Repression of Telomerase Components in Isogenic Normal, Immortal and Tumorigenic Cells

Repression of telomerase components in isogenic normal, immortal and tumorigenic cells

A thesis submitted to the University of New South Wales in fulfilment of the requirements for the degree of

DOCTOR OF PHILOSOPHY

Michelle Frances Maritz

Children’s Cancer Institute Australia for Medical Research and University of New South Wales Faculty of Medicine, School of Women’s and Children’s Health Sydney, NSW, Australia.

2013

ORIGINALITY STATEMENT

‘I hereby declare that this submission is my own work and to the best of my knowledge it contains no materials previously published or written by another person, or substantial proportions of material which have been accepted for the award of any other degree or diploma at UNSW or any other educational institution, except where due acknowledgement is made in the thesis. Any contribution made to the research by others, with whom I have worked at UNSW or elsewhere, is explicitly acknowledged in the thesis. I also declare that the intellectual content of this thesis is the product of my own work, except to the extent that assistance from others in the project's design and conception or in style, presentation and linguistic expression is acknowledged.’

Signed ……………………………………………......

Date ……………………………………………......

Acknowledgements Firstly, I would like to say a huge thank you to my supervisor Karen Mackenzie for taking me on as a PhD student and her continued support throughout the years. Thank you for giving me the opportunity to be a part of your group and to develop my research skills under your supervision.

I would also like to extend my thanks to CCIA for opportunity to undertake my PhD within the institute and for the postgraduate CCIA scholarship award and all the support they have given me. It was a great privilege to do my PhD with you. I would also like to thank UNSW for the International scholarship and Cancer Institute New South Wales for the research scholar award.

To both past and present members of Cancer Cell Development group and Leukemia Biology groups and all at CCIA who have help me during PhD. Jun, Erwin, Flora, Laura, Georg, Phil, Carlotta thanks for all help in the lab when I first began and your continued friendship. To Arjanna, Lisa and Ashu thanks for all your support in the latter part of my PhD. I must also thank Rosemary; I will forever be indebted to you for the optimisation of 384 well qRT-PCR setup. Thanks to Nick and Colin for being so eager and volunteering to help with the many RNA extractions and RT-PCRs.

Thank you also to Eddy for all your help with the immunofluorescence and to Omesha Perera and Tracy O’ Bryan for the Meta-TIF analysis. Thank you to Dr W Kaplan and Dr M. Cowley for your guidance in the microarray experiments and analysis. To Aldona, thanks for always keeping me in check in the lab and to all my fellow students Melinda, Donya, Guy, Patrick, Jeyran, Miriam, thanks for keeping company on the weekends in the lab and all the coffee catch ups.

To Miriam and Nico, aka my Australian family, I can’t say thank you enough for all your support. To my other Miriam, thanks for hopping in the boat with me. To my family, Mom, Dad, Andre and Amy and Johnny and Monique and Grandpa, your support from across the miles was never failing.

To my love Davide thanks for joining me on this adventure… It’s your turn now.

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Publications Kan, CY, Petti, C, Bracken, L, Maritz, M, Xu, N, O’Brien, R, Yang, C, Liu, T, Yuan, J, Lock RB and MacKenzie, KL. Upregulation of survivin during immortalization of human myofibroblasts is linked to repression of tumour suppressor p16INK4a and confers resistance to oxidative stress (2013) Journal Biological Chemistry 288(17) 12032-12041.

Maritz, MF, Richards LA and Mackenzie KL. Assessment and quantitation of telomerase enzyme activity (2013) Methods in Molecular Biology 965: 215-231.

Maritz, MF, van der Watt, PJ, Holderness, N, Birrer MJ, and Leaner, VD. Inhibition of AP-1 suppresses cervical cancer cell proliferation and associates with p21 expression (2010) Biological Chemistry 392 (5): 439-448.

Maritz, MF, Napier, CE, Wen, VW and Mackenzie KL Targeting Telomerase in Hematologic malignancy (2010) Future Oncology 6 (5): 769-89.

Whibley CE, McPhail KL, Keyzers RA, Maritz MF, Leaner VD, Birrer MJ, Davies- Coleman MT, Hendricks DT (2007) Reactive oxygen species mediated apoptosis of oesophageal cancer cells induced by marine triprenyl toluquinones and toluhydroquinones. Molecular Cancer Therapeutics 9:2535-43.

Presentations Maritz, MF, Cowley MJ, Kaplan, W, Kavallaris, M, MacKenzie, KL (2012) Independent mechanisms mediate the acute effects of siRNA-mediated repression of hTERT or dyskerin. Australian Telomere and DNA damage and Repair workshop 29 October. Power house Museum, Sydney. Oral presentation.

Maritz, MF, Cowley MJ, Kaplan, W, Kavallaris, M, MacKenzie, KL (2012) Repression of essential telomerase components mediates anti-proliferative effects in isogenic immortal and tumorigenic cells, but not normal cells. European Association for Cancer Research-22 (EACR22) Barcelona, Spain. Poster presentation.

Maritz, MF, Cowley MJ, Kaplan, W, Kavallaris, M, MacKenzie, KL (2012) Gene expression analysis following repression of telomerase components in normal, immortal and tumorigenic cells. ASMR NSW Annual Scientific Meeting, Sydney, NSW. Poster presentation.

Maritz, MF, Cowley MJ, Kaplan, W, Kavallaris, M, MacKenzie, KL (2012) Potent and specific anti-proliferative effects following the repression of telomerase components in immortal and tumorigenic cells. 24TH Lorne Cancer Conference, Melbourne, Victoria. Poster presentation.

Maritz, MF, Mackenzie, KL (2010) Consequences of directly targeting telomerase enzyme components in isogenic normal, immortal and tumorigenic cells, The Coast Association TOW Research Day. 9 November. Edmund Blacket Lecture Theatre, Prince of Wales Hospital. Oral presentation.

Maritz, MF, Mackenzie, KL. (2010) Functional and molecular consequences of directly targeting telomerase enzyme components in isogenic normal, immortal and tumorigenic cells. Australian Telomere and DNA damage and Repair workshop 29 October. Power house Museum, Sydney. Oral presentation.

Maritz, MF, Mackenzie, KL. (2010) Direct targeting of telomerase enzyme components in isogenic normal, immortal and tumorigenic cells, The Lowy Symposium, Discovering Cancer Therapeutics, 16-18 May. John Niland Scientia Building, University of New South Wales, Poster presentation.

Abbreviations ADP Adenosine diphosphate ALT Alternative lengthening of telomerase ɑMEM Alpha minimal essential medium AML Acute myeloid leukaemia APC Allophycocyanin ATM Ataxia Telangiectasia Mutated ATCC American Tissue Culture Centre ATP Adenosine triphosphate °C Degrees Celsius BCA Bicinchoninic acid BFB Breakage fusion bridge bp Base pair BrdU Bromodeoxyuridine BSA Bovine serum albumin CDK Cyclin dependent kinase CHAPS 3-[(3-cholamidopropyl) dimethylammonio]-1-propanesulphonate C-terminal Carboxyl terminal cDNA Complementary DNA CML Chronic Myeloid leukaemia CO2 Carbon dioxide CR Conserved region DAPI 4’, 6-diamidino-2’-phenylindole dihydrochloride DAT Dissociates activities of telomerase DBA Diamond Blackfan anemia DC Dyskeratosis congenita d-loop Displacement loop DKCLD Dyskerin like domain DKC1 Dyskerin gene DMEM Dulbecco’s modified Eagle’s medium DMSO Dimethyl sulphoxide DN Dominant negative DNA Deoxyribonucleic acid DNA PK DNA protein kinase dNTP Deoxynucleoside triphosphate DSB Double stranded breaks dsRNA Double stranded RNA DTT Dithiothreitol EDTA Ethylenediamine-tetra-acetate EGFR Epidermal growth factor receptor EGTA Ethylene glycol-bis (2-aminoethylether)-N,N,N’,N’-tetraacetate EMT Epithelial to mesenchymal transition vi

FACS Fluorescence-activated cell sorting FCS Foetal calf serum FDR False discovery Rate FITC Fluorescein isothiocyanate g Gram g Gravitational force G Guanine G0 Cell cycle resting phase G1 Cell cycle growth phase 1 G2 Cell cycle growth phase 2 GFP Green fluorescent protein G418 Geneticin GP Gene Pattern GSEA Gene Set Enrichment Analysis HDV Hepatitis D virus HCC Human hepatocellular carcinoma HGF Hepatocyte growth factor HMEC Human mammary epithelial cell hr Hour hnRNP Human nuclear ribonucleoprotein HPV Human papilloma virus HRP Horseradish peroxidise hTERT Human telomerase reverse transcriptase hTR Human telomerase RNA component Hsp90 Heat shock protein 90 IRES Internal ribosomal entry site Kb Kilobase Kbp Kilo base pairs kDa Kilo Dalton L Litre LB Luria-Bertani broth LRR Leucine rich repeat LT Large T antigen µ Micro M Molar MCS Multiple cloning site Min Minutes MDV Marek's disease herpes virus miRNA Micro RNA mL Millilitre MLS Mitochondrial localisation signal MRC5 Human lung foetal fibroblast MRC5hTERT hTERT-transduced MRC5 MRN MRE/RAD50/NBS1 vii

mRNA Messenger RNA MTS Mitochondrial targeting sequence MSCV Murine Stem Cell Virus mtDNA Mitochondrial DNA MTT 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide µ Micro N Nano NADH Reduced nicotinamide adenine dinucleotide NMDA N-methyl-D-aspartic NSCLC Non-small cell lung cancer N-terminal Amine terminus OD Optical density O/N Overnight OSCC Oral squamous cell carcinomas Opti-MEM Opti Minimal essential media PBS Phosphate buffered saline PCR Polymerase chain reaction PD Population doublings PI Propidium iodide PNA Polynucleotide nucleic acid Polybrene Hexadimethrine bromide PSG Penicillin, streptomycin, L-glutamine PUA Pseudouridine synthase and archaeosine transglycosylase PVDF Polyvinylidene fluoride qRT-PCR Quantitative real-time polymerase chain reaction qTRAP Quantitative telomerase repeat amplification protocol RAP1 Repressor activator protein 1 RDRP RNA-dependent RNA polymerase RID1 RNA interacting domain 1 RID2 RNA interacting domain 2 RISC RNA-induced silencing complex RMRP RNA component of mitochondrial RNA processing endoribonuclease RIPA Radio immunoprecipitation assay RNA Ribonucleic acid RNAi RNA interference RNP Ribo nuclear protein rRNA Ribosomal RNA ROS Reactive oxygen species rt Room temperature RT Reverse transcriptase S Seconds SA-B-gal Senescence associated Beta-galactosidase Sc Scrambled viii

scaRNA Small nuclease RNA SDS Sodium Dodecyl Sulphate SEM Standard error of the mean shRNA Short hairpin RNA siRNA Short interfering RNA SSC Sodium chloride, tri-sodium citrate SOC Super optimal catabolite STS Staurosporine SV40 Simian virus 40 Taq Thermus aquaticus TBS Tris buffered saline t-circles Telomeric circles TBE Tris-borate EDTA TBS Tris buffered saline TTBS Tris buffered saline with Tween-20 TE Tris-EDTA TIF Telomere dysfunction induced foci TERRA Telomeric Repeat-containing RNA T-loop Telomere loop TRF Telomere restriction fragment tRNA Transfer RNA Tris Tris (hydroxymethyl) aminomethane UV Ultra violet V Volts VCM Virus-containing media VEGF Vascular endothelial growth factor WDR79 WD repeat domain 79 WRN Werner syndrome wt Wild type

Summary

Cellular immortalisation is a fundamental step in the development of virtually all cancers. Activation of the enzyme telomerase underlies immortality in approximately 90% of human malignancies, where it functions to circumvent telomere shortening at chromosome ends. The active core of the human telomerase holoenzyme is a ribonuclear protein complex that includes a reverse transcriptase, hTERT, an RNA template for synthesis of telomeric repeats, and the RNA binding and modifying protein, dyskerin. In addition to the function of telomerase in telomere maintenance, substantial evidence indicates that the individual telomerase components may have extracurricular functions that contribute to survival, proliferation and other properties of tumour cells. This evidence provides compelling rationale for investigating the consequences of specifically targeting the individual telomerase components as a potential approach for halting the replication of immortal cancer cells.

In this body of work, the functional and molecular consequences of siRNA-mediated targeting of hTERT, hTR, dyskerin were directly compared in a model of mesenchymal tumorigenesis. The model employed was comprised of normal MRC5 foetal lung myofibroblasts, hTERT-transduced immortal MRC5 (MRC5hTERT) and an N-Ras transformed tumorigenic derivative of MRC5hTERT cells (MRC5hTERT- TZT). Isogenic SV40-transformed MRC5 cells that either spontaneously upregulated endogenous telomerase (MRC5V1) or activated the alternative lengthening of telomeres mechanism known as ALT (MRC5V2) were also incorporated in the study, as well as a fibrosarcoma derived cell line HT1080 that expresses endogenous telomerase.

The first Results chapter (Ch3) of this thesis describes the molecular characterisation of the tumorigenic MRC5hTERT-TZT cells and optimisation of siRNA-mediated repression of hTERT, dyskerin and hTR in the isogenic MRC5 model. The MRC5hTERT-TZT cells were found to have a defective p53/p21 pathway and N-Ras overexpression was confirmed. A series of non-specific scrambled siRNAs were screened for off-target effects. For each of the three telomerase components, two independent siRNAs that effectively repressed gene expression and suppressed

telomerase activity were selected for the functional studies in the second Results chapter (Ch4).

Investigations described in Chapter 4 demonstrate the impact of siRNA-mediated repression of hTERT, dyskerin and hTR on cell proliferation. It is shown that repression of hTERT and dyskerin did not significantly impede the proliferation of normal MRC5 cells in a short-term assay, but dramatically impaired the expansion of telomerase-immortalised cells. siRNA-mediated repression of dyskerin or hTERT also impaired the anchorage-independent growth of the tumour-derived cells. In contrast to the effects of dyskerin or hTERT repression, down regulation of telomerase by treatment with hTR siRNA or the small molecular weight telomerase enzyme inhibitor BIBR1532 had no acute effect on any of the cell lines in these short-term assays. Since the short time frame of these experiments precluded gradual telomere shortening as the mechanism underlying the proliferative impairment, the growth arrest observed in dyskerin and hTERT siRNA-transfected cells appeared to be due to a telomerase-independent mechanism. This result was supported by data that showed repression of dyskerin also impaired the proliferation of a telomerase- negative cell line, MRC5V2. Since the tumorigenic MRC5hTERT-TZT cells have a defective p53 pathway, the results also demonstrated that down regulation of hTERT and dyskerin halt proliferation via a p53-independent mechanism. Consistent with those observations, inhibition of p53 function by stable suppression of p53 in MRC5hTERT cells did not override the proliferative impairment induced by hTERT or dyskerin siRNA.

To determine whether the proliferative defect mediated by inhibition of dyskerin was a result of destabilisation of hTR, siRNA-mediated inhibition of dyskerin was performed using MRC5hTERT and HT1080 cells overexpressing hTR from a retroviral vector. Cell proliferation assays showed that hTR overexpression heightened telomerase activity, but failed to rescue the cells from the proliferative impairment induced by dyskerin siRNA. These results further substantiated the conclusion that down regulation of dyskerin halted proliferation via a telomerase- independent mechanism.

To determine whether dyskerin and hTERT contributed to the malignant phenotype of tumorigenic cells, the effect of stable repression of hTERT, dyskerin and hTR in HT1080 and MRC5hTERT-TZT cells was investigated. The third Results chapter (Ch5) describes the construction of retroviral vectors encoding shRNA targeting hTERT, dyskerin and hTR, as well as scrambled shRNA. It is shown that vector driven shRNA inhibited gene expression and suppressed telomerase activity, albeit less potently than siRNA transfection. Nevertheless, anchorage-independent growth was impaired in both cell lines transduced with hTERT and dyskerin shRNA. In contrast, no effects were apparent in cells transduced with hTR or Sc shRNA. The effect of stable suppression of the telomerase components on subcutaneous tumour formation in mice was next examined. An initial tumour growth delay was evident in mice xenografted with HT1080 cells expressing hTERT shRNA or dyskerin shRNA. However, no significant differences in tumour formation were observed in mice engrafted with MRC5hTERT-TZT cells expressing the shRNA vectors. This was most likely due to overgrowth of tumour cells that did not have hTERT or dyskerin sufficiently silenced.

In the fourth Results chapter (Ch6), microarray gene expression analysis was performed to identify pathways responsible for the specific and potent effects of targeting dyskerin and hTERT in immortal cells. Differentially expressed gene sets that corresponded to normal, immortal and tumorigenic cells subjected to repression of the different telomerase components were identified by Gene Set Enrichment Analyses (GSEA). Meta-analysis was then performed to compare gene sets corresponding to the repression of each component in normal, immortal and tumorigenic cells. Gene sets that were specifically altered in immortal and/or tumorigenic cells by siRNA-mediated ablation of hTERT or dyskerin were identified. The gene sets highlighted distinct telomerase-independent mechanims of hTERT and dyskerin that were evident upon down regulation in immortal and tumorigenic cells. Pathways involved in DNA damage and repair, chromatin remodelling and epigenetic silencing, correlated most significantly with hTERT repression, while several distinct gene sets relating to the retinoblastoma pathway were associated with dyskerin repression.

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In conclusion, these studies showed that in contrast to normal cells, immortal and tumorigenic cells were vulnerable to the siRNA-mediated down regulation of hTERT and dyskerin. The investigations provided further evidence of non -canonical functions of hTERT and dyskerin in immortal and tumorigenic cells. The therapeutic potential of targeting dyskerin and hTERT was highlighted by the potent anti- proliferative effects that occurred only in immortal and tumour cells upon repression of hTERT and dyskerin, irrespective of initial telomere length, progressive telomere shortening and p53 status. The distinct telomerase-independent pathways mediating the functions of hTERT and dyskerin in immortal and tumorigenic cells that were identified by microarray and bioinformatic analyses provide insight to the downstream effector pathways that may be exploited in the development of new molecular-targeted therapeutics for the potential application in treatment of human malignancies.

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Table of Contents 1. Literature Review ...... 1 1.1 Human cancer and cellular immortality ...... 1 1.2 The replicative lifespan of normal cells ...... 2 1.2.1 Hayflick limit of senescence ...... 2 1.2.2 Telomere hypothesis of replicative senescence ...... 3 1.2.2.1 Structure and function of telomeres ...... 3 1.2.2.2 Three state model of telomere protection ...... 6 1.2.2.3 Telomere shortening limits replicative lifespan ...... 8 1.3 Telomere maintenance mechanisms ...... 12 1.3.1 Telomerase ...... 12 1.3.2 Alternative Lengthening of Telomeres (ALT) ...... 17 1.4 The telomerase holoenzyme ...... 17 1.4.1 Human telomerase reverse transcriptase, hTERT ...... 17 1.4.2 Human telomerase RNA component, hTR ...... 18 1.4.3 Dyskerin ...... 21 1.4.4 Telomerase assembly ...... 23 1.4.5 Trafficking of telomerase components and recruitment to the telomere 23 1.5 Regulation of telomerase components ...... 25 1.5.1 Transcriptional regulation of telomerase components ...... 26 1.5.2 Epigenetic regulation of telomerase components ...... 27 1.5.3 Post-transcriptional regulation of telomerase components ...... 27 1.5.4 Post-translation modification of telomerase components ...... 28 1.5.5 Gene amplification and mutations ...... 28 1.6 Telomerase-mediated telomere extension ...... 29 1.6.1 Immortalisation via overexpression of hTERT ...... 31 1.7 Extra-telomeric functions of telomerase components ...... 33 1.7.1 Extra-telomeric functions of hTERT ...... 34 1.7.1.1 Cell survival and proliferation ...... 34 1.7.1.2 DNA damage and repair ...... 40 1.7.1.3 Malignant phenotype, metastasis and angiogenesis ...... 42 1.7.1.4 hTERT function in the mitochondria ...... 43 1.7.2 Extra-telomeric functions of hTR ...... 45 1.7.3 Extra-telomeric functions of dyskerin ...... 47 1.8 Telomerase inhibition as a therapeutic approach to cancer ...... 50

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1.8.1 Reverse transcriptase inhibitors ...... 51 1.8.2 Dominant-negative inhibition of telomerase activity ...... 55 1.8.2.1 Non-nucleosidic small molecule telomerase inhibitors...... 56 1.8.3 G-quadruplex stabilisation ...... 58 1.8.4 Gene targeted nucleic-acid based strategies...... 59 1.8.4.1 Ribozymes ...... 59 1.8.4.2 Antisense oligonucleotides and peptidic nucleic acids ...... 60 1.8.4.3 Short interfering RNA (siRNAs) ...... 64 1.9 Hypothesis ...... 67 1.10 Aim ...... 68

2. Materials and Methods ...... 69 2.1 Reagents and solutions ...... 69 2.2 Mammalian cell culture ...... 73 2.2.1 Passaging of mammalian cells ...... 73 2.2.2 Cryopreservation and thawing of cells ...... 73 2.2.3 Calculation of expansion and population doublings (PDs) ...... 75 2.2.4 Mycoplasma testing ...... 75 2.3 Retroviral gene transfer ...... 75 2.3.1 Bacterial transformation with plasmid DNA ...... 75 2.3.2 Preparation of plasmid DNA...... 76 2.3.2.1 Mini-preparation of plasmid DNA ...... 76 2.3.2.2 Maxi-preparation of plasmid DNA ...... 76 2.3.2.3 Preparation of plasmid DNA for transfection ...... 77 2.3.2.4 Spectrophotometric analysis of DNA ...... 77 2.3.2.5 DNA agarose gel electrophoresis ...... 77 2.3.2.6 Preparation of glycerol stocks ...... 78 2.3.2.7 Transfection of packaging cells ...... 78 2.3.2.8 Retroviral transduction of mammalian cells ...... 78 2.4 Suppression of gene expression with siRNA inhibition ...... 79 2.4.1 siRNA design ...... 79 2.4.2 Transient siRNA transfection ...... 79 2.5 Construction of retroviral shRNA vectors ...... 79 2.5.1 Design and annealing of double stranded oligonucleotides ...... 84 2.5.2 Ligation of shRNA and vector DNA ...... 84 2.5.3 Selection of transfectants ...... 86 2.6 Polymerase chain reaction (PCR) ...... 86 2.6.1 Genomic DNA extraction ...... 86 2.6.2 PCR analysis of genomic DNA ...... 87 2.7 Real Time qRT-PCR analysis (qRT-PCR) ...... 87 2.7.1 RNA extraction ...... 87 2.7.2 cDNA synthesis ...... 89 2.7.3 PCR amplification of cDNA and Real-Time qRT-PCR analysis ...... 89 2.8 Assessment of telomerase activity by the quantitative telomerase repeat amplification assay (qTRAP) ...... 90 2.8.1 Preparation of cell lysate and protein quantification by Bradford assay 90 2.8.2 Preparation of controls for qTRAP ...... 90

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2.8.3 qTRAP reaction and analysis ...... 91 2.8.4 Telomere restriction fragment length (TRF) analysis ...... 92 2.9 Western blot analysis ...... 92 2.9.1 Preparation of cell lysate for western blot analysis...... 92 2.9.2 SDS-polyacrylamide gel electrophoresis ...... 92 2.9.3 Immunoblotting ...... 93 2.9.4 Immunodetection ...... 93 2.10 Immunofluorescence staining for detection of γH2AX ...... 95 2.11 Meta-telomere dysfunction induced foci (Meta-TIF) assay ...... 95 2.12 Cell cycle, cell death and senescence ...... 96 2.12.1 Propidium iodide staining ...... 96 2.12.2 Annexin V-APC staining ...... 96 2.12.3 Beta-galactosidase (SA-ß-gal) assay ...... 97 2.13 Anchorage-independent growth assay ...... 97 2.14 In vivo tumorigenesis assay with Nude Balb-C mouse model ...... 97 2.15 Microarray analysis of gene expression ...... 98 2.15.1 RNA extraction for microarray analysis ...... 99 2.15.2 RNA quantitation and analysis of RNA quality ...... 99 2.15.3 cRNA amplification ...... 99 2.15.3.1 Synthesis of first and second strand cDNA synthesis ...... 99 2.15.3.2 cDNA purification ...... 100 2.15.3.3 In vitro transcription and purification of cRNA ...... 100 2.15.4 Labelling, hybridisation and scanning ...... 101 2.16 Bioinformatic analysis of gene expression data ...... 101 2.16.1 Pre-processing data for analysis ...... 101 2.16.2 Limma Gene Pattern analysis ...... 102 2.16.3 Gene Set Enrichment Analysis (GSEA) ...... 103 2.16.3.1 META-GSEA ...... 104 2.16.3.2 Leading edge gene analysis ...... 104 2.17 Statistical analyses ...... 104

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3. Repression of telomerase components in isogenic normal, immortal and tumorigenic myofibroblasts ...... 105 3.1 Introduction ...... 105 3.2 Results ...... 107 3.2.1 Molecular characterisation of tumorigenic MRC5 human myofibroblasts (MRC5hTERT-TZT cells) ...... 107 3.2.2 Quantification of hTERT, dyskerin, hTR expression and telomerase activity in isogenic MRC5 cells ...... 110 3.2.3 Optimisation of siRNA-mediated repression of telomerase components …………………………………………………………………….115 3.2.3.1 Effective siRNA delivery of the cell line panel ...... 115 3.2.3.2 Selection of non-specific control siRNA ...... 115 3.2.3.3 Design of siRNA targeting hTR ...... 120 3.2.3.4 Optimisation of siRNA-mediated repression of telomerase components ...... 123 3.2.4 siRNA-mediated repression of hTERT, dyskerin and hTR gene expression and telomerase activity ...... 129 3.3 Discussion ...... 133 3.3.1 Tumorigenic MRC5hTERT-TZT cells express NRas, have a defective p53 pathway and have long telomeres ...... 133 3.3.2 siRNA-mediated targeting of hTERT, hTR and dyskerin is an effective means of inhibiting target gene expression and telomerase activity ...... 134

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4. Repression of either hTERT or dyskerin but not hTR halts the replication of immortal and tumorigenic cells via a telomerase independent mechanism ...... 138 4.1 Introduction ...... 138 4.2 Results ...... 139 4.2.1 Downregulation of hTERT or dyskerin inhibits proliferation of immortal and tumorigenic cells, but not normal cells ...... 139 4.2.2 Repression of hTERT or dyskerin impairs anchorage-independent growth of tumorigenic cells ...... 145 4.2.3 Repression of dyskerin impairs proliferation of immortal cells via a mechanism that is independent of hTR depletion ...... 147 4.2.4 Cell cycle kinetics following repression of hTERT and dyskerin ...... 150 4.2.5 Induction of senescence-like growth arrest following repression of hTERT in immortal cells ...... 153 4.2.6 Accumulation of cells in G1 phase following dyskerin repression is mediated by p53 ...... 155 4.2.7 Immortal cells are dependent upon continued expression of hTERT or dyskerin for replication ...... 161 4.3 Discussion ...... 166 4.3.1 Proliferation arrest induced by the repression of hTERT or dyskerin is mediated by a telomerase-independent mechanism ...... 166 4.3.2 Repression of dyskerin selectively impairs the proliferation of immortal and tumorigenic cells ...... 168 4.3.3 Repression of hTERT selectively impairs proliferation of immortal and tumorigenic cells ...... 170 4.3.4 Potential for the therapeutic targeting of telomerase components dyskerin and hTERT ...... 171

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5. Long term analysis of telomerase suppression in immortal and tumorigenic cells ...... 173 5.1 Introduction ...... 173 5.2 Results ...... 175 5.2.1 Construction of replication-defective retroviral vectors for stable repression of telomerase components...... 175 5.2.2 Retroviral transduction of MRC5hTERT and HT1080 cells with dominant-negative hTERT vector (MIG+DNhTERT) ...... 178 5.2.3 shRNA-mediated suppression of hTERT, hTR, dyskerin gene expression and telomerase activity ...... 181 5.2.4 Comparison of the effects of telomerase inhibition via expression of DNhTERT and shRNA targeting the telomerase components ...... 187 5.2.4.1 Anchorage independent growth...... 187 5.2.4.2 Tumour formation in xenografted mouse model ...... 190 5.2.4.3 Replicative lifespan ...... 194 5.3 Discussion ...... 200

6. Gene expression pathways associated with the potent anti-proliferative effects elicited by repression of hTERT and dyskerin ...... 204 6.1 Introduction ...... 204 6.2 Results ...... 206 6.2.1 Microarray analysis of gene expression changes following siRNA- mediated repression of telomerase components ...... 206 6.2.1.1 Microarray design and control of RNA quality ...... 206 6.2.1.2 Identification of differentially expressed genes using Limma analysis 211 6.2.1.3 Identification of gene sets that correlate with genes regulated by the repression of telomerase components in normal, immortal and tumorigenic cells 216 6.2.2 Identification of pathways associated with the repression of each telomerase components common to normal, immortal and tumorigenic cells .. 222 6.2.3 Identification of pathways associated with the repression of specific telomerase components ...... 228 6.2.3.1 Pathways associated with the repression of hTR in normal, immortal and/or tumorigenic cells ...... 228 6.2.3.2 Pathways that distinguish the response of immortal and tumorigenic cells from normal cells following the repression of hTERT or dyskerin ...... 234 258 6.2.4 Repression of hTERT activates a DNA damage response in immortal and tumorigenic cells ...... 259 6.3 Discussion ...... 266 6.3.1 GSEA reveals pathways associated with the repression of telomerase components ...... 266 6.3.2 Distinct mechanisms underlie the proliferative defect induced by repression of dyskerin ...... 268 6.3.3 Distinct mechanisms underlie the proliferative defect induced by repression of hTERT ...... 270

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7. Conclusions and future perspectives ...... 275 7.1 Potential therapeutic utility of directly targeting the telomerase components hTERT or dyskerin ...... 275 7.2 Directly targeting hTERT in human cancer ...... 276 7.3 Directly targeting dyskerin in human cancer ...... 280 7.4 Directly targeting of hTERT or dyskerin in combination with chemotherapeutic agents ...... 282 7.5 Development of clinically relevant approaches to targeting hTERT or dyskerin...... 285 7.6 Concluding remarks ...... 285

A. Appendix ...... 287

8. References ...... 312

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List of figures Chapter 1 Figure 1.1 Schematic representation of telomeres ...... 5 Figure 1.2 Semi-conservative DNA replication of chromosome ends ...... 9 Figure 1.3 Telomere maintenance underlies cancer cell immortality ...... 13 Figure 1.4 Structure of the telomerase ribonuclear protein complex ...... 15 Figure 1.5 Telomerase-mediated extensions of telomeres ...... 30 Chapter 3 Figure 3.1 Molecular characterisation of the tumorigenic MRC5hTERT-TZT cell line ...... 108 Figure 3.2 Expression of telomerase components and telomerase enzyme activity in MRC5 cell line panel ...... 111 Figure 3.3 Optimisation of siRNA delivery ...... 116 Figure 3.4 Effects of siRNA transfection of scrambled siRNA control on cell proliferation and viability ...... 119 Figure 3.5 Design and evaluation of hTR siRNA ...... 121 Figure 3.6 Optimal conditions for siRNA-mediated repression of telomerase components ...... 124 Figure 3.7 Effective siRNA-mediated repression of dyskerin and hTR telomerase components in MRC5hTERT-TZT cells ...... 127 Figure 3.8 siRNA-mediated inhibition of telomerase components effectively suppresses gene expression and telomerase activity ...... 131 Chapter 4 Figure 4.1 siRNA mediated repression of hTERT and dyskerin impedes the short- term proliferation of immortal and tumorigenic cells, but not normal cells ...... 140 Figure 4.2 Inhibition of telomerase by BIBR1532 had no acute proliferative effects on normal, immortal and tumorigenic cells ...... 144 Figure 4.3 Repression of hTERT or dyskerin impairs anchorage independent growth ...... 146 Figure 4.4 Overexpression of hTR does not rescue from the proliferative impairment induced by dyskerin repression...... 148 Figure 4.5 Cell cycle analysis of immortal and tumorigenic cells following repression of telomerase components ...... 151 xxiii

Figure 4.6 Cell cycle analysis of immortal and tumorigenic cells following repression of telomerase components ...... 152 Figure 4.7 Senescence associated (SA)-β-galactosidase activity in MRC5hTERT cells following repression of hTERT ...... 154 Figure 4.8 shRNA targeting p53 inhibits expression and function of p53 in MRC5hTERT cells ...... 156 Figure 4.9 Inhibition of p53 overcomes accumulation of cells in G1 phase, but is not sufficient to circumvent the proliferative impairment induced by dyskerin depletion...... 158 Figure 4.10 Cell cycle analysis of MRC5hTERT cells expressing GFPshRNA or p53shRNA...... 160 Figure 4.11 Overexpression of hTERT in normal MRC5 cells ...... 162 Figure 4.12 Repression of hTERT and dyskerin selectively impairs the proliferation of immortal cells ...... 164 Chapter 5 Figure 5.1 Cloning strategy for generation of pSRpuro retroviral vectors expressing shRNA ...... 176 Figure 5.2 Transduction of HT1080 and MRC5hTERT-TZT cells with pSR retroviral vectors expressing shRNA ...... 179 Figure 5.3 Retroviral transduction of HT1080 and MRC5hTERT-TZT with pMIG+GFP and pMIG+DNhTERT vectors ...... 182 Figure 5.4 Suppression of hTERT, hTR and dyskerin and telomerase activity in MRC5hTERT-TZT and HT1080 cells ...... 185 Figure 5.5 Impaired anchorage independent growth of HT1080 and MRC5hTERT- TZT cells transduced with shRNA targeting telomerase components or DNhTERT ...... 188 Figure 5.6 Effects of shRNA on tumour formation in mice ...... 192 Figure 5.7 shRNA suppression of telomerase does not affect long-term replicative lifespan ...... 195 Figure 5.8 Suppression of gene expression and telomerase activity by shRNA and DNhTERT over replicative lifespan...... 198

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Chapter 6 Figure 6.1 Suppression of hTERT, dyskerin and hTR gene expression in siRNA- 208 Figure 6.2 Gene expression analysis pipeline using Gene Pattern software and GSEA analysis ...... 209 Figure 6.3 Scale normalisation and suppression of hTERT and dyskerin in siRNA- transfected cells ...... 213 Figure 6.4 GSEA analysis showing the top twenty of upregulated and downregulated gene sets that correlated with the repression of each component in normal, immortal and tumorigenic cells ...... 218 Figure 6.5 Gene sets correlating with the repression of hTERT, dyskerin or hTR in normal, immortal and tumorigenic cells ...... 224 Figure 6.6 Leading edge genes of gene sets related to pRb/E2F pathway that correlated with the repression of dyskerin in immortal and tumorigenic cells ...... 245 Figure 6.7 Repression of hTERT upregulates Myc repressors ...... 253 Figure 6.8 Leading edge genes of gene sets related to DNA damage and chromatin remodelling pathways that correlated with gene downregulated by the repression of hTERT in immortal and tumorigenic cells ...... 257 Figure 6.9 Repression of hTERT activates the formation of γH2AX foci ...... 260 Figure 6.10 DNA damage foci at the telomeres by Meta-TIF assay ...... 263 Appendix Figure A.1 Protein Marker: Kaleidoscope protein Marker (Biorad)………………287 Figure A.2 DNA ladders…………………………………………………………...287 Figure A.3 Repression of hTERT or dyskerin impairs soft agarose colony formation…………………………………………………………………………...288 Figure A.4 ShRNA targeting hTERT or dyskerin impairs colony formation in soft agarose……………………………………………………………………………..289

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List of Tables Table 1.1 Extra-telomeric functions of telomerase and its components ...... 35 Table 1.2 Therapeutic approaches to targeting telomerase ...... 52 Table 2.1 General Reagents and kits ...... 69 Table 2.2 Buffers and Solutions ...... 72 Table 2.3 Cell lines used in this study ...... 74 Table 2.4 Retroviral Vectors used in this study ...... 80 Table 2.5 Cell lines derived from this study ...... 81 Table 2.6 siRNA sequences ...... 83 Table 2.7 shRNA sequences generated ...... 85 Table 2.8 Primers sequences ...... 88 Table 2.9 Antibodies ...... 94 Table 3.1 Characterisation of isogenic MRC5 cell line panel and HT1080 cells .... 114 Table 6.1 Number of differentially expressed genes and gene sets enriched in normal, immortal and tumorigenic cells following the repression of telomerase components hTERT, dyskerin and hTR ...... 215 Table 6.2 Gene sets correlating with gene expression changes induced by repression of each telomerase component that were common in normal, immortal and tumorigenic cells (Top 20) ...... 226 Table 6.3 Gene sets correlating with gene expression changes induced by repression of hTR in normal, immortal and/or tumorigenic cells (Top 20) ...... 230 Table 6.4 Gene sets correlating with gene expression changes induced by repression of dyskerin or hTERT in normal cells (Top 20) ...... 236 Table 6.5 Gene sets correlating with gene expression changes induced by repression of dyskerin in immortal and/or tumorigenic cells (Top 20) ...... 239 Table 6.6 Gene sets correlating with gene expression changes induced by the repression of hTERT in immortal and/or tumorigenic cells ...... 247 Table 6.7 Overview of altered pathways associated with the repression of hTR or dyskerin or hTERT ...... 250 Table 6.8 Gene expression alterations of Myc related genes ...... 254 Table A.1 Additional gene sets correlating with gene expression changes induced by repression of hTERT that were common in normal, immortal and tumorigenic cells (Top 100)…………………………………………………………………………..290

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Table A.2 Additional gene sets correlating with gene expression changes induced by repression of hTR in normal, immortal and/or tumorigenic cells (Top 100)………293 Table A.3 Additional gene sets correlating with gene expression changes induced by repression of dyskerin or hTERT in normal cells (Top 100)………………………301 Table A.4 Additional gene sets correlating with gene expression changes induced by repression of dyskerin in immortal and/or tumorigenic cells (Top 100)…………..305 Table A.5 Additional gene sets correlating with gene expression changes induced by repression of hTERT in immortal and/or tumorigenic cells (Top 100)……………309

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1. Literature Review

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1.1 Human cancer and cellular immortality Human cells engage a complex network of mechanisms to ensure genomic integrity is preserved during cellular replication. In cancer cells, these homeostatic controls are disrupted and through the sequential accumulation of genetic and epigenetic mutations, cells acquire a selective growth advantage which promotes clonal expansion [1, 2]. During the multistep process of malignant transformation cells, normal cells acquire the six hallmarks that include; self-sufficiency in growth signals, insensitivity to growth inhibitory signals, evasion of programmed cell death, limitless replicative potential (immortality), sustained angiogenesis, and tumour invasion and metastasis [1, 3-7]. Two “emerging hallmarks” of cancer cells namely; evasion of immunological destruction and reprogramming of cellular metabolism have recently been added. The significance of genomic instability and mutation and tumour promoting inflammation for the acquisition of tumorigenic capabilities has also been recognised [1].

The majority of current cancer therapeutics, such as radiation and chemotherapeutics exhibit poor selectivity for cancer cells and are associated with toxic side effects on normal tissue [8]. Increased knowledge of the genes and pathways that govern the cancer specific characteristics combined with more specific targeting strategies, has enabled the exploitation of these pathways in the development of novel cancer- specific targeted therapeutics [1, 9]. Targeted therapies aimed at cancer specific pathways are likely to be associated with minimised detrimental side effects on normal tissue and improve the quality of life and survival outcomes for cancer patients.

This thesis specifically focuses on the molecules involved in the process that allows cancer cells to proliferate indefinitely, ie the immortalisation process. Cellular immortality is a common feature of most cancer cells. It is also one of the defining properties of cancer initiating cells, the small population of cells that drive tumour 1

CHAPTER 1:LITERATURE REVIEW growth and are thought to be largely responsible for relapse and resistance to chemotherapy [10-12]. Several lines of compelling evidence indicate that the immortalisation is an essential step in tumorigenesis [3, 13]. Hence, the molecular mechanisms underlying immortalisation provide ideal therapeutic targets [14, 15]. Rationale for the use of in vitro human model systems to investigate the molecular pathways that promote immortalisation are provided by the findings that the molecular alterations that occur during in vitro immortalisation, commonly occur during carcinogenesis. The molecular alterations have also been extensively studied in mice but species specific differences of the regulation of immortalisation, have highlighted the value of using human cell culture models to study the molecular alterations of immortalisation in human cancer [16, 17].

1.2 The replicative lifespan of normal cells

1.2.1 Hayflick limit of senescence All normal diploid somatic cells have an intrinsically limited replicative lifespan predetermined by a number of cell divisions or population doublings (PDs), known as the Hayflick Limit [18]. When the cells reach this limit, they succumb to an irreversible growth arrest in G1 phase of the cell cycle. This process is referred to as replicative senescence.

Senescent cells are typically characterised by an enlarged flattened morphology, multi-nucleation, large cytoplasmic vacuoles, decreased protein and RNA synthesis and altered gene expression. Although the cells are not actively dividing, they remain metabolically active for long periods of time [19, 20]. Under standard tissue culture in vitro conditions, human embryonic fibroblasts, various epithelial cells, vascular cells and endothelial cells are shown to enter replicative senescence, at varying PDs [21-25]. Senescence associated β-galactosidase (SA-β-gal) activity (detected at an acidic pH of 6.0), is a well-established biomarker for replicative senescence [26]. Senescent cells exhibit SA-β-gal activity irrespective of whether the stimulus that caused the growth arrest is initiated intrinsically during replicative senescence at the Hayflick limit or is induced by other stresses that result in premature senescence such as oncogenic stress or oxidative stress [26].

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The tumour suppressor gene pathways governed by p53 and pRb play key roles in the induction and maintenance of senescence [27-31]. The causal role of tumour suppressor pathways in senescence was demonstrated in studies that showed overexpression of p21CIP1, p16INK4a and p53 induced a senescent phenotype in early passage human diploid fibroblasts and immortalised cells [32-34]. Elevated levels of p53 and the cyclin dependent kinase inhibitors, p21CIP1 and p16INK4a are detected in senescent cells, while pRb is downregulated and activated in its hypo phosphorylated state [34]. In senescent cells, p53 functions to transcriptionally activate p21CIP1, which prevents cell cycle progression via the inhibition of cyclins that are required for progression into S phase of the cell cycle [27, 35]. p21CIP1 may also inhibit cell cycle progression by inactivating, an essential DNA replication factor, proliferating cell nuclear antigen [28]. In human fibroblasts, activation of both p21/p53 and p16/Rb pathways occurs during senescence, but only one of these pathways is needed to initiate senescence [28, 31, 36]. p16INK4a controls the cyclin dependent kinases (cyclin D/CDK4/6) complexes that are responsible for the phosphorylation of pRb. Increased expression of p16INK4a in senescence cells inactivates the cyclin D/CDK4/6 complexes and thereby prevents the phosphorylation of pRb [37]. Hypophosphorylated pRb in senescent cells sequesters the E2F transcription factors from their target genes, thereby controlling the expression of genes involved in DNA synthesis and G1-S phase cell cycle progression including cyclin E, cyclin A and PCNA. The cells are unable to progress through the cell cycle and enter a permanent growth arrest in the G1 phase of the cell cycle [38].

1.2.2 Telomere hypothesis of replicative senescence

1.2.2.1 Structure and function of telomeres The limited replicative ability of normal cells is largely attributed to the shortening of chromosomal-end structures, referred to as telomeres [38]. Telomeres are DNA/protein complexes made up of a guanine rich hexanucleotide repeats and a number of telomere associated proteins that interact to form a specialised structure that protect the end of linear chromosomes from damage, degradation and chromosomal fusions and in doing so, regulates and preserves genomic stability and integrity during

CHAPTER 1:LITERATURE REVIEW cellular replication [39, 40] The first suggestion that telomeres protected the chromosome ends from genomic instability emerged from investigations by Muller and Mc Clintock [39, 41]. Their studies observed that in contrast to random DNA double stranded breaks (DSB), natural chromosomal ends did not form end to end fusions and therefore must be protected somehow [39, 41]. The protective role of the telomere cap was later confirmed later by Blackburn et al., 1991 [40].

The presence of telomeres at the chromosome end is evolutionary conserved, although sequences, length and dynamics vary among species. Human germ line cells have average telomere length of 10-15 kbp whereas murine cells have considerably longer telomeres of 25-50 kbp telomeres [42, 43]. Mammalian telomeres are composed of tandem 5’-TTAGGG-3’ repeats and end in a single stranded 3’overhang of approximately 130 to 210 nucleotides in length (Figure 1.1) [43, 44]. The single stranded 3’ G-rich overhang at the telomeric DNA invades the double stranded region of the telomere to form a lariat structure consisting of a large telomere (T)-loop and a smaller displacement (D)-loop (Figure 1.1) [43, 45, 46]. Within the telomere cap, the 3’ single stranded region end is hidden which protects it from chromosomal aberrations during cellular division to ensure chromosomal integrity [46, 47].

Proteins associated with telomeric DNA aid in the stabilisation of the functional telomere or telomere cap [40, 42]. Six proteins are found in the core telomeric complex, which is termed shelterin. These telomere associated proteins include three telomere specific proteins: telomeric repeat binding factors 1 and 2 (TRF1, TRF2) and protection of telomeres 1 (POT1). These three proteins bind directly to telomeric DNA. TRF1 and TRF2 form homodimers that associate with the double stranded T- loop, whereas POT1 binds to the G-rich single stranded overhang that forms the D- loop and interacts with TRF2 at the base of the T-loop (Figure 1.1). The three other core shelterin proteins TIN2 (TRF1-Interacting Nuclear Factor 2), TPP1 (TIN2 and POT1 interacting protein 1) and RAP1 (repressor activator protein 1) bind indirectly to telomeres through interactions with TRF1, TRF2, POT1 [42, 46].

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Figure adapted from Martinez et al., 2011 [48].

Figure 1.1 Schematic representation of telomeres Human telomeres are composed of several kilobases of tandem telomeric hexanucleotide repeats of TTAGGG and end in a single-stranded 3’overhang of approximately 130 to 210 nucleotides in length. The 3’ G-rich overhang invades the double-stranded telomeric region to form a lariat structure consisting of a large telomere (T)-loop and a smaller displacement (D)-loop. Numerous proteins associate and aid in the stabilisation of this secondary structure to form a functional telomere or telomere cap. The six proteins of the core telomeric complex, shelterin, includes TRF1, TRF2, POT1 that bind directly to telomeric DNA, while TIN2, TPP1 and RAP1, which interact by binding with TRF1, TRF2 or POT1.

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Additional proteins that associate with the shelterin complex include Tankyrase 1 and 2 (TRF1 interacting ankyrin related polyADP-ribose polymerase (PARP) and Pin2 interacting protein X1 (PINX1) as well as the helicase WRN [42].

A large array of proteins involved in DNA damage and repair have been identified in association with telomeric DNA. These include the DNA damage response protein, Ataxia-Telangiectasia Mutated (ATM), the DNA damage sensor of double stranded breaks (DSB) MRE11/RAD50/NBS1 (MRN) complex, and the double stranded break DNA damage repair, Ku complex [49, 50]. These DNA damage complexes are found to interact with the telomere binding proteins TRF1 and TRF2 at the telomere and are crucial in the protection of telomeres as evidenced by the activation of ATM dependent DNA damage response upon depletion of TRF2 [51, 52].

The formation of G-quadruplexes at the telomere introduces an additional level of structural complexity. The 3’ overhang of the telomere is rich in guanine repeats and is therefore prone to G-quadruplex formation [53-56]. G-quadruplexes are made up of four blocks of repeated guanines that are engaged into a quartet structure. These structures are paired by intra- or intermolecular Hoogsteen bonds and held together by stacking interactions [53, 54, 56]. The G-overhang can fold into at least two different intramolecular quadruplexes that differ in the position of the adjacent loop regions [57-60]. The molecular driving forces that determine when and how these quadruplexes form remains to be fully elucidated. The formation of these structures occurs readily in vitro, with telomere DNA sequences and stabilisation by monovalent ions [53, 61]. The only direct evidence of the formation of G- quadruplexes at the telomeres in vivo has been demonstrated in Stylonychia ciliate and E.coli [62, 63]. Evidence that specific G-quadruplex ligands interact with the terminal regions of human chromosomes have supported the proposed role of these G-quadruplexes for the stabilisation and protection of telomere from genomic instability and chromosomal aberrations [60, 61].

1.2.2.2 Three state model of telomere protection The end capping of telomeres is not a static but rather a dynamic process, which is strictly regulated. This flexibility allows for transient uncapping which is necessary for telomere length regulation during cell cycle progression [49, 64, 65]. A three- 6

CHAPTER 1:LITERATURE REVIEW state model of telomere protection has been proposed; it includes a closed-state, intermediate-state and an uncapped-state [66].

Closed-state telomeres are fully protected and do not activate or induce DNA damage response and chromosomal aberrations. Telomeres are hypothesised to be in the T-loop closed conformation in G1 of the cell cycle [66, 67] (Figure 1.1). During S-phase, when telomeres are replicated, telomeres unfold from the closed-state [66, 67]. Failure of the telomeres to fold back into the closed-state following replication, results in an intermediate-state of protection of telomeres, and the activation of the DNA damage response [66, 67]. DNA damage foci at these telomeres are known as telomere dysfunction induced foci (TIFs). These TIFs are formed by the assembly of classic protein markers for DSB, including phosphorylated histone maker (γH2AX), Ataxia Telangiectasia Mutated (ATM), phosphorylated Checkpoint kinase 1 and 2 (Chk1 and Chk2) [52]. The intermediate state of telomere protection is likely to occur as a result of excessive telomere shortening or partial depletion of telomere binding proteins, however there is no evidence of chromosomal aberrations [67]. Cells with telomeres in an intermediate state of protection are still able to progress through the cell cycle. Recent studies have reported that in wild type (wt) p53 cells, five TIFs can be tolerated in G1 phase before the cells undergo a telomere-length independent growth arrest [67, 68].

An uncapped-telomere state occurs when telomeres are critically short and not able to bind telomere binding proteins. This may result from defects in the mechanisms that maintain telomere length and secondary structure [49, 52, 67, 69]. Dysfunctional telomeres are associated with increased DNA damage foci and promote the formation of chromosomal aberrations, fusion and recombination [67, 69]. Chromosomal aberrations arise through breakage-fusion-bridge cycles that involve the formation of anaphase bridges, chromosome breakages, misintegration of chromosomes, chromosomal rearrangements and the formation of aneuploidy [40]. In cells with intact tumour suppressor mechanisms, these types of chromosomal aberrations result in the induction of either senescence or apoptosis depending on the cell type [70].

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1.2.2.3 Telomere shortening limits replicative lifespan The incomplete replication of chromosome ends results in progressively shorter telomeres with each cellular division [39, 43]. During semi-conservative DNA replication, both strands of DNA serve as a template for DNA polymerase to synthesise new complementary DNA (Figure 1.2). The leading strand is synthesised continuously in the 5’-3’ direction as the 3’hydroxyl (OH) group of the 3’-5’ DNA template strand acts a primer for DNA polymerase. On the complementary 5’-3’ DNA strand however, there are no free 3’OH group for DNA polymerase to initiate synthesis. DNA synthesis on the lagging strand is therefore primed by short RNA primers and synthesis occurs via the production of discrete Okazaki fragments in the 5’ to 3’ direction. Following the polymerisation step, degradation of the RNA primers leave gaps, which are subsequently filled by DNA polymerase 1 and DNA ligase join the Okazaki fragments to form a continuous DNA strand. However, at the 5’end of the lagging strand the primer gap remains unfilled forming a 3’single strand overhang [71, 72] (Figure 1.2). The DNA end-replication problem is therefore a major contributing factor to telomere shortening.

Compelling evidence implicating telomere length as a critical determinant of the replicative potential of human somatic cells has led to the development of the telomere hypothesis of replicative aging [73-75]. This hypothesis elaborates on the “end DNA replication problem,” suggesting that telomere shortening with each cellular division acts as the “molecular clock” that determines the replicative lifespan of a cell [40, 76]. Progressive telomere shortening is however not solely dependent on the end DNA replication problem [77, 78]. Telomere attrition is accelerated by exposure to oxidative stress as well as endonuclease activity [79-81]. It has been demonstrated that with every cell division, 50-200 bp of telomeric DNA is lost at the chromosomal ends, resulting in progressively shorter chromosomes each cellular division [80, 81].

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Figure 1.2 Semi-conservative DNA replication of chromosome ends During each cellular division, both strands of DNA serve as a template for DNA replication. DNA polymerase synthesises the new leading 5’-3 strand continuously using the free hydroxyl group of the 3’-5’DNA template as a primer to initiate synthesis. However, due to the inability of DNA polymerase to synthesise DNA in the 3’ to 5’ direction and the requirement for primers to initiate synthesis, the lagging 3’-5’ strand is synthesised in a series of Okazaki fragments, primed by short RNA primers. RNA primers are degraded, once the fragments are synthesised. DNA polymerase 1 and DNA ligase fill the gaps left by the primers and the Okazaki fragments are joined to form a continuous strand. However, the most terminal gap left by the primer on the 5’end of the lagging strand remains unfilled, resulting in a 3’single strand overhang and progressive loss of DNA from the end of linear chromosomes.

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It was originally proposed that donor age was inversely related to telomere shortening and replicative lifespan of human fibroblasts [23, 74, 82, 83]. Correlative evidence showed that telomeres were shorter in somatic tissues from older individuals, when compared with younger individuals [42, 84]. However, this premise was later refuted by studies which controlled for health status of the patients and quality of biopsies and which showed that the disease state of patients affected telomere shortening [85-87]. A retrospective review of studies revealed that there was also a publication bias of reports supporting the inverse relationship between donor age, telomere shortening and replicative lifespan of human fibroblasts, due to study size [87]. A significant correlation between the replicative lifespan of human fibroblasts and donor age was only found in the comparison of studies with individuals with premature aging syndromes, and not for the studies with healthy individuals [87]. These results suggest that the rate of telomere shortening may be a crucial factor for the correlation between replicative lifespan of human fibroblasts, telomere shortening and age of the donor, which still remains a topic of debate.

There is however substantial evidence demonstrating that telomeres of hematopoietic cells do shorten with age, albeit on a population basis [88-90]. Telomere shortening of hematopoietic cells with age is likely to be as a result of telomere shortening in tissue stem cells and progenitor populations. For example, telomere shortening of blood leukocytes is presumed to reflect telomere loss in hemapoetic stem cells and occurs at rate of 40-100 bp/PD in healthy individuals in vivo during aging [91]. A comparison of telomere length of fetal liver, cord blood and adult bone marrow of human hemapoetic cells showed that fetal cells had both the longest telomeres and greatest proliferative potential [92, 93]. The rate of telomere shortening of hematopoetic cells is not constant and is demonstrated to be greatest in childhood compared to adulthood, possibly due to increased haematopoiesis and rapid cell turnover [94]. These investigations indicate that telomere length is a crucial factor in the functioning of hemapoetic cells and highlight the importance of telomere shortening in aging diseases or hematological malignancies marked by cells with excessively shortened telomeres.

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Human cells enter senescence following the reduction of telomeres to a critical average length. The telomere length at which telomere-initiated senescence occurs varies depending on the cell type and telomere protection state [76, 88, 95-97]. In both MRC5 human foetal lung fibroblasts and BJ human fibroblasts, critically short telomere-initiated senescence results in the full activation of the DNA damage response and subsequent activation of the tumour suppressor p53/p21 and/or p16/Rb pathways [70, 96].

There is considerable evidence indicating that the activation of the DNA damage response is the major pathway driving senescence in response to shortened telomeres, however alternate mechanisms have been proposed [98-100]. For example, a transcriptional silencing mechanism due to telomere positioning effects has been proposed, which occurs via the activation of senescence genes in proximal regions to the telomere. When telomeres reach a critical length, these normally repressed genes become activated as a result of structural rearrangements of the chromosomal DNA [99, 100]. However, later studies using primary human senescent cells and microarray analysis showed no preferential reactivation of telomere proximal genes. Instead altered senescent gene expressions were determined at four distal chromosomal loci [101, 102], which were further supported by earlier studies that have identified two senescent gene loci further away from proximal telomere regions [103].

Another mechanism proposed, suggests overall telomere state rather than telomere length to be critical for activation of senescence [104, 105]. Studies in support of this possibility have shown that overexpression of the telomere binding protein TRF2 delayed senescence from a telomere length of 7 to 4 kbps in human fibroblasts in vitro [105]. An additional mechanism for the activation of telomere-initiated senescence was proposed, suggesting that increased concentrations of unbound telomeric binding proteins interact with senescence proteins to serve as signalling molecules to activate senescence, although direct evidence of this interaction has yet to emerge [106, 107]. The latter two proposed mechanisms highlight the importance of telomere binding proteins to guard against telomere dysfunction and are consistent with the three state model of telomere protection [66, 105].

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1.3 Telomere maintenance mechanisms The maintenance of telomere length involves the precise regulation of factors mediating telomere attrition and extension. In contrast to normal somatic cells, cancer cells and germ line cells are able to maintain stable telomere lengths as a consequence of the activation of telomere maintenance mechanisms (Figure 1.3). Notably, normal cells generally have longer telomeres than those of tumour cells obtained from the same individual [75, 108].

During the process of immortalisation, cells overcome the replicative barriers that normally function as tumour suppressors [109]. The timing and control of the tightly regulated replicative lifespan show cell type specificity and may be overcome by a number of mechanisms including the alterations of p53 and Rb tumour suppressor gene pathways and/or the activation of telomere maintenance mechanisms [21, 110- 112]. In human cells, the spontaneous escape from the replicative lifespan barrier of senescence is a very rare event. Cell hybrids between immortal cancer and normal cells were shown to senesce, indicating that replicative senescence is dominant over immortality [113, 114]. In 80-90% cancers, telomeres are maintained through the activation of the enzyme telomerase, while in 10% of cancers, tumour cells use a mechanism referred to as Alternative Lengthening of Telomeres (ALT) to maintain their telomere length [14, 115, 116].

1.3.1 Telomerase The most commonly used telomere maintenance mechanism by cancer cells is the activation of telomerase, which synthesises telomeric repeats at chromosomal ends [14, 117, 118]. Immortalised and human cancer cells were first shown to have functional telomerase activity using a primer extension assay to detect enzyme activity and telomere restriction fragments (TRF) analysis to measure telomere length [14, 119, 120]. The development of a highly sensitive PCR based assay known as telomere repeat amplification protocol (TRAP) improved the ability to detect telomerase activity in cells and allowed the significance of telomerase mediated immortalisation of cancer cells to be demonstrated. These studies revealed expression of telomerase activity in immortalised cell lines and cancer cells, while little or no telomerase activity was detected in normal cells [14, 121, 122].

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Figure 1.3 Telomere maintenance underlies cancer cell immortality Normal cells progressively lose telomeric repeats with each cellular division. Once the telomeres reach a critical length, they are sensed by DNA damage repair machinery, or destabilisation of telomere binding proteins, a G1 arrest is initiated and the cells enter replicative senescence. In contrast, cancer cells are able to avoid senescence and become immortal due to their ability to maintain their telomeres at a constant length via the activation of telomere maintenance mechanisms ALT or telomerase activation.

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Importantly, from these studies the link between telomerase, the maintenance of telomeres and immortality of cancer cells was established [14]. Telomerase activity is detectable in a wide range of cancer types although its activity levels vary [14, 108, 123]. Telomerase enzyme activity has been detected in normal human germ line cells and various rapidly proliferating stem and progenitor cells including; progenitors of endometrial and lymphoid tissue, basal keratinocytes and hematopoietic cells [124-127]. Telomerase activity is suppressed during the differentiation of somatic cells, although low and/or transient levels of endogenous hTERT and telomerase activity have been detected in differentiated normal somatic cells including; differentiated epithelial cells, breast, liver cells and human fibroblasts has also been demonstrated [128-134].

The human telomerase holoenzyme is as a ribonuclear protein multimer complex consisting of a reverse transcriptase (hTERT) catalytic domain, an RNA component (hTR) that includes an RNA template domain for the synthesis of telomeric repeats and the RNA binding and modifying protein dyskerin (Figure 1.4 A) [135]. hTERT and hTR were previously considered sufficient for telomerase activity [136], but more recently all three components were shown to be required for a catalytically active telomerase enzyme [135]. Dyskerin binds to hTR in complex with small ribonuclear proteins including GAR1, NHP2 and NOP10 [137].

Numerous other proteins including telomerase associated protein (Tep1) and the chaperone proteins Heat shock protein 90 (Hsp90) and p23, as well as helicases and ATPases associate with the telomerase holoenzyme complex but appear to be dispensable for telomerase activity in vitro [135, 138, 139]. Additionally, the telomerase Cajal Body protein1/WD repeat domain 79 (TCAB1/WDR79) associates with the holoenzyme and is required for accumulation of mature hTR in Cajal bodies (Figure 1.4 A) [140]. Cajal bodies are mobile sub nuclear structures that are required for the accumulation of telomerase and serve as important intracellular sites for maturation of the holoenzyme [141, 142]. The additional proteins do not appear to be essential for telomerase enzyme activity, however they contribute to the biogenesis and assembly of the enzyme and/or play a role in the regulation of telomerase activity through trafficking and recruitment of telomerase to the telomere [135].

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Figure 1.4 Structure of the telomerase ribonuclear protein complex A) The human telomerase holoenzyme exists as a ribonuclear protein complex. Three components that are essential to its function are the catalytic reverse transcriptase domain, hTERT, an RNA component with a template for the synthesis of telomeric repeats, hTR, and the RNA binding and modifying protein, dyskerin. hTR binds dyskerin together with three other conserved ribonuclear proteins GAR1, NHP2 and NOP10. Numerous other proteins, including telomerase associated protein (Tep1) and chaperone proteins; heat shock protein 90 (Hsp90) and p23, and the telomerase Cajal body protein 1 (TCAB1/WDR79) also associate with the holoenzyme but are dispensable for active telomerase enzyme activity. B) hTR is a 451 nucleotide RNA that adopts a secondary structure with universally conserved structural domains and can be divided into two functional regions: Template/pseudoknot region and the H/ACA region. hTERT is a 127 kDa protein that includes a central catalytic reverse transcriptase (RT) domain with seven evolutionary conserved RT motifs that are essential for catalysis. Both regions outside the RT domain of hTERT are required to reconstitute telomerase activity in vitro. The N-terminus includes the DAT (dissociates activities of telomerase) domain is responsible for localisation of telomerase to the telomere [143]. hTERT binds to hTR via two RNA interacting domains (RID 1 and RID 2). Dyskerin includes an N- terminal dyskerin-like domain (DKCLD), a TruB domain with Pseudouridine synthase and archaeosine transglycosylase RNA binding domain (PUA) and a lysine rich region (LRR) at the C-terminus.

CHAPTER 1:LITERATURE REVIEW

hTERT

B Domains of hTR

- Pseudouknot domain RNA template 5' -CUAACCCUAAC-3' Template /pseudoknot region - Template boundary reg ion - CR4-CR5 domain - H/ACA box domain CAB box l ] H/ACA region CR7 domain - BIO box Domains of hTERT

N-terminus RTdomain C-terminus

Domains of Dyskerin

Figure adapted from Chen et al., 2000, Zhang et al., 2011, Armbuster, et al., 2001, Angrisani, et al., 2001[143-146].

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1.3.2 Alternative Lengthening of Telomeres (ALT) ALT mediated telomere maintenance occurs by a homologous recombinant mechanism between telomeres [115, 116]. Human cell lines that utilise ALT are characterised by long heterogeneous telomere lengths and ALT-associated promyelotic leukaemia nuclear bodies consisting of the promyelotic leukaemia protein, the telomeric proteins TRF1 and TRF2 and proteins involved in DNA recombination and repair [115, 147]. The ALT mechanism is most commonly found in telomerase-negative human mesenchymal sarcomas and neuroepithelial tumours [116, 148]. The lack of telomerase expression in ALT tumour cells has been shown to occur in association with repressive chromatin remodelling at the promoter regions of telomerase hTR and hTERT component genes. Disruption of histone methylation and acetylation by treatment with trichostatin A and 5-azadeoxycytidine treatment reactivated hTERT and hTR expression in ALT tumour cells, resulting in reconstitution of telomerase activity [149].

The existence of ALT-mediated telomere extension and evidence to suggest that telomerase and ALT may coexist in human cells, have important implications for possible resistance to anti-telomerase therapy [150]. The possibility that ALT may provide a mechanism of resistance to anti-telomerase therapy was highlighted by a report of a mismatch repair deficient colon cancer cell line in which, a telomerase- independent ALT-like telomere elongation was activated following telomerase inhibition [151].

1.4 The telomerase holoenzyme

1.4.1 Human telomerase reverse transcriptase, hTERT The 127 kDa protein human telomerase reverse transcriptase hTERT protein is encoded from a single copy gene located on chromosome 5p15.33 that includes 16 exons [152, 153]. The protein includes a central catalytic reverse transcriptase (RT) domain with seven evolutionary RT conserved motifs that are essential for catalysis [152, 154, 155] (Figure 1.4 B). The RT domain is flanked on either side by N- and C- terminal regions that are required to reconstitute telomerase activity in vitro [143, 154, 156]. The N-terminus is more highly conserved than the C-terminus and deletion of the first 350 amino acids abolishes telomerase activity [157]. The N- 17

CHAPTER 1:LITERATURE REVIEW terminus of hTERT contains a region also known as DAT (dissociates the activities of telomerase domain) which is dispensable for telomere catalytic activity in vitro but essential for telomere elongation in vivo possibly through telomere localisation defects [143, 158, 159].

Detailed investigations of the functions of this region suggest that the DAT domain is involved in the association of telomerase to the telomeres through interactions with other telomerase binding proteins [143, 158]. Additionally, the N-terminus is required for hTR binding as hTERT holds two RNA interacting domains (RID 1 and RID 2), that bind to hTR, one with relatively high affinity and one with lower affinity [143, 160, 161] (Figure 1.4 B). The C-terminus of hTERT is a highly divergent region between species and is not very well characterised. Since it includes a CRM1 (exportin1) site and multimerisation sites, it may be involved in the nuclear export of the protein and in the multimerisation of hTERT molecules [157, 159, 162]. hTERT is regarded as the rate limiting component of telomerase enzyme activity. In support of this view, there are numerous studies demonstrating that overexpression of hTERT in normal cells is sufficient to reconstitute telomerase activity [163, 164]. Furthermore, it was demonstrated that the exogenous transduction of hTERT is sufficient to reconstitute telomerase activity, elongate telomeres and immortalise not all but several normal human diploid cells [73, 165, 166]. Additional evidence is provided by studies that confirmed the expression of the hTERT component is limited to cells that exhibit telomerase activity [14, 163]. Immortal and tumour cells express less than 1-5 copies of hTERT mRNA transcripts per tumour cell and hTERT mRNA has half-life of only 2 hrs [167, 168].

1.4.2 Human telomerase RNA component, hTR The TERC gene is located on chromosome 3q26 that encodes telomerase RNA molecule (hTR) [169, 170]. hTR is highly divergent among eukaryotes differing in primary sequences, secondary structure and size, although some regions remain conserved [169, 170]. The transcriptional machinery responsible for the transcription of the telomerase RNA also differs between species. In humans, hTR is transcribed by RNA polymerase II to yield a mature 451 nucleotide product that adopts a secondary structure with four universally conserved structural domains that 18

CHAPTER 1:LITERATURE REVIEW constituent two functionally distinct regions of the hTR molecule [170, 171] (Figure 1.4 B).

The 5’ region of hTR includes the telomerase template, which is made up of two domains, a pseudoknot domain (nucleotides 33-147) and a conserved region 4 and 5 (CR4-CR5 domain) (nucleotides 243-326). Both these two domains are essential for telomerase activity (Figure 1.4) [172, 173]. The pseudoknot domain is characterised by stem-loop-base pairing that forms a pseudoknot secondary structure and includes the template RNA region and the putative hTERT binding site [169, 170]. The telomerase RNA template consists of a short 11 nucleotide sequence of 5’- CUAACCCUAAC-3’, which is complementary to the sequence of human telomeric repeat DNA and is encoded between nucleotides 46-52 of the hTR sequence. hTR includes a defined upstream template boundary hairpin region (P1) on the 5’ of the template that ensures the precise copying of the telomere sequence from this small region of hTR [170].

The CRC4-CR5 domain interacts with high affinity to hTERT independently of the pseudoknot domain, which interacts with hTERT with lower affinity and is crucial for telomerase activity [144, 160, 161, 174]. Deletion studies have revealed that specific residues found within the CRC4-CR5 of hTR function separately in catalysis from those regions that bind to hTERT [175]. The RNA structural element referred to as hTR J6 within the CRC4-CR5 domain, forms a stable hairpin interrupted by a single nucleotide bulge and an asymmetric internal loop which introduces a twist in the RNA structure that positions the domain to from an optimal active site [175, 176] (Figure 1.4). Within the CRC4-CR5, the P6.1 hairpin undergoes pseudouridylation which results in conformational change that increases the stability of hTR but has subtle effects on telomerase activity [177].

The 3’ region of hTR includes an H/ACA box and conserved region 7 (CR7) (nucleotides 211 to ~237 and ~334 to 451), which are essential for RNA stability [137, 144]. In vivo reconstitution of telomerase requires the pseudoknot domain and template sequence and the H/ACA box domain indicating that hTR stabilisation is an important factor for functional telomerase enzyme [137, 172, 173]. The H/ACA

CHAPTER 1:LITERATURE REVIEW domain of hTR is also a common feature of small nucleolar RNAs (snoRNA) and small Cajal body RNAs (scaRNAs) [137, 178, 179]. SnoRNAs are mainly responsible for the methylation and pseudouridylation of ribosomal RNA, tRNAs and small nuclear RNAs (snRNAs). ScaRNAs are small nucleolar RNAs which specifically localise to the Cajal bodies for maturation, nuclear organelles that are sites for biogenesis of small nuclear ribonucleoproteins. scaRNAs are responsible for the methylation and pseudouridylation of splicesomal RNAs [178, 179]. The RNA modifying protein dyskerin associates with hTR directly through binding at this H/ACA region in complex with three other ribonuclear proteins (RNP) NOP10, NHP2, and GAR1 [173].

Within the CR7 domain of hTR, there are two additional motifs. A four nucleotide Cajal body localisation sequence (CAB box) within the CR7 domain, suggests that hTR may function as a scaRNA. The telomerase Cajal Body protein 1 (TCAB1/WDR79) binds at the CAB box and the direct association of TCAB1 increases the concentration of mature RNP in Cajal bodies [140, 180, 181]. An additional BIO box motif is found in the CR7 domain, which is required for mature RNP accumulation in vivo and complete processing of 3’ end of hTR [182, 183]. Increased efficiency of RNP assembly by the 3’ hairpin and BIO Box region distinguishes the hTR H/ACA domain from other human H/ACA RNAs [182, 184].

The complexity of hTR secondary architecture and the well-defined regions of hTR are crucial to its function in the telomerase enzyme [173]. Not only does hTR serve as the template for telomere synthesis and has a role in telomerase alignment to the telomere; it also contributes to the structure and function of the active telomerase catalytic site [161]. Interactions between hTR and the RNA modifying proteins and trafficking proteins (TCAB1/WDR79) ensure proper stability and RNP biogenesis and maturation of telomerase enzyme [180, 184]. hTR is widely expressed in cells of different tissue types, including cells with undetectable telomerase activity. However, it is found to be present in normal somatic cells at lower levels than in cancer-derived cell lines [167, 170, 185]. A number of studies have revealed varied expression levels of hTR in human cancer

CHAPTER 1:LITERATURE REVIEW cells [186]. The transcription half-life (∼3 weeks) of hTR is increased in normal cells overexpressing hTERT or in cancer cells expressing endogenous hTERT [167]. This stabilisation of hTR in the presence of hTERT indicates the importance of hTR function for telomerase activity. In contrast to the previously thought notion that hTERT is the sole rate limiting factor of telomerase, there are a few studies that suggest under certain conditions in cells that express hTERT, hTR may also become a limiting factor for telomerase activity [186-188]. In telomerase positive HT1080 cells, overexpression of hTR alone increased telomerase activity to much higher extent than hTERT overexpression alone [187]. In human mammary epithelial cells with amplified TERT, amplified TERT was found to correlate with low levels of hTR, telomerase activity and telomere function, while exogenous expression of hTR caused an increase in telomerase activity and telomere lengthening [186]. hTR was also found to be limiting for telomerase activity and telomerase-mediated telomere length maintenance of hTERT-transduced bone marrow endothelial cells under mild hyperoxia conditions [188]. These studies indicating that in telomerase positive cells, hTR may also limit telomerase activity and telomere maintenance are consistent with findings that hTR levels limit telomere maintenance in X- linked DC [189].

1.4.3 Dyskerin The DKC1 gene is located at chromosomal region Xq28. It is 2.6 kb in length and is divided into 15 exons that encode a highly conserved 57 kD nucleoprotein [173, 190]. The N-terminus of dyskerin protein holds two TRuB catalytic domains. This domain includes a Pseudouridine synthase and archaeosine transglycosylase (PUA) RNA binding domain through which hTR binds (Figure 1.4). Dyskerin associates with small ribonuclear protein including GAR1, NHP2 and NOP10 and binds as a complex to the H/ACA box of both snoRNAs and scaRNAs including hTR through the PUA domain that is crucial for hTR stability [105,158,159-161]. The TRuB catalytic domain is responsible for the telomerase-independent function of dyskerin as a pseudouridine synthase that functions in the processing of rRNA and assembly of ribosomes [190]. As a pseudouridine synthase it functions in the site specific modifications of scaRNA and snoRNAs, catalysing the conversion of uridine residues to a pseudouridine that are involved in ribosomal RNA (rRNA) and splicesomal RNA modifications respectively [190-192]. Dyskerin also has an N- terminal dyskerin-like domain (DKCLD) with unknown function. A lysine-rich 21

CHAPTER 1:LITERATURE REVIEW repeat (LRR) domain on its C-terminus has been identified and may be involved in protein–protein interactions but its function has yet to be established.

A homozygous deletion of DKC1 is embryonically lethal in mice, whereas TERT or TERC knockout mice do not show impaired phenotype for four to five generations [171, 193-195]. Mutation of the DKC1 is the causal factor of the X-linked inherited disease dyskeratosis congenita (DC) [173]. DC-associated deletion mutants of DKC1, impair dyskerin function but do not ablate dyskerin expression completely. In DC, mutated dyskerin primarily affects tissues and results in the dysfunction of tissues with high cell turnover, such as skin and haematopoietic tissue due to impaired cell proliferation of these cells [173]. The use of X-linked DC deletion mutants of DKC1 has been pivotal for the elucidation of dyskerin’s function. Studies of DC were the first to indicate that dyskerin is functionally important for telomerase activity and implicated telomerase insufficiency as a crucial factor in the pathogenesis of this disease [173, 189].

Dyskerin is not only a core component of the telomerase holoenzyme, but also plays a crucial role in the assembly and regulation by influencing the maturation, accumulation and localisation of telomerase RNA. The crucial role of dyskerin (in association with GAR1, NHP2 and NOP10) in hTR RNA stability has been established from studies of DC that have showed missense mutations in DKC1 result in defective hTR biogenesis and stability [173]. A large proportion of the DKC1 mutations in DC occur within the PUA domain, which may account for instability of hTR [196]. An 80% reduction of hTR levels compared to normal hTR levels was demonstrated in patients with mutated DKC1, short telomeres and defective telomerase activity [173, 189, 197, 198] (Figure 1.4 B). Disruption of the hTR binding sites confirmed that the stabilisation role of dyskerin with hTR is essential for enzymatically active telomerase [135, 199].

The expression of dyskerin is not limited to cells with detectable telomerase activity and expression levels of dyskerin were shown to be variable in various human sporadic tumours of different histological origins including breast, lung, colon and B- cell lymphomas [173, 190, 200]. Elevated levels of dyskerin are associated with a poor prognosis in breast, oral squamous cell carcinoma, colon, prostate, 22

CHAPTER 1:LITERATURE REVIEW hepatocellular carcinoma. There is also evidence of elevated levels and deregulated expression of dyskerin in neuroblastoma [201-204]. Modulation of telomerase activity in breast cancer cells by dyskerin was shown to occur via the regulation of hTR levels and was independent of hTERT expression demonstrating that dyskerin expression levels modulates telomerase activity via the regulation of hTR levels [200].

1.4.4 Telomerase assembly A number of proteins facilitating in telomerase assembly also play a role in regulating telomerase activity [205, 206]. The assembly of hTR RNP and subsequent assembly of hTR with hTERT is a highly chaperoned process [138, 139]. Chaperone Heat shock protein 90 (Hsp90) and p23 proteins specifically bind to hTERT protein and remain associated with functional telomerase enzyme. These two proteins influence the proper assembly of the holoenzyme with template RNA and govern the multimerisation of hTERT [138]. The telomere protein 1 (Tep1) was shown to be involved in the assembly or localisation of the telomerase RNP in vivo [139].

The DNA helicases pontin and reptin, which exhibit ATPase activity, interact with both hTERT and dyskerin. shRNA-mediated inhibition of pontin in HeLa cells that deplete both pontin and reptin, showed a marked reduction in telomerase activity and hTR RNP assembly indicating that pontin and reptin function in the assembly and biogenesis of telomerase [205]. Similar findings of the AAA-ATPase Nuclear VCP/p97-like (NVL2) mediated ribosomal biogenesis were demonstrated. NVL2 interacted and co-localised with hTERT in the nucleolus and was implicated to function in the assembly of a catalytically competent telomerase, from which it would dissociate once formed [206]. Furthermore H/ACA RNP assembly is aided by a complex of the nuclear RNA binding protein NUFIP, which bind to NHP2 and the helicases RUVBL1 and RUVBL2, which bind to dyskerin [207]. Depletion of these chaperone activities reduces mature hTR accumulation in vivo [205, 207-210].

1.4.5 Trafficking of telomerase components and recruitment to the telomere Telomerase is mostly located within the nucleolus and is targeted to the telomere during DNA replication [211]. Accordingly, telomerase activity levels are found to be highest during S-phase, when telomeres are synthesised [212-214]. Cell cycle 23

CHAPTER 1:LITERATURE REVIEW regulation of telomerase was reported in an early study that quantitated telomerase activity after arresting human cancer cell lines in the different phases of the cell cycle. This study showed that telomerase activity peaked in S phase but was lacking in G2/M cells [212]. The cell cycle regulation of telomerase activity was however debated by a contrasting report, which demonstrated that telomerase was not cell cycle regulated but present in all phases of the cell cycle in a variety of immortal cells, including; fibrosarcoma, non-small cell lung carcinoma, renal cell carcinoma cells [214]. The apparent discrepancies between these studies was possibly due to the chemical agents used to block cells in particular cell cycle phase and the different methods of detection of telomerase [214]. Later studies confirmed the cell cycle regulation of telomerase by examining the subcellular localisation of endogenous hTR and hTERT levels in HeLa cells within the cell phases [213]. These studies showed endogenous hTR and hTERT are recruited to subset of telomeres during S phase and the recruitment peaks at mid S phase, indicating that the transport of telomerase to the telomeres is tightly regulated with the cell cycle.

The detection of low and transient levels of telomerase in S phase of normal cells also suggested that telomerase may be cell cycle regulated in normal cells [128-134]. Evaluation of endogenous hTERT mRNA expression, telomerase activity and hTERT promoter-based transgene expression in a panel of 11 normal cell lines and 12 cancer cell lines, however demonstrated the contrary, that the cell cycle regulation of telomerase was specific to cancer cells [215]. This study showed that hTERT expression or telomerase activity remained undetected in all of the normal cells, while variable levels of hTERT and telomerase activity was determined in all of the cancer cells, with some showing higher levels within G1/S phase [215]. The lack of cell cycle regulation of telomerase in normal cells suggests that the cell cycle regulation of telomerase in cancer cells is related to its function for telomere synthesis.

Following transcription and assembly of hTR with the H/ACA core proteins, hTR is shunted to the Cajal bodies. In cancer cells, hTR was shown to remain in the Cajal bodies during cell cycle progression [216], while hTERT localised to nucleoli [213]. In normal primary cells, hTR is distributed throughout the nucleoplasm and does not

CHAPTER 1:LITERATURE REVIEW accumulate in the Cajal bodies. Dyskerin localises to the p80 coilin protein compartment within the nucleolus [168]. During S phase, hTERT concentrates in nucleoli and hTR-containing Cajal bodies move to the nucleolar periphery. Only co- localised hTERT and hTR associated with Cajal bodies are observed to co-localise with telomeres [141, 142, 213]. The co-localisation of Cajal bodies with telomeres during S-phase, when telomeres are replicated, suggested that Cajal bodies are important for the recruitment of telomerase to the telomere [141, 142, 216]. Together, these data are consistent with the recruitment of hTR to the Cajal bodies as an important factor in the regulation of telomerase-mediated telomere maintenance in cancer cells [140, 216, 217].

The telomerase Cajal body protein 1 (TCAB1/WDR79) binds to hTR, through the CAB box and also through a direct interaction with dyskerin [180]. TCAB1/ WDR79 was previously shown to localise to the telomeres, in a telomerase-dependent manner and was involved in the accumulation of telomerase to the Cajal bodies [180]. Hence, it was perceived that TCAB1 recruited telomerase to the telomere in a Cajal body dependent manner. However, a recent study demonstrated that TCAB1/WDR79 had a more direct role in the recruitment of telomerase to the telomere [218]. This study showed that TCAB1/WDR79 was recruited to the telomere in a telomerase dependent but a Cajal body independent manner [218]. This recruitment was also found to be independent of N-terminal DAT region of hTERT, which is important region for telomerase recruitment to the telomere [143, 218]. Gene mutations of TCAB1/WDR79 have recently been identified to cause dyskeratosis congenita, highlighting the significance of TCAB1/WDR79 for telomere maintenance [219]. Telomere binding proteins also play a role in recruiting telomerase to the telomere [106]. TTP1 includes an hTERT-interacting domain that functions in the recruitment of telomerase to the telomere [220, 221].

1.5 Regulation of telomerase components The regulation of telomerase enzyme activity is complex and controlled at a variety of levels. The differential activation of telomerase within normal and cancer cells is largely due to the transcriptional regulation of the telomerase components, but other contributing regulatory controls include epigenetic, post transcriptional, post- translation regulation, gene amplification, as well as trafficking, biogenesis and 25

CHAPTER 1:LITERATURE REVIEW assembly of telomerase enzyme components into a functional telomerase enzyme [149, 180, 206, 213, 222-225].

1.5.1 Transcriptional regulation of telomerase components The promoter of the gene encoding hTERT (TERT) is activated in most immortal and cancerous cells, while it is inactivate in transformed pre-immortal and most normal cells corresponding with telomerase enzyme activity [222]. The TERT promoter was shown to be activated by a number of transcription factors, steroid hormones and oncogenes including Sp-1, estrogen and retinoic acid receptors, c-Myc and Human papilloma virus (HPV) 16 E6 oncogenes and the Ets family of transcription factors [226-229]. Suppressors of the TERT promoter include tumour suppressor’s p53, pRb1 and Wilm’s tumour 1 (WT1), transcription factors E2F1 and signalling molecules such as tumour growth factor beta (TGF-β) [230-234]. Myc and Mad1 function antagonistically to regulate hTERT transcription [235]. More complex interactions are also evident on the TERT promoter. In human breast cancer cells, TGF-β signalled via the phosphorylation of Smad3, which interacted with c- Myc at the hTERT promoter. The binding of this complex to the hTERT gene promoter repressed hTERT transcription and inhibited telomerase activity [223, 224]. The paired domain–containing transcription factor, Pax 8, was shown to directly bind to both TERT and TERC promoters and activate expression and telomerase activity in glioma patient cells, indicating Pax 8 to be an important regulator of telomerase activity [236]. hTERT promoter is also a direct transcriptional target of the β- catenin/TCF4 activator complex and is critical for the reactivation of telomerase in cancer cells [237].

The transcriptional response elements responsible for TERC promoter activity are included within the 231 bp of the transcriptional start site. TERC is amongst others activated by Sp-1 and repressed by Sp-3 and Nuclear factor Y (NF-Y) [238, 239]. The tumour suppressor pRb was also shown to induce hTR promoter activity possibly by interactions with other transcriptional regulators [240]. Mdm2 was found to negatively regulate TERC gene promoter activity by two different mechanisms. Mdm2 was found to repress the transcriptional activity of Sp1, preventing the activation of TERC by Sp1 and Mdm2. Additionally, Mdm2 repressed TERC

CHAPTER 1:LITERATURE REVIEW promoter activity through a direct association of Mdm2 with the TERC core promoter in vivo [238].

Direct transcriptional activation of the DKC1 gene by the oncogenes Myc and the transcription factor Sp-1 has recently been demonstrated [241]. Regulation of DKC1 by c-Myc occurs in the absence of de novo protein synthesis [241]. DKC1 was identified as a direct transcriptional target of both n-Myc and c-Myc and was found in the cluster of genes associated with MYCN expression in neuroblastoma samples [241, 242].

1.5.2 Epigenetic regulation of telomerase components Epigenetic regulation and chromatin remodelling control both TERT and TERC gene expression and play key roles in the silencing of hTERT and hTR transcription in normal cells and its activation in immortalisation [149, 225]. Both the TERT and TERC promoters have a number of CpG islands and both TERT and TERC promoters are found to be methylated in normal somatic cells [149, 243]. Only a few (30%) ALT cell lines have been identified as hTR negative [244], however TERC methylation has not been reported in these ALT cell lines. Similarly, unmethylated states of TERT promoter are also found in some telomerase negative and non- immortalised cells, suggesting additional mechanisms must exist to prevent TERT and TERC expression in these cells [245]. Hypermethylation of the TERT promoter was recently demonstrated to be associated with tumour progression and poor prognosis of paediatric brain tumours [246, 247]. Histone deacetylation functions in the repression of hTERT and hTR of normal cells. Inhibition of histone deacetylation in normal and telomerase negative cells resulted in re-activation of telomerase activity and hTERT and hTR mRNA expression [149, 225, 248].

1.5.3 Post-transcriptional regulation of telomerase components Thirteen different mRNA variants of hTERT have been identified but only the full length hTERT transcript is associated with telomerase activity. The two most common variants are the α- and β-variants [249-251]. Different variants often coexist in cells, however it is yet to be determined whether the ratios between the full- length hTERT mRNA and its spliced forms affect telomerase activity [152, 252].

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Post-transcriptional processing of hTR is another level of regulation of telomerase activity [253]. It was shown that splicing of the 3’ terminus of hTR is required to yield a mature hTR [253]. An additional splice variant of dyskerin, which encodes a truncated form of dyskerin has recently been discovered. This splice variant is thought to function independently of telomerase in cell adhesion and localises to the cytoplasm [146].

1.5.4 Post-translation modification of telomerase components Phosphorylation and dephosphorylation of hTERT by various signalling molecules regulate hTERT post-translationally [167, 254]. For example, protein kinase C (PKC) enhances telomerase activity through the phosphorylation of hTERT, while protein phosphatase 2A (PP2A) inhibits telomerase activity following dephosphorylation of hTERT [255, 256]. Akt is responsible for the phosphorylation of hTERT on Ser 824. This phosphorylation event is associated with the upregulation of telomerase and is involved in growth signalling through the phosphatidylinositol- 3-kinase (PI3K) pathway [257]. In contrast, DNA damage activated c-Abl tyrosine kinase oncogene, associated and phosphorylated hTERT inhibited telomerase in breast cancer cells [258]. Dyskerin holds multiple phosphorylation sites but their roles are yet to be elucidated [185].

1.5.5 Gene amplification and mutations Gene amplification and chromosomal gains on the 5p and 3q chromosomal arms, which encode hTERT (5p15) and hTR (3q26), frequently occur in human tumours [186, 259]. Amplification of both TERT and TERC are found in a number of solid tumours [169, 186, 259, 260]. Recent evidence indicates that TERT promoter mutations frequently occur in bladder cancer, thyroid cancers, melanoma and glioblastomas [261-266]. Single nucleotide Polymorphisms (SNPs) within and around the TERT locus have been identified to be associated with risks of numerous cancers including prostate, breast and ovarian cancer [267-269].The chromosomal region where DKC1 (Xq28) is located, is also subjected to chromosomal gains and amplifications in human cancers, but no gene amplification of DKC1 have thus far been identified [270, 271].

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1.6 Telomerase-mediated telomere extension The ability of telomerase to synthesis tandem hexanucleotide repeats and elongate eukaryotic telomeres was first described in the protozoa Tetahymena [44, 272, 273]. Telomerase elongates telomeres processively in repeated cycles of extension and translocation (Figure 1.5 A-D). Telomere synthesis is initiated by the recruitment of telomerase to the telomeric DNA (Figure 1.5 A). Telomerase binds to the 3’end of the telomeric strand by aligning the 11 nt template region (5’-CUAACCCUAAC-3’) of hTR to complementary telomeric DNA strand. The 5’ region of the telomeric DNA is secured through a separate anchor site of hTR [170, 274]. This is followed by template directed sequential addition of deoxynucleotides to the 3’end of telomeric DNA strand by hTERT-mediated reverse transcription of the template region of hTR (Figure 1.5 B). Once the telomere repeat is synthesised (Figure 1.5 C), the 5’ telomere-end is extended by DNA polymerase 1. Telomerase translocates without dissociation and repositions the enzyme for another round of elongation. The process ends when telomerase dissociates from the telomere [274, 275].

Telomerase-mediated telomere synthesis is precisely regulated to ensure properly controlled elongation without excessive extension. The regulation of telomerase enzyme activity ensures that telomerase activity reaches sufficient activity levels for elongation of telomeres in only a few cell types including stem cells, germ cells and cancer cells [74, 276]. Telomeres of telomerase positive cancer cells maintain their telomeres in the range of 2-8 kbp, although some neuroblastoma and leukaemia cells maintain longer telomeres up to 15 kbp [201, 277]. Telomeric DNA is however not static and stabilisation of telomeres by telomerase occurs at steady levels [187, 278].

Telomere attrition and other factors including limited access of telomerase to the telomere by telomere binding proteins, ensure that high levels of telomerase do not always result in continuous telomere elongation. The access of telomerase to the telomeres is governed by the telomere binding proteins of the shelterin complex and telomere architecture [56, 279]. The formation of T-loops and G-quadruplexes at the telomeres can prevent the action of telomerase and telomere function [53, 55]. It was formerly viewed that all that all G-quadruplexes, in any particular conformation were not amenable to telomerase-mediated telomere extension [53].

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Figure adapted from Maritz et al., 2010, Zhang et al., 2011 [145, 280].

Figure 1.5 Telomerase-mediated extensions of telomeres A) The core telomerase complex binds and aligns to the single stranded 3’overhang of the telomere end. B) hTERT catalyses the addition of TTAGGG telomeric repeats using a region of hTR as a template for reverse transcription of telomeric DNA.C) Once the telomeric repeat is synthesised, telomerase translocates without dissociation to enable another round of telomeric DNA synthesis to occur. D) The process continues until telomerase dissociates from the telomere and then the 5’ telomere-end is extended by DNA polymerase 1.

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In vitro studies in ciliates later revealed that some G-quadruplex structures are recognised and prime telomerase for telomere extension [56, 281].

The epigenetic status of telomeric chromatin is also an important regulator of telomere elongation. Both telomeric DNA and subtelomeric DNA are highly methylated and loss of this methylation results in excessive telomere elongation [282]. Additionally, telomeric RNAs (TERRAs), which are transcribed from telomeric DNA associate with telomeric chromatin and act to inhibit telomerase and negatively regulate telomere length [283]. Consistent with this effect, TERRAs are found to be downregulated during cancer progression [283].

Continued telomere shortening in the presence of high telomerase activity and extended periods of proliferation have previously been attributed to an additional capping function of a catalytically active hTERT [65, 284, 285]. Introduction of catalytically active hTERT into telomerase negative pre-crisis fibroblasts protected telomeres from telomere dysfunction, recognition by the DNA damage response and cellular death, suggesting that telomerase provides a protective telomere cap [65, 284, 285]. Another explanation for the apparent lack of telomere lengthening in the presence of telomerase activity is suggested by results that demonstrated that when telomerase levels are limiting, elongation in vivo is less processive on longer telomeres and telomerase preferentially elongates the shortest telomeres [278, 286, 287]. Hence, while no net telomere lengthening may be apparent, the shortest telomeres may be maintained preventing senescence and cell death. Support for the lack of apparent telomere lengthening is also given by evidence of a novel rapid telomere shortening trimming mechanism that regulates telomere length in cells with increased telomerase activity [288]. Heterogeneous telomere length are evident in cells with increased telomerase activity and telomeric DNA is trimmed off in the formation of telomeric circles (t-circles) to prevent the over-lengthening of telomeres and [288].

1.6.1 Immortalisation via overexpression of hTERT The role of telomerase and its individual components in the maintenance of telomeres and in the acquisition of cellular immortalisation was demonstrated by specific manipulation of telomerase in human cells with cloned components of the 31

CHAPTER 1:LITERATURE REVIEW enzyme in vitro, as well as in telomerase component knock-out mice and in the pathogenesis of DC [3, 289-291]. The spontaneous upregulation of hTERT and telomerase activity together with telomere elongation was demonstrated in the rare cells that escape senescence and overcome a period of delayed growth (crisis) following transformation with viral oncogenes that disable tumour suppressor pathways and are subsequently immortalised [108, 125, 155]. Activation of telomerase in these cells corresponds with their immortalisation.

Several studies have shown that ectopic hTERT expression in normal human diploid cells, including human foreskin fibroblasts, retinal epithelial cells and endothelial cells is sufficient to reconstitute telomerase activity, elongate telomeres and enable proliferation beyond senescence [73, 165, 166]. These hTERT immortalised cells were devoid of any disruptions to p53 and pRb pathways and conferred no signs of genetic instability or malignant transformation [73, 165, 292]. The ability of ectopic expression of hTERT to immortalise cells independently of malignant transformation has been utilised as a powerful cell culture tool for the immortalisation of a variety of cells in vitro [3]. However, several other studies have shown that activation of telomerase activity by hTERT is not sufficient for immortalisation and additional molecular alterations are required. These observations were made in a variety of hTERT immortalised cell types including lung fibroblasts, bone marrow endothelial cells, keratinocytes, breast epithelial cells and MRC5 lung fibroblasts [123, 284, 293- 297]. Overexpression of hTERT in MRC5 cells resulted in a transient telomere- length independent crisis, which was overcome by inactivation of p16 but the regulation of p53/p21CIP1 remained unchanged [294, 296, 297]. The inhibitor of apoptosis family member survivin was shown to be upregulated during the process of hTERT immortalisation [298]. The upregulation of survivin was linked to silencing of p16 and conferred survival of immortalised cells via the protection from apoptosis [298, 299]. These studies suggested other molecular alterations are required for immortalisation of virus/oncogenically transformed cells that have undergone additional molecular alterations.

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1.7 Extra-telomeric functions of telomerase components The possibility that telomerase and/or its components have extra-telomeric functions that contribute to malignant transformation, tumour growth and survival, has been a subject of rigorous debate [300-302]. Some contend that the pro-survival and proliferative effects of telomerase that occur in the absence of obvious telomere length changes are explained by the ability of telomerase to preferentially maintain critically short telomeres [278, 287], while others have shown that the extra- telomeric functions can be dissociated from telomerase activity and localisation to the telomeres, with the use of catalytically inactive mutants of hTERT and hTR and telomere localisation mutants of hTERT. The unravelling of the mechanisms mediating the extra-telomeric functions of telomerase will allow a better understanding of how these additional functions of telomerase support tumour cell survival and progression, independently of telomere maintenance.

Low and/or transient expression of hTERT and telomerase activity has been detected in numerous normal somatic cells including; differentiated epithelial cells, breast, liver cells and human fibroblasts. Since these low levels of telomerase are inadequate for the elongation of telomeres, it has been proposed that they may contribute to other cellular processes that are essential for cell replication and viability suggesting that extra-telomeric functions of hTERT may not be specific to tumour cells [128- 134].

The telomerase ribonucleoprotein has demonstrated to have additional nuclease functions independent of telomere maintenance. Nuclease activity of cell extracted or recombinant purified human telomerase was shown to remove 3' non-telomeric nucleotides from a substrate containing 5' telomeric DNA, followed by extension of the newly exposed telomeric sequences [233]. hTERT was also shown to possess transferase activity, independent of telomere elongation activities [303, 304]. The transferase activity of hTERT was demonstrated in reticulocyte lysate and not whole cells and showed that both yeast and hTERT protein are able to function as a template- and RNA-independent terminal transferase in DNA synthesis [303, 304].

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1.7.1 Extra-telomeric functions of hTERT Telomere-length independent effects of hTERT components was demonstrated in investigations that showed hTERT promoted tumour proliferation and survival by gene expression changes, alterations to DNA damage and repair pathways, chromatin remodelling and interactions of signaling pathways [126, 134, 298, 305, 306]. Proposed mitochondrial functions of hTERT that are functionally independent of telomere elongation have also been described [128, 134, 302, 307, 308] (Table 1.2). The extra-telomeric functions of hTERT have been classified into activities that require catalytically active reverse transcriptase activity and those that do not require a catalytically active reverse transcriptase activity [134, 302, 307, 308].

1.7.1.1 Cell survival and proliferation The first evidence of telomere-length independent functions of telomerase reverse transcriptase was derived in vivo in murine models [309]. Transgenic mice constitutively overexpressing TERT in basal keratinocytes of stratified epithelial were found to have a higher susceptibility to develop tumours, when compared to wild type mice [309]. Due to the inherent long lengths of telomeres in early generation mice, this phenotype was not attributed to the telomere length maintenance function of overexpressed TERT [16, 194]. When TERT overexpressing transgenic mice were crossed into a TERC deficient background, the TERT/TERC-/- mice showed a reduction in tumour growth compared to TERC null mice, suggesting TERC was required for the tumour promoting effect of TERT [309]. In contrast to the requirement for TERC for tumour growth in that study, subsequent investigations showed that overexpression of TERT in mouse skin epithelium, promoted the growth of murine hair and skin epidermal stem cells through the mobilisation and proliferation of hair follicle cells [310, 311]. These effects also occurred in TERC-deficient mice and mice expressing the catalytically inactive TERT or TERC mutants, indicating the effects were independent of telomerase catalytic activity and mTR [310, 311].

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Table 1.1 Extra-telomeric functions of telomerase and its components Telomerase Extra-telomeric roles Reverse transcriptase References subunit activity required hTERT Nuclease activity Yes [304] Nuclease of 3' non-telomeric nucleotides from a 5' telomeric DNA and extension Growth promotion Yes/No1 [300, 302, 309-312] pRb, TGF-β, EGFR, bFGF, Cytokine, mitogens Regulation of gene expression No [313, 314] Transcriptional regulator, WNT /β-Catenin -Brg1, NFKB1 Cell survival and apoptosis No [129, 298, 301, 315-320] p53, bFGF, TRAIL Protection of mitochondria mtDNA damage [321-325] Various mtDNAs, NADH: ubiquinone oxidoreductase Yes (complex 1) Post transcriptional gene silencing Yes [326]. RNA component of mitochondrial RNA processing endoribonuclease (RMRP) Epigenetic silencing Yes [327] DNA methyltransferase I Transferase activity Yes [303] In presence of Mn2+ ions adds nucleotides independently of the template RNA. Chromosome healing function Yes [275, 328, 329] De novo telomere synthesis Modulation of DNA damage response No [134, 330] Chromatin remodeling, ATP metabolism Metastasis, Angiogenesis and tumour progression Yes/No1 [331-336] EGFR/bFGF/VEGF/ or Integrins/HGF/c-Met

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Table 1.1 continued Telomerase Extra-telomeric roles Reverse transcriptase References subunit activity required hTR Modulation of gene expression No [337] Genes involved in tumour growth or metastasis Modulation of DNA repair and cell cycle arrest No [338, 339] ATR –p53-CHK Dyskerin rRNA processing and ribosome biogenesis No [340-343] Pseudouridine synthase DNA damage response Yes1 [344] ATM/p53 IRES mediated translation No [200, 345-347] p27, p53, BcL-XL Regulation of microRNAs No [348] H/ACA snoRNA-derived miRNA Notes: Abbreviations ATM: Ataxia Telangiectasia Mutated CHK: Checkpoint kinase, bFGF; fibroblast growth factor TRAIL, TNF- related apoptosis-inducing ligand, snoRNA; small nucleolar RNA, miRNA; microRNA, IRES; Internal ribosome entry site. 1See text for details

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Further studies using mouse models showed that ectopic expression of mTERT protected mouse embryonic fibroblasts from serum starvation-induced apoptosis and stress-induced apoptosis [129, 315, 349]. mTERT deficient generation 1 (G1) mouse embryonic fibroblasts (MEFS) showed an increased sensitivity, following exposure to staurosporine (STS) and N-methyl-D-aspartic (NMDA) acid, while transgenic mTERT MEFS showed resistance and increased survival to both drugs in vitro [254]. The anti-apoptotic effects were confirmed in vivo with the use of wild-type and mTERT deficient transgenic mice [129]. The protective role of mTERT was found to be independent of telomerase activity as deletion of TERC gene had no effect on the sensitivity of the cells [129]. mTERT overexpression has also been shown to cause hyperplasia and hypertrophy of murine cardiac myocytes, independent of telomere elongation, however these effects were dependent on active reverse transcriptase activity [307].

The telomere-length independent functions of mTERT were however recently questioned in a study of mTERT-/+ or mTERC-/+ haplo-insuffucient mice [350]. This authors of this study strongly argued that because these mice had similar phenotypes that deteriorated with each generation, the phenotypes resulting from loss of mTERT or mTERC function solely depended on telomere shortening [350]. In addition, they showed there was no evidence of telomerase-independent functions of mTERT on the mobilisation of hair follicle mediated by the Wnt pathway and argued that this effect was an artefact resulting from the overexpression of mTERT and representing a gain of function by mTERT overexpression which was not linked to its normal physiological function [350].

A role for hTERT in human tumorigenesis, independent of telomere-length maintenance was substantiated by studies demonstrating the failure of ALT to substitute for hTERT in vivo and in vitro in Ras-induced transformation [300]. However, these findings contrasted with studies that showed hTERT, caused no particular growth advantage when the ALT immortalised cells were implanted into the kidney capsule, rather than subcutaneously injected under skin [351]. Together these studies suggest that the tumour microenvironment may influence the extra- telomeric functions of hTERT in tumorigenesis [351].

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Extra-telomeric roles of hTERT in proliferation and survival were demonstrated in a variety of different human cells. Overexpression of hTERT enabled the proliferation and limited apoptosis of primary human epithelial cells in mitogen-deficient medium [302]. This proliferative function of hTERT was attributed to a catalytically active telomerase that was independent of telomere elongation [302]. Antisense inhibition of hTERT in human prostate cancer cell lines, reduced proliferation of prostate cancer cells and induced cell death by apoptosis, independent of effects on telomere length [309, 352]. Constitutive expression of hTERT in colon cancer cells and Burkitt's lymphoma cells antagonised p53 induced apoptosis after exposure to the DNA damaging agents, mitomycin C and fluorouracil [315]. Similar effects were also demonstrated in the latter study following the expression of catalytically inactive hTERT mutant, confirming the anti-apoptotic role of hTERT to be independent of its role in telomerase-mediated telomere maintenance [315]. In addition, hTERT was also shown to promote cell survival and prevent cell death via mechanisms independent of telomere maintenance in cultured neurons [129, 317, 353]. Specifically, the expression of hTERT during the development of neurons provided protection against numerous forms of neuronal damage and showed decreased neurotoxicity of amyloid B peptide in Alzheimer’s disease [129, 317, 353]. In human hematopoietic progenitors and leukaemia cell lines, overexpression of hTERT was associated with increased cell survival when vital growth factors were withdrawn [224]. A non-canonical function of hTERT in the survival of hemapoetic cells was also demonstrated in a study using a zebra fish model [354]. hTERT has been reported to promote cell survival through a variety of apoptotic pathways, including both the intrinsic mitochondrial and extrinsic death receptor pathways [318, 355]. In a breast cancer stem cell line, antisense inhibition of hTERT resulted in p53 and PARP-dependent induction of apoptosis. No changes to telomere length were evident by telomere restriction fragment analysis following hTERT inhibition and the inability of a catalytically inactive hTERT mutant to induce apoptosis, confirmed this function of hTERT to be independent of telomere shortening and telomerase activity [128, 356]. Overexpression of hTERT protected acute promyelocytic leukaemia cell lines via the extrinsic death receptor pathway of TNF–related apoptosis inducing ligand (TRAIL) from the induction of apoptosis

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[318]. The ectopic expression of hTERT in immortalised human foetal fibroblasts suppressed the activation of p53 protein expression by the induction of bFGF in the response to doxorubicin treatment and consequently increased resistance to apoptosis via a mechanism that was independent of telomere lengthening [320]. An additional hTERT regulated anti-apoptotic mechanism was demonstrated in hTERT- immortalised fibroblasts. Repression of hTERT resulted in the downregulation of the inhibitor of apoptosis protein, survivin and increased the sensitivity of these cells to stress- induced apoptosis [298]. Together these studies indicate that hTERT plays an important pro-survival role by the regulation of the apoptotic response, although the mechanisms between different cell types may vary.

Several studies have suggested that the extra-telomeric functions of hTERT in growth promotion occur as a result of effects on the regulation of gene expression and interaction with signalling pathways that control cell cycle progression [310, 313, 357-359]. In support of this possibility, ectopically expressed hTERT has been associated with increased expression of proliferative genes and suppression of anti- proliferative genes [357-359]. These include several genes involved in cell cycle regulation including the D-type cyclins that form complexes with CDK4/6 to phosphorylate and inactivate the pRb/E2F pathway and hence regulate G1 phase progression [357-359]. hTERT also interacts with various signalling pathways to promote cellular proliferation and contribute to tumorigenesis independently of telomere extension functions. hTERT overexpression resulted in the induction of the epidermal growth factor receptor (EGFR) in human mammary epithelial cells, while in murine cells, overexpression of mTERT in MEFs affected the TGF-beta network of growth factors [300, 312]. The growth promoting function of overexpressed mTERT for the proliferation of stem cells and mobilisation of hair follicle were found to be mediated via the transcriptional control of Myc- and Wnt- related developmental program, independent of telomerase catalytic activity [310, 311]. The expression of an endogenously tagged version of TERT knocked into mouse embryonic stem cells, revealed the growth promoting functions of mTERT acted as a transcriptional regulator through an interaction with the chromatin remodelling factor BRG1, to

CHAPTER 1:LITERATURE REVIEW directly activate the Wnt/β-catenin signalling pathway [313]. Co-transfection of hTERT expression vector and Wnt reporter plasmids in HeLa cells subjected to shRNA depletion of BRG1 or overexpression of hTERT in SW-13 cells (that lack BRG1 expression), failed to activate the Wnt pathway, confirming the interaction of hTERT with BRG1 was required for regulation of Wnt pathway [313]. Telomerase has also been demonstrated to directly regulate NFKB1 dependent transcription by binding to NFKB1 p65 subunit [314].

The extra-telomeric functions of hTERT were also linked to epigenetic gene silencing. Overexpression of hTERT in fibroblasts resulted in activation of and the maintenance of the DNA methyltransferase 1 gene (DNMT1), which counteracted the age-related decreased expression of DNMT1 [327]. It still remains to be determined whether the regulation of DNMT1 via hTERT occurs independently of telomerase activity and the exact mechanisms involved. The regulation may potentially involve EGFR signalling to the transcription factor STAT3, which has been found to induce expression of DNMT1 [327, 360].

A chromosomal healing function of hTERT has also been demonstrated [275, 328, 329]. hTERT is found associated with chromatin at multiple sites along the chromosomes, revealing that it has the potential to heal broken genomic DNA ends by de novo telomere addition at DNA break sites. This property of hTERT suggests hTERT may be able to assist in the repair of double stranded breaks and hence sustain cell proliferation [361]. The DNA healing mechanism of hTERT is proposed to function in the protection of cells from stress-induced apoptosis and necrosis [349].

1.7.1.2 DNA damage and repair There is a well-established link between telomerase and the DNA damage response in telomere stability and insights into extra-telomeric functions of hTERT in DNA damage and repair have strengthened this link [52, 330]. A previous study by Sharma et al., 2003 revealed hTERT associates with the telomere to enhance DNA repair and genomic stability in a telomere-length independent mechanism [330]. Ectopically expressed hTERT in human foreskin fibroblasts was found to cross link to telomeric DNA and alter the interaction of the telomeres with the nuclear matrix to enhance the 40

CHAPTER 1:LITERATURE REVIEW transcription of DNA repair and chromatin remodelling genes [330]. The mechanism was found to be dependent on telomerase activity as functionally inactive mutants of the RT domain and mutants of hTR, which failed to carry out this function [330]. Overexpression of hTERT also conferred an enhanced DNA repair capacity possibly due to increased nucleotide pools and ATP metabolism [330]. Decreased spontaneous chromosomal damage and telomeric associations were evident in the hTERT overexpressing cells, although an antibody directed to hTERT did not impact on DNA end joining or meiotic recombination, suggesting an indirect role for hTERT in these processes. Chromatin remodelling and DNA repair functions, as well as the influence of ATP metabolism on chromatin remodelling provide potential mechanisms underlying the regulation of hTERT on DNA damage response. Additionally, the identification of a N-terminus domain of hTERT that was shown to interact with telomere DNA and the C-terminus of hTERT independently of telomere elongation in vitro and in vivo, provides a potential mechanism of how hTERT cross links to the telomere as proposed by Sharma et al., 2003 and/or plays a role in the regulation of gene expression, independent of telomere maintenance [330, 362].

The transient low levels of hTERT present in the S phase of BJ human fibroblasts was found to function in the remodelling of chromatin structure during DNA replication that was required for effective DNA damage response and repair following ionising radiation [126, 134]. An impaired DNA damage response followed shRNA-mediated inhibition of hTERT in normal BJ fibroblasts treated with ionising radiation. It was reported that shRNA-mediated suppression of hTERT affected the post-translational modifications of histone tails of telomeric 3’overhang, affected overall chromatin state and impaired DNA damage without affecting telomere length [126, 134]. hTERT suppression also altered the capacity of these cells to repair DNA [134]. Both the DAT mutant of hTERT, which reconstituted telomerase biological activity but failed to elongate telomeres, and the catalytically inactive DNhTERT, failed to rescue the impaired DNA damage response indicating that these effects were unrelated to overall telomere length and telomerase activity [134, 143, 163]. In contrast to ablation of hTERT gene expression in human cells, TERT (-/-) murine cells with telomeres that had not yet undergone shortening to a critical length, had no affect the ATM-dependent response to DNA damage. These

CHAPTER 1:LITERATURE REVIEW findings suggest that the function of hTERT in DNA damage response may not be conserved across species and exemplifies differences in murine and human telomerase biology [363].

Evidence of roles for hTERT in the DNA damage and repair pathways implicates additional mechanisms via which hTERT may contribute to proliferation, survival and progression of cancer cells. Whether hTERT mediates the DNA damage response by similar mechanisms in normal cells shown to have transient low levels of hTERT and cancer cells with higher levels of hTERT, remains to be clarified. Further investigations of the mechanisms mediating these effects in both normal and tumour cells could solve these discrepancies between mechanisms described.

1.7.1.3 Malignant phenotype, metastasis and angiogenesis Telomere-length independent roles of hTERT have been shown to promote tumour formation, metastasis and angiogenesis via mechanisms that are independent of telomerase activity and telomere-length maintenance [331, 364, 365]. In glioblastoma cells, shRNA-mediated inhibition of hTERT was shown to reduce the growth of xenografted glioblastoma tumour cells, which exhibited reduced angiogenesis and necrosis [366]. Consistent with these findings, other studies showed that inhibition of hTERT in combination with interferon treatment decreased migration, angiogenesis, invasion and tumour progression of glioblastoma cells [333, 334]. Additionally, downregulation of hTERT in differentiating glioma stem cells suggested a role for hTERT in the promotion of cancer stemness via EGFR pathway independent of telomerase activity [332].

The ectopic expression of hTERT in a metastasis-derived non-small cell lung cancer cell line conferred a more aggressive growth potential in vitro and tumour volume in vivo [331]. This observation was corroborated by a study that showed that siRNA depletion of hTERT in gastric cancer cells suppressed Epithelial Mesenchymal Transition (EMT) and gastric cancer stemness [367]. Studies have also shown that suppression of hTERT inhibited tumorigenicity and motility of HCT116 human colon cancer cells via the impairment of integrins and hepatocyte growth factor (HGF)/cMet signalling pathways [335]. More recently, a telomerase-independent positive feedback regulatory loop involving hTERT and the critical vascular 42

CHAPTER 1:LITERATURE REVIEW endothelial growth factor (VEGF) was demonstrated in normal human fibroblasts overexpressing hTERT and HeLa tumour cells [365]. Inhibition of hTERT by DNhTERT effectively reduced the malignant phenotype of neuroblastoma cells in vitro and in vivo tumour formation of xenografted neuroblastoma cells in mice via the regulation of apoptosis effectors [336]. These studies demonstrate that the extra- telomeric functions of hTERT in the regulation of the malignant phenotype and metastatic potential of various cancer cells are mediated through via a variety of pathways that may have cell type specificity [333, 334, 336, 365].

1.7.1.4 hTERT function in the mitochondria Past reports suggest that the extra-telomeric functions of hTERT may not be limited to the nucleus. A conserved mitochondrial targeting sequence within the first 20 amino acids of the N-terminus of hTERT, identified by mitochondrial targeting sequence (MTS) predicting software may potentially target it to the mitochondria [322]. Under oxidative stress, a recombinant protein of enhanced GFP fused to the C- terminus of wild type hTERT, showed mitochondrial localisation [321]. Evidence that the mitochondrial localisation of hTERT was not an artefact of overexpression was verified by the mutation of two amino acids residues within the MTS that abolished localisation to the mitochondria [321, 322].

The functional significance of hTERT in the mitochondria remains controversial. In human cell lines, mitochondrial localised hTERT was promoted mitochondrial DNA (mtDNA) damage, following oxidative stress induced by treatment of cells with 200

µM hydrogen peroxide. mtDNA damage was significantly increased in cells in hTERT overexpressing cells, in comparison to their normal isogenic cells [321, 322]. However, conflicting results were obtained by Ahmed et al., 2008, who demonstrated that mitochondrial accumulation of hTERT, lowered mtDNA damage under oxidative stress and therefore preserved mtDNA [323]. A similar protective role was demonstrated in human umbilical vein endothelial cells (HUVECS) in vitro and in vivo. A substantial portion of endogenous hTERT localised and became bound to the mitochondrial matrix resulting in the protection of coding sequences for reduced nicotinamide adenine dinucleotide (NADH): ubiquinone oxidoreductase complex 1, that are major sites for ROS production [324]. Mitochondrial hTERT has also been shown to bind to proteins that interact with mtDNA binding proteins and 43

CHAPTER 1:LITERATURE REVIEW may play a role in their stabilisation [323]. Additional evidence of a mitochondrial localised hTERT, derives from studies that demonstrated telomerase activity in purified mitochondrial extracts and detection of endogenous hTERT expression in mitochondria by western blot analysis and immunofluorescence [322-325, 368]. However, at the time of those publications no reliable antibodies were readily available for the detection of endogenous hTERT by western blot and immunofluorescence, so the strength of evidence from those studies is uncertain [369]. The discrepancies of the roles for hTERT in the mitochondria should be resolved by better detection methods of endogenous hTERT levels for accurate evaluation of the physiological roles of hTERT in the mitochondria [369].

In addition to the N-terminal MTS, hTERT also has nuclear export signal (NES) found between amino acids 978-987. A recent study showed that mutations that disrupted the nuclear export signal (NES) of hTERT prevented hTERT from leaving the nucleus and impaired both mitochondrial function and telomere function and suggested the mitochondrial functions may also be required for telomerase-telomere mediated immortalisation [370].

A novel mitochondrial function of catalytically active hTERT as a RNA dependent RNA polymerase that functions independently of hTR was also demonstrated [326]. It was shown that hTERT interacted with the RNA component of the mitochondrial RNA processing endoribonuclease (RMRP) to form a hTERT-RMRP ribonucleoprotein complex and regulated RMRP levels by a negative feedback loop [368]. The hTERT-RMRP ribonucleoprotein complex acts as a RNA-dependent RNA polymerase (RDRP) to produce dsRNAs using RMRP as a template. These dsRNAs are then processed by Dicer into 22-nucleotide siRNAs that specifically silence the expression of RMRP itself and possibly other RNAs [368]. Using reverse genetics, it was shown that the enhanced proliferation by ectopically expressed hTERT in human mammary epithelial cells was dependent on hTERT catalysed decreased expression of the non-coding RNA, RMRP [371]. Using an extensive panel of mutants, this study confirmed the effect on proliferation by hTERT in mitogen-deficient medium was independent of telomere extension, the regulation of the DNA damage response, Wnt-signalling, but linked to the decreased expression of

CHAPTER 1:LITERATURE REVIEW non-coding RNA, RMRP. These results were confirmed in studies that showed shRNA targeting RMRP enhanced proliferation [368]. The cellular functions of RMRP include the processing of RNA primers for mitochondrial DNA replication, pre-rRNA processing during rRNA maturation and mRNA cleavage of cell cycle genes [368]. The exact mechanisms of how the hTERT-RMRP complex impacts proliferation requires further investigation but these studies suggest that a post transcriptional mechanism of hTERT regulates gene expression by RMRP generated siRNAs, that are likely to be associated by changes in cell cycle regulators [371]. The implications are that hTERT-RMRP regulation may have a more extensive role in the putative extra-telomeric functions of hTERT than previously envisioned [371]. Consistent with the findings that hTERT can interact with other RNAs in the mitochondria, were the findings that showed hTERT functions as an hTR- independent reverse transcriptase in association mtDNAs and tRNAs in the mitochondria, independently of hTR to regulate the metabolism of mtDNAs [325].

The diverse array of telomere-length independent functions of hTERT in resistance to apoptosis, self-sufficiency to growth signals, cell cycle progression and proliferation, DNA damage and repair, metastasis and tumour progression indicate that in addition to the role of hTERT for telomere elongation and immortalisation, hTERT facilitates in the development of other hallmarks of cancer cells [1]. Consolidation of the multiple mechanisms that mediate the extra-telomeric functions of hTERT, will allow investigators to determine whether similar mechanisms are shared between different tumour types or in normal cells with lower transient expression levels of hTERT.

1.7.2 Extra-telomeric functions of hTR The ubiquitous expression of hTR in most cells suggests that hTR plays an important role in normal cellular function (Table 1.1). A number of studies have demonstrated that hTR has functions in proliferation and tumorigenesis that are separable from telomere-length regulation in murine models and human cancer cell lines [169, 194, 309, 338]. Telomere-length independent functions of hTR are argued to be related to its upregulation as an early event in tumorigenesis and explain why hTR levels correlate more closely with tumour grade than telomerase activity or hTERT [186, 372, 373]. 45

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The tumour-promoting effects of TR were described in a natural virus-host infection model of Marek's disease herpes virus (MDV) alphaherpesvirus induced lymphomagenesis in chicken fibroblast cell line DF-1. In this study, overexpression of the viral form of TR (vTR) transformed the DF-1 cells in vitro [374]. The early upregulation of mTR in the early stages of mouse models of tumorigenesis of islet cell carcinoma and squamous cell carcinoma and mouse mammary tumours, was found to independent of telomere shortening [194, 309, 364]. Owing to the inherent longer lengths of mouse telomeres in early generations, this upregulation could not be attributed to telomere shortening [194, 309, 364].

Although mTR levels increased in early pre-neoplastic stages and increased with progression, mTR levels did not correlate with telomerase activity in early disease stages, only in late stage tumours suggesting that the early upregulated mTR may have been required for additional cellular functions [194, 364]. More evidence of extra-telomeric functions of mTR came from experiments using early generation (G1) mTERC-/- knockout mice, which lack telomerase but still have long telomeres [171, 194]. Following chemical induced skin carcinogenesis, less skin tumours developed in the knockout mice compared with wild-type mice indicating effects of hTR that were independent of telomerase [375].

Extra-telomeric functions of hTR in human cancer cells were demonstrated with the use of hTR shRNA and transient hTR siRNA inhibition [337, 338]. shRNA inhibition of hTR in human melanoma, colon, bladder and breast cancer cell caused a p53-independent growth arrest with no evidence of bulk telomere shortening. However, the growth arrest was not demonstrated in telomerase-negative cells indicating that these anti-proliferative effects were dependent on the presence of catalytically active hTERT and may have been due to a telomere uncapping mechanism [337, 376, 377]. Additional studies showed siRNA inhibition of hTR inhibited the growth of human HCT116 cancer cells independently of p53 or telomere length, with no telomere uncapping evident [337]. Instead hTR siRNA- inhibition modulated genes involved in tumour growth, angiogenesis and metastasis independent of telomere length regulation, suggesting a role of hTR in the malignant phenotype of colon cancer cells [337].

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Evidence of a p53-dependent telomerase independent function of hTR has so far only been demonstrated in one study [338]. shRNA inhibition of hTR in MCF7 cells lead to a short-term anti-proliferative effect, telomerase inhibition and ATR-mediated activation of p53-Chk1. In this study, the cells arrested without the induction of a p53-Chk1 mediated DNA damage response [338]. This study also conversely showed that overexpression of hTR in these cells impaired ATR activity and ATR- mediated G2/M checkpoint in response to ionising radiation. Upon treatment of UV, hTR was upregulated independent of hTERT expression, providing potential evidence of a protective function of hTR in DNA damage response. This effect was also determined in telomerase negative cells, indicating a function independent of telomerase activity [338].

The apparent discrepancy between the reports on the involvement of p53 and telomerase of the extra-telomeric functions of hTR may be resolved by clarification of the exact mechanisms by which RNA molecule exerts it functions independent of telomere length and/or hTERT. The complexity of the functional domains of hTR provides scope for varied function of hTR, but so far these domains have only been linked to telomerase-dependent functions.

1.7.3 Extra-telomeric functions of dyskerin As a pseudouridine synthase, dyskerin is guided by snoRNAs to perform posttranscriptional site-specific modification of rRNA [190]. Dyskerin binds to H/ACA snoRNAs and C/D snoRNAs in complex with ribonucleo proteins to ensure the accurate processing of rRNA [190, 378]. Murine studies with hypomorphic DKC1 mutants suggested that dyskerin’s tumour suppressive role was linked to its ribosome function [342-344, 378, 379]. These studies showed that the disease associated with mutated or under expressed dyskerin resulted from reduced rates of rRNA uridylation and rRNA processing and translation of specific proteins [378, 380]. The rRNA processing function of DKC1 was found to be essential for cell division and proliferation of mouse liver cells, but was not required for cell survival [342]. Ablation of dyskerin in mouse liver cells decreased ribosome production and resulted in the reduction of subsets of H/ACA snoRNAs, but increased C/D snoRNAs [343]. In mouse embryonic stem cells, mutated dyskerin decreased a subset of H/ACA snoRNAs [379]. 47

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Consistent with these findings, decreased cell proliferation of MEFs and spleen cells that carry a mutant form of dyskerin (DKC1Δ15) was demonstrated [344]. The DKC1 mutant used in that study has a 3′ truncation of exon 15 of the DKC1 gene that results in a 21 amino acid truncated dyskerin protein [344]. The growth impairment was lost when mice harbouring this mutation were crossed in first generation (G1) TERT-/- and TERC-/- background, indicating that growth impairment of the mutant DKC1 required an active telomerase complex. The proliferation impairment was found to be independent of telomere length, as telomeres in G1 mice would not have undergone significant telomere shortening [278, 381]. The growth impairment was associated with a heightened DNA damage response via the activation of p53 that was dependent of telomerase but did not involve telomere elongation [344].

The role of dyskerin in rRNA processing functions in human cells still requires further investigation. Previous investigations of DC function with primary human cells harbouring mutated dyskerin have showed no marked effect of dyskerin mutation on rRNA processing [173, 189]. However, recently gene expression and mass spectrophotometry profiling of DC patient samples demonstrated that the subsets of H/ACA small RNA were perturbed in cells from DC patients associated with DKC1 mutations of DC patients [382]. Furthermore, this study demonstrated that the pseudouridylation synthase activity of dyskerin was required for accurate hematopoietic stem cell differentiation and confirmed that in addition to the telomerase associated activities of dyskerin, the rRNA processing functions of dyskerin also contribute to the pathogenesis of the DC [382].

In human cancer cells, siRNA-mediated depletion of dyskerin function impaired the proliferation of telomerase positive oral cancer cells, cervical cancer cells and telomerase negative osteosarcoma cells [340, 341]. The impaired proliferation was found to be independent of hTERT/telomerase activity, and only partially related to its rRNA processing function as evidence by only a transient disruption of rRNA maturation [340]. Similar findings were demonstrated following the depletion of dyskerin in human breast cancer cells, which was associated with disrupted rRNA pseudo-uridylation and suppression of telomerase activity [383].

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Overexpression of dyskerin is associated with poor prognosis of hepatocellular carcinoma and correlated with proliferative potential of HCC cancer cells [384]. Additionally, the upregulation of dyskerin significantly correlated with proliferation of oral squamous cell carcinoma (OSCC) cells. The upregulation of dyskerin in OSCC cells was dissociated from increased telomerase levels as levels of dyskerin did not correlate with hTERT and telomerase activity. Antisense inhibition of hTERT and telomerase activity confirmed the upregulation of dyskerin to be independent of telomerase activity, indicating an additional role for dyskerin in proliferation [340]. Further evidence for telomerase-independent functions of dyskerin emerged from a study that showed immortalised mammary epithelial cells, showed upregulation of dyskerin did not consistently correlate with hTERT and telomerase activity [186]. The overexpression of dyskerin independently of telomerase in cancer cells, may be indicative of a need of cancer cells to support increased RNA and protein biosynthesis, as enhanced ribosomal biogenesis is a common feature of numerous cancer cells [385]. Therefore a transient delay in rRNA processing induced by inhibition of dyskerin may be adequate to impair proliferation, cell survival or halt of malignant transformation [340, 341].

There is also evidence to suggest that dyskerin regulates internal ribosome entry site (IRES)-mediated translation of specific cell cycle regulatory proteins, including p27kip1 and p53 [200, 345-347]. Regulation of IRES-mediated translation of tumour suppressors p27kip1 and anti-apoptotic proteins of BcL-xL and X-linked Inhibitor of Apoptosis protein (XIAP) was demonstrated using DKC1 mutant mice and cells from X-Linked DC patients [345, 347]. siRNA-mediated inhibition of dyskerin in human cells have also been shown to be associated with defects in IRES-mediated translation of p53 leading to decreased p53 [200, 345, 383, 386]. In contrast to the reduced IRES-mediated translation of those proteins, it was shown that depletion of dyskerin in breast cancer cell lines caused an increase in IRES-mediated translation of the vascular epidermal growth factor, VEGF, providing first evidence that dyskerin depletion is able to upregulate IRES-mediated translation of specific mRNAs [387]. Interestingly, sporadic breast cancer tumours with low dyskerin expression, retained wild type p53, but had reduced p53 activity and defective rRNA

CHAPTER 1:LITERATURE REVIEW pseudouridylation, suggesting that dyskerin may function to inactivate p53 in some tumours [200, 383].

A role for dyskerin in regulating the malignant phenotype of numerous tumour types is also emerging. siRNA-mediated repression of dyskerin decreased anchorage independent growth of both telomerase negative U2OS and telomerase positive HeLa cells [341]. The recent identification of dyskerin alternative splice variant required for a yet to be defined role in cell adhesion also may potentially function in the malignant properties of cells and are consistent with the findings that show DKC1 gene silencing alters cell adhesion of prostate cancer cells [146, 388].

Depletion of dyskerin was also shown to reduce the accumulation of a subset of H/ACA snoRNA-derived microRNAs, which are a class of small non-coding RNAs responsible for the regulation of post transcriptional gene expression [348]. These microRNAs regulate numerous cellular functions including cell proliferation and few have also been implicated in tumorigenesis [389]. It is yet to be determined whether the regulation of these microRNAs by dyskerin contributes to normal cellular functions, proliferation and/or tumorigenesis.

The extra-telomeric functions of the telomerase components may explain why cancer cells become dependent on telomerase and provide rationale for the finding new approaches to targeting telomerase [390]. The biological consequences of inhibiting the additional functions of the telomerase components are likely to provide significant insights into the application of anti-telomerase therapeutics as targeting these extra-telomeric function represent an alternate means of telomerase inhibition that may be more potent than telomerase inhibitors that target telomerase mediated telomere maintenance [391].

1.8 Telomerase inhibition as a therapeutic approach to cancer Numerous approaches to the inhibition of telomerase have been tested in vitro and have entered into in vivo pre-clinical models (Table 1.2). Therapeutic strategies for targeting telomerase that have been examined include reverse transcriptase inhibitors, genetic strategies for inhibition of enzymatic addition of telomere repeats; peptide nucleic acids (PNAs), hammer head ribozymes, siRNA, modified/anti-sense 50

CHAPTER 1:LITERATURE REVIEW oligonucleotides strategies; small-molecule inhibitors, G-quadruplex stabilizers and telomerase immunotherapy as reviewed in [280]. Approaches developed towards targeting the telomere maintenance function of telomerase are associated with broad efficacy but despite the ideal properties of telomerase for targeting in cancer, only a limited number of telomerase-based therapeutic approaches have entered clinical trials in both solid and haematological malignancies [280]. The challenges to targeting telomerase include overcoming the limitations associated the phenotypic delay of currently used telomerase inhibitors as they depend on telomeres to reach a critical short length before anti-proliferative effects are evident [163, 280, 392-394].

1.8.1 Reverse transcriptase inhibitors Soon after the characterisation of hTERT as a reverse transcriptase, a number of existing reverse transcriptase inhibitors and nucleoside analogues that had been shown to terminate retroviral reverse transcriptases, were amongst the first anti- telomerase therapeutics tested for their ability to suppress telomerase activity [395, 396]. Some of these, including dideoxyguanosine and azidothymidine (AZT) inhibited telomerase activity in vitro and caused progressive telomere shortening in T-cell and B-cell lines. However, efficacies varied among different cultures and were negligible in long-term proliferation assays [395, 396]. It was proposed that this was due to the operation of both telomerase-dependent and -independent functions separable from telomere-maintenance mechanism in these cells. In one study suppression of cell proliferation was transient and reversible when the drug was removed [397]. In AZT-treated breast and melanoma cancer cells, telomerase activity was inhibited and growth was suppressed following rapid telomere shortening [398]. Reduced tumorigenicity of AZT-treated metastatic mouse mammary tumour cells was evident by a decreased number and size of spontaneous metastases in mice [399].

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Table 1.2 Therapeutic approaches to targeting telomerase Class of Examples Mechanism of action Models employed Dependent Progressed References inhibitor on T/S to clinical trials (Y/N) Nucleoside AZT Reverse transcriptase B and T lymphoid cell lines, breast, Y Y [395, 396, 398- inhibitor inhibition, blocks dNTPs melanoma cancer cells, Breast cancer 403] incorporation into DNA xenografted mice Vector-expressed DNhTERT Dominant-negative Solid and haematological tumour cell Y N [163, 242, 336, cDNA inhibition of telomerase lines and xenografted mice 392, 404-408] enzyme activity Neuroblastoma cell line1 N1 Mutant hTR Mutated hTR template Prostate, colon and breast, bladder Y N [169, 194, 309, sequence cancer cell lines 337, 338]

Antisense Antisense hTERT Antisense inhibition of Leukaemic cell lines and primary Y N [352, 409-411] oligonucleotide oligonucleotides hTERT cultures established from AML and Cantide® CML patients’ cells; breast cancer cells Prostate, bladder, HCC gastric cancer N2 cell lines2 HCC xenografted mice Orthotropic model of PHL Antisense Antisense hTR Antisense inhibition of Prostate, colon and breast, bladder Y N [377, 412-419] oligonucleotide oligonucleotides hTR cancer cell lines for hTR N3'-P5' modified hTR template antagonist Multiple myeloma, non-Hodgkin’s Y Y [393, 420-431] thio- lymphoma cell lines, primary patient phosphoroamidate cells, liver, breast, lung3, prostate oligonucleotide oesophageal tumour cell lines and [422, 423, 425, GRN163L xenografted mice, Glioblastoma tumor N3.4 432-434] initiating cells.4

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Table 1.2 continued

Class of Examples Mechanism of action Models employed Dependent Progressed References inhibitor on T/S to clinical trials (Y/N) PNAs hTR PNAs PNAs complementary Solid tumour derived breast, colon and Y [412, 435]. to template region of melanoma, ovarian cell lines [170, 435] hTR Block interactions between telomerase and telomeric DNA. Ribozymes hTR ribozymes Ribozymal cleavage of Solid tumour derived breast, colon and Y [436-441]. hTR RNA melanoma, ovarian cell lines, melanoma (HDV) hTR cells in vivo, liver and colon cancer ribozyme (g.RZ57) delivered cell lines5 N5 Ribozymes hTERT ribozymes Ribozymal Cleavage of Breast cancer and ovarian cancer cell N N [442-444] hTERT RNA lines shRNA and hTERT siRNA Endonucleolytic Solid tumor derived cell lines, breast, N N [126, 134, siRNA directed hTR siRNA cleavage of target colon and melanoma, ovarian 298, 305, 306, to hTERT, hTR RNA through RNAi 333, 346, 348, and dyskerin 366, 377, 445, 446] G-quadruplex TMPyP4, Stabilisation of Hematological cell lines, primary patient Y N [447-458] interacting PIPER,BRACO19, telomeric G- cells, xenografted mice ligands RHPS4,Telomestatin quadruplex prevents telomerase access to telomeres Non-nucleosidic TMPI, Rhodacyanine Interferes with Solid tumor derived cell lines, breast, Y N [394, 459-465] (FJ5002) telomerase enzyme lung, colon, fibrosarcoma, cervical, small molecule processivity melanoma, ovarian and hematological inhibitors BIBR1532 cell lines, primary patient cells, normal N6 Mixed type non- cord blood progenitor cells6 competitive

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Table 1.2 continued Notes: Effects that are not dependent on critical telomere shortening suggest that the inhibitors are functioning independently of telomerase-mediated telomere maintenance. Description of acute effects of inhibitors that have previously been shown to function via critical telomere shortening but also have acute effects in specific models are annotated by subscript numbers 1-6.

1 Extra-telomeric function of hTERT has been demonstrated in the metastasis of neuroblastoma cells via the regulation of effectors of apoptosis [336]. 2 AS PS-ODN was shown to exert immediate anti-proliferative effects in the absence of telomere shortening in prostate, bladder, human hepatocellular carcinoma (HCC) and gastric cancer cell lines [352, 409-411] 3 Lipid moiety and other structural features of GRN163L alter cell adhesion, which is a crucial determinant of metastatic potential of lung carcinoma cells [434] 4 GRN163L increased radiation-induced DNA damage of both cell types and after one week of treatment GRN163L reduced the colony forming ability of oesophageal cancer cells [425, 432]. 5 Hepatitis D virus (HDV) ribozyme (g.RZ57) directed against hTR was shown to induce a growth arrest and spontaneous apoptosis of liver and colon cancer derived cell lines after 72 hrs [466]. 6 A low concentration of 10 µM had no effect on short-term proliferation or survival, whereas higher concentrations (50 µM to 80 µM) were acutely cytotoxic to haematological cell lines, primary patient cells, and normal cord blood progenitor cells [394].

Abbreviations: AE, acute effects, AML, Acute Myeloid leukaemia, CML, Chronic Myeloid leukaemia, HCC, human hepatocellular carcinoma, hTERT, human reverse transcriptase, HDV hepatitis D virus, NSLC, non-small cell lung cancer, PHL, primary hepatic lymphoma, PNAs, Peptide Nucleic Acid, T/S, Telomere shortening, DNhTERT, Dominant negative hTERT, siRNA, small interfering RNA,

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The most promising results for AZT, were obtained in a clinical trial of 19 T-cell leukaemia virus I–infected (HTLV-I) adult T-cell leukaemia/lymphoma (ATL) cells patients, where AZT was administered in combination with interferon-alpha treatment [400, 401]. Prolonged treatment of the ATL cells effectively inhibited telomerase activity, resulting in telomere attrition and subsequent activation of p53- dependent senescence [402]. In accordance with these results, remission was induced in AZT-treated ATL patients who had wild-type p53, but not in those with mutated p53 [402]. A high rate of clinical response, with only mild hematologic toxicity, was also observed in a clinical trial of 19 ATL patients where AZT was used in combination with interferon-alpha treatment [400, 401, 467]. However, as telomere shortening was not confirmed, it remains to be determined whether the observed effects were a result of inhibition of telomerase or suppression of other critical enzymes [400, 401, 467]. The progression of reverse transcriptase inhibitors and nucleoside analogues into clinically relevant anti-telomerase therapy has not occurred as result of very high drug concentrations required for effective enzyme inhibition as well as variable and poor anti-proliferative activity [396, 403]. A better understanding of functioning of AZT is also required as under certain conditions AZT has potential tumorigenic properties [468].

1.8.2 Dominant-negative inhibition of telomerase activity Proof of principal experiments that supported the specific targeting of telomerase were demonstrated by the overexpression of DN-hTERT in human cell lines derived from solid tumours [163, 392]. Those pioneering studies demonstrated telomerase inhibition by the expression of DNhTERT, decreased the replicative lifespan and tumorigenicity of tumour cells as a result of telomerase activity suppression and critical telomere shortening [163, 392]. The proliferative arrest and death induced by the overexpression of DN-hTERT occurred either via p53-dependent or p53- independent mechanisms depending on the cell type [3, 392, 469]. It was also shown that expression of DN-hTERT inhibited anchorage independent growth and prevented the formation of tumours in mice xenografted with solid or haematological tumour cell lines [404-408]. The expression of DN-hTERT has also been shown to reduce the tumorigenic capacity of ovarian and neuroblastoma cancer cells in vivo [163, 336].

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More recently, telomerase inhibition via DN-hTERT was shown to increase the efficacy to radiotherapy, when utilised in combination with chemotherapy in neuroblastoma cells [242]. In contrast to previous studies that showed inhibition of hTERT by DN-hTERT resulted in a growth arrest that was dependent on progressive telomere shortening, use of DN-hTERT in malignant neuroblasts has recently demonstrated acute anti-metastatic effect independent of telomere length via the regulation of effectors of apoptosis [336]. These findings suggest a cell type specific response of neuroblastoma cells to the inhibition of hTERT by DN-hTERT. Since the gene therapy method of delivery is associated with concerns of safety, it has not reached its full potential as a therapeutic means. The loss of DN-hTERT integration and loss of telomerase inhibition by the subsequent upregulation of hTERT following the expression of DNhTERT also raises concerns for the therapeutic use of DN- hTERT [406, 470]. The proportion of completely telomerase negative clones is limited and clonal variation would be problematic for clinical application of this approach.

Vector expressed mutant hTRs that inhibit hTR expression and prevent the accurate synthesis of telomeres, were shown to reduce the proliferation of prostate and breast cancer cells in vitro, as a result of a telomere uncapping mechanism and induction of DNA damage and apoptosis via TRF2-ATM pathway [337, 339, 376, 377]. Surprisingly, even low levels of mutant hTR RNA was able to decrease growth of prostate and breast cancer cells through a p53-independent pathway, independent of telomere shortening. However, cell type specific responses to these mutant hTRs were also demonstrated, where expression of mutant hTRs to inhibit hTR in HT1080 cells caused no dramatic effects on the proliferation [337, 377].

1.8.2.1 Non-nucleosidic small molecule telomerase inhibitors A number of small non-nucleoside inhibitors have been identified through various screening regimes, including large scale screening of chemical libraries, comparative screening for compounds that have similar pharmacological properties to known moderate telomerase inhibitors and screening against telomerase containing nuclear extracts [453-456]. These approaches have yielded a range of different classes of compounds, including 2- [3-(trifluoromethyl)phenyl]isothiazolin-3-one (TMPI),

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Rhodacyanine (FJ5002) and (E)-2-(3-(naphthalene-2-yl)but-2enamido)benzoic acid (BIBR1532).

Each of these compounds effectively inhibited telomerase enzyme activity in a variety of different tumor cell types [459, 460, 462]. The most extensively characterised small molecular-weight telomerase inhibitor is BIBR1532. BIBR1532 is as a mixed type non-competitive inhibitor, specific for human telomerase [471]. Although it was found to target hTR and hTERT directly, its mechanism of action does not rely on the blocking of the template copying catalytic steps. Instead it functions by specifically impairing the elongation of the DNA substrate after its extension to the 5′-end of the template [394, 471] and was demonstrated to mediate its effect by interfering with in vitro processivity of telomerase [471].

This telomerase inhibitor was the first small molecule inhibitor shown to cause progressive telomere shortening and impede proliferation in fibrosarcoma (HT1080) cells, prostate (DU145), breast (MDA-MB-231) and lung (NCI-H460) cancer cells and hemapoetic cancer cells in vitro [394, 472]. BIBR1532 was subsequently shown to impede survival and inhibit proliferation of a B lymphoid cell line, as well as cells derived from primary AML and CLL patients in a short-term in vitro assay [462, 472]. In these studies, a low concentration of 10 µM had no effect on short-term proliferation or survival, whereas higher concentrations (50 µM to 80 µM) were acutely cytotoxic. Notably this acute toxicity was also observed when telomerase- negative leukemic cells were treated with the compound [462, 472]. The cytotoxic effects of BIBR1532 were attributed to telomere uncapping, which was evidenced by the formation of chromosome end-to-end fusions, a reduction in expression of the telomere binding protein TRF2 and the upregulation of tumor suppressor p53 [462, 472]. BIBR1532 at concentrations of up to 120 M had no apparent effect on the proliferation, viability or clonogenicity of normal cord blood-derived progenitor cells [462, 472]. However, it did halt the proliferation of normal fibroblasts after a lag period that was proportionate to the length of telomeres [462, 472]. BIBR1532 also showed in vivo efficacy to limit tumour formation when tumour cell lines were subcutaneously injected into immune compromised mice [394].

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The development of this drug was hampered by results that showed higher concentrations of BIBR153 were required to inhibit telomerase activity in cell extracts than those previously reported in assays with the purified enzyme [463]. In comparison, BIBR1532 was less effective in inhibiting telomerase activity, when compared with other telomerase strategies in various cell types, suggesting further development was required to gain broader response in different cellular contexts [464, 465].

1.8.3 G-quadruplex stabilisation The stabilisation of G-quadruplex structures of telomeric DNA provides an alternate indirect means for inhibiting telomerase enzyme function. Several ligands that bind and stabilise G-quadruplexes have been tested for telomere effects. These include ligands of different compound classes, such as cationic porphyrins (TMPyP4), perylenes (PIPER), acridine derivatives (BRACO19, RHPS4), quinoline-substituted triazines and natural products, such as Telomestatin, which was derived from Streptomyces anulatus [449-453].

Telomestatin functions by binding to the telomeric overhang and impairs its single- stranded conformation [454, 455]. At non-toxic concentrations, these compounds diminished telomerase enzyme activity and halted proliferation of tumor cell lines after a lag period [453, 456]. There is also evidence that G-quadruplex stabilising agents may facilitate the action of standard chemotherapeutic agents [451, 456]. Telomestatin exhibited the highest selectivity for cancer cells and potency in comparison with other G-quadruplex ligands [447, 448, 451, 454, 473, 474]. Different tumour cell types treated with telomestatin show delayed growth arrest and/or apoptosis following quickened telomere shortening [448].

Overall, these results highlight the potential of G-quadruplex stabilising agents for the treatment of malignancy. However, some of these ligands are also likely to target non-telomeric GC-rich regions of DNA, which are prone to G-quadruplex formation. This possibility was highlighted by a study that showed TMPyp4 stabilised a G- quadruplex structure within the promoter region of the c-Myc oncogene, resulting in its down regulation [457, 458]. Non-specific effects such as these may confer unacceptable toxicity toward non-malignant cells. 58

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1.8.4 Gene targeted nucleic-acid based strategies All of the major nucleic-acid based gene-silencing molecules that function by the sequence specific targeting of mRNAs to inhibit gene expression have been utilised for the inhibition of telomerase in the search for potential therapeutic agents. These include ribozymes, chemically modified antisense oligodeoxyribonucleic acids (ODNs), peptidic nucleic acids (PNAs), and siRNAs [134, 298, 305, 306, 346, 348, 366, 377, 415-418, 422, 423, 425, 432, 433, 435, 446].

1.8.4.1 Ribozymes Ribozymes are small RNA molecules that bind and catalytically cleave target RNAs. However, ribozymes may also exert anti-sense effects, promoting RNAse-mediated degradation of the target mRNA [475]. Ribozyme targeting hTR or hTERT are included in the repertoire of approaches for the specific therapeutic targeting of telomerase [476, 477].

Numerous investigations in solid tumor derived cell lines demonstrated that ribozymes targeting the template region of hTR inhibited telomerase activity in dose dependent manner and caused progressive telomere shortening. These effects however were not determined in all cell lines and the effects on cell proliferation were variable [436-441]. An important study indicating a novel role for telomerase activity in the progression of metastatic melanoma was demonstrated following ribozyme-mediated knockdown of murine telomerase RNA that inhibited metastasis of murine melanoma cells in vivo [441]. More recently, a hepatitis D virus (HDV) ribozyme (g.RZ57) directed against hTR was shown to induce a growth arrest and spontaneous apoptosis of liver and colon cancer delivered cell lines [466].

Ribozymes directed for the catalytic cleavage of hTERT have also been utilised for the downregulation of telomerase activity in tumor cell lines [442, 443]. In breast cancer cell lines, this approach mediated telomere shortening and reduced the rate of proliferation, which was attributed to increased apoptosis [443]. In addition, breast cancer cells transfected with this ribozyme were shown to be more sensitive to the cytotoxic effects of topoisomerase II inhibitors. The same hTERT-directed ribozyme was also successfully expressed from a replication-defective adenoviral vector in

CHAPTER 1:LITERATURE REVIEW ovarian cancer cells, where it very potently induced apoptosis in a short-term assay [444].

The variability of responses of different cancer cells to hTR and hTERT directed ribozymes, underscore the obstacles needed for ribozymes to become a realistic therapeutic strategy. Factors which affect optimal ribozymal activity include dependence of divalent cations, efficient delivery into cells and steric hindrance of ribozyme binding to RNA. The steric hindrance is most notable in interactions of ribozymes with ribonucleoproteins, of which telomerase is [478]. Furthermore, the effects of ribozymal targeting of hTERT and hTR on normal cells have yet to be investigated. There is no evidence of ribozymes targeting dyskerin.

1.8.4.2 Antisense oligonucleotides and peptidic nucleic acids Complementary oligonucleotides specifically targeting mRNA encoded by TERC and TERT genes have shown promise as an anti-telomerase therapeutic approach. Initial studies targeting human hTR with antisense ODNs or PNAs confirmed the targeting hTR is a valid approach for targeting telomerase. The effectiveness of this approach was demonstrated in the specific inhibition of telomerase enzyme activity and subsequent induction of apoptosis in tumor cell lines [412, 435]. PNAs complementary to the template region of hTR were considerably more effective than other chemically modified oligonucleotides or PNAs that targeted other regions of hTR [435]. Success of targeting the template region was attributed to effective competitive binding of the oligomer at the template region of hTR, which blocked interactions between telomerase and telomeric DNA. These findings revealed that oligomers could be employed to inhibit telomerase activity effectively through both competitive inhibition as well as the catalytic degradation of the mRNA.

Results from initial studies of hTR directed antisense ODNs led to the design of more complex oligonucleotides with an unlimited content of chemically modified bases that would potentially improve their stability and binding affinity in vivo. In addition to PNAs, other chemical modifications that have been shown to confer favorable stability and efficacy include 2’-O-methyl RNA (2MeRNA), 2’-O- methoxyethyl (2’MOE) RNA, adenylate and phosphorothioate linkage (reviewed in [415-418]. In human tumor cell lines, 2’-O-MeRNA, phosphoramidate and PNA 60

CHAPTER 1:LITERATURE REVIEW oligomers complementary to hTR were shown to mediate telomere shortening with the subsequent onset of apoptosis over long-term culture periods [416-419].

In particular, a N3’-P5’ thio-phosphoroamidate 13-mer oligonucleotide targeted to the template region of hTR, referred to as GRN163 was shown to be effective against multiple myeloma and non-Hodgkin’s lymphoma cell lines and patient cells in vitro and in xenografted mice [479, 480]. The specificity of this compound was evidenced by telomere shortening and by the relatively higher sensitivity of multiple myeloma cells with short telomeres, compared with cells with longer telomeres [479, 480]. A lipid conjugated form of N3’-P5’ thio-phosphoroamidate, referred to as GRN163L or Imetelstat, exhibited improved cellular uptake and inhibited telomerase activity more effectively. Telomerase inhibition by GRN163L resulted in rapid telomere shortening and induced more rapid growth arrest than the non-lipidated compound [422, 481].

GRN163L is the most clinically progressed and highly developed telomerase therapeutic to date [421, 422]. The efficacy of GRN163L has been demonstrated in a variety of pre-clinical models, including mice xenografted with human cell lines derived from liver, breast, lung, multiple myeloma and chronic myeloproliferative diseases. The long-term treatment of GRN163L caused progressive telomere shortening and after several weeks resulted in anti-proliferative effects and induction of cell death or senescence [393, 420-423]. Telomerase inhibition by GRN163L was also shown to inhibit the metastatic spread of breast and lung cancer cells in animal models [393, 421]. Long-term treatment with GRN163L led to telomerase inhibition, progressive telomere shortening and reduced rates of proliferation, which caused eventual cell death of primary human glioblastoma tumour-initiating cells and oesophageal cancer cells [425, 432]. These two independent studies also showed that there was increased radiation-induced DNA damage of both cell types 72 hrs after treatment with GRN163L. Additionally, after one week of treatment, GRN163L reduced the colony forming ability of oesophageal cancer cells [425, 432]. These findings indicate that GRN163L had both telomere length-dependent and telomere length-independent effects in primary human glioblastoma tumour-initiating cells and oesophageal cancer cells [425, 432].

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In addition to the sequence specific duplex formed by GRN163L, GRN163L has also been demonstrated to form G-quadruplex complexes under physiological conditions, but the effects of telomerase inhibited have yet to be fully elucidated [433, 482]. Other telomerase-independent anti-metastatic effects of GRN163L were shown to occur via a mechanism that was independent of hTR and telomerase [434]. It appears that the lipid moiety and other structural features of GRN163L alter cell adhesion, which is a crucial determinant of metastatic potential [434]. While this telomerase- independent effect may add to the potency of GRN163L, potential effects on normal tissue remain to be fully explored.

Irrespective of the questions surrounding the possible additional non-telomeric effects of GRN163L, preclinical studies to date have validated GRN163L as an effective compound for targeting the template region of hTR and inhibiting telomerase. The efficacy of targeting hTR has been demonstrated in various in vitro cancer cell line models of lung, breast, prostate, liver, brain, bladder origin, multiple myeloma and T-lymphocytic leukaemia cell lines as well as in patient cells in vitro and in xenografted mice [479](as reviewed in [429]. Following the success of the pre-clinical investigations, clinical trials with GRN163L were initiated in 2005 of numerous solid and haematological malignancies.

Phase II/III clinical trials are currently underway in patients with chronic lymphoproliferative disease, refractory or relapsed multiple myeloma some advanced solid tumours (http://clinicaltrials.gov). In three of these trials, GRN163L has been used as a single agent, while in others it is used in combination studies. The most common toxicities reported included; cytopenia, prolonged clotting, side effects to the gastrointestine, fatigue and peripheral neuropathy but appeared to be well tolerated [390, 433]. However, the limited efficacy of GRN163L in the treatment of malignancies other than those with short telomeres was recently demonstrated in an failed phase II clinical trial of patients with breast and lung cancer .[483] An adverse side effect following GRN163L treatment noted in this failed trial was that of decreased platelets or thrombocytopenia .[483] Notably, the side effects of decreased platelets are however a desired effect for the treatment of some blood malignancies, characterised by an excess of blood cells [483]. These findings and another trial showing eight out of nine multiple myeloma patients treated with GRN163L had 62

CHAPTER 1:LITERATURE REVIEW decreased number of circulating cancer stem cells [483], promoted GRN163L as the lead therapeutic candidate for hematological malignancies, however the mechanisms underlying these effects on the hematopoietic cells, still remains to be elucidated.

Another consequence of GRN163L treatment that warrants further investigation is its propensity to augment the activity of other therapeutic agents [422, 423]. It is generally appreciated that due to the lag period that occurs as telomeres are gradually shortened, this approach would be best employed in conjunction with other therapies. Evidence from independent studies further supports this notion [424, 425, 430, 431]. GRN163L mediated telomerase inhibition was found to work co-operatively in conjunction with ionising radiation in breast cancer treatment as well as other anticancer agents such as the microtubule stabiliser paclitaxel, cell cycle stage specific DNA damaging agents, etoposide and cisplatin and the HER2 receptor- monoclonal antibody, trastuzumab [424, 430, 431]. A phase 1 trial of GRN163L in combination with trastuzumab has recently been initiated in patients who have not responded to trastuzumab treatment alone. So far, it has been reported that this combination treatment decreases HER2 levels comparable to the reduction of HER2 levels seen by trastuzumab treatment in trastuzumab se nsitive cells [483].

The effects of GRN163L treatment on human glioblastoma (GBM) tumour-initiating cells were also enhanced when the compound was used in combination with radiation or temozolomide [425]. Pre-clinical investigations showed promise for the potential use of GRN163L with debulking therapy. Inhibition of telomerase by GRN163L decreased cancer stem cell populations of breast, prostate, pancreatic cancer that resulted in growth inhibition and decreased self-renewal potential [425, 426, 432, 483, 484]. However, these effects were not seen in the recent failed II clinical trial of patients with breast and lung cancer [483].

Antisense approaches aimed at ablating hTERT mRNA expression have also been investigated. Repression of hTERT expression with phosphothiodated antisense oligodeoxynucleotides (AS PS-ODN) was shown to exert immediate anti- proliferative effects in the absence of telomere shortening in prostate, bladder, human hepatocellular carcinoma (HCC) and gastric cancer cell lines [352, 409-411]. The

CHAPTER 1:LITERATURE REVIEW outcome of anti-sense mediated hTERT repression contrasted with results obtained when prostate tumour cells were transfected with hTR-directed AS PS-ODN, which showed no short-term (72 hours) effect on cell viability, despite effective inhibition of telomerase activity [352]. Transfection of leukemic cell lines and primary cultures established from AML and CML patients’ cells with AS PS-ODN directed towards hTERT mRNA was shown to suppress telomerase activity and sensitised the cells to the anti-cancer drug cisplatin [485].

An antisense oligonucleotide that targets the 3′-untranslated sequences in human hTERT mRNA, referred to as Cantide ® specifically downregulated hTERT mRNA, telomerase activity and induced apoptosis in HCC cells [411]. Cantide® was subsequently shown to have anti-tumour activity and enhanced the therapeutic effectiveness of chemotherapeutic drugs on tumour formation in HCC xenografted mice [486, 487]. Most recently, Cantide was administered in combination with 5- Fluoracil in an orthotropic model of primary hepatic lymphoma (PHL) and showed inhibitory effects on tumour growth [488]. The results from these studies have provided the basis for the development of a clinical trial for the treatment of PHL yet to be initiated [488].

1.8.4.3 Short interfering RNA (siRNAs) siRNAs directly targeting any of the three core telomerase components (hTERT, dyskerin, hTR) have validated directly targeting the telomerase components as an effective approach for the inactivation of telomerase activity [134, 298, 305, 306, 346, 348, 366, 377, 446, 489, 490].

RNAi takes advantage of the natural cellular antiviral response mechanism of gene silencing by mRNA cleavage. It is meditated by double stranded RNA, which is cleaved by the DICER enzyme into duplexes of 21-23 nucleotide length with a 2-nt overhang at the 3’end of each strand [491]. The siRNA duplexes are incorporated into an enzyme complex that includes Argonaute 2 (AGO2) and the RNA induced silencing complex (RISC). The sense strand is cleaved forming the activated AGO2/RISC complex. Guided by the antisense strand of the duplex siRNA, the activated AGO2/RISC complex searches and binds to the complementary target mRNA. The target mRNA is then cleaved resulting in specific gene silencing [492]. 64

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Custom made siRNAs resembling the DICER products, specifically inhibit gene expression of target genes when transfected into various mammalian cell lines [491].

The highly specific nature of siRNA has made it a very useful tool in studies of gene function [493]. A single nucleotide mismatch in a 19-nt targeting sequence dramatically reduces its ability to suppress gene expression [494]. Effective delivery and duration of siRNA in the cells are important factors that influence the efficacy of siRNA-mediated inhibition [491]. siRNA-mediated inhibition is generally transient, shown to occur within 8-24 hrs of transfection, which usually recovers after 96 to 120+ hrs or 3 to 5 cell divisions after transfection, as the siRNAs are not integrated and hence diluted out or lost as the cells divide [495]. Factors including mRNA transcription rate and stability, as well as the rate of protein turnover, dilution and longevity of the siRNA affect onset and duration of siRNA-mediated inhibition. As these factors may vary depending on cell type, cell specific effect variations to siRNA-mediated inhibition may arise [491, 493, 495]. These factors highlight the importance of empirically determining effective siRNA-mediated inhibition between different cell types. siRNA directed to hTERT have proven to be an effective means for suppressing expression of hTERT and downregulating telomerase activity in tumor cell lines [134, 298, 305, 306, 366, 377, 446, 490]. Furthermore, stable suppression of hTERT- mediated by vector-expressed siRNA resulted in telomere shortening and impeded tumor growth in xenografted mice [306, 366]. siRNA targeting the template region of hTR has been tested in human melanoma, breast, colon, prostate and bladder cell lines in vitro and in xenografted mice [445]. Similarly, a number of studies have used siRNA directed to dyskerin successfully to unveil additional functions of dyskerin [218, 346, 348]. siRNA-mediated suppression of hTERT, hTR and dyskerin have been employed to probe the putative telomere length-independent functions of these telomerase components as reviewed in section 1.7.

Owing to the more acute effects of hTERT inhibition, hTERT siRNAs have been also been shown to be relatively effective in combination with other cancer therapies. For example, the effects of decreased proliferation and invasion of human

CHAPTER 1:LITERATURE REVIEW glioblastoma cell lines following telomerase inhibition by hTERT siRNA was greatly enhanced when used in combination with IFN-gamma treatment [333]. The therapeutic application of directly targeting the telomerase components will be gained by investigations of investigating the effects of siRNA targeting in normal cells.

The clinical application of siRNAs as therapeutic agents has not yet been realised as delivery, stability and in vivo effectiveness still require optimization. To date, only five siRNA treatments have been tested in clinical trials [496, 497]. Ongoing modifications and/or improvements should ensure the development of more clinically relevant techniques to deliver siRNA efficiently and specifically to malignant cells [493]. Alternatively, the siRNA targeting may be applied as a read out in drug screening investigations to identify small molecules that downregulated target genes as an effective as a means for the identification of new therapeutics [498, 499].

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1.9 Hypothesis Telomerase presents a promising target for new therapeutics due to its activity in most cancer cells [3, 14, 163, 311, 500]. Studies of telomerase inhibition have utilised approaches that interfere with the telomere maintenance function of telomerase without directly ablating subunits of holoenzyme [163, 280, 392-394]. However, as these approaches including the DNhTERT, small molecule inhibitor BIBR1532 and template antagonist GRN163L, are dependent on gradual telomere shortening, they are associated with a delay before tumour cell replication ceases.

Past studies have shown that telomerase components contribute to survival and/or proliferation of tumour cells independently of their roles in the telomerase holoenzyme and telomere maintenance and provide strong rationale for investigating the utility of directly targeting telomerase components as a therapeutic strategy [134, 300, 338, 341]. It is anticipated that directly targeting telomerase components will elicit more rapid and potent effects than telomerase inhibitors that are dependent on telomere shortening and be a more effective approach to inhibiting the proliferation of immortal cells than solely interfering with telomere extension. However, it is yet to be empirically determined which of the components of the telomerase holoenzyme will be the most effective target in terms of efficacy and specificity for cancer cells.

The impact of directly targeting the telomerase components in normal cells has not been fully elucidated and is an important question in relation to the potential toxicity of anti- telomerase therapeutics. Defining the molecular and functional consequences of directly targeting each of these components in normal, immortal and tumorigenic cells will provide insight for the development of an effective therapeutic approach to ablating telomerase activity and halting the replication of immortal cells, while sparing normal human cells.

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1.10 Aim To demonstrate the molecular and functional consequences of telomerase inhibition via siRNA-mediated targeting of hTERT, hTR and dyskerin and to determine whether this approach induces similar effects in normal and neoplastic cells

Specific Aim 1: Optimise siRNA-mediated inhibition of the telomerase components hTERT, hTR and dyskerin telomerase in normal, immortal and tumorigenic MRC5 human foetal lung fibroblasts Specific Aim 2: Define the effects of repression of the telomerase components hTERT, dyskerin and hTR on cell proliferation, viability and cell cycle kinetics in isogenic normal, immortal and tumorigenic MRC5 cells. Specific Aim 3: Demonstrate the effects of stable repression of telomerase components on the malignant properties of tumorigenic MRC5 cells and Ht0180 fibrosarcoma Specific Aim 4: Characterise the gene expression pathways that are altered by the repression of the telomerase enzyme components hTERT, hTR and dyskerin in isogenic normal, immortal and tumorigenic MRC5 cells. .

CHAPTER 2: MATERIALS AND METHODS ______

2. Materials and Methods

______

2.1 Reagents and solutions Table 2.1 General Reagents and kits Reagents Source Location General reagents Acetic acid Fronine Riverstone, NSW, AUS Agarose , DNA grade Applichem Darmstadt, Germany Annexin V-APC BD Biosciences New Jersey, USA AmpliTaq DNA polymerase Gold Applied Biosystems Foster City, CA,USA Ampicillin Sigma St Louis, MO, USA BIBR1532 Tocris Biosciences Bristol, UK Bgl11 restriction enzyme New England Biolabs Ipswich, MA, USA β-mercaptoethanol Sigma St Louis, MO, USA Bromophenol blue International New Haven, CT, USA Biotechnologies Bovine serum albumin (BSA) Roche Mannheim, Germany Chloroform Sigma St Louis, MO, USA Criterion XT Bis-Tris Gel 4–12% Biorad Carliformia,USA polyacrylamide gel Deoxynucleoside triphosphates (dNTPs) Roche Mannheim, Germany Dimethyl sulphoxide (DMSO) Sigma St Louis, MO, USA Dithiothreitol (DTT), 0.1 M Invitrogen Carlsbad, CA, USA Dimethylformamide (DMF) Amersco Solon, OH, USA

DNase and RNase-free H2O Gibco, Invitrogen Carlsbad, CA, USA dNTPs Invitrogen Carlsbad, CA, USA EcoRI restriction enzyme New England Biolabs Ipswich, MA, USA Ethanol 100% Molecular Grade Sigma St Louis, MO, USA Ethidium bromide Amresco Solon, OH, USA Ethylene diamine tetra acetic acid (EDTA) Sigma St Louis, MO, USA First strand buffer, 5X Invitrogen Carlsbad, CA, USA Glycerol Sigma St Louis, MO, USA Geneticin (G418) Sigma St Louis, MO, USA HindIII, restriction enzyme New England Biolabs Ipswich, MA, USA Hydrochloric acid Fronine Riverstone, NSW, AUS Immobilon-P polyvinylidene fluoride Merck Milipore Darmstadt, Germany (PVDF) iQ™ SYBR Green Supermix BioRad Hercules, CA, USA Isoamyl alcohol Sigma St Louis, MO, USA Isopropanol Fronine Riverstone, NSW, AUS

CHAPTER 2: MATERIALS AND METHODS

Sample buffer 4xNupage for Invitrogen Carlsbad, CA, USA immunoblotting Magnesium chloride (MgCl2), PCR grade Applied Biosystems Foster City, CA,USA

Magnesium sulphate (MgSO4) Sigma St Louis, MO, USA Maleic acid Sigma St Louis, MO, USA Methanol Fronine Riverstone, NSW,AUS Nonidet P-40 (NP-40) Sigma St Louis, MO, USA NuPAGE MES SDS running buffer Invitrogen Carlsbad, CA, USA NuPAGE MOPS SDS running buffer Invitrogen Carlsbad, CA, USA Paraformaldehyde 16% methanol-free Polysciences Inc Warrington, PA. USA PCR SYBR green Master mix Applied Biosystems Foster City, CA, USA Phosphate Buffered Saline (PBS) Invitrogen Carlsbad, CA, USA Polymerase Chain Reaction (PCR) buffer Applied Biosystems Foster City, CA without MgCl2, 10X Polyoxyethylene-sorbitan monolaureate MP Biomedicals Aurora, OH, USA (Tween-20) Ponceau S Sigma St Louis, MO, USA Propidium iodide Sigma St Louis, MO, USA Protease cocktail inhibitor Roche Mannheim, Germany Proteinase K Sigma St Louis, MO, USA Random primers Invitrogen Carlsbad, CA, USA Reducing agent 20x NuPage Invitrogen Carlsbad, CA, USA RNAse Out Invitrogen Carlsbad, CA, USA RsaI, restriction enzyme New England Biolabs Ipswich, MA, USA Sample buffer 4x NuPage Invitrogen Carlsbad, CA, USA Tris-Saturated phenol Invitrogen Carlsbad, CA, USA Skim milk powder Coles Sydney, NSW, AUS Sodium acetate (NaOAc) Sigma St Louis, MO, USA Sodium chloride (NaCl) Fronine Riverstone, NSW Sodium dodecyl sulphate (SDS) MP Biomedicals Aurora, OH, USA Sodium fluoride (NaF) Sigma St Louis, MO, USA Sodium hydroxide (NaOH) Fronine Riverstone, NSW Sodium orthovanadate Sigma St Louis, MO, USA SuperSignal West Pico Chemiluminescent Pierce, Thermofisher Rockford, IL, USA Substrate scientific Superscript II reverse transcriptase Invitrogen Carlsbad, CA, USA SYBR green Master Mix Applied Biosystems Foster City, CA, USA Transfer buffer for immunoblotting Invitrogen Carlsbad, CA, USA Trapeze lysis buffer 1x CHAPS Merck Milipore Darmstadt, Germany Tris (hydroxymethyl)aminomethane Fronine Riverstone, NSW Tris acetate EDTA (TAE), 50x Invitrogen Carlsbad, CA, USA Tri-sodium citrate Fronine Riverstone, NSW, AUS Tris-saturated phenol Sigma St Louis, MO, USA Triton X-100 MP Biomedicals Aurora, OH, USA Trizol® Invitrogen Carlsbad, CA, USA 70

CHAPTER 2: MATERIALS AND METHODS

Trypan blue 0.4% Sigma St Louis, MO, USA VECTASHIELD® Mounting Medium Vector Laboratories Burlingame, CA.USA. with 4',6-diamidino-2-phenylindole (DAPI) Xho1 restriction enzyme New England Biolabs Ipswich, MA, USA General kits Illumina® TotalPrep™ RNA Amplification Applied Biosystems Foster City, CA kit (Ambion®) Bi-chinchonic acid (BCA) Protein Assay Pierce Rockford, IL, USA kit Quick Start ®Bradford Protein Assay BioRad Hercules, CA, USA Digoxigenin (DIG)-Chem-Link Labelling Roche Mannheim, Germany and Detection kit DNA Gel extraction kit Qiagen Hilden, Germany Human Ht-12v4 Beadchips Illumina San Diego, CA,USA PureLink® HiPure Plasmid Filter Invitrogen Carlsbad, CA, USA Maxiprep kit RNAeasy Minikit Qiagen Hilden, Germany Purelink RNAeasy Minikit Invitrogen Carlsbad, CA, USA TeloTAGGG kit Roche Mannheim, Germany Wizard® Plus SV Minipreps DNA Promega Madison, WI, USA Purification System Cell culture reagents and kits Alpha Minimum Essential media (αMEM) Invitrogen Carlsbad, CA, USA Dulbecco’s Modified Eagle’s media Invitrogen Carlsbad, CA, USA (DMEM) Foetal calf serum (FCS) Thermo Trace Noble Park, VIC, AUS Hexadimethrine bromide (polybrene) Sigma St Louis, MO, USA L-glutamine Invitrogen Carlsbad, CA, USA Lipofectamine 2000 Invitrogen Carlsbad, CA, USA Lipofectamine RNAiMAX Invitrogen Carlsbad, CA, USA Low melting Agarose Lonza Rockland Inc, USA MycoAlert® Mycoplasma Detection Kit Lonza Rockland Inc, USA OptiMEM Gibco Gibco, Invitrogen Carlsbad, CA, USA Penicillin (10000 U/mL), streptomycin Invitrogen Carlsbad, CA, USA (10000 g/mL), l- glutamine (29.2 mg/mL) (PSG) Puromycin Sigma St Louis, MO, USA Trypsin EDTA solution Invitrogen Carlsbad, CA, USA X-galactosidase Sigma St Louis, MO, USA

CHAPTER 2: MATERIALS AND METHODS Table 2.2 Buffers and Solutions Solutions Constituents Preparation and storage Annealing buffer 10 mM Tris, pH 7.5–8.0, 50 mM NaCl, 1 -20ºC mM EDTA Binding buffer 10 mM HEPES, 140 mM NaCl, 2.5 mM 4°C CaCl2, 5mM KCl, 1 mM MgCl2, pH 7.4 Cell freezing media 20% FCS, 10% DMSO Fresh 0.1% Triton X-100/KCM 120 mM KCl, 20 mM NaCl, 10 mM Tris (pH 4°C buffer 7.5), 0.1% (v/v) Triton X-100, 0.5 EDTA Chloroform:isoamyl alcohol 24:1 chloroform:isoamyl alcohol Fresh Chloroform:isoamyl 24:1:25 chloroform:isoamyl alcohol:tris- Fresh alcohol:tris-saturated phenol saturated phenol BIBR1532 1 mM stock dissolved in DMS0 -20ºC DNA loading dye, 6x 0.25% bromophenol blue, 0.25% xylene RT cyanol, 40% sucrose FACS buffer 0.2% BSA, 0.1% Sodium azide, PBS RT Fixative 2% formaldehyde/ 0.2% glutaraldehyde gDNA lysis solution 10 mM Tris pH 7.5, 10 mM EDTA, 50 mM RT (TESS buffer) NaCl, 0.1% SDS, 0.2 mg/mL proteinase K LB agar plates with ampicillin 37 g per 1 L H2O, add 100 µg /mL ampicillin Autoclave when agar is 40-50oC before use LB broth 20 g per 1 L H2O pH 7.5 Polybrene, 100x 800 μg/mL in PBS 4°C Ponceau S 0.1% Ponceau S / 5% acetic acid RT Radio immunoprecipitation 50 mM Tris pH 7.5, 150 mM NaCl, 1% 4°C assay (RIPA) buffer NP40, 0.5% sodium deoxycholate, 0.1% SDS, 50 mM NaF, 0.1 mM Na3VO4, H20 Transfer buffer, 10x 25 mM Tris, 192mM Glycine, RT

Tris buffered saline (TBS) 20 mM Tris, 100 mM NaCl, H20 pH 7.5, RT Tris-borate EDTA buffer 0.9 M Tris, 0.9 M boric acid, 40 mM EDTA, RT (TBE), 10x H20 TBS with Tween-20 (TTBS) 20 mM Tris, 100 mM NaCl, 0.1% Tween-20 pH 7.5 RT

Tris-EDTA buffer (TE) 10 mM Tris, pH 8.0, 1 mM EDTA, H20 RT x-gal buffer 40 mM Citric acid, sodium phosphate 4°C, dark Na2HPO4 pH 6, 0, 5 mM potassium ferrocyanide K3[Fe(CN)6], 150 mM NaCl, 2mM MgCl2, H20 Southern blotting solutions Blocking buffer 1% blocking reagent in maleic acid buffer fresh Denaturation solution 0.5 M NaOH, 1.5 M Tris fresh Detection buffer 0.1 M Tris, 0.1 M NaCl pH 9.5 Maleic acid buffer 100 mM maleic acid, 150 mM NaCl fresh Neutralisation solution 0.5 M Tris, 1.5 M NaCl pH 7.5 Sodium chloride, tri-sodium 3 M NaCl, 0.3 M tri-sodium citrate fresh citrate (SSC), 20x Stringent wash buffer I 2x SSC/0.1% SDS fresh Stringent wash buffer II 0.2x SSC/ 0.1% SDS fresh Wash buffer 100 mM maleic acid, 150 mM NaCl, 3% pH 7.5 Tween-20 72

CHAPTER 2: MATERIALS AND METHODS 2.2 Mammalian cell culture Cells were maintained in media supplemented with heat inactivated 10% FCS and

PSG. Cells were maintained at 37ºC in a 95% air and 5% CO2 incubator. See Table 2.3 for details of culturing media for each cell line.

2.2.1 Passaging of mammalian cells Cells were grown in T-75 mm dishes to 60-80% confluence. For passaging, media was aspirated and cells were washed with 4 mLs PBS. The PBS was aspirated and incubated in 4 mLs trypsin EDTA solution for two min at 37ºC for enzymatic dissociation. The flasks were shaken gently until the cells detached. Eight mLs of serum containing media was added to neutralise the trypsin. Cells were transferred into fresh flasks at a dilution of 1:10 or 1:20. For cell counting, 20 µL of the cell suspension was mixed with 20 µL trypan blue 0.4% solution. Live and dead cells were counted using a haemocytometer under an inverted Zeiss microscope and a 10 x objective. The remaining cells were centrifuged for 7 min at 490 x g at 4C and made into cell pellets for molecular biology assays or used for cryopreservation. For cell pellets, cells were transferred to a microcentrifuge, washed with 1x PBS and centrifuged for 1 min at 12 000 x g at RT. PBS was aspirated off and cell pellets were frozen at -80C.

2.2.2 Cryopreservation and thawing of cells Cryopreservation of cells was routinely performed by resuspension of cells in 1 mL cell freezing media and transferred to a cryovial for storage at -80ºC for up to one week and then moved to liquid nitrogen for long term storage. Frozen cells were thawed in a 37ºC water bath with agitation until only a small frozen core of cells remained. Cells were added to 9 mL of pre-warmed culture media containing 20% FCS and centrifuged at 490 x g for 7 min at RT. The supernatant was aspirated off and cells were resuspended in the appropriate media containing 20% FCS and placed into a new flask. 20 µL of the cell suspension was taken for cell count as described above. Fresh media was replaced on the following day.

CHAPTER 2: MATERIALS AND METHODS

Table 2.3 Cell lines used in this study Cell line Lineage Exogenous genes Description Culture media Source of cell line Phoenix A cells Retroviral Packaging cells Retroviral helper genes Derived from HEK293 10% DMEM [501] and SV40 PG13 Retroviral Packaging cells Retroviral helper genes Derived from NH3T3 10% DMEM [502]

MRC5 Human foetal lung fibroblasts None Normal diploid 10% ɑMEM [503] ATCC

MRC5hTERT Human foetal lung fibroblasts hTERT/GFP tpz hTERT immortalised 10% ɑMEM [296, 504]

MRC5hTERT-TZT Human foetal lung fibroblasts hTERT/GFP tpz/N-RAS hTERT immortalised, 10% ɑMEM Generated in our lab

MRC5V1 Human foetal lung fibroblasts SV40 SV40 immortalised, 10% ɑMEM Gift from Lily Huschtcha [505] hTERT MRC5V2 Human foetal lung fibroblasts SV40 SV40 immortalised, 10% ɑMEM Gift from Lily Huschtcha [505] ALT HT1080 Fibrosarcoma None Tumour derived 10% DMEM ATCC [506]

HeLa Human epithelial carcinoma None Tumour derived 10% DMEM ATCC [507]

SK-N-SH Neuroblastoma None Tumour derived 10% DMEM ATCC [508]

Note: Abbreviations: ATCC; American Tissue Culture centre, ALT, Alternative lengthening telomeres; ɑMEM; alpha minimal essential media, DMEM; Dulbecco’s modified Eagle’s medium; GFP(TZP) –Green fluorescent protein, hTERT; telomerase reverse transcriptase; SV40, Simian virus 40

CHAPTER 2: MATERIALS AND METHODS

2.2.3 Calculation of expansion and population doublings (PDs) Cell concentration was calculated with the formula: Cells/ml = [(number of counted cells) / (number of squares)] x (dilution factor) x 104 Cell expansion was calculated using the following formula: Expansion = (Number of cells harvested)/ (Number of cells seeded) Cumulative expansion was calculated by multiplying the expansion of the previous passages by the expansion of the current passage. Population doublings (PDs) was calculated using the following formula: Population doubling = [log (expansion)]/ (log 2) Cumulative PDs were calculated by addition of the PDs from the previous passages to the current passage. Viability was calculated from the ratio of viable cells (unstained) to total cell number containing live and blue stained dead cells.

2.2.4 Mycoplasma testing Mycoplasma testing was performed using the MycoAlert® Mycoplasma Detection Kit. Cells were centrifuged and 100 µL of cell supernatant was transferred to a 96 well plate. MycoAlert® Reagent (100 μL) was added to each sample followed by a 5 min incubation and then first reading (Reading A) was measured on the Wallace

1420 Victor. One hundred μL of MycoAlert® Substrate was then added to each sample and further incubated for 10 min and the second Reading B was measured. Ratio was calculated using the following equation Reading B/Reading A.

2.3 Retroviral gene transfer

2.3.1 Bacterial transformation with plasmid DNA Transformation was performed with MAX Efficiency® Stbl2™ Competent Cells (Invitrogen), which are highly competent bacterial cells engineered for the cloning of sequences prone to recombination. Two µg of each plasmid was used to transform 50 µL of thawed competent cells. Bacterial cells and plasmid DNA were mixed by gently flicking. The tubes were placed on ice for 30 minutes before a 30 second heat shock in a 42ºC water bath. The tubes were returned to ice for 5 minutes. SOC medium (950 µL) was added to the bacteria and DNA mixture and incubated for 1 hr at 37ºC with shaking. One hundred µL of each transformation was streaked onto LB plates containing 100 µg/mL ampicillin. 75

CHAPTER 2: MATERIALS AND METHODS

2.3.2 Preparation of plasmid DNA

2.3.2.1 Mini-preparation of plasmid DNA Mini-preparation of plasmid DNA was performed for restriction enzyme analysis and sequencing reactions using the Wizard®Plus SV Miniprep kit according to the manufacturer’s instructions. Single colonies were isolated and inoculated into 5 mL LB broth containing 100 µg/mL ampicillin and incubated overnight at 37ºC with shaking. The following morning, the 5 mL overnight starter cultures were centrifuged at 490 x g for 10 min at RT. The supernatant was removed and the pellet resuspended in 250 µL cell resuspension buffer and transferred to a new tube. Bacterial cells were lysed in 250 µL cell lysis buffer and the solution inverted four times for mixing. After a five min incubation at RT, 10 µL Alkaline Phosphatase was added and mixed by inversion four times and incubated for an additional 5 min. The solution was neutralised with 350 µL cell neutralisation Solution and mixed by inversion four times. The solution was centrifuged at 16 000 x g for 10 min at RT and the supernatant was transferred to the Wizard®Plus SV DNA-binding Spin Column and centrifuged at 16 000 x g for 1 min. The flow through was discarded and 750 µL of wash solution was added to the columns and centrifuged for 1 min at 16 000 x g at RT. The columns were washed again in 250 µL Wash Solution and centrifuged for 2 min at 16 000 x g at RT. Columns were placed in 1.5 microfuge tubes and eluted with 20 µL of DNAse/RNAse free H20 by centrifugation 16 000 x g for 1 min.

2.3.2.2 Maxi-preparation of plasmid DNA Single colonies were isolated and inoculated into 5 mL LB broth containing 100 µg/mL ampicillin and incubated for 8 hr. The 5 mL starter culture was diluted into 500 mL LB containing 100 µg/mL ampicillin and grown at 37ºC overnight with vigorous shaking. Cells were harvested by centrifugation at 6000 x g on a Beckman ™ JA-10 centrifuge for 15 min at 4ºC. For the preparation of a working stock, plasmid DNA was isolated using the Qiagen Plasmid Maxi Kit. The supernatant was removed and the pelleted bacterial cells were resuspended in 10 mL resuspension buffer containing 0.1 mg/mL RNAse A. For lysis, 10 mL of lysis buffer was added and mixed thoroughly by inverting 4-6 times for 5 mins at RT. To neutralise, 10 ml of chilled neutralisation buffer was then added to the solution, incubated on ice for 76

CHAPTER 2: MATERIALS AND METHODS

20 min and mixed thoroughly by inverting up to 10 times as the solution became cloudy. The samples were then centrifuged at 20 000 x g for 30 min at 4ºC and the supernatant containing the plasmid DNA was then centrifuged again for 15 min at 4ºC. The supernatant was transferred to an equilibrated Qiagen-tip 500 Spin Column and the plasmid DNA was allowed to enter the silica membrane by gravity flow. The membrane was then washed twice with 30 mL wash buffer to remove all contaminants. DNA was eluted with 15 mL of elution buffer . The eluted DNA was precipitated by addition of 10.5 mL RT isopropanol and centrifuged at 15 000 x g for 30 min at 4ºC. The DNA pellet was then washed with 70% ethanol and centrifuged at 15000 x g. The pellet was air dried for 5-10 min and redissolved in 50 µL TE buffer, pH 8.

2.3.2.3 Preparation of plasmid DNA for transfection DNA was prepared for transfection in biological safety hood to ensure it remained sterile. Twelve µg of plasmid DNA was precipitated overnight in an eppendorf tube with 1/10 volume sterile 3M NaAc and 2 volumes of ethanol. The precipitated DNA was then centrifuged for 30 min at 4ºC and rinsed with 500 µL cold 70% ethanol, centrifuged for 15 mins at 4 oC and air dried. DNA was resuspended in sterile

DNAse/RNAse-free H20 to a final concentration of 0.04 µg/µL.

2.3.2.4 Spectrophotometric analysis of DNA Absorbance of DNA was measured at 260 nm and 280 nm a ND 1000 Nanodrop spectrophotometer (Thermofisher, USA). The concentrations were determined as follows: c (µg/ml) = A260 nm X 50 µg/mL X 10. Quality of DNA was assessed by ratio of A260 nm/A280 nm. A ratio of between 1.8 and 2 was considered acceptable quality [509].

2.3.2.5 DNA agarose gel electrophoresis DNA was electrophoresised through a 1-2% agarose gel buffered in 1x TAE at 80V for 1.5 hr. Gels were stained with 0.5 µg/µL ethidium bromide and viewed on the Gel Doc™ EZ Imager (BioRad).

CHAPTER 2: MATERIALS AND METHODS

2.3.2.6 Preparation of glycerol stocks Single bacterial colonies were inoculated into 5 mL selective LB medium containing 100 µg/µL of ampicillin and grown until stationery phase (with an OD 600 nm of 1- 2) at 37ºC with vigorous shaking overnight. Eight hundred µL of bacterial cells was added to 200 µL sterile 80% glycerol solution and stocks stored at -80ºC.

2.3.2.7 Transfection of packaging cells Transfection of plasmid DNA into the retroviral packaging cells was performed using Lipofectamine 2000 reagent. Packaging cells were plated at 0.8 X106 cells/ well in a 6 well plate. Four µg of plasmid DNA was diluted in 250 µL OptiMEM and left for 5 min at RT. Ten µL Lipofectamine 2000 was added to a total volume of 250 µL OptiMEM and left f1or 5 mins at RT. The DNA solution and Lipofectamine solution of 500 µL were mixed and left for 20 mins at RT. Media was aspirated off the packaging cells and 750 µL OptiMEM including 10% FCS was added. Five hundred µL of the transfection solution was added drop wise to the cells and incubated overnight. The media was then changed to 2 mLs 10% FCS/DMEM and PSG and 24 hr later the media on the packaging cells was changed to that of the target cells.

2.3.2.8 Retroviral transduction of mammalian cells Target cells were seeded (1x105) one day prior to infection in a 6-well plate. Transduction of target cells was performed via multiple rounds of infections with virus containing media (VCM) collected from the packaging cells. At the infection step, VCM was collected from the packaging cells, filtered through a 0.45 µM filter and mixed with polybrene to a final concentration of 8 µg/mLs VCM (1.5 mL) was placed on the target cells and incubated for 12 hrs at 37C in a humidified 5% CO2 incubator following spin inoculation at 30ºC, 490 x g for 1.5 hrs. The packaging cells were replenished with fresh media of the target cells and incubated until next infection. After the last round of infection, VCM was replaced with fresh media. Three days after the infection, the infected cells were passaged and checked for Green Fluorescent Protein (GFP) expression on a FACS Calibur flow cytometer (Becton Dickinson) and analysed using CELL Quest software (Becton Dickinson) or subjected to antibiotic selection (Table 2.4 and Table 2.5).

CHAPTER 2: MATERIALS AND METHODS

2.4 Suppression of gene expression with siRNA inhibition

2.4.1 siRNA design siRNA inhibition of gene expression was performed using siRNA directed specifically against hTERT, hTR, dyskerin. Control siRNA (siSc) was used to discount any potential non-specific effects of siRNAs. All siRNAs were synthesised by Sigma and purified by desalted purification (Table 2.6).

2.4.2 Transient siRNA transfection For siRNA transfection, 1x105 cells were plated in 10% FCS/αMEM in T-25 mm flasks and allowed to settle overnight. Cells were transiently transfected with 50 nM siRNA. Six µL of Lipofectamine RNAimax reagent was added to 300 uL of serum- free OptiMEM media and incubated at RT for 5 min. Two µL of 50 µM siRNA was then added to the lipofectamine RNAImax/OptiMEM solution and incubated at RT for 20 min. Culture media was removed and cells were transfected by drop wise addition of 300 µL transfection mix to the cells, and 1700 µL fresh 10% FCS/culture media lacking antibiotics which was added to the cells to facilitate transfection and incubated for 16 hrs before the media was changed. Cells were harvested by trypsinisation and resuspended in 1x PBS and counted as described Section 1.2.1. Cell pellets were stored at -80ºC and harvested for real-time qRT-PCR and qTRAP analysis 24-96 hr post-transfection.

2.5 Construction of retroviral shRNA vectors Stable short hairpin RNAs targeting the telomerase genes were specifically designed for insertion into the retroviral vector system, pSuperRetro-puro (pSRpuro) vector according to the method of Brummelkamp et al.,2002 [494, 510] (Table 2.7). This vector uses the polymerase-III H1-RNA gene promoter and produces a small RNA transcript lacking a polyadenosine tail. It has a well-defined transcriptional start and a termination signal consisting of five thymidines in a row (T5). The cleavage of the transcript at the termination site occurs after the second uridine, so the transcript resembles the ends of synthetic siRNAs, which also contain two 3’ overhanging T or U nucleotides. The pSRpuro includes an ampicillin selectable marker for selection of bacterial transformants and a puromycin selectable marker for selection of transfected mammalian cells.

CHAPTER 2: MATERIALS AND METHODS

Table 2.4 Retroviral Vectors used in this study Vector name Backbone Promoter driven Selectiion Mammalian Exogenous Reference exogenous sequence markerSelecti Selection sequence of on marker marker interest pMND A pMND Human U1 snRNA Ampicillin Neomycin hTR [188] pMND-hTR A pMND Human U1 snRNA Ampicillin Neomycin pBABEshRNAGFP C pBABE SV40 Ampicillin Puromycin GFPshRNA [299, 511] pBABEshRNAp53 C pBABE SV40 Ampicillin Puromycin pMIG+GFPB pMIG+GFP IRES Ampicillin GFP [153, 163] pMIG+hTERT B pMIG+GFP IRES Ampicillin GFP hTERT pMIG+DNhTERT B pMIG+GFP IRES Ampicillin GFP DNhTERT pSRshRNA ScD pSuperRetro-puro Human H1-RNA Ampicillin Puromycin [494] pSRshRNA T7 pSuperRetro-puro Human H1-RNA Ampicillin Puromycin pSRshRNA T8 pSuperRetro-puro Human H1-RNA Ampicillin Puromycin pSRshRNA DKC1-2 pSuperRetro-puro Human H1-RNA Ampicillin Puromycin pSRshRNA DKC1-3 pSuperRetro-puro Human H1-RNA Ampicillin Puromycin pSRshRNATR151 pSuperRetro-puro Human H1-RNA Ampicillin Puromycin

Notes: pMIG+DNhTERT has two point mutations (D868.D869/A868.A869) within the reverse transcriptase domain of hTERT which confer dominant negative function (DNhTERT). Abbreviations: pMIG- pMSCV-IRES-GFP Murine Stem Cell Virus –Internal Ribosome Entry Site Green Fluorescent protein vector.

CHAPTER 2: MATERIALS AND METHODS

Table 2.5 Cell lines derived from this study Cell line generated Parental cell line Retroviral Vector Packaging Selection Antibiotic Exogenous cell marker concentration sequence CHAPTER 4

MRC5hTERT-pMND MRC5hTERT pMND A PhA cells Neomycin 400 µg/mL hTR MRC5hTERT-pMNDhTR MRC5hTERT pMND-hTR A PhA cells Neomycin 400 µg/mL HT1080-pMND HT1080 pMND PhA cells Neomycin 800 µg/mL HT1080-pMNDhTR HT1080 pMND-hTR PhA cells Neomycin 800 µg/mL

MRC5hTERT-GFPshRNA MRC5hTERT pBABEshRNAGFP C PhA cells Puromycin 0.8 µg/mL GFPsh MRC5hTERT-p53shRNA MRC5hTERT pBABEshRNAp53 C PhA cells Puromycin 0.8 µg/mL p53sh

MRC5-GFPM MRC5 pMIG+GFPB PG13 GFP GFP MRC5-hTERTM MRC5 pMIG+hTERT B PG13 GFP hTERT MRC5-DNhTERTM MRC5 pMIG+DNhTERT B PG13 GFP DNhTERT

CHAPTER 5 HT1080-shSc HT1080 pSRshRNA ScD PhA cells Puromycin 0.8 µg/mL Scsh HT1080-shTERT7 HT1080 pSRshRNA T7 PhA cells Puromycin 0.8 µg/mL TERT7sh HT1080-shTERT8 HT1080 pSRshRNA T8 PhA cells Puromycin 0.8 µg/mL TERT8sh HT1080-shDKC1-2 HT1080 pSRshRNA DKC1-2 PhA cells Puromycin 0.8 µg/mL DKC1-2sh HT1080-shDKC1-3 HT1080 pSRshRNA DKC1-3 PhA cells Puromycin 0.8 µg/mL DKC1-3sh HT1080-shTR151 HT1080 pSRshRNATR151 PhA cells Puromycin 0.8 µg/mL TR151sh HT1080-shTR2 HT1080 pSRshRNATR2 PhA cells Puromycin 0.8 µg/mL TR2sh HT1080-GFP HT1080 MIG+GFP B PhA cells GFP GFP HT1080-DNhTERT HT1080 MIG+DNhTERT B PhA cells GFP DNhTERT

CHAPTER 2: MATERIALS AND METHODS

Table 2.5 continued Cell line generated Parental cell line Retroviral Vector Packaging cell Selection Antibiotic Exogenous marker concentration sequence CHAPTER 5 MRC5hTERT-TZT-shSc MRC5hTERT-TZT pSRshRNA ScD PhA cells Puromycin 0.8 µg/mL Scsh MRC5hTERT-TZT-shTERT-T7 MRC5hTERT-TZT pSRshRNA T7 PhA cells Puromycin 0.8 µg/mL TERT7sh MRC5hTERT-TZT-shTERT-T8 MRC5hTERT-TZT pSRshRNA T8 PhA cells Puromycin 0.8 µg/mL TERT8sh MRC5hTERT-TZT-shDKC1-2 MRC5hTERT-TZT pSRshRNA DKC1-2 PhA cells Puromycin 0.8 µg/mL DKC1-2sh MRC5hTERT-TZT-shDKC1-3 MRC5hTERT-TZT pSRshRNA DKC1-3 PhA cells Puromycin 0.8 µg/mL DKC1-3sh MRC5hTERT-TZT-shTR151 MRC5hTERT-TZT pSRshRNATR151 PhA cells Puromycin 0.8 µg/mL TERC151sh MRC5hTERT-TZT-shTR2 MRC5hTERT-TZT pSRshRNATR2 PhA cells Puromycin 0.8 µg/mL TR2sh MRC5hTERT TZT-GFP MRC5hTERT-TZT MIG+GFP PhA cells GFP GFP MRC5hTERT TZT-DNhTERT MRC5hTERT-TZT MIG+DNhTERT PhA cells GFP DNhTERT

Note: Derived cell lines grown in media of the parental cell line shown in Table 2.1. Superscript indicates corresponding vector in Table 2.4 Abbreviations: GFP, Green fluorescent protein; DN-hTERT, dominant negative; hTERT, hTERT human reverse transcriptase, MIG+ Murine Stem Cell Virus IRES GFP; shRNA-short hairpin RNA; Ph A, Phoenix A cells; pSR, pSuperRetro.

CHAPTER 2: MATERIALS AND METHODS

Table 2.6 siRNA sequences Target gene siRNA ID Strand siRNA Sequence 5-3’ Position Gene source hTERT sihTERT-T7 Sense GGAGCAAGUUGCAAAGCAU[dT][dT] 1797-1817 NM_198254 Antisense AUGCUUUGCAACUUGCUCC[dT][dT] sihTERT-T8 Sense AGAACGUUCCGCAGAGAAA[dT][dT] 1980-2000 NM_198254 Antisense UUUCUCUGCGGAACGUUCU[dT][dT] DKC1 siDKC1-2 Sense GCUGCACAAUGCUAUUGAA[dT][dT] 683-702 NM_001363 Antisense UUCAAUAGCAUUGUGCAGC[dT][dT] siDKC1-3 Sense CGGAAGUCAUUGCCAGAAGAAGA[dT][dT] 265-287 NM_001363 Antisense UCUUCUUCUGGCAAUGACUUCCG[dT][dT] hTR sihTR151 Sense CCGUUCAUUCUAGAGCAAA[dT][dT] 151-170 NR_001566.1 Antisense UUUGCUCUAGAAUGAACGG[dT][dT] sihTR2 Sense GUCUAACCCUAACUGAGAAUU 44–62 NR_001566.1 Antisense UUCUCAGUUAGGGUUAGACUU sihTR3. Sense GCCUUCCACCGUUCAUUCUtt 143-162 NR_001566.1 Antisense AGAAUGAACGGUGGAAGGCtt sihTR4 Sense CCUUCCACCGUUCAUUCUAtt 144-164 NR_001566.1 Antisense UAGAAUGAACGGUGGAAGGtt sihTR5 Sense CACCCACUGCCACCGCGAAtt 284-303 NR_001566.1 Antisense UUCGCGGUGGCAGUGGGUGtt Non-specific siSc Sense GUUCUUGCGAUUGUCUCUAUU Antisense UAGAGACAAUCGCAAGAACUU AlexaFluoroRedSc Sense CAGUCGCGUUUGCGACUGG Antisense CCAGUCGCAAACGCGACUG Notes: Abbreviations si-small interfering; siSc Scramble siRNA. All siRNA was synthesised by Sigma (St Louis, MO, USA).

CHAPTER 2: MATERIALS AND METHODS

2.5.1 Design and annealing of double stranded oligonucleotides To adapt the siRNA sequence to form double stranded shRNA constructs for insertion into the pSRpuro retroviral vector system, a Bgl11 site was added on the 5’end of the sense target sequence of the siRNA. A hairpin spacer sequence of 5’TTCAAGAGA3’ was inserted after the sense target sequence, preceding the reverse complement of target sequence. A terminal sequence of five thymidines was added onto the 3’ end of the reverse complement target sequence followed by Xho1 site added at the 3’ end to form the shRNA (See Table 2.7).

The sense and antisense oligomers were desalted purified and synthesised separately and dissolved in sterile, nuclease-free H2O at a concentration of 3 mg/mL and annealed to generate double stranded shRNA constructs. The oligomers were annealed by adding 1 μL of each sense and antisense oligomer to 48 μL of annealing buffer. The annealing reaction was then incubated at: 94ºC for 4 min, 84ºC for 4 min, 75 for 4 min and 70ºC for 10 min. Annealed oligomers were cooled slowly to RT on the bench and then placed on ice and either stored at -20ºC or used immediately for ligation into the linearised pSRpuro vector.

2.5.2 Ligation of shRNA and vector DNA pSRpuro (2 µg) vector was linearised with a double restriction enzyme digest in a 40 µL reaction of 10 U Bgl11, 20 U Xho1 and 4 µL 10x RE Buffer, 0.4 µL (10 mg/mL)

BSA, and made up 40 µL with H20 for 2 hrs at 37ºC. The linearised plasmid was electrophoresised through a 1% agarose gel for 1 hour at 80 V in TAE buffer and excised from the gel using the Qiagen Gel Extraction kit. The ligation was set up in a 10 µL reaction with 20 ng vector DNA, 120 ng annealed oligo with 1 µL of ligase and 1 µL of ligase buffer, made up with H20 and incubated overnight at 16ºC. The cloning of shRNA for hTR required additional cloning steps, the shRNA oligo constructs were synthesised with a phosphorylated 5’end and the vector was dephosphorylated by antarctic phosphatase prior to ligation. The annealed oligomers were ligated into the vector between the unique BglII and Xho1 enzyme sites, positioning the forward oligomer at the correct position downstream from the H1 promoter’s TATA box.

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Table 2.7 shRNA sequences generated

Sc shRNA Strand shRNA Sequence 5-3’ Corresponding siRNA Sense GATCCCCCGTTCTTGCGATTGTCTCTATTTTCAAGAGATAGAGACAATCGCAAGAACTTTTTTTC siSc Antisense TCGAGAAAAAAAGTTCTTGCGATTGTCTCTATCTCTTGAAAATAGAGACAATCGCAAGAACGGG hTERT-T7 Sense GATCCCCCGGAGCAAGTTGCAAAGCATTTCAAGAGAATGCTTTGCAACTTGCTCCTTTTTC sihTERT-T7 Antisense TCGAGAAAAAGGAGCAAGTTGCAAAGCATTCTCTTGAAATGCTTTGCAACTTGCTCCGGG hTERT-T8 Sense GATCCCCAGAACGTTCCGCAGAGAAATTCAAGAGATTTCTCTGCGGAACGTTCTTTTTTC sihTERT-T8 Antisense TCGAGAAAAAAGAACGTTCCGCAGAGAAATCTCTTGAATTTCTCTGCGGAACGTTCTGGG DKC1-2 Sense GATCCCCGCTGCACAATGCTATTGAATTCAAGAGATTCAATAGCATTGTGCAGCTTTTTC siDKC1-2 Antisense TCGAGAAAAAGCTGCACAATGCTATTGAATCTCTTGAATTCAATAGCATTGTGCAGCGGG DKC1-3 Sense GATCCCCCGGAAGTCATTGCCAGAAGAAGATTCAAGAGATCTTCTTCTGGCAATGACTTCCGTTTTTC siDKC1-3 Antisense TCGAGAAAAACGGAAGTCATTGCCAGAAGAAGATCTCTTGAATCTTCTTCTGGCAATGACTTCCGGGG hTR151 Sense GATCCCCGTCTAACCCTAACTGAGAAGGTTCAAGAGACCTTCTCAGTTAGGGTTAGACTTTTTC sihTR151 Antisense TCGAGAAAAAGTCTAACCCTAACTGAGAAGGTCTCTTGAACCTTCTCAGTTAGGGTTAGACGGG hTR2 Sense GATCCCCCCCGTTCATTCTAGAGCAAACTTCAAGAGAGTTTGCTCTAGAATGAACGGTTTTTC sihTR2 Antisense TCGAGAAAAACCGTTCATTCTAGAGCAAACTCTCTTGAAGTTTGCTCTAGAATGAACGGGGG Notes: Colour to denote sequence regions; Orange: Hairpin spacer sequence, Red: Terminator sequence, Underlined Black: siRNA target sequence and reverse complement target sequence (5’-3), Green: Xho 1 restriction enzyme site, Blue: Bgl11restiction enzyme site. Abbreviations: shRNA-short hairpin RNA; siRNA-small interfering RNA. All shRNA were synthesised by Sigma (St Louis, MO, USA). shTR2 and shTR151 were synthesised phosphorylated on 5’ end. 85

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When expressed in mammalian cells, a 19-base pair stem-loop precursor is transcribed and then rapidly cleaved in the cell to produce a functional siRNA [494,

510].

2.5.3 Selection of transfectants The ligated plasmids were transformed into bacteria as described in section 2.5.2. Single colonies were isolated and inoculated into starter cultures and plasmid DNA recovered by mini-preparation as described in section 2.3.2.1. Restriction enzyme digest with Bgl11, or a double digest with Xho 1 and EcoRI was performed for 2 hr in a 37ºC water bath. Digested DNA was electrophoresised through a 2% agarose gel and visualised on the Gel Doc™ EZ Imager (BioRad). One hundred ng plasmid DNA (10 ng/µL) was sent with sequencing primers (see Table 2.8) for sequencing at Applied Genetic Diagnostics, Melbourne. Maxi-preparation of the correctly sequenced shRNA vectors was performed using the Qiagen Plasmid Maxi Kit and prepared as described in section 2.3.2.2. Glycerol stocks of the correctly sequenced shRNA vectors were made as described in section 2.3.5. Integration of vector DNA was assessed by PCR amplification of PCR amplification using primers for amplification of 1332-1922 region of the pSR retroviral vector (Table 2.8 for primer sequences).

2.6 Polymerase chain reaction (PCR)

2.6.1 Genomic DNA extraction Cell pellets (2x106) were harvested and washed in 1x PBS as previously described and lysed overnight at 37oC in 510 L of TESS gDNA lysis solution with 0.2 mg/mL proteinase K. The following day, lysates were transferred to DNAse-free sterile eppendorf tubes and 500 L 24:1:25 chloroform:isoamyl alcohol:tris-saturated phenol was added. The tubes were shaken vigorously for 3 min and then centrifuged at 12 000 x g for 10 min at RT. The upper aqueous phase was transferred to another eppendorf and the addition of phenol mixture and centrifugation steps repeated twice more, removing the upper aqueous phase to a new tube each time. The aqueous phase was transferred to a clean, labelled microfuge tube and the gDNA precipitated overnight at -20oC using two volumes of 100% ethanol and 1/10 volume of 3 M NaOAc

CHAPTER 2: MATERIALS AND METHODS gDNA was then harvested by centrifugation at 12 000 x g for 30 min at 4oC and washed with 1 mL of ice cold 70% ethanol and centrifuged at 12 000 x g for 15 min at 4oC. The resulting pellet was air dried at RT and resuspended in 50 L of DNAse and RNAse-free H2O. The concentration of gDNA was determined by measuring the absorbance at 260 nm using a ND 1000 Nanodrop Spectrophotometer (Thermofisher Scientific Inc, CA, USA). gDNA was diluted to a working concentration of 0.5 g/L and stored at -20ºC.

2.6.2 PCR analysis of genomic DNA PCR was carried out in 0.2 mm thin walled PCR tubes. The PCR reaction contained a final concentration mix of PCR buffer 0.2 mM dNTP mix, 2 U AmpliTaq DNA polymerase, 0.4 M of each primer (Table 2.8) and 200 ng of gDNA template to final volume of 50 L per reaction. The tubes were placed in the Biorad PCR thermal cycler and cycled with the program described in Table 2.8.

2.7 Real Time qRT-PCR analysis (qRT-PCR)

2.7.1 RNA extraction RNA was extracted using the RNAeasy mini kit (Qiagen) according to the manufacturer’s directions. Cell pellets (1x105-1x106) were lysed in 350 µL of lysis buffer and vortexed until the cell pellet had completely dispersed. Three hundred and fifty µL 70% ethanol was added and mixed thoroughly. Seven hundred μL of the sample was transferred to the RNA spin column pre-inserted in a collection tube and centrifuged at 12 000 x g for 15 s at RT. The flow-through was discarded and the cartridge was re-inserted in the collection tube. The column was washed with 350 μL of wash buffer and centrifuged at 12 000 x g for 15 s at RT. To remove DNA contamination, a DNAse I digestion with 70 μL DNAse Buffer and 10 μL DNAse I (1 unit/μL) was added to the centre of the column and incubated for 15 min at RT. Next, 700 μL of wash buffer was added to the sample and centrifuged at 12 000 x g for 15 s at RT. The column was washed twice in 500 µL of second wash buffer and centrifuged again at 12 000 x g for 15 s at RT. The column was centrifuged at 12 000 x g for 1 min to dry the column with attached RNA. RNA was eluted in 25 L

DNAse and RNAse-free H2O by centrifugation at 12 000 x g for 2 min.

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Table 2.8 Primers sequences Primers Sequence 5'-3' PCR cycling References Primers for RT-RT PCR β2M forward CCGTGGACTCTTCGGAGAAC 94ºC for 10 min; 40 cycles: 94ºC for 30 s, 60ºC for 30 [512] β2M reverse GGGACAACAGCGGTTCTTGC s,72ºC for 30 s, 72ºC for 10 min hTERT forward TGACACCTCACCTCACCCAC 94ºC for 10 min; 40 cycles: 94ºC for 30 s, 60ºC for 30 s, [298] hTERT reverse CACTGTCTTCCGCAAGTTCAC 72ºC for 30 s, 72ºC for 10 min DKC1 forward CATGGCGGATGCGGAAGTAAT 94ºC for 10 min; 40 cycles: 94ºC for 30 s, 58ºC for 30 s, Designed by L.Veas DKC1 reverse GTCAAGATTAATGAAACCTG 72ºC for 30 s, 72ºC for 10 min hTR forward CGCTGTTTTTCTCGCTGACTT 94ºC for 10 min; 40 cycles: 94ºC for 30 s, 65ºC for 30 s, [298] hTR reverse TGCTCTAGAATGAACGGTGGAA 72ºC for 30 s, 72ºC for 10 min OAS1 forward CAAGCTCAAGACCTCATCC 94ºC for 10 min; 40 cycles: 94ºC for 30 s, 57ºC for 30 s, Designed by M.Maritz OAS2 reverse TGGGCTGTGTTGAAATGTGT 72ºC for 30 s, 72ºC for 10 min PKR forward ACGCTTTGGGGCTAATTCTT 94ºC for 10 min; 40 cycles: 94ºC for 30 s, 57ºC for 30 s, Designed by M.Maritz PKR reverse TTCTCTGGGCTTTTCTTCCA 72ºC for 30 s, 72ºC for 10 min IFIT forward GCAGAACGGCTGCCTAATTT 94ºC for 10 min; 40 cycles: 94ºC for 30 s, 57ºC for 30 s, Designed by M. Maritz IFIT reverse CATTCTGGCCTTTCAGGTGT 72ºC for 30 s, 72ºC for 10 min Primers for genomic and vector DNA N-ras forward CGAAGGCTTCCTCTGTGTAT 94ºC for 10 min; 40 cycles: 94ºC for 30 s, 60ºC for 30 s, Designed by K.MacKenzie N-ras reverse GGCTTCAGCTGGTGGTGATATTG 72ºC for 30 s, 72ºC for 10 min pSuperRetro forward CCCTTGAACCTCCTCGTTCGACC 94ºC for 10 min; 40 cycles: 94ºC for 30 s, 60ºC for 30 s, [494] pSuperRetro reverse GAGACGTGCTACTTCCATTTGTC 72ºC for 30 s, 72ºC for 10 min β-actin gDNA forward GTGGGGCGCCCCAGGCACCA 94ºC for 10 min; 40 cycles: 94ºC for 30 s, 60ºC for 30 s, [284] β-actin gDNA reverse CTCCTTAATGTCACGCACGATTTC 72ºC for 30 s, 72ºC for 10 min Sequencing primer GGAAGCCTTGGCTTTTG [494] pSuperRetro Primers for qTRAP ACX primer GCGCGG[CTTACC]3CTAACC 30 °C for 12 min, 95 °C for 10 min, 40 cycles of 95 [513] TS primer AATCCGTCGAGCAGAGTT °C for 30 s, 60 °C for 1 min. Dissociation of 95°C for [513] 15 s, 60°C for 1 min, 95°C for 15 s, 60°C for 15 s Notes: All primers were synthesised by Sigma (St Louis, MO, USA)

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The concentration of RNA determined using the Nano Drop (ND) 1000 spectrophotometer (Thermofisher Scientific Inc, CA, USA) and the following formulae applied: Total RNA (μg) = OD260 × [40 μg/ (1 OD260nm × 1 mL)] × dilution factor × total sample volume (mL). RNA quality was determined by 260 nm/280 nm and 230 nm/280 nm ratios. 1.8-2 were regarded as acceptable RNA ratios. If ratios were too low, RNA was re-precipitated in 2x volume 100% ethanol and 1/10 x volume NaOAc for 30 min at -80ºC. Precipitated RNA was centrifuged at 12 000 x g for 30 min at 4ºC. The pellet was washed in 75% ethanol and then centrifuged at 8000 x g for 15 min at 4ºC. Supernatant was removed and RNA was air dried. RNA concentrations were determined as described above.

2.7.2 cDNA synthesis For cDNA synthesis, 1µg of RNA was added to 1 µg of random primers and 1 µL of

10 mM dNTPs in a total reaction volume of 13 µL made up with H20 in a 0.2 mL thin walled PCR tube. RNA was primed by incubation at: 65oC for 5 min and then placed on ice. Seven µL of reverse transcriptase master mix (4 µL of 5 x first strand buffer, 1 µL of 0.1 M DTT, 1 µL of 40 U/ µL RNAse Out and 1 L of 200 U/L Superscript III reverse transcriptase) was added to the RNA and incubated at: 25oC for 5 min, 50oC for 60 min, 70oC for 15 min and then placed on ice. For subsequent real time qRT-PCR the synthesised cDNA was diluted 1 in 10 in H20 and 2 µL of working solution was used per reaction.

2.7.3 PCR amplification of cDNA and Real-Time qRT-PCR analysis Synthesised cDNA (2 µL) was added to real time qRT-PCR reactions consisting of 12.5 µL of iQ™ SYBR Green Supermix, 10 µM each of forward and reverse primers of gene of interest (Table 2.7) and made up to 25 µL with DNAse and RNAse-free

H2O. In a separate reaction, amplification of a housekeeping gene, β-2-microglobulin (β2m) was used an internal control for each sample and performed in parallel on the same plate. Control cell samples (MRC5 and HeLa) and negative control samples

(H20 and no reverse transcriptase reaction) were performed with every analysis. All samples were run in duplicate on a MyIQ real-time PCR detection system (BioRad) and analysed using MyIQ 2.0 software (BioRad). To calculate the threshold cycle

(Ct) value, the threshold was placed in the logarithmic range of amplification curve.

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The Ct was used to calculate relative gene expression calculated using the 2-CT method [514] as follows. Target gene expression was first normalised to housekeeping β2m gene using the formula: ΔCt = CtaveSample – Ctave β2m. Β2m normalised target gene expression was then normalised to control gene expression using this formula: Ct = Ctsample – Ctcontrol. To calculate fold difference in expression, the normalised target gene expression relative to control (Ct) was entered into the formula: 2 –ΔΔCt. HeLa tumour cells are immortalised by the upregulation of endogenous hTERT and have endogenous levels of hTERT, hTR, dyskerin gene expression and telomerase activity. Normalisation was performed relative to the HeLa tumour cell line, which serves as a positive control reference for comparison between experiments. Values are expressed as the mean±SEM of three independent experiments.

2.8 Assessment of telomerase activity by the quantitative telomerase repeat amplification assay (qTRAP) qTRAP laboratory setup and precautions are detailed in Maritz et al.,2013 [513].

2.8.1 Preparation of cell lysate and protein quantification by Bradford assay Cells (1x105-2x105) were harvested, washed in PBS and stored at -80ºC. Cells were lysed in 50 µL ice-cold 1x TRAPeze lysis buffer with 1x protease inhibitors and resuspended by pipetting. The cell lysate was vortexed briefly, incubated on ice for 30 mins and centrifuged at 12 000 x g for 15 min at 4°C and the supernatant removed. Protein was quantified by the Bradford assay (BioRad) [515], according to the manufacturer’s protocol and using Bovine Serum Albumin (BSA) as a standard. The absorbance of the samples was read at 595 nm on a Microtitre plate reader (BioRad). Samples were diluted to a concentration of 5 ng/µL in 1x TRAPeze lysis buffer for qTRAP analysis and stored at -80ºC until required.

2.8.2 Preparation of controls for qTRAP The positive control stock lysate was prepared separately from samples in a biological safety cabinet. SKN-SH cell line was used as a positive control. HeLa tumour cell line was used as a positive control reference for comparison between experiments. The cells (1x106) were lysed in 200 µL TRAPeze lysis buffer plus protease inhibitors and quantitated as described above. The lysate was prepared to a 90

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1 µg/mL stock and diluted to a concentration of 100 ng/µL and stored in 5 µL aliquots at -80oC. For generating a standard curve, a 100 ng/µL aliquot was diluted to a concentration of 12.5 ng/µL, 5 ng/µL, 2.5 ng/µL, 0.5 ng/µL, 0.25 ng/µL and 0.05 ng/µL. Gloves were changed after handling SK-N-SH lysates to avoid cross-over contamination. Negative controls included samples with low telomerase activity (normal MRC5 cells) and were prepared as described in previous section 2.8.1.Other negative control included in each run were Lysis buffer only, H20 and a heat inactivated SKN-SH sample that was incubated at 85ºC for 10 min.

2.8.3 qTRAP reaction and analysis Enough qTRAP master mix was prepared for samples in duplicate plus one well extra with the following reagents (12,5 µL SYBR Green Master Mix, 2.5 µL 10 mM EGTA, pH 8.5, 1.0 µL 100 ng/µL TS primer, 1.0 µL 10 ng/µL ACX primer and 6.0

µL RNAse, DNase-free H20 (Table 6.8). Twenty three µL of q-TRAP master mix was added to each well of a 96 well plate. Two µL of sample (5 ng/µL), controls or standard was added to each duplicate wells, starting with negative controls first, then samples and dilutions of the standard last, working from low concentration to high. The 96-well plate was covered with the optical adhesive film cover and centrifuged for 30 sec for collection of the reaction mixture to the bottom of the well.

Telomerase products were then amplified using a 4-step PCR program as in Table 6.8 in an ABI 7500 Real-Time Cycler. The real-time PCR data was evaluated using the ABI sequence detection system and analysis software. Amplification plots were used to retrieve the threshold cycle (Ct value) for each sample. Ct value is the cycle number at which the fluorescence signal reaches the threshold. It reflects the point at which amplicons have accumulated to a statistically significant point above the baseline. The melting or dissociation curves generated were used to confirm the specific amplification of telomerase products by identification of uniform peaks.

For quantitation of telomerase enzyme activity, a standard curve was plotted using the average duplicate Ct values for the SKN-SH dilution series versus log10 protein amount (ng). For accurate quantitation, only graphs with an r2 > 0.98 and a slope close between -1.5 to -1.1 were used. The unknown test sample data was converted to Relative Telomerase Activity (RTA) using the linear equation of the standard 91

CHAPTER 2: MATERIALS AND METHODS curve. The HeLa tumour cell line, with endogenous telomerase activity was used a positive control for telomerase activity and reference for comparison between experiments. The assay was repeated three times to ensure reproducibility and values expressed as means ± SEM.

2.8.4 Telomere restriction fragment length (TRF) analysis Mean telomere length was measured using the TeloTAGGG telomere length assay kit (Roche) according to manufacturer’s instructions. Eleven g of gDNA was isolated from MRC5, MRC5hTERT, MRC5hTERT-TZT and HT1080 cells and telomeric restriction fragment assay was performed by L.Richards and mean telomere length quantified by K MacKenize according to the previously described method in Taylor et al., 2004 [296].

2.9 Western blot analysis

2.9.1 Preparation of cell lysate for western blot analysis Total protein was harvested from cell pellets by resuspension in RIPA lysis buffer in eppendorf tube. Lysis was performed by incubation on ice for 30 min, with occasional vortexing. The lysed cells were centrifuged at 12 000 x g for 15 min at 4ºC. The supernatant was transferred to a fresh eppendorf tube. Protein concentrations were determined using the Bicinchoninic Acid (BCA) assay [516], according to the manufacturer’s protocol and using BSA as a standard. The absorbance of the samples was read at 570 nm on a Biota II plate reader (GE Healthcare). Samples were stored at -80ºC until required.

2.9.2 SDS-polyacrylamide gel electrophoresis For the separation of proteins, 15 - 20 µg of proteins were electrophoresised through a 12% Criterion XT™ Precast Gel. Nu Page 4 x Sample Buffer (7.5 μL) and 1.5 μL Nu Page 20 x Reducing Agent was added to each protein sample and made up to 30 uL with dH20. Samples were heated to 95°C for 5 min on a dry heating block. Proteins were electrophoresised at a voltage of 200V for 45 minutes in 1X Nu Page MES Running Buffer. Five µL of pre-stained Biorad Kaleidoscope Marker (BioRad) with protein sizes ranging from 250-10 kDa was also loaded onto the gel to determine protein sizes.

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2.9.3 Immunoblotting Following electrophoresis, the gel was soaked for 15-20 min in transfer buffer. The immobilon-P polyvinylidene fluoride (PVDF) transfer membrane was pre-wet in methanol for 30 s and washed in miliQ H2O for 5 min at RT and then washed in 1x transfer buffer for 5-10 min. Proteins were subsequently transferred to the membrane using a tank system in 1X transfer buffer at 100V for 1 hr. A cassette was assembled to form a transfer sandwich in the following order: pre wet sponge on cathode side, pre wet Whatman 3M filter paper, soaked gel, immobilon-P polyvinylidene fluoride (PVDF) transfer membrane, pre wet Whatman 3M filter paper, pre wet sponge on the anode side of the cassette. The cassette was placed in the correct orientation into the transfer tank and the tank was filled with 1x transfer buffer (1X Nu PAGE transfer

Buffer with 20% methanol/ miliQ H2O). After transfer, the membrane was placed into a small container and stained with Ponceau S for 5 min with gentle shaking at

RT and rinsed in miliQ H2O three times. Transferred bands were checked for even transfer and images scanned by a canon scanner.

2.9.4 Immunodetection Membranes for immunoblotting were placed back into container and blocked in 10% (w/v) skim milk/TTBS for 2 hr at RT or overnight with gentle shaking at 4°C to prevent non-specific binding of the antibody to the membrane. The membranes were incubated with primary and secondary antibodies with gentle shaking. Antibodies used are described in Table 2.9. Following antibody incubations membranes were washed three times for 15, 10 and 5 minutes in 1X TBS-0.05%Tween-20 to remove unbound antibody. After incubation with secondary antibody, membranes were washed 3 times for 10 min with 1x TBS-0.05% Tween-20, followed by detection. For detection of immunoreactive bands, SuperSignal West Femto Maximum Sensitivity Substrate was added to the membrane and incubated for 5 mins. The membranes were placed into an X-ray cassette between plastic sheets and exposed to X-ray film for varying exposure times in the dark.

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Table 2.9 Antibodies Antibody Source Antibody type Primary incubation Secondary Diluent Size(kDa) incubation 0.5% Skim milk Actin Sigma Donkey anti-rabbit O/N 4°C 1:5000 1hr at RT 42 in TBS-T

Santa Cruz 0.5% Skim milk p53 (DO-1) Sheep anti-mouse O/N 4°C 1:1000 1hr at RT 53 Biotechnology in TBS-T

0.5% Skim milk p21 (SX118) BD PharMingen Sheep anti-mouse O/N 4°C 1:500 1hr at RT 21 in TBS-T

0.5% Skim milk pan NRas Merck Sheep anti-mouse O/N 4°C 1:1000 1hr at RT 21 in TBS-T

Hilda Picket , 0.5% Skim milk Dyskerin Sheep anti-rabbit 3 hrs RT 1:2000 1hr at RT 56 CMRI in TBS-T

0.5% Skim milk γH2Ax (Ser139) Biolegends Sheep anti-mouse O/N 4°C 1:1000 1hr at RT 19 in TBS-T

0.5% Skim milk Donkey anti-rabbit HRP GE Healthcare 1 hr RT 1:10000 1hr at RT NR in TBS-T

0.5% Skim milk Sheep anti-mouse HRP GE Healthcare 1 hr RT 1:5000 1hr at RT NR in TBS-T

Notes: Abbreviations O/N, overnight, RT, room temperature, hr, hour, kDa, kilo Daltons, TBST Tris buffered Saline with Tween-20, CMRI, Children’s Medical Research Institute

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The film was subsequently developed for detection of antibody specific reactive bands. Immunoblots were gently stripped for reprobing by incubation with 1 M Glycine HCl pH 2.5 for 7 min at RT. The membrane was turned over and incubated for a further 7 min and neutralised with 1/10 of volume 1M Tris HCl pH 7.5. Densitometric analysis was performed with Versadoc and quantitated using Quantity One.

2.10 Immunofluorescence staining for detection of γH2AX The detection of γH2AX foci is a measure of DNA damage within the cell [517]. Immunofluorescence staining to detect phosphorylated γH2Ax was performed using 8-well chamber slides. Cells were plated at a density of 15 000 per well and transfected with siRNA as described in Section 2.4.2. After 8 hrs of transfection, the cells were rinsed quickly with warm 1x PBS (37ºC). The cells were fixed for 15 min at RT with 4% paraformaldehyde and rinsed with PBS. Cells were permeabilised for 12 min at RT with 0.1% Triton X-100 in KCM buffer and rinsed quickly in 1x PBS. Cells were incubated for 20 min at RT in 10% FCS/PBS blocking solution. The primary antibody was diluted 1/250 in antibody solution of 5% FCS/ PBS and 200 µL was added per well. Following hr incubation at 37ºC, five washes of 10 mins each were performed with 0.1% Tween/PBS under agitation at RT. The secondary antibody Alexa 488-conjugated anti-mouse antibody was diluted 1/1000 in 200 µL 5% FCS/PBS and incubated for 45 min at RT in a humidified chamber. The slides were washed five times for 10 mins with 0.1% Tween/PBS under agitation at RT. The slides were rinsed quickly with water and air dried. VECTASHIELD® Mounting Medium containing 1.5 µg/µL DAPI stain was dropped onto the slides which were then mounted with a cover slip and sealed off with nail polish. γH2AX staining was photographed using the × 60 objective of an Axiovert 200M fluorescent microscope coupled to an AxioCamMR3 camera (Carl Zeiss, Munich, Germany). Percentages of cells stained with γH2Ax were quantified from 20 fields of views with approximately 25 cells in each.

2.11 Meta-telomere dysfunction induced foci (Meta-TIF) assay MRC5hTERT-TZT cells were transfected with siRNA as described in section 2.4.2. The assay was performed by PhD student Omesha Perera, at the Childrens Medical Research Institute (Westmead Institute) as in Cesare et al., 2007 [67]. 95

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2.12 Cell cycle, cell death and senescence

2.12.1 Propidium iodide staining To determine the cell cycle distribution, 5x105-2x106 cells were harvested and subjected to propidium iodide (PI) staining. All media that the cells were grown in, the pre-trypsin wash and trypsinised cells were harvested and transferred to a 15 mL centrifuge tube. Cells were counted as described in section 2.2.3 and then washed in 5 mL 1x cold PBS and centrifuged at 490 x g for 8 min at 4°C. The cells were resuspended in 3 mL cold 70% ethanol added drop wise while gently vortexing and fixed in the dark at -20°C overnight or over the weekend. Fixed cells were centrifuged at 1980 x g for 10 min at 4°C. Cells were washed with 0.1% BSA/PBS and centrifuged twice between washes at 1980 x g for 10 min at 4°C. Cells (1x106) were resuspended in 400 µL PI/RNAse solutions and incubated at 4°C for 30 min in the dark. Two mLs of cold 0.1% BSA/PBS was added and cells were centrifuged at 1980 g for 10 min at 4°C. The supernatant was removed and cells were resuspended in 400 µL FACS buffer and analysed on the FL-2 channel on FACS Calibur (BD Biosciences). Cells were analysed using Cell Quest software using the FL-2 channel for propidium iodide. The percentages of cells in G0, G1, S and G2M phases of the cell cycle were quantified using Cell Quest and Modfit software. Cell doublets were excluded using the FL-A and FL-W dot plot.

2.12.2 Annexin V-APC staining To distinguish non-viable and apoptotic cells, cells were subjected to staining with Annexin V-APC staining. Cells were harvested by trypsin digestion and washed in 1x cold PBS. Cells (1X106) were resuspended in 1 mL 1x Binding buffer. One hundred µL of the cells were transferred to a flow tube and incubated at RT for 15 mins with 5 µL Annexin V-APC. An additional 100 µL binding buffer containing propidium iodide, added to a final concentration of 2 µg/mL and incubated for a further 15 mins at RT. Analyses by flow cytometry on the APC channel (FL-4) and PI (FL-2 channel. The percentages of late apoptotic and early apoptotic cells were quantified using the Cell Quest software.

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2.12.3 Beta-galactosidase (SA-ß-gal) assay For detection of senescent cells, cells were set up in 6 well plates in triplicate with 0.5 x 106 cells per well. The next day cells were transfected with siRNA as described in section 2.4.2. Ninety-six-120 hrs after transfection, the cells were assayed for senescence associated ß-galactosidase activity as previously described [26]. Briefly, cells were rinsed with PBS, then fixed in 2% formaldehyde/0.2% glutaraldehyde for 5 min and stained with 1 mg/mL 5-Bromo-4-chloro-3-indolyl -D-galactopyranoside (x-gal) diluted in x-gal buffer, pH 6.0 for 12-16 hr at 37C. Images were captured using an inverted Olympus CKX41 camera microscope and Capture Pro v6 software.

2.13 Anchorage-independent growth assay To quantify anchorage independent growth, cells were set up in SeaPlaque low melting temperature agarose. A bottom layer of 0.5% agarose (Culture media/20%FCS) was set in 35 mm culture dishes in triplicate. Cells were counted and 2000 cells per dish were added in a top layer of 0.33% agarose (Culture media/20%FCS). The plates were left at 4ºC for 5-10 min to set. Dishes were then transferred to a large plate with wells of water without lids to keep them hydrated and incubated at 37ºC in 5% CO2 for 14 days. Colonies were fixed with addition of 1 mL of in 1% glutaraldehyde in PBS. Colonies were counted under phase light at 4 x objective with inverted light microscope CK2 (Olympus). Pictures of colonies were taken by camera (Image Pro 6.2 software) with light microscope TE2000-U (Eclipse, Nickon).

2.14 In vivo tumorigenesis assay with Nude Balb-C mouse model All animals experiments were approved by the animal ethic committee, UNSW Ethics number (ACEC# 11/5B). Mice were housed in the Animal Facility of the Children's Cancer Institute Australia (CCIA) within the Lowy Cancer Research building (C25 UNSW). The mice were placed in micro-isolator cages, with a maximum of 6 mice per cage. Food and water were available ad libitum and cages were maintained under specific pathogen-free conditions for the duration of the study. Female Nude Balb-C mice at 6-8 week of age were acclimatised for at least one week after delivery and prior to any experimental procedure. One week after arrival in the animal facility, ears of mice were clipped for identification and mice

CHAPTER 2: MATERIALS AND METHODS were weighed for a baseline weight. Mice were distributed to their cages 4 mice were placed into each cage (2 cages per experimental group).

Cells (5x 106) were harvested by trypsinisation and resuspended in 200 µL of serum- free media. Just prior to inoculation with the cell suspension, the mice were placed on a heated pad and anaesthetised using a gaseous anaesthetic machine to administer 4% isofluorane mixed with oxygen, which is delivered at 1 L per minute. The use of anaesthetic minimises pain and distress and circumvents the need to restrain animals during the procedure. Once induced, the mice will be maintained on 2% isofluorane and 0.5 L/min oxygen. For subcutaneous injection, the cells were injected beneath the skin of the hind flank using a 26G needle. The mice were allowed to recover with 100% oxygen and monitored for signs of consciousness. Once the mice had regained consciousness, they were returned to their cages. Mice were monitored daily for in activity, signs of malaise or distress, including hunching, ruffled fur, non- responsiveness, isolation, poor colour and weight loss. Mice were weighed weekly and assessed for tumour growth twice weekly. Tumours were measured with digital calipers and tumour volumes were calculated using the formula (LxW2)/2. All mice were sacrificed by CO2 euthanasia once tumours reached a diameter of 1.2 cm, or the animal showed signs of malaise, or if there was persistent weight loss of 20% of original body weight. Mice that did not develop a tumour and remained healthy throughout the experiment were sacrificed 26 weeks after initiation of the experiment.

2.15 Microarray analysis of gene expression Microarray experiments were designed and analysed in collaboration with Dr Warren Kaplan and Mark Cowley (Peter Wills Bioinformatic Centre, Garvan Institute for Medical Research). MRC5, MRC5hTERT and MRC5hTERT-TZT cells were transfected with siSc, sihTERT-T8, siDKC1-2 and sihTR151 in three independent experiments. A fourth replicate of siSc siRNA was included and two replicates of untreated cells were also included. Cells were harvested for RNA extraction 48 hrs after siRNA transfection. Balanced randomisation of the samples was incorporated into the study design to eliminate batch effects of sample preparation and processing of slides.

CHAPTER 2: MATERIALS AND METHODS

2.15.1 RNA extraction for microarray analysis MRC5, MRC5hTERT, MRC5hTERT-TZT (3.6x 105) cells were seeded in T-75 mm flasks and the next day were transfected with siSc, sihTERT-T8, siDKC1-2, and sihTR151 as described in section 2.4.2. Cells were harvested 48 hrs after siRNA transfection and stored. RNA extraction was performed using Purelink Invitrogen columns according to the manufacturer’s instructions and which follows a similar method as described in section 2.7.1. For elution of RNA, 30 μL of nuclease-free H20 was added to the centre of the spin columns and incubated at RT for 1 min before centrifugation for 2 min at 12 000 x g at RT.

2.15.2 RNA quantitation and analysis of RNA quality For microarray analysis, RNA was quantified on the Nanodrop spectrophotometer as described in section 2.7.1. To ensure that RNA with good quality and integrity was used the quality of purified total RNA was analysed on the Agilent 2100 Electrophoresis Bioanalyzer (Agilent Technologies, Palo Alto, CA) at the Ramaciotii Center (UNSW) using the RNA 6000 Nano LabChip kit [518]. Two µL of isolated RNA at 100 ng/ µL were sent for analysis. The bioanalyzer was used to assess integrity of RNA and generate a RNA integrity number (RIN) number. The ratio of

18 and 28S rRNA and ratios of A260nm/280nm and A230nm/260nm were also confirmed. The RIN number standardises RNA quality using an alogorithim based on the different degradation states of RNAs in samples [519]. A good RIN is a value of 10. All RIN over 9.5 were used in this study.

2.15.3 cRNA amplification cRNA was amplified for hybridisation to the microarray slides from extracted RNA using an Illumina® TotalPrep™ RNA Amplification kit. Twenty samples were processed at a time to ensure good quality cRNA amplification. The amplification of cRNA was performed in two stages; first and second strand cDNA was synthesised and purified followed by in vitro transcription (IVT) and purification of cRNA.

2.15.3.1 Synthesis of first and second strand cDNA synthesis RNA samples were brought up to a volume of 11 µL with Nuclease-free Water and 9 μL of the Reverse Transcription Master Mix (1 μL T7 Oligo(dT) Primer, 2 μL 10X First Strand Buffer, 4 μL dNTP Mix, 1 μL RNAse Inhibitor, 1 μL ArrayScript) was

CHAPTER 2: MATERIALS AND METHODS added. Samples were mixed thoroughly by pipetting up and down 2-3 times and centrifuged briefly to collect the reaction in the bottom of the tube before incubation for 2 hrs at 42ºC. Eighty µL of the Second Strand Master Mix (63 μL Nuclease-free Water, 10 μL 10X Second Strand Buffer, 4 μL dNTP Mix, 2 μL DNA Polymerase, 1 μL RNase H) was then added to each sample to a volume of 100 µL and incubated for 2 hrs at 16ºC before purification.

2.15.3.2 cDNA purification cDNA Binding Buffer (250 μL) was added to each sample and the solution placed on the center of a cDNA Filter Cartridge. The solution passed through the filter by centrifugation for 1 min at 10 000 x g. The samples were then washed with 500 μL wash buffer and centrifuged for 1 min at 10 000 g to pass through the filter. cDNA was then eluted with 19 μL pre-warmed (50–55°C) Nuclease-free Water and centrifuged for 90s at 10 000 x g.

2.15.3.3 In vitro transcription and purification of cRNA For in vitro transcription of cRNA, 7.5 μL of IVT Master Mix (2.5 μL T7 10 Reaction Buffer, 2.5 μL T7 Enzyme Mix, 2.5 μL Biotin-NTP Mix) was added to each cDNA sample to a volume of 25 µL and mixed thoroughly followed by incubation for 14 hrs at 37ºC. The reaction was stopped by the addition of 75 μL of preheated Nuclease-free Water (55ºC) to each sample and brought to a final volume of 100 μL.

For purification of cRNA, the cRNA Filter Cartridge was placed into the collection tubes and 350 μL of cRNA Binding Buffer was added to each sample. Two hundred and fifty μL of 100% ethanol was added and mix thoroughly. Precipitation of cRNA begins with the addition of ethanol and samples were passed thoroughly immediately after mixing. The cRNA Filter Cartridges were centrifuged at 10 000 x g and washed with 650 μL Wash Buffer and centrifuged again. cRNA was eluted with 100 μL of preheated Nuclease-free Water and quantified using the ND 1000 Nanodrop spectrophotometer (Thermofisher, USA). Any cRNA with low 230/260 ratios were re-precipitated with the addition of 2X volume ethanol and 1/10th volume of 5 M NaOAc and incubated at -20ºC for 30 mins. Re-precipitated cRNA was centrifuged at 12 000 x g for 15 min at 4°C. The supernatant was removed and cRNA washed in

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70% ethanol and centrifuged again for 15 min at 4ºC. Once the cRNA had air dried, it was resuspended in 50 µL Nuclease- Free water and re-quantified.

2.15.4 Labelling, hybridisation and scanning Labelling, hybridisation and scanning was performed by staff at the Ramaciotti Center (UNSW). Amplified cRNA (500 ng) was sent to the Ramaciotti Center and labeled and hybridised to Illumina Human HT-12 V4 expression Bead chips. Scanning of the bead chips was performed using the Illumina bead array iScan system.

2.16 Bioinformatic analysis of gene expression data The bioinformaticians, Dr W. Kaplan and Dr M. Cowley (Peter Wills Bioinformatic Centre, Garvan Institute for Medical Research) provided advice for the design and analysis of the experiment. However, all the bioinformatic analyses were performed by the candidate. The microarray data generated by the Ramaciotti Center was transferred to the Peter Wills Bioinformatics Center (PWBC) and uploaded into the caARRAY microarray data management system, (https://cabig.nci.nih.gov/tools/caArray), which forms part of the cancer biomedical informatics grid (caBIG) system. The experimental data was downloaded and retrieved as Illumina IDAT files in a single ZIP file. For analysis of microarray data, Gene Pattern software (Broad Institute) using the Garvan Gene Pattern Server was used.Gene pattern is a web interface that combines many complex statistical programming into a series of modules that allows quick and uncomplicated analysis [520]. A number of modules developed or modified by bioinformaticians at the Peter Wills Bioinformatics Center (PWBC) (Garvan Institute) and those available from Broad Institute were used.

2.16.1 Pre-processing data for analysis The raw Illumina Bead array data files were converted to the gct (tab delimited) file using the Illumina file creator module. The gct file describes the gene expression data with samples distributed across columns, gene probes distributed across rows and the expression measurement for each gene in each sample. “No collapse mode” was used and “background subtraction” was performed to remove signal due to non-specific hybridisation. Background is defined as the average signal intensity estimated from

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CHAPTER 2: MATERIALS AND METHODS the negative control beads. Background subtraction was calculated as the mean of the negative control signal values subtracted from all the gene probe values but not the control probe values. These negative control probes are made up of 800-1600 random sequence probes selected to have no corresponding targets in the genomes [521]. A cls file, describing the categorical phenotype of the experimental dataset was created. The cls file associates each sample in the expression data with sample names. Samples in the gct file were labelled and re-ordered and the expression dataset log transformed and normalised with “scale normalisation” to eliminate variation between slides. Scale normalisation adjusts the expression levels on each array to have the same median (MAD) across arrays [522-524]. The data was log transformed as the limma module used to determine differential gene expression requires the data to be log transformed.

2.16.2 Limma Gene Pattern analysis To identify genes that were differentially expressed between scramble siRNA (siSc) and siRNA-transfected cells, the Limma Gene Pattern (GP) module was used. For each cell line, each siRNA targeting each of the telomerase components (sihTERT- T8, siDKC1-2 and sihTR2) was compared to that of cells transfected with (siSc). Limma GP uses moderated t-statistic based on the empirical Bayes method described by [525]. For each probe it generates a raw unadjusted p-value and a moderated t- statistic which gives an indication of the ratio of fold change to standard error. Limma GP was performed by selection of best probe method. This method collapses multiple probes to one value per gene, choosing the best performing probe, which is defined as the probe with largest absolute t-statistic. The 34, 694 feature dataset was collapsed to 34, 170 genes symbols. The number of differentially expressed genes were reported with a significance of False discovery Rate (FDR) to control for multiple hypothesis testing (FDR <0.1) using the Benjamini-Hochberg model [526- 528].

The Limma GP analysis generated three files, a rank file that lists the probe ID and moderated t-statistic of the most up to the most downregulated genes based on the moderated t-statistic [525]. It also generated an excel file containing all Limma GP results with a summary of differentially expressed genes and the corresponding p- values, fold change and FDR for each comparison. In addition, an odf file was also 102

CHAPTER 2: MATERIALS AND METHODS generated for subsequent analysis and viewing of the results using the comparative marker selection module [529].

2.16.3 Gene Set Enrichment Analysis (GSEA) The rank file generated from Limma GP was used for GSEA using the Pre- rankedGSEA module in suite of Gene Pattern modules. GSEA identifies functionally related groups of genes whose expression pattern correlates with the pattern of a ranked list of genes [530]. It uses a modified Kolmogorov-Smirnov-style statistic to identify groups of genes that are enriched toward the top or bottom of a ranked list of genes based on a running sum statistic [530].

The pre-ranked list was submitted to GSEA and evaluated against the Molecular Signatures Database (MSigDB) collection of annotated gene sets, namely the C2.all.v3 curated gene set collection [530, 531]. The C2 curated gene set collection consists of a total of 3272 gene sets, which includes sub-categories of chemical and genetic perturbations (CGP) with 2392 gene sets as well as canonical pathways (CP) with 880 gene sets, which is subdivided into three categories: Biocarta gene sets (217 gene sets), KEGG gene sets (186 gene sets) and Reactome (430 gene sets) [531]. Gene sets with a minimum size of 15 genes and a maximum size of 500 genes were included in the analysis. GSEA analysis was performed with 1000 permutations [530]. The number of permutations indicates the precision of the analysis. For a 1000 permutations, the smallest possible p-value is 0.001, and the uncertainty of a p-value of 0.05 is 1% [532].

An enrichment score (ES) for each gene set was calculated and was adjusted for multiple hypothesis testing to account for the different sizes of the gene sets to a normalised enrichment score (NES). The proportion of false positives was controlled by calculating the FDR corresponding to each NES. The FDR indicates the estimated probability that a gene set with a given NES, represents a false positive finding [528, 530]. A significant FDR <0.1 was used in GSEA analysis, indicating that at most the significant list of results has at most 10% false positives[528]. Enrichment plots were generated for each gene set, displaying a running enrichment score as a function of the rank-ordered probes in the microarray data set, reflecting how well the gene sets correlated with the gene expression profile. 103

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2.16.3.1 META-GSEA The GSEA results from gene expression analysis of cells transfected with siRNA were spooled into one file using the META-GSEA tool within the Gene Pattern suite of module (Dr M. Cowley, unpublished). The META-GSEA file was then used in for Venn diagram analysis to identify distinct or overlapping gene sets between the different samples. The online tool, VENNY was used for these analyses [533].

2.16.3.2 Leading edge gene analysis The leading edge genes are the core genes of the gene set that account for the correlation [530]. The GSEA results were submitted to the leading edge gene analysis module of GSEA (Broad Institute) and a heat map of genes with gene expression values were generated. The fold changes and statistical significance of the genes were retrieved from limma analysis.

2.17 Statistical analyses Data are expressed as the means ±standard error of the mean (SEM) and analysed using ANOVA or followed by Dunnet's post-test or student t-test comparison as indicated in the figure legends. The GraphPad Prism program was used. A P-value of <0.05 was considered as statistically significant. Survival curves were plotted by the Kaplan-Meier survival method and tested for differences with the log-rank statistic [534, 535].

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______

3. Repression of telomerase components in isogenic normal, immortal and tumorigenic myofibroblasts

______

3.1 Introduction The central objective of this project was to use siRNA-mediated gene targeting to compare the effects of downregulating specific telomerase components in normal, immortal and tumorigenic cells. The highly specific nature of siRNAs has made it a useful tool for silencing gene expression and modelling of therapeutic molecular targeting. When appropriately applied and controlled, siRNA-mediated inhibition of gene expression allows the analysis of a phenotype resulting from the ablation of the target gene product [491]. However, gene silencing is not always certain and cell type and target gene specific variations have been observed [491, 495]. In addition to the silencing of target genes, the introduction of siRNA into cells can result in non- sequence-specific gene silencing and off-target effects. Upregulation of genes involved in the interferon (IFN) response, including protein kinase R (PKR), interferon-induced protein with tetratricopeptide repeats 1 (IFIT1) and 2′-5′- oligoadenylate (2–5A) synthase (OAS1) are frequently observed as off-target effects of cellular uptake of long double stranded RNA and siRNA [536-539]. For successful use of siRNA, experimental conditions must therefore be optimised to ensure mRNA is specifically knockdown with minimal off-target effects [495].

In the current investigations siRNAs were designed and tested to specifically target the essential components of the telomerase holoenzyme; hTERT, dyskerin and hTR. Isogenic normal, immortalised and malignantly transformed human foetal lung MRC5 myofibroblasts were employed in this study to enable comparison of the effects of directly targeting these telomerase components in matched normal and neoplastic cells at the different stages of the transformation process.

The immortal cells used in this study were established by genetically modifying MRC5 cells to overexpress hTERT (MRC5hTERT) [298, 504]. The MRC5 cells transduced with a control GFP vector entered senescence and ceased proliferation at 105

CHAPTER 3: RESULTS approximately 65 PD, while the MRC5hTERT cells proliferated beyond senescence and were eventually immortalised. Immortalisation of MRC5hTERT cells was preceded by a “crisis” period and spontaneously silencing the tumour suppressor gene p16INK4a and upregulation of the inhibitor of apoptosis family member survivin. These molecular changes conferred an increased resistance to stress-induced cell death [298, 299, 504]. However, the immortal MRC5hTERT cells retained function of p53 and hypophosphorylated pRb in response to genotoxic stress [296]. These cells were not capable of anchorage-independent growth and were non-tumorigenic when xenografted in immune-compromised mice [296]. A tumorigenic derivative of the MRC5hTERT cells was developed by transduction with a retrovirus encoding oncogenic N-RAS [296, 540]. The N-RAS transduced MRC5hTERT cells were injected into immune-compromised mice and generated subcutaneous tumours after a long latent period. The MRC5hTERT-TZT cell line was established in vitro from dissociated tumour tissue.

The in vitro isogenic model of mesenchymal tumorigenesis was extended to include MRC5 cells immortalised by simian virus 40 (SV40) infection with spontaneous activation of either telomerase (MRC5V1) or ALT (MRC5V2) [505]. HT1080 fibrosarcoma cells were also used to represent cells expressing endogenous telomerase. The investigations in this chapter described the molecular characterisation of the tumorigenic MRC5hTERT-TZT cells of the isogenic model for the first time and the optimisation steps for effective siRNA inhibition of telomerase components in this model.

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3.2 Results

3.2.1 Molecular characterisation of tumorigenic MRC5 human myofibroblasts (MRC5hTERT-TZT cells) Prior to these studies, the MRC5hTERT-TZT cell line was established and shown to form tumours in xenografted mice. The MRC5hTERT cells were transduced with L9NrIGFP vector that encodes a mutant N-RAS (N-RAS13 with a G-C transversion at position 763) and a yellow fluorescence protein reporter gene (GFPtpz) that is linked by an IRES [296, 540]. As part of the characterisation of this cell line, PCR analysis and western blotting was performed to confirm transduction and overexpression of N-Ras in these cells. For the PCR analysis, primers spanning the L9NrIGFP vector sequence and 3’region of N-RAS cDNA were employed. Vector-derived Nras DNA was confirmed in MRC5hTERT-TZT cells and as expected was not detected in MRC5 and MRC5hTERT or fibrosarcoma HT1080 cells [541, 542] (Figure 3.1 A). Western blot analysis confirmed N-Ras protein overexpression in MRC5hTERT- TZT cells (Figure 3.1 B).

The tumour suppressor pathways of p53/pRb have previously been characterised in MRC5 cells and non-tumorigenic immortalised MRC5hTERT cells [294, 296, 504]. To investigate the status of p53 in the tumorigenic MRC5hTERT-TZT cells, p53 and its downstream target p21CIP1 were assessed by western blot analysis. Low basal p53 and p21CIP1 protein expression were detected in normal MRC5 cells (35 PDs) and immortal MRC5hTERT (295 PDs), while high levels p53 expression were detected in MRC5V1 and MRC5V2 cells immortalised by SV40 cells (Figure 3.1 B). The latter observation of high p53 levels in MRC5V1 and MRC5V2 cells is consistent with the known ability of SV40 Large T-antigen to bind and stabilise p53. In this bound state p53 is unable to function properly [543, 544].

In comparison to normal MRC5 cells, p53 expression was higher and p21CIP1 protein expression levels were lower in MRC5hTERT-TZT cells (Figure 3.1 B). Since elevated p53 protein levels in tumorigenic cells may be indicative of p53 mutations or p53 pathway perturbations, further investigation was performed to assess p53/p21 response pathway to DNA damage [545]. For these investigations MRC5hTERT- TZT cells were treated with DNA damaging agent, etoposide for 8 and 24 hrs. 107

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Figure 3.1 Molecular characterisation of the tumorigenic MRC5hTERT-TZT cell line A) PCR amplification of N-RAS cDNA of retroviral vector L9NrIGFP [296, 540]. Genomic DNA was subjected to PCR using primers that span from the retroviral LN- RAS vector sequence into the 3’region of N-RAS cDNA. PCR products were electrophoresised through 1% agarose gel at 80Vand visualised on the Gel Doc™ EZ Imager (BioRad). The expected 2300 bp N-RAS PCR product was detected as shown. PCR amplification of the β-actin gene served as a control. B) Western blot analysis of p53, p21CIP1 and N-Ras proteins of isogenic MRC5 cells, HT1080 fibrosarcoma cells and HeLa cells C) Control MRC5hTERT-BABE cells and tumorigenic MRC5hTERT-TZT cells were treated with 25 µmol/L etoposide or DMSO as a control for 8 hrs and 24 hrs and accumulation of p53 and downstream target p21CIP1 were analysed by western blot analysis. Blots were reprobed with actin to control for loading. White breaks between gels indicate cuts through the image of the same gel to re-arrange lanes for presentation purposes. D) Telomere restriction fragment analysis of telomere length of isogenic MRC5 (34 PD), MRC5hTERT (295 PD), MRC5hTERT-TZT and HT1080 cells. Molecular weight markers (Appendix A) are indicated by white numbers and Multigauge software (Fujifilm) was used for quantification of mean telomere length indicated by white bars. Telomere Restriction Fragment analysis was performed by L.Richards and quantification by Dr. K.MacKenzie.

108

CHAPTER 3: RESULTS )> 0 PD) (295 TZT - PD) Marker (34 80 DNA C5 Ill VII Kb PD) MRC5hTERT MR MRC5hTERT HT10 1 labelled - 0" -c PD) DIG (295 marker marker MW MW I DNA 0 0 0 (.,.) TERT TERT-TZT POL (34 h h 80 Kb ~ z (/) I 1 DNA DNA 0 I ~I\) 0>0 Ul 00 M1 M3 MRC5 MRC5 MRC5 M2 HT10 . I g. :::J )> 1j;) D 295) OJ (PD -TZT 34) (PD 80 LA He MRC5 MRC5hTERT MRC5hTERT MRC5V1 MRC5V2 ;!<; 0 !ll HT10 j 0'1 (,.) 0'1 0 -c L_j I ~ (") r l ~ _.. N -c 0 N I ! L_j I z (/) !ll ::0 I 0 N L__j ~I l (") :::J )> ..... ~ I I V>Ul --.10 L_j mnlflll s::: s::: -;-i -i -i -i m ::0 :::r ::0 N 0'1 0'1 (") CD CD (") -;-i -i )> m m ::0 ::0 :::r (") s::: (/) 0 I + + c.. 0 !Q. ° -c :::r :::r co (/) ...., ""' (il N 0'1 0 co 3 (il ""' (il :::r :::r N Nmo "'C ;!<; m•ro o:::::: 0'1 (,.) -c 0'1 0 ~ (") _.. N -c 0 N ~- (") :::J )> v.>(J'l --.10

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In response to etoposide treatment, control MRC5hTERT cells that had been transduced with an empty retroviral vector pBABE (MRC5hTERT-BABE), showed elevated p53 expression and induced the expression of its downstream transcriptional target p21CIP1 (Figure 3.1 C). MRC5hTERT-TZT cells exhibited elevated levels of p53 expression upon treatment of etoposide, but failed to induce the expression of p21CIP1 (Figure 3.1 C). These findings indicate that MRC5hTERT-TZT cells have a defective p53/p21 pathway.

The mean telomere length of MRC5 (36 PD) cells and MRC5hTERT (92-111 PDs) cells were previously demonstrated to be 8.1 kbp and over 20 kbp respectively [294, 296, 504]. However, the telomere length of MRC5hTERT-TZT cells had not yet been assessed. Telomere length was measured using Southern Blot analysis of terminal restriction fragments (TRF) length assay. In comparison to the shorter telomere lengths observed in MRC5 cells at 35 PDs, ranging from 6-10 kbp, the mean telomere length of MRC5hTERT-TZT cells was found to be similar to MRC5hTERT cells at 295 PDs, ranging from 21 kbp up to lengths of greater than 30 kbp (Figure 3.1 D). HT1080 cells were confirmed to have a shorter mean telomere length of 5 kbp consistent with other reports showing that telomerase maintains telomeres at a relatively short length in these cells [546].

3.2.2 Quantification of hTERT, dyskerin, hTR expression and telomerase activity in isogenic MRC5 cells The gene expression levels of hTERT, hTR and dyskerin within the isogenic MRC5 cells and HT1080 cells were evaluated by real time qRT-PCR analysis (Figure 3.2 A). Expression of hTERT was limited to immortal MRC5hTERT, MRC5hTERT- TZT, HT1080 and MRC5V1 cells. The highest expression detected in MRC5hTERT-TZT and HT1080 cells (Figure 3.2 A). In comparison to normal MRC5 cells, hTERT expression was significantly higher in MRC5hTERT cells (P<0.01, Dunnet’s post-test) and MRC5hTERT-TZT, HT1080 cells (P<0.001 Dunnet’s post-test). The expression of hTERT was negligible in MRC5 and MRC5V2 cells.

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Figure 3.2 Expression of telomerase components and telomerase enzyme activity in MRC5 cell line panel A) Expression of hTERT, dyskerin and hTR genes were determined by Real time qRT PCR analysis (left axis). Telomerase enzyme activity was ∆∆ measured by qTRAP analysis (right axis). Gene expression was normalised to β2 microglobulin (β2M) and compared to HeLa cells using the Ct method. Relative telomerase activity was compared to HeLa levels. HeLa levels are indicated by the dotted line. B) Proteins were harvested from cell lines and were subjected to western blot analysis for dyskerin protein expression levels. Blots were reprobed with actin as a loading control. Results represent the mean and ±SEM of 3-5 independent experiments and assay run each performed in duplicate.*P<0.05, **P<0.01, ***P<0.001 in Dunnet’s post-test comparison to MRC5 cells.

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Telomerase A D hTERT • Dyskerin D hTR -enzyme act1..v1ty

§ 8 ·cn *** en Q) o_ 6 ** ~ *** ** Q)

cQ) 4 0) Q) 2 £ro Q) ~ 0::: c

1- 1- 0:::w 0::: w 1- 1- ...- N ..c ..c 0 > > 1.0 1.0 co 1.0 1.0 <( 0 0 0..- 0 0 _J 0::: 0::: 1- 0::: 0::: Q) ~ ~ I ~ ~ I k~:j I Dyskerin ~======~ ~~ -L--1------' j Actin 112

CHAPTER 3: RESULTS

Dyskerin mRNA levels were significantly 4-5 fold higher in MRC5hTERT-TZT cells and HT1080 cells (p<0.01, Dunnet’s post-test) compared to MRC5 cells which had very low dyskerin mRNA expression. MRCV1 cells had a significant two fold higher expression of dyskerin (p<0.01, Dunnet’s post-test) compared to the normal MRC5 cells. Low dyskerin levels were evident in MRC5V2 and MRC5hTERT cells. No hTR expression was detected in MRC5V2 cells and low expression of hTR was demonstrated in MRC5 cells. The expression of hTR in HT1080, MRC5hTERT and MRC5hTERT-TZT cells was relatively similar. MRC5V1 cells had the highest hTR levels relative to the other cell lines. The expression of hTR was significantly higher in MRC5hTERT (p<0.05, Dunnet’s post-test) and MRC5V1 cells (p<0.001, Dunnet’s post-test) compared to normal MRC5 cells.

Telomerase activity of the panel of isogenic cells was evaluated by qTRAP analysis (Figure 3.2 A). Telomerase activity levels tended to parallel with hTERT expression. Telomerase activity of MRC5hTERT and MRC5V1 cells were similar, while highest telomerase activity was detected in MRC5hTERT-TZT cells and HT1080 cells. Telomerase activity of MRC5hTERT-TZT (p<0.001, Dunnet’s post-test) and HT1080 cells (p<0.01, Dunnet’s post-test) was found to be significantly higher compared to normal MRC5 cells. No telomerase activity was detected in both the telomerase-negative MRC5 and MRC5V2 cells.

Dyskerin protein expression levels were evaluated by western blot analysis. Two molecular weight forms are apparent in some of the cell lines, indicating a potential post translation modification of dyskerin. Across the panel of cells dyskerin protein expression did not appear to correlate precisely with dyskerin mRNA expression levels or telomerase activity. MRC5hTERT-TZT, MRC5V1 and MRC5V2 showed the highest levels of dyskerin protein expression, compared to lower levels detected in HT1080, MRC5hTERT, and MRC5 cells (Figure 3.2 B). The expression levels of telomerase components hTERT, hTR, dyskerin and telomerase activity across the panel of cell lines utilised in this study are summarised in Table 3.1.

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Table 3.1 Characterisation of isogenic MRC5 cell line panel and HT1080 cells Characteristics Cell lines Replicative Tumorigenic p16INKA/Rb p53/p21CIP1 Ectopic gene hTERT Dyskerin hTR Telomerase References capacity status* expression expression expression expression activity MRC5 Mortal No Wt p16 Wt p53 None - + + - [294, 296]

MRC5hTERT Immortal No Silenced Wt p53 hTERT ++ ++ ++ +++ [294, 296]

MRC5hTERT-TZT Immortal Yes Silenced Defective p53 hTERT, N-RAS +++ ++ +++ +++ Unpublished pathway # # MRC5V1 Immortal Yes Inactivated Dysfunctional SV40 T-antigen ++ ++ ++ ++ [505]

# # MRC5V2 Immortal Yes Inactivated Dysfunctional SV40 T-antigen - ++ - - [505]

HT1080 Immortal Yes Wt p16 Wt p53 None + ++ +++ +++ [547]

Notes: + denotes levels of expression, – denotes lack of expression * Forms tumours in sc. xenografted mice # inactivated by SV40 large T antigen. Abbreviations: wt- wildtype, SV40, Simian virus 40.

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3.2.3 Optimisation of siRNA-mediated repression of telomerase components

3.2.3.1 Effective siRNA delivery of the cell line panel Prior to the comparison of the effect of siRNA-mediated inhibition of hTERT, hTR and dyskerin in the cell line panel, the transfection efficiencies of the MRC5, MRC5hTERT, MRC5hTERT-TZT and HT1080 cells were determined using a fluorescent (ALexa-Fluora-555) labelled siRNA at a range of concentrations. Uptake of siRNA was determined by FACs analysis 24 hrs after transfection (Figure 3.3 A). All cell lines tested exhibited transfection efficiencies above 90 % at concentrations of 50 nM - 200 nM (Figure 3.3 B.). The transfection efficiency of 20 nM siRNA was reduced to 80 % in HT1080 cells. These results showed that the efficiency across the cell lines was similar; indicating that any variation in the response to siRNA- mediated inhibition of gene expression was not due to differences in cellular uptake and the concentration of 50 nM of siRNA was selected for further investigations.

3.2.3.2 Selection of non-specific control siRNA The non-specific control siRNA (siSc) was previously designed and used in our lab [298]. The induction of IFN response is an important effect that should be minimised in siRNA experiments. To investigate the extent of the induction of an IFN response and to compare siRNAs from different companies, the expression of key IFN genes namely PKR, OAS1 and IFIT1 in MRC5hTERT and HT1080 cells transfected with control siRNA oligomers (siScQ, siScHP, siScDS), was assessed by real time qRT- PCR analysis. siScQ, siScHP, siScDS were similar in length and GC content of the specific siRNAs. siScHP and siScDS were synthesised by the same manufacturer but purified by either high performance liquid chromatography or desalting, while siScQ was manufactured by a different company.

The induction of IFN genes upon transfection with the different control siRNAs varied between the MRC5hTERT and HT1080 cells, when compared to mock- transfected cells (Lipofectamine only) (Figure 3.3 C). In MRC5hTERT cells, both siScQ and siScHP caused a two to four induction of OAS1 gene expression in comparison to mock-transfected cells. There was no apparent change in the expression of OAS1 in these cells transfected with siScDS (Figure 3.3 C Left panel).

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Figure 3.3 Optimisation of siRNA delivery (A-B) MRC5, MRC5hTERT, MRC5hTERT and HT1080 cells were transfected with Alexa-555 labelled siRNA at the indicated concentrations and analysed by FACs 24 hrs post-siRNA transfection. A) Representative FACS plots of MRC5hTERT-TZT 24 hrs after transfection with Alexa-555 labelled siRNA B) Graphical representation of transfection efficiencies of cell line panel determined by FACS. Values are means from 2 independent experiments. C) Real time qRT-PCR analysis of interferon response genes OAS1, IFIT1 and PKR 48 hrs post-transfection of three different scrambled siRNAs, siScQ, siScHP, and siScDS. Gene expression was normalised to ∆∆ β2 microglobulin (β2M) and compared to mock transfected cells using the Ct method. Results are presented as the mean ±Standard Deviation (SD) of two independent experiments where samples were assayed in duplicate. Levels of cells transfected with mock are indicated by the dotted line. D) Percentage of apoptotic MRC5hTERT and HT1080 cells following transfection with 50 nM siScQ, siScHP, and siScDS siRNAs. Cells were subjected to Annexin V1 and propidium iodide (PI) staining 48 hrs post-transfection and analysed by FACs. Early apoptotic cells were identified in lower right quadrant (Annexin V+/PI-) and late apoptotic cells were identified in the upper right quadrant (Annexin V+/PI+). Results are presented as the mean ± SD of two experiments.

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FSC-H

B 0 20 nM siRNA 0 50 nM siRNA •100 nM s•RNA • 200 nM siRNA 100 _, - ,.... '$. , - r- .,~80 rT 0 ~ 60 c 0 i3 40 .g; .=~ 20 0 - MRC5 MRC5hTERT MRC5hTERT HT1080 -TZT c MRC5hTERT HT1080 iS 0 ~ 1.5 c • siSc Q 0 siSc HP 0 siSc OS ·0u; (/) ~ 1.0 a. )( Q) Q) :ii 0.5 0> Q) > ~ Q) 0.0 0:: OAS1 IFIT1 PKR

0 Early apoptotic cells • Late apoptotic cells D >c .,x c c !S. .!!! ~ .!:! 0 0. 0 a. <( '$.

MRC5hTERT Ht1080

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None of the three control siRNAs induced OAS1 gene expression in HT1080 cells (Figure 3.3 C right panel). However, transfection of MRC5hTERT cells with siScQ resulted in a dramatic induction of IFIT1 expression. A moderate induction of IFIT1 expression was observed upon transfection with siScHP and siScDS. All three control siRNAs caused a modest increase in PKR expression in MRC5hTERT cells. In contrast, there was no induction of PKR expression in HT1080 cells transfected with any of the three control siRNAs. The expression levels of each of the three interferon response genes was higher in transfected HT1080 cells compared to MRC5hTERT cells, indicating an increased sensitivity of HT1080 cells siRNA transfection. These findings revealed that of the siSc tested, transfection of siScDS had the least effect on the expression of the IFN response.

Cell toxicity resulting from siRNA transfection is another non-specific effect of siRNA transfection that should be avoided. The sensitivity of cells to transfection with control siRNA was evaluated by Annexin V/PI staining. FACs analysis of MRC5hTERT cells transfected with any of the three control siRNAs showed little evidence of apoptosis (Figure 3.3 D). The HT1080 cells were more sensitive to siRNA transfection, as evidenced by a higher total apoptosis in siSc transfected cells compared to MRC5hTERT cells. There was no substantial difference in the percentage of early or late apoptotic cells in MRC5hTERT and HT1080 cells transfected with any of the three Sc siRNA in comparison to mock-transfected cells. siScDS was selected as the most appropriate control for RNAi experiments as it induced the minimal IFN response and caused no dramatic effects on apoptosis upon siRNA transfection in MRC5hTERT and HT1080 cells.

To ensure that siScDS did not mediate any non-specific effects on proliferation, the effect of 50 nM and 100 nM siScDS on cell proliferation and viability of MRC5hTERT-TZT cells was assessed at 24 hr intervals post-transfection by manual counting with the trypan blue exclusion assay. Transfection of siScDS at the lower concentration of 50 nM, had no effect on cell proliferation (Figure 3.4 A), while a small decrease in viability was evident following transfection with 100 nM siSc in comparison to mock-transfected cells.

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Figure 3.4 Effects of siRNA transfection of scrambled siRNA control on cell proliferation and viability MRC5hTERT-TZT cells were transfected with 100 nM or 50 nM siSc and cell proliferation and viability was measured by trypan blue exclusion assay at indicated time points post-transfection. Results presented as means ±SEM from three independent experiments. No significant changes were demonstrated in Two-way Anova and Dunnet’s post-test comparison to mock-transfected cells.

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The viability of the cells remained above 95% for the duration of the time course, with no cell death seen following transfection with either 50 or 100 nM concentrations (Figure 3.4 B). These findings indicate that transfection with 100 nM concentration may have detrimental effects on proliferation. Hence, the lower concentration of 50 nM was used for gene expression silencing experiments in future investigations.

3.2.3.3 Design of siRNA targeting hTR The design of siRNAs used in this study was based on the rules suggested by Elbashir et al., 2001 [491]. This includes a GC content of the duplexes kept between 40-55% and avoidance of multiple identical nucleotides in series to ensure off-target effects are minimal. Blast searches were performed to check specificity against the target gene and avoid potential off-target effects. siRNA of 21 nucleotides with 3'- d(TT) or (UU) overhangs were included in the design for all siRNAs used in this study [491]. Two independent siRNAs targeting different regions of each of the telomerase components hTERT (sihTERT-T7 and sihTERT-T7) and dyskerin (siDKC1-2 and siDKC1-3) were previously designed and tested in our lab, however only one siRNA targeting hTR was previously validated in our lab [298]. sihTR2 was designed to target the template region of hTR and has previously been reported to inhibit telomerase activity in MCF7 and MRC5hTERT cells [298, 338].

The secondary structure of target RNAs are a crucial aspect to be considered while designing specific siRNAs [491, 495]. The hTR molecule is a highly specialised RNA sequence with an intricate secondary structure, that includes a number of GC rich areas that form double-stranded regions and non-GC rich regions areas forming loops (Figure 3.5 A) [145, 170]. For selection of a second siRNA targeting hTR, three additional hTR siRNAs were designed taking the secondary structure of hTR into account. The GC rich areas of hTR were avoided and different loop regions were targeted by sihTR144, sihTR151 and sihTR343 (Figure 3.5 A).

The hTR siRNAs were assessed in their ability to decrease hTR expression and telomerase activity of MRC5hTERT, MRC5hTERT-TZT and HT1080 cells at a concentration of 50 nM 48 hrs post-transfection by real time qRT-PCR analysis and qTRAP analysis (Figure 3.5 B). 120

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Figure 3.5 Design and evaluation of hTR siRNA A) Diagram of hTR secondary structure showing regions targeted by four different hTR siRNAs (sihTR2, sihTR4, sihTR151). Target regions are indicated by red crosses. B) MRC5hTERT, MRC5hTERT-TZT and HT1080 cells were transfected with 100 nM (left) and 50 nM (right) of different hTR siRNA for 48 hrs. hTR gene expression measured by Real time qRT-PCR analysis. Gene expression was normalised to β2 microglobulin (β2M) and then compared to HeLa using the ∆∆CT method. C) Telomerase activity measured by qTRAP analysis 48 hrs post transfection of MRC5hTERT, MRC5hTERT-TZT and HT1080 cells with 50 nM hTR2 or hTR151 siRNA. HeLa levels are indicated by the dotted line. Results are presented as means ± SEM from two independent experiments and assays repeated three times performed in duplicate. *P<0.05, **P<0.01, ***P<0.001 in Dunnet’s post-test comparison with mock-transfected cells.

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sihTR343

sihTR2

B c

• stSc • SthTR2 0 SthTR144 0 sihTR151 sthTR343 0 sihTR151 • sihTR2 ro 300 400 • siSc _l Q) Ic 0 ~ro- 300 ·u; ·- _l Vl t)QI Q) <(I Ci Q) 0 X Q) ~; 200 n::: lii - ~ E]! ~ 0 Q) Q) > 1--a;n:::1 ~ Qi n:::

MRC5hTERT MRC5hTERT HT1080 MRC5hTERT MRC5hTERT-TZT HT1080 -TZT 122

CHAPTER 3: RESULTS qRT-PCR analysis of hTR gene expression 48 hrs post siRNA-transfection showed that the extent of hTR suppression was variable among the multiple hTR siRNAs tested, although sihTR2 and sihTR151 elicited the most effective suppression of hTR in all three cell lines (Figure 3.4 B). Up to 84% repression of hTR gene expression was demonstrated in MRC5hTERT cells, 60% repression in MRC5hTERT-TZT cells and up to 70% repression in HT1080 cells transfected with sihTR2 and sihTR151 compared to cells transfected with siSc.

The suppression of telomerase activity in all three cell lines transfected with sihTR2 and sihTR151 was very effective. Repression of telomerase activity reached up to 83% in MRC5hTERT cells and 96% in MRC5hTERT-TZT and HT1080 cells, compared to cells transfected with siSc (Figure 3.5 C). Therefore, sihTR2 which targets the template region and sihTR151, which targets the upstream loop region were selected for further studies (Figure 3.5 A).

3.2.3.4 Optimisation of siRNA-mediated repression of telomerase components To test whether 50 nM of sihTERT- T8 would inhibit hTERT expression and telomerase activity effectively, MRC5hTERT-TZT cells were transfected with 50 nM and 100 nM sihTERT-T8 and both gene expression and telomerase activity was assessed at 24 hr intervals post-transfection. Over a 96 hr time course, both concentrations of sihTERT-T8 significantly decreased hTERT gene expression by 80-90 % compared to mock-transfected cells (Figure 3.6 A).

To determine whether siRNA-mediated inhibition of hTERT gene expression affected the expression of other telomerase components, dyskerin mRNA (Figure 3.6 B) and hTR (Figure 3.6 C) was assessed following transfection with sihTERT-T8. No changes to hTR or dyskerin expression were evident in the MRC5hTERT-TZT cells upon repression of hTERT, indicating that sihTERT-T8 targeted hTERT specifically and did not affect the expression of the other telomerase components. The transfection of 50 nM and 100 nM sihTERT-T8 also inhibited telomerase activity effectively well for up to 96 hrs with maximum suppression at 48-72 hrs post-transfection (Figure 3.6 D).

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Figure 3.6 Optimal conditions for siRNA-mediated repression of telomerase components MRC5hTERT-TZT cells were transfected with 100 nM or 50 nM hTERT-T8 siRNA. Cells were harvested at the indicated times post- transfection. Gene expression following hTERT siRNA transfection measured by real time qRT-PCR analysis of A) hTERT expression B) dyskerin expression. C) hTR expression. Gene expression was normalised to β2 microglobulin (β2M) and then compared to HeLa cells using the ∆∆Ct method. D) Telomerase activity measured qTRAP analysis following siRNA transfection. HeLa levels are indicated by the dotted line. Results are presented as mean ± SEM from three independent experiments. *P<0.05, **P<0.01, ***P<0.001 in Dunnet’s post-test comparison to mock-transfected cells.

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• Mock D siSc 50 nM D sihTERT-T8 50 nM lal siSc 100 nM • sihTERT-T8 100 nM

A B m _j ~ 2000 (J) (J) 500 I I c --c --0 0 .Ui .Ui 1500 (/) 400 ,I (/) (J) (J) L- L- Q. n. >< 300 1000 (J) ,.. ~ .I, c L... ~ (J) .:£. 200 w (/) 1- 500 >. ..r::: TI (J) • (J) 100 - - - > • • • • • • • • ...... > ~ 0 n lflr · rfi• ·· .n• .. i n~ · ro (J) 24 48 72 96 (J) 0 0::: 0::: 48 72 96 Time (hrs) 24 Time (hrs) c ro D ~ _j (J) 250 ~ 600 I -m c _j --0 200 (J) .Ui I (/) (J) -- 400 L- >. Q. 150 :'= >< > L (J) t5 l 0::: m 1- 100 ...... ~ J I . .c ··J ·· ~ 200 (J) m > 50 ill * ~ro E * 0 (J) r- I n ~- - fl i-- 0::: 0 ~ 0 ~ I 24 48 72 96 24 48 72 96 Time (hrs) Time (hrs) 125

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No substantial differences in hTERT, hTR and dyskerin gene expression were apparent when mock-transfected cells were compared with cells transfected with 50 nM siSc. Hence, the effects of siRNA-mediated inhibition of the telomerase genes determined were compared to siSc throughout this study.

To determine the efficacy of two dyskerin siRNA in MRC5hTERT-TZT cells, the expression levels of dyskerin, hTERT and hTR were evaluated following siRNA transfection with 50 nM siDKC1-2 and siDKC1-3 (Figure 3.7 A). Transfection of either siRNA mediated up to 80-90 % suppression of dyskerin mRNA expression over 96 hrs, compared to siSc transfected cells (Figure 3.7 A, right graph).

Repression of dyskerin caused a significant decrease in hTR levels (Figure 3.7 A, middle graph), which is consistent with the role of dyskerin in the stabilisation of hTR [173]. In contrast hTERT expression remained largely unchanged by dyskerin siRNA (Figure 3.7 A, left graph). Dyskerin protein expression was evaluated by western blots analysis 24-48 hrs post siRNA transfection of MRC5hTERT-TZT cells. Inhibition of dyskerin protein expression by either siRNA targeting dyskerin was effective at those concentrations correlating with the inhibition of dyskerin mRNA (Figure 3.7). No decrease of dyskerin protein expression was evident upon siRNA-mediated inhibition of hTR.

To demonstrate the efficacy of siRNA-mediated inhibition of hTR, hTR gene expression was evaluated in MRC5hTERT-TZT cells following siRNA transfection with sihTR2 and sihTR151. The effect of siRNA-mediated inhibition of hTR on the expression levels of the other telomerase components was also assessed. While both sihTR2 and sihTR151 effectively inhibited hTR gene expression over 24-96 hrs, no reduction of dyskerin and hTERT expression was evident following siRNA-mediated inhibition of hTR confirming the inhibition of hTR does not affect the expression of the other telomerase components.

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Figure 3.7 Effective siRNA-mediated repression of dyskerin and hTR telomerase components in MRC5hTERT-TZT cells A) MRC5hTERT-TZT cells were transfected with 50 nM DKC1 siRNA and cells were harvested at indicated time points post-siRNA transfection. Gene expression of dyskerin (left), hTR (middle) and hTERT (right) was determined by real time qRT-PCR analysis. B) Western blot analysis of dyskerin protein expression of MRC5hTERT-TZT transfected with lipofectamine only, siSc, siDKC1-2, siDKC1-3 and sihTR151 at indicated time points post-transfection. White breaks between gels indicate cuts through the image of the same gel to re-arrange lanes for presentation purposes. Blots were re-probed with actin to control for loading. C) MRC5hTERT-TZT cells were transfected with 50 nM hTR siRNA and cells were harvested at indicated time points post-transfection. Gene expression of dyskerin (left), hTR (middle) and hTERT (right) was determined by real time qRT-PCR analysis. Gene expression was normalised to β2 microglobulin (β2M) and compared to HeLa cells using the ∆∆Ct method. HeLa levels are indicated by the dotted line. Results are presented as means ± SEM from three independent experiments and assays each performed in duplicate **P<0.01, ***P<0.001 in Dunnet’s post-test comparison to siSc-transfected cells.

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A • Mock • siSc D siDKC1-2 D siDKC1-3 Dyskerin expression "' 240 hTR expression ....J"' 3000 hTERT expression ....J Q) Q) :X: ~ c 0 0 180 ·u; ·u; "'~ 2000 T "'~ a. a. X X Q) T Q) 120 ..... a::: a::: ~ w 1000 ~ Q) .~ iii Qi a::: 24 48 72 96 24 24 48 72 96 Time (hrs) Time (hrs) Time (hrs) B Mock siSc siDKC1-2 siDKC1-3 sihTR151 24 48 24 48 24 48 24 48 24 48

c • Mock siSc D sihTR2 D sihTR151 ~ 1500 500 3000 Q) ....J"' Dyskerin expression ....J"' hTR expression Q) hTERT expression Q) ~ 0 ~ 400 ~ ·u; 0 0 ·u; ·u; e 1000 "' 2000 a. "'~ 300 ~ X a. a. Q) X X Q) l c: Q) ·c: a::: ..... Q) T ..... 200 a::: .:.! 500 1 ..c: .....w 1000 Tl "'>. Q) ..c: 0 . ~ Q) Q) iii 100 . ~ . ~ Qi iii iii a::: Qi Qi a::: a::: 0 -I ~ 0 0 24 48 72 96 24 48 128 72 96 24 48 72 96 Time (hrs) Time (hrs) Time (hrs) CHAPTER 3: RESULTS

3.2.4 siRNA-mediated repression of hTERT, dyskerin and hTR gene expression and telomerase activity To determine whether siRNA targeting of the telomerase components inhibited hTERT, hTR, dyskerin gene expression levels and telomerase activity to comparable levels within the different cell lines utilised in this study, siRNA inhibition of hTERT, hTR and dyskerin and telomerase activity was assessed by real time qRT- PCR analysis and qTRAP analysis at 24-96 hrs post-siRNA transfection across the panel of cell lines. Isogenic MRC5 cells and HT1080 cells were transfected with two independent specific siRNAs for hTERT (sihTERT-T7, sihTERT-T8), dyskerin (siDKC1-2, siDKC1-3) and hTR (sihTR2, sihTR151) using the optimised conditions. The effect of siRNA-mediated inhibition on mRNA levels of the telomerase components was analysed by real time qRT-PCR analysis and compared to gene expression levels of the HeLa tumour cells as a control (Figure 3.8 A-F). Some variation to the gene expression levels of hTERT, dyskerin and hTR was noted in cells transfected with siSc during the 96 hr time course.

The results showed that hTERT gene expression was reduced by 80-90% in MRC5hTERT-TZT, MRC5hTERT, HT1080 and MRC5V1 cells following transfection of both siRNA targeting hTERT (sihTERT-T7, sihTERT-T8) compared to siSc transfected cells (Figure 3.8). hTERT gene expression recovered slightly by 96 hrs in HT1080 cells (Figure 3.8 F). It was also shown in MRC5hTERT-TZT and HT1080 cells, which had the highest basal hTERT expression levels, that although hTERT expression was inhibited up to 80-90% by siRNA targeting hTERT, compared to siSc transfected cells and the hTERT mRNA levels remained similar to that of HeLa cells (Figure 3.8 A and F). Dyskerin expression was effectively suppressed by the two independent siRNAs targeting dyskerin (siDKC1-2, siDKC1- 3) in MRC5hTERT-TZT, MRC5hTERT, HT1080, MRC5V2 and MRC5 cells reaching up to 80-90% inhibition compared with siSc transfected cells. The MRC5V2 cells were previously shown to have no hTERT or hTR (Figure 3.2) (Figure 3.8 E). Inhibition of dyskerin gene expression in MRC5V2 cells transfected with either siDKC1-2 and siDKC1-3 siRNA was also effective reaching below 95 % at 24-96 hrs post-transfection (Figure 3.8 E). Dyskerin expression in MRC5 and MRC5V2 cells transfected with siSc decreased over the duration of experiment. 129

CHAPTER 3: RESULTS siRNA-mediated inhibition of hTR expression of MRC5hTERT, MRC5hTERT-TZT, HT1080, MRC5V1 and MRC5 cells was confirmed following transfection with sihTR2 and sihTR151.

Analysis of telomerase activity by qTRAP analysis showed repression of any of the three telomerase components quenched telomerase enzyme activity for up to 72 hrs (Figure 3.8 A-C right panel). Maximum suppression of telomerase activity was evident at 48 hrs in MRC5hTERT, MRC5hTERT-TZT, HT1080 and MRC5V1 cells. Telomerase activity recovered by 96 hrs in MRC5hTERT-TZT, MRC5hTERT and HT1080 cells. The recovery of telomerase activity was most evident notable in cells transfected with siRNAs targeting hTERT and correlated with recovered levels of hTERT gene expression at 96 hrs. The telomerase negative MRC5V2 and normal MRC5 cells (Figure 3.8 E and F) were previously shown to have no telomerase activity. In comparison to siRNA-mediated inhibition of telomerase activity by hTERT and dyskerin siRNA, both siRNAs targeting hTR were found to be more effective in inhibiting telomerase activity in this panel of the cells

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Figure 3.8 siRNA-mediated inhibition of telomerase components effectively suppresses gene expression and telomerase activity Isogenic MRC5 and HT1080 cells were transfected with 50 nM siSc, sihTERT-T7, sihTERT-T8, siDKC1-2, siDKC1-3, sihTR2 and sihTR151. Expression of hTERT, dyskerin and hTR was quantified by real time qRT-PCR (left panel) and telomerase activity measured by qTRAP analysis (right panel) at indicated time points 24 hr - 96 hrs post siRNA transfection. A) MRC5hTERT-TZT B) MRC5hTERT C) MRC5 D) MRC5V1 E) ∆∆ MRC5V2 and F) HT1080 cells. Gene expression was normalised to β2 microglobulin (β2M) and compared to HeLa cells using the Ct method. Relative telomerase activity was compared to HeLa cells. HeLa levels are indicated by the dotted line. No telomerase activity or hTERT expression detected in MRC5 and MRC5V2 cells. Results are presented as mean ±SEM of 3-4 independent experiments and assays each performed in duplicate. *P<0.05, ** P<0.01, ***P<0.001 in Two-way Anova comparison to siSc-transfected cells.

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sihTERT siDKC1 sihTR Telomerase activity ?:'~ 8 <(...J 15 z<> z ~~ a:: '"I a:: I "., A 0::w-.2: ~ -~ TZT t--.!!1 ~~ EiO .c<> ... OQ; .. ~a; ~ ... ~ L-_J~~~~==~ ·· ~~ 24 48 72 96 24 48 72 96 Time (hrs) Time (hrs) <04 Time (hrs) <(...J :I ,.. ,... , <(-' z <> z <> a:: '"I O::I 3 ...E .,o E£ B MRCShTERT 0:: .2: t--.!!1w- .c"' [f. 0~ ***,.. 0 24 48 72 96 24 48 72 96 Time (hrs) Time (hrs) ro- 2.0 siSc <(...J <-' Z "' • a:: I 1.5 a::z I'""' sihTERT-T7 E£ E£ • MRCS ... ., 1.0 ... -. ~-~~- -- ...... sihTERT-T8 c 0:: .2: 0~ :<:ro • ~I§ siDKC1-2 .<:., 0.5 OQ; 0:: ~ .. • ~ 0.0 ~~~-.--~~· ·· siDKC1-3 0 24 48 72 96 • Time (hrs) sihTR151 (04 <(...J 2 . 0~ <(Q; Z"''" ZI * sihTR2 a:: I O::o Q; E£ E- <:r * I- ., '" D MRC5V1 a:: .2: ~£ w- :,::rou~ t--.!!1 .c"' 0"' 1-~~ li:rffi ~ 0.4 (.):.= 0::~ Q.):,: 0.5 ~ ~ 0.2 5~1 ~ ~ g ~ 0.5 0:: ~ ~0 . 0~ ~0 o.oe-- --tl---- .. ;! ~ 0 . ~------·-· 0 24 48 72 96 0 24 48 72 96 0 24 48 72 96 0 24 48 72 96 Time (hrs) ro- 10 Time (hrs) <(<010 ro 1.0 ------Time-(llr&}------~ :§"" 8 Time (hrs) <-' -' z., z ~ 8 "Q) a:: I 8 O::J: ~ ~ 0.8 !;l ~!§ EiO .<:., 2 >-., ., 0.2 o - ~ 0~ .... ------·:: 0:: 0 ~ o. o~~~~-~-=='~' ;!~ OJ__::;_:::Sil.-;io::j~__:: 24 48 72 96 0 24 48 72 96 0 24 48 72 96 0 24 48 72 96 Time (hrs) Time (hrs) Time (hrs) Time (hrs)

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3.3 Discussion

3.3.1 Tumorigenic MRC5hTERT-TZT cells express NRas, have a defective p53 pathway and have long telomeres These investigations characterised the MRC5hTERT-TZT cells for the first time, demonstrating ectopic expression of N-Ras, long telomeres and a defective p53/p21 pathway. A defective p53 pathway and activation of N-Ras are key alterations found in many cancer types, including mesenchymal-derived sarcomas, epithelial cancers and leukaemias [541, 542, 545, 548-552]. The tumorigenic MRC5hTERT-TZT cells provide a relevant model of human mesenchymal tumorigenesis.

The comparison of the effects of directly targeting the telomerase components in MRC5hTERT-TZT cells with a defective p53 pathway and MRC5hTERT cells with functional p53 pathway will be important for the elucidation of p53-dependent or p53-independent mechanisms following the repression of the telomerase components. The p53 pathway is commonly defective in cancers due to a mutated p53 allele or MDM2 ubiquitin-mediated degradation of p53 or other pathway perturbations [545, 552-554]. Elevated p53 protein levels are commonly associated with inactivating p53 mutations; however in order to confirm whether the defective p53/p21 pathway within the MRC5hTERT-TZT cells is due to mutation rather than a pathway perturbation, sequencing of the p53 gene should be performed.

Varied expression levels of the telomerase components and telomerase activity were detected across the panel of MRC5 cell lines. The tumorigenic MRC5hTERT-TZT ectopically overexpressing hTERT showed elevated mRNA expression levels of all of the components and telomerase activity. The levels of hTERT, hTR and telomerase activity within the MRC5hTERT cells were lower than what had previously been reported in our lab [296, 504]. The differences in PDs of the MRC5hTERT cells assessed in the previous study, may account for the differences in telomerase activity and hTERT levels detected, but these discrepancies will need to be further evaluated.

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The apparent lack of hTERT mRNA expression and telomerase in normal human MRC5 cells in this study are consistent with other published reports that confirm hTERT expression is limited to cells with telomerase activity and normal cells have negligible levels [14, 215, 248, 504, 505, 555]. However, some reports have demonstrated hTERT mRNA and telomerase activity in normal somatic cells such as BJ fibroblasts [128-134]. These reports suggest that low levels of hTERT and telomerase activity are present in normal somatic cells at transient levels that are insufficient for telomere maintenance but may confer telomere-independent effects on survival and proliferation [128-134].

Dyskerin mRNA and protein expression levels varied across the panel of normal and immortal cells. It was notable that the dyskerin mRNA expression levels of dyskerin in Sv40 transformed tumorigenic cells, MRC5hTERT-TZT and HT1080 cells were elevated compared to the normal MRC5 cells and non-transformed MRC5hTERT cells were not much higher than levels detected in normal MRC5 cells. Elevated levels of dyskerin have been detected in tumour types of different histological origins, including breast, lung and colon, as well as B-cell lymphomas and neuroblastoma [200, 340, 341, 383, 384, 388]. Furthermore, elevated levels of dyskerin associated with a poor prognosis in breast, oral squamous cell carcinoma, colon, prostate, hepatocellular carcinoma [200, 340, 341, 383, 384, 388]. The expression of dyskerin in MRC5 and MRC5V2 cells, which lack hTERT expression and have undetectable telomerase activity is consistent with the expression of non- elevated levels of dyskerin in normal adult somatic cells that lack telomerase activity [173, 190]. The inclusion of MRC5hTERT and MRC5hTERT-TZT cells with telomere lengths of 21 kbp and above and HT1080 cells with a shorter telomere length of 5 kbp in this study of telomerase inhibition will be useful for distinguishing mechanisms that are independent or dependent on gradual telomere shortening.

3.3.2 siRNA-mediated targeting of hTERT, hTR and dyskerin is an effective means of inhibiting target gene expression and telomerase activity The results in this chapter show that siRNA-mediated targeting of hTERT, dyskerin and hTR expression effectively supressed gene expression of the telomerase components and quenched telomerase activity. In the present study, two siRNAs were shown to effectively inhibit hTERT gene expression and telomerase activity in 134

CHAPTER 3: RESULTS the telomerase-positive cells of the isogenic panel. These two siRNAs (sihTERT-T7 and sihTERT-T8) were previously selected from a screening of a panel of eight different siRNAs targeting hTERT, for ability to effectively inhibit hTERT mRNA levels and telomerase activity in MRC5hTERT cells [298]. No significant variations to the inhibition of hTERT or telomerase activity following siRNA transfection of sihTERT-T7 and sihTERT-T8 between the different cell lines were found in the current study. Consistent with the knockdown demonstrated in his study, siRNA- mediated inhibition of hTERT two breast MCF-7 and MDA-MB-453 cancer cells mediated 75% inhibition of hTERT expression [556]. However, the ability of two different hTERT siRNAs sequences to inhibit hTERT gene expression is not always the same between cancer cells. This was demonstrated by a previous study that showed two different siRNA targeting hTERT supressed hTERT mRNA expression to different extents in two different prostate (PC-3 and DU145) cancer cell lines [305]. At the time these experiments were performed, no reliable antibody for the detection of hTERT was available and hence inhibition of hTERT protein was not determined. The availability of an hTERT antibody able to detect overexpressed levels of hTERT will be used in future experiments.

In this study, siRNAs targeting dyskerin mediated potent inhibition of dyskerin mRNA as well as telomerase activity across the panel of cells. The depletion of dyskerin protein expression in MRC5hTERT-TZT cells however, did not precisely reflect depletion of dyskerin mRNAs, suggesting a more complex regulation of dyskerin protein expression. Notably, these siRNAs mediated effective inhibition of dyskerin mRNA in the MRC5V2 and MRC5 cells that lacked telomerase activity. Effective inhibition of dyskerin in the telomerase negative MRC5V2 cell line that utilise ALT to maintain their telomeres will be important for the elucidation of telomerase-independent effects of dyskerin repression. siRNA-mediated inhibition of up to 80% dyskerin mRNA and protein was previously demonstrated in HeLa cells and telomerase-negative osteosarcoma cells, as well as breast cancer cells [341, 346]. However, there are no prior studies investigating the siRNA targeting of dyskerin in normal cells, which will be important for the evaluation of whether targeting dyskerin in normal proliferating cells is associated with any undesirable toxicity. The siRNAs targeting dyskerin also decreased hTR levels, while hTERT levels were

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CHAPTER 3: RESULTS unaffected [137]. These findings are consistent with previous studies that showed that dyskerin expression levels modulated telomerase activity via the regulation of hTR levels independently of hTERT, and highlight the important role of dyskerin for the stabilisation of hTR [173, 383]. In contrast, siRNA-mediated inhibition of hTERT did not affect dyskerin or hTR expression. Similarly, siRNA depletion of hTR did not result in inhibition of dyskerin or hTERT mRNA.

The targeted sequences of sihTR2 and sihTR151 had similar GC content but targeted different loop structures. Similar sequences were employed as vector-driven shRNA sequences in a study by Kedde, et al., 2006 [338]. The two shRNA used by Kedde et al., 2006, reduced endogenous levels of hTR in MCF7 breast carcinoma cells to a similar extent as the siRNA used in this study. However, in the study by Kedde et al., 2006, there was significant variation in the extent of hTR inhibition of 19 % in comparison to 67 %, which correlated with varying ability to ablate telomerase activity [338]. No significant variation of the repression of hTR gene expression or telomerase activity was demonstrated between the two different siRNA sequences utilised in this study.

In this study, each of the siRNAs targeting the three different telomerase components effectively quenched telomerase activity in the isogenic telomerase-positive cells to below detected levels in HeLa tumour cells although telomerase levels recovered at 96 hrs. Previous investigations have shown that telomerase activity can be ablated by the specific targeting of individual telomerase components hTERT, dyskerin and hTR with specific siRNA in numerous human carcinoma and sarcoma cells [298, 305, 306, 346, 348, 366, 377, 446, 490]. However, prior to the current study, the comparison of telomerase inhibition by targeting the individual telomerase core components hTERT, hTR and dyskerin within matched normal, immortal and tumorigenic panel of cells, had not yet been performed. Notably, telomerase activity was more effectively suppressed by siRNA-mediated inhibition of hTR than hTERT or dyskerin in the majority of cell lines. These results underscore the importance of hTR for telomerase activity. More effective inhibition of telomerase activity by siRNA targeting hTR was also demonstrated in a study that compared siRNA targeting of hTR and hTERT in carcinoma and sarcoma cells. In that study, hTR

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CHAPTER 3: RESULTS siRNA, with a similar sequence to sihTR2 mediated 75% suppression of telomerase activity, while hTERT siRNA-mediated 55% suppression [144, 446]. Increasing the concentrations of siRNA or the simultaneous administration of both hTERT and hTR siRNAs, did not cause any greater inhibition, suggesting a maximum threshold level to the inhibition of telomerase activity [144, 446].

In summary, this chapter characterised the tumorigenic MRC5hTERT-TZT cells for the first time. Two independent siRNAs targeting hTERT, hTR and dyskerin potently inhibited gene expression and telomerase activity within the isogenic matched cell line panel of normal, immortal and tumorigenic cells. Collectively, this chapter describes an effective system for investigating and comparing the consequences of telomerase inhibition via siRNA-mediated inhibition of the telomerase components hTERT, dyskerin and hTR within the isogenic cell line panel.

Contribution by others: Telomere restriction fragment analysis was performed by Laura Richards and Karen MacKenzie (Figure 3.1).

A number of RNA extractions and RT-PCR assays were performed by Colin Kong (MRC5hTERT-TZT cell line) and Nick Lister (MRC5V1 and MRC5V2) (Figure 3.8).

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______

4. Repression of either hTERT or dyskerin but not hTR halts the replication of immortal and tumorigenic cells via a telomerase independent mechanism

______

4.1 Introduction Defining the consequences of directly targeting hTERT, hTR or dyskerin components of the telomerase enzyme in normal, immortal and tumorigenic cells will provide insight for the development of an effective therapeutic approach to ablating telomerase activity and halting the replication of immortal cells, while sparing normal human cells. Towards the primary objective of comparing the effects of targeting specific components of telomerase in normal and neoplastic cells, the investigations in the previous chapter established a well characterised panel of normal, immortal and transformed cells and optimised siRNA-mediated downregulation of hTERT, dyskerin and hTR in these cells.

The present study compared the direct targeting of hTERT, hTR and dyskerin in normal and neoplastic cells by siRNA-mediated silencing of hTERT, dyskerin and hTR in the isogenic model of normal, immortal and tumorigenic MRC5 cells. The consequences of siRNA-mediated inhibition of the telomerase components on cell proliferation, viability and cell cycle kinetics of normal, immortal and tumorigenic cells were evaluated. The effects of siRNA targeting of the telomerase components were also compared with the effects of telomerase inhibition by treatment of the small molecular weight inhibitor of telomerase, BIBR1532 that blocks telomerase- mediated telomere maintenance function of telomerase [394].

The results show that repression of hTERT or dyskerin induced an immediate proliferative arrest in immortal and tumorigenic cells but had no acute effect on normal cells. In contrast, hTR knockdown had no immediate effects on the proliferation of normal, immortal and tumorigenic cells.

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4.2 Results

4.2.1 Downregulation of hTERT or dyskerin inhibits proliferation of immortal and tumorigenic cells, but not normal cells To directly compare the effects of targeting the telomerase components hTERT, dyskerin and hTR in normal and neoplastic cells, isogenic normal, immortal and tumorigenic MRC5 cells, as well as HT1080 fibrosarcoma cells were transfected with two siRNA targeting each of those telomerase components. Proliferation of the siRNA-transfected cells was assayed over a 96 hr time course by manual cell counting, using trypan blue. The results revealed that repression of either hTERT or dyskerin caused a dramatic decrease in the proliferation of MRC5hTERT-TZT tumour and MRC5hTERT cells in comparison to siSc transfected cells over a 96 hr time course (Figure 4.1 A and B, left and middle left). In contrast, transfection of the same cells transfected with hTR siRNA, had no effect on short-term proliferation (Figure 4.1 A and B, middle right graph). The viability of the cell transfectants remained above 95% with no cell death observed throughout the course of the experiment.

Since, repression of hTR supressed telomerase activity as effectively as hTERT or dyskerin suppression, the proliferation impairment induced by downregulation of hTERT and dyskerin in neoplastic cells appears to be independent of effects on telomerase activity. In the absence of telomerase, telomeres of MRC5 cells shorten at a rate of 72-75 bp/PD and enter senescence at a critical short length of 5 kbp [504]. The MRC5hTERT and MRC5hTERT-TZT cells proliferate at a rate of 0.4 to 0.8 PD/day, it would therefore require approximately 270 to 540 days for the long telomeres of over 21 kbp of MRC5hTERT and MRC5hTERT-TZT cells to reach a critical short length of 5 kbp, if the cells were completely depleted of telomerase. The lack of immediate effect following telomerase inhibition by hTR repression contrasted strongly with the acute anti-proliferative effects observed in immortal and tumorigenic cells following telomerase inhibition via siRNA-mediated inhibition of hTERT or dyskerin.

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Figure 4.1 siRNA mediated repression of hTERT and dyskerin impedes the short-term proliferation of immortal and tumorigenic cells, but not normal cells Isogenic normal, immortal and tumorigenic MRC5 cells and HT1080 fibrosarcoma cells were transfected with 50 nM siRNA targeting hTERT (sihTERT-T7, sihTERT-T8), dyskerin (siDKC1-2, siDKC1-3), or hTR (sihTR151, sihTR2). As a control, the cells were transfected with a control oligonucleotide (siSc). Cell proliferation and viability was determined at 24, 48, 72 and 96 hrs post-siRNA transfection using the trypan blue exclusion assay. Results are represented as mean ± SEM of 3-5 independent experiments. ***P<0.001, **P<0.01 by Two-way Anova comparison to matched siSc transfected cells.

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sihTERT siDKC1 sihTR Viabitity

..... 750 ~ 800 ..... 640 10 0 0 ~ 600 >< x 600 x480 ,-!;' 95 A MRCShTERT- c 450 ] 90 :::l § 400 §320 0 0 "' 8 300 () ~ 85 TZT *** " 0 ~ 150 ~ 200 *** ~1 60 80 u o:. u u n=3 n=3 0 24 48 72 0 24 48 72 0 24 48 72 96 750 24 48 72 96 Time (hrs) Time (hrs) Time (hrs) Time (hrs) -.1"150 ..... 140 ~ 150 10 0 0 0 x 105 -~95 ~100 c ~ 100 ] 90 c: :::l c: MRCShTERT :::l 70 :::l B 0 0 "' 8 50 50 ~ 85 *** Qj" Qj .L ** Qj" 80 () u () n=4 n=3 75 n=3 24 48 72 96 24 48 72 0 96 0 24 48 72 96 Time (hrs) Time (hrs) Time (hrs) -.t160 ..... 200 10 < 0 ~200 0 0 • siSc x120 x150 x150 ~ 95 c §100 1i 90 • sihTERT-T7 :J 80 0 §100 ·:;: c MRCS 0 0 "' () 85 0~ " Qj" • sihTERT-T8 Qj 80 u u n=3 n=3 n=3 75 n=3 • siDKC1-2 24 48 72 96 24 48 72 96 96 0 24 48 72 96 Time (hrs) Time (hrs) ..... 180 Time (hrs) • siDKC1-3 ..... 180 0 ~200 10 0 0 sihTR151 x150 ~ 95 + ~ 120 ~120c: c: :J 1i 90 D MRCSV1 :::l §100 ·:;: + sihTR2 0 "' 85 8 ~ 60 ~ Qj 50 0 Qj () Qj" 80 () n=5 u n=4 0 75 n=3 24 48 0 24 48 72 96 24 48 72 0 24 48 72 96 72 96 Time (hrs) Time (hrs) ..... 240 Time (hrs) Time (hrs) ~240 0 ..... 240 10 0 x 180 0 x180 x 180 ~ 95 MRCSV2 § 120 c 1i 90 E §120 0 :::l 120 0 0 ·;; " () "' 85 Qj 0~ Qj " () 80 n=4 () n=4 n=4 n=3 24 48 72 96 75 72 96 Time (hrs) 24 48 96 0 96 ..... 250 ..... 300 ..... 300 Time (hrs) 10 ~ 200 ~250 ~250 >< ,-!;' 95 c 150 ~200 ~200 ] 90 :J § 150 § 150 8100 0 "' F HT1080 ~ 100 ~100 ~ 85 Qj 50 ** Qj 50 50 80 () ** () ~ 0 n=4 n=3 75 n=3 0 24 48 72 96 0 24 48 72 141 24 48 72 96 0 24 48 72 96 Time (hrs) Time (hrs) Time (hrs) Time (hrs)

CHAPTER 4: RESULTS

To determine whether the repression of dyskerin or hTERT also affected the proliferation of normal cells, early passage normal MRC5 cells were also subjected to siRNA-mediated inhibition of the telomerase components. The MRC5 cells employed in these investigations were at early passage (33-35 PDs) and proliferated at a similar rate to the immortalised MRC5hTERT cells; nevertheless there was no change to the proliferation of the normal cells following transfection with any of the siRNA (Figure 4.1 C). This lack of change to the proliferation of MRC5 cells following hTERT repression was predictable, as MRC5 cells do not express substantial hTERT (Figure 3.2 in Chapter 3). However, as MRC5 cells do express hTR or dyskerin, the apparent lack of effect on the proliferation of MRC5 cells following hTR or dyskerin repression was not predictable. The possibility that the lack of effect on the proliferation of normal cells was due to a lower transfection efficiency was excluded as all cell lines showed similar transfection efficiency (Figure 3.3B in Chapter 3). Importantly, the extent of repression of dyskerin or hTR mRNA was similar in both immortal and normal cells (Figure 3.8 in Chapter 3). These results indicate that the apparent lack of an effect of hTR or dyskerin repression on the proliferation of normal cells was not due to a lower transfection efficiency of normal cells or lack of effective inhibition of hTR or dyskerin gene expression in normal cells.

To evaluate whether the consequences of directly targeting telomerase components was specific to telomerase-mediated immortalised cells, the effect of hTERT, dyskerin or hTR repression on the proliferation of isogenic MRC5V1 and MRC5V2 cells was investigated. These cell lines were immortalised by SV40 and endogenous telomerase (MRC5V1) or ALT (MRC5V2). Since the MRC5V2 cells, do not express hTERT and hTR expression and lack telomerase activity (Chapter 3), they provide an ideal tool for characterising telomerase-independent consequences of targeting dyskerin. Similar to the anti-proliferative effects of hTERT and dyskerin repression in MRC5hTERT and MRC5hTERT-TZT cells, siRNA-mediated inhibition of hTERT impaired the proliferation of MRC5V1 cells, while inhibition of hTR had no apparent effect on these cells (Figure 4.1 D). In contrast, siRNA-mediated inhibition of hTERT or hTR had no significant effect in MRC5V2 cells (Figure 4.1 E, left, middle right). This result is consistent with the lack of hTERT expression in these cells and confirms that there were no significant off-target effects on proliferation of 142

CHAPTER 4: RESULTS hTERT and hTR siRNAs. Repression of dyskerin impaired the proliferation of both telomerase positive MRC5V1 and telomerase-negative MRC5V2 cells, providing further evidence that the proliferation arrest mediated by the repression of dyskerin occurs independently of telomerase function of dyskerin (Figure 4.1 D and E middle left).

To determine whether the proliferative defect induced by the repression of hTERT or dyskerin would extend beyond MRC5 cell lines, the proliferation of tumour-derived fibrosarcoma HT1080 cells was assessed following siRNA transfection with telomerase components. A dramatic decrease in HT1080 cell proliferation was observed following the inhibition of hTERT or dyskerin. Again, cells transfected with hTR siRNA continued to proliferate (Figure 4.1 F). These findings suggest that the repression of dyskerin and hTERT impairs the proliferation of immortal and tumorigenic cells, but has no apparent effect on normal cells and this impairment is not limited to MRC5 cells.

The small molecular weight compound BIBR1532 inhibits telomerase activity and mediates anti-proliferative effects after a lag period that is dependent upon gradual telomere shortening [394]. To compare the effects of telomerase inhibition via siRNA targeting of the telomerase components with inhibition of telomerase activity at the telomere, MRC5, MRC5hTERT, MRC5hTERT-TZT and HT1080 cells were treated with 5 µM or 10 µM BIBR1532. Cell proliferation was assayed over a 96 hr treatment period and the qTRAP assay was used to confirm that BIBR1532 suppressed telomerase activity (Figure 4.2 A). The results show that telomerase activity was inhibited by greater than 80 % in immortal MRC5hTERT cells treated with BIBR1532, compared to DMSO treated cells. Telomerase levels in the BIBR1532-tretaed cells were well below that detected in HeLa tumour cells. Telomerase activity was suppressed to a lesser extent in MRC5hTERT-TZT and HT1080 cells treated with BIBR1532 (Figure 4.2 A). The extent of inhibition of telomerase activity by BIBR1532 at 96 hrs however was comparable to inhibition mediated by siRNAs directly targeting hTERT, dyskerin and hTR shown in Chapter 3 (Figure 3.8).

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Figure 4.2 Inhibition of telomerase by BIBR1532 had no acute proliferative effects on normal, immortal and tumorigenic cells A) Isogenic normal, immortal and tumorigenic MRC5 and HT1080 cells were treated with 5 µM and 10 µM BIBR1532 or control DMSO for 96 hrs. Telomerase activity was measured after 96 hrs treatment with BIBR1532 by qTRAP analysis. Telomerase activity was calculated relative to HeLa cells, indicated by dotted line B) Proliferation of isogenic MRC5 and HT1080 cells following 96 hr treatment with BIBR1532 determined by trypan blue exclusion assay. Fold expansion at 96 hrs calculated relative to control DMSO.

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Treatment of cells with BIBR1532 at either concentration of 5 µM and 10 µM had no significant impact on the proliferation of MRC5, MRC5hTERT, MRC5hTERT-TZT and HT1080 cells over 96 hrs (Figure 4.2 B). These findings are consistent with the requirement for critical telomere shortening for the anti-proliferative effects mediated by BIBR1532 treatment.

4.2.2 Repression of hTERT or dyskerin impairs anchorage-independent growth of tumorigenic cells In cell culture, growth in soft agar demonstrates the ability of the cells to grow in an anchorage independent manner, a distinct characteristic phenotype of transformed tumorigenic cells [40, 41]. To determine if siRNA-mediated inhibition of hTERT and dyskerin affected the ability of the tumorigenic cells to grow in an anchorage independent manner, siRNA transfected HT1080 and MRC5hTERT-TZT cells were grown in soft agarose and colony formation was assessed following two weeks incubation at 37°C. Representative soft agarose colonies of MRC5hTERT-TZT cells post-siRNA transfection are shown in in the appendix in Figure A.3.

The efficiency of colony formation was much higher in MRC5hTERT-TZT cells, compared to HT1080 cells as evidenced by the greater number of MRC5hTERT- TZT colonies formed (Figure 4.3). Consistent with the previous adherent proliferation assays, inhibition of hTERT or dyskerin significantly decreased the ability of HT1080 and MRC5hTERT-TZT tumorigenic cells to form colonies in comparison to siSc transfected cells (Figure 4.3). Repression by sihTERT-T7, sihTERT-T8 reduced the colony forming ability of HT1080 cells by 70% and 91% respectively compared with siSc transfected cells (Figure 4.3 A).

The colony forming ability of MRC5hTERT-TZT was reduced by 65 % following hTERT repression by both siRNAs targeting hTERT (Figure 4.3 B). Transfection of HT1080 cells with siDKC1-2 and siDKC1-3 resulted in a 50% decrease in colony formation (Figure 4.3 B). siRNA-mediated inhibition of dyskerin resulted in a 60 % reduction in colony formation of MRC5hTERT-TZT cells (Figure 4.3 B). In contrast, no significant reduction in number of colonies of HT1080 or MRC5hTERT-TZT cells was demonstrated following inhibition of hTR with sihTR151 and sihTR2 (Figure 4.3 A and B). 145

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Figure 4.3 Repression of hTERT or dyskerin impairs anchorage independent growth Anchorage independent growth of tumour cells following transient transfections of siRNA and treatment with telomerase inhibitor BIBR1532. Colonies were grown on soft agarose and counted after 14 days using a 40 x objective of an inverted microscope. The number of colonies counted in A) MRC5hTERT-TZT and B) HT1080. Results presented as mean ± SEM from four independent experiments from with triplicate plates in each experiment ***P<0.001, **P<0.01, *P<0.05 in Dunnet’s post-test comparison to siSc transfected cells. The dotted line indicates the number of colonies of siSc transfected cells.

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Treatment with BIBR1532 also had no significant effects on colony formation of either cell line (Figure 4.3 A and B). No reduction in the size of the colonies was observed in either cell lines. The contrasting results with siRNA-mediated targeting of hTERT or dyskerin and the treatment with BIBR1532 further highlight that the effect of hTERT and dyskerin on proliferation is separable from telomerase suppression.

4.2.3 Repression of dyskerin impairs proliferation of immortal cells via a mechanism that is independent of hTR depletion Consistent with the role of dyskerin in stabilising hTR, siRNA-mediated inhibition of dyskerin reduced hTR levels (Figure 3.7, Chapter 3). To determine whether the acute anti-proliferative effect mediated by the repression of dyskerin was dependent upon the depletion of hTR expression, the effects of siRNA-mediated inhibition of dyskerin were investigated in MRC5hTERT and HT1080 cells genetically modified to overexpress hTR. For these investigations, HT1080 and MRC5hTERT cells were transduced with a replication-defective retroviral vectors encoding hTR under the control of the U1 promoter (MND-hTR) and encoding a neomycin selectable marker. A vector encoding neomycin selectable marker was used as a control (MND) [188]. Following ten days of selection in geneticin, overexpression of hTR was confirmed in cells transduced with MND-hTR by qRT-PCR analysis (Figure 4.4 A left). Upregulation of telomerase activity was also confirmed in the hTR-overexpressing MRC5hTERT and HT1080 cells (Figure 4.4 A right).

Both siRNAs targeting dyskerin effectively suppressed dyskerin mRNA levels MRC5hTERT and HT1080 cells transduced with MND and MND-hTR. In contrast hTR siRNA had no effect on dyskerin expression (Figure 4.4 B left). siRNA- mediated inhibition of dyskerin reduced hTR expression levels in MND transduced cells to similar levels induced by sihTR151 siRNA (Figure 4.4 B right). However, siRNA targeting either dyskerin failed to reduce hTR expression in MND-hTR transduced MRC5hTERT and HT1080 cells (Figure 4.4 B right). These results demonstrate that hTR overexpression overcame the inhibitory effects of the siRNA targeting hTR expression, confirming this system could be used to dissociate the effects of dyskerin depletion from hTR depletion.

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Figure 4.4 Overexpression of hTR does not rescue from the proliferative impairment induced by dyskerin repression. MRC5hTERT and HT1080 cells were transduced with a MND retroviral vector encoding hTR (MND-hTR) or a control (MND) vector, each encoding a neomycin selectable marker. The transduced cells were subjected to ten days selection in 400 µg/mL (MRC5hTERT) or 800 µg/mL (HT1080) geneticin A) qRT-PCR analysis of hTR expression and qTRAP analysis of telomerase activity two weeks post- transduction. (B-D) Transduced MRC5hTERT and HT1080 cells were transfected with siRNA and suppression of gene expression and telomerase activity was assessed 96 hrs post-transfection. B) qRT-PCR analysis confirmed siRNA-mediated inhibition of hTR (left) and dyskerin mRNA (right). C) Suppression of telomerase activity determined by qTRAP analysis (right). Gene expression was normalised to β2 microglobulin (β2M) and then compared to HeLa cells using the ∆∆Ct method. Telomerase activity was normalised to HeLa cells. HeLa levels are indicated by the dotted line. Results are presented as means ± SEM from two independent experiments with assay performed in duplicate. D) Cell proliferation was determined by trypan blue exclusion assay and fold cell expansion calculated relative to expansion of cells transfected with siSc. Results are presented as means ± SEM from three independent experiments ***p<0.001 in Dunnet’s post-test comparison to siSc treated cells.

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A CJ pMND • pMNO·hTR

MRC5hTERT HT1080 B • siSc 0 siDKC1-2 0 siDKC1-3 0 sihTR151 c 250 ·u;0 ~ 200 a. ~ co 150 ..- _J ()G> o~ I- 100 ------.~ 50 Q)ro a::: 0 ...... pMND pMND-hTR pMND pMNO-hTR MRC5hTERT HT1080 c 300

>, ~ co -~ _J ~ :1!. 200 Q) ..._ co(/) ....(/) "-..C Q) !5 ~ 100 Q)ro I-

pMND pMND-hTR pMND pMND-hTR MRCShTERT HT1080

(.) 150 D (/) ·u; ..._ c 0 "(ii c co Q_ X Q) "0 :§ .._(/) ..c

pMND pMND-hTR pMND pMND-hTR n=3 MRCShTERT HT1080

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Proliferation of the vector-transduced cells was evaluated 96 hrs after transfection with siRNA targeting dyskerin and hTR siRNA were transfected as used as a control. Both cells transduced with MND or MND-hTR arrested following siRNA-mediated inhibition of dyskerin (Figure 4.4 D). As expected, siRNAs targeting hTR and siSc had no effect on proliferation of any of the cell lines.

These findings demonstrated that overexpression of hTR failed to rescue the cells from the proliferative impairment induced by siRNA-mediated suppression of dyskerin and indicate that the proliferative defect induced by depletion of dyskerin resulted from a mechanism that was independent of the effect of dyskerin on hTR stability. These results are consistent with the notion that the proliferative defect in dyskerin siRNA treated cells was the result of the disability of a telomerase independent mechanism.

4.2.4 Cell cycle kinetics following repression of hTERT and dyskerin To determine the mechanism of the proliferation arrest induced by the depletion of hTERT and dyskerin, cell cycle analysis was performed using propidium iodide and FACs analysis of DNA content. The percentage of cells in the different phases of the cell cycle was evaluated by flow cytometry. Aggregates and debris were excluded from the analysis by gating on a FL2-Area versus FL2-Width dot plot. The proportion of cells in the G1, S and G2/M phases of the cell cycle was analysed and quantified by Modfit software 96 hrs post-transfection with siRNA. Representative cell cycle histograms are shown in Figure 4.5.

Only subtle cell cycle changes were evident following the repression of hTERT in comparison in each of the three cell lines at 96 hrs post-siRNA transfection (Figure 4.6 A). There was no significant accumulation of cells within a particular cell cycle phase. These results indicated that hTERT repression caused an overall slower progression of cells through the cell cycle, resulting in impaired proliferation of these cells.

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Figure 4.5 Cell cycle analysis of immortal and tumorigenic cells following repression of telomerase components MRC5hTERT, MRC5hTERT-TZT and HT1080 cells were harvested 96 hrs post- transfection with siRNA targeting hTERT, dyskerin and hTR or control siSc. Cell cycle analysis by FACS with propidium iodide staining and flow cytometry of A) MRC5hTERT, B) MRC5hTERT-TZT and C) HT1080 cells 96 hrs post-siRNA transfection. Representative histogram plots of cells in G1, S and G2M phase from one experiment of six independent experiments by Modfit analysis software. 151

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Figure 4.6 Cell cycle analysis of immortal and tumorigenic cells following repression of telomerase components MRC5hTERT, MRC5hTERT-TZT and HT1080 cells were harvested 96 hrs post- transfection with siRNA targeting hTERT, dyskerin and hTR or control siSc. Ethanol fixed cells were stained with propidium iodide and assessed by flow cytometry. The percentages of cells in G1 phase, S phase and G2/M phase of the cell cycle was determined by Modfit analysis software of A) MRC5hTERT, MRC5hTERT-TZT and HT1080 cells. B) Percentage of cells in sub G0 phase 96 hrs post-transfection was quantified by cell quest analysis software. Results are presented as means ± SEM from 3-6 independent experiments ***p<0.001, **p<0.01, *p<0.05 in Dunnet’s post-test comparison to cells transfected with siSc.

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CHAPTER 4: RESULTS siRNA-mediated inhibition of dyskerin in MRC5hTERT cells resulted in an accumulation of cells in G1 phase 96 hrs post-siRNA transfection. A significant increase in the percentage of cells in G1 phase coincided with a significant decrease of cells in S phase of the cell cycle, when compared to siSc transfected cells (p<0.01 in Dunnet’s post-test) (Figure 4.6 A). However, no accumulation of cells in G1 was evident in MRC5hTERT-TZT cells transfected with dyskerin siRNA, suggesting that the proliferative defect observed in the cells was due to an overall slower progression through the cell cycle. The contrasting cell cycle profiles of MRC5hTERT and MRC5hTERT-TZT cells transfected with dyskerin siRNA may be a consequence of the defective p53 pathway in MRC5hTERT-TZT cells, implicating a role for p53 in the accumulation of cells in G1 following the repression of dyskerin (Figure 4.6 A).

There were no significant changes to the cell cycle kinetics upon repression of hTR in comparison to siSc transfected cells in all three cell lines (Figure 4.6 A). In HT1080 cells, there were no consistent changes to cell cycle kinetics upon siRNA- mediated inhibition of hTERT or dyskerin repression. Further investigation of siRNA-mediated inhibition of dyskerin and hTERT in this cell line is therefore required (Figure 4.6 A).

No significant changes to sub G0 population of cells, indicative of cell death, was evident in any of the cell lines following 96 hrs repression of hTERT and dyskerin in comparison to siSc transfected cells. This result is consistent with the trypan blue exclusion assay, which showed no change in cell viability following the inhibition of dyskerin or hTERT (Figure 4.6 B).

4.2.5 Induction of senescence-like growth arrest following repression of hTERT in immortal cells Since no cell death was evident following the repression of hTERT or dyskerin demonstrated the cells were assayed for pH-dependent detection of senescence associated (SA)-β-galactosidase activity. Representative MRC5hTERT cells 96-120 hrs post-siRNA transfections are shown in Figure 4.7 A. β-galactosidase positive cells were only evident MRC5hTERT cells following the depletion of hTERT, but not MRC5hTERT-TZT or HT1080 cells.

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Figure 4.7 Senescence associated (SA)-β-galactosidase activity in MRC5hTERT cells following repression of hTERT MRC5hTERT, MRC5hTERT-TZT and HT1080 were stained with X-gal for detection of (SA)-β-galactosidase activity 96-120 hrs post-siRNA transfection. A. The graph shows the percentage of cells stained positive for (SA)-β-galactosidase. Results are represented as mean ± SEM from three independent experiments performed in duplicate ***P<0.001, **P<0.01 in Dunnet’s post-test comparison to siSc transfected cells. B. Cells were visualised by Olympus CKX41 camera under 4x objective and captured using the Capture Pro v6. Software. White scale bar indicates 100 µM.

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At 96-120 hrs post transfection, a significant proportion of MRC5hTERT cells stained positive for (SA)-β-galactosidase activity following the repression of hTERT with sihTERT-T7 (30%) or sihTERT-T8 (15%) (p<0.01, Dunnet’s post-test), compared to siSc transfected cells (Figure 4.7 B). There was no substantial increase in the percentage of cells of β-galactosidase positive cells following siRNA inhibition of dyskerin or hTR in MRC5hTERT cells, or after the depletion of any of the telomerase components in HT1080 and MRC5hTERT-TZT cells (Figure 4.7 B). These finding indicate that the tumour cells have additional alterations that prevent them from entering a senescent-like growth arrest.

4.2.6 Accumulation of cells in G1 phase following dyskerin repression is mediated by p53 The proliferation arrest induced by depletion of dyskerin in MRC5hTERT cells resulted in an accumulation of cells in G1, while there was no apparent accumulation of cells in G1 phase following the repression of dyskerin in MRC5hTERT-TZT cells with a p53 defective pathway (Figure 4.6). The role of p53 in mediating the proliferative arrest in dyskerin depleted MRC5hTERT cells was directly assessed by expressing shRNA targeting p53 in the MRC5hTERT cells. MRC5hTERT cells were retrovirally transduced with the retroviral vectors BABEp53shRNA or BABEGFPshRNA and BABE (Empty Vector) as controls [511].

Following selection in puromycin, suppression of p53 expression in cells transduced with BABEp53shRNA was confirmed by western blot analysis (Figure 4.8 A). The disability of p53 function in the BABEp53shRNA transduced cells was further validated by treating the cells with the DNA damaging agent, etoposide for 8 and 24 hrs. Immunoblotting for p53 and p21 showed that p53 function was retained in cells transduced with BABEGFPshRNA but was disabled in cells transduced with BABEp53shRNA (Figure 4.8 B). This was evidenced by failure of BABEp53shRNA transduced cells to induce the p53 transcriptional target, p21CIP1 following treatment with etoposide (Figure 4.8 B).

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Figure 4.8 shRNA targeting p53 inhibits expression and function of p53 in MRC5hTERT cells MRC5hTERT cells were transduced with retroviral vectors carrying shRNA targeting p53 (BABEp53shRNA) or BABEGFPshRNA as a control A) Inhibition of p53 and transcription target p21CIP1 protein expression determined by western blot analysis 10 days following selection in 0.8 µg/mL puromycin B) Cells were treated with 25 µmol/L etoposide for 8 hrs and 24 hrs. p53 and p21CIP1 were analysed by western blot analysis. Blots were reprobed with actin to control for loading.

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To demonstrate the role of p53 in the response to depletion of hTERT or dyskerin repression, the cell proliferation of cells transduced with BABEGFPshRNA and BABEp53shRNA was assessed 96 hrs post-siRNA transfection. siRNA-mediated inhibition of the telomerase genes and suppression of telomerase activity was confirmed 96 hrs post-transfection (Figure 4.9 A and B). Notably, there was a significant increase in hTERT expression (p-value<0.05, Student t-test) and telomerase activity (p<0.01, Student t-test) in MRC5hTERT cells transduced with BABEp53shRNA, compared to MRC5hTERT cells transduced with BABEGFPshRNA. This is consistent with previous studies that have demonstrated the ability of p53 to inversely regulate hTERT mRNA expression in breast cancer cells [557]. The increased hTERT mRNA expression was effectively inhibited by hTERT in the cells transduced with BABEp53shRNA, although the inhibition was greater in cells transduced with BABEGFPshRNA (Figure 4.9 A). Telomerase activity was inhibited to similar levels in cells transduced with either BABEGFPshRNA or BABEp53shRNA (Figure 4.9 B).

Repression of either hTERT or dyskerin mediated a similar reduction in cell proliferation, irrespective of p53 function (Figure 4.9 C). This result is consistent with data from non-transduced MRC5hTERT cells and MRC5hTERT-TZT cells (Figure 4.2 B and C). Also consistent with earlier results, inhibition of hTR had no effect on the proliferation of these cells. These results confirm that p53 function was dispensable for the proliferative impairment mediated by the repression of hTERT and dyskerin.

To determine whether p53 played a role in the accumulation of cells in G1 following the repression of dyskerin in MRC5hTERT cells, cell cycle analysis was performed following siRNA transfection of cells transduced with BABEGFPshRNA or BABEp53shRNA. Repression of hTERT mediated a similar cell cycle effect in both cells transduced with BABEGFPshRNA or BABEp53shRNA (Figure 4.9 D). There was no apparent accumulation of cells within a particular cell cycle phase. These results were consistent with cell cycle effects of non-transduced MRC5hTERT and MRC5hTERT-TZT cells transfected with hTERT siRNA (Figure 4.6 A). Representative cell cycle histograms are shown in Figure 4.10.

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Figure 4.9 Inhibition of p53 overcomes accumulation of cells in G1 phase, but is not sufficient to circumvent the proliferative impairment induced by dyskerin depletion. MRC5hTERT cells transduced with BABEp53shRNA or BABEGFPshRNA were transfected with siRNA targeting telomerase components or siSc siRNA. A) qRT- PCR analysis to confirm siRNA-mediated inhibition of gene expression of hTERT, hTR and dyskerin 96 hrs post-transfection. B) Suppression of telomerase activity determined by qTRAP analysis at 96 hrs. Gene expression was normalised to β2 microglobulin (β2M) and then compared to HeLa using the ∆∆Ct method. Telomerase activity was normalised to HeLa, which is indicated by the dotted line. C) Cell proliferation determined by trypan blue exclusion assay 96 hrs post-transfection. Fold expansion was calculated relative to siSc transfected cells. D) Cell cycle analysis by FACS with propidium iodide staining and flow cytometry 96 hrs post- siRNA transfection. Results are presented as mean ± SEM from four independent experiments.**p<0.01,*p<0.05 in Student t-test for hTERT gene expression and telomerase activity.*** p<0.001 in Dunnet’s post-test comparison of proliferation and cell cycle relative to siSc transfected cells.

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A • siSc • sihTERT-T7 D sihTERT-T8 D siDKC1 -2 D siDKC1 -3 D sihTR151 • sihTR2

MRC5hTERT MRC5hTERT MRC5hTERT 600 100 ------150 ro ro ro al 500 iil 80 ...J ~ ~ I"' ~ 400 ~ 100 ------· ~ 60 ~ 300 0:: 0:: E 40 E ~ 200 0:: 50 w 0 I I .e .e 5 20 o~~==-~~-;= 0 p53sh GFPsh p53sh ,..,.. B -~ ro1 28r------iii------

:;::; .....1 30 u Q) roi Q) -- ._~ ..c.~ 20 Q)(!) § ~ 10 Q5ro 1- 0 u Cl) GFPsh p53sh "iii 12 c c --0 "iii c ro a. X Q) "'C :§ 1/)...... c =6

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Figure 4.10 Cell cycle analysis of MRC5hTERT cells expressing GFPshRNA or p53shRNA. MRC5hTERT cells transduced with BABEp53shRNA or BABEGFPshRNA were transfected with siRNA targeting telomerase components or siSc siRNA. Cell cycle analysis with propidium iodide staining and flow cytometry 96 hrs post-siRNA transfection. Representative histogram plots of cells in G1, S and G2M phase from one experiment of four independent experiments by Modfit analysis software.

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A significant increase of cells in G1 phase and decrease of cells in S phase was observed in MRC5hTERT cells transduced with BABEGFPshRNA following dyskerin repression, compared to siSc transfected cells (p<0.01, Dunnet’s post-test) (Figure 4.9 D). This accumulation of cells in G1 phase was consistent with the effect of dyskerin siRNA knockdown in MRC5hTERT cells (Figure 4.6 A). In contrast to BABEGFPshRNA transduced cells, there was no apparent accumulation of BABEp53shRNA transduced cells in G1 phase following the repression of dyskerin (Figure 4.9 D). This result was similar to the cell cycle arrest of the MRC5hTERT- TZT cells transfected with dyskerin siRNA (Figure 4.6 A). These results indicate that while p53 may not be essential to impose the proliferation impairment induced by the repression of dyskerin, p53 did appear to play a crucial role in the accumulation of cells in G1 phase of the MRC5hTERT cells. Consistent with earlier results, inhibition of hTR had no effect on the cell cycle in MRC5hTERT cells transduced with either BABEGFPshRNA or BABEp53shRNA.

4.2.7 Immortal cells are dependent upon continued expression of hTERT or dyskerin for replication siRNA-mediated inhibition of hTERT or dyskerin induced a proliferation arrest in non-transformed immortal cells, but no proliferation arrest was evident in matched normal cells. To determine whether the dependence on hTERT or dyskerin was a specific feature of cells that had become immortal, MRC5 cells (35 PDs) were retrovirally transduced with an hTERT overexpression retroviral vector (MIG+hTERT). MRC5 cells were also transduced with MIG+GFP and MIG+DNhTERT vector constructs as controls. The DNhTERT constructs has two mutations (D868A, D869A) at critical catalytic aspartates within the reverse transcriptase domain of hTERT [153, 392, 408]. The cells generated were named MRC5-GFPM, MRC5-hTERTM and MRC5-DNhTERTM to prevent confusion with the previously established MRC5hTERT cells used this study.

GFP expression levels in MRC5-GFPM, MRC5-hTERTM and MRC5-DNhTERTM cells was confirmed by fluorescence microscopy and transduction efficiencies quantified by FACs analysis (Figure 4.11 A and B).

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Figure 4.11 Overexpression of hTERT in normal MRC5 cells Normal MRC5 cells (PD 35) were transduced with MIG+ retroviral vectors encoding hTERT or DNhTERT and GFP or control vector encoding GFP. MRC5-GFPM, MRC5-hTERTM and MRC5-DNhTERTM were generated A) Photomicrographs of GFP expression in transduced MRC5 cells taken and captured using the fluorescent microscope and CRXX1 camera under the 4x objective (upper panel). White scale bars indicate 50 µM. B) Graphical representations of transduction efficiencies of transduced cells determined by FACs analysis (lower panel). C) Cell proliferation was determined by trypan blue exclusion assay over 350 days and PDs calculated to determine replicative lifespan of newly transduced MRC5 cells overexpressing hTERT. 162

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Under the microscope, the brightness of GFP of the MRC5-hTERTM and MRC5- DNhTERTM cells was dimmer in comparison to the MRC5-GFPM cells (Figure 4.11 A). This is a common feature of bicistronic vectors, from which two genes are expressed from the same IRES [558]. The percentage of GFP expressing cells were however found to be similar of 94 %, 92 % and 89 % for MRC5-GFPM, MRC5- hTERTM and MRC5-DNhTERTM 72 hrs post-transduction respectively (Figure 4.11 B).

To confirm that overexpression of hTERT/telomerase activity rendered normal cells immortal, the replicative lifespan of the transduced MRC5-hTERTM cells was evaluated over 350 days. The MRC5-hTERTM continued to grow beyond the point at which the control MRC5-GFPM and MRC5-DNhTERTM cells entered senescence (55 PDs) (Figure 4.11 C).

To determine whether the dependence of hTERT or dyskerin was a specific feature of cells that become immortal or whether it would also be observed in cells immediately upon upregulation of hTERT, the transduced MRC5 cells were transfected with siRNA targeting the telomerase components 20 days post- transduction (45 PDs) and the effect on proliferation was assessed. At that time, the MRC5-hTERTM cells overexpressed hTERT and telomerase activity (Figure 4.12 A and B) but were not yet immortalised. siRNA-mediated inhibition of hTERT, dyskerin and hTR gene expression and telomerase activity was confirmed by qRT- PCR and qTRAP analysis (Figure 4.12 A and B). Expansion of MRC5-GFPM, MRC5-hTERTM and MRC5-DNhTERTM cells was determined by the trypan blue exclusion assay 96 hrs post-siRNA transfection and fold expansion relative to siSc was calculated. The results showed that the transduced cells transfected with siRNAs targeting hTERT or dyskerin continued to proliferate similarly to the cells transfected with siSc (Figure 4.12 C). These findings revealed that the contrasting response of MRC5 and MRC5hTERT cells to the repression of hTERT or dyskerin was not simply a consequence of hTERT expression, but was a reflection of addiction of immortalised cells to the expression of two telomerase components. hTERT and dyskerin repression selectively impaired the proliferation of immortalised cells that become dependent on their expression for proliferation.

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Figure 4.12 Repression of hTERT and dyskerin selectively impairs the proliferation of immortal cells MRC5-GFPM, MRC5-hTERTM and MRC5-DNhTERTM transduced cells were transfected with siSc, sihTERT-T7, sihTERT-T8, siDKC1-2, siDKC1-3, sihTR151 A) Suppression of gene expression of hTERT (top), dyskerin (middle) and hTR (bottom) determined by RT-PCR analysis of transduced cells 96 hrs post-transfection. Gene expression was normalised to β2 microglobulin (β2M) and then compared to HeLa cells using the ∆∆Ct method B) Relative telomerase activity of transduced cells was measured by qTRAP analysis 96 hrs post-transfection and compared to HeLa cells which is indicated by the dotted line. Results are presented as mean ± SEM from two independent experiments with assays performed in duplicate. ***p<0.001 , ** p<0.01, *p<0.05 in Dunnet’s post-test comparison of hTERT and dyskerin siRNA transfected cells relative to siSc transfected cells. Student t-test was used for analysis of gene expression of hTR siRNA transfected cells relative to siSc transfected cells. C) Cell proliferation was determined by trypan blue exclusion assay 96 hrs post-transfection and fold change relative to siSc transfected cells was calculated. Results are presented as mean ± SEM from three independent experiments with assays performed in duplicate. No significant changes to proliferation were determined by One-way Anova and Dunnet’s post-test comparison to siSc transfected cells.

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• si Sc • sihTERT-T? D sihTERT-T8 D siDKC1-2 D siDKC1-3 • sihTR151

hTERT siRNA ctl A _J B Q) I 120 <( .;:::- z ·:; 100 0::: i) E C1J 80 f- Q) C1J 0::: (J) _J ._IC1J Q) 60 ~1 w-- * ..c E ** 0 40 MRC5-GFPM ai f- 20 *** 0

DKC1 siRNA MRC5-GFPM MRC5-hTERTM MRC5-DNhTERTM ctl1 _J Q) I <( z 0::: E ..... ~1 c 200 0 c *** 0 ()) MRC5-GFPM MRC5-hTERTM MRC5-DNhTERTM ffi 150 c.. X 0 <1>(/) "0 '()) 100 ._o-- - hTR siRNA cu 1 (J)..__ ~ __l .c 50 Q) <.0 I en <( z 0 - '---- 0:: MRC5-GFPM MRC5-hTERTM MRC5-DNhTERTM E 0:: 1- _c

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4.3 Discussion

4.3.1 Proliferation arrest induced by the repression of hTERT or dyskerin is mediated by a telomerase-independent mechanism The results in this study show that repression of either hTERT or dyskerin induced an acute proliferative defect in immortal and tumorigenic cells but had no apparent effect on the proliferating isogenic normal cells. In contrast, siRNA-mediated depletion of hTR or treatment with BIBR1532 had no immediate effect on proliferation in any of the cell lines. The lack of an immediate effect of telomerase suppression via hTR depletion implicated a telomerase-independent mediated mechanism in the proliferation arrest following hTERT or dyskerin repression. The proliferative defect was also evidenced in soft agarose assays, which showed repression of hTERT or dyskerin diminished the anchorage independent growth of tumorigenic cells, while no effects were demonstrated in cells transfected with hTR siRNA or treated with BIBR1532. While decreased anchorage independent growth induced by the repression of hTERT or dyskerin may be in part a consequence of decreased proliferation exerted by the repression of hTERT or dyskerin, anchorage independent growth also depends on other properties of malignant cells. Hence, these findings may also reflect an effect of hTERT or dyskerin repression on the malignant phenotype of these tumour cells. The lack of apparent effects on the anchorage independent growth of cells transfected with hTR siRNA or treated with BIBR1532 indicates the potential effect of hTERT or dyskerin repression on the malignant phenotype is mediated via a telomerase independent mediated mechanism. In support of these results, extra-telomeric functions of hTERT or dyskerin in malignant transformation, tumour formation and progression of a number of different cancer cells have previously been demonstrated [334, 335, 341, 367, 388].

The short-time frame required for the proliferation arrest demonstrated in these investigations was not sufficient for telomeres to shorten to a critical length, and therefore provided further evidence that repression of hTERT or dyskerin mediated anti-proliferative effects via mechanisms independent of telomerase-mediated telomere maintenance. In the absence of telomerase, telomere shortening of MRC5 cells occurs at a rate of ∼72-75 bp/PD in vitro until they reach a critical short length

166

CHAPTER 4: RESULTS of 5 kbp and undergo senescence [504]. The MRC5hTERT undergo 0.4 PD/Day [298], while the MRC5hTERT-TZT undergo 0.8 PD/Day. Therefore in 96 hrs these cells would undergo 1.6 or 3.2 PDs with a resultant telomere loss of ∼120 or 300 bp in the absence of telomerase. This telomere loss would be insufficient to result in a critical short telomere length in MRC5hTERT and MRC5hTERT-TZT cells over 96 hrs. From a starting telomere length of 21 kbp of MRC5hTERT and MRC5hTERT- TZT cells and in the presence of a normal telomere shortening rate in the absence of telomerase, the time required for the telomeres of MRC5hTERT or MRC5hTERT- TZT to reach a critical short length, would be approximately 280 or 550 days, respectively. Additionally, if the proliferation was dependent on critical telomere shortening, a delay in the proliferation impairment between immortal and tumorigenic MRC5 cells with longer telomeres and HT1080 cells with shorter telomeres would be evident. However, no delay was observed.

The proliferative defect induced in telomerase-negative immortal MRC5V2 cells upon the repression of dyskerin is consistent with results from a previous report that showed siRNA-mediated inhibition of dyskerin arrested the proliferation of telomerase negative U2OS osteosarcoma cells [341]. In the present study, it was also shown that dyskerin repression impaired proliferation independently of its effect in hTR stabilisation. Together with the results that inhibition of hTR had no effect of the proliferation of immortal and tumorigenic cells, these findings are consistent with the conclusion that dyskerin supports the replication of immortal and tumorigenic cells via a telomerase-independent mechanism.

Results that showed inhibition of hTR had no effect on the short-term proliferation of immortal and tumorigenic cells, confirm that hTR support the proliferation of immortal and tumorigenic cells, solely through its role in telomerase-mediated telomere extension. The lack of telomerase-independent roles of hTR are consistent with studies of TERC knockout mice, which were viable and showed no phenotype in early generations [381]. In subsequent generations, the TERC-/- mice developed phenotypes associated with telomere shortening and lack of telomerase, including premature aging, proliferative failure in regenerative tissue and increased susceptibility to cancer [381]. However, independent reports have demonstrated that

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CHAPTER 4: RESULTS downregulation of hTR with siRNA induced an acute anti-proliferative effect in human cancer cell lines, including human melanoma, colon, bladder, breast, HCC cancer cell lines [169, 194, 337, 338, 377]. These reports suggested that hTR repression may mediate extra-telomeric functions by mechanisms including regulation of gene expression and via the regulation of DNA damage response [337, 338, 377]. The great structural complexity and multiple functional domains of hTR have thus far only been linked its role as part of the telomerase holoenzyme. The possibility that the unique architecture of the RNA molecule may allow hTR to execute extra-telomeric functions has yet to be addressed. Lack of effects following hTR repression, in this study indicate that the gene expression alterations following hTR repression were not sufficient to impair the proliferation of normal and neoplastic mesenchymal cells and indicate the previously demonstrated functions of hTR may be tumour cell type specific.

Treatment with the small molecular weight inhibitor BIB1532 effectively suppressed telomerase activity in MRC5hTERT, MRC5hTERT-TZT and HT1080 cells. However, it had no effect on the short-term proliferation of any of the cells used in this study. It was similarly reported that concentrations of 10-50 µM had no effects on short-term proliferation of other cancer and leukaemia cell lines [394, 471]. Prolonged treatment of 10 µM BIBR1532, resulted in progressive telomere shortening at a rate of 30 bp/PD in fibrosarcoma (HT1080) cells and led to a growth arrest once the telomeres had reached a critical length over 200 PDs [394]. Similar effects on long term proliferation that was dependent on telomere shortening, were observed when prostate, breast and lung cancer cells were treated with BIBR1532 [394].

4.3.2 Repression of dyskerin selectively impairs the proliferation of immortal and tumorigenic cells The telomerase-independent proliferation arrest induced by the repression of dyskerin in immortal and tumorigenic cells indicated these cells become dependent on dyskerin for their replication. The levels of expression of dyskerin in MRC5hTERT cells were similar to the expression levels in MRC5 cells (Chapter 3), however only the MRC5hTERT cells arrested following the repression of dyskerin. This finding suggested that the proliferative defect mediated by the repression of 168

CHAPTER 4: RESULTS dyskerin in immortal cells was not due to elevated levels of dyskerin expression, but rather due to different functions of dyskerin in immortal and tumorigenic cells compared to normal cells.

The proliferative role for dyskerin in normal cells has come from murine studies using mutated DKC1 constructs and demonstrated mechanisms related to rRNA processing were impaired [378, 379]. siRNA depletion of dyskerin impaired the proliferation of telomerase positive prostate and breast cancer cells via either telomerase-dependent and telomerase-independent mechanisms [200, 340, 341, 383]. The increased dependency on ribosome biogenesis for tumour cell proliferation, poses the possible explanation for the dependency of immortal and tumorigenic cells on dyskerin for their proliferation [340, 341].

There were no signs of senescence or apoptosis following dyskerin repression in any of the cells used in this study. Similarly, no evidence of apoptosis or senescence resulting from the downregulation of dyskerin was demonstrated in prostate carcinoma and osteosarcoma cell lines [341, 388]. p53 function was not found to be essential for the proliferation arrest mediated by the repression of dyskerin, as repression of dyskerin arrested both p53 competent immortal cells and tumorigenic cells with a defective p53 pathway. However, the mechanism that induced proliferation arrest was influenced by p53. The accumulation of cells in G1 triggered by dyskerin repression was overcome by the loss of the p53 pathway. Defects in rRNA processing and ribosomal biogenesis trigger a p53-dependent G1 arrest [559, 560]. The p53-dependent accumulation of cells in G1 in p53 competent immortal cells following the repression of dyskerin in this study, suggests a possible link to defective rRNA processing. However, p53 is not the only regulator of rRNA processing and additional p53 independent mechanisms involving E2F/pRb pathway have also been demonstrated to be induced upon impaired rRNA processing [559- 561]. Varied p53-dependent and p53-independent mechanisms have been reported in relation to impaired functioning of dyskerin. In p53 competent U2OS osteosarcoma cells, depletion of dyskerin caused a p53-independent growth arrest that resulted in accumulation of cells in G2/M leading to atypical mitosis with multipolar spindle formation [341]. In the present study however, no effects on G2/M were evident

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CHAPTER 4: RESULTS following dyskerin repression in any of the cell lines. Further kinetic experiments may be performed to elaborate on the mechanism of the accumulation of cells in G1 phase of the cell cycle.

The HT1080 cells with endogenous telomerase, express mutant NRas and have wild type p53 [541, 542]. The proliferation of these cells was impaired following the repression of dyskerin and hTERT, similar to effects demonstrated in MRC5hTERT and MRC5hTERT-TZT cells. However, the proliferation arrest was associated with lack of any consistent cell cycle effects and hence requires further investigations are required. A more detailed analysis of changes to the percentage of cells within different cell cycle phases at different time points of HT1080 cells may be gained using BrdU analysis.

4.3.3 Repression of hTERT selectively impairs proliferation of immortal and tumorigenic cells The finding in this study demonstrated that hTERT repression induced an immediate proliferation arrest in immortal and tumorigenic cells independent of its telomerase activities, while having no detrimental effects on normal cells. Results that showed no proliferation impairment following hTERT inhibition in early passage mortal MRC5 overexpressing hTERT indicate that hTERT is not essential for the proliferation of non-immortalised cells [134]. This was in strong contrast to the response of immortalised cells that appeared to be highly dependent on hTERT for their replication and hence more vulnerable to its repression. These findings are consistent with previous studies showing that hTERT provides tumour cells with a proliferative advantage, independent of its telomere maintenance functions [14, 163].

In this study, there were no signs of apoptosis or decreased cell viability following hTERT repression. Instead, hTERT repression caused a defective progression of cells through the cell cycle. The senescent-like features adopted by the cells was most likely induced by oncogenic stress or other stresses (e.g DNA damage or oxidative stress), following hTERT repression, rather than replicative senescence, due to the insufficient time for critical telomere shortening to occur [26]. Alterations in pathways within the tumour cells may function to prevent the cells from entering a senescence-like growth arrest. It will be important to evaluate whether the defective 170

CHAPTER 4: RESULTS p53 pathway was responsible for the lack of senescent-like growth arrest in MRC5hTERT-TZT cells. The cell cycle effects are consistent with the previous investigations in our lab which showed siRNA-mediated inhibition of hTERT in immortalised hTERT overexpressing MRC5 cells resulted in a defective progression of cells through the cell cycle [298].

The shRNA-mediated inhibition of p53 increased hTERT mRNA and telomerase activity of MRC5hTERT cells. This observation is consistent with previous studies that demonstrated wild type p53 downregulated hTERT expression in breast cancer cells [557]. In the present study, siRNA-mediated inhibition of hTERT impaired the proliferation of p53-competent immortal cells, tumorigenic cells with a p53 defective pathway and immortal cells transduced with p53 shRNA. Thus it was clear that p53 is not essential for the proliferative arrest mediated by the repression of hTERT. Previous studies have demonstrated that shRNA-mediated inhibition of hTERT regulates p53 by a feed-back loop mechanism, however both p53-dependent and p53-independent mechanisms function in response to hTERT repression within different cellular contexts [128, 356]. shRNA-mediated inhibition of hTERT reduced proliferation of p53 competent human embryonic kidney cells via p53-dependent and reduced the proliferation of p53-null mammary cancer by a p53-independent manner [356].

4.3.4 Potential for the therapeutic targeting of telomerase components

dyskerin and hTERT

Results from these investigations indicate that of the three telomerase components targeted, dyskerin and hTERT are the most promising as therapeutic targets. The lack of apparent effects of targeting hTERT or dyskerin on the proliferation of matched normal cells suggests that targeting hTERT or dyskerin may be relatively non-toxic to proliferation of normal cells. Repression of hTERT or dyskerin were found to mediate the proliferation arrest by distinct mechanisms

In contrast to the results from these studies that showed proliferating normal MRC5 cells tolerated siRNA targeting hTERT, studies by Masutomi et al., 2003 demonstrated that shRNA-mediated inhibition of hTERT in normal BJ fibroblasts 171

CHAPTER 4: RESULTS slowed down their proliferation. The BJ fibroblasts cells express transient low levels of hTERT/telomerase activity [126]. The slower proliferation was proposed to be due to the disruption of telomere end structure [126]. However in that study, the anti- proliferative effects were also demonstrated following inhibition of telomerase activity by the expression of DNhTERT, indicating that the maintenance of telomere structure was dependent of a catalytically active telomerase [126]. Another study by Masutomi et al., 2005, showed that shRNA-mediated inhibition of hTERT in BJ fibroblasts impaired a DNA damage response to ionising radiation or DNA damaging agents via the remodelling of chromatin structure [134]. No loss of telomere length was evident and both the catalytically inactive DNhTERT and a telomere localisation defective mutant of hTERT failed to restore the impaired DNA damage response, indicating that these effects were unrelated to overall telomere length and telomerase activity [126, 134, 143, 163].

Discrepancies between findings of that study and this one may be due to the inherent differences between foreskin BJ fibroblasts and MRC5 myofibroblasts used as well as the different means of hTERT inhibition of shRNA or siRNA utilised for these studies [10, 25]. In addition, the BJ fibroblasts were transduced with shRNA at very early PD (20 PDs) in comparison to this study in which the MRC5 cells were transfected with siRNA at 34-35 PDs [10, 25]. Direct targeting of dyskerin by siRNA in normal cells has not been previously demonstrated. Ongoing investigations in our lab have recently confirmed that inhibition of hTERT or dyskerin also selectively inhibits the proliferative of hTERT-immortalised WI38 foetal lung fibroblasts, but no effects of the proliferation of normal W138 control cells were demonstrated following the repression of hTERT or dyskerin.

Collectively, the results of this study provide further evidence that hTERT and dyskerin engage telomerase-independent mechanisms that are required for their proliferation. Furthermore, these studies support the notion that targeting the extra- telomeric functions of telomerase components will be a more rapid and effective therapeutic approach to halt the replication of immortal and tumorigenic cells than currently used telomerase inhibitors, that target the telomere maintenance function and which require time for telomeres to shorten.

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______

5. Long term analysis of telomerase suppression in immortal and tumorigenic cells

______

5.1 Introduction Previous studies have demonstrated that the telomerase components hTERT, hTR and dyskerin may contribute to various processes involved in tumorigenic growth and malignancy independent of telomerase activity and telomere regulation [337, 341, 366]. For instance, shRNA-mediated inhibition of hTERT was shown to reduce the growth of xenografted glioblastoma tumour cells in vivo prior to a substantial shortening of telomere length [366]. siRNA mediated repression of dyskerin decreased anchorage independent growth of both telomerase negative U2OS and telomerase positive HeLa cells, independent of telomerase [341]. siRNA mediated repression of hTR in colon HCT116 cells was associated with gene expression changes of genes involved in tumour growth and metastasis, independent of telomerase activity and telomere lengthening mechanisms [337]. Furthermore, previous studies of telomerase inhibition by the retroviral expression of the catalytically inactivate DNhTERT, that blocks telomere synthesis has also be shown to decrease tumour capacity of ovarian in vivo via telomerase suppression and telomere shortening [163, 550].

In the previous chapter, it was shown that siRNA-mediated repression of hTERT and dyskerin impaired the ability of tumorigenic cells to grow in an anchorage independent manner, which is fundamental property of malignant cells. The transient nature of siRNA-mediated inhibition of gene expression is however limited for the evaluation of longer term effects [495, 562]. Adenoviral, lentiviral and retroviral vector-based expression of shRNAs have been successfully used to determine longer term effects of gene suppression both in vitro and in vivo as they allow the persistent suppression of gene expression and better efficacy in vivo [493, 563].

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The pSuperRetro RNAi retroviral system employed in this study, was previously used in our lab to supress expression of p16INK4 and has been employed for the stable suppression of a variety of other genes, including p53 and Cadherin-1 (CDH1) by others [494, 510]. shRNAs are transcribed from the pSuperRetro (pSR) vector into a double-stranded short hairpin precursor mRNA transcript. This precursor is rapidly cleaved to produce functional siRNA [494, 510].

In the investigations described in this chapter, pSR retroviral vectors encoding shRNA for targeting each of the telomerase components (hTERT, dyskerin and hTR) and a control scrambled shRNA were constructed. The consequences of stable shRNA-mediated suppression of hTERT, hTR and dyskerin were assessed on anchorage independent growth in vitro and tumour growth in vivo and compared with the long-term enzymatic inhibition of telomerase activity by the retroviral expression of DNhTERT.

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5.2 Results

5.2.1 Construction of replication-defective retroviral vectors for stable repression of telomerase components Transient siRNA-mediated repression of the telomerase components over the 96 hr time course demonstrated that repression of hTERT and dyskerin mediated an immediate anti-proliferative effect of immortal and tumour cells and reduced the anchorage-independent growth of immortal tumour cells. To determine whether the anti-proliferative effects or impairment of anchorage-independent growth would be evident in longer-term assays, retroviral vectors expressing stable shRNAs targeting hTERT, hTR and dyskerin were generated using the pSR retroviral vectors that include a puromycin marker for selection in mammalian cells (pSRpuro). The previously designed siRNA sequences targeting the telomerase components were specifically adapted into 60-62 bp double-stranded shRNA according to Brummelkamp et al., 2002. The shRNAs were annealed and ligated to the pSRpuro retroviral vector as shown in Figure 5.1 shRNA cloned into the pSRpuro vectors was identified by restriction enzyme digestion. Following double digestion with Xho1 and EcoR1, the presence of a 281 bp fragment was indicative of vector with the 62- 65 bp shRNA insert. The Bgl1 site was destroyed while cloning of the insert into the vector and therefore positive transfectants with shRNAs remained uncut upon digestion with the Bgl11 restriction enzyme.

Successful cloning of hTR151 shRNA and hTR2 shRNA required an additional step, possibly due to the GC rich secondary structure of hTR. The shRNA oligomers were synthesised with a phosphorylated 5’end and the pSRpuro vector was dephosphorylated by Antarctic phosphatase prior to ligation. Successful cloning of hTERT-T7 shRNA and hTERT-T8 shRNA, DKC1-2 and DKC1-3 shRNA, hTR151 shRNA and hTR2 shRNA and the control shRNA (Sc shRNA) was confirmed by sequencing.

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Figure 5.1 Cloning strategy for generation of pSRpuro retroviral vectors expressing shRNA A) shRNA were synthesised from the double stranded siRNA target sequence with the following additions according to Brummelkamp et al., 2002; a restriction enzyme Bgl11 site (red) on the 5 end, a hairpin spacer sequence (light blue), a terminator sequence of five thymidines (dark blue) and a Xho1 site on the 3’ end (green). Arrows indicate 5’to 3’ direction of siRNA target and reverse complementary target sequence. B) pSuperRetro-puro (pSRpuro) retroviral vector was linearised by a double restriction enzyme digest with Bgl11 and Xho1. Linearised vectors were electrophoresed through a 1% agarose gel and extracted. C) Annealed shRNA were ligated to linearised pSRpuro vector. D) Bacterial transformation of the shRNA constructs into the pSRpuro retroviral vector. E) Restriction enzyme digest and sequencing performed to confirm shRNA within pSRpuro vector. F) pSRpuro vector with shRNA were transfected into Phoenix A packaging cells G) The supernatant containing replicative-defective retrovirus used to transduce HT1080 and MRC5hTERT-TZT cells. H) Cells were selected in 0.8 µg/mL puromycin for two weeks. I) Gene expression and telomerase activity was assessed by qRT-PCR and qTRAP analysis. J) Effects on anchorage independent growth by soft agarose cloning and tumour formation in mice were assessed.

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.... Q) ~.... ro E + - .0 • shRNA ~ - + •• Bgl11 ------7 . 5'.....__ ...... ____ " + 1 " 1 •I I 3 1---- ·· ,Xho1 .,____ H1 promoter J I • - PKG promoter 1"'""'-- - 3'LTR pufO - A. shRNA design and synthesis B. Linearisation of vector C. Anneal shRNA to vector

J. Effects on in vitro replicative lifespan, soft agarose and tumour formation in mice

t D. Bacterial transformation and plasmid preparation I. Inhibition of gene expression and telomerase activity assessed

E. shRNA vectors confirmed by t RE digest and Sequencing H. Selection in puromycin

t G. Retroviral transduction of vectors F. Transfect plasmid into Phoenix into HT1080 and MRCShTERT-TZT ~ A packaging cells cells 177

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The pSRpuro vectors encoding shRNAs targeting hTERT, dyskerin or hTR or scrambled control Sc shRNA were transfected into phoenix A packaging cells. The supernatant containing replicative-defective retrovirus was collected and MRC5hTERT-TZT and HT1080 tumour cells were transduced with pSRpuro vectors encoding the shRNAs. After 72 hrs, the infected cells were selected in 0.8 µg/ mL puromycin for 2 weeks. Vector integration of HT1080 and MRC5hTERT-TZT cells was confirmed by PCR amplification of the pSRpuroshRNA vector DNA using primers for a 643 bp region of the vector that extended over the shRNA region. All the retrovirally transduced cells were positive for this sequence (Figure 5.2 A and B).

5.2.2 Retroviral transduction of MRC5hTERT and HT1080 cells with dominant-negative hTERT vector (MIG+DNhTERT) To distinguish between telomerase-independent effects resulting from shRNA- mediated inhibition or effects dependent on telomere shortening, the consequences of shRNA-mediated inhibition of hTERT, dyskerin and hTR were compared with the enzymatic inhibition of telomerase activity by expression of DNhTERT. HT1080 and MRC5hTERT-TZT tumour cells stably expressing MIG+DNhTERT and MIG+GFP control were generated.

HT1080 and MRC5hTERT-TZT cells were transduced using stable packaging phoenix A cells carrying the MIG+GFP or MIG+ DNhTERT vectors. No pSRpuro vector DNA was apparent in these cells, which was used as PCR negative control (Figure 5.2 A and B). Successful transduction was confirmed by flow cytometric analysis of GFP cells (Figure 5.3 A). Transduction efficiencies of 95.61 % and 73.55 % were obtained for HT1080-GFP and HT1080-DNhTERT respectively (Figure 5.3 A, upper panel). The transduction efficiency of the MRC5hTERT-TZT cells was less efficient with 81.19 % and 42.62 % for GFP and DNhTERT transduced cells, respectively (Figure 5.3 A, upper panel).

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Figure 5.2 Transduction of HT1080 and MRC5hTERT-TZT cells with pSR retroviral vectors expressing shRNA Genomic DNA was isolated from A) HT1080 (14 PD post-transduction) and B) MRC5hTERT-TZT (19 PD post-transduction) cells transduced with shRNA targeting telomerase components and a scrambled control shRNA at indicated time points post-transduction. PCR amplification was performed to amplify a vector product of 643 bp fragment spanning the 1332-1922 region of the pSR retroviral vector according to Brummelkamp et al., 2002 [494]. PCR amplification for pSR retroviral vector of cells transduced with DNhTERT and GFP was performed as negative controls. PCR amplification of β-actin gene (540 bp) was performed as a PCR control. PCR products were fractionated by electrophoresis through a 1% agarose gel which was visualised by staining with ethidium bromide and viewed on the Gel Doc™ EZ Imager (BioRad).

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HT1080pSR HT1080 ~ A Q) ~ co ""0 E .I::. .I::. Q) en en (.) en f'-. co .I::. .I::. .I::. ::J I- ::J I- I- en en en ""0 .I::. .,.... en 0::: 0.. ~ ~ .,....~ .,....'? en l{) c w 0.. .I::. 0::: 0::: .I::. .I::. N .,.... I- ~ (L .I::. ..0 en w w en 0 0 en 0::: 0::: +-' ~ (.) I- I- (.) ~ ~ (.) I- I- c LL z (f) .I::. .I::. (f) 0 0 (f) .I::. .I::. :J C) 0

1000 850 650 pRSpuro vector (643 bp) 500 650 - 13-actin (540bp) 500 -

B (D MRC5h TERT-TZTpSR MRC5hTERT-TZT ~ co ""0 E .I::. .I::. Q) en en (.) en f'-. co .I::. .I::. .I::. ::J I- ::J I- I- en en en ""0 0::: .I::. .,.... en 0.. ~ ~ .,....~ .,....'? en l{) c w 0.. .I::. .I::. .I::. N .,.... I- 0::: 0::: ~ (L .I::. ..0 en w w en 0 0 en 0::: 0::: +-' ~ (.) I- I- (.) ~ ~ (.) I- I- c LL z (f) .I::. .I::. (f) 0 0 (f) .I::. .I::. :J C) 0 Kbp 1000 850 650 pRSpuro vector (643 bp) 500 650 13-actin (540 bp) 500

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Since the MRC5hTERT-TZT cells already expressed the yellow fluorescence protein reporter gene (GFPtpz) and both the GFPtpz and GFP are read on the same FL-1 channel of FACs Calibur flow cytometer (BD Biosciences), the transduction efficiencies were likely to be less accurate (Figure 5.3 A upper panel). Both cell lines were FACS sorted for enriched GFP. The post-sorted HT1080-GFP and HT1080- DNhTERT cells had highly enriched GFP populations of 98.48 % or 93.82 %. The enriched GFP population of post-sorted MRC5hTERT-TZT-GFP and MRC5hTERT- TZT-DNhTERT cells were increased from 81.19 % and 42.62 % to 95.48 % and 62.54 % respectively (Figure 5.3 A lower panel, right).

Following FACs sorting, suppression of telomerase activity by DNhTERT was confirmed by qTRAP analysis. Telomerase activity was suppressed by 64% in HT1080 cells, while in MRC5hTERT-TZT cells, expression of DNhTERT decreased telomerase activity by 45 % compared to cells transduced with GFP. The apparently less effective inhibition of telomerase activity detected in MRC5hTERT-TZT cells may have resulted from the lower transduction efficiency or very high levels of ectopic hTERT (Figure 3.2). While DNhTERT expression in HT1080 cells decreased telomerase activity to levels below those detected in HeLa cells, levels of telomerase activity in MRC5hTERT-TZT cells remained similar to levels detected in HeLa cells.

5.2.3 shRNA-mediated suppression of hTERT, hTR, dyskerin gene expression and telomerase activity HT1080 and MRC5hTERT-TZT cells transduced with shRNAs targeting the telomerase components were subjected to selection in 0.8 µg/ mL puromycin for two weeks and then evaluated for suppression of gene expression and telomerase activity by qRT-PCR analysis and qTRAP analysis and levels compared to HeLa cells (Figure 5.4). In HT1080 and MRC5hTERT-TZT cells, hTERT-T7 shRNA mediated less effective inhibition of hTERT gene expression and telomerase activity than hTERT-T8 shRNA.

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Figure 5.3 Retroviral transduction of HT1080 and MRC5hTERT-TZT with pMIG+GFP and pMIG+DNhTERT vectors HT1080 and MRC5hTERT-TZT cells were retrovirally transduced with pMIG+GFP and pMIG+DNhTERT vectors A) FACs analysis of HT1080 (left) and MRC5hTERT-TZT cells (right) 72 hrs post transduction before FACs sort (upper panel) and after sort (lower panel). B) Suppression of telomerase activity by DNhTERT measured by qTRAP analysis and expressed relative to HeLa cells. HeLa levels are indicated by the dotted line. Results represented as mean ±SEM from three assays each performed in duplicate ***P<0.001 in Student t-test comparison to GFP transduced cells.

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A HT1080 HT1080- HT1080- MRCShTERT- MRCShTERT- MRCShTERT-TZT- GFP DNhTERT TZT TZT-GFP DNhTERT Pre-sort Pre-sort a.. a.. LL. LL. (9 (9

,... ~~ .q:-.. ...,.,.. 000 000 Post-sort Post-sort a.. LL. a.. (9 LL. (9

FSC-H FSC-H HT1080 MRC5hTERT- TZT B 5'600 --- ::5. ro __J Q) ::: 400 • GFP >. > D DNhTERT nro Q) l(l 200 "- Q) E £ Q) 1-- 0

183

CHAPTER 5: RESULTS shRNA-mediated inhibition by hTERT-T8 shRNA in both cell lines, reduced hTERT gene expression by greater than 80% and telomerase activity by greater than 60%, compared to cells transduced with Sc shRNA (Figure 5.4 A and B left). Only hTERT-T8 shRNA reduced hTERT expression levels of MRC5hTERT-TZT and HT1080 cells to levels lower than those detected in HeLa cells, however telomerase activity remained above the endogenous levels detected in HeLa cells, in both cell lines (Figure 5.4 A and B left).

In HT1080 and MRC5hTERT-TZT transduced with DKC1-2 and DKC1-3 shRNA, dyskerin expression was significantly reduced to 30-40% relative to that of cells transduced with Sc shRNA and telomerase activity was supressed to a similar extent (Figure 5.4 A and B middle). Both shRNAs targeting dyskerin suppressed dyskerin gene expression of MRC5hTERT and HT1080 cells to lower levels than those detected in HeLa cells, however the suppression of telomerase activity did not reach below the endogenous levels to HeLa cells. Suppression of dyskerin protein expression by DKC1-2 and DKC1-3 shRNA in HT1080 and MRC5hTERT-TZT was confirmed by western blot analysis for dyskerin (Figure 5.4 C). Inhibition of dyskerin protein expression was more evident in the HT1080 cell line than MRC5hTERT-TZT cells with greater inhibition demonstrated in HT1080 cells transduced with DKC1-2 shRNA (Figure 5.4 C).

In MRC5hTERT-TZT and HT1080 cells transduced with hTR shRNA, hTR gene expression was significantly repressed by greater than 55% compared Sc shRNA transduced cells (Figure 5.4 A right). The repression of hTR gene expression approximated with the repression of telomerase activity (Figure 5.4 A and B right). In HT1080 cells, both hTR shRNAs mediated a similar reduction of telomerase activity, compared to cells transduced with Sc shRNA (Figure 5.3 B). However, neither shRNAs targeting hTR supressed hTR in MRC5hTERT-TZT and HT1080 cells to below endogenous expression levels of HeLa cells. Importantly, suppression of telomerase activity by shRNA targeting hTERT, hTR and dyskerin was comparable to suppression of telomerase activity with DNhTERT relative to HeLa cells in HT1080 and MRC5hTERT-TZT cells (Figure 5.3).

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Figure 5.4 Suppression of hTERT, hTR and dyskerin and telomerase activity in MRC5hTERT-TZT and HT1080 cells Protein and RNA was isolated from HT1080 and MRC5hTERT-TZT cells transduced with shRNA targeting telomerase components following selection in puromycin at 10-16 PD post-transduction. A) RT-PCR analysis was used to quantify gene hTERT mRNA (left), dyskerin mRNA (middle) and hTR (right). Gene expression levels were normalised to β2 microglobulin (β2M) housekeeping gene and compared to Sc shRNA using the ∆∆Ct method and compared to HeLa cells. HeLa levels are indicated by the dotted line. Results are presented as mean ± SEM from three experimental repeats. B) Inhibition of telomerase activity by vectors encoding shRNA for hTERT, dyskerin and hTR in MRC5hTERT-TZT and HT1080 cells determined by qTRAP analysis. Results are presented as means ± SEM from three experimental repeats C) Proteins extracted from shRNA transduced cells were subjected to western blot analysis using a dyskerin antibody and reprobed for actin as a control. *P <0.05, **P<0.01, ***P<0.001 in Dunnet’s post-test comparison to Sc shRNA transduced cells.

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A • Scsh • hTERT-T?sh D hTERT-T8sh D DKC1-2sh D DKC1-3sh D hTR151sh • hTR2sh

MRC5hTERT-TZT HT1080 MRC5hTERT-TZT HT1 080 c 400 § 15001 0 - .Ui ro 1000 1/) _J -~ ~ I Q) Q) 300 ~::¥: * C. I Q) 0 500 • >< 0 Q) - a>- Q) Q) 200 c .:: c Q) 3001 Q)- c;.!!! Q) crcr 100 1 ~- ~ .s:::. i ~~ llo----i.n-- 0

MRCShTERT-TZT HT1 080 MRC5hTERT-TZT HT1080 800 1000

>- ~ >- ~ ~ ro ~ l1l -~ _J -~ _J 800 ts Q) 600 u Q) roi ro i Q) 0 Q) 0 600 w- w- ro Q) 400 l1l Q) ..._ > .... > a>.:; a> :;:; 400 E.!!! E.!!! 0 Q) 0 Q) Qilr 200 Qilr t-- t- - 200 0 0 c MRCShTERT-TZT HT1080

..c: ..c: ..c: ..c: r/) r/) r/) r/) N (") N (") ...--I ...--I ...--I ...--I ..c: ..c: (/) u u (/) u u (.) ~ ~ (.) ~ ~ (/) 0 0 (/) 0 0 kDa Dyskerin 56 kDa 5o - 5o_,------..------~ Actin 42 kDa 37~------~~------~

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5.2.4 Comparison of the effects of telomerase inhibition via expression of DNhTERT and shRNA targeting the telomerase components

5.2.4.1 Anchorage independent growth siRNA-mediated repression of hTERT and dyskerin caused an impaired anchorage independent growth of HT1080 and MRC5hTERT-TZT cells. To determine whether similar or more robust effects would be evident following shRNA-mediated inhibition of the telomerase components, transduced tumour cells were assayed for anchorage independent growth in vitro, by culturing cells in soft agarose. MRC5hTERT-TZT and HT1080 cells at early PD (10-30 PD post-transduction) expressing shRNAs vectors targeting hTERT, dyskerin, hTR or a control Sc shRNA were grown in soft agarose and the number of colonies were assessed and compared to MRC5hTERT-TZT and HT1080 cells expressing GFP and DNhTERT (Figure 5.5). Representative soft agarose colonies of MRC5hTERT-TZT cells expressing shRNA are shown in the appendix in Figure A.4.

The efficiency of colony formation was much higher in MRC5hTERT-TZT cells, compared to HT1080 cells and a lower number of colonies were formed by HT1080 and MRC5hTERT-TZT cells transduced with hTR shRNAs, compared to either cell line transduced with hTERT and DKC1 shRNA (Figure 5.5). A lower number of colonies were also formed in HT1080 cells transduced with GFP and DNhTERT in comparison to the HT1080 cells transduced with shRNA. shRNA-mediated repression of hTERT by both shTERT-T7 and shTERT-T8 in MRC5hTERT-TZT caused a significant 40% decrease in soft agarose colony formation compared to cells transduced with the Sc shRNA (p<0.001, One way ANOVA, post-Dunnet’s test) (Figure 5.5 A left). Although both DKC1 shRNA decreased colony formation by MRC5hTERT-TZT cells, this only reached statistical significance for the DKC1-2 transduced cells (p<0.01, One way ANOVA, post-Dunnet’s test) (Figure 5.5 A middle left). No effect on colony formation was observed in MRC5hTERT-TZT expressing either hTR shRNA. Similarly, the expression of GFP and DNhTERT had no effect on the number of colonies produced by MRC5hTERT-TZT cells (Figure 5.5 A middle right).

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Figure 5.5 Impaired anchorage independent growth of HT1080 and MRC5hTERT-TZT cells transduced with shRNA targeting telomerase components or DNhTERT Cells (1000 cells/plate) were plated and colonies were grown in soft agarose and viewed and counted after 14 days incubation at 37°C. Number of colonies generated by A) MRC5hTERT-TZT and B) HT1080 cells (10-30 PD post-transduction) with vectors encoding shRNA for hTERT, dyskerin and hTR and GFP and DNhTERT. Results presented as mean ± SEM from three-four independent experiments performed with triplicate plates in each experiment. *P<0.05, **P<0.01, ***P<0.001 in Dunnet’s post-test comparison to Sc shRNA transduced cells or student t-test for cells transduced with DNhTERT compared to cells transduced with GFP.

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A MRC5hTERT-TZT (10-30 PDs)

"0 640 1!! ro 15.. .'!2 Q) u 0 *** 0 0 0 0 ..... ** 0 (i; (i; ~ .!!:! .!!:! .!!:! c: c: c: 0 0 0 0 u 1 8 1 8 0 0 0 0 z z0 z0 '---Lo_ .._ n =4 B HT1 080 cells (1 0-30 PDs)

"0 180 "0 -o1 "0 2

• Scsh • Scsh • Scsh • hTERT-T7sh 0 hTERT-T8sh 0 DKC1-2sh 0 DKC1-3sh • hTR151sh . hTR2sh • GFP 0 DNhTERT

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Expression of hTERT shRNA also impaired colony formation in HT1080 cells with hTERT-T8 shRNA (p<0.01, One way ANOVA, post-Dunnet’s test) (Figure 5.5 B left). shRNA-mediated inhibition of dyskerin by DKC1 shRNA also significantly reduced the number of HT1080 colonies, compared to HT1080 cells transduced with ScshRNA (Figure 5.5 B middle left). Neither hTR shRNAs had an effect on colony formation by HT1080 cells (Figure 5.5 B middle right). The expression of DNhTERT did not affect colony formation of MRC5hTERT-TZT cells but significantly reduced colony formation of HT1080 cells, compared to HT1080 cells transduced with MIG+GFP (p<0.001, Student’s t-test) (Figure 5.5 A and B right). This result may be explained by the dramatic difference in telomere length between MRC5hTERT-TZT and HT1080 cells.

The reduction of colony formation mediated by shRNA-mediation inhibition of hTERT or dyskerin was less effective and more variable than siRNA-mediated inhibition of hTERT or dyskerin as effects were not demonstrated by both shRNAs targeting each component. In MRC5hTERT-TZT cells, hTERT-T8 shRNA was more effective at inhibiting hTERT gene expression, however, the expression of either hTERT-T7 or hTERT-T8 shRNA, mediated a similar reduction of colony formation of MRC5hTERT-TZT cells. In comparison, only the expression of hTERT-T8 shRNA reduced colony formation of HT1080 cells, possibly relating to the greater extent of hTERT-T8 shRNA to inhibit gene expression. DKC1-2 shRNA reduced colony formation of MRC5hTERT-TZT cells and HT1080 cells to a greater extent than DKC1-3 shRNA. Based on these findings, the more effective shRNA (hTERT- T8 shRNA, DKC1-2 shRNA and hTR2 shRNA) were selected for subsequent investigations of subcutaneous tumour formation in mice and compared with cells transduced with MIG+ GFP and MIG+ DNhTERT.

5.2.4.2 Tumour formation in xenografted mouse model To determine whether shRNA-mediated inhibition of hTERT or dyskerin would affect the malignant phenotype of the tumour cells in vivo, the shRNA transduced HT1080 and MRC5hTERT-TZT cells were assessed for subcutaneous (s.c) tumour formation in immuno-compromised mice. Five million HT1080 cells transduced with hTERT-T8 shRNA, DKC1-2 shRNA, shTR2 shRNA, MIG+DNhTERT and control

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HT1080 cells transduced with Sc shRNA or MIG+GFP were injected into the hind flank of BALBC/nude mice. Eight mice per experimental group were used. Weight and s.c tumour formation were monitored until tumours reached an endpoint of 1.2 cm diameter, at which time the mice were sacrificed. S.c tumour volumes (mm3) and percentage survival curves were calculated.

All mice xenografted with HT1080 cells grew rapidly developed. Tumour growth was evident within 7 days of the injection. An initial delay in tumour growth was observed in all mice injected with HT1080 cells transduced with hTERT-T8 shRNA and DKC1-2 shRNAs, however all tumours subsequently reached an end point of 1.2 cm diameter within 21 days of the injections. No significant differences in tumour volumes were evident between the experimental groups evaluated over the 21 days (Figure 5.6 A left).

Survival curves for mice xenografted with transduced HT1080 cells showed the percentage survival of mice xenografted with HT1080 cells transduced with hTERT- T8 shRNA was significantly greater compared to mice xenografted with HT1080 cells transduced with Sc shRNA (p.value 0.0385, log-rank statistical test) (Figure 5.6 B left). The percentage survival of mice xenografted with HT1080 cells transduced with DKC1-2 shRNA was also greater compared to mice xenografted with HT1080 cells transduced with Sc shRNA cells, but did not reach significance (p. value 0.0698, log-rank statistical test). The median days of survival calculated from the percentage survival curves of mice injected with HT1080 cells transduced with hTERT-T8 shRNA and DKC1-2 shRNA was greater (19 days and 15 days), in comparison to the median survival of mice xenografted with HT1080 cells transduced with Sc shRNA (13 days), however this was not statistically different (Figure 5.6 C left). The median survival of mice xenografted with HT1080 transduced with hTR2 shRNA of 11 days was less compared to the median survival of mice xenografted with HT1080 transduced with Sc shRNA (13 days) and similar to mice xenografted with HT1080 transduced with MIG+GFP and MIG+DNhTERT of 12 days each (Figure 5.6 C left).

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Figure 5.6 Effects of shRNA on tumour formation in mice Tumour formation was evaluated following subcutaneous injection of Balb/c Nude mice with 5x106 HT1080 or MRC5hTERT-TZT cells transduced with shRNA targeting the telomerase component or DNhTERT. A) Volumes of tumours (mm3) formed by HT1080 cells (left) and MRC5hTERT-TZT cells (right) transduced with shRNA targeting telomerase components or DNhTERT. B) Kaplan-Meier percentage survival curve of mice injected with HT1080 cells (left) and MRC5hTERT-TZT cells (right) transduced with shRNA targeting telomerase components or DNhTERT. C) Median survival time of mice. Mice were sacrificed when tumours reached an end point of 1,2 cm diameter or if no tumours formed 26 weeks after the initiation of the experiment. The graphs show results of n=8 mice per group.

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HT1 080-pSRpuro MRC5hTERT-pSRpuro

A ,. Scsh

M' • ~ 2000 E 1 ' §. ' g • 4 Q) Q) .. I.a. E 1500 E ' ::::; :::l 0 I • 0 > > ' .... 1000 :J 5 0 0 E E :J ::::; 1- 1-

I I 0 15 18 21 20 40 60 80 100 Days post s.c injection Days post s.c injection

B HT1 080-pSRpuro MRC5hTERT-pSRpuro 1:- Scsh - DKC1 -2sh - GFP rl - Scsh - DKC1-2sh - GFP - hTERT-T8sh - hTR2sh - DNhTERT - hTERT-T8sh - hTR2sh - ONhTERT ~100 t100 Qi ,_ Qi E !-- E ro :6 80 :.0 80 !-- E E (.) ..._ (.) "'! 60 "'! 60 .,...... ,. co (ij .:::. 40 > 40 2: ·~ :J :::l rJ> ...... rJ> c 20 c 20 Q) CIJ (.) ....(.) (i; Q) a.. 0 l a.. 0 0 6 12 18 24 0 20 40 60 80 100 Days post s.c injection Days post s.c injection

MRC5hTERT-pSRpuro

• Scsh 4 DKC1-2sh c • hTERT-T8sh f IITR2sh 25 100 ,.-... --. •• • ~ 20 ~ 80 ro • ro •• • ~ • • • ~ • (ij •• + •• (ij > 15 - ~ " > 60 + -i- ·~ I- ·~ .... ~ •• :::l • .... ::::; • "" • • -±- rJ> rJ> ...::v- " + "ffi .p. +., t: 10 • • •• t: 40 • • ns ro ••• •• ro ...... :.0 ns """ ns :.0 ns CIJ ns Q) ~ 5 ~ 20 ns ns ns

0 0

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The previous in vivo investigations were performed with transduced MRC5hTERT- TZT cells of 10 PD post transduction. In comparison to tumour formation by HT1080 cells, the MRC5hTERT-TZT tumours grew relatively slowly with tumours appearing after 33 days post-injections. Not all mice grew tumours and some mice developed a small subcutaneous growth at the site of injection that regressed after day 23. More specifically, the number of mice xenografted with MRC5hTERT-TZT cells per experimental group that formed tumours included; 6/8 (Sc shRNA), 5/8 (hTERT-T8 shRNA), 7/8 (DKC1-2 shRNA), 4/8 (hTR2 shRNA), 7/8 (pMIG+GFP) and 5/8 (pMIG+ DNhTERT). No significant reduction in tumour volumes was evident between the experimental groups evaluated over 93 days (Figure 5.6 A right). The survival curves for mice xenografted with transduced MRC5hTERT-TZT cells evaluated over 93 days showed no differences in the percentage survival of mice xenografted with MRC5hTERT-TZT cells transduced with shRNA targeting hTERT, hTR or dyskerin compared to mice xenografted with MRC5hTERT-TZT cells transduced with Sc shRNA (Figure 5.6 B right). Similarly, there was no difference in the percentage survival of mice xenografted with MRC5hTERT-TZT cells transduced with pMIG+ DNhTERT, compared to mice xenografted with cells transduced with pMIG+GFP. There were also no significant differences in median day survival of the mice (Figure 5.6 B right). For the calculation of median days and percentage survival, mice that did not grow tumours were not included, and hence sample size was reduced. Based on this data no significant effects of shRNA- mediated inhibition of hTERT, dyskerin or hTR were observed in these tumour formation assays.

5.2.4.3 Replicative lifespan No immediate effects on the in vitro proliferation of MRC5hTERT-TZT and HT1080 cells transduced with shRNA targeting any of the telomerase components were evident. These lack of immediate arrest were contrary to the acute anti-proliferative effects following siRNA-mediated inhibition of hTERT and dyskerin and suggested that shRNA-mediated inhibition of gene expression may not have been sufficient to cause acute effects as elicited by putative telomerase-independent functions of dyskerin and hTERT.

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Figure 5.7 shRNA suppression of telomerase does not affect long-term replicative lifespan Transduced MRC5hTERT-TZT and HT1080 cells were cultured between 100-200 days and population doublings (PDs) were calculated from weekly cell counts by trypan blue staining. Growth curves of MRC5hTERT-TZT (left panel) and HT1080 (right panel) transduced with A) hTERT-T7 shRNA and hTERT-T8 shRNA B) DKC1-2 shRNA and DKC1-3 shRNA C) hTR151 shRNA and hTR2 shRNA D) MIG+DNhTERT and MIG+GFP constructs.

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A HT1080 MRC5hTERT-TZT 250 ~ 180 0"' 0"' ~ 200 ~ "'0) .s: / ~"' 120 :0 150 :;) :0 0 / ' :;::) 0 - hTERT-T7sh 0 / •sc•h .J!• 0 c 100 ...;1'~ /' hTERT-T8sh 0 -- c p 0 60 (1) .A' p :; J!' (1) a. 50 ~"}' :; 0 a. "1 0 a.. ~ a.. 0 0 0 100 200 300 0 60 120 180 Days post transducllon (Days) Days post transduclton (Days) B 150 150 ~ 0"' 0"' ~ ~ f100"' "'~ 1 00 :0 :0 :;) :;) - Scsh 0 0 0 0 • DKC1-2sh c c • DKC1-3sh 0 0 ~ 50 -::> 50 (1) (1) :; :; a. a. 0 a.. a..0 0 60 120 180 0 60 120 180 c Tlme post transduction (Days) Time post transduction (Days) 150 150 c;;- 0 0"' ~ ~ "'g> 100 f100"' i5 :0 :;) :;::) • Scsh 0 0 0 0 - hTR1 51sh c c - hTR2sh 0 0 :c 50 ~ 50 ro (1) :; :; a. a. 0 0 a.. a.. 0 50 100 150 0 50 100 150 D Time post transducllon (Days) Time post transductton (Days) 150 150

~ ~ 0"' 0"' ~ ~ -"'~ 100 f"' 100 :0 :0 :;) :;) - GFP 0 0 '0 '0 DNhTERT c c 0 0 c 50 ~ 50 ro (1) :; :; a. a. 0 a.. a..0

50 100 150 200 50 100 150 200 Ttme post transduction (Days) Time post transduction (Days)

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To determine whether shRNA targeting the different telomerase components had an effect on the long-term replicative capacity of the cells in vitro, the PDs of transduced cells were calculated over at least 150 days without selective pressure. Growth curves showing the long-term proliferation of HT1080 and MRC5hTERT- TZT cells transduced with control Sc shRNA, hTERT-T7 shRNA, hTERT-T8 shRNA, DKC1-2 shRNA, DKC1-3 shRNA, hTR151 shRNA, hTR2 shRNA, MIG+GFP (control vector) or MIG+ DNhTERT are shown in Figure 5.7. There was no significant effect on replicative lifespan of HT1080 and MRC5hTERT-TZT cells with any of the shRNA targeting the telomerase components or the expression of DNhTERT.

Effective shRNA repression of gene expression and telomerase activity was previously confirmed two weeks post-selection at the early 10-16 PDs approximately 15 days post transduction (Figure 5.4). Suppression of gene expression and telomerase activity with at least one of the shRNAs for each target genes in both cell lines at this earliest PD was reshown in Figure 5.8. To determine whether gene expression and telomerase activity remained stably suppressed over the replicative lifespan, telomerase activity and gene expression were evaluated by qRT-PCR analysis and qTRAP analysis at later time points post-transduction. The results showed the effects of shRNA-mediated inhibition of gene expression and telomerase activity of MRC5hTERT-TZT and HT1080 cells transduced with shRNAs targeting the telomerase components, varied over time and there was a tendency for gene expression levels and telomerase activity to recover over time (Figure 5.8 A-C). Suppression of telomerase activity by the expression of DNhTERT in MRC5hTERT and HT1080 was also progressively lost over time (Figure 5.8 D).

The recovery of gene expression and telomerase activity over time would have compromised both the long term in vitro investigations of replicative lifespan and the in vivo investigations of tumour formation in this study. Failure of hTERT or dyskerin gene expression to reach below detected HeLa levels at later PDs is likely the reason why apparent effects of HT1080 tumour formation were only evident in the earlier stages of tumour formation.

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Figure 5.8 Suppression of gene expression and telomerase activity by shRNA and DNhTERT over replicative lifespan. Gene expression and telomerase activity were evaluated in MRC5hTERT-TZT (left) and HT1080 (right) tumorigenic cells transduced with A) hTERT shRNA B) DKC1 shRNA C) hTR shRNA or D) GFP and DNhTERT vectors at indicated days post-transduction. Gene expression levels ∆∆ were determined by qRT-PCR analysis and normalised to β2 microglobulin (β2M) housekeeping gene and then compared to HeLa using the Ct method. Telomerase activity determined by qTRAP analysis at indicated time points and compared to HeLa. Results presented as mean ± SEM from two assays performed in duplicate.

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MRC5hTERT-TZT HT1080 Gene expression Telomerase activity Gene expression Telomerase activity

A 1500 1000 A 400 1000 c - ~ro 800 iii<> "i"' ~ 5 800 300 > I • Scsh ~ ~ .[ 1000 gr u".. :r uo 600 600 • hTERT-T7sh w x - .,-" 0 Q) .. 200 .. .. hTERT-T8sh 1- f "' 400 400 trro 500 ~ ~ ..c wo; 0 .. f 0:: 100 ...... 'ii cr .s:.~ 200 r-- 200 ····• ····································· ... 0 OL-~--~--~----~ 0 0 B 14 25 70 95 14 25 70 95 B 10 53 172 201 254 1000 Days post-transduction 1200 !lays post ttansduc. t1on 600 c 500 Days post-transduction 0 'Vi ·~· 2!- -- 800 ~~- >~ :""' -' a. I.. ~ :1:: 800 400 ~ :1:: 400 • Scsh 600 300 ~ :; n ~.9 ~B • DKC1-2sh "'c: ..> ~ ~ "'., ., ., 400 200 / 4; .~ DKC1-3sh 0) .. E~ 400 ! !§ ~ 200 uo:~-- 100 ...... ,_... ..:..-T...... >- - 0 ·~··· · ~.. ::- •••••• H ••••••••••••••••••••••• ... f;H...... OL-~------~-- O L-~----~------~-- - 0 0 10 55 110 10 110 14 34 110 14 34 110 c Days post-transduction Days post transduchon c Days pOst hansduct1on Days post-transduction 400 800 300 1000 c: c: 0 -- <> - I • Scsh 0:: -~~ fS'soo - ~ ~ 300 tl"' • hTR2sh 1- ~ I 200 "'I ..c e.~ )(0 ., 0 600 * hTR151sh : ~ 200 ...... - .."'- ., ~~ ~ ~ 400 ~ ~ 0)~ 100 ... 0 .. ~ I 0:: CY. 100 a: a: ~ ~200 I.s:. - I.s:. ·- ...... / ..... 0 w 15 44 71 113 44 16 44 82 113 16 44 87 113 >. Days pnst-uansd,IC:flon Days post-transduction Days posHransduchon Days post-transduction ·+-':;; :.;::; D D C) I- 400 ro >- 0:: -~ ro Q.) ~ ~ 300 w + GFP CJ) I- "'0 ro ~; 200 • DNhTERT I...... c ~ > Q.) z -;;;~~ 0: 100 E 0 I ~- 0 10 OL--1 0~--~5c5----, ~,o~~,,6~,- ~ Days post transduc.hon Days post-transduchon

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5.3 Discussion These investigations describe the construction of shRNA retro-viral vectors that supress the expression of hTERT, hTR and dyskerin. shRNA-mediated inhibition of telomerase component gene expression and telomerase activity was most effective at early time points post transduction, although there was some variation of shRNA- mediated inhibition of gene expression and telomerase activity observed between the two different shRNA sequences. Overall the shRNA-mediated inhibition of gene expression and telomerase activity was not as effective as with siRNA as demonstrated in Chapter 3 and there was tendency for it to recover over time. There are a number of factors that influence the effectiveness of shRNA-mediated inhibition of gene expression [491]. For example, some reports suggest shRNA transduced cells have the potential to adapt to shRNA, while others have reported that overexpression of shRNA can exhaust the cellular machinery for RNA silencing or cell turnover [564]. Premature elimination of shRNA-expressing cell clones or outgrowth of non-transduced cells are also probable factors that affect stable shRNA inhibition [564].

Contrary to the results from siRNA gene targeting hTERT or dyskerin, no immediate anti-proliferative effects were evident following shRNA-mediated inhibition of hTERT or dyskerin. This suggests that a threshold level of suppression must be achieved for the induction of acute effects. Notably, with a number of the shRNA constructs, gene expression and telomerase activity in the transduced cells did not fall below levels detected in HeLa cells. Nevertheless, at early 10-30 PDs, shRNA- mediated repression of hTERT and dyskerin impaired the anchorage-independent growth of both HT1080 and MRC5hTERT-TZT cells. Decreased colony formation in soft agarose by shRNA-mediated inhibition of hTERT and dyskerin was consistent with the effects on anchorage-independent growth following siRNA-mediated inhibition of hTERT and dyskerin to reduce the malignant phenotype of these tumour cells. The impairment of anchorage-independent growth induced by shRNA- mediated inhibition of hTERT or dyskerin was however less robust compared with effects of siRNA-mediated inhibition of hTERT or dyskerin. These differences are likely a result of less effective inhibition of gene and telomerase activity suppression mediated by shRNA-mediated inhibition. The reduced anchorage-independent 200

CHAPTER 5: RESULTS growth of tumour cells following the shRNA-mediated repression of hTERT and dyskerin are consistent with previous studies suggesting that hTERT and dyskerin contribute to malignant phenotype of various tumour cells [333-335, 367]. The results for the current study demonstrate the impairment to anchorage independent growth is separable from impaired cell replication that was noted by siRNA suppression of hTERT or dyskerin.

This study compared the effects of shRNA-mediated inhibition of hTERT, dyskerin and hTR with the effects of telomerase inhibition by expression of DNhTERT. The extent of telomerase inhibition by expression of DNhTERT in HT1080 cells was similar to that mediated by the repression of telomerase genes and similarly impaired the capacity of HT1080 cells for anchorage independent growth. As the HT1080 cells have shorter telomeres of 5 kbp, the impaired anchorage-independent growth of HT1080 cells induced by expression of DNhTERT in HT1080, may be a result of telomere shortening. Notably, anchorage independent growth of MRC5hTERT-TZT cells, which have long telomeres, was not impaired by expression of DNhTERT. Similar to the effect of telomerase suppression by expression of DNhTERT in HT1080 cells, a previous study showed a shorter-term growth effect only 15-35 days post-transduction of DNhTERT in leukemia cells that was dependent on telomere shortening [406]. An additional explanation for these effects is given by a more recent extra-telomeric function of hTERT demonstrated by the expression of DNhTERT in neuroblastoma cells [336, 406]. In the latter study, the expression of DNhTERT caused a reduction in colony formation of in vitro and subcutaneous tumour formation in mice in vivo, which occurred without net telomere loss [336]. Future investigations of telomere-lengths of these colony forming cells will be important to determine whether the impaired anchorage independent growth was dependent on critical telomere shortening.

There was no evidence of a long-term growth arrest resulting from shRNA transduction or DNhTERT expression in HT1080 and MRC5hTERT-TZT. However, expression of the same DNhTERT construct used in this study in leukemia cells, resulted in telomere shortening followed by telomere dysfunction, increased apoptosis and reduced tumour clonogenicity in comparison to GFP or hTERT

201

CHAPTER 5: RESULTS transduced cells [408]. Similarly, DNhTERT expression in K562 cells resulted in rapid telomere shortening of 110 bp/PD in four of seven clonal derivatives and subsequent growth arrest [394, 565]. However, telomere shortening as a result of telomerase inhibition does not always affect growth kinetics. In that same study, prolonged treatment of over 450 days of K562 cells with BIBR1532 resulted in the gradual shortening of telomeres (13 bp/PD) to a critical length of 3-5 kbp, but no changes to growth kinetics were evident [394, 565]. A similar absence of an elicited growth arrest following telomerase inhibition in germ cell tumours and chondrosarcoma cell lines have been reported [465, 566]. The lack of any apparent proliferation arrest or effect on replicative lifespan in the present study was most likely a consequence of residual telomerase activity, since telomerase in transduced MRC5hTERT-TZT and HT1080 cells remained above the levels detected in HeLa cells for a significant time of those experiments. To determine whether the levels of telomerase inhibition reached a level that was insufficient to induce critical telomere shortening, telomere-length analysis of transduced MRC5hTERT-TZT and HT1080 cells over the replicative lifespan should be performed.

The lack of stable shRNA suppression of gene expression and telomerase activity due to either the outgrowth of non-transduced cells or loss of shRNA silencing over time may have masked the effects of stable shRNA-mediated suppression in long- term cultures and tumour formation. The interpretation of these results was therefore hampered. The overgrowth of cells is particularly an issue in gene silencing experiments, where the target gene is required for cell proliferation [567]. The relatively long period that preceded the appearance of tumours of transduced xenografted MRC5hTERT-TZT cells formed, combined with the inability to administer selective pressure to the transduced cells in vivo, may have allowed time for the overgrowth of tumour cells without effective gene suppression. To address the concerns of applying selective pressure in vitro and in vivo, inducible shRNA vector expression system will be used in future investigations to enable more stable and effective shRNA inhibition. This vector system may also help to clarify the biological significance of the slight delay to tumour growth by hTERT and DKC1 shRNA transduced HT1080 cells xenografted in mice. Such a vector is currently in use in our lab by other members.

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Future investigations will be performed to verify these results and to rectify some of technical issues encountered in this study including, lack and/or variation of stable shRNA suppression, minimal sample size and the dynamics of tumour initiation and formation. Re-calculation of sample size and larger experimental sample sizes could be incorporated to increase statistical analysis and power of the in vivo investigations. To enhance efficacy of tumour initiation of MRC5hTERT-TZT cells, s.c injections of transduced MRC5hTERT-TZT cells can be performed with matrigel to provide a substrate for initiation of tumour formation. The number of HT1080 cells injected can be reduced to allow for an extended monitoring time of HT1080 tumour formation.

Retroviral vectors targeting telomerase components were shown to supress gene expression and telomerase activity for up to ~15 days and impaired anchorage independent growth of the tumorigenic cells. Inhibition of hTERT and dyskerin did show a marginal early delay of HT1080 tumour formation in vivo. However, gradual recovery of gene expression compromised the capacity for these investigations to address questions regarding potential long-term effects on proliferation and malignant phenotype. The issues underlying the problems are intrinsic to gene silencing experiments where the target gene is required for cell proliferation and may be mitigated by the use of inducible vector systems.

Contribution by others: RNA extractions and RT-PCR analysis of HT1080 shRNA targeting hTERT and DKC1 and Sc shRNA transduced cells were performed by Colin Kong

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______

6. Gene expression pathways associated with the potent anti- proliferative effects elicited by repression of hTERT and dyskerin

______

6.1 Introduction The results described in Chapter 4 showed that repression of telomerase by siRNA- mediated inhibition of either hTERT or dyskerin impaired the proliferation of immortal and tumorigenic cells, but had no apparent effect on the proliferation of isogenic normal cells. The investigations described in this chapter were to identify pathways that distinguished the response of immortal and tumorigenic cells from that of normal cells when subjected to repression of hTERT or dyskerin. These pathways are likely to contribute to the proliferation arrest induced by hTERT or dyskerin. To investigate the mechanisms responsible for the proliferative arrest induced by repression of hTERT and dyskerin in immortal and tumorigenic cells, microarray gene expression analysis was performed on normal, immortal and tumorigenic cells subjected to siRNA-mediated repression of hTERT, hTR or dyskerin.

In gene expression analysis, there has been a shift from a focus on the identification of individual genes to gene set/pathway analysis to identify biological processes that are perturbed [568, 569]. Gene Set Enrichment Analysis (GSEA) is a sensitive and robust pathway analysis tool that utilises the Molecular Signatures Database (MSigDB) collection of annotated gene sets [530, 531]. It uses the modified Kolmogorov-Smirnov-style statistical algorithm to link newly generated ranked data to collections of gene sets to uncover biological trends [530, 570]. GSEA uses the differientially expressed dataset with no cut offs to generate an unbaised identification of pathways and processes [530].

One of the strengths of GSEA is that it allows for comparisons across multiple microarray platforms, species and cell types [530, 569, 571-574]. GSEA was sucessfully utilised to resolve metabolic pathways involved in human diabetes, large B cell lymphoma, colon cancer and the response of prostate cancer to rapamycin inhibition. In addition GSEA, has been used to and to uncover p53 regulated 204

CHAPTER 6: RESULTS pathways in cancer cell lines [530, 569, 571-574]. It has also been for used to compare the transcriptional profiling of myogenic sarcomas and non-myogenic sarcomas [575]. In combination with another anlaysis program Significance Analysis of Microarray (SAM), GSEA was used to identify the altered transcriptional regulation of Myc and Wnt signalling implicated in the impaired proliferation of mouse epidermal hair follicles following the repression of hTERT [310]. Analysis of the subset of genes within the gene sets identified by GSEA (leading edge analysis) has then been used to identify the genes crucial to the pathways identified [530].

In contrast to GSEA, gene expression analysis at the individual gene level relies on significant thresholds and cut offs that are subjective and less likely to provide broad biological insight. This was exemplified by a previous study that used individual gene expression analysis or GSEA to analyse gene expression of profiles of lung cancer patient tumours from two independent studies. No common statistically significant genes between the two studies were identifed by individual gene expression. In contrast, GSEA analysis identified statistically significant genes that were shared between the two lung cancer data sets [530, 576, 577]. These findings indicated that GSEA overcomes the limitations of individual gene expression analysis, including poor reproducibility between independent studies of same disease [530, 578] and the failure to detect statistically significant genes linked in biologically meaningful way [569]. In addition it has been shown that GSEA boost signal to noise ratio allowing for detection of modest biological changes [530].

In this study, GSEA of microarray data was used to identify pathways associated with the proliferative impairment induced by repression of dyskerin and hTERT inhibition in immortal and tumorigenic cells. Alterations in the expression of genes involved in Rb/E2F regulation of the cell cycle and DNA replication correlated with dyskerin repression and several pathways relating to DNA damage response were found to correlate with hTERT repression.

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6.2 Results

6.2.1 Microarray analysis of gene expression changes following siRNA- mediated repression of telomerase components

6.2.1.1 Microarray design and control of RNA quality To delineate the mechanisms responsible for the proliferation arrest induced by the repression of hTERT or dyskerin, microarray gene expression analysis was performed. The microarray study described in this chapter was designed in collaboration with bioinformaticians Dr W. Kaplan and Dr M. Cowley (Peter Wills Bioinformatic Centre, Garvan Institute for Medical Research). Three biological independent repeats of siRNA transfections of normal (MRC5), immortal (MRC5hTERT) and tumorigenic MRC5hTERT-TZT cells were performed using sihTERT-T8, siDKC1-2 and sihTR151. A fourth siSc transfection was incorporated into the study to increase reproducibility between biological repeats for good statistical power. Two samples of untreated cells were included in the study for each cell line providing basal gene expression levels and to identify inherent differences in the expression of genes between normal, immortal and tumorigenic cells. However, these analyses have not been included in the thesis.

A 48 hr time course was chosen for these experiments because gene expression and telomerase activity was maximally supressed at this time point and the gene expression changes at 48 hrs were considered to be involved in the proliferation arrest observed at 72 hrs. Therefore RNA was extracted and gene expression analysis performed at 48 hrs post-siRNA transfection. The quality of RNA was assessed on the Agilent 2100 electrophoresis bioanalyzer (Ramaciotti Center). Good quality RNA has optical density (OD) ratios of A260nm/280nm and A230nm/260nm between 1.8-2 and hence only RNAs with ratios between 1.8-2 were used in this study. RNA Integrity Numbers (RIN) is a separate measure of RNA integrity that standardises RNA quality using an alogorithim based on the different degradation states of RNAs in samples [519]. RIN values range from 1-10 from totally degraded RNA to that of intact RNA. In this study, all RNAs used gave RIN values over 9.5.

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CHAPTER 6: RESULTS siRNA-mediated suppression of hTERT, dyskerin and hTR gene expression was confirmed by qRT-PCR analysis prior to cRNA amplification and hybridisation to the slides (Figure 6.1). Negligible levels of hTERT were demonstrated in MRC5 cells and hTERT-T8 siRNA inhibited hTERT expression of MRC5hTERT and MRC5hTERT-TZT cells to below the level of hTERT in HeLa tumour cells (Figure 6.1 A). It was shown that siDKC1-2 inhibited dyskerin expression to similar levels in MRC5, MRC5hTERT and MRC5hTERT-TZT cells (Figure 6.1 B). hTR expression was repressed to similar levels by the both siDKC1 and sihTR151, which is consistent with the known role of dyskerin in stabilising hTR (Figure 6.1 C). The levels of siRNA-mediated inhibition of gene expression were consistent with the results from siRNA transfections described in Chapter 3, Figure 3.8. The RNA was converted to cRNA, amplified and labelled with biotin in a process that involved the reverse transcription of RNA into cDNA, second strand cDNA synthesis and subsequent in vitro transcription for synthesis of cRNA. The quality of amplified, labelled cRNA was confirmed by assessment of A260nm/280nm and A230nm/260nm OD ratios between 1.8-2.

Non-biological experimental variation such as batch effects across multiple experiments or position effects due to sample hybridisation can significantly impact outcomes of gene expression microarray experiments and increase the likelihood of false positive results [579]. To eliminate non-biological variation, balanced randomisation [579], was used to split the samples into workable groups for RNA extraction and the samples were randomly distributed to the slides with respect to cell lines and siRNA. The cRNA samples were sent to the Ramaciotii center and hybridised to Illumina Human (HT-12V.4) Bead Chips, washed and scanned on the Illumina iScan reader at the Ramaciotti Center. The microarray data files generated were deposited into the caArray microarray data management system (https://cabig.nci.nih.gov/tools/ caArray) and retrieved for analysis. The gene expression pipeline used in this study is outlined in Figure 6.2.

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Figure 6.1 Suppression of hTERT, dyskerin and hTR gene expression in siRNA- transfected cells A) RNA was extracted from siRNA-transfected cells harvested at 48 hrs post-siRNA transfection. Real time qRT-PCR analysis was performed to assess expression of hTERT, dyskerin and hTR. Gene expression was normalised to the housekeeping gene β2 microglobulin and then compared to HeLa cells using the ∆∆Ct method. HeLa levels are indicated by the dotted line. Results are presented as means ±SEM of two-four independent siRNA transfection experiments with assays performed in duplicate.

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Figure 6.2 Gene expression analysis pipeline using Gene Pattern software and GSEA analysis A) Gene expression files from microarray analysis were created and uploaded onto the Gene Pattern server (Garvan institute). Data was normalised and log transformed prior to analysis. B) Suppression of hTERT and dyskerin expression was confirmed with the heat map viewer. No probe for TERC gene was present on the array. C) Limma analysis was performed to compare differentially expressed genes for suppression of each component to the matched cell lines transfected with control siRNA. Limma rank lists of the differentially expressed genes ranked up to downregulated genes based on a moderated t-statistic were generated. D) The limma ranked lists were submitted and evaluated for gene set enrichment against the curated gene set collection C2v3 within the Molecular Signatures Database (MSigDB) collection of annotated gene sets using the GSEA pre-ranked gene pattern module. An enrichment score (ES) was assigned to each gene set to reflect the degree of correlation. Gene sets that correlate with upregulated gene expression changes (Positive ES) or downregulated gene expression changes (Negative ES) following the repression of each of the telomerase components were identified. E) GSEA files were submitted to META-GSEA module and the significantly enriched gene sets with an FDR <0.1 were compared by Venn diagram analysis using the online software program VENNY. F) Pathways were identified from the significantly gene sets that correlated with the upregulated and downregulated genes following the repression of each of the telomerase components in normal, immortal and/or tumorigenic cells G) Gene sets related to selected pathways were uploaded to leading edge gene analysis tool of GSEA and crucial genes of pathways were identified.

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A. Pre-processing data B. Heat map viewer c. Limma analysis

• Download from CaArray • Confirm gene suppression • Preprocessed get file uploaded to limma GP module • Create gene expression get file • Upload to Gene Pattern (GP) ::..!:! ::;:;;;:;- ~ • Differentially expressed genes of hTERT, DKC1 and hTR i i ---- ~~~ ;.:: s~c • Normalise and log transform data I lJ ::.!!!!!!. www ooo a:o:::o:::: siRNA transfected cells compared to cells transfected with • Background subtraction !§~~~~iii~~~ iii siSc identifed MRCS I I I I I I I I I - IO . MRCShTERT I I I I I I I I I - 0:0 Dysker.1n • Genes ranked from most up to down regulated genes to MRCShTERT-TZT I I I 1•..::0::0 express1on ~ create limma ranked file MRCS ~I ~I ~I ~I ~-~:::I:. I M I M hTERT MRCShTERT I I I I I I M I M . MRC5hTERT-TZT I I M M =-.-. M I I I I expreSSIOn

G. Leading edge gene analysis D. Gene Set Enrichment Analysis (GSEA)

• Enriched gene sets related to pathways uploaded to • Entire limma ranked list was uploaded to GSEA Pre-ranked module GSEA Leading edge gene analysis tool • Compared against MSigDC2.V3 curated gene set collection • Crucial genes within specific pathways identified • Identification gene sets that correlated with upregulated genes or downregulated genes following the repression of each component in t each cell line F. Identify pathways Positive ES Upregulated No correlation • Pathways associated with gene sets correllating specifically ~ 1::~::~ DR<0 . 1 ;::1:~ DR>0 . 1 w ith the repression of each component in immortal and/or .. ,_ 1Oo J.M tumorigenic cells or normf cells identified Gene set 1 ~ _~ _Ill J WWJL ~~I!:.IIJ IIIJJJ 1 Limma, I •·~ ----...____- l I;,.• ----...,__ _

,I E. Geneset comparisons ranked hsts _.. J - - ·---·-- --~-- D 1 I (X} IJ ·· - ·- ·-___·- - ·- -- LL 'e:7 ]- Enrichment profile- Hits Ranking metric scores J GSEA results for each comparison uploaded to METAGSEA module • Correlation indicated by enrichment score (ES) • Gene sets compared using venn diagram analysis using Venny online software ·Significance defined as False Discovery Rate (FOR) <0.1 • Gene sets correlating specifically with the repression of each component • ES converted to normalised enriched score (NES) to account for in normal, immortal and/or tumorigenic cells were identifed multiple hypothesis testing

210

CHAPTER 6: RESULTS The raw data files were converted into a gene expression data set (gct file) for Gene Pattern software analysis, using the gene pattern Illumina convertor tool. The analysis was conducted under the guidance of Dr W. Kaplan and Dr M Cowley. Background subtraction was performed to remove signal due to non-specific hybridisation. The expression data in the gct file was normalised by scale normalisation to adjust for potential non-biological variation between slides (Figure 6.3 A). The median is the midpoint bar within 25th-75th percentile box that becomes aligned after normalisation (Figure 6.3 A). Scale normalisation adjusts the samples so that the expression levels have the same median expression across arrays and in order to minimise false positives [522-524]. Data was also log transformed prior to analysis.

The Gene Pattern heat map viewer module was used to confirm the repression of dyskerin and hTERT gene expression in all three cell lines subjected to dyskerin and hTERT siRNAs, after hybridisation to the arrays (Figure 6.3 B). To view the hTERT gene on the heap map viewer, the two probes for hTERT gene were collapsed using the median of the two probes to one gene expression value for hTERT. There was only one probe for dyskerin. High mRNA expression levels of dyskerin and hTERT in MRC5hTERT-TZT cells was indicated by red expression values, whereas very low hTERT expression was demonstrated in MRC5 cells, indicated by blue expression values. Repression of dyskerin and hTERT was similar for all replicate experiments. No probe for the hTR gene (TERC) was present on the array.

6.2.1.2 Identification of differentially expressed genes using Limma analysis To identify genes that were differentially expressed following repression of telomerase components, Limma analysis [525] was performed. Gene expression of normal (MRC5), immortal (MRC5hTERT) and tumorigenic MRC5hTERT-TZT cells transfected with siRNA targeting hTERT, dyskerin and hTR were compared to matched cells transfected with siSc. The non-collapsed gct file was uploaded to the limma module and multiple probes for each gene were collapsed to one value by the best probe method. Limma analysis uses a more statistically powerful moderated t- statistic based on the empirical Bayes method described by [525]. The best performing probe is defined as the probe with largest absolute t-statistic [525].

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CHAPTER 6: RESULTS Limma analysis generated a ranked list of the most up to downregulated differentially expressed genes for each comparison, based on the moderated t-statistic [525]. The number of significantly upregulated genes with a fold change (FC) >1.5 and downregulated genes with a FC<0.66 were identified. Significance was defined with a False Discovery Rate (FDR) <0.1 according to the Benjamini-Hochberg (BH) model to account for multiple hypothesis testing [528, 530] (Table 6.1).

The highest number of total differentially expressed genes was evident in all three cell types transfected with hTERT siRNA; MRC5 (3769 genes), MRC5hTERT (2481 genes) and MRC5hTERT-TZT (1781 genes). In comparison, a lower total number of differentially expressed genes were demonstrated in MRC5 (197 genes) and MRC5hTERT-TZT (111 genes) transfected with hTR siRNA, while a higher number (741) differentially expressed were identified in MRC5hTERT cells transfected with hTR siRNA. MRC5hTERT cells transfected with dyskerin siRNA also had the highest number of differentially expressed genes (1277 genes) compared to MRC5 cells (334 genes) and MRC5hTERT-TZT cells (240 genes) transfected with dyskerin siRNA (Table 6.1). The differences in number of differentially expressed genes suggest that the gene expression alterations that result from repression of the individual telomerase components in normal, immortal and/or tumorigenic cell types are different.

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Figure 6.3 Scale normalisation and suppression of hTERT and dyskerin in siRNA-transfected cells A) Expression levels were normalised by scale normalisation using the normalisation module within the Gene Pattern suite. Gene expression levels of each sample were adjusted to have the same median expression across arrays. Box and whisker plot of expression levels of samples showing the minimum value, lower hinge (25th percentile), median indicated by the midpoint bar in the box, maximum hinge (75th percentile) and maximum value. Samples are arranged from left to right Unt (n=2), siSc (n=4), shTERT (n=3), siDKC1 (n=3) and sihTR (n=3) for each MRC5, MRC5hTERT and MRC5hTERT-TZT cell line. B) Amplified cRNA samples were hybridised to Illumina Human (HT-12V.4) Bead Chips and scanned using the Illumina iScan reader. Scanned data files were converted to gct files and processed using gene pattern software. The heat map viewer module within the Gene Pattern suite was used to generate the Heat Map. Each coloured cell represents the gene expression value for each replicate sample. The highest gene expression values are represented as deep red, the lowest values in dark blue and intermediate values in lighter shades of red/pink or blue as shown by the scale. Yellow boxes highlight knockdown of dyskerin and hTERT.

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Table 6.1 Number of differentially expressed genes and gene sets enriched in normal, immortal and tumorigenic cells following the repression of telomerase components hTERT, dyskerin and hTR Limma analysis GSEA analysis Number of differentially expressed genes (FDR<0.1) Number of enriched gene sets ( FDR<0.1) siRNA Upregulated Downregulated Total Upregulated Downregulated Total (FC> 1.50) (FC< 0.66) (+NES) (-NES) sihTERT_vs_siSc 1082 2714 3796 180 662 842 MRC5 siDKC1_vs_siSc 178 156 334 671 47 718 sihTR_vs_siSc 105 92 197 406 9 415 sihTERT_vs_siSc 797 1684 2481 101 674 775 MRC5hTERT siDKC1_vs_siSc 883 394 1277 947 169 1116 sihTR_vs_siSc 40 714 754 0 1436 1436 sihTERT_vs_siSc 681 1100 1781 593 417 1010 MRC5hTERT- siDKC1_vs_siSc 46 194 31 589 240 620 TZT sihTR_vs_siSc 38 73 111 58 751 809 Notes: For Limma analysis, numbers indicate upregulated genes with a FC>1.5 and downregulated genes (FC) <.66. Significance using a False Discovery Rate (FDR) <0.1 was calculated using Benjamini-Hochberg (BH) model [528]. For GSEA analysis, numbers indicate gene sets with a positive NES, which correlated with upregulated genes and gene sets with a negative NES, which correlated with downregulated genes following the repression of each component in each cell line. A significance of FDR<0.1 was used [530]. Abbreviations: vs- in comparison to, FDR: False Discovery Rate, NES- Normalised Enrichment Score, siSc: Scrambled siRNA.

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6.2.1.3 Identification of gene sets that correlate with genes regulated by the repression of telomerase components in normal, immortal and tumorigenic cells For GSEA analysis, the Limma ranked lists of differentially expressed genes for each comparison were evaluated against the curated C2_all gene set collection, version 3 (C2.v3) within the Molecular Signatures Database (MSigDB) collection of annotated gene sets [531]. The default FDR for GSEA significance cut off is 0.25, however in this study there was a large number of gene sets up to the significance of (FDR) <0.1. A FDR of 0.05 was potentially too stringent for this study and may have overlooked potentially significant results, hence the (FDR) <0.1 was used as in [530].

Gene sets correlating with gene expression changes induced by the repression of each of the three telomerase components in normal, immortal and tumorigenic cells were identified. Gene sets that correlated with genes downregulated by the repression of each telomerase component were assigned a negative ES (downregulated gene sets) and gene sets that correlated with genes upregulated by the repression of each telomerase component were assigned a positive ES (upregulated gene sets). The downregulated and upregulated gene sets were analysed independently (Table 6.1). The ES was normalised to form a NES that accounted for multiple hypothesis testing and different gene set sizes. Within the GSEA module, appropriate normalisation of the ES is based on the scaling of the Kolmogorov-Smirnov algorithm distribution as a function of gene set sizes [530].

The differences in the number of gene sets correlating with the repression of each component in cell panel are consistent with the substantial differences observed for the number of differentially expressed genes for each comparison (Table 6.1). In normal cells with negligible hTERT expression, there were 842 gene sets that correlated with the repression of hTERT. In comparison, only 47 gene sets correlated with genes downregulated by the repression of dyskerin in normal cells and even fewer (9) gene sets correlated with genes downregulated by hTR repression in normal cells. It was notable that in immortal cells, no gene sets were identified that correlated with the repression of hTR, consistent with the low number of differentially expressed genes identified by Limma analysis in those cells. 216

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The GSEA results for each data set were collected together using the META-GSEA module of gene pattern (M. Cowley et al, unpublished), that was used in a previous study [580]. The twenty most significant gene sets (based on FDR <0.1), which correlated with the genes downregulated or upregulated by the repression of each component in normal, immortal and tumorigenic cells are shown in Figure 6.4 A-C. Variations in the gene expression alterations induced by the repression of the individual telomerase components, was evident by the opposing correlation of gene sets with genes regulated by the repression of each of the telomerase components within the cell panel (Figure 6.4 A-C). For example, gene sets related to proliferation correlated with genes downregulated by hTERT repression in all three cell types, as well as genes downregulated by the repression of dyskerin in tumorigenic cells. In contrast, these gene sets correlated with genes upregulated by hTR repression in normal cells correlated with (Figure 6.4 C).

Within the top 20 gene sets (FDR<0.1), there were also a large proportion of gene sets related to the IFN response (Figure 6.4 A-C). These gene sets illustrated non- specific effects of siRNA transfection and were evident in all three cell lines transfected with siRNA. Gene sets related to the IFN response were particularly prevalent among the top twenty gene sets correlating with hTR repression in immortal and tumorigenic cells, suggesting that transfection of hTR siRNA may be associated with more non-specific effects on the IFN response, than the siRNAs targeting hTERT or dyskerin. There were also a large number of IFN related gene sets, which correlated with genes downregulated by the repression of each telomerase component, indicating IFN response was stronger upon transfection with siSc (Figure 6.4 A-C).

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Figure 6.4 GSEA analysis showing the top twenty of upregulated and downregulated gene sets that correlated with the repression of each component in normal, immortal and tumorigenic cells The ranked Limma files for each comparison of the repression of hTERT, dyskerin and hTR to the matched cells transfected with control siRNA were submitted and evaluated against the C2_all gene set collection, version 3 (C2.v3) within the MSigDB. The top twenty most significantly upregulated and downregulated gene sets according to the highest or lowest normalised enrichment score (NES) with a significant False Discovery Rate (FDR<0.1) are shown.

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A. Gene Sets - Normal cells Normalised enrichment score (NES) FOR <0.1 -4 -3 -2 -1 0 2 3 4

sihTERT_vs_siSc ROSTY CERVICAL CANCER PROLIFERATION CLUSTER KEGG LYSOSOME - KOBAYASHI "EGFR SIGNALING""24HR DN STEARMAN TUMOR FIELD"""EFFECT UP SOTIRIOU BREAST"t:ANCER GRADE f"VS :>UP A:PPEL IM"ATINIB" RESPOfiiSE KANG DO"XORUBICThl RESIS'TJINCElJP KOBAYASHI EGFILVCAN DEGRADATION GRAHAM CML DIVIDING VS NORMAL ""QUIE"SCENT UP KEGG"" ECM RECEPTOR" INTERPCTION GRAHAM NORMAL- QUIESCEliiT VS NORM111.. DIVIDINGL)N DAVICIONI MOLECULAR ARM S"" VS ERMS DN -- REPCTOMEI:ELL CYCLE MITC1TIC - NJIKAJI M:A M:AST C"ELL LEE EARCY T CYMPHO't:YTE UP KEGG GLYCOSAMINOGLYCAN BIOSYNTHESIS CHONDROinN SULFATE PU.TANA BR"CJ\2 PCC NETWORK - CHIARADONNA NEOPLASTIC TRANSFORMATIONlUP MAHAOEVAN RESPONSE TO MP47(f'"UP SCHUETZ BREAST CANCER OlJCTAL INVASIVE"" UP - SANA TNF ~IGfiiALINGlJP - -wANG S"MARCE1 iARGETS"lJP RAD/lE.VA RESP"ONSE TO IFNA1UP NEWMJW ERCCSIARGETSI)N EINAV INTERFEROfil SIGNATURE lfl CANCER CHARAFE BREAST CANCER LOMINAL "VS BASAL-ON - ""XU AKT1 T ARcrETS SHR CHIARADONNA ~OPLASTIC TRANSFORMATRlN- CDC25l)N DAUER- STAT:J TARGETS DN YPD TEMPORAL RESPONSE T

CHAPTER 6: RESULTS

B. Gene Sets -Immortal cells Normalised enrichment score (NES) FOR <0. 1

-5 -4 -3 -2 -1 0 2 3 sihTERT_vs_siSc KOBAYASHI EGFR SIGNALING 24HR DN YAD TEMPORAL RESPONSE TO PROGESTERONE CLUSTER 16 ROSTY CERVICAL CANCER" PRODFERATION""CLUSTER - - KEGG tCM RECEPTOR Tfo.ITERACTION "SOTIRIOU BREAST CANCER GRADE l VS 3 UP -- KEGt> LYSOSOME BROWJiiE INTERFERON m:SPONSIVE GEfJES REACTOME CELLEXTRACELLUL.AR MATRIX INTERACTIONS TAKEDA TARGETS OF NUP98 110XA9 FUSION"" 3D UP - VERRECCHIA RESPONSE TO TGFB1 CS - CROONQUISI IL6 D"EPRIVATTO"DN BROWNE 11CMV INFECTIOfJ 18HR LlN SANA RESPONSE TO IFNG- UP KEGG "DILATED CARDIOMYOPATHY PUJAiilA XPRSS lfJT ffi:TWO"RK FARMER ~EAST CJINCER CLUSTER 5 WINNEPENNINCKX MELAiiiOMA METASTASIS UP - ONDER "CDH1 TARGETS 3 0P GRAHAM NORMAL QUIESCENT VS NORMAL DIVIDING- ON ONDER- CDH11ARGETS- 2- UP - -KANG DOXORliBICIN RESISTANCElJP KEGG HYPERTROPHIC CAADIOMYOPATHY""ffi:M - BENPORATI1 PROLIFERAlTON - - GU PDEF TARGETS UP DAUER ST.6:t3 TARGETS DN PAPASPYRIDONOS UNSTABLE ATEROSCLEROTIC PL.AQUE""bN REACTOME CELL CVCLE MITOTIC MCMURRAY""TP53 HRAS" COOPERATION RESPONS!:UP PUJANA tiRCAT PCC ffi:TWORK VERRECCHIA EARLY RESP

CHAPTER 6: RESULTS

C. Gene sets- Tumorigenic cells Normalised enrichment score (NES) FOR <0.1

-3 -2 -1 0 2 3 4

sihTERT_ vs_siSc ROSTY CERVICAL CANCER PROLIFERATION CLUSTER MISSI.I!GLIA REGULATED BY METHYLATION UP SOTIRIOU BREAST CANCER GRADE i VS 3 UP - - IASU IL6 SIGNAONG"l.JP WINNEPENNINCKX MELAiiiOMA M'ETASTASIS UP ONDER COH1 TARGETS 3-UP GRAHANI NORMAL QUIESCE'JIIT VS NORI'IIAL DIVIDING- ON COWLING 'MYCN TARGETS - WHITEFORD PED'f.6.TRIC CANCER MARKE'RS HESS TARGETS OF HOXA!l AND'"'MEIS1 ON PUJAN"A BRCA CENTERED "NETWORK STEARMAR' LUNG CIINCER EARLY VS LAT~DN 'BENPORATH PROOFERATION KINSEY TJIRGETS OF EWSR1 FLII FOSION'"ON GRAHAM CML DIVIDING VS NORMAL QUIESCENT UP - - l.IAN LfPA TJIRGETS""6M - - REPCTOI'IIE CELL ~YCLE MITOIIC WAMUNYOKOLI OVARINI CNICER LMPIJN CROONQUIST NRAS SIGNJ!LING ON WAMUNYOKOLI OVARIJj$1 CANCER GRADES 1 2lJN KANG DOXORUSlCIN RESISTANC~UP STEARMAN TUMOR FIELD EFFECIUP - MANJII..O HYPOXIA'"DN CHIARADONNA NEOPLASTIC TRJINSFOR'MATIOfl CDC25'"0N FERREIRA EWINGS SARCOMA UNSTABLE VS STABLE"'UP - - KONDO EZH'2 TARGETS "MISSI..aGOA REGULATED BY METHYLATIONL)N VERHAAK ANIL WITH NPM'1 MUTATED UP - CA'ANG CYCLING GENES ROTEL[A RES'PONS'E TO HGF"l.JP LEE_EARLY_T_LYMPHOCYTE_UP - LE_SKI_TARGETs:::uP siDKC1_vs_siSc PUJANA XPRSS INT NETWORK SENESE HDP£:1 AND HDP£:2 TARGETS ON REPCTOM~CELL CYCCE MITOTIC REN ALVEOLAR "RHA8DOMYO'SARCOMA-DN PUJANA 'BRCA2' PCC NETWORK - - VALK AML WITH EVI1 SOTIRIOU BREAST CANt:ER GRADE- 1 VS 3 UP BARRIER COLON CANCER RE'CUR"RENCE ON REPCTOME MITOTIC M M Gf' PffAS'ES SA B CELL RECEPTOR COMPLEXES SHEDDEN LUNG CJINCER POO"R SURVIVAL /J6 - - COWLING MYCN TARGETS KOBAYASAI EGFR SIGNALTNG 24HR 'ON SEITZ NEOPLASTIC TRANSFORMATION BY 'liP DEC'ETION UP MITSIADES~ESPONSE TO APLIDIN"'DN - - WOTTOfiJ RONJ('TARGETS"l.JP REPCTOME MITOTIC 'PROMETAPHASE REPCTOME "XENOBIOTICS TARTE PLASMA CELL VS PLASMABLAST ON TAKEDA TARGETS OF NUP98 HOXA9 FOSION 100 ON I{ANG DOXORUBICIN RESISTANC~UP - - - SJINA TNF SIGNAONG'"ON ROSTY CERVICAL CAA'CER PROLIFERATION CLUSTER KANG IMMORTJII..IZE'D BY TERIUP - WONG EMBRYONIC STEM CELL CORE NEWMAN ERCC61ARGETS'"DN - BENPORATH PROLIFERATION BERENJENO TRANSFORMED- BY RHOA- ON REPCTOME PROCESSING OF CAPPED INTRON CONTAII'IING PRE MRNA HUPER BREAST BASAL VS' LUMINAL- ON - MISSI..aGLIA REGULATED BY METHYLATION ON COULOUARN TEMPORAL-TGFBl' SIGNATURE'"DN Ll WILfiiS TUMOR VS FETAL KIDNEY 1 DN L'EE NEURAL '"CREST- STEM CELC UP WINNE'PENNINCKX ME[ANOMA METASTASTS-UP GAUSSMANN MLL AF4 "'FUSION"'TARGETS F""UP CROONQUIST NRAS SIGNALINGL)N PONSE TO OXIDIZE'D PAOSPHODPIDS GREY'"UP RE.PCTO'ME_G'CS_TRANSiffON -- WAfiG_SMARCE1_TAR'GETS:::UP sihTR_vs_siSc MILl PSEUDOPODIA HAPTOTAXIS UP - SHEN SMARC'A2 TARGETS- UP OPCOSTA UV RESPONSE VIA ERCC3- COMMON"'DN - - CHEN 'AOX115 TARGETS 9HR"l.JP REPCTOME PROCESSING OF CAPPED INTRO'fl CONIAINING PRE MRNA - - - MITSOOES RESPONSE TO APLIDlN ON ZHANG BREAST- CANCER PR'OGENITORS"'UP - - RAMAOiO STEMNESS"l.JP SCHLOSSER MYC TARGETS REPRESSED BY SERUM REPCTOME ELONGATION AND 'PROCESSING OF CAPPED TRANSCRIPTS - - - REAI:TOME GENE EXPRESSION REPCTOME MRNA SPLICING REPCTOME METABOLISI'II OF RNA MARTINEZ RESPONSE"'TO TRABECTEDIN ON REPCTOME FORMATION AND M..UURATION OF fiiRNA TRANSCRIPT REPCTOME TRANSPORT OF MATURE MRNADERIVED FR'OM JIN INTRON CON~NING-TRANSCRIPT ------GARY COS "TARGETS ON BOY.AIJLT LIVER CANCER"'SUSt:LASS G:f'UP DAZARD RESPONSE" TO UV NA'EI\DN HAMAI_APOPTOSISYIA:rRAIL:::UP 221

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6.2.2 Identification of pathways associated with the repression of each telomerase components common to normal, immortal and tumorigenic cells To delineate the pathways associated with the repression of each telomerase components within the different cell lines, gene sets correlating with the repression of each telomerase component were compared by Venn diagram analysis. Gene sets that correlated with genes regulated by the repression of the telomerase components in all three cell types were identified. Cell line specific gene sets that correlated with genes regulated by the repression of the telomerase components in the different cell lines were also identified (Figure 6.5 A).

The gene sets that were common to all three cell types are indicative of the response of cells to siRNA transfection and/or the repression of the different components irrespective of whether they are telomerase positive or negative transformed or normal cells (Figure 6.5 A). The top twenty common gene sets are shown in Table 6.2 and further 80 gene sets with FDR< 0.1 in Appendix Table A1. Gene sets related cell cycle, proliferation and siRNA metabolism and growth factor signalling including EGFR, TGF-β signalling, correlated with genes regulated by the repression of hTERT in all three cell types (Table 6.2). As the normal MRC5 cells were previously demonstrated to have no detectable hTERT expression, the identification of specific pathways that correlated with genes downregulated and upregulated by hTERT repression in all three cell types upon hTERT was notable. The possibility that normal cells express very low levels of endogenous hTERT, cannot be excluded. Low transient levels of hTERT in other normal fibroblast strains, including BJ foreskin fibroblasts or WI38 fibroblasts has been demonstrated [126]. With consideration to that possibly, it is conceivable that specific gene expression changes identified in all three cell types following the repression of hTERT may indicate pathways altered by the repression of hTERT, but that are insufficient to induce the proliferation arrest. This possibility was suggested by finding that the gene sets Kobayashi_EGFR_signalling_24hr_ dn and Kobayashi_EGFR_signalling_24hr_up, comprised of genes downregulated or upregulated in non-small cell lung cancer cells treated with EGFR inhibitor [581], correlated with genes that were downregulated and upregulated by hTERT repression respectively, in all three cell types. These 222

CHAPTER 6: RESULTS results demonstrate that gene expression alterations of the EGFR pathway occurred in all three cell types upon the repression of hTERT, but inhibition of EGFR signalling appears to be insufficient to induce the proliferation arrest.

Also in consideration of the possibility the transient low level of hTERT may be expressed in normal cells, it was that also found that the gene set Ren_alveolar_rhabdomyosarcoma_dn, comprised of genes commonly downregulated in alveolar rhabdomyosarcoma and a mouse model of this disease [582], correlated with genes upregulated by the repression of hTERT, as well as dyskerin repression, in all three cell types. Genes upregulated by hTERT repression also correlated with the gene set Davicioni_molecular_arms_vs_erms_dn, comprised of genes downregulated in alveolar rhabdomyosarcoma compared to embryonal rhabdomyosarcoma [583]. The upregulation of the genes by the repression of hTERT or dyskerin, which are commonly downregulated in alveolar rhabdomyosarcoma, indicates hTERT or dyskerin may be important for the regulation of genes involved in the pathogenesis of rhabdomyosarcoma.

In comparison to the repression of hTERT, fewer genes sets correlated with the genes regulated by dyskerin or hTR repression in all three cell lines (Figure 6.5A middle and right). Indeed, no gene sets were identified with FDR<0.1 that correlated with genes downregulated by the repression of dyskerin in all three cell lines (Figure 6.5 A, middle). The majority of the 18 gene sets that correlated with upregulated genes upon dyskerin repression were related to the IFN response or growth factor signalling (Table 6.2). No gene sets were identified that correlated with genes upregulated by hTR repression in all three cell lines. This was due to the lack of gene sets correlating with genes upregulated by the repression of hTR in immortal cells (Figure 6.5 A right). Only five gene sets were found to correlate with genes downregulated by hTR repression in all three cell lines, which were also associated with IFN response or growth factor signalling (Table 6.2).

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Figure 6.5 Gene sets correlating with the repression of hTERT, dyskerin or hTR in normal, immortal and tumorigenic cells The GSEA results were collected together with Meta-GSEA using the MetaGSEA module of gene pattern server and compared Venn diagram analysis using VENNY online software. A) Venn diagrams of gene sets that correlated with the repression of hTERT (left), dyskerin (middle) and hTR (right) in normal, immortal and/or tumorigenic cells. Gene sets that correlated with the repression of each telomerase component in normal (pink), immortal (yellow), tumorigenic (green) cells and gene sets common to immortal and tumorigenic (light green) cells were identified. B) Venn diagrams showing the number of gene sets that correlated with the repression of hTERT (blue), dyskerin (purple) and hTR (white) specifically in normal cells only, immortal cells only, tumorigenic cells only or immortal and tumorigenic cells. Gene sets were filtered separately according to downregulated (upper panel) and upregulated (lower panel) normalised enrichment scores (NES) and a significant FDR of less than 0.1 was used as a cut-off.

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A sihTERT si0KC1 sihTR

"'0 Q) -ctl :::J C') ....Q) c.. ) ::J FOR< 0.1 Q Normal ( Immortal Q Tumorigenic

B Normal cells Immortal cells Tumorigenic cells Immortal and only only only Tumorigenic cells only "'0 Q) -ctl :::J C') ....Q) c $ 0 0

"'0 Q) ~- :::J C') ....Q) c ::J

Q sihTERT 0 si0KC1 0 sihTR FOR< 0.1

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Table 6.2 Gene sets correlating with gene expression changes induced by repression of each telomerase component that were common in normal, immortal and tumorigenic cells (Top 20) Gene sets correlating with repression of hTERT Tumorigenic Immortal Normal Common to normal, immortal and tumorigenic cells (Negative NES n=20/335) Pathways Gene set size NES FDR NES FDR NES FDR KOBAYASHI_EGFR_SIGNALING_24HR_DN T1 249 -3.33 0.00 -3.44 0.00 -2.91 0.00 CROONQUIST_IL6_DEPRIVATION_DN 78 -3.27 0.00 -3.12 0.00 -2.83 0.00 PUJANA_BRCA2_PCC_NETWORK 417 -3.26 0.00 -2.99 0.00 -2.72 0.00 PUJANA_XPRSS_INT_NETWORK 166 -3.24 0.00 -3.11 0.00 -2.81 0.00 GRAHAM_NORMAL_QUIESCENT_VS_NORMAL_DIVIDING_DN T2 87 -3.18 0.00 -3.10 0.00 -2.75 0.00 PUJANA_BRCA_CENTERED_NETWORK 93 -3.13 0.00 -2.92 0.00 -2.64 0.00 BENPORATH_PROLIFERATION T2 144 -3.12 0.00 -3.09 0.00 -2.68 0.00 GRAHAM_CML_DIVIDING_VS_NORMAL_QUIESCENT_UP 182 -3.12 0.00 -2.98 0.00 -2.75 0.00 REACTOME_CELL_CYCLE_MITOTIC T2 301 -3.11 0.00 -3.03 0.00 -2.75 0.00 CROONQUIST_NRAS_SIGNALING_DN T2 61 -3.10 0.00 -2.92 0.00 -2.62 0.00 KANG_DOXORUBICIN_RESISTANCE_UP 54 -3.09 0.00 -3.09 0.00 -2.86 0.00 MANALO_HYPOXIA_DN 284 -3.08 0.00 -2.91 0.00 -2.59 0.00 FERREIRA_EWINGS_SARCOMA_UNSTABLE_VS_STABLE_UP 147 -3.06 0.00 -2.96 0.00 -2.60 0.00 MISSIAGLIA_REGULATED_BY_METHYLATION_DN 97 -3.05 0.00 -2.94 0.00 -2.68 0.00 CHANG_CYCLING_GENES T2 49 -3.05 0.00 -2.93 0.00 -2.47 0.00 LEE_EARLY_T_LYMPHOCYTE_UP 82 -3.04 0.00 -2.79 0.00 -2.72 0.00 REACTOME_MITOTIC_M_M_G1_PHASES T2 157 -3.01 0.00 -2.96 0.00 -2.70 0.00 MARKEY_RB1_ACUTE_LOF_DN T2 205 -3.01 0.00 -2.80 0.00 -2.67 0.00 PUJANA_BREAST_CANCER_WITH_BRCA1_MUTATED_UP 54 -2.99 0.00 -2.81 0.00 -2.61 0.00 Common to normal, immortal and tumorigenic cells (Postive NES n=20/37) Pathways Size NES FDR NES FDR NES FDR CHIARADONNA_NEOPLASTIC_TRANSFORMATION_CDC25_DN T2 139 2.32 0.00 1.69 0.08 1.96 0.02 KEGG_LYSOSOME 121 2.63 0.00 2.19 0.00 2.55 0.00 KOBAYASHI_EGFR_SIGNALING_24HR_UP T1 98 2.49 0.00 1.77 0.05 2.36 0.00 ONDER_CDH1_TARGETS_3_UP 16 2.42 0.00 2.06 0.01 1.69 0.06 REN_ALVEOLAR_RHABDOMYOSARCOMA_DN T4 407 2.62 0.00 1.91 0.03 1.64 0.08 HENDRICKS_SMARCA4_TARGETS_UP 47 2.27 0.00 1.81 0.05 1.72 0.05 HUANG_FOXA2_TARGETS_DN 36 2.25 0.00 1.88 0.03 2.06 0.01 WANG_SMARCE1_TARGETS_UP 159 2.19 0.00 1.83 0.04 1.59 0.10 ONDER_CDH1_TARGETS_1_UP 135 2.17 0.00 1.67 0.08 1.99 0.01 BERENJENO_TRANSFORMED_BY_RHOA_DN 354 2.14 0.00 1.83 0.04 1.69 0.06 DAVICIONI_MOLECULAR_ARMS_VS_ERMS_DN T4 172 2.04 0.00 1.77 0.06 2.14 0.00 COULOUARN_TEMPORAL_TGFB1_SIGNATURE_DN T1 118 2.01 0.00 1.73 0.06 1.65 0.08 LIU_PROSTATE_CANCER_DN 461 2.02 0.00 1.90 0.03 1.80 0.04 MILI_PSEUDOPODIA_CHEMOTAXIS_DN 431 2.01 0.00 1.89 0.03 1.81 0.04 KEGG_GLYCOSAMINOGLYCAN_BIOSYNTHESIS_CHONDROITIN_SULFATE 22 2.02 0.00 1.98 0.02 2.09 0.00 VERRECCHIA_RESPONSE_TO_TGFB1_C5 T1 20 2.00 0.00 2.17 0.01 1.80 0.04 MCMURRAY_TP53_HRAS_COOPERATION_RESPONSE_UP 25 1.96 0.01 1.98 0.02 2.07 0.01 Notes: Gene sets corresponding to pathways labelled R1-4, D1-4, T1-4 are indicated in Table 6.7. NES Normalised enrichment scores, FDR- False Discovery Rate. 226

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Gene sets correlating with repression of dyskerin Tumorigenic Immortal Normal Common to normal, immortal and tumorigenic cells (Negative NES n=0) Pathways Gene set size NES FDR NES FDR NES FDR Common to normal, immortal and tumorigenic cells (Postive NES n=18) Pathways Gene set size NES FDR NES FDR NES FDR SENESE_HDAC1_AND_HDAC2_TARGETS_DN 223 2.26 0.01 2.16 0.00 2.53 0.00 REN_ALVEOLAR_RHABDOMYOSARCOMA_DN D4 407 1.98 0.07 2.47 0.00 2.88 0.00 VALK_AML_WITH_EVI1 24 1.79 0.08 1.63 0.02 1.64 0.04 SA_B_CELL_RECEPTOR_COMPLEXES 24 1.79 0.08 2.05 0.00 2.07 0.00 COWLING_MYCN_TARGETS D2 41 1.80 0.08 1.71 0.01 1.90 0.01 SANA_TNF_SIGNALING_DN D1 81 1.81 0.09 1.66 0.02 1.91 0.01 KANG_IMMORTALIZED_BY_TERT_UP 88 1.80 0.09 1.62 0.03 1.92 0.01 NEWMAN_ERCC6_TARGETS_DN 36 2.04 0.09 1.80 0.01 2.33 0.00 BERENJENO_TRANSFORMED_BY_RHOA_DN D1 354 1.77 0.09 2.10 0.00 2.25 0.00 COULOUARN_TEMPORAL_TGFB1_SIGNATURE_DN D1 118 1.77 0.09 1.94 0.00 2.01 0.00 LEE_NEURAL_CREST_STEM_CELL_UP 146 1.88 0.09 1.53 0.05 2.10 0.00 WANG_SMARCE1_TARGETS_UP 159 1.99 0.09 1.78 0.01 2.33 0.00 DACOSTA_ERCC3_ALLELE_XPCS_VS_TTD_DN 28 1.81 0.09 1.72 0.01 2.05 0.00 BROWNE_HCMV_INFECTION_20HR_DN D3 109 1.89 0.10 1.99 0.00 1.89 0.01 BROWNE_HCMV_INFECTION_16HR_DN D3 84 1.81 0.10 2.06 0.00 1.61 0.05 CHIARADONNA_NEOPLASTIC_TRANSFORMATION_CDC25_DN D2 139 1.85 0.10 1.94 0.00 2.26 0.00 KEGG_LYSOSOME 121 1.81 0.10 2.13 0.00 2.80 0.00 ONDER_CDH1_TARGETS_2_UP 254 1.89 0.10 2.13 0.00 2.25 0.00

Gene sets correlating with repression of hTR Tumorigenic Immortal Normal Common to normal, immortal and tumorigenic cells (Negative NES n=5) Pathways Gene set size NES FDR NES FDR NES FDR NICK_RESPONSE_TO_PROC_TREATMENT_DN R3 27 -1.95 0.00 -2.06 0.00 -2.14 0.01 BERNARD_PPAPDC1B_TARGETS_UP 35 -1.93 0.00 -1.95 0.00 -2.04 0.02 BIOCARTA_EIF4_PATHWAY R2 23 -1.89 0.00 -1.55 0.04 -1.94 0.05 GAZDA_DIAMOND_BLACKFAN_ANEMIA_MYELOID_UP 29 -1.66 0.02 -1.91 0.00 -1.99 0.03 BIOCARTA_AKT_PATHWAY R2 22 -1.63 0.03 -1.96 0.00 -1.87 0.09 Common to normal, immortal and tumorigenic cells (Postive NES n=0) Pathways Gene set size NES FDR NES FDR NES FDR Notes: Gene sets corresponding to pathways labelled R1-4, D1-4, T1-4 are indicated in Table 6.7. NES Normalised enrichment scores, FDR- False Discovery Rate.

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6.2.3 Identification of pathways associated with the repression of specific telomerase components The pathways associated with the gene sets that correlated with the repression of each specific telomerase component in the different cell lines were indicative of the cell line specific responses to the repression of the individual telomerase components (Figure 6.5 A). To identify gene specific responses to the repression of telomerase components, the gene sets that correlated the repression of each telomerase component in normal cells only, immortal cells only, tumorigenic cells only, or both immortal and tumorigenic cells were compared and extracted by Venn diagram analysis (Figure 6.5 B). The gene sets extracted from Venn diagram analyses distinguished pathways underlying the repression of hTERT, hTR, dyskerin in normal, immortal and/or tumorigenic cells. The top twenty enriched gene sets (based on FDR<0.1) from these analyses are shown in Tables 6.3-6.6 and an overview of the associated pathways are shown in Table 6.7. Additional enriched gene sets up to 100 gene sets (based on FDR<0.1) for each comparison are shown in the Appendix tables A2-5.

6.2.3.1 Pathways associated with the repression of hTR in normal, immortal and/or tumorigenic cells Repression of hTR had no effect on the proliferation of any of the cells analysed in this study. Nevertheless, there were a large number of gene sets that correlated with genes regulated by the repression of hTR specifically in the different cell lines and there was very little overlap between these gene sets and those that correlated with genes regulated by the repression of hTERT or dyskerin (Figure 6.7 B). The top twenty gene sets (based on FDR< 0.1) that correlated specifically with genes regulated by hTR repression are shown in (Table 6.3) and up to the top 100 gene sets are listed in the Appendix Table A2. The gene sets following the repression of hTR in normal, immortal and/or tumorigenic cells, give an indication of gene expression alterations that occur directly upon targeting of hTR in those cell types.

The only gene set that correlated with genes downregulated by hTR repression in normal cells was the reactome gene set related to the p53-dependent DNA damage 228

CHAPTER 6: RESULTS pathway [531] (Table 6.3). However, three hundred and thirty gene sets correlated with genes upregulated by hTR repression specifically in normal cells. The pathways associated with these gene sets were related to cell proliferation, growth factor signalling and cell cycle regulation and DNA replication (Table 6.3) (Appendicx Table A.2). Additionally, genes upregulated by the repression of hTR in normal cells correlated with genes conferring resistance to doxorubicin (Kang_doxorubicin_resistance_up) (Table 6.3) [584] and genes downregulated by the NRas inhibitor (Blum_response_to_salirasib_ dn) [585] (Table 6.3).

Six hundred and five gene sets correlated with genes downregulated by hTR repression in immortal cells. Various gene sets related to the IFN response, protein degradation and growth factor signalling also correlated with genes regulated by hTR repression in immortal cells (Table 6.3). Notably, genes downregulated by hTR repression in immortal cells correlated with the reactome gene set Reactome_p53_independent_DNA_damage_response [531] (Table 6.3). These findings highlight a difference in hTR gene regulation of the DNA damage response between normal and immortal cells, as genes downregulated by hTR repression in normal cells, correlated with a p53-dependent DNA damage response. Additionally in tumorigenic cells, gene sets related to differentiation correlated with genes regulated by hTR repression (Table 6.3), while a large number of gene sets related to mRNA and rRNA processing and splicing were found to correlate with genes downregulated by hTR repression in immortal and tumorigenic cells (Table 6.3).

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Table 6.3 Gene sets correlating with gene expression changes induced by repression of hTR in normal, immortal and/or tumorigenic cells (Top 20)

Gene sets correlating with the repression of hTR Tumorigenic Immortal Normal Unique to normal cells (Negative NES n=1 gene sets) Pathway Gene set size NES FDR NES FDR NES FDR AMUNDSON_DNA_DAMAGE_RESPONSE_TP53 R5 16 -1.13 0.43 -1.23 0.22 -2.14 0.01 Unique to normal cells (Positive NES n=20/33 0 gene sets) Pathway Gene set size NES FDR NES FDR NES FDR BENPORATH_PROLIFERATION R6 144 -1.39 0.10 -1.54 0.05 2.48 0.00 BLUM_RESPONSE_TO_SALIRASIB_DN R7 336 -1.45 0.07 -1.62 0.03 2.44 0.00 CHANG_CYCLING_GENES R6 49 0.99 0.86 1.31 0.31 2.82 0.00 CHIANG_LIVER_CANCER_SUBCLASS_PROLIFERATION_UP R6 136 -0.88 0.73 -1.64 0.03 2.44 0.00 CROONQUIST_IL6_DEPRIVATION_DN 78 1.24 0.70 -1.43 0.10 2.86 0.00 CROONQUIST_NRAS_SIGNALING_DN 61 1.26 0.73 -1.50 0.07 2.86 0.00 CROONQUIST_NRAS_VS_STROMAL_STIMULATION_DN R1 80 -0.92 0.68 -1.08 0.50 2.52 0.00 FUJII_YBX1_TARGETS_DN 139 -1.41 0.09 -1.69 0.02 2.54 0.00 GRAHAM_CML_DIVIDING_VS_NORMAL_QUIESCENT_UP R6 182 -1.04 0.49 -1.54 0.05 2.44 0.00 GRAHAM_NORMAL_QUIESCENT_VS_NORMAL_DIVIDING_DN R6 87 -0.85 0.78 -1.41 0.12 2.57 0.00 KANG_DOXORUBICIN_RESISTANCE_UP R7 54 0.97 0.87 -1.13 0.43 2.79 0.00 KAUFFMANN_MELANOMA_RELAPSE_UP 57 -1.31 0.15 -1.90 0.00 2.48 0.00 KOBAYASHI_EGFR_SIGNALING_24HR_DN R1 249 -1.52 0.05 -1.52 0.06 2.76 0.00 LEE_EARLY_T_LYMPHOCYTE_UP 82 1.35 0.63 -1.04 0.57 2.57 0.00 MISSIAGLIA_REGULATED_BY_METHYLATION_DN 97 -1.00 0.54 -1.33 0.18 2.70 0.00 MORI_IMMATURE_B_LYMPHOCYTE_DN 53 1.15 0.72 -1.00 0.63 2.76 0.00 MORI_LARGE_PRE_BII_LYMPHOCYTE_UP 52 0.98 0.86 -1.03 0.57 2.61 0.00 NAKAYAMA_SOFT_TISSUE_TUMORS_PCA2_UP 88 1.55 0.39 0.99 0.72 2.56 0.00 PUJANA_BRCA_CENTERED_NETWORK 93 -1.52 0.05 -1.91 0.00 2.59 0.00 PUJANA_XPRSS_INT_NETWORK 166 -1.76 0.01 -2.12 0.00 2.61 0.00 Notes: Gene sets corresponding to pathways labelled R1-11 are indicated in Table 6.7. NES Normalised enrichment scores, FDR-False Discovery Rate.

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Gene sets correlating with the repression of hTR Tumorigenic Immortal Normal Unique to immortal cells (Negative NES n= 20/605 gene sets) Pathway Gene set size NES FDR NES FDR NES FDR BROWNE_INTERFERON_RESPONSIVE_GENES R3 68 2.69 0.00 -3.27 0.00 -1.29 0.41 DAUER_STAT3_TARGETS_DN 38 3.08 0.00 -2.96 0.00 -0.87 0.82 DER_IFN_BETA_RESPONSE_UP R3 82 -1.12 0.43 -2.76 0.00 0.86 0.79 EINAV_INTERFERON_SIGNATURE_IN_CANCER R3 27 2.69 0.00 -2.88 0.00 -1.21 0.46 HAHTOLA_MYCOSIS_FUNGOIDES_CD4_UP R3 64 -1.23 0.29 -2.29 0.00 0.86 0.79 MOSERLE_IFNA_RESPONSE R3 30 3.33 0.00 -3.00 0.00 1.19 0.32 NAGASHIMA_EGF_SIGNALING_UP R1 57 -1.06 0.54 -2.25 0.00 1.24 0.27 NAGASHIMA_NRG1_SIGNALING_UP R1 171 -1.35 0.17 -2.31 0.00 1.24 0.26 PELLICCIOTTA_HDAC_IN_ANTIGEN_PRESENTATION_DN 49 -1.40 0.12 -2.50 0.00 1.03 0.51 RADAEVA_RESPONSE_TO_IFNA1_UP R3 32 2.00 0.01 -2.73 0.00 -1.14 0.49 REACTOME_APOPTOSIS 128 -1.26 0.25 -2.23 0.00 -1.02 0.61 REACTOME_P53_INDEPENDENT_DNA_DAMAGE_RESPONSE R5 43 -1.41 0.12 -2.27 0.00 -0.74 0.94 REACTOME_REGULATION_OF_ORNITHINE_DECARBOXYLASE 47 -1.40 0.12 -2.29 0.00 -0.85 0.84 REACTOME_SCF_BETA_TRCP_MEDIATED_DEGRADATION_OF_EMI1 R6 48 -1.29 0.21 -2.24 0.00 -0.78 0.91 REACTOME_SCF_SKP2_MEDIATED_DEGRADATION_OF_P27_P21 R6 52 -1.40 0.12 -2.35 0.00 1.01 0.55 REACTOME_VIF_MEDIATED_DEGRADATION_OF_APOBEC3G R8 47 -1.43 0.10 -2.33 0.00 -0.85 0.84 TAKAO_RESPONSE_TO_UVB_RADIATION_DN R5 90 -1.54 0.05 -2.24 0.00 -1.12 0.51 XU_AKT1_TARGETS_6HR 24 1.82 0.04 -2.37 0.00 0.96 0.64 KOKKINAKIS_METHIONINE_DEPRIVATION_48HR_UP R1 128 -1.21 0.31 -2.17 0.00 1.34 0.20 Unique to immortal cells (Positive NES n=0 gene sets) Pathway Gene set size NES FDR NES FDR NES FDR Notes: Gene sets corresponding to pathways labelled R1-11 are indicated in Table 6.7. NES Normalised enrichment scores, FDR-False Discovery Rate.

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Gene sets correlating with the repression of hTR cont. Tumorigenic Immortal Normal Unique to tumorigenic cells (Negative NES n=20/59 gene sets) Pathway Gene set size NES FDR NES FDR NES FDR IVANOVA_HEMATOPOIESIS_EARLY_PROGENITOR R9 101 -1.87 0.00 -1.31 0.15 1.63 0.05 PRAMOONJAGO_SOX4_TARGETS_DN R9 50 -1.77 0.01 -1.11 0.38 -1.24 0.46 BONOME_OVARIAN_CANCER_POOR_SURVIVAL_UP 31 -1.77 0.01 -0.95 0.63 1.13 0.38 DORSAM_HOXA9_TARGETS_UP R9 32 -1.74 0.01 -1.24 0.21 1.25 0.26 REACTOME_DUAL_INCISION_REACTION_IN_GG_NER 20 -1.72 0.02 -1.27 0.19 -1.05 0.59 LOPEZ_MBD_TARGETS_IMPRINTED_AND_X_LINKED 16 -1.72 0.02 -1.09 0.40 -0.80 0.89 REACTOME_PLATELET_AGGREGATION_PLUG_FORMATION 26 -1.71 0.02 -1.00 0.55 0.71 0.94 MAHAJAN_RESPONSE_TO_IL1A_DN R3 62 -1.71 0.02 -0.92 0.68 1.30 0.23 SANA_RESPONSE_TO_IFNG_DN R3 78 -1.71 0.02 -1.36 0.12 1.25 0.26 HORIUCHI_WTAP_TARGETS_DN 300 -1.70 0.02 -1.32 0.14 2.13 0.00 BIOCARTA_MEF2D_PATHWAY R9 18 -1.70 0.02 -0.79 0.85 -1.46 0.36 YANG_BREAST_CANCER_ESR1_LASER_DN 37 -1.69 0.02 -1.20 0.25 1.44 0.14 BIOCARTA_NDKDYNAMIN_PATHWAY 18 -1.68 0.02 -1.35 0.12 -0.67 0.97 MATTIOLI_MULTIPLE_MYELOMA_WITH_14Q32_TRANSLOCATIONS 36 -1.67 0.02 -1.38 0.11 1.27 0.24 REACTOME_GLYCOGEN_BREAKDOWN_GLYCOGENOLYSIS 16 -1.65 0.03 1.14 0.74 -0.78 0.91 TOMLINS_PROSTATE_CANCER_UP 34 -1.64 0.03 -1.37 0.11 -1.29 0.41 WAGNER_APO2_SENSITIVITY 25 -1.64 0.03 0.94 0.88 -1.22 0.46 SIG_REGULATION_OF_THE_ACTIN_CYTOSKELETON_BY_RHO_GTPASES 35 -1.64 0.03 0.91 0.93 -0.85 0.84 LUI_THYROID_CANCER_PAX8_PPARG_UP R9 44 -1.62 0.03 -1.24 0.21 -1.14 0.49 STEIN_ESRRA_TARGETS_RESPONSIVE_TO_ESTROGEN_UP 29 -1.62 0.03 -1.08 0.42 1.97 0.01 Unique to tumorigenic cells (Positive NES n=20/ 30 gene sets) Pathway Gene set size NES FDR NES FDR NES FDR BENNETT_SYSTEMIC_LUPUS_ERYTHEMATOSUS R3 23 2.80 0.00 -2.79 0.00 -1.19 0.46 DAUER_STAT3_TARGETS_DN 38 3.08 0.00 -2.96 0.00 -0.87 0.82 EINAV_INTERFERON_SIGNATURE_IN_CANCER 27 2.69 0.00 -2.88 0.00 -1.21 0.46 FARMER_BREAST_CANCER_CLUSTER_1 R3 40 2.88 0.00 -2.84 0.00 1.13 0.39 MOSERLE_IFNA_RESPONSE 30 3.33 0.00 -3.00 0.00 1.19 0.32 ZHANG_INTERFERON_RESPONSE R3 21 3.02 0.00 -2.70 0.00 0.86 0.80 UROSEVIC_RESPONSE_TO_IMIQUIMOD R3 16 2.44 0.00 -2.58 0.00 -1.19 0.46 SANA_RESPONSE_TO_IFNG_UP 68 2.29 0.00 -3.12 0.00 0.85 0.82 BECKER_TAMOXIFEN_RESISTANCE_UP R3 38 2.22 0.00 -2.10 0.00 1.35 0.19 TAKEDA_TARGETS_OF_NUP98_HOXA9_FUSION_8D_UP R9 151 2.21 0.00 -2.85 0.00 1.27 0.24 TAKEDA_TARGETS_OF_NUP98_HOXA9_FUSION_10D_UP R9 188 2.14 0.00 -2.89 0.00 1.16 0.35 ZHAN_MULTIPLE_MYELOMA_LB_DN 41 2.00 0.01 -2.62 0.00 1.24 0.27 RADAEVA_RESPONSE_TO_IFNA1_UP 32 2.00 0.01 -2.73 0.00 -1.14 0.49 ICHIBA_GRAFT_VERSUS_HOST_DISEASE_D7_UP R3 93 1.97 0.02 -2.90 0.00 1.15 0.36 XU_HGF_TARGETS_INDUCED_BY_AKT1_6HR R1 17 1.92 0.03 -2.09 0.00 -0.80 0.89 URS_ADIPOCYTE_DIFFERENTIATION_UP R9 61 1.86 0.04 -0.96 0.61 1.32 0.21 TSAI_RESPONSE_TO_RADIATION_THERAPY 31 1.83 0.04 -2.38 0.00 1.07 0.46 XU_AKT1_TARGETS_6HR 24 1.82 0.04 -2.37 0.00 0.96 0.64 DER_IFN_ALPHA_RESPONSE_UP R3 57 1.79 0.05 -2.76 0.00 0.95 0.64 CHANG_IMMORTALIZED_BY_HPV31_DN 47 1.78 0.05 -2.30 0.00 -0.85 0.84 Notes: Gene sets corresponding to pathways labelled R1-11 are indicated in Table 6.7. NES Normalised enrichment scores, FDR-False Discovery Rate. 232

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Gene sets correlating with the repression of hTR cont. Tumorigenic Immortal Normal Unique to immortal and tumorigenic cells (Negative NES n=20/607 gene sets) Pathway Gene set size NES FDR NES FDR NES FDR CHEN_HOXA5_TARGETS_9HR_UP R9 219 -2.39 0.00 -2.74 0.00 -1.34 0.41 DACOSTA_UV_RESPONSE_VIA_ERCC3_COMMON_DN R5 415 -2.45 0.00 -2.31 0.00 -1.55 0.30 GARY_CD5_TARGETS_DN 422 -2.25 0.00 -2.41 0.00 -1.27 0.43 KEGG_SPLICEOSOME R8 126 -2.19 0.00 -2.28 0.00 -1.04 0.59 MARTINEZ_RESPONSE_TO_TRABECTEDIN_DN R11 218 -2.26 0.00 -2.38 0.00 1.14 0.37 MILI_PSEUDOPODIA_HAPTOTAXIS_UP 459 -2.50 0.00 -2.39 0.00 -1.43 0.37 RAMALHO_STEMNESS_UP 193 -2.32 0.00 -2.25 0.00 -1.46 0.35 REACTOME_ELONGATION_AND_PROCESSING_OF_CAPPED_TRANSCRIPTS R8 133 -2.28 0.00 -2.26 0.00 -1.12 0.51 REACTOME_FORMATION_AND_MATURATION_OF_MRNA_TRANSCRIPT R8 151 -2.26 0.00 -2.30 0.00 -1.22 0.45 REACTOME_GENE_EXPRESSION 423 -2.28 0.00 -2.28 0.00 -1.50 0.32 REACTOME_METABOLISM_OF_RNA R8 96 -2.27 0.00 -2.29 0.00 -1.04 0.59 REACTOME_MRNA_SPLICING R8 106 -2.27 0.00 -2.23 0.00 -0.97 0.68 REACTOME_PROCESSING_OF_CAPPED_INTRON_CONTAINING_PRE_MRNA R8 137 -2.36 0.00 -2.25 0.00 -1.09 0.55 SHEN_SMARCA2_TARGETS_UP 417 -2.49 0.00 -2.32 0.00 1.41 0.15 SCHLOSSER_MYC_TARGETS_REPRESSED_BY_SERUM R1 158 -2.30 0.00 -2.22 0.00 -1.23 0.46 DAZARD_RESPONSE_TO_UV_NHEK_DN R5 259 -2.23 0.00 -2.17 0.00 -1.13 0.50 MOREAUX_MULTIPLE_MYELOMA_BY_TACI_DN 131 -2.20 0.00 -2.15 0.00 1.11 0.41 REACTOME_TRANSPORT_OF_MATURE_MRNA_DERIVED_FROM_AN_INTRON_CONTAINING_TRANSC R8 51 -2.25 0.00 -2.09 0.00 -1.07 0.57 RIPT ZHANG_BREAST_CANCER_PROGENITORS_UP 373 -2.34 0.00 -2.09 0.00 1.74 0.03 Unique to immortal and tumorigenic (Postive NES n= 0 gene sets) Pathway Gene set size NES FDR NES FDR NES FDR Notes: Gene sets corresponding to pathways labelled R1-11 are indicated in Table 6.7. NES Normalised enrichment scores, FDR-False Discovery Rate.

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CHAPTER 6: RESULTS hTR repression also correlated with genes sets related to resistance mechanism and responses of chemotherapeutic treatment in immortal and/or tumorigenic cells. Genes downregulated by hTR repression correlated with the gene set Honma_docetaxel_resistance, comprised of gene upregulated following treatment of the anti-mitotic chemotherapeutic docetaxel in a resistant breast cancer cell line [586] (Table 6.3). In immortal and tumorigenic cells and, genes downregulated by hTR repression correlated with the gene set Martinez_response_to_trabectedin_dn, comprised of genes downregulated by treatment with the trabectadin in sarcoma cells [587] (Table 6.3).

6.2.3.2 Pathways that distinguish the response of immortal and tumorigenic cells from normal cells following the repression of hTERT or dyskerin To identify pathways that distinguish the response of immortal and tumorigenic cells from normal cells to the repression of hTERT or dyskerin, the gene sets that correlated with the repression of hTERT or dyskerin in either normal or immortal and/or tumorigenic cells were analysed separately. It was hypothesised that the gene sets correlating with hTERT or dyskerin repression in normal cells are indicative of the gene expression alterations that may be involved in mechanisms that protect them from the proliferation arrest, following the repression of hTERT or dyskerin. Similarly, the gene sets that correlated with the repression of hTERT or dyskerin in immortal and/or tumorigenic cells may have contributed to the proliferation arrest.

6.2.3.2.1 Pathways associated with gene expression alterations in normal cells To identify the pathways that were specifically altered in normal cells, the gene sets that correlated with the repression of either dyskerin or hTERT only in normal cells were extracted by Venn diagram analysis (Figure 6.5 B left). The top twenty gene sets (based on FDR< 0.1) identified in these analyses are shown in Table 6.4 and up to 100 gene sets in Appendix Table A3.

There were only two gene sets that correlated with genes downregulated by the repression of dyskerin and 205 gene sets that correlated with genes upregulated by the repression of dyskerin repression. No apparent potential protective mechanisms identified following analyses of the gene sets correlating with the repression of dyskerin in normal cells (Table 6.4). These findings indicated that the pathways 234

CHAPTER 6: RESULTS altered by the repression of dyskerin in immortal and/or tumorigenic cells are more likely to account for the specificity of the proliferation arrest in immortal and tumorigenic cells.

Since, hTERT mRNA was not detected in normal MRC5 cells, the altered pathways identified in normal cells upon hTERT repression were envisaged to be associated with non-specific effects due to metabolism of siRNA, rather than specific protective mechanisms. Genes upregulated by hTERT repression in normal cells correlated with purinergic receptor the gene set Reactome_nucleotide_like_purinergic_receptors [531]. The signalling of purines and pyrimidines function in the modulation of cell proliferation and differentiation, however purinergic receptor signalling also regulates the immune response [588] (Table 6.4). These findings may indicate that the regulation of hTERT on purinergic receptor signalling genes may therefore be non-specific effect in response to siRNA. However, if the possibility that the normal cells have low transient levels of hTERT is taken into consideration, the specific upregulation of purinergic receptor signalling genes may indicate a mechanism mediated by hTERT repression that protects the cells from the proliferation arrest.

Another protective mechanism mediated by the repression of hTERT in normal cells was suggested by the correlation of genes downregulated by hTERT repression with three reactome gene sets related to chaperon-mediated protein folding in normal cells. These included the gene sets; Reactome_prefoldin_mediated_transfer_of_ substrate_to_cct_tric, Reactome_post_chaperonin_tubulin_folding_pathway, and Reactome_chaperonin_mediated_protein_folding, comprised of genes essential for the correct folding by chaperon proteins. Chaperon-mediated folding mediates the folding of various cytoskeletal proteins, oncogenes and tumour suppressors [589]. The correct folding of crucial proteins may therefore protect normal cells from the proliferation arrest mediated by hTERT (Table 6.4 and Appendix Table A3).

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Table 6.4 Gene sets correlating with gene expression changes induced by repression of dyskerin or hTERT in normal cells (Top 20) Gene sets correlating with repression of dyskerin in normal cells Tumorigenic Immortal Normal Unique to normal cells (Negative NES n= 2 gene sets) Pathway Gene set size NES FDR NES FDR NES FDR REACTOME_BASE_EXCISION_REPAIR 18 -1.44 0.11 1.21 0.27 -1.74 0.09 YANG_BREAST_CANCER_ESR1_DN 19 -1.44 0.11 -1.28 0.24 -1.75 0.09 Unique to normal cells (Positive NES n=20/205 gene sets) Pathway Gene set size NES FDR NES FDR NES FDR KEGG_SPHINGOLIPID_METABOLISM 40 1.38 0.28 1.38 0.11 2.21 0.00 KEGG_OTHER_GLYCAN_DEGRADATION 15 1.18 0.42 1.11 0.39 2.21 0.00 APPEL_IMATINIB_RESPONSE D6 33 1.61 0.15 1.15 0.34 2.16 0.00 PEREZ_TP63_TARGETS 340 1.25 0.37 1.24 0.23 2.06 0.00 BOYAULT_LIVER_CANCER_SUBCLASS_G5_DN D5 26 1.35 0.31 -1.81 0.03 1.99 0.01 REACTOME_SIGNALING_BY_NOTCH 16 1.82 0.10 0.87 0.75 1.98 0.01 ONDER_CDH1_TARGETS_2_DN 456 1.36 0.30 -1.32 0.22 1.98 0.01 SMIRNOV_CIRCULATING_ENDOTHELIOCYTES_IN_CANCER_UP 156 1.32 0.33 1.09 0.42 1.96 0.01 LIEN_BREAST_CARCINOMA_METAPLASTIC_VS_DUCTAL_UP 80 1.25 0.37 1.25 0.21 1.95 0.01 KONDO_EZH2_TARGETS 142 1.53 0.21 1.17 0.31 1.94 0.01 GOUYER_TATI_TARGETS_DN 17 1.51 0.21 0.96 0.63 1.93 0.01 SMID_BREAST_CANCER_NORMAL_LIKE_UP 453 1.26 0.36 -1.29 0.24 1.90 0.01 ELVIDGE_HYPOXIA_UP 165 -1.26 0.25 1.25 0.22 1.90 0.01 CONCANNON_APOPTOSIS_BY_EPOXOMICIN_UP 233 -1.31 0.20 1.39 0.11 1.89 0.01 HUPER_BREAST_BASAL_VS_LUMINAL_UP 54 1.58 0.18 1.10 0.41 1.88 0.01 HOFMANN_MYELODYSPLASTIC_SYNDROM_LOW_RISK_DN 25 1.14 0.47 1.30 0.17 1.88 0.01 HAN_SATB1_TARGETS_DN 329 1.19 0.41 1.26 0.21 1.88 0.01 VECCHI_GASTRIC_CANCER_EARLY_DN 345 1.65 0.14 1.07 0.46 1.87 0.01 BOQUEST_STEM_CELL_CULTURED_VS_FRESH_DN 31 -0.61 0.99 0.76 0.89 1.87 0.01 MCBRYAN_PUBERTAL_BREAST_3_4WK_UP 185 0.93 0.74 1.24 0.23 1.87 0.01 Notes: Gene sets corresponding to pathways labelled D1-6 are indicated in Table 6.7. NES Normalised enrichment scores, FDR-False Discovery Rate.

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Gene sets correlating with repression of hTERT cont. Tumorigenic Immortal Normal Unique to normal cells (Negative NES n=20/174 gene sets) Pathway Gene set size NES FDR NES FDR NES FDR KEGG_PATHOGENIC_ESCHERICHIA_COLI_INFECTION T3 59 1.35 0.15 1.04 0.47 -1.97 0.00 GENTILE_UV_RESPONSE_CLUSTER_D4 48 -1.39 0.13 -1.32 0.18 -1.96 0.00 DING_LUNG_CANCER_EXPRESSION_BY_COPY_NUMBER 93 -1.21 0.29 -1.37 0.14 -1.93 0.00 RODRIGUES_NTN1_TARGETS_UP 17 -1.42 0.10 -1.42 0.11 -1.92 0.00 REACTOME_PREFOLDIN_MEDIATED_TRANSFER_OF_SUBSTRATE_TO_CCT_TRIC T5 28 -1.22 0.29 -1.18 0.33 -1.88 0.00 TIEN_INTESTINE_PROBIOTICS_6HR_DN 165 1.24 0.23 -1.30 0.20 -1.87 0.00 DE_YY1_TARGETS_DN 89 -1.33 0.17 -1.38 0.14 -1.84 0.00 REACTOME_POST_CHAPERONIN_TUBULIN_FOLDING_PATHWAY T5 19 -1.43 0.10 -1.25 0.24 -1.84 0.00 GINESTIER_BREAST_CANCER_ZNF217_AMPLIFIED_UP 67 -0.99 0.60 -0.93 0.66 -1.82 0.00 DACOSTA_UV_RESPONSE_VIA_ERCC3_XPCS_DN 75 -1.33 0.18 -1.16 0.35 -1.81 0.00 BIOCARTA_CHREBP2_PATHWAY 42 -1.02 0.56 -0.98 0.58 -1.79 0.00 MASRI_RESISTANCE_TO_TAMOXIFEN_AND_AROMATASE_INHIBITORS_DN T7 20 -0.97 0.64 -0.78 0.86 -1.78 0.00 SANA_RESPONSE_TO_IFNG_DN T3 78 -1.10 0.43 -1.04 0.51 -1.77 0.00 YAO_TEMPORAL_RESPONSE_TO_PROGESTERONE_CLUSTER_17 167 -1.35 0.16 -1.43 0.11 -1.76 0.00 GINESTIER_BREAST_CANCER_20Q13_AMPLIFICATION_UP 107 -1.07 0.49 -1.25 0.24 -1.76 0.00 REACTOME_GENES_INVOLVED_IN_APOPTOTIC_CLEAVAGE_OF_CELLULAR_PROTEINS 36 -1.31 0.19 -1.20 0.30 -1.74 0.01 YANAGIHARA_ESX1_TARGETS 23 -1.25 0.25 -1.31 0.19 -1.74 0.01 REACTOME_COSTIMULATION_BY_THE_CD28_FAMILY 65 -1.42 0.11 -1.43 0.11 -1.72 0.01 WU_HBX_TARGETS_2_UP 22 -1.05 0.52 -1.24 0.26 -1.72 0.01 FAELT_B_CLL_WITH_VH_REARRANGEMENTS_DN 48 -1.01 0.58 -1.16 0.35 -1.72 0.01 Unique to normal cells (Positive NES n=19 gene sets) Pathway Gene set size NES FDR NES FDR NES FDR HOFFMANN_IMMATURE_TO_MATURE_B_LYMPHOCYTE_UP 22 1.31 0.18 0.97 0.60 1.95 0.02 KRIGE_AMINO_ACID_DEPRIVATION 26 1.34 0.15 0.90 0.71 1.89 0.03 REACTOME_NUCLEOTIDE_LIKE_PURINERGIC_RECEPTORS T6 16 -1.10 0.44 -0.65 0.96 1.73 0.05 SMID_BREAST_CANCER_RELAPSE_IN_PLEURA_DN 26 0.87 0.76 1.20 0.30 1.74 0.05 NIKOLSKY_BREAST_CANCER_16P13_AMPLICON 116 0.83 0.80 1.44 0.17 1.74 0.05 WOTTON_RUNX_TARGETS_UP 18 1.17 0.29 -1.02 0.53 1.70 0.06 LUI_THYROID_CANCER_PAX8_PPARG_DN 44 1.19 0.27 1.11 0.39 1.70 0.06 KEGG_GALACTOSE_METABOLISM 26 1.28 0.20 1.17 0.32 1.69 0.06 LEE_LIVER_CANCER_ACOX1_DN 64 1.43 0.11 1.62 0.10 1.68 0.07 REACTOME_GLUCOSE_AND_OTHER_SUGAR_SLC_TRANSPORTERS 82 1.36 0.15 1.04 0.47 1.63 0.08 NIKOLSKY_BREAST_CANCER_22Q13_AMPLICON 17 -1.05 0.51 1.61 0.10 1.63 0.08 WANG_BARRETTS_ESOPHAGUS_AND_ESOPHAGUS_CANCER_UP 25 1.40 0.12 0.85 0.79 1.61 0.09 KERLEY_RESPONSE_TO_CISPLATIN_UP T7 39 1.34 0.15 1.29 0.24 1.60 0.09 ACEVEDO_LIVER_TUMOR_VS_NORMAL_ADJACENT_TISSUE_DN 267 1.27 0.21 -1.25 0.24 1.57 0.10 REACTOME_PHASE_1_FUNCTIONALIZATION 15 1.08 0.40 1.01 0.51 1.58 0.10 REACTOME_NCAM1_INTERACTIONS 44 0.78 0.87 1.38 0.19 1.57 0.10 DACOSTA_UV_RESPONSE_VIA_ERCC3_COMMON_UP 55 1.27 0.20 -0.98 0.58 1.56 0.10 MOREAUX_B_LYMPHOCYTE_MATURATION_BY_TACI_UP 71 1.22 0.25 1.53 0.13 1.56 0.10 NIKOLSKY_BREAST_CANCER_20Q12_Q13_AMPLICON 147 1.11 0.36 1.25 0.27 1.55 0.10 Notes: Gene sets corresponding to pathways labelled T1-7 are indicated in Table 6.7. NES Normalised enrichment scores, FDR-False Discovery Rate. 237

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6.2.3.2.2 Pathways associated with the proliferation arrest induced by the repression of dyskerin in immortal and/or tumorigenic cells Pathways underlying the proliferation arrest mediated by the repression of dyskerin were identified by analysis of the gene sets that correlated with the repression of dyskerin specifically in immortal or tumorigenic cells (Table 6.5). Gene sets related to energy/metabolism, metastasis and cell adhesion, proteasome and rRNA processing and the pRb/E2F pathways correlated with genes regulated by dyskerin repression in immortal and/or tumorigenic cells (Table 6.5 and Appendix Table A.4).

The gene sets related to energy/metabolism pathways included; Mootha_glycogen_metabolism, Kegg_oxidative_phosphorylation, Mootha_voxphos, Reactome_glucose_regulation_of_insulin_secretion, Reactome_regulation_of_ insulin_secretion [531]. These gene sets correlated with genes upregulated by dyskerin repression in immortal cells; however the latter three gene sets also correlated with genes downregulated by dyskerin repression in tumorigenic cells (Table 6.5). Additional energy/metabolism pathways implicated in these analyses included gene sets related to p450 cytochromes. These enzymes are involved in metabolic processes of steroid, lipid and drug metabolism and play important roles in cancer formation and treatment [590]. The p450 related gene sets included; Kegg_steroid_biosynthesis, comprised of p450 cytochrome enzymes and other enzymes involved in steroid biosynthesis [531], which correlated to genes upregulated by the repression of dyskerin in immortal and tumorigenic cells (Table 6.5). The two gene sets that correlated with upregulated genes following the repression of dyskerin in tumorigenic cells only were also shown to have upregulated p450 cytochromes in the leading edge genes. These included Reactome_xenobiotics, related to steroid and drug metabolism [531] and the gene set Wotton_runx_targets_up gene set, comprised of genes upregulated by the RUNX family of transcription factors in cancers [591].

The leading edge genes of the Wotton_runx_targets_up gene set, also included the upregulation of the extracellular surface protein neural cell adhesion molecule (NCAM1) and the tissue inhibitor of metalloproteinase-4 (TIMP4), which is a matrix metalloproteinase inhibitor. 238

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Table 6.5 Gene sets correlating with gene expression changes induced by repression of dyskerin in immortal and/or tumorigenic cells (Top 20) Gene sets correlating with repression of dyskerin Tumorigenic Immortal Normal Unique to immortal cells (Negative NES n=7 gene sets) Pathway Gene set size NES FDR NES FDR NES FDR BIOCARTA_TH1TH2_PATHWAY D3 19 -1.22 0.31 -1.78 0.03 1.02 0.50 BIOCARTA_TALL1_PATHWAY D3 15 -1.31 0.21 -1.71 0.05 -0.72 0.94 REACTOME_CLASS_A1_RHODOPSIN_LIKE_RECEPTORS D3 290 1.40 0.26 -1.67 0.06 0.87 0.76 REACTOME_G_ALPHA_I_SIGNALLING_EVENTS D3 176 0.82 0.89 -1.62 0.07 0.94 0.64 REACTOME_NA_CL_DEPENDENT_NEUROTRANSMITTER_TRANSPORTERS D3 18 0.60 0.99 -1.61 0.08 1.48 0.09 PODAR_RESPONSE_TO_ADAPHOSTIN_DN 18 -1.41 0.13 -1.57 0.09 -0.88 0.81 WU_HBX_TARGETS_2_DN 15 -1.40 0.14 -1.58 0.09 -1.09 0.52 Unique to immortal cells (Positive NES n=20/551 gene sets) Pathway Gene set size NES FDR NES FDR NES FDR CAIRO_HEPATOBLASTOMA_UP D8 202 -1.63 0.03 2.19 0.00 1.43 0.12 SHEN_SMARCA2_TARGETS_UP D1 417 -2.42 0.00 2.22 0.00 1.35 0.16 SPIELMAN_LYMPHOBLAST_EUROPEAN_VS_ASIAN_UP 469 -1.99 0.00 2.19 0.00 1.17 0.31 YAO_TEMPORAL_RESPONSE_TO_PROGESTERONE_CLUSTER_13 160 -1.80 0.01 2.23 0.00 -1.01 0.61 MAHAJAN_RESPONSE_TO_IL1A_DN D3 62 -1.28 0.24 2.14 0.00 1.28 0.20 MOOTHA_VOXPHOS D7 85 -1.79 0.01 2.16 0.00 -1.27 0.34 BORCZUK_MALIGNANT_MESOTHELIOMA_UP D8 298 -2.17 0.00 2.16 0.00 1.46 0.10 GINESTIER_BREAST_CANCER_ZNF217_AMPLIFIED_DN 316 -1.43 0.12 2.12 0.00 1.33 0.17 VERRECCHIA_EARLY_RESPONSE_TO_TGFB1 D1 51 1.26 0.36 2.09 0.00 1.14 0.34 MOOTHA_GLYCOGEN_METABOLISM D7 20 0.81 0.90 2.10 0.00 1.14 0.34 KEGG_OXIDATIVE_PHOSPHORYLATION D7 120 -1.32 0.20 2.09 0.00 1.02 0.50 SOTIRIOU_BREAST_CANCER_GRADE_1_VS_3_DN 51 -0.89 0.78 2.09 0.00 1.03 0.48 REACTOME_GLUCOSE_REGULATION_OF_INSULIN_SECRETION D7 147 -1.91 0.00 2.08 0.00 -1.55 0.18 REACTOME_REGULATION_OF_INSULIN_SECRETION D7 198 -1.70 0.02 2.08 0.00 -1.35 0.26 YAO_TEMPORAL_RESPONSE_TO_PROGESTERONE_CLUSTER_17 167 -1.71 0.02 2.07 0.00 -1.14 0.47 RICKMAN_METASTASIS_UP D8 330 -1.85 0.01 2.07 0.00 1.40 0.13 STARK_PREFRONTAL_CORTEX_22Q11_DELETION_DN 435 -1.95 0.00 2.07 0.00 -1.25 0.35 BIOCARTA_MAL_PATHWAY D1 19 1.09 0.50 2.07 0.00 1.36 0.15 BIOCARTA_SPRY_PATHWAY D1 18 1.03 0.59 2.05 0.00 1.19 0.28 REACTOME_FORMATION_OF_THE_TERNARY_COMPLEX_AND_SUBSEQUENTLY_THE_43S_COMPLEX D9 49 -1.90 0.00 2.05 0.00 0.80 0.86 Notes: Gene sets corresponding to pathways labelled D1-11 are indicated in Table 6.7. NES Normalised enrichment scores, FDR-False Discovery Rate.

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Gene sets correlating with repression of dyskerin cont. Tumorigenic Immortal Normal Unique to tumorigenic cells (Negative NES n=20/458 gene sets) Pathway Gene set size NES FDR NES FDR NES FDR REACTOME_CELL_CYCLE_MITOTIC D2 301 -2.83 0.00 1.37 0.12 -1.11 0.50 PUJANA_BRCA2_PCC_NETWORK 417 -2.81 0.00 1.16 0.32 -1.65 0.12 REACTOME_MITOTIC_M_M_G1_PHASES D2 157 -2.76 0.00 1.16 0.32 -1.13 0.48 KOBAYASHI_EGFR_SIGNALING_24HR_DN D1 249 -2.72 0.00 -1.38 0.18 -1.45 0.23 TARTE_PLASMA_CELL_VS_PLASMABLAST_DN 304 -2.66 0.00 1.39 0.11 -1.65 0.12 ROSTY_CERVICAL_CANCER_PROLIFERATION_CLUSTER D2 139 -2.65 0.00 1.04 0.50 -1.46 0.22 WONG_EMBRYONIC_STEM_CELL_CORE 331 -2.64 0.00 1.63 0.02 -1.44 0.23 WINNEPENNINCKX_MELANOMA_METASTASIS_UP 157 -2.61 0.00 1.19 0.28 -1.40 0.24 REACTOME_G1_S_TRANSITION D2,D10 100 -2.60 0.00 -1.36 0.20 -1.03 0.59 BLUM_RESPONSE_TO_SALIRASIB_DN D11 336 -2.58 0.00 1.54 0.05 -1.34 0.28 CROONQUIST_IL6_DEPRIVATION_DN D3 78 -2.58 0.00 -0.89 0.73 -1.42 0.23 REACTOME_GENE_EXPRESSION D2 423 -2.57 0.00 1.74 0.01 -1.48 0.21 REACTOME_MRNA_SPLICING D2 106 -2.57 0.00 1.42 0.09 -1.68 0.10 REACTOME_S_PHASE D2,D10 102 -2.56 0.00 -1.16 0.35 -0.84 0.85 ZHANG_BREAST_CANCER_PROGENITORS_UP 373 -2.56 0.00 1.69 0.01 -1.36 0.26 REACTOME_CELL_CYCLE_CHECKPOINTS D2, D10 108 -2.55 0.00 -1.36 0.20 -1.14 0.48 PYEON_CANCER_HEAD_AND_NECK_VS_CERVICAL_UP 180 -2.54 0.00 0.99 0.58 -1.08 0.54 GRAHAM_NORMAL_QUIESCENT_VS_NORMAL_DIVIDING_DN 87 -2.54 0.00 0.75 0.89 -1.43 0.24 REACTOME_FORMATION_AND_MATURATION_OF_MRNA_TRANSCRIPT D9 151 -2.53 0.00 1.58 0.03 -1.37 0.25 BIDUS_METASTASIS_UP D8 208 -2.49 0.00 1.79 0.01 -1.39 0.24 Unique to tumorigenic cells (Positive NES n=2 gene sets) Pathway Gene set size NES FDR NES FDR NES FDR REACTOME_XENOBIOTICS D7 15 1.87 0.09 -1.43 0.16 -1.15 0.46 WOTTON_RUNX_TARGETS_UP D7, D8 18 1.79 0.08 0.84 0.80 -0.88 0.81 Notes: Gene sets corresponding to pathways labelled D1-11 are indicated in Table 6.7. NES Normalised enrichment scores, FDR-False Discovery Rate.

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Gene sets correlating with repression of dyskerin cont. Tumorigenic Immortal Normal Unique to immortal and tumorigenic cells (Negative NES n=15 gene sets) Pathway Gene set size NES FDR NES FDR NES FDR CHANG_CYCLING_GENES D10 49 -2.36 0.00 -1.62 0.08 -1.61 0.13 VERNELL_RETINOBLASTOMA_PATHWAY_UP D10 39 -2.29 0.00 -1.85 0.02 -1.38 0.26 REACTOME_E2F_MEDIATED_REGULATION_OF_DNA_REPLICATION D10 31 -2.27 0.00 -1.72 0.04 -1.38 0.26 REACTOME_E2F_TRANSCRIPTIONAL_TARGETS_AT_G1_S D10 20 -2.15 0.00 -1.77 0.04 -1.43 0.24 BROWNE_HCMV_INFECTION_8HR_UP D3 103 -1.83 0.01 -2.13 0.00 1.12 0.36 KOKKINAKIS_METHIONINE_DEPRIVATION_96HR_DN D1 72 -1.81 0.01 -1.56 0.10 -1.32 0.29 TOOKER_GEMCITABINE_RESISTANCE_UP D11 78 -1.71 0.02 -1.56 0.10 1.11 0.37 DER_IFN_BETA_RESPONSE_UP D3 82 -1.7 0.02 -3.08 0.00 -1.26 0.34 PELLICCIOTTA_HDAC_IN_ANTIGEN_PRESENTATION_DN D9 49 -1.7 0.02 -2.12 0.00 0.94 0.63 TOOKER_RESPONSE_TO_BEXAROTENE_DN D11 78 -1.68 0.03 -1.65 0.06 1.11 0.37 MURAKAMI_UV_RESPONSE_6HR_DN 20 -1.58 0.05 -1.66 0.06 -1.42 0.24 MURAKAMI_UV_RESPONSE_24HR 18 -1.52 0.07 -1.97 0.01 -1.46 0.22 AMIT_SERUM_RESPONSE_40_MCF10A D1 33 -1.48 0.09 -1.72 0.05 -1.12 0.49 TONKS_TARGETS_OF_RUNX1_RUNX1T1_FUSION_MONOCYTE_UP D3 200 -1.46 0.1 -1.86 0.02 1.09 0.39 DUTTA_APOPTOSIS_VIA_NFKB 31 -1.46 0.1 -1.79 0.03 -1.39 0.25 Unique to immortal and tumorigenic cells (Positive NES n= 1 gene sets) Pathway Gene set size NES FDR NES FDR NES FDR KEGG_STEROID_BIOSYNTHESIS D7 17 1.83 0.09 1.89 0 1.15 0.32 Notes: Gene sets corresponding to pathways labelled D1-11 are indicated in Table 6.7. NES Normalised enrichment scores, FDR-False Discovery Rate.

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Both of these genes are implicated in metastasis [591]. Gene sets related to cell adhesion and metastasis included; Rickman_metastasis_up comprised of genes upregulated in metastatic compared to non-metastatic head and neck squamous cell carcinoma (HNSCC) samples [592], Borczuk_malignant_mesothelioma_up, comprised of genes that are upregulated in mixed subtypes compared to epithelial type malignant mesothelioma [593], and the gene set Bidus_metastasis_up, comprised of genes associated with lymph node metastasis [594] (Table 6.5). These three gene sets correlated with genes upregulated by repression of dyskerin in immortal cells, but correlated with genes downregulated by the repression of dyskerin in tumorigenic cells. The gene set Ouellet_ovarian_cancer_invasive_ vs_lmp_up, comprised of genes upregulated in invasive ovarian cancer compared to ovarian cancers of low malignant potential [595], correlated with genes upregulated by the repression of dyskerin in tumorigenic cells only.

Gene sets related to proteosome and rRNA processing included the reactome and kegg gene sets; Kegg_ribosome and Reactome_formation_of_the_ternary_complex _and_subsequently_the_43s_complex, correlated with genes downregulated by repression of dyskerin in tumorigenic cells, but genes upregulated by dyskerin repression in immortal cells [531] (Table 6.5). In addition, the reactome gene set Reactome_SNRNP_assembly correlated with genes downregulated by dyskerin repression in tumorigenic cells only (Appendix Table A.4). Genes downregulated by dyskerin repression in immortal and tumorigenic cells also correlated with the gene set Pellicciotta_HDAC_in_antigen_presentation_dn, comprised of proteosomal genes in response to the HDAC inhibition on presentation of telomerase antigen in lymphoblastoid cell line [596]. These findings provide evidence that dyskerin mediated alterations to proteasomal and rRNA processing pathways that may contribute to the proliferation arrest induced by dyskerin repression in immortal and tumorigenic cells (Table 6.5).

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From these analyses it was clear that the gene sets related to energy/metabolism, metastasis, proteasome and rRNA processing correlated with the genes regulated by dyskerin repression; however it was also evident that some of these gene sets related to these pathways correlated differently between immortal and tumorigenic cells. The reasons for these opposing correlations were not clear and further investigations of the differential gene expression changes of these pathways is required before the contribution of these pathways to the proliferation arrest can be made. The differences in correlation of these pathways between immortal and tumorigenic cells indicated that the gene sets that correlated with dyskerin repression in both immortal and tumorigenic cells, were possibly more suited to identify pathways that contributed to the proliferation arrest induced by the repression of dyskerin in immortal and tumorigenic cells.

The top four gene sets that correlated with genes downregulated by dyskerin repression in both immortal and tumorigenic cells, were related to pRb/E2F regulated pathways involved in cell cycle regulation and DNA replication (Table 6.5). These included the gene sets; Chang_cycling_genes, comprised genes of a non-core serum response of fibroblasts [597], and Vernell_retinoblastoma_pathway_up, comprised of genes upregulated by E2Fs and down-regulated by p16 and pRB [598]. Two related reactome gene sets also correlated with dyskerin repression in immortal and tumorigenic cells included; Reactome_E2F_mediated_regulation_of_DNA _replication and Reactome_E2F_transcriptional_targets_at_G1_S [531].

Leading edge gene analysis of these four gene sets was used to identify the genes that underpinned the correlation between the repression of dyskerin and the pRb/E2F pathway. The leading edge genes were then uploaded to the heat map viewer used to show the expression levels of individual leading edge genes (Figure 6.6). Even though these four gene sets did not significantly correlate with the repression of dyskerin in normal cells, some of the leading edge genes were found to be repressed in normal cells. The black labelled leading edge genes were genes modulated in normal cells and immortal and/or tumorigenic cells.

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DKC1 gene was found within the leading edge of Chang_cycling_genes and was highly repressed in all three cell lines (Figure 6.6). Interferon genes (IFIT1), proliferation and cell cycle regulator genes (TFDP1, CCNE, E2F2, BTG3) and other genes involved in DNA replication (RFC3) were also repressed by dyskerin in all three cell types (Figure 6.6). Only the homeo box transcription factor NANOG, which plays an essential role in the self-renewal of mesenchymal stem cells and maintenance of pluripotency [599], was specifically downregulated in normal cells (indicated in red). Notably, not all the leading edge genes were as highly repressed as dyskerin as indicated by the lighter blue squares.

The leading edge genes identified in these analyses included 10 genes specifically modulated in immortal cells only, 30 genes specifically modulated in tumorigenic cells only (indicated by red) and 10 genes that were specifically modulated in both immortal and tumorigenic cells but not normal cells (indicated by green) (Figure 6.6). The 10 Rb/E2F pathway genes that were downregulated by the repression of dyskerin in immortal and tumorigenic cells, but unaltered in normal cells (green) included; tumour suppressor Rb1, the transcription factor FOX05, S-phase kinase- associated protein 2 E3 ubiquitin ligase (SKP2), origin recognition complex, subunit 1-like (ORC1L), DNA replication factor (CDT1), minichromosome maintenance complex component 7 (MCM7) and chromatin assembly factor 1, subunit A CHAF1A or (p150). The downregulation of these pRb/E2F regulated genes by dyskerin repression indicates that perturbations of the pRB/E2F pathway may contribute to the proliferation arrest mediated by dyskerin repression in immortal and tumorigenic cells.

Genes regulated by the repression of dyskerin in immortal and/or tumorigenic cells also correlated with gene sets related to responses of chemotherapeutic drugs. Genes downregulated by the repression of dyskerin in tumorigenic cells only, correlated with the gene set Martinez_response_to_trabectedin_dn, comprised of genes downregulated by the alkaloid trabectedin in sarcoma cell lines [587] (Table 6.5).

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Figure 6.6 Leading edge genes of gene sets related to pRb/E2F pathway that correlated with the repression of dyskerin in immortal and tumorigenic cells Gene sets related to pRb/E2F pathway that correlated with the repression of dyskerin were submitted to leading edge analysis tool of GSEA software (Broad institute). Leading edge genes were extracted from the gene sets and visualised in heat map view. Each coloured cell represents the expression value of the leading edge gene and expression levels are indicated by the scale. Genes marked in red were modulated specifically in each cell type and genes marked in green were modulated in immortal and tumorigenic cells, but not normal cells. Genes marked in black were modulated in normal, immortal and/or tumorigenic cells. 245

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Additionally, genes downregulated by the repression of dyskerin correlated with the two gene sets from the study by Tooker etal., 2007. These two gene sets included; Tooker_gemcitabine_resistance_up, comprised of genes associated with gemcitabine resistance in NSCLC and Tooker_response_to_bexarotene_dn, comprised of genes downregulated by treatment with the retinoid x-receptor-selective agonist bexarotene treatment in NSCLC cells [600] (Table 6.5).

6.2.3.2.3 Pathways associated with the proliferation arrest induced by the repression of hTERT in immortal and/or tumorigenic cells To determine altered pathways underlying the proliferation arrest induced by hTERT repression, gene sets that correlated specifically with hTERT repression in immortal and/or tumorigenic cells were extracted by Venn diagram analysis (Table 6.6 and Appendix table A.5). Gene sets related to hematopoiesis/platelet formation, cell adhesion and metastasis, cardio vascular/myopathy, Myc signalling pathways correlated with genes regulated by hTERT repression in immortal and/or tumorigenic cells (Table 6.6). Additionally, in immortal and tumorigenic cells, gene sets related to the previous studies of extra-telomeric functions of hTERT (NFKB signalling and DNA damage repair and chromatin remodelling) and gene sets related to the regulation of hTERT (Epigenetic silencing/DNA methylation) also correlated with genes regulated by hTERT repression (Table 6.6).

The majority of the cardio vascular/myopathy related gene sets correlated with genes upregulated by hTERT repression in immortal cells (Table 6.6). These included the Kegg pathway gene sets of dilated_cardiomyopathy, cardiac_muscle_contraction, arrhythmogenic_right_ventricular_cardiomyopathy and vascular_smooth_muscle contraction [531]. The leading edge genes of these my gene sets included multiple genes involved in myopathy including integrins and ECM-interacting genes, which are also regulated in myofibroblast differentiation. These findings indicate that myofibroblast differentiation may contribute to the proliferation arrest induced by the repression of hTERT.

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Table 6.6 Gene sets correlating with gene expression changes induced by the repression of hTERT in immortal and/or tumorigenic cells Gene sets correlating with repression of hTERT Tumorigenic Immortal Normal Unique to immortal cells (Negative NES n=20/34 gene sets) Pathway Gene set size NES FDR NES FDR NES FDR SEKI_INFLAMMATORY_RESPONSE_LPS_UP T3 74 2.18 0.00 -2.27 0.00 -1.01 0.56 BROWNE_HCMV_INFECTION_10HR_UP T3 100 -1.41 0.11 -2.14 0.00 -1.30 0.19 OKUMURA_INFLAMMATORY_RESPONSE_LPS T3 183 -1.23 0.27 -1.83 0.00 -1.37 0.13 HINATA_NFKB_TARGETS_FIBROBLAST_UP T12 65 1.84 0.01 -1.77 0.01 -1.18 0.33 BIOCARTA_TNFR1_PATHWAY 29 -1.72 0.01 -1.76 0.01 -1.83 0.00 BIOCARTA_NFKB_PATHWAY T12 22 1.09 0.38 -1.75 0.01 -0.96 0.63 LINDSTEDT_DENDRITIC_CELL_MATURATION_B 47 1.09 0.39 -1.70 0.02 -1.02 0.56 BOYAULT_LIVER_CANCER_SUBCLASS_G56_DN 16 0.91 0.68 -1.68 0.02 -0.83 0.81 YAO_TEMPORAL_RESPONSE_TO_PROGESTERONE_CLUSTER_15 31 -1.32 0.18 -1.66 0.03 -1.32 0.18 BROWNE_HCMV_INFECTION_20HR_UP T3 233 -1.19 0.31 -1.65 0.03 -1.22 0.28 BILD_MYC_ONCOGENIC_SIGNATURE T11 194 -1.22 0.28 -1.62 0.03 -1.28 0.22 KEGG_NOD_LIKE_RECEPTOR_SIGNALING_PATHWAY 59 1.60 0.05 -1.62 0.03 -1.38 0.13 MITSIADES_RESPONSE_TO_APLIDIN_UP 433 1.71 0.03 -1.62 0.03 -1.22 0.27 SMID_BREAST_CANCER_RELAPSE_IN_BRAIN_UP T9 39 -1.43 0.10 -1.62 0.03 -1.35 0.16 OSWALD_HEMATOPOIETIC_STEM_CELL_IN_COLLAGEN_GEL_UP T8 221 1.74 0.03 -1.60 0.04 -1.18 0.33 OSWALD_HEMATOPOIETIC_STEM_CELL_IN_COLLAGEN_GEL_DN T8 221 1.74 0.03 -1.60 0.04 -1.18 0.32 CROONQUIST_NRAS_SIGNALING_UP T1 30 1.03 0.46 -1.60 0.04 -0.86 0.77 MARZEC_IL2_SIGNALING_UP T1 107 -1.18 0.33 -1.58 0.04 -1.27 0.22 SHAFFER_IRF4_TARGETS_IN_ACTIVATED_DENDRITIC_CELL 64 -1.28 0.22 -1.55 0.05 -1.20 0.30 BIOCARTA_CD40_PATHWAY 15 -1.03 0.55 -1.54 0.06 -1.21 0.29 Unique to immortal cells (Positive NES n=20 gene sets) Pathway Gene set size NES FDR NES FDR NES FDR KEGG_DILATED_CARDIOMYOPATHY T10 92 1.42 0.11 2.10 0.01 1.50 0.12 KEGG_ARRHYTHMOGENIC_RIGHT_VENTRICULAR_CARDIOMYOPATHY_ARVC T10 76 1.15 0.31 1.91 0.03 1.47 0.13 KEGG_GNRH_SIGNALING_PATHWAY 100 1.13 0.34 1.84 0.04 1.19 0.29 REACTOME_FURTHER_PLATELET_RELEASATE T8, T9 23 0.96 0.59 1.81 0.04 -0.81 0.83 REACTOME_OTHER_SEMAPHORIN_INTERACTIONS 16 1.42 0.12 1.79 0.05 1.14 0.34 REACTOME_FORMATION_OF_PLATELET_PLUG T8, T9 184 1.37 0.14 1.79 0.05 -1.06 0.49 BIOCARTA_INTRINSIC_PATHWAY 23 0.91 0.69 1.76 0.05 1.22 0.26 REACTOME_PLATELET_ACTIVATION T8,T9 165 1.37 0.14 1.76 0.05 -0.93 0.67 LEE_METASTASIS_AND_ALTERNATIVE_SPLICING_UP T9 74 1.28 0.20 1.76 0.05 1.43 0.14 DEBIASI_APOPTOSIS_BY_REOVIRUS_INFECTION_DN 224 -1.23 0.27 1.77 0.05 1.43 0.14 REACTOME_SIGNALLING_TO_ERKS T1 34 1.42 0.11 1.73 0.06 -1.00 0.59 KEGG_VASOPRESSIN_REGULATED_WATER_REABSORPTION 44 0.97 0.57 1.72 0.07 -0.94 0.66 KEGG_GLYCEROPHOSPHOLIPID_METABOLISM 76 1.20 0.26 1.71 0.07 1.36 0.18 BIOCARTA_AMI_PATHWAY 20 0.71 0.93 1.71 0.07 0.94 0.64 REACTOME_AXON_GUIDANCE 159 1.02 0.48 1.70 0.07 -1.08 0.46 CHARAFE_BREAST_CANCER_LUMINAL_VS_BASAL_UP T9 362 1.22 0.25 1.67 0.08 1.49 0.13 KAYO_CALORIE_RESTRICTION_MUSCLE_UP T10 69 1.22 0.24 1.65 0.09 1.44 0.14 KEGG_VASCULAR_SMOOTH_MUSCLE_CONTRACTION T10 114 0.90 0.71 1.64 0.09 -0.95 0.64 REACTOME_PLATELET_DEGRANULATION T8, T9 84 1.15 0.32 1.64 0.10 1.09 0.40 KEGG_CARDIAC_MUSCLE_CONTRACTION T10 74 1.15 0.32 1.63 0.10 1.19 0.29 Notes: Gene sets corresponding to pathways labelled T1-15 are indicated in Table 6.7. NES Normalised enrichment scores, FDR-False Discovery Rate. 247

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Gene sets correlating with repression of hTERT cont. Tumorigenic Immortal Normal Unique to tumorigenic cells (Negative NES n= 13 gene sets) Pathway Gene set size NES FDR NES FDR NES FDR NIKOLSKY_BREAST_CANCER_21Q22_AMPLICON 15 -1.77 0.01 -0.95 0.64 -0.96 0.64 STARK_HYPPOCAMPUS_22Q11_DELETION_DN 18 -1.67 0.02 -0.93 0.66 -1.16 0.36 GINESTIER_BREAST_CANCER_ZNF217_AMPLIFIED_DN 316 -1.63 0.02 1.08 0.42 -0.89 0.73 OHM_METHYLATED_IN_ADULT_CANCERS T13, T14 27 -1.57 0.04 -1.24 0.26 -0.84 0.80 ELVIDGE_HYPOXIA_DN 143 -1.56 0.04 -1.27 0.23 -1.21 0.29 SCHLOSSER_SERUM_RESPONSE_AUGMENTED_BY_MYC T11 105 -1.54 0.05 -1.12 0.40 -1.25 0.24 REACTOME_RNA_POLYMERASE_III_TRANSCRIPTION_INITIATION_FROM_TYPE_2_PROMOTER 20 -1.51 0.06 -0.77 0.88 -0.76 0.89 HADDAD_T_LYMPHOCYTE_AND_NK_PROGENITOR_DN 63 -1.50 0.06 -1.39 0.13 -1.28 0.21 YAO_TEMPORAL_RESPONSE_TO_PROGESTERONE_CLUSTER_4 15 -1.50 0.06 -1.09 0.44 -1.31 0.18 MMS_MOUSE_LYMPH_HIGH_4HRS_UP 34 -1.49 0.07 -1.37 0.14 -1.39 0.12 ROYLANCE_BREAST_CANCER_16Q_COPY_NUMBER_UP 34 -1.47 0.08 -0.93 0.66 -1.03 0.53 LIAO_HAVE_SOX4_BINDING_SITES 38 -1.46 0.08 -0.93 0.66 -1.16 0.35 BIOCARTA_RACCYCD_PATHWAY T1 26 -1.45 0.09 -1.19 0.31 -1.41 0.10 Unique to tumorigenic cells (Positive NES n=20/413 gene sets) Pathway Gene set size NES FDR NES FDR NES FDR COWLING_MYCN_TARGETS T11 41 2.41 0.00 1.06 0.45 1.54 0.11 KONDO_EZH2_TARGETS T1 142 2.32 0.00 -1.08 0.44 1.29 0.22 WAMUNYOKOLI_OVARIAN_CANCER_LMP_DN T10 184 2.34 0.00 -1.07 0.46 -1.33 0.17 HAHTOLA_MYCOSIS_FUNGOIDES_CD4_UP 64 2.27 0.00 -1.36 0.15 1.16 0.33 MOLENAAR_TARGETS_OF_CCND1_AND_CDK4_UP 65 2.24 0.00 1.53 0.13 1.51 0.11 HINATA_NFKB_TARGETS_KERATINOCYTE_UP T12 71 2.20 0.00 -2.04 0.00 1.27 0.23 GARGALOVIC_RESPONSE_TO_OXIDIZED_PHOSPHOLIPIDS_GREY_UP 18 2.19 0.00 -0.91 0.68 0.88 0.75 SEKI_INFLAMMATORY_RESPONSE_LPS_UP T3 74 2.18 0.00 -2.27 0.00 -1.01 0.56 TONKS_TARGETS_OF_RUNX1_RUNX1T1_FUSION_HSC_UP 184 2.17 0.00 -1.39 0.13 -1.07 0.48 CHARAFE_BREAST_CANCER_LUMINAL_VS_BASAL_DN T9 443 2.17 0.00 -1.66 0.02 -1.34 0.16 LENAOUR_DENDRITIC_CELL_MATURATION_DN 100 2.15 0.00 -1.27 0.23 1.44 0.14 HUANG_DASATINIB_RESISTANCE_UP T15 75 2.15 0.00 -1.73 0.01 -1.13 0.39 GRAHAM_CML_QUIESCENT_VS_CML_DIVIDING_UP 22 2.12 0.00 -1.56 0.05 -0.74 0.91 GUENTHER_GROWTH_SPHERICAL_VS_ADHERENT_DN T9 26 2.09 0.00 1.38 0.19 1.32 0.20 MCLACHLAN_DENTAL_CARIES_UP 201 2.08 0.00 -1.56 0.05 1.49 0.13 REACTOME_PACKAGING_OF_TELOMERE_ENDS 48 2.08 0.00 -2.04 0.00 1.20 0.28 ADDYA_ERYTHROID_DIFFERENTIATION_BY_HEMIN E 63 2.08 0.00 -1.29 0.21 1.39 0.16 GAURNIER_PSMD4_TARGETS 59 2.07 0.00 -2.38 0.00 0.98 0.56 ELVIDGE_HYPOXIA_UP 165 2.07 0.00 1.36 0.21 1.49 0.13 BILD_HRAS_ONCOGENIC_SIGNATURE T1 248 2.06 0.00 -1.56 0.05 1.22 0.27 Notes: Gene sets corresponding to pathways labelled T1-15 are indicated in Table 6.7. NES Normalised enrichment scores, FDR-False Discovery Rate.

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Gene sets correlating with repression of hTERT cont. Tumorigenic Immortal Normal Unique to immortal and tumorigenic cells (Negative NES n= 8 gene sets) Pathway Gene set size NES FDR NES FDR NES FDR WALLACE_JAK2_TARGETS_UP 20 -1.82 0.00 -1.65 0.03 -1.38 0.13 LOPES_METHYLATED_IN_COLON_CANCER_DN T13, T14 26 -1.74 0.01 -1.77 0.01 -1.3 0.2 COLLIS_PRKDC_SUBSTRATES T13 16 -1.69 0.01 -1.51 0.07 -1.37 0.14 FLOTHO_PEDIATRIC_ALL_THERAPY_RESPONSE_DN T13 29 -1.63 0.02 -1.78 0.01 -1.24 0.26 RASHI_RESPONSE_TO_IONIZING_RADIATION_6 T13 77 -1.61 0.03 -1.84 0.00 -1.36 0.14 YEGNASUBRAMANIAN_PROSTATE_CANCER T13,T14 126 -1.6 0.03 -1.63 0.03 -1.42 0.1 MARIADASON_RESPONSE_TO_CURCUMIN_SULINDAC_5 23 -1.5 0.07 -1.48 0.09 -1.36 0.15 KEGG_ONE_CARBON_POOL_BY_FOLATE T14 17 -1.49 0.07 -1.68 0.02 -1.15 0.36 Unique to immortal and tumorigenic cells (Positive NES n=20/27 gene sets) Pathway Gene set size NES FDR NES FDR NES FDR BROWNE_HCMV_INFECTION_16HR_DN T3 84 2.1 0.00 1.88 0.03 1.35 0.19 SENESE_HDAC2_TARGETS_DN T14 123 1.90 0.01 1.82 0.04 1.45 0.13 ONDER_CDH1_TARGETS_2_UP T13, T14 254 1.98 0.01 2.03 0.01 1.46 0.13 KEGG_VIBRIO_CHOLERAE_INFECTION T3 56 1.91 0.01 1.91 0.03 1.29 0.22 CLASPER_LYMPHATIC_VESSELS_DURING_METASTASIS_DN T9 49 1.91 0.01 1.83 0.04 1.51 0.11 SENESE_HDAC1_AND_HDAC2_TARGETS_DN T14 223 1.91 0.01 1.64 0.1 1.39 0.17 PAPASPYRIDONOS_UNSTABLE_ATEROSCLEROTIC_PLAQUE_DN T8 43 1.89 0.01 1.99 0.02 1.36 0.18 RAGHAVACHARI_PLATELET_SPECIFIC_GENES T8 69 1.87 0.01 1.68 0.08 1.35 0.18 SENESE_HDAC1_TARGETS_DN T14 243 1.85 0.01 1.91 0.03 1.33 0.2 LE_EGR2_TARGETS_DN 99 1.85 0.01 1.79 0.05 1.48 0.13 BERTUCCI_MEDULLARY_VS_DUCTAL_BREAST_CANCER_DN T9 160 1.82 0.02 1.85 0.04 -1.05 0.52 RODRIGUES_THYROID_CARCINOMA_ANAPLASTIC_DN T9 496 1.77 0.02 1.67 0.08 -1.11 0.42 HELLEBREKERS_SILENCED_DURING_TUMOR_ANGIOGENESIS T9, T8 56 1.77 0.02 1.65 0.09 1.52 0.11 FRASOR_TAMOXIFEN_RESPONSE_UP T15 49 1.77 0.02 1.83 0.04 1.23 0.26 NATSUME_RESPONSE_TO_INTERFERON_BETA_DN T3 36 1.73 0.03 1.76 0.05 1.56 0.1 LANDIS_ERBB2_BREAST_PRENEOPLASTIC_DN T9 51 1.69 0.03 1.73 0.07 1.53 0.11 REACTOME_HEMOSTASIS 272 1.70 0.03 1.73 0.06 1.19 0.29 YAO_TEMPORAL_RESPONSE_TO_PROGESTERONE_CLUSTER_16 76 1.70 0.03 2.35 0.00 1.38 0.17 LIU_NASOPHARYNGEAL_CARCINOMA 59 1.67 0.04 1.89 0.03 1.35 0.19 HALMOS_CEBPA_TARGETS_DN 41 1.66 0.04 1.7 0.08 1.42 0.15 Notes: Gene sets corresponding to pathways labelled T1-15 are indicated in Table 6.7. NES Normalised enrichment scores, FDR-False Discovery Rate.

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Table 6.7 Overview of altered pathways associated with the repression of hTR or dyskerin or hTERT Pathways altered following hTR repression Dyskerin repression hTERT repression Common to normal immortal, tumorigenic cells R1. Growth factor signalling D1. Growth factor signalling T1. Growth factor signalling R2. Cell proliferation and cell cycle D2. Cell proliferation and cell cycle T2. Cell proliferation and cell cycle R3. IFN response D3. IFN response T3. IFN response D4. Alveolar rhabdomyosarcoma T4. Alveolar rhabdomyosarcoma Normal cells R5. DNA damage D5. TP63 pathway T5. Chaperon mediated protein folding R6. Cell cycle and replication T6. Purinergic receptor signalling R7. Resistance to Doxorubicin D6. Response to Imatanib T7. Response to Tamoxifen, Response to Salisrab Resistance to Cisplatin Response to Cisplatin Immortal and/or tumorigenic cells R8. mRNA and rRNA processing D7. Energy/metabolism T8. Hematopoiesis/Platelet formation R9. Differentiation D8. Metastasis T9. Metastasis/Angiogenesis R10. Protein degradation D9. Proteasome and rRNA processing T10. Cardiomyopathy D10. pRb/E2F pathway- Cell cycling genes and T11. Myc signalling DNA replication T12. NFKB1 signalling T13. DNA damage and repair /Chromatin remodelling T14. Epigenetic silencing/DNA methylation R11. Response to Trabectedin D11. Response to Salisrib T15 Resistance to Dasatinib Resistance to Doxetaxal Response to Trabectedin Resistance to Tamoxifen. Response to Fenretinide Resistance to Gemcitabine Response to Cisplatin Response to Bexarotene Response to Trabectedin Notes: See Table 6.2 and Appendix table A.1 for gene sets belonging to each pathway indicated R1-R4, D1-4, T1-4. See Table 6.3 and Appendix table A.2 for gene sets belonging to each pathway indicated R5-R11. See Table 6.4 and Appendix table A.3 for gene sets belonging to each pathway indicated D5-D6 and T5-T7. See Table 6.5 and Appendix table A.4 for gene sets belonging to each pathway indicated D7-D11. See Table 6.6 and Appendix table A.5 for gene sets belonging to each pathway indicated T8-T15. 250

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Genes regulated by the repression of hTERT in tumorigenic cells correlated with gene sets related with Myc signalling in B-lymphoma. For instance, the gene set Schlosser_serum_response_augmented_by_myc_up, comprised of genes in B lymphocytes that were upregulated in response to combination of tetracycline- regulated myc gene and serum stimulation [601], correlated with genes downregulated by the repression of hTERT. Also gene sets related to Myc signalling in B-lymphoma correlated with genes upregulated by the repression of hTERT in tumorigenic cells. These included the Cowling_mycn_targets, comprised of MYC target genes repressed by overexpression of MYCN [602], Odonnell_targets_of_myc_and_tfrc_up [603], comprised of genes down-regulated by Myc in B lymphoma cells and upregulated in response to siRNA-inhibition of the Transferrin receptor protein 1 (tfrc) as well as the gene set Mori_E_myc lymphoma_by_onset_time_dn [604], comprised of genes that were downregulated in the E-myc transgenic mouse lymphoma model (Table 6.6, Appendix Table A.5). The associated genes upregulated by the repression of hTERT with genes downregulated by Myc, suggests downregulation of hTERT may perturb hTERT - Myc signalling pathway by a feedback mechanism, as Myc is a known transcriptional activator of hTERT [235].

Leading edge genes from GSEA that underpinned the correlation between genes upregulated by hTERT repression with Myc signalling pathways were identified in tumorigenic cells. The gene expression heatmap was condensed to one line as no genes overlapped between the selected gene sets. These data show that the Max dimerisation partner, Mdx4, which functions as a MYC repressor, was found to be upregulated following hTERT repression in tumorigenic cells (Figure 6.7 A). To further investigate the relationship between Myc-related genes and genes regulated by hTERT repression, differential gene expression results from Limma analysis were mined for Myc related genes (Table 6.8). The Limma results showed that while Myc, N-Myc and L-Myc genes remained unchanged, Mdx4 was confirmed to be significantly upregulated by hTERT repression in immortal and tumorigenic cells, but not normal cells. Additionally, Max and the Max binding protein, Mnt were also upregulated. The upregulation of Myc repressors alludes to a mechanism for the hTERT feedback regulation of Myc.

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Gene sets related to NFKB signalling also correlated with the genes regulated by hTERT repression in immortal and/or tumorigenic cells (Table 6.6). The biocarta gene set biocarta_NFKB_pathway, comprised of genes of the NFKB pathway correlated with genes downregulated by hTERT in immortal cells only [531]. Additionally, two gene sets from the study by Hinata et al., 2003 were shown to differentially correlate with genes regulated by hTERT repression in immortal and tumorigenic cells [605]. Genes upregulated by hTERT repression in tumorigenic cells correlated with the gene sets Hinata_NFKB_targets_fibroblast_up and Hinata_NFKB_targets_keratinocyte_up, comprised of genes upregulated by overexpression of NFKB in primary fibroblasts and keratinocytes respectively. These genes set however were found to correlate with genes downregulated by hTERT repression in immortal cells, indicating a different response between immortal and tumorigenic cells.

As predicted by previous studies of extra-telomeric functions of hTERT, gene sets related to pathways of DNA damage and repair and chromatin remodelling [134, 330], also correlated with hTERT repression in immortal and/or tumorigenic cells (Table 6.6) (Appendix Table A5). In addition, gene sets related to pathways associated with the regulation of hTERT including epigenetic silencing and DNA methylation were also found to correlate with hTERT repression [149, 243, 247, 264] (Table 6.6) (Appendix Table A5). Six of the eight gene sets that correlated with genes downregulated by hTERT repression in immortal and tumorigenic cells were related to DNA damage and repair and chromatin remodelling and epigenetic silencing and DNA methylation pathways (Table 6.6).

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Figure 6.7 Repression of hTERT upregulates Myc repressors A) Gene sets related to Myc pathway that correlated with genes upregulated by hTERT repression in tumorigenic cells were submitted to leading edge analysis tool of GSEA software (Broad institute). Leading edge genes were extracted from the gene sets and visualised in heat map view. Each coloured cell represents the expression value of the leading edge gene and expression levels indicated by the scale. B) Limma analysis of Myc related genes showing fold change of gene expression in cells transfected with siRNA compared to cells transfected with siSc in each cell line. The unadjusted p-value assigned to gene by Limma analysis is also indicated.

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B Table 6.8 Gene expression alterations of Myc related genes

Tumorigenic Immortal Normal Gene symbol Gene name FC p.value FC p.value FC p.value MYC v-myc myelocytomatosis viral oncogene homolog (avian) 1.26 0.29 -1.27 0.28 -1.21 0.39 MYCN v-myc myelocytomatosis viral related oncogene, neuroblastoma derived (avian) 1.15 0.01 1.05 0.4 -1.06 0.28 MYCL1 v-myc myelocytomatosis viral oncogene homolog 1, lung carcinoma derived (avian) 1.13 0.08 -1.1 0.18 -1.07 0.4 MAX MYC associated factor X 1.54 0.00 1.39 0.00 1.36 0.00 MXD1 MAX dimerization protein 1 1.03 0.87 -1.13 0.56 -1.03 0.89 MXD3 MAX dimerization protein 3 -1.23 0.32 -1.34 0.17 -1.54 0.04 MXD4 MAX dimerization protein 4 1.81 0.01 1.51 0.07 1.25 0.33 MXI1 MAX interactor 1 1.26 0.05 1.13 0.28 1.13 0.28 MNT MAX binding protein 1.36 0.05 1.63 0.00 1.75 0.00 Notes: Fold change (FC) of gene expression in cells transfected with hTERT siRNA compared to cells transfected with siSc in each cell line with associated unadjusted p.value indicated. Genes that were significantly upregulated by hTERT repression are highlighted in red.

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These gene sets included Lopes_methylated_in_colon_cancer_dn, comprised of genes un-methylated in colon cancer cells [606], Collis_prkdc_substrates, comprised of DNA repair substrates of DNA-dependent protein kinase [607], Flotho_pediatric_all_therapy_response_dn, comprised of genes downregulated following chemotherapy and associated with minimal residual disease in acute lymphoblastic leukemia patients [608], Rashi_response_to_ionising_radiation_6, comprised of genes activated in ATM-deficient tissues by double stranded breaks [609], Yegnasubramanian_prostate_cancer, comprised of genes regulated by hypomethylation in prostate cancer [610] and Kegg_one_carbon_pool_by_folate, comprised of genes involved in folate synthesis [531]. As folate acts as methyl donor for epigenetic regulation of gene expression by DNA methylation [611], the gene set Kegg_one_carbon_pool_folate, that correlated with genes downregulated by hTERT repression in immortal and tumorigenic cells, supported the notion that hTERT repression alters pathways involved in DNA methylation and epigenetic silencing.

Leading edge gene analysis of these six selected gene sets was performed to determine the gene expression alterations responsible for the correlation of hTERT repression with the DNA damage and repair, chromatin remodelling, epigenetic silencing and DNA methylation pathways (Figure 6.8). Only one gene overlapped between gene sets, namely dihydrofolate reductase (DHFR), which was common to gene sets Yegnasubramanian_prostate_cancer and Kegg_one_carbon_pool_folate (Figure 6.8). Therefore the gene expression heatmap was condensed to one line per cell line. Even though these five gene sets did not significantly correlate genes downregulated by hTERT repression in normal cells, some of leading edge genes were also found to be downregulated in normal cells. There were nineteen downregulated genes in normal cells (shown in red in normal cells) (Figure 6.8).

Twelve genes were specifically downregulated in immortal cells only (shown in red), 20 genes specifically modulated in tumorigenic cells only (shown in red) and 13 genes that were modulated (shown in green) in both immortal and tumorigenic cells (Figure 6.8). The downregulated leading edge genes included an array of genes involved in DNA repair including mismatch repair, MutL homolog 1 (MLH1) and MutS homolog 6 (MSH6), repair of double stranded breaks including the enzyme

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DNA cross-link repair 1C (DCLRE1C) and the X-ray repair cross-complementing protein 4 (XRCC4) and homologous recombination repair protein Rad associated protein 1 (RAD51AP1) [612, 613]. Additionally, genes involved in DNA replication including the helicase WRN as well as the replication protein A (RPA1,2,3) were identified in the downregulated leading edge genes, of which RPA 1 was downregulated in immortal and tumorigenic cells, but not normal cells (Figure 6.8). TERT was identified within the leading edge genes of Lopes_methylated_in_colon_cancer_dn and was shown to be within the downregulated leading edge genes in both immortal and tumorigenic cells but not normal cells. Other downregulated leading edge genes that were specific to immortal and tumorigenic cells included TRIM 36, 68 genes of the innate immunity response and the tumour specific antigens MAGEA1 and MAGEA3. The downregulation of DNA repair genes by hTERT repression indicates alterations to the DNA repair pathways that most likely contribute to the proliferation arrest mediated by hTERT. Consistent with the correlation of hTERT repression with DNA damage response and repair pathways, gene sets related to the chemotherapeutic treatment responses that induce DNA damage including cisplatin and the alkaloid trabectedin were found to correlate with hTERT repression, but in tumorigenic cells only (Appendix Table A5).

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Figure 6.8 Leading edge genes of gene sets related to DNA damage and chromatin remodelling pathways that correlated with gene downregulated by the repression of hTERT in immortal and tumorigenic cells Gene sets related to DNA damage and chromatin remodelling that correlated with genes downregulated by hTERT in immortal and tumorigenic cells. GSEA results were submitted to leading edge analysis tool of GSEA software (Broad institute). Leading edge genes were extracted from the gene sets. Selected downregulated gene sets labelled A-F. Each coloured cell represents the gene expression value of each leading edge gene and expression levels are indicated by scale. Genes marked in red were modulated specifically in each cell type and genes marked in green were modulated in immortal and tumorigenic cells, but not normal cells. Genes marked in black were modulated in normal cells and immortal and/or tumorigenic cells

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6.2.4 Repression of hTERT activates a DNA damage response in immortal and tumorigenic cells The previously established link between hTERT and the DNA damage response together with the findings that gene sets related to DNA damage correlated with genes regulated by hTERT repression in this study, provided the impetus to determine whether the proliferative arrest that followed hTERT repression in immortal and tumorigenic cells was mediated via the DNA damage response. Detection of the well-established DNA damage marker, γH2AX [517], was assessed by immunofluorescence for the detection of γH2AX foci of interphase cells of normal, immortal and tumorigenic MRC5 as well as in the fibrosarcoma HT1080 cells.

The DNA damage response was previously shown to be mediated 8 hrs post- treatment of the DNA damaging drug etoposide (Figure 4.7). Hence, the detection of γH2AX was performed 8 hrs post-siRNA transfection. Cells were transfected with hTERT-T7 and hTERT-T8, as well as siRNA targeting dyskerin and hTR as controls. Treatment with etoposide showed increased γH2AX in all cell types and served as a positive control for the activation of DNA damage response. DMSO treatment was used as a negative control. Effective siRNA-mediated inhibition was demonstrated 8 hrs post-siRNA transfection of the different cells (Figure 6.9 A). No activation of the DNA damage response was detected in cells treated with DMSO or cells transfected with siSc, siDKC1-2, siDKC1-3 or sihTR151 (Figure 6.9 B, C). Activation of DNA damage response was evident by γH2AX foci in immortal and tumorigenic MRC5, as well as HT1080 cells transfected with either sihTERT-T7 or sihTERT-T8 (Figure 6.9 B, C). Quantification of the percentage of cells stained with γH2AX revealed that normal, immortal and tumour cells had endogenous levels of γH2AX, which remained unchanged following treatment with DMSO or transfection with siSc, siDKC1-2, siDKC1-3 and sihTR (Figure 6.9 C).

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Figure 6.9 Repression of hTERT activates the formation of γH2AX foci Cells were transfected with siRNA and γH2AX detected 8 hrs post-siRNA transfection by immunofluorescence. Cells were treated with 25 µM etoposide as a positive control or DMSO as a negative control. Cells were fixed, permeabilised and incubated with γH2AX mouse primary antibody and secondary Alexa-555 conjugated anti-mouse antibody for detection of γH2AX (green). Cover slips were mounted onto slides in mounting media with DAPI (blue) for nuclear staining. A) Gene expression analysis of hTERT, dyskerin and hTR in cells 8 hrs post-transfection ∆∆ with siRNA. Gene expression was normalised to β2 microglobulin (β2M) and then compared to HeLa cells using the Ct method. HeLa levels are indicated by the dotted line and values presented as means from two independent experiments B) Representative immunofluorescence photomicrographs showing activation of γH2AX in MRC5hTERT cells 8 hrs post-transfection with siRNA targeting hTERT, dyskerin and hTR and cells treated with controls. Photos were taken on the Axiovert 200 M Microscope coupled to an AxioCamMR3 camera (Carl Zeiss, Munich, Germany) using the 60x objective. White scale bar represents 100 µM C) Percentage of cells positive for γH2AX 8 hrs post-siRNA transfection with siRNA targeting the telomerase components in MRC5, MRC5hTERT, MRC5hTERT-TZT and HT1080 cells. The number of positive cells for γH2AX was calculated from 25 fields of view with an average of 30-50 cells per view on the Axiovert 200 M Microscope (Carl Zeiss, Munich, Germany) using the 60x objective. A basal threshold for a cell positive for γH2AX was set to greater than or equal to two foci per cell. Results are presented as the means ±SEM from two independent transfections. ***P.values <0.001 from Dunnet’s post-test comparison to siSc transfected cells.

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The variable endogenous levels of γH2AX in normal and tumour cells are consistent with previous studies that show normal and tumour cells may have varied endogenous levels of γH2AX [614, 615]. A 30-40% increase in γH2AX staining in MRC5hTERT cells, MRC5hTERT-TZT and HT1080 cells transfected with sihTERT-T7 or sihTERT-T8 compared to cells transfected with siSc. In contrast to immortal and tumorigenic cells, no increase in the percentage of γH2AX positive cells was evident in normal cells (Figure 6.9 C). There was also no increase in γH2AX following the repression of dyskerin or hTR, confirming that the response was mediated by the specific effect of hTERT repression.

Overall γH2AX levels do not discriminate between DNA damage at non-telomeric DNA or telomeric DNA. The detection of γH2AX foci at dysfunctional telomeres, which are known as telomere-dysfunctional induced foci (TIFs) and as dysfunctional telomeres tend to aggregate in interphase, the detection of TIFs is far more accurate in metaphase cells as meta-TIFs [67, 68].

To determine whether the DNA damage foci identified in immortal and tumorigenic cells upon the repression of hTERT, occured at the telomere, the meta-TIF assay was performed in collaboration with Dr Tracy Bryan and Omesha Perera at Children’s Medical Research Institute (CMRI) (Figure 6.10 A). For this assay, MRC5hTERT- TZT cells were fixed in meta-phase with colecimid 48 hrs post-siRNA transfection and staining for meta-TIFs performed as described [67]. A significant increase in the number of meta-TIFs at the telomere was evident by the co-localisation of γH2AX (red) with telomere (green) in MRC5hTERT-TZT cells transfected with hTERT-T7 and hTERT-T8 siRNA (Figure 6.10 A and B left). These results provide evidence that hTERT protects the cells from the activation of the DNA damage response at the telomere.

It was previously shown that p53 wild type fibroblasts are able to tolerate a threshold level of 5 meta-TIFs associated with dysfunctional telomeres, above which the cells undergo a telomere-length independent senescence [68]. However, the MRC5hTERT-TZT cells transfected with siSc continued to proliferate in the presence of greater than 5 meta-TIFs (Figure 6.10 B left).

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Figure 6.10 DNA damage foci at the telomeres by Meta-TIF assay MRC5hTERT-TZT cells were transfected with siSc, sihTERT-T7 or sihTERT-T8 and fixed in metaphase with colcemid 48 hrs post-transfection. Meta-TIFs (telomere- dysfunction induced foci) were detected by immunofluorescence. Cells were stained with γH2AX antibody and conjugated secondary antibody (red), telomeres were labelled with FISH probe (TTAGGG green) and the nuclear stain DAPI (blue) was used. Co-detection of γH2AX (red) at the telomere (green) is representative of one TIF. White scale bar represents 10 µM. A) Representative photomicrographs of Meta-TIFs taken 48 hrs post-transfection with hTERT siRNA (upper panel). The blocked area was amplified four times and arrows indicate meta-TIFs (lower panel). B) The number of Meta-TIFs per cell was scored by counting (left panel). Results presented as scatter dot plot for individual counts. Quantification of percentage of cells with greater than or equal to 10 TIFs. The number of cells with greater than 10 Meta-TIFs per cell post-transfection with siRNA are shown (right panel). Results presented as the means from two independent experiments. C) Quantification of the number of chromosome ends positive for telomere probe per cell.

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It was observed that the number of MRC5hTERT-TZT cells transfected with siSc had less cells with 10 meta-TIFs/cell than MRC5hTERT-TZT cells transfected with siTERT-T7 and sihTERT-T8 (Figure 6.10 B left). The percentage of cells with ≥10 TIFs was therefore analysed. Twenty percent of MRC5hTERT-TZT cells transfected with sihTERT-T7 or sihTERT-T8 had greater than 10 meta-TIFs/per cells, compared to 5% of cells transfected with siSc (Figure 6.10 B right), indicating that a greater proportion of cells with more than 10 meta-TIFs/per cells subjected to hTERT repression may contribute to the proliferation arrest.

The majority of individual MRC5hTERT-TZT cells had detectable telomeres (positive for telomere fish probe) on all four chromosomal ends of each of the 46 chromosomes following repression of hTERT (Figure 6.10 C). This finding provided further evidence that there was insufficient time during these assays for telomeres to erode to a critical short length. Overall, the data demonstrated the activation of the DNA response following hTERT repression was independent of telomerase- mediated maintenance of telomere-length. The DNA response was specific to hTERT repression in immortal and tumorigenic cells indicating a mechanism underlying the anti-proliferative effect mediated by hTERT.

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6.3 Discussion

6.3.1 GSEA reveals pathways associated with the repression of telomerase components The focus of Chapter 6 was to use GSEA to delineate the mechanistic basis of the effects of ablating the telomerase components dyskerin and hTERT in immortal and malignant cells. Therefore cell line specific responses following the repression of each individual telomerase component were first identified, followed by the identification of gene expression changes that were specific to repression of each gene. This approach allowed the identification of pathways that distinguished the response of immortal and tumorigenic cells from the response of normal cells following the repression of dyskerin and hTERT.

The curated C2.MSigD gene set collection is made up of a diverse array of gene sets of Reactome, Biocarta and Kegg pathways and many other gene sets which are generated from previous studies [530, 569, 571-574]. One caveat of GSEA is that the pathways identified are dependent on the gene sets present within the collection. If studies relevant to these investigations are missing, then important pathways may be overlooked. If the pathways of interest are known to the investigations, this issue may be overcome by utilising a more focused gene set collection of known pathways. Other potential issues with GSEA relates to the method of comparing upregulated and downregulated genes separately, which may not always be ideal for analysis, as many biological pathways have a combination of up and downregulated genes, and hence [530, 616]. Despite these caveats associated with GSEA approach, the identification of pathways related to previously established extra-telomeric functions of dyskerin and hTERT provided confidence that GSEA analysis approach utilised in this study was applicable for the identification of pathways that underpinned the proliferation arrest mediated by the repression of hTERT or dyskerin.

Pathways involved in the regulation of cell proliferation and growth factor signalling were altered in all three cell types upon the repression of the telomerase components. In particular, growth factor signalling by TGF-β and EGFR were modulated in normal, immortal and tumorigenic cells following the repression of hTERT. TGF-β 266

CHAPTER 6: RESULTS and EGFR pathways were previously implicated in extra-telomeric functions of hTERT [300, 312]. The large number of differentially regulated genes induced by the repression of hTERT in normal cells and the correlation of gene sets related to specific pathways with genes regulated by hTERT repression in normal cells, was unforeseen as normal cells were demonstrated to have negligible hTERT expression. However, if the possibility that transient low levels of hTERT may be found in the normal cells, which was demonstrated by the study by Masutomi, et al., 2003 [126], these pathways may be indicative of hTERT regulated gene expression pathways that were not sufficient for inducing a proliferation arrest, as the proliferation of normal cells was not affected by the repression of hTERT. The insufficiency of these gene expression alterations to induce a proliferation arrest, indicate that either normal cells have a protective mechanism that prevent the proliferation arrest from occurring or that the immortal and tumorigenic cells have heightened sensitivity to changes in these pathways as they become addicted to them. Support for the possibility that normal cells may have transient low levels of hTERT, were also indicated by the identification of possible protective mechanisms specifically mediated by hTERT in normal cells, including chaperon-mediated folding and purinergic receptor signalling.

Pathways related to the IFN response and metabolism of siRNA that correlated with the repression of each telomerase component in all three cell lines highlighted the problem of non-specific effects associated with siRNA transfection [536-539]. Some genes sets of the interferon response correlated with genes downregulated by the repression of each of the telomerase components, indicating that the transfection of cells with siSc was possibly associated with an increased interferon response and hence the response was downregulated when compared to the siRNAs targeting the telomerase components.

Gene sets correlating with genes regulated by hTR repression within the different cell lines implicated pathways that were altered upon the repression of hTR specifically, but that were not sufficient to induce an immediate effect on proliferation of any of the cell lines. Pathways found to be altered by hTR repression

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CHAPTER 6: RESULTS were related to cell proliferation and cell cycle and the downregulation of DNA damage response.

6.3.2 Distinct mechanisms underlie the proliferative defect induced by repression of dyskerin Venn analysis of gene sets revealed that repression of dyskerin in immortal and tumorigenic cells altered pathways involved in energy/metabolism and metastasis. The metabolism of cancer cells is highly divergent from that of normal cells and dysregulated metabolism is also linked to drug resistance mechanisms [1, 617]. Similarly, the gene expression alterations involved in metastasis represent cancer specific alterations [1]. The regulation of dyskerin on pathways involved in energy/metabolism and metastasis may provide dyskerin dependent pathways that are necessary for tumour cell proliferation and/or malignant phenotype. However, as genes regulated by the repression of dyskerin correlated differentially to the gene sets of these pathways in immortal and tumorigenic cells, the gene expression changes responsible for differential correlation of these pathways in immortal and tumorigenic cells will need to be further investigated before the role of dyskerin in these pathways is clarified.

Genes involved in proteosomal and rRNA processing also correlated with gene expression changes induced by repression of dyskerin., however some differential gene set correlation were also noted between genes regulated by dyskerin in immortal or tumorigenic cells. The regulation of dyskerin on rRNA processing is consistent with extra-telomeric function as a pseudouridine synthase that functions in the processing of rRNA and assembly of ribosomes [190]. Mutated dyskerin has been shown to impact on rRNA processing and ribosomal function and reduce the proliferation of mouse liver and embryonic stem cells [343, 344, 378]. In human cells, however the effect of rRNA processing is less clear. No effects of mutated dyskerin have been demonstrated in DC patients [173, 189] and only a transient delay in rRNA processing was demonstrated following siRNA depletion of dyskerin in oral squamous carcinoma, cervical cancer and osteosarcoma cell lines [341]. Future investigations using the isogenic model of normal, immortal and tumorigenic cell and metabolic labelling of rRNA following the repression of dyskerin will be

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CHAPTER 6: RESULTS valuable to determine whether dyskerin repression impacts on rRNA processing and contributes to the proliferation arrest of immortal cells. pRb/E2F regulated pathways involved in cell cycle progression and DNA replication were downregulated following the repression of dyskerin in both immortal and tumorigenic cells indicating that disruption of pRb/E2F signalling most likely contributes to the proliferation arrest mediated by the repression of dyskerin. As the pRb/E2F pathway is involved in G1/S cell cycle transition, these findings are consistent with the results that showed repression of dyskerin induced an accumulation of cells in G1 in the immortal cells. The leading edge genes of the pRB/E2F pathway that were downregulated by the repression of dyskerin in both immortal and tumorigenic cells included RB1, FOX05, SKP2, ORC1L, CDT1, MCM7, CHAF1A or (p150). These pRb/E2F regulated genes are essential for G1/S phase progression of the cell cycle and DNA replication in S phase and since these genes are specifically downregulated in immortal and tumorigenic cells, these findings suggest that their downregulation may contribute to the proliferation arrest mediated by dyskerin repression. Additionally, the genes pRB/E2F cell cycle genes (TFDP1, CCNE, E2F2, BTG3) and genes involved in DNA replication (RFC3) were also downregulated by the repression of dyskerin in all three cell types. While the genes downregulated by dyskerin in all three cell types may not be sufficient for the proliferation arrest, they may however be essential for the proliferation arrest mediated by dyskerin repression in immortal and tumorigenic cells that become addicted to dyskerin and the pRB/E2F pathway for their proliferation. TFDP1 was the most highly repressed leading edge gene in all three cell types following the repression of dyskerin. TFDP1 is the dimerisation partner of E2F transcription factors that forms E2F1/TFDP1 complex to regulate other cell cycle genes [618]. It is found to be overexpressed in numerous cancers including breast and hepatocellular carcinomas and its overexpression is related to tumour progression of hepatocellular carcinomas [619, 620]. These findings warrant further investigations to determine whether TFDP-1 is essential for the proliferation arrest mediated by the repression of dyskerin.

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6.3.3 Distinct mechanisms underlie the proliferative defect induced by repression of hTERT Pathways associated with cardio vascular/myopathy correlated with hTERT repression closely in immortal cells. Multiple integrins, ECM interacting genes and TGF-β regulated genes were present in the leading edge genes of the cardio vascular/ myopathy related gene sets. Some of the transcriptional regulators involved in integrin-ECM interactions have been shown to induce myofibroblasts differentiation in human mesenchymal cells [621]. As MRC5 cells are myofibroblasts, the correlation of hTERT repression with the cardio vascular/ myopathy pathways, indicate that myofibroblast differentiation may contribute to the proliferation arrest induced by the repression of hTERT.

The MYC oncogene is an important regulator of tumour cell proliferation and MYC overexpression correlates with hTERT expression in a variety of cancers, including prostate and breast cancers [622-626]. The transcriptional activation of TERT promoter activity by Myc is likely to be responsible this correlation [622-626]. A previous GSEA study that investigated gene expression alterations responsible for the impaired proliferation of hair follicle in epidermal mouse skin following mTERT repression, found that genes downregulated by mTERT repression correlated with genes downregulated by the repression of Myc [310]. In contrast to the findings of this study however, there was no evidence that genes upregulated by TERT repression correlated with genes downregulated by myc or vice versa [310], which may represent a species difference in regulation. There are no prior reports of extra- telomeric functions of hTERT impacting on the proliferation of human tumour cells via Myc driven pathways.

The regulation of Myc target genes is dependent on the dimerisation of Myc with the repressor, Myc associated factor X (Max) or with other transcription factors including Sp1 and Miz-1 [226, 627, 628]. Max is also able to bind with the Mad family of repressors (Mnt, Mdx1, Mdx3, Mdx4 and Mxi1) that compete with Myc for the binding with Max [629, 630]. While the myc genes c-Myc, N-Myc and L- Myc were not directly altered by hTERT repression, Max and the Max dimerization partners Mxd4 and Mnt were amongst the genes found to upregulated by hTERT

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CHAPTER 6: RESULTS repression. These findings indicate that hTERT repression may be involved in a feedback loop mechanism in the regulation of Myc, which is driven by the upregulation of Myc repressors. The findings that the repression of hTERT modulates Myc signalling indicate that targeting of hTERT may also be effective for the treatment of Myc-driven cancers.

The correlation of genes regulated by hTERT repression with NFKB-regulated genes involved in divergent effects on proliferation of fibroblast and keratinocytes [605], indicated that alterations of NFKB signalling may contribute to the proliferation arrest mediated by hTERT repression. The regulation of hTERT on NFKB signalling is in agreement with the recently demonstrated extra-telomeric role for telomerase to directly regulate NFKB1-dependent transcription [314].

DNA damage response and repair and chromatin remodelling pathways were identified by GSEA to be altered following hTERT repression in immortal and/or tumorigenic cells. Extra-telomeric roles of hTERT in DNA damage response and repair and chromatin remodelling pathways have previously been demonstrated [134, 330]. Ectopically expressed hTERT in human foreskin fibroblasts was demonstrated to cross link to the telomeric DNA and interact with the nuclear matrix to alter the transcription of DNA repair and chromatin remodeling genes [330]. However, as functionally inactive mutants of the RT domain and mutants of hTR failed to cross link to DNA and alter DNA repair, the extra-telomeric mechanism mediated by hTERT was demonstrated to be dependent on telomerase activity [330]. In normal BJ fibroblasts that express only transient levels of hTERT in S phase [126], shRNA- mediated inhibition of hTERT impaired the DNA damage response following ionising radiation by influencing the remodelling of chromatin structure during DNA replication. hTERT suppression also impaired the capacity of these cells to repair DNA [134]. Both the telomere localisation defective DAT mutant of hTERT and the catalytically inactive DNhTERT, failed to rescue the impaired DNA damage response indicating that these effects were unrelated to overall telomere length and telomerase activity [134, 143, 163]. Both of these studies were consistent in showing that hTERT alters DNA repair, however there are discrepancies between the mechanisms. These discrepancies may highlight a difference in mechanisms between

271

CHAPTER 6: RESULTS normal cells expressing transient low levels of hTERT subjected shRNA-mediated inhibition of hTERT or cells overexpressing hTERT [134, 330].

In the present study, hTERT impaired the expression of DNA repair genes, which is consistent with those previous studies [134, 330]. The activation of the DNA damage response following hTERT repression was mediated specifically in immortal and tumorigenic cells of this study. There was no activation of DNA damage in normal cells of this study as the MRC5 cells. These findings contrasted with the impaired DNA damage response following shRNA-mediated inhibition of hTERT in normal BJ fibroblasts when treated with ionising radiation [134], which most likely reflects a cell-type specific response of BJ fibroblasts to hTERT repression.

The activation of the DNA damage response at the telomere following hTERT repression was found to be a specific effect of hTERT repression that can be dissociated from its telomerase activities, as no activation of the DNA damage response was demonstrated following the repression of dyskerin and hTR. Furthermore, the increased level of meta-TIFs in MRC5hTERT-TZT cells subjected to the repression of hTERT was demonstrated. Spontaneous dysfunctional telomeres associated with DNA damage foci (TIFs) form as a result from structural changes of telomeric DNA during telomere DNA replication [49, 52, 67, 69]. It has previously been demonstrated that cells can tolerate a small number of meta-TIFs without affecting proliferation and meta-TIFs can progress from mitosis to G1 of the cell cycle [49, 52, 67, 69]. However, a threshold level of 5 meta-TIFs associated with dysfunctional telomeres, causes the induction of a telomere-length independent senescence at G1 phase of the cell cycle in p53 wild type cells [68]. Findings from the study by Kaul et al., 2012, showed that p53-defective cells continued to proliferate with more than 5 meta-TIFs per cell [68]. The accumulation of greater than 5 meta-TIFs per cell in p53-defective MRC5hTERT-TZT cells transfected with siSc that still proliferate, indicate these cells may be able to tolerate a higher number of meta-TIFs, consistent with the study by Kaul et al., 2012 [68]. hTERT repression resulted in an increased percentage of cells with greater than 10 meta-TIFs per cell and the cells were no longer able to proliferate. The increased level of TIFs to above 10 TIFs/cell in MRC5hTERT-TZT cells most likely contributes to the dramatically

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CHAPTER 6: RESULTS reduced cell cycle progression and proliferation demonstrated in Chapter 4. It will be important to determine whether similar number of meta-TIFs will accumulate in p53 wild type MRC5hTERT cells following the repression of hTERT. These investigations are currently ongoing in A/Prof Bryans lab.

The activation of the DNA damage response and impairment of DNA repair genes most likely underpins the specific anti-proliferative effects elicited by hTERT repression in immortal and tumorigenic cells. Activation of the DNA-damage response arrests cell-cycle progression until damage is removed [612, 631, 632]. However, if other downstream effector pathways including DNA repair mechanisms are perturbed, DNA damage may persist and the cells undergo a permanent arrest [612, 631, 632]. In the present study, DNA repair mechanisms were identified to be downregulated following hTERT repression. A subset of DNA repair genes was identified by leading edge gene analysis to be downregulated by the repression of hTERT in all three cell types. These included the downregulation of DNA mismatch repair genes (MLH1, MSH6), genes involved in the repair of double stranded breaks (DCLRE1C, XRCC4) and homologous recombination repair (RAD51AP1), as well as the replication proteins (RPA2, RPA3) [612, 613]. The downregulation of these genes in normal cells indicate that these genes may not be sufficient for proliferation arrest, however they may still contribute to the proliferation arrest mediated by hTERT repression in immortal and tumorigenic cells. The DNA replication protein A1 (RPA1) was found to be downregulated specifically in immortal and tumorigenic cells by hTERT repression. RPA1 is a single stranded DNA binding protein that functions in DNA replication and repair, cell cycle and DNA damage checkpoints [633]. It is an independent prognostic marker of oesophageal, colon and bladder urothelial cancer [634-636]. Previous studies have demonstrated that the treatment of colon cancer cells with a cytotoxin inhibited the proliferation of colon cancer cells via the inhibition of DNA replication and RPA1 was suggested to be a target [637]. The downregulation of RPA1 specifically in immortal and tumorigenic cells by hTERT repression indicate RPA1 may be essential to the proliferation arrest mediated by hTERT.

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An alternative approach to delineate the pathways associated with the repression of each component following GSEA would be by first comparing gene expression changes associated with the repression of each telomerase component. This analysis flow would be most suited to identifying telomerase-independent gene specific responses that can then be compared between cell types to allow responses that are unique to normal cells or immortal and tumorigenic cells to be identified. This alternative approach described would also utilise two rounds of Venn analysis to generate gene sets specific to the repression of each component. Since the gene sets would be different, the output of this alternate approach may be different. However, as the ranked lists of genes submitted to GSEA are the same, and the pathways associated with the gene sets identified by both analysis approaches should differ dramatically. This possibility will be addressed by further bioinformatic analyses.

In this chapter, microarray analysis implicated distinct pathways in the proliferation arrest that followed the repression of hTERT or dyskerin in immortal and tumorigenic cells. Pathways involved in energy/metabolism and metastasis and pRb/E2F regulated pathways were found to be altered by the repression of dyskerin. The alterations of the Rb/E2F pathway that followed the repression of dyskerin provide insight to the mechanisms through which repression of dyskerin may exert anti-proliferative effects in immortal and tumorigenic cells. Pathways involved in myofibroblast differentiation, the regulation of Myc signalling, DNA repair and chromatin remodelling were found to be altered by the repression of hTERT. Activation of the DNA damage response following the repression of hTERT specifically in immortal and tumorigenic cells was confirmed indicating hTERT may contribute to the proliferation of immortal and tumorigenic cells by preventing the activation of DNA damage response. Together these results provide pathways that may underlie the proliferation arrest mediated by the repression of hTERT or dyskerin that have potential for exploitation to selectively halt the replication of immortal and tumorigenic cells.

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7. Conclusions and future perspectives

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7.1 Potential therapeutic utility of directly targeting the telomerase components hTERT or dyskerin The primary objective of this study was to compare the impact of directly targeting each of the telomerase components hTERT, dyskerin and hTR in isogenic normal, immortal and tumorigenic cells in order to empirically determine a safe and effective approach to therapeutically targeting telomerase. The potency of targeting hTERT or dyskerin was demonstrated by an immediate halt to the proliferation of immortal cells, while normal cells continued to proliferate. In contrast, although inhibition of hTR was equally effective at quenching telomerase activity, there was no immediate impact on proliferation. Treatment with small molecule inhibitor of telomerase activity, BIBR1532 similarly had no acute effect on cell proliferation in vitro [394, 472]. Together these findings indicate the proliferative defects induced by the repression of hTERT or dyskerin were not a direct consequence of the suppression of telomerase. The vulnerability of immortal and tumorigenic cells to dyskerin or hTERT repression underscores the potential of these telomerase components as therapeutic targets. Directly targeting hTERT or dyskerin was not associated with the phenotypic delay that accompanies the targeting of the telomere maintenance function of telomerase [163, 280, 392-394] and hence provides a more potent and effective way of halting the replication of immortal cells.

The possibility that sustained long-term telomerase inhibition may result in detrimental side effects on highly proliferative normal tissue, such as bone marrow, is underscored by the pathogenesis of dyskeratosis congenita (DC). Missense mutations of the DKC1 gene that result in low levels of dyskerin, cause skin and bone marrow failure syndrome in DC patients [173]. Mutations in TERT and TERC genes have also been identified in DC patients [291]. The possible detrimental effects on highly proliferating progenitor cells present consideration for the therapeutic utility of targeting of hTERT or dyskerin [173]. The results in this thesis suggest that inhibiting hTERT or dyskerin directly may have more immediate therapeutic effect 275

CHAPTER 7: CONCLUSIONS AND FUTURE PERSPECTIVES than strategies that target the telomere maintenance function of telomerase. This approach may therefore be less likely associated with detrimental effects on progenitor cells due to inhibition of telomerase activity. However, the potential side effects of the loss of hTERT or dyskerin in highly proliferating progenitor cells and stem cells, needs to be addressed. A window of opportunity for the targeting of hTERT may be provided by the higher levels of hTERT expression in cancer cells compared with normal cells [14]. The elevated levels of dyskerin in specific cancers and the increased need of rRNA processing in cancer cells also alludes to a therapeutic window for the targeting of dyskerin [200, 340, 345, 384, 388, 638, 639]. The potential therapeutic application of targeting hTERT or dyskerin directly is broad and highly pertinent for the improvement of cancer treatments for broad range of cancer including mesenchymal-derived tumours.

7.2 Directly targeting hTERT in human cancer The therapeutic targeting of hTERT is broadly applicable for human cancer treatment as hTERT underlies immortality in 90 % of all human cancers [14]. Immortality is also a property of cancer stem cells, a small subset of cells that drive tumour formation, which express hTERT and are responsible for resistance and relapse to currently used anti-cancer chemotherapeutics [10, 11]. Previous studies have shown that hTERT contributes to tumour cell proliferation independent of their functions in telomere maintenance [134, 173, 300, 341]. In addition, extra-telomeric functions of hTERT in the maintenance of breast, glioblastoma and gastric cancer stem cells have been demonstrated [300, 312, 327, 367, 640, 641]. The findings presented in this study underscore the value of targeting hTERT to halt the replication of immortal cancer cells and cancer stem cells that may become addicted hTERT expression for their proliferation.

The transcriptional activation of hTERT expression is implicated in the correlation of high Myc and hTERT expression in Myc-driven cancers [622-626]. The GSEA results in this thesis showing that repression of hTERT altered Myc signalling pathways indicates that the targeting of hTERT may be effective for the treatment of Myc-driven cancers, such as hormone associated prostate, ovarian and breast cancers, lymphomas as well as neuroblastoma [622-626, 642]. In this study, the regulation of Myc signalling by hTERT repression was potentially driven by the 276

CHAPTER 7: CONCLUSIONS AND FUTURE PERSPECTIVES upregulation of Myc repressors. In future studies, it will be important to evaluate the whether the Myc repressors identified to be upregulated including Max, Mxd4 and Mnt contribute to the proliferative arrest associated hTERT repression. These Myc repressors also play a role in the regulation of Myc target genes by competing with Myc for binding to Max [629, 630]. To determine whether the upregulation of Myc repressors by hTERT repression affects the regulation of Myc target genes by modulating the binding of Myc to Max, chromatin immunoprecipitation analysis of Myc target genes could be performed in combination with the inhibition of Myc repressors.

The results in this study were generated using a model of mesenchymal tumorigenesis and hence provide the impetus for further evaluation of targeting hTERT as a therapeutic target in mesenchymal-derived tumours, including sarcomas. Sarcomas are diverse set of tumour arising from soft tissues including connective tissue (fibrosarcoma), muscle (embryonal or alveolar rhabdomyosarcomas), cartilage (chondrosarcoma), arising from bone (osteosarcomas) [643]. Ewing’s sarcoma is a sarcoma that manifests in the bone or soft tissue that is characterised by the chromosomal translocation of the EWS gene with one of the five ETS family of transcription factors including FLI1, ERG, ETV1/ER81, EIAF/PEA3 and FEV [644, 645]. The main treatment methods of sarcomas include surgical removal, radiation, chemotherapy, but most are associated with a poor prognosis and 40-50% are often associated with relapse due to metastasis [643].

A major problem with radiation treatment of sarcomas is that radiation-induced human sarcomas emerge at the edge of radiation field [646, 647]. These radiation induced sarcomas have been demonstrated to develop p53 mutations and are associated with high frequency of MYC gene amplifications [646, 647]. The impaired proliferation of both wild type p53 and cells with a defective p53 pathway and the alterations to Myc signalling following the repression of hTERT, support the notion that directly targeting of hTERT may be effective for the treatment of radiation-induced human sarcomas.

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In this study, the correlation of genes upregulated by hTERT repression with genes commonly downregulated in alveolar rhabdomyosarcoma, indicated that hTERT may also be suitable target for the therapeutic application of alveolar rhabdomyasarcomas. Alveolar rhabdomyasarcoma is an aggressive soft tissue sarcoma in children characterised by the fusion gene of paired domain–containing transcription factors with the forkhead family of transcription factors (PAX-FKHR) [583]. Pax 8 of the Pax transcription factor family was demonstrated to directly bind to the TERT promoter and activate expression of hTERT and telomerase activity in glioma cells, indicating possible regulatory mechanisms between Pax transcription factors and hTERT expression [236]. These findings substantiate further investigations of the utility of targeting hTERT in alveolar rhabdomyosarcoma.

Activating mutations in N-RAS have been demonstrated in 35-44% of human rhadomyosarcomas and soft tissue sarcomas [548-550, 575]. More specifically, N- RAS is frequently mutated in embryonic rhadomyosaromas (ERMS) and is essential for the proliferation and survival of embryonic rhadomyosarcomas cell lines [550, 648]. In the present study, the repression of hTERT halted the proliferation of tumorigenic MRC5hTERT-TZT cells, which overexpress N-Ras, as well as HT1080 fibrosarcoma cells in which cells, N-Ras is activated by a single base mutation that alters an amino acid at position 61 of the N-Ras product [541, 542]. These findings indicate that targeting hTERT may be an effective approach to halt the proliferation of cancer cells with activated N-Ras.

The findings that hTERT is a transcriptional target of the ETS transcription factors and hTERT is found to be highly expressed in Ewing’s sarcoma, indicate that hTERT may be a suitable therapeutic target for this sarcoma [644, 645]. Furthermore, the chimeric fusion proteins encoded by the EWS/ETS chromosomal translocation that drives tumorigenesis has been demonstrated to activate telomerase activity via the upregulation of hTERT expression [644, 645]. The importance of hTERT for the tumorigenesis of Ewing’s sarcoma substantiates further investigations of the repression of hTERT in Ewing’s sarcoma in vitro and in vivo tumour models to investigate the therapeutic utility of targeting hTERT in Ewing’s sarcomas.

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The activation of the DNA damage response and downregulation of DNA repair genes following the repression of hTERT specifically in immortal and tumorigenic cells indicated mechanisms that underpinned the proliferation arrest induced by the repression of hTERT. The activation of the DNA damage response acts as barrier to oncogenic transformation [632]. The DNA damage response is also activated by a number of clinically relevant cancer therapeutics including topoisomerase inhibitors that have been used in therapy of many cancers including breast and lung cancers, leukemia, lymphomas and sarcomas [649-651]. The topoisomerase inhibitors function by inhibiting Type 1 or Type 2 topoisomerase enzymes, which are essential enzymes that change DNA structure [650, 651]. Activation of DNA damage occurs due to the introduction of single stranded breaks by Type I inhibitors, including camptothecin and topotecan or double stranded breaks by Type II inhibitors, including etoposide, doxorubicin, and mitoxantrone [650, 651]. Other DNA damaging inducing agents function by the induction of stalled replication forks, or depletion of nucleotide pools [612, 632, 652-654]. However, a major issue with these chemotherapeutic agents is that they are not selective for cancer cells and therefore elicit toxicity toward normal tissue [654]. In contrast to those chemotherapeutic agents, the DNA damage response induced by the repression of hTERT was not activated in normal cells and hence is less likely to be associated with any detrimental effects on normal tissue.

The downregulation of DNA repair and replication genes, possibly results from transcriptional effects of hTERT on the DNA repair pathways. In future work, it will be important to confirm that the downregulation of DNA repair and replication genes identified by GSEA, contribute to the activation of the DNA damage response and proliferation arrest mediated by hTERT repression. This may be performed by the evaluation of the effects of hTERT repression on proliferation and the activation of DNA damage response in combination with small-molecule inhibition or siRNA- mediated inhibition or overexpression of genes such as the RPA1 implicated to be downregulated in immortal and tumorigenic cells specifically. Whether the impairment of DNA repair genes occurs from physical absence of hTERT crosslinking to the DNA as demonstrated by Sharma et al., 2003 [330] also remains to be determined. Functional effects on DNA repair and chromatin remodelling

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CHAPTER 7: CONCLUSIONS AND FUTURE PERSPECTIVES pathways may be further evaluated by DNA repair assays in combination with small- molecule inhibition or siRNA-mediated inhibition of critical genes such as RPA1 within these pathways.

Mesenchymal-derived cancer stem cells have been identified in in rhabdomyosarcomas, osteosarcomas, soft tissue sarcomas and Ewing’s sarcomas [655, 656]. DNA repair pathways are associated with resistance mechanisms of cancer stem cells to treatment [10, 657]. The gene expression data presented in Chapter 6 showed that targeting hTERT alters DNA repair pathways indicating that targeting hTERT may be an effective approach to eliminating cancer stem cells. The use of pre-clinical models that utilise cancer stem cells would be of great value for future investigations of the effects of directly targeting hTERT in tumours that relapse due to failed depletion cancer stem cells.

7.3 Directly targeting dyskerin in human cancer The utility of targeting dyskerin is also broadly applicable as the overexpression of dyskerin has been demonstrated in numerous cancers types including prostate cancer, breast cancers and oral squamous cell carcinoma (OSCC), hepatocellular carcinoma (HCC) has been associated with poor prognosis of these cancers [200, 340, 341, 384, 388]. Furthermore, the overexpression of dyskerin correlated with the proliferation of HCC cancer cells and oral squamous cell carcinoma cells [340, 384], providing the impetus for investigating the therapeutic application of targeting dyskerin in HCC and OSCCs. Future studies of targeting of dyskerin in cancers overexpressing dyskerin should be performed in conjunction with tumour cell type matched normal cells to further assess potential toxicity of this approach for these cancers.

The targeting of dyskerin as a therapeutic target in mesenchymal derived sarcomas was also provided by the utilisation of the mesenchymal-derived model to demonstrate the effects of dyskerin repression on proliferation. The results in this thesis demonstrated that repression of dyskerin impaired proliferation of immortal and tumorigenic cells via mechanisms that were separable from the effects of telomerase suppression, indicate that targeting dyskerin is applicable for the treatment of telomerase-negative cancers that require dyskerin for their proliferation. This has important relevance for the treatment of sarcomas, as the prevalence of ALT 280

CHAPTER 7: CONCLUSIONS AND FUTURE PERSPECTIVES is higher in sarcomas, than in carcinomas [148]. In particular, the majority of osteosarcomas lack telomerase expression and use ALT to maintain their telomeres. Previous studies have shown that osteosarcomas can harbour both telomerase activity and ALT and those with telomerase activity are associated with unfavourable clinical outcome [658]. The depletion of dyskerin in telomerase-negative osteosarcoma was previously demonstrated to reduce the proliferation osteosarcoma cells, however in that study the effects of targeting of dyskerin in normal cells was not established [341]. The selective impairment of proliferation of telomerase negative cells by dyskerin repression demonstrated in this study, indicate the therapeutic targeting of dyskerin may be applicable for treatment of osteosarcomas that dependent on dyskerin for their proliferation.

The downregulation of pRb/E2F signalling pathways involved in cell cycle and DNA replication in immortal and tumorigenic cells indicated mechanisms that underpinned the proliferation arrest induced by the repression of dyskerin. The regulation of dyskerin on the pRb/E2F for the proliferation of tumour cells has not been previously demonstrated. The inactivation of pRb is frequent in soft tissue sarcomas, bone sarcomas, small lung cell cancers, breast cancer and prostate cancers [659-661]. In future work, it will be important to confirm the downregulation of these major regulators of the Rb/E2F pathway including those downregulated specifically in immortal and tumorigenic cells (RB1, SKP2), as well as those downregulated in all three cells (TFDP1). siRNA-mediated inhibition or overexpression of the validated gene targets will then be performed to ascertain whether the disruption of pRb/E2F signalling directly modulates the proliferation arrest mediated by the repression of dyskerin.

Although perturbed ribosomal biogenesis is normally associated with activation of p53 and a G1 arrest, recent studies have shown that a pRb-mediated mechanism is activated in response to impaired rRNA processing and hinders cell cycle progression at the G1–S phase transition [547, 548]. Results from this study that revealed Rb1 and other genes involved in the pRb/E2F pathway are downregulated by dyskerin repression, providing a potential link between the extra-telomeric function of dyskerin in rRNA processing and the pRb/E2F pathway. These findings

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CHAPTER 7: CONCLUSIONS AND FUTURE PERSPECTIVES also provide impetus for investigating whether the pRb/E2F pathway and rRNA processing pathways interact to mediate the proliferation arrest associated with dyskerin repression. .

Dyskerin binds to H/ACA snoRNAs and C/D snoRNAs in complex with ribonucleotide proteins to ensure the accurate processing of rRNA [190, 343]. In addition dyskerin also binds to precursors of five H/ACA snoRNA-like miRNAs [662]. The regulation of H/ACA snoRNAs or miRNAs by dyskerin has also thus far not been linked to the proliferation of human normal or tumour cells. Hence, further investigations to determine whether rRNA processing functions of dyskerin contribute to the proliferation of normal or tumour human cells are needed. It will be valuable to demonstrate the impact of dyskerin repression on the levels of H/ACA snoRNAs and C/D snoRNAs and H/ACA snoRNA-encoded miRNAs in normal and immortal cells to determine whether the regulation of H/ACA snoRNAs and C/D snoRNAs and H/ACA snoRNA-encoded miRNAs by dyskerin contributes to the proliferation of normal and/or tumour human cells.

7.4 Directly targeting of hTERT or dyskerin in combination with chemotherapeutic agents Targeted therapies may be used to sensitise tumour cells to chemotherapy to enable the use of lower drug doses and thus minimise treatment associated toxicity [9]. Synthetic lethality refers to the process of targeting two co-operating, independent pathways that are not lethal to cells when targeted individually, but if targeted in combination kill the cell. Drugs combinations that function by a synthetically lethal mechanism by targeting pathways essential for the viability of cancer cells, but not normal cells, are likely to have an increased therapeutic index [663, 664].

The results from GSEA showed that the repression of telomerase components correlated with gene sets comprised of genes that were regulated by certain drug treatment responses and/or genes regulated associated with the resistance mechanisms of certain drug treatments. These findings provide insight for ways of targeting hTERT or dyskerin that may be effectively used to reduce drug dose. For future in vitro studies, chemotherapeutic and molecular targeted compounds are to be tested in combination cytotoxicity assays with inducible shRNA-mediated inhibition 282

CHAPTER 7: CONCLUSIONS AND FUTURE PERSPECTIVES of hTERT or dyskerin. Compounds might be selected based on gene sets related to drug treatments responses altered by the repression of hTERT (dasatanib, imatinib) or dyskerin (bexacarotene, salirasib). Additionally, chemotherapeutic and molecular targeted compounds may be selected based on pathways identified to be altered by the repression of hTERT or dyskerin in GSEA and/or related to the extra-telomeric functions of hTERT (cisplatin, DNA damaging drugs) or dyskerin in rRNA processing (mTOR inhibitor, rapamycin).

In previous studies, repression of hTERT increased the sensitivity of various cancer cells to the treatment of clinically relevant drugs including DNA cross linkers; cisplatin, mitomycin C as well as the topoisomerase inhibitors, etoposide and doxorubicin [316, 424, 665, 666]. The downregulation of DNA repair genes demonstrated by hTERT repression in this study provides a possible explanation for this increased sensitivity. Future investigations to determine whether the activated DNA damage response mediated by hTERT repression contributes to the sensitisation of normal and tumour cells to DNA cross linkers or damaging agents will be valuable. To compare the sensitivity of neoplastic and normal cells, cytotoxicity assays with isogenic normal, immortal and tumorigenic cells transduced with inducible shRNA targeting of hTERT or a scrambled shRNA should be performed in combination with treatment of these agents. Decreased efficacy of anticancer DNA damaging agents is associated with the emergence of resistance mechanisms to DNA damaging agents in tumour cells [651]. It will be valuable to determine whether the downregulation of DNA repair genes by hTERT repression is able to circumvent the resistance mechanisms associated with DNA damaging agents and in so doing, be able to increase the efficacy of DNA damaging drugs.

GSEA linked genes downregulated by the repression of dyskerin to genes upregulated in NSCLC cancer cells upon treatment with the nucleoside analogue gemcitabine, which are associated with gemcitabine resistance [600]. Multiple gemcitabine resistance mechanisms have been described, particularly in relation to the treatment of pancreatic and lung cancer [600, 667]. In the study by Tooker et al., 2007, the genes upregulated by the treatment of gemcitabine that were associated with gemcitabine resistance, were also found to be downregulated following the

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CHAPTER 7: CONCLUSIONS AND FUTURE PERSPECTIVES treatment of the retinoid X receptor agonist, bexacarotene. Their findings indicated that bexacarotene is able to re-sensitise gemcitabine-resistance tumour cells [600]. Bexarotene has previously been demonstrated to be effective in overcoming multidrug resistance in combination with different chemotherapeutic drugs in preclinical studies of advance breast, prostate and NSCLC cancer cells [600]. The findings that the repression of dyskerin repression correlates with genes regulated by bexacarotene treatment, indicate that targeting of dyskerin may also be able to re- sensitise resistant tumour cells and be applicable for the treatment of multidrug resistance cancer cells.

Genes that are commonly altered by the repression of all three components indicate effects associated with the repression of telomerase activity. The repression of all three telomerase components correlated with genes altered by the chemotherapeutic drug trabectedin in immortal and/or tumorigenic cells. Trabectedin is a marine- derived alkaloid used for treatment of advanced soft tissue sarcoma, that have failed standard treatment with anthracyclines and ifosfamide [668, 669]. Trabectedin acts by binding in the minor groove of DNA. This binding of trabectedin to the DNA, blocks the activation of several transcription factors and interacts with DNA repair mechanism that can result in activation of DNA damage response [668, 669]. Previous studies have demonstrated that telomerase decreases the efficacy of trabectadin in melanoma cells [670]. Directly targeting dyskerin or hTERT may therefore represent a novel approach to increase the efficacy of trabectadin.

Other bioinformatic tools including Connectivity Map (cMAP) Broad institute may be used to confirm the correlation of the drug treatments that were identified by GSEA to correlate with the repression of telomerase components. This software matches gene lists to a collection of gene expression data from cultured cells treated with small molecules using a pattern-matching algorithm [671]. This analysis may also identify additional drug treatments that may co-operate with the repression of the dyskerin or hTERT in order to sensitise tumour cells to lower doses of drug treatment.

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7.5 Development of clinically relevant approaches to targeting hTERT or dyskerin To date, there are no pharmalogical inhibitors specifically directed towards hTERT or dyskerin. The full benefit of siRNA for therapeutic application awaits the development of clinically relevant techniques for efficient delivery of siRNA to malignant cells in patients [493]. In addition to demonstrating the therapeutic potential of directly targeting hTERT or dyskerin, this study has highlighted the utility of our isogenic mesenchymal tumour model for distinguishing compounds that selectively halt the proliferation of immortal and cancer cells without adverse effect on proliferating normal cells. Consequently, this model has now been applied in a high throughput screen of a chemical library of 11, 000 structurally diverse compounds to identify compounds that mimic the effects of targeting dyskerin or hTERT. Compounds with efficacy toward immortal and tumorigenic cells will have potential therapeutic relevance to a broad-spectrum of childhood and adult malignancies, with particular relevance to those of mesenchymal origin. Effective compounds will firstly be tested in relevant cancer cell line models and then extended to demonstrate efficacy on tumour formation in vivo.

An alternative approach to identify specific inhibitors of hTERT or dyskerin would be to use gene expression-based drug screen with mRNA expression levels or defined gene expression signatures as a read out [672-674]. Previous applications of gene expression-based drug screens have previously been successfully utilised to identify compounds that modulate the Hsp90 pathway and PDGF signalling and induce differentiation in leukemic cells [672-674].

7.6 Concluding remarks In conclusion, this study demonstrates that hTERT or dyskerin are required for the proliferation of immortal and tumorigenic myofibroblasts, and illustrate telomerase independent consequences of targeting these components. Activation of a DNA damage response following the repression of hTERT and engagement of pRb/E2F tumour suppressor pathway were implicated as central mediators of these effects. Collectively, the findings of this study demonstrate the exploitation of hTERT and

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APPENDIX

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A. Appendix

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Figure A.1. Protein Marker: Kaleidoscope protein Marker (Biorad) The ladder of Precision Plus Protein standard Biorad Kaleidoscope Marker run on a 4-20% Tris-HCL gel

Figure A.2. DNA ladders DNA ladders used in this study include A) 1Kb Plus Marker (Invitrogen), B) 1 kb ladder ( Invitrogen), C) Marker III (Roche), D) Marker VI (Roche).

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Figure A.3: Repression of hTERT or dyskerin impairs soft agarose colony formation Cells (1000 cells/plate) were plated and colonies were grown in soft agarose and viewed and counted after 14 days incubation at 37°C. Representative photomicrographs in duplicate of colonies formed by MRC5hTERT-TZT cells transfected with siRNA and treated with BIBIR1532 showing no difference in colony size. Colonies were counted under phase light with 4 x objective with light inverted microscope CK2 (Olympus). Pictures of colonies were taken by camera and captured by Image Pro 6.2 software with light inverted microscope TE2000-U (Eclipse, Nickon).White scale bar indicates 100 µM.

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Figure A.4. ShRNA targeting hTERT or dyskerin impairs colony formation in soft agarose. Cells (1000 cells/plate) were plated and colonies were grown in soft agarose and viewed and counted after 14 days incubation at 37°C. Representative photomicrographs in duplicate of colonies formed by MRC5hTERT-TZT cells (10-30 PD post-transduction) with vectors encoding Sc shRNA and shRNA targeting hTERT or dyskerin. Colonies were counted under phase light with 4 x objective with light inverted microscope CK2 (Olympus). Pictures of colonies were taken by camera and captured by Image Pro 6.2 software with light inverted microscope TE2000-U (Eclipse, Nickon).White scale bar indicates 100 µM..

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Table A.1 Additional gene sets correlating with gene expression changes induced by repression of hTERT that were common in normal, immortal and tumorigenic cells (Top 100) Gene sets correlating to the repression of hTERT Tumorigenic Immortal Normal Common to normal, immortal and tumorigenic cells (Negative NES n=80/335) Pathways Size NES FDR NES FDR NES FDR BLUM_RESPONSE_TO_SALIRASIB_DN 336 -2.95 0.00 -2.81 0.00 -2.49 0.00 ODONNELL_TFRC_TARGETS_DN 132 -2.95 0.00 -2.82 0.00 -2.53 0.00 FUJII_YBX1_TARGETS_DN T1 139 -2.95 0.00 -2.85 0.00 -2.59 0.00 HOFFMANN_LARGE_TO_SMALL_PRE_BII_LYMPHOCYTE_UP 94 -2.89 0.00 -2.81 0.00 -2.57 0.00 MORI_IMMATURE_B_LYMPHOCYTE_DN 53 -2.87 0.00 -2.70 0.00 -2.61 0.00 MOLENAAR_TARGETS_OF_CCND1_AND_CDK4_DN T2 56 -2.87 0.00 -2.86 0.00 -2.56 0.00 HORIUCHI_WTAP_TARGETS_DN 300 -2.86 0.00 -2.79 0.00 -2.63 0.00 LI_WILMS_TUMOR_VS_FETAL_KIDNEY_1_DN 161 -2.85 0.00 -2.71 0.00 -2.42 0.00 LINDGREN_BLADDER_CANCER_CLUSTER_3_UP 318 -2.85 0.00 -2.81 0.00 -2.45 0.00 MITSIADES_RESPONSE_TO_APLIDIN_DN 246 -2.84 0.00 -2.62 0.00 -2.52 0.00 MORI_LARGE_PRE_BII_LYMPHOCYTE_UP 52 -2.83 0.00 -2.75 0.00 -2.55 0.00 MUELLER_PLURINET 294 -2.83 0.00 -2.63 0.00 -2.37 0.00 REACTOME_G2_M_CHECKPOINTS T2 41 -2.83 0.00 -2.75 0.00 -2.40 0.00 LE_EGR2_TARGETS_UP T1 100 -2.81 0.00 -2.59 0.00 -2.49 0.00 REACTOME_ACTIVATION_OF_THE_PRE_REPLICATIVE_COMPLEX 29 -2.81 0.00 -2.60 0.00 -2.24 0.00 BASAKI_YBX1_TARGETS_UP 287 -2.80 0.00 -2.87 0.00 -2.53 0.00 FRASOR_RESPONSE_TO_SERM_OR_FULVESTRANT_DN 42 -2.79 0.00 -2.62 0.00 -2.40 0.00 FARMER_BREAST_CANCER_CLUSTER_2 32 -2.77 0.00 -2.72 0.00 -2.53 0.00 ODONNELL_TARGETS_OF_MYC_AND_TFRC_DN 44 -2.76 0.00 -2.60 0.00 -2.29 0.00 KAUFFMANN_MELANOMA_RELAPSE_UP 57 -2.76 0.00 -2.68 0.00 -2.48 0.00 FOURNIER_ACINAR_DEVELOPMENT_LATE_2 273 -2.75 0.00 -2.68 0.00 -2.54 0.00 REACTOME_G1_S_TRANSITION T2 100 -2.75 0.00 -2.79 0.00 -2.44 0.00 REACTOME_ACTIVATION_OF_ATR_IN_RESPONSE_TO_REPLICATION_STRESS T2 36 -2.74 0.00 -2.68 0.00 -2.28 0.00 GARCIA_TARGETS_OF_FLI1_AND_DAX1_DN 150 -2.71 0.00 -2.51 0.00 -2.24 0.00 KEGG_CELL_CYCLE 126 -2.69 0.00 -2.59 0.00 -2.42 0.00 CHIANG_LIVER_CANCER_SUBCLASS_PROLIFERATION_UP T2 136 -2.67 0.00 -2.62 0.00 -2.33 0.00 REACTOME_DNA_STRAND_ELONGATION T2 30 -2.66 0.00 -2.51 0.00 -2.21 0.00 PYEON_HPV_POSITIVE_TUMORS_UP 87 -2.65 0.00 -2.44 0.00 -2.29 0.00 PYEON_CANCER_HEAD_AND_NECK_VS_CERVICAL_UP 180 -2.63 0.00 -2.46 0.00 -2.36 0.00 KEGG_DNA_REPLICATION T2 36 -2.63 0.00 -2.42 0.00 -2.14 0.00 BIDUS_METASTASIS_UP 208 -2.62 0.00 -2.24 0.00 -2.33 0.00 NAKAYAMA_SOFT_TISSUE_TUMORS_PCA2_UP 88 -2.62 0.00 -2.60 0.00 -2.46 0.00 LY_AGING_OLD_DN 47 -2.60 0.00 -2.64 0.00 -2.41 0.00 EGUCHI_CELL_CYCLE_RB1_TARGETS T2 19 -2.60 0.00 -2.58 0.00 -2.25 0.00 REACTOME_DNA_REPLICATION_PRE_INITIATION T2 75 -2.59 0.00 -2.65 0.00 -2.26 0.00 AMUNDSON_GAMMA_RADIATION_RESPONSE 35 -2.59 0.00 -2.53 0.00 -2.51 0.00 FURUKAWA_DUSP6_TARGETS_PCI35_DN 66 -2.58 0.00 -2.79 0.00 -2.46 0.00 REACTOME_CELL_CYCLE_CHECKPOINTS T2 108 -2.58 0.00 -2.62 0.00 -2.39 0.00 290

Gene sets correlating to the repression of hTERT Tumorigenic Immortal Normal CHEMNITZ_RESPONSE_TO_PROSTAGLANDIN_E2_UP 140 -2.57 0.00 -2.47 0.00 -2.32 0.00 REACTOME_E2F_MEDIATED_REGULATION_OF_DNA_REPLICATION T2 31 -2.57 0.00 -2.51 0.00 -2.24 0.00 KAUFFMANN_DNA_REPAIR_GENES T2 204 -2.56 0.00 -2.50 0.00 -2.40 0.00 PUJANA_BREAST_CANCER_LIT_INT_NETWORK 100 -2.54 0.00 -2.41 0.00 -2.32 0.00 KEGG_SPLICEOSOME 126 -2.54 0.00 -2.39 0.00 -2.41 0.00 BOYAULT_LIVER_CANCER_SUBCLASS_G23_UP 52 -2.52 0.00 -2.36 0.00 -1.98 0.00 FINETTI_BREAST_CANCER_BASAL_VS_LUMINAL 16 -2.51 0.00 -2.42 0.00 -2.26 0.00 GARGALOVIC_RESPONSE_TO_OXIDIZED_PHOSPHOLIPIDS_TURQUOISE_DN 52 -2.50 0.00 -2.52 0.00 -2.07 0.00 KAUFFMANN_DNA_REPLICATION_GENES T2 128 -2.50 0.00 -2.50 0.00 -2.17 0.00 REACTOME_G2_M_TRANSITION T2 80 -2.49 0.00 -2.42 0.00 -2.42 0.00 REACTOME_DNA_REPAIR T2 104 -2.48 0.00 -2.38 0.00 -2.31 0.00 REACTOME_METABOLISM_OF_RNA 96 -2.47 0.00 -2.32 0.00 -2.13 0.00 FINETTI_BREAST_CANCER_KINOME_RED 15 -2.46 0.00 -2.39 0.00 -2.21 0.00 BENPORATH_ES_1 372 -2.45 0.00 -2.46 0.00 -2.25 0.00 NAKAMURA_CANCER_MICROENVIRONMENT_DN 46 -2.43 0.00 -2.17 0.00 -1.96 0.00 REACTOME_ELONGATION_AND_PROCESSING_OF_CAPPED_TRANSCRIPTS 133 -2.40 0.00 -2.07 0.00 -2.28 0.00 REACTOME_EXTENSION_OF_TELOMERES 27 -2.39 0.00 -2.23 0.00 -2.11 0.00 REACTOME_LAGGING_STRAND_SYNTHESIS 20 -2.39 0.00 -2.26 0.00 -1.97 0.00 LY_AGING_MIDDLE_DN 15 -2.38 0.00 -2.28 0.00 -2.22 0.00 REACTOME_FORMATION_AND_MATURATION_OF_MRNA_TRANSCRIPT 151 -2.37 0.00 -2.10 0.00 -2.30 0.00 PAL_PRMT5_TARGETS_UP 182 -2.37 0.00 -2.36 0.00 -2.28 0.00 ALCALAY_AML_BY_NPM1_LOCALIZATION_DN 181 -2.36 0.00 -2.36 0.00 -1.96 0.00 BOYAULT_LIVER_CANCER_SUBCLASS_G3_UP 184 -2.36 0.00 -2.25 0.00 -2.32 0.00 MORI_EMU_MYC_LYMPHOMA_BY_ONSET_TIME_UP 94 -2.35 0.00 -2.16 0.00 -2.08 0.00 NADERI_BREAST_CANCER_PROGNOSIS_UP 37 -2.34 0.00 -2.21 0.00 -1.96 0.00 KEGG_HOMOLOGOUS_RECOMBINATION 28 -2.34 0.00 -2.14 0.00 -1.97 0.00 REACTOME_CENTROSOME_MATURATION 68 -2.31 0.00 -2.16 0.00 -2.25 0.00 REACTOME_LOSS_OF_NLP_FROM_MITOTIC_CENTROSOMES 60 -2.31 0.00 -2.17 0.00 -2.28 0.00 GARY_CD5_TARGETS_DN 422 -2.30 0.00 -2.40 0.00 -2.34 0.00 CROONQUIST_NRAS_VS_STROMAL_STIMULATION_DN 80 -2.30 0.00 -2.19 0.00 -1.84 0.00 REACTOME_DOUBLE_STRAND_BREAK_REPAIR 21 -2.30 0.00 -1.98 0.00 -1.89 0.00 KEGG_MISMATCH_REPAIR 23 -2.29 0.00 -2.18 0.00 -2.09 0.00 REACTOME_E2F_TRANSCRIPTIONAL_TARGETS_AT_G1_S 20 -2.29 0.00 -2.15 0.00 -1.91 0.00 BIOCARTA_MCM_PATHWAY 18 -2.29 0.00 -2.32 0.00 -2.09 0.00 KEGG_RNA_DEGRADATION 55 -2.26 0.00 -2.33 0.00 -1.82 0.00 REACTOME_HIV_LIFE_CYCLE 103 -2.25 0.00 -2.15 0.00 -2.22 0.00 NAKAMURA_TUMOR_ZONE_PERIPHERAL_VS_CENTRAL_UP 276 -2.25 0.00 -1.99 0.00 -2.10 0.00 REACTOME_GLOBAL_GENOMIC_NER 33 -2.25 0.00 -2.16 0.00 -2.23 0.00 KIM_WT1_TARGETS_DN 448 -2.25 0.00 -2.33 0.00 -2.33 0.00 REACTOME_LATE_PHASE_OF_HIV_LIFE_CYCLE 90 -2.23 0.00 -2.10 0.00 -2.16 0.00 REACTOME_M_G1_TRANSITION 61 -2.22 0.00 -2.47 0.00 -2.09 0.00 LINDGREN_BLADDER_CANCER_CLUSTER_1_DN 369 -2.22 0.00 -2.30 0.00 -1.93 0.00

291

Gene sets correlating to the repression of hTERT Tumorigenic Immortal Normal GEORGES_CELL_CYCLE_MIR192_TARGETS 59 -2.21 0.00 -2.08 0.00 -2.07 0.00 Common to normal, immortal and tumorigenic cells (Postive NES n=17/37) Pathways Size NES FDR NES FDR NES FDR BROWNE_HCMV_INFECTION_18HR_DN T3 174 1.96 0.01 2.14 0.01 1.80 0.04 VALK_AML_CLUSTER_9 35 1.94 0.01 1.86 0.04 1.59 0.10 VANTVEER_BREAST_CANCER_ESR1_UP 139 1.92 0.01 1.77 0.06 1.64 0.08 FARMER_BREAST_CANCER_CLUSTER_5 19 1.88 0.01 2.11 0.01 2.05 0.01 GARGALOVIC_RESPONSE_TO_OXIDIZED_PHOSPHOLIPIDS_YELLOW_UP 31 1.83 0.01 1.77 0.05 1.79 0.04 MANALO_HYPOXIA_UP 200 1.83 0.02 1.79 0.05 1.89 0.03 LEE_TARGETS_OF_PTCH1_AND_SUFU_UP 78 1.82 0.02 1.77 0.05 1.82 0.04 UDAYAKUMAR_MED1_TARGETS_DN 237 1.82 0.02 1.82 0.04 1.55 0.10 KEGG_OTHER_GLYCAN_DEGRADATION 15 1.82 0.02 1.68 0.08 2.14 0.00 PARK_APL_PATHOGENESIS_DN 38 1.73 0.03 1.65 0.09 1.61 0.09 SWEET_KRAS_TARGETS_DN T1 25 1.72 0.03 1.69 0.08 1.59 0.09 KEGG_GLYCOSPHINGOLIPID_BIOSYNTHESIS_GANGLIO_SERIES 15 1.71 0.03 1.75 0.06 1.58 0.10 MCCLUNG_DELTA_FOSB_TARGETS_8WK 44 1.70 0.03 1.86 0.04 1.58 0.10 LUND_SILENCED_BY_METHYLATION 16 1.70 0.03 1.93 0.03 1.93 0.02 REACTOME_INTEGRIN_CELL_SURFACE_INTERACTIONS 81 1.69 0.03 1.77 0.06 1.76 0.05 VERRECCHIA_RESPONSE_TO_TGFB1_C2 T1 19 1.67 0.04 1.89 0.03 1.90 0.03 ZHAN_MULTIPLE_MYELOMA_MS_UP 47 1.62 0.05 1.67 0.08 1.69 0.06 Notes: Gene sets corresponding to pathways labelled T1-4 are indicated in Table 6.7. NES Normalised enrichment scores, FDR-False Discovery Rate.

292

Table A.2 Additional gene sets correlating with gene expression changes induced by repression of hTR in normal, immortal and/or tumorigenic cells (Top 100) Gene sets correlating to the repression of hTR Tumorigenic Immortal Normal Unique to normal cells only (Positive NES n=80/330 gene sets) Pathways Gene set size NES FDR NES FDR NES FDR REN_BOUND_BY_E2F R6 47 -1.13 0.35 -1.65 0.03 2.64 0.00 RHODES_UNDIFFERENTIATED_CANCER 60 -1.23 0.22 -1.38 0.14 2.46 0.00 ROSTY_CERVICAL_CANCER_PROLIFERATION_CLUSTER R6 139 1.06 0.76 -1.31 0.19 3.09 0.00 SESTO_RESPONSE_TO_UV_C6 39 1.46 0.51 1.11 0.55 2.39 0.00 SOTIRIOU_BREAST_CANCER_GRADE_1_VS_3_UP R5 150 -0.85 0.78 -1.45 0.09 2.88 0.00 WHITEFORD_PEDIATRIC_CANCER_MARKERS 92 0.83 1.00 -1.34 0.17 2.74 0.00 ZHAN_MULTIPLE_MYELOMA_PR_UP 44 0.92 0.90 -1.25 0.25 2.61 0.00 ZHANG_RESPONSE_TO_IKK_INHIBITOR_AND_TNF_DN R3 101 -1.43 0.08 -1.55 0.05 2.53 0.00 LY_AGING_OLD_DN 47 -0.96 0.61 -1.41 0.12 2.34 0.00 RIZ_ERYTHROID_DIFFERENTIATION_CCNE1 39 -0.79 0.85 -1.11 0.45 2.35 0.00 MARKEY_RB1_ACUTE_LOF_DN R6 205 -1.74 0.01 -1.40 0.09 2.36 0.00 REACTOME_DNA_STRAND_ELONGATION R6 30 -0.73 0.91 -1.52 0.06 2.38 0.00 REACTOME_TELOMERE_MAINTENANCE 75 -1.81 0.01 1.34 0.29 2.38 0.00 AMUNDSON_GAMMA_RADIATION_RESPONSE R5 35 1.08 0.74 -0.94 0.72 2.30 0.00 SHEDDEN_LUNG_CANCER_POOR_SURVIVAL_A6 451 -1.69 0.01 -1.93 0.00 2.30 0.00 KEGG_DNA_REPLICATION R6 36 -0.97 0.60 -1.52 0.06 2.30 0.00 BASAKI_YBX1_TARGETS_UP 287 -1.39 0.10 -1.50 0.07 2.31 0.00 WINNEPENNINCKX_MELANOMA_METASTASIS_UP 157 -1.73 0.01 -1.60 0.03 2.28 0.00 REACTOME_G2_M_CHECKPOINTS R6 41 -1.31 0.15 -1.76 0.01 2.27 0.00 PUJANA_BREAST_CANCER_WITH_BRCA1_MUTATED_UP 54 -1.64 0.02 -1.94 0.00 2.25 0.00 PUJANA_BRCA2_PCC_NETWORK 417 -1.68 0.01 -2.01 0.00 2.25 0.00 ODONNELL_TARGETS_OF_MYC_AND_TFRC_DN 44 -1.77 0.01 -1.06 0.45 2.26 0.00 REACTOME_CELL_CYCLE_MITOTIC R6 301 -1.54 0.04 -1.78 0.01 2.24 0.00 LI_WILMS_TUMOR_VS_FETAL_KIDNEY_1_DN 161 -1.47 0.06 -1.86 0.00 2.25 0.00 FRASOR_RESPONSE_TO_SERM_OR_FULVESTRANT_DN R1 42 -0.95 0.63 -1.28 0.22 2.23 0.00 KEGG_MISMATCH_REPAIR R6 23 -0.75 0.89 -1.45 0.09 2.24 0.00 REACTOME_POST_TRANSLATIONAL_PROTEIN_MODIFICATION 40 -0.85 0.78 0.88 0.84 2.22 0.00 FERREIRA_EWINGS_SARCOMA_UNSTABLE_VS_STABLE_UP 147 -1.30 0.16 -1.86 0.00 2.22 0.00 MOLENAAR_TARGETS_OF_CCND1_AND_CDK4_DN R6 56 -0.96 0.62 -1.31 0.19 2.22 0.00 SCIAN_CELL_CYCLE_TARGETS_OF_TP53_AND_TP73_DN R6 22 0.79 1.00 -0.83 0.87 2.22 0.00 HOFFMANN_LARGE_TO_SMALL_PRE_BII_LYMPHOCYTE_UP 94 -1.18 0.27 -1.55 0.05 2.21 0.00 YU_MYC_TARGETS_UP 37 -0.94 0.64 -1.29 0.21 2.21 0.00 PYEON_CANCER_HEAD_AND_NECK_VS_CERVICAL_UP 180 -1.62 0.02 -1.83 0.01 2.19 0.00 REACTOME_ACTIVATION_OF_ATR_IN_RESPONSE_TO_REPLICATION_STRESS R6 36 -1.25 0.20 -1.72 0.02 2.19 0.00 LE_EGR2_TARGETS_UP R1 100 -0.88 0.73 -1.64 0.03 2.19 0.00 WU_APOPTOSIS_BY_CDKN1A_VIA_TP53 R6 36 1.30 0.76 -0.96 0.70 2.19 0.00 RIGGI_EWING_SARCOMA_PROGENITOR_DN 181 -1.51 0.05 1.39 0.24 2.16 0.00 293

Gene sets correlating to the repression of hTR Tumorigenic Immortal Normal LINDGREN_BLADDER_CANCER_CLUSTER_3_UP 318 -1.70 0.01 -1.71 0.02 2.16 0.00 ALONSO_METASTASIS_UP 156 -1.53 0.04 -1.22 0.29 2.15 0.00 EGUCHI_CELL_CYCLE_RB1_TARGETS R6 19 0.84 1.00 -1.14 0.41 2.15 0.00 FINETTI_BREAST_CANCER_BASAL_VS_LUMINAL 16 -1.09 0.40 -1.13 0.43 2.13 0.00 HORIUCHI_WTAP_TARGETS_DN 300 -1.32 0.14 -1.70 0.02 2.13 0.00 LY_AGING_MIDDLE_DN 15 1.35 0.66 -0.94 0.72 2.12 0.00 BEGUM_TARGETS_OF_PAX3_FOXO1_FUSION_UP 50 -0.92 0.67 0.95 0.76 2.12 0.00 XU_HGF_TARGETS_INDUCED_BY_AKT1_48HR_DN 18 1.44 0.48 -1.13 0.42 2.12 0.00 REACTOME_REPAIR_SYNTHESIS_OF_PATCH_27_30_BASES_LONG_BY_DNA_POLYM R6 ERASE 15 0.96 0.87 -1.12 0.44 2.11 0.00 REACTOME_LAGGING_STRAND_SYNTHESIS R6 20 -0.85 0.77 -1.44 0.10 2.11 0.00 CROMER_TUMORIGENESIS_UP 44 -1.41 0.09 1.23 0.39 2.10 0.00 FINETTI_BREAST_CANCER_KINOME_RED 15 -1.15 0.32 -1.15 0.40 2.09 0.00 VERNELL_RETINOBLASTOMA_PATHWAY_UP R6 39 -1.52 0.05 -1.09 0.48 2.09 0.00 VANTVEER_BREAST_CANCER_METASTASIS_DN 106 -1.13 0.34 -1.69 0.02 2.08 0.00 SONG_TARGETS_OF_IE86_CMV_PROTEIN 47 -1.50 0.05 -1.85 0.00 2.08 0.00 LY_AGING_PREMATURE_DN 24 -1.07 0.42 -0.67 0.97 2.07 0.00 SASAKI_ADULT_T_CELL_LEUKEMIA 139 -1.75 0.01 -1.31 0.20 2.06 0.00 OLSSON_E2F3_TARGETS_DN R6 27 -0.71 0.92 -0.58 0.99 2.06 0.00 KEGG_GLYCOSYLPHOSPHATIDYLINOSITOL_GPI_ANCHOR_BIOSYNTHESIS 25 -0.82 0.81 -0.66 0.97 2.06 0.00 REACTOME_MITOTIC_PROMETAPHASE R6 92 -1.27 0.18 -1.60 0.04 2.05 0.00 GAL_LEUKEMIC_STEM_CELL_DN 240 -1.37 0.11 -0.81 0.88 2.05 0.00 TOYOTA_TARGETS_OF_MIR34B_AND_MIR34C 447 -1.65 0.02 -2.13 0.00 2.04 0.00 CONCANNON_APOPTOSIS_BY_EPOXOMICIN_DN 166 -0.95 0.63 -1.08 0.51 2.04 0.00 CLASPER_LYMPHATIC_VESSELS_DURING_METASTASIS_DN 36 0.78 1.00 0.97 0.75 2.04 0.00 REACTOME_EXTENSION_OF_TELOMERES 27 -0.84 0.79 -1.44 0.10 2.04 0.00 VECCHI_GASTRIC_CANCER_ADVANCED_VS_EARLY_UP 164 -1.51 0.05 -1.36 0.16 2.04 0.00 FARMER_BREAST_CANCER_CLUSTER_2 32 0.75 1.00 -1.60 0.04 2.03 0.00 NAKAMURA_CANCER_MICROENVIRONMENT_DN 46 -1.47 0.06 -1.67 0.02 2.03 0.00 PYEON_HPV_POSITIVE_TUMORS_UP 87 -1.02 0.52 -1.63 0.03 2.03 0.00 KORKOLA_TERATOMA 32 -1.04 0.48 -0.53 1.00 2.03 0.00 CHIARADONNA_NEOPLASTIC_TRANSFORMATION_KRAS_UP 117 -1.27 0.24 -1.58 0.03 2.03 0.00 CHEN_LVAD_SUPPORT_OF_FAILING_HEART_DN 42 -1.29 0.17 1.14 0.50 2.02 0.00 PAL_PRMT5_TARGETS_UP 182 -1.51 0.05 -1.77 0.01 2.02 0.00 NADERI_BREAST_CANCER_PROGNOSIS_UP 37 0.96 0.88 -0.95 0.71 2.02 0.00 CHANG_CORE_SERUM_RESPONSE_UP R1 69 -1.21 0.24 -1.33 0.18 2.01 0.00 ONDER_CDH1_TARGETS_2_UP 254 -1.25 0.20 -1.07 0.52 2.01 0.00 KEGG_ECM_RECEPTOR_INTERACTION 83 1.04 0.79 -0.82 0.87 2.01 0.00 SENGUPTA_NASOPHARYNGEAL_CARCINOMA_UP 275 -2.12 0.00 -1.73 0.02 2.00 0.00 ALONSO_METASTASIS_EMT_UP 30 -0.85 0.78 -1.07 0.51 2.00 0.00 KIM_WT1_TARGETS_DN 448 -1.91 0.00 -1.94 0.00 1.99 0.00 CUI_TCF21_TARGETS_UP 36 -0.73 0.91 0.93 0.80 1.99 0.00 REACTOME_FANCONI_ANEMIA_PATHWAY 17 -1.03 0.49 -1.34 0.17 1.99 0.00 Notes: Gene sets corresponding to pathways labelled R5-7 are indicated in Table 6.7. NES Normalised enrichment scores, FDR-False Discovery Rate.

294

Gene sets correlating to the repression of hTR cont. Tumorigenic Immortal Normal Unique to immortal cells only (Negative NES n=585/605 gene sets) Pathways Gene set size NES FDR NES FDR NES FDR REACTOME_TOLL_RECEPTOR_CASCADES 83 -1.36 0.15 -2.15 0.00 -1.25 0.45 RASHI_RESPONSE_TO_IONIZING_RADIATION_2 119 -1.31 0.20 -2.13 0.00 -1.47 0.33 REACTOME_ORC1_REMOVAL_FROM_CHROMATIN 63 -1.40 0.12 -2.13 0.00 1.10 0.42 CONCANNON_APOPTOSIS_BY_EPOXOMICIN_UP 233 -1.34 0.17 -2.11 0.00 -1.40 0.37 XU_HGF_SIGNALING_NOT_VIA_AKT1_48HR_UP R1 33 -1.36 0.16 -2.11 0.00 -1.19 0.46 HELLER_HDAC_TARGETS_SILENCED_BY_METHYLATION_DN 242 -1.37 0.15 -2.09 0.00 1.60 0.06 XU_HGF_TARGETS_INDUCED_BY_AKT1_6HR 17 1.92 0.03 -2.09 0.00 -0.80 0.89 REACTOME_INNATE_IMMUNITY_SIGNALING 103 -1.24 0.27 -2.10 0.00 -1.18 0.46 KEGG_APOPTOSIS 88 -1.42 0.11 -2.10 0.00 -1.43 0.36 REACTOME_TOLL_LIKE_RECEPTOR_3_CASCADE R3 56 -1.38 0.13 -2.07 0.00 -1.31 0.41 TONKS_TARGETS_OF_RUNX1_RUNX1T1_FUSION_HSC_UP 184 -1.03 0.58 -2.07 0.00 1.38 0.17 WINTER_HYPOXIA_METAGENE 217 -1.36 0.16 -2.07 0.00 1.19 0.31 BIOCARTA_TID_PATHWAY 19 -1.24 0.27 -2.06 0.00 -1.28 0.42 GRUETZMANN_PANCREATIC_CANCER_UP 343 -1.22 0.30 -2.06 0.00 1.62 0.06 HOFFMANN_PRE_BI_TO_LARGE_PRE_BII_LYMPHOCYTE_DN 52 -0.93 0.73 -2.05 0.00 0.99 0.58 CORRE_MULTIPLE_MYELOMA_UP 67 -1.26 0.24 -2.05 0.00 -1.35 0.40 REACTOME_SIGNALING_BY_WNT R1 58 -1.17 0.36 -2.04 0.00 -0.88 0.81 TARTE_PLASMA_CELL_VS_B_LYMPHOCYTE_DN 38 -1.37 0.15 -2.04 0.00 -1.40 0.36 WANG_HCP_PROSTATE_CANCER 80 -1.26 0.25 -2.03 0.00 -1.13 0.49 CASORELLI_ACUTE_PROMYELOCYTIC_LEUKEMIA_UP 168 -1.39 0.13 -2.03 0.00 1.44 0.14 AMIT_SERUM_RESPONSE_60_MCF10A R1 56 -1.24 0.27 -2.03 0.00 1.48 0.11 BIOCARTA_P38MAPK_PATHWAY 39 -1.24 0.27 -2.03 0.00 -0.87 0.82 SAGIV_CD24_TARGETS_DN 45 -0.87 0.82 -2.02 0.00 -0.95 0.71 KOKKINAKIS_METHIONINE_DEPRIVATION_96HR_DN R1 72 -1.26 0.25 -2.02 0.00 1.54 0.09 KEGG_PRION_DISEASES 35 -1.12 0.43 -2.02 0.00 -0.92 0.74 RICKMAN_TUMOR_DIFFERENTIATED_WELL_VS_POORLY_DN R9 369 -1.23 0.28 -2.02 0.00 -1.40 0.37 BILBAN_B_CLL_LPL_UP 61 -1.40 0.12 -2.01 0.00 0.95 0.65 MARSON_FOXP3_TARGETS_DN 38 -1.38 0.14 -2.01 0.00 -1.34 0.40 LEE_LIVER_CANCER_MYC_TGFA_UP 59 -0.65 0.98 -2.01 0.00 -0.97 0.68 WANG_CISPLATIN_RESPONSE_AND_XPC_DN 141 -1.29 0.21 -2.01 0.00 -1.42 0.36 GENTILE_UV_LOW_DOSE_DN 18 -0.81 0.88 -2.00 0.00 -1.36 0.40 HONMA_DOCETAXEL_RESISTANCE R11 31 -1.02 0.60 -2.00 0.00 -1.08 0.56 DAZARD_RESPONSE_TO_UV_SCC_UP 83 -1.35 0.16 -2.00 0.00 1.77 0.02 DANG_REGULATED_BY_MYC_DN 241 -0.99 0.65 -1.99 0.00 1.43 0.14 RUTELLA_RESPONSE_TO_HGF_VS_CSF2RB_AND_IL4_UP 405 -1.32 0.19 -1.99 0.00 1.19 0.31 HESS_TARGETS_OF_HOXA9_AND_MEIS1_DN R9 69 1.10 0.56 -1.99 0.00 0.99 0.58 GALE_APL_WITH_FLT3_MUTATED_DN 16 0.93 0.81 -1.99 0.00 0.91 0.71 ELVIDGE_HYPOXIA_UP 165 -1.41 0.12 -1.99 0.00 1.48 0.11 GENTILE_UV_LOW_DOSE_UP 18 -0.79 0.90 -1.98 0.00 -1.39 0.37 WU_HBX_TARGETS_3_UP 17 -1.21 0.30 -1.98 0.00 -1.53 0.30 DAZARD_UV_RESPONSE_CLUSTER_G1 38 -0.80 0.89 -1.98 0.00 1.06 0.48 LEE_LIVER_CANCER_DENA_UP 59 -1.02 0.60 -1.97 0.00 1.18 0.32 HOFMANN_MYELODYSPLASTIC_SYNDROM_LOW_RISK_DN 25 -1.32 0.19 -1.97 0.00 -1.42 0.36 295

KEGG_NOTCH_SIGNALING_PATHWAY 47 -1.18 0.35 -1.97 0.00 -1.16 0.48 APPIERTO_RESPONSE_TO_FENRETINIDE_UP 32 -0.94 0.72 -1.97 0.00 -0.96 0.69 GERY_CEBP_TARGETS 115 -1.21 0.30 -1.96 0.00 1.60 0.06 MORI_SMALL_PRE_BII_LYMPHOCYTE_UP 60 -1.38 0.14 -1.95 0.00 -1.39 0.37 TAVOR_CEBPA_TARGETS_UP 44 -1.36 0.15 -1.95 0.00 -1.54 0.30 MONNIER_POSTRADIATION_TUMOR_ESCAPE_DN 320 -1.06 0.52 -1.95 0.00 1.78 0.02 LIU_TARGETS_OF_VMYB_VS_CMYB_DN 42 -1.34 0.17 -1.94 0.00 1.06 0.48 REACTOME_INTRINSIC_PATHWAY_FOR_APOPTOSIS 29 -1.10 0.47 -1.94 0.00 1.19 0.32 LEE_RECENT_THYMIC_EMIGRANT 99 -1.17 0.37 -1.94 0.00 1.98 0.00 DORN_ADENOVIRUS_INFECTION_24HR_DN 38 -0.91 0.77 -1.93 0.00 -0.96 0.69 KEGG_EPITHELIAL_CELL_SIGNALING_IN_HELICOBACTER_PYLORI_INFECTION 67 -1.24 0.27 -1.92 0.00 0.98 0.59 KEGG_NEUROTROPHIN_SIGNALING_PATHWAY 126 -1.20 0.32 -1.92 0.00 -1.33 0.41 HOUSTIS_ROS 30 1.14 0.50 -1.92 0.00 -1.55 0.31 RODWELL_AGING_KIDNEY_UP 334 -0.92 0.76 -1.92 0.00 1.13 0.38 HADDAD_B_LYMPHOCYTE_PROGENITOR 283 -1.41 0.12 -1.92 0.00 1.30 0.22 KRIGE_AMINO_ACID_DEPRIVATION 26 -1.43 0.11 -1.92 0.00 1.02 0.53 AMIT_SERUM_RESPONSE_240_MCF10A R1 57 -0.93 0.74 -1.92 0.00 -1.06 0.58 SUNG_METASTASIS_STROMA_UP 108 -1.36 0.15 -1.92 0.00 1.63 0.06 FERRARI_RESPONSE_TO_FENRETINIDE_UP R11 17 -1.39 0.13 -1.92 0.00 1.06 0.48 DORN_ADENOVIRUS_INFECTION_48HR_DN 34 -1.39 0.13 -1.91 0.00 -1.08 0.55 HARRIS_HYPOXIA 79 -1.33 0.18 -1.91 0.00 -1.20 0.46 BIOCARTA_DEATH_PATHWAY 33 -1.32 0.18 -1.91 0.00 -1.09 0.55 RAGHAVACHARI_PLATELET_SPECIFIC_GENES 69 -0.81 0.88 -1.91 0.00 -1.02 0.61 OUYANG_PROSTATE_CANCER_PROGRESSION_UP 18 1.01 0.69 -1.90 0.00 1.18 0.33 KOKKINAKIS_METHIONINE_DEPRIVATION_96HR_UP 117 -1.09 0.49 -1.90 0.00 1.25 0.26 AMIT_EGF_RESPONSE_120_MCF10A R1 42 -1.22 0.29 -1.90 0.00 -1.09 0.55 GAUSSMANN_MLL_AF4_FUSION_TARGETS_F_UP 131 0.95 0.76 -1.90 0.00 1.31 0.22 VARELA_ZMPSTE24_TARGETS_UP 39 -1.38 0.14 -1.89 0.00 -0.89 0.79 LEE_LIVER_CANCER_ACOX1_UP 59 -0.67 0.97 -1.89 0.00 -1.28 0.42 XU_HGF_SIGNALING_NOT_VIA_AKT1_6HR 24 -0.82 0.87 -1.89 0.00 -1.12 0.51 ZHAN_MULTIPLE_MYELOMA_UP 52 -1.20 0.32 -1.89 0.00 -1.22 0.46 LIANG_HEMATOPOIESIS_STEM_CELL_NUMBER_LARGE_VS_TINY_DN 42 -1.57 0.05 -1.88 0.00 -1.37 0.38 MORI_IMMATURE_B_LYMPHOCYTE_UP 40 -1.31 0.19 -1.87 0.00 -1.33 0.41 DAVICIONI_TARGETS_OF_PAX_FOXO1_FUSIONS_UP 252 -1.12 0.44 -1.87 0.00 -1.21 0.46 GARGALOVIC_RESPONSE_TO_OXIDIZED_PHOSPHOLIPIDS_RED_DN 24 1.05 0.62 -1.87 0.00 -1.37 0.38 CHUANG_OXIDATIVE_STRESS_RESPONSE_UP 29 -0.75 0.93 -1.87 0.00 -1.07 0.56 ENK_UV_RESPONSE_EPIDERMIS_UP 290 -1.13 0.42 -1.87 0.00 1.26 0.26 Notes: Gene sets corresponding to pathways labelled R1-11 are indicated in Table 6.7. NES Normalised enrichment scores, FDR-False Discovery Rate.

296

Gene sets correlating to the repression of hTR cont. Tumorigenic Immortal Normal Unique to tumorigenic cells only (Negative NES n=39/59 gene sets) Pathways Gene set size NES FDR NES FDR NES FDR JAATINEN_HEMATOPOIETIC_STEM_CELL_UP 311 -1.61 0.04 -1.27 0.18 1.59 0.07 LUI_THYROID_CANCER_CLUSTER_2 41 -1.61 0.04 -1.27 0.18 -0.94 0.72 REACTOME_RHO_GTPASE_CYCLE R1 119 -1.59 0.04 -1.22 0.23 -1.37 0.38 BIOCARTA_CALCINEURIN_PATHWAY 18 -1.59 0.04 -0.55 0.99 -0.71 0.95 KEGG_CYSTEINE_AND_METHIONINE_METABOLISM 34 -1.58 0.04 -1.33 0.13 -1.09 0.55 REACTOME_INTEGRIN_ALPHAIIBBETA3_SIGNALING 23 -1.58 0.04 -1.18 0.28 -0.85 0.84 REACTOME_BRANCHED_CHAIN_AMINO_ACID_CATABOLISM 17 -1.57 0.05 -1.23 0.22 -1.29 0.42 CHEN_HOXA5_TARGETS_9HR_DN R9 41 -1.56 0.05 -0.94 0.64 1.57 0.07 BIOCARTA_TGFB_PATHWAY R1 18 -1.55 0.05 -1.24 0.21 0.61 0.98 SWEET_KRAS_TARGETS_UP 21 -1.54 0.05 -1.06 0.44 -1.10 0.53 SOTIRIOU_BREAST_CANCER_GRADE_1_VS_3_DN 51 -1.54 0.05 -0.87 0.75 1.55 0.08 ASTON_MAJOR_DEPRESSIVE_DISORDER_UP 47 -1.53 0.06 -1.27 0.19 -0.73 0.94 CHEOK_RESPONSE_TO_MERCAPTOPURINE_AND_LD_MTX_DN 20 -1.53 0.06 -0.92 0.68 1.13 0.38 GENTILE_RESPONSE_CLUSTER_D3 44 -1.53 0.06 -1.39 0.10 1.62 0.06 BREDEMEYER_RAG_SIGNALING_NOT_VIA_ATM_DN 55 -1.53 0.06 -1.23 0.22 1.53 0.09 CERVERA_SDHB_TARGETS_2 110 -1.52 0.06 -1.28 0.18 -1.14 0.49 HASLINGER_B_CLL_WITH_17P13_DELETION 16 -1.52 0.06 -1.18 0.27 -0.91 0.76 LIANG_HEMATOPOIESIS_STEM_CELL_NUMBER_SMALL_VS_HUGE_DN 32 -1.52 0.06 -0.94 0.64 0.84 0.82 CHANG_POU5F1_TARGETS_UP 15 -1.52 0.06 -0.90 0.70 1.10 0.42 DEBIASI_APOPTOSIS_BY_REOVIRUS_INFECTION_DN 224 -1.51 0.06 -1.19 0.26 -1.26 0.43 ST_T_CELL_SIGNAL_TRANSDUCTION 44 -1.51 0.06 -1.25 0.20 -1.50 0.33 REACTOME_PYRUVATE_METABOLISM 17 -1.51 0.07 -0.57 0.99 0.94 0.67 VALK_AML_CLUSTER_6 33 -1.50 0.07 -1.27 0.18 1.25 0.26 BIOCARTA_MYOSIN_PATHWAY 31 -1.49 0.07 -1.22 0.23 -0.93 0.72 GU_PDEF_TARGETS_UP 68 -1.49 0.07 -1.01 0.53 1.52 0.10 REACTOME_ASSOCIATION_OF_TRIC_CCT_WITH_TARGET_PROTEINS_DURING_BIOS YNTHESIS 29 -1.49 0.08 -1.35 0.13 -0.84 0.85 REACTOME_BASE_EXCISION_REPAIR 18 -1.48 0.08 -1.01 0.53 1.07 0.46 REACTOME_REMOVAL_OF_DNA_PATCH_CONTAINING_ABASIC_RESIDUE 15 -1.48 0.08 -0.98 0.57 1.08 0.44 JAEGER_METASTASIS_UP 43 -1.48 0.08 -1.19 0.27 1.67 0.04 BROWNE_HCMV_INFECTION_12HR_DN 99 -1.47 0.08 -1.28 0.17 1.71 0.03 CADWELL_ATG16L1_TARGETS_DN 57 -1.47 0.08 -1.03 0.49 1.18 0.33 NIKOLSKY_BREAST_CANCER_6P24_P22_AMPLICON 22 -1.47 0.08 -0.91 0.69 -0.90 0.78 VANTVEER_BREAST_CANCER_BRCA1_DN 38 -1.47 0.08 -1.29 0.17 1.37 0.17 BIOCARTA_RHO_PATHWAY 32 -1.46 0.09 -1.21 0.24 -1.27 0.43 REACTOME_E2F_MEDIATED_REGULATION_OF_DNA_REPLICATION 31 -1.46 0.09 -1.27 0.18 1.67 0.04 RODWELL_AGING_KIDNEY_DN 110 -1.45 0.09 -1.29 0.17 1.62 0.06 REACTOME_GRB2_SOS_PROVIDES_LINKAGE_TO_MAPK_SIGNALING_FOR_INTERGR INS_ 15 -1.45 0.09 -1.10 0.38 0.75 0.91 BIOCARTA_MCM_PATHWAY 18 -1.45 0.09 -1.27 0.19 1.65 0.05

297

Unique to tumorigenic cells only (Positive NES n=10/30 gene sets) Pathways Gene set size NES FDR NES FDR NES FDR MISSIAGLIA_REGULATED_BY_METHYLATION_UP 99 1.75 0.06 -2.55 0.00 1.47 0.12 DIRMEIER_LMP1_RESPONSE_LATE_DN 25 1.75 0.06 -1.48 0.06 1.15 0.36 RORIE_TARGETS_OF_EWSR1_FLI1_FUSION_DN 22 1.73 0.06 0.70 1.00 0.80 0.87 FURUKAWA_DUSP6_TARGETS_PCI35_UP 66 1.71 0.06 -2.55 0.00 1.12 0.40 BROWNE_HCMV_INFECTION_6HR_UP R3 68 1.72 0.07 -2.31 0.00 -0.91 0.76 KEGG_METABOLISM_OF_XENOBIOTICS_BY_CYTOCHROME_P450 70 1.70 0.07 1.46 0.53 1.44 0.13 TAVOR_CEBPA_TARGETS_DN 23 1.66 0.09 -2.24 0.00 -0.85 0.84 GOUYER_TATI_TARGETS_DN 17 1.65 0.09 -0.98 0.58 1.30 0.23 LOPEZ_EPITHELIOID_MESOTHELIOMA 16 1.64 0.09 -0.90 0.71 1.20 0.31 Notes: Gene sets corresponding to pathways labelled R1-11 are indicated in Table 6.7. NES Normalised enrichment scores, FDR-False Discovery Rate.

298

Gene sets correlating to the repression of hTR cont. Tumorigenic Immortal Normal Unique to immortal and tumorigenic cells only (Positive NES n=80/607 gene sets) Pathways Gene set size NES FDR NES FDR NES FDR HAMAI_APOPTOSIS_VIA_TRAIL_UP 332 -2.22 0.00 -2.02 0.00 0.17 1.38 BOYAULT_LIVER_CANCER_SUBCLASS_G3_UP 184 -2.24 0.00 -2.00 0.00 0.09 1.54 MILI_PSEUDOPODIA_CHEMOTAXIS_UP 65 -2.20 0.00 -1.96 0.00 0.22 -1.71 MILI_PSEUDOPODIA 40 -2.19 0.00 -1.95 0.00 0.21 -1.76 MANALO_HYPOXIA_DN 284 -2.17 0.00 -1.94 0.00 0.02 1.78 DAZARD_UV_RESPONSE_CLUSTER_G6 125 -2.18 0.00 -1.92 0.00 0.57 -1.07 REACTOME_DEADENYLATION_OF_MRNA R8 22 -2.19 0.00 -1.85 0.00 0.38 -1.37 GEORGES_CELL_CYCLE_MIR192_TARGETS R8 59 -2.16 0.00 -1.85 0.00 0.05 1.63 BIDUS_METASTASIS_UP 208 -2.16 0.00 -1.74 0.01 0.01 1.93 MITSIADES_RESPONSE_TO_APLIDIN_DN 246 -2.34 0.00 -1.69 0.01 0.03 1.70 SHAFFER_IRF4_TARGETS_IN_ACTIVATED_B_LYMPHOCYTE 79 -2.15 0.00 -2.04 0.00 0.81 -0.88 ENK_UV_RESPONSE_KERATINOCYTE_DN 477 -2.15 0.00 -2.22 0.00 0.21 1.31 MONNIER_POSTRADIATION_TUMOR_ESCAPE_UP 358 -2.16 0.00 -2.04 0.00 0.46 -1.23 REACTOME_RNA_POLYMERASE_II_TRANSCRIPTION R8 91 -2.14 0.00 -2.15 0.00 0.41 -1.32 UDAYAKUMAR_MED1_TARGETS_UP 131 -2.15 0.00 -1.99 0.00 0.36 -1.41 REACTOME_SNRNP_ASSEMBLY R8 50 -2.14 0.00 -2.18 0.00 0.84 -0.85 BIOCARTA_EIF_PATHWAY 16 -2.12 0.00 -1.39 0.10 0.36 -1.44 REACTOME_TRANSLATION R8 120 -2.12 0.00 -2.01 0.00 0.22 -1.72 PUJANA_XPRSS_INT_NETWORK 166 -2.12 0.00 -1.76 0.01 0.00 2.61 SCHUHMACHER_MYC_TARGETS_UP 69 -2.12 0.00 -1.82 0.00 0.91 -0.77 TOYOTA_TARGETS_OF_MIR34B_AND_MIR34C R8 447 -2.13 0.00 -1.65 0.02 0.00 2.04 SCHLOSSER_MYC_TARGETS_AND_SERUM_RESPONSE_DN 47 -2.12 0.00 -2.27 0.00 0.61 0.97 RICKMAN_TUMOR_DIFFERENTIATED_MODERATELY_VS_POORLY_DN 41 -2.14 0.00 -1.75 0.01 0.55 1.01 SEIDEN_ONCOGENESIS_BY_MET 87 -2.12 0.00 -2.07 0.00 0.25 1.26 RICKMAN_TUMOR_DIFFERENTIATED_MODERATELY_VS_POORLY_UP 41 -2.12 0.00 -1.79 0.01 0.54 1.01 WONG_EMBRYONIC_STEM_CELL_CORE 331 -2.10 0.00 -1.77 0.01 0.02 1.77 REACTOME_HIV_LIFE_CYCLE 103 -2.10 0.00 -2.05 0.00 0.49 -1.14 REACTOME_METABOLISM_OF_MRNA R8 46 -2.10 0.00 -2.00 0.00 0.51 -1.12 REACTOME_LATE_PHASE_OF_HIV_LIFE_CYCLE 90 -2.09 0.00 -2.11 0.00 0.46 -1.21 NOUZOVA_TRETINOIN_AND_H4_ACETYLATION 128 -2.09 0.00 -2.27 0.00 0.54 1.02 DING_LUNG_CANCER_EXPRESSION_BY_COPY_NUMBER 93 -2.06 0.00 -2.36 0.00 0.36 1.15 REACTOME_GTP_HYDROLYSIS_AND_JOINING_OF_THE_60S_RIBOSOMAL_SUBUNIT R8 106 -2.05 0.00 -2.09 0.00 0.21 -1.74 REACTOME_NEP_NS2_INTERACTS_WITH_THE_CELLULAR_EXPORT_MACHINERY 29 -2.04 0.00 -1.98 0.00 0.79 0.86 MUELLER_PLURINET 294 -2.05 0.00 -1.86 0.00 0.01 1.97 REACTOME_VPR_MEDIATED_NUCLEAR_IMPORT_OF_PICS 31 -2.04 0.00 -1.98 0.00 0.82 0.85 ACEVEDO_NORMAL_TISSUE_ADJACENT_TO_LIVER_TUMOR_UP 169 -2.04 0.00 -1.90 0.00 0.36 1.15 PUJANA_BREAST_CANCER_LIT_INT_NETWORK 100 -2.04 0.00 -1.90 0.00 0.05 1.66 HOSHIDA_LIVER_CANCER_SUBCLASS_S2 115 -2.05 0.00 -1.75 0.01 0.49 1.05 REACTOME_TRANSPORT_OF_RIBONUCLEOPROTEINS_INTO_THE_HOST_NUCLEUS R8 29 -2.04 0.00 -1.99 0.00 0.90 -0.79 299

Gene sets correlating to the repression of hTR cont. Tumorigenic Immortal Normal GAZDA_DIAMOND_BLACKFAN_ANEMIA_PROGENITOR_DN R4 64 -2.05 0.00 -1.46 0.07 0.18 1.36 REACTOME_TRANSPORT_OF_THE_SLBP_INDEPENDENT_MATURE_MRNA R8 32 -2.04 0.00 -1.85 0.00 0.57 -1.07 SAKAI_CHRONIC_HEPATITIS_VS_LIVER_CANCER_UP 81 -2.03 0.00 -2.39 0.00 0.46 -1.23 REACTOME_NUCLEAR_IMPORT_OF_REV_PROTEIN 30 -2.03 0.00 -2.08 0.00 0.82 0.84 OSMAN_BLADDER_CANCER_UP 384 -2.03 0.00 -2.09 0.00 0.11 1.49 DANG_REGULATED_BY_MYC_UP 67 -2.02 0.00 -1.61 0.03 0.76 -0.91 REACTOME_REV_MEDIATED_NUCLEAR_EXPORT_OF_HIV1_RNA 31 -2.02 0.00 -2.00 0.00 0.69 0.92 REACTOME_TRANSLATION_INITIATION_COMPLEX_FORMATION R8 56 -2.02 0.00 -1.93 0.00 0.22 -1.72 LAIHO_COLORECTAL_CANCER_SERRATED_DN 82 -2.01 0.00 -2.10 0.00 0.41 -1.30 PUJANA_BRCA2_PCC_NETWORK 417 -2.01 0.00 -1.68 0.01 0.00 2.25 GRESHOCK_CANCER_COPY_NUMBER_DN 336 -2.00 0.00 -1.77 0.01 0.46 -1.20 DANG_MYC_TARGETS_UP 129 -2.00 0.00 -1.93 0.00 0.66 -0.98 VANHARANTA_UTERINE_FIBROID_WITH_7Q_DELETION_UP 65 -2.00 0.00 -1.93 0.00 0.35 1.16 Notes: Gene sets corresponding to pathways labelled R1-11 are indicated in Table 6.7. NES Normalised enrichment scores, FDR-False Discovery Rate.

300

Table A.3 Additional gene sets correlating with gene expression changes induced by repression of dyskerin or hTERT in normal cells (Top 100) Gene sets correlating to the repression of dyskerin Tumorigenic Immortal Normal Unique to normal cells (Positive NES n= 80/205 gene sets) Pathways Gene set size NES FDR NES FDR NES FDR AMIT_SERUM_RESPONSE_240_MCF10A D1 57 1.11 0.48 1.14 0.35 1.86 0.01 ABRAHAM_ALPC_VS_MULTIPLE_MYELOMA_UP 25 1.37 0.29 1.27 0.20 1.86 0.01 AMIT_SERUM_RESPONSE_60_MCF10A D1 56 0.84 0.86 1.03 0.51 1.84 0.02 YAO_HOXA10_TARGETS_VIA_PROGESTERONE_DN 18 1.40 0.26 0.84 0.80 1.84 0.02 RHEIN_ALL_GLUCOCORTICOID_THERAPY_UP 75 1.12 0.48 -1.12 0.38 1.84 0.02 SMID_BREAST_CANCER_RELAPSE_IN_BONE_DN 298 1.13 0.47 1.24 0.23 1.84 0.02 CONRAD_STEM_CELL 37 1.27 0.36 0.98 0.59 1.83 0.02 WEST_ADRENOCORTICAL_TUMOR_MARKERS_DN 20 1.54 0.19 0.77 0.87 1.83 0.02 RUTELLA_RESPONSE_TO_CSF2RB_AND_IL4_UP D3 337 -1.09 0.47 1.75 0.01 1.83 0.02 RUTELLA_RESPONSE_TO_HGF_VS_CSF2RB_AND_IL4_UP D3 405 -1.07 0.51 1.09 0.42 1.82 0.02 STOSSI_RESPONSE_TO_ESTRADIOL 36 1.58 0.17 0.92 0.68 1.82 0.02 KLEIN_PRIMARY_EFFUSION_LYMPHOMA_UP 50 1.39 0.27 1.35 0.13 1.82 0.02 DANG_REGULATED_BY_MYC_DN 241 1.39 0.27 1.27 0.20 1.82 0.02 XU_HGF_SIGNALING_NOT_VIA_AKT1_48HR_UP 33 1.09 0.50 0.95 0.65 1.81 0.02 LI_CISPLATIN_RESISTANCE_DN D6 34 1.65 0.14 -0.84 0.80 1.81 0.02 ROSS_AML_WITH_CBFB_MYH11_FUSION 50 1.33 0.33 1.25 0.21 1.81 0.02 ELVIDGE_HYPOXIA_BY_DMOG_UP 126 -1.34 0.18 1.16 0.33 1.80 0.02 FRIDMAN_SENESCENCE_UP 77 1.51 0.21 -1.20 0.31 1.80 0.02 RODWELL_AGING_KIDNEY_UP 334 0.81 0.90 -1.48 0.13 1.80 0.02 COLIN_PILOCYTIC_ASTROCYTOMA_VS_GLIOBLASTOMA_DN 27 1.11 0.49 1.39 0.11 1.79 0.02 HELLER_HDAC_TARGETS_UP 273 1.30 0.34 1.32 0.16 1.79 0.02 HELLER_HDAC_TARGETS_SILENCED_BY_METHYLATION_UP 401 1.59 0.17 1.21 0.26 1.79 0.02 GARGALOVIC_RESPONSE_TO_OXIDIZED_PHOSPHOLIPIDS_BLUE_UP 130 -1.16 0.37 1.38 0.11 1.78 0.02 ROZANOV_MMP14_TARGETS_DN 35 1.37 0.29 -1.09 0.42 1.78 0.02 LIN_SILENCED_BY_TUMOR_MICROENVIRONMENT 103 1.17 0.42 0.77 0.88 1.77 0.02 MISHRA_CARCINOMA_ASSOCIATED_FIBROBLAST_DN 23 1.64 0.13 1.37 0.12 1.77 0.02 ROY_WOUND_BLOOD_VESSEL_UP 48 1.15 0.45 0.94 0.65 1.77 0.02 STEARMAN_TUMOR_FIELD_EFFECT_UP 32 1.29 0.34 -0.95 0.64 1.76 0.02 FLECHNER_PBL_KIDNEY_TRANSPLANT_OK_VS_DONOR_DN 39 -1.18 0.36 1.26 0.21 1.75 0.02 YAO_TEMPORAL_RESPONSE_TO_PROGESTERONE_CLUSTER_8 45 -0.94 0.70 1.09 0.42 1.75 0.02 MARSON_FOXP3_TARGETS_UP 55 -1.19 0.33 1.27 0.20 1.75 0.02 REACTOME_UNFOLDED_PROTEIN_RESPONSE 19 -1.27 0.25 0.86 0.77 1.75 0.03 WINNEPENNINCKX_MELANOMA_METASTASIS_DN 45 0.85 0.85 1.38 0.11 1.75 0.03 NING_CHRONIC_OBSTRUCTIVE_PULMONARY_DISEASE_DN 61 -0.77 0.91 1.37 0.12 1.74 0.03 MCBRYAN_PUBERTAL_BREAST_4_5WK_UP 241 1.27 0.36 1.26 0.21 1.74 0.03 WOOD_EBV_EBNA1_TARGETS_DN 45 0.81 0.89 1.30 0.17 1.74 0.03 RODRIGUES_NTN1_TARGETS_DN 155 1.34 0.31 1.34 0.14 1.74 0.03 CHARAFE_BREAST_CANCER_LUMINAL_VS_MESENCHYMAL_UP 437 1.19 0.40 1.30 0.18 1.74 0.03 301

Gene sets correlating to the repression of dyskerin Tumorigenic Immortal Normal BILD_HRAS_ONCOGENIC_SIGNATURE 248 -1.10 0.46 -1.16 0.35 1.73 0.03 WATTEL_AUTONOMOUS_THYROID_ADENOMA_DN 24 0.84 0.86 1.08 0.43 1.73 0.03 GAUSSMANN_MLL_AF4_FUSION_TARGETS_F_DN 29 1.27 0.36 0.67 0.95 1.73 0.03 KUMAR_TARGETS_OF_MLL_AF9_FUSION 370 1.37 0.29 -1.34 0.21 1.72 0.03 RIGGI_EWING_SARCOMA_PROGENITOR_UP 407 1.14 0.45 1.16 0.33 1.72 0.03 REACTOME_EFFECTS_OF_PIP2_HYDROLYSIS 16 1.10 0.49 1.22 0.25 1.72 0.03 ALCALAY_AML_BY_NPM1_LOCALIZATION_UP 137 1.46 0.22 1.31 0.17 1.72 0.03 VANTVEER_BREAST_CANCER_ESR1_DN 223 -1.68 0.02 1.29 0.18 1.72 0.03 CHEN_LVAD_SUPPORT_OF_FAILING_HEART_UP 103 0.97 0.68 1.40 0.10 1.72 0.03 RICKMAN_TUMOR_DIFFERENTIATED_WELL_VS_MODERATELY_DN 108 1.49 0.21 1.24 0.23 1.71 0.03 LIU_VAV3_PROSTATE_CARCINOGENESIS_UP 87 1.59 0.17 -2.15 0.00 1.71 0.03 JISON_SICKLE_CELL_DISEASE_UP 178 1.29 0.34 -2.45 0.00 1.71 0.03 GARCIA_TARGETS_OF_FLI1_AND_DAX1_UP 43 1.05 0.56 1.32 0.15 1.70 0.03 BILD_MYC_ONCOGENIC_SIGNATURE 194 -1.21 0.31 1.37 0.12 1.70 0.03 HOSHIDA_LIVER_CANCER_SURVIVAL_UP 73 1.34 0.31 1.18 0.29 1.69 0.03 CORRE_MULTIPLE_MYELOMA_UP 67 -1.12 0.44 1.01 0.55 1.68 0.04 SCHUETZ_BREAST_CANCER_DUCTAL_INVASIVE_DN 83 0.64 0.99 1.27 0.20 1.68 0.04 CHIANG_LIVER_CANCER_SUBCLASS_POLYSOMY7_UP 65 1.06 0.54 1.27 0.20 1.68 0.04 VALK_AML_CLUSTER_10 32 1.66 0.14 1.23 0.24 1.68 0.04 RORIE_TARGETS_OF_EWSR1_FLI1_FUSION_DN 22 1.66 0.14 -0.50 0.99 1.68 0.04 RUGO_ENVIRONMENTAL_STRESS_RESPONSE_UP 28 0.96 0.70 1.21 0.26 1.67 0.04 WANG_METHYLATED_IN_BREAST_CANCER 32 0.87 0.83 1.33 0.14 1.67 0.04 DORSEY_GAB2_TARGETS 22 0.57 0.99 1.24 0.23 1.67 0.04 SHAFFER_IRF4_MULTIPLE_MYELOMA_PROGRAM 36 -1.42 0.12 0.82 0.82 1.67 0.04 MARSON_FOXP3_TARGETS_DN 38 -0.99 0.63 1.11 0.40 1.67 0.04 KYNG_ENVIRONMENTAL_STRESS_RESPONSE_UP 30 0.94 0.72 1.23 0.24 1.67 0.04 REACTOME_AMINO_ACID_AND_OLIGOPEPTIDE_SLC_TRANSPORTERS 48 0.86 0.84 -0.95 0.64 1.67 0.04 ZHAN_MULTIPLE_MYELOMA_MS_DN 43 1.00 0.64 1.37 0.12 1.67 0.04 CERVERA_SDHB_TARGETS_1_UP 112 1.68 0.13 1.21 0.26 1.66 0.04 TURASHVILI_BREAST_LOBULAR_CARCINOMA_VS_DUCTAL_NORMAL_DN 90 1.02 0.61 1.31 0.17 1.66 0.04 WU_CELL_MIGRATION 182 -0.96 0.68 0.97 0.61 1.66 0.04 ZHAN_V1_LATE_DIFFERENTIATION_GENES_UP 31 1.38 0.28 1.38 0.11 1.65 0.04 REACTOME_SIGNALING_BY_BMP 23 1.21 0.39 1.32 0.16 1.65 0.04 FINAK_BREAST_CANCER_SDPP_SIGNATURE D1 25 1.29 0.34 1.03 0.51 1.65 0.04 LEE_LIVER_CANCER_ACOX1_UP 59 0.79 0.91 -1.10 0.41 1.64 0.04 HESS_TARGETS_OF_HOXA9_AND_MEIS1_DN 69 1.21 0.39 -1.38 0.18 1.64 0.04 HOSHIDA_LIVER_CANCER_LATE_RECURRENCE_UP 55 -1.08 0.50 1.10 0.40 1.64 0.04 TAKEDA_TARGETS_OF_NUP98_HOXA9_FUSION_8D_DN 193 1.25 0.37 0.96 0.63 1.63 0.05 BROWNE_HCMV_INFECTION_14HR_UP D3 154 -1.66 0.03 1.17 0.31 1.63 0.05 REACTOME_MYD88_CASCADE 19 1.72 0.12 1.01 0.55 1.63 0.05 GOTTWEIN_TARGETS_OF_KSHV_MIR_K12_11 62 -1.04 0.55 -1.18 0.33 1.63 0.05 KEGG_VIRAL_MYOCARDITIS D3 73 0.97 0.69 -1.06 0.46 1.63 0.05 Notes: Gene sets corresponding to pathways labelled D1-11 are indicated in Table 6.7. NES Normalised enrichment scores, FDR-False Discovery Rate.

302

Gene sets correlating to the repression of hTERT Tumorigenic Immortal Normal Unique to normal cells only (Negative NES 80/174 gene sets) Pathways Gene set size NES FDR NES FDR NES FDR REACTOME_MEMBRANE_TRAFFICKING 78 1.13 0.34 1.06 0.45 -1.72 0.01 REACTOME_CHAPERONIN_MEDIATED_PROTEIN_FOLDING T5 50 -1.02 0.56 -1.00 0.56 -1.72 0.01 KEGG_UBIQUITIN_MEDIATED_PROTEOLYSIS 137 -1.16 0.35 -1.16 0.34 -1.71 0.01 BIOCARTA_STRESS_PATHWAY T3 25 -0.93 0.70 -1.26 0.24 -1.71 0.01 REACTOME_GENERIC_TRANSCRIPTION_PATHWAY 35 -0.77 0.91 -1.25 0.24 -1.71 0.01 DITTMER_PTHLH_TARGETS_DN 72 -1.16 0.35 -1.36 0.15 -1.71 0.01 CHNG_MULTIPLE_MYELOMA_HYPERPLOID_DN 28 -1.42 0.11 -1.30 0.20 -1.71 0.01 BIOCARTA_P53HYPOXIA_PATHWAY 22 -1.32 0.18 -1.15 0.36 -1.70 0.01 BYSTRYKH_HEMATOPOIESIS_STEM_CELL_AND_BRAIN_QTL_CIS 61 -1.34 0.17 -1.14 0.36 -1.68 0.01 POTTI_CYTOXAN_SENSITIVITY 33 -1.07 0.48 1.21 0.30 -1.68 0.01 GAZDA_DIAMOND_BLACKFAN_ANEMIA_MYELOID_UP 29 -1.36 0.15 -1.37 0.14 -1.65 0.02 ALONSO_METASTASIS_UP 156 -1.16 0.36 -1.44 0.10 -1.65 0.02 MORI_PRE_BI_LYMPHOCYTE_DN 64 1.24 0.23 -1.40 0.13 -1.64 0.02 REACTOME_IRS_RELATED_EVENTS 78 -1.19 0.31 -1.12 0.39 -1.63 0.02 GENTILE_UV_RESPONSE_CLUSTER_D7 31 -1.05 0.52 -1.39 0.13 -1.63 0.02 KEGG_PROSTATE_CANCER 88 -1.37 0.14 -1.30 0.20 -1.62 0.02 WATANABE_RECTAL_CANCER_RADIOTHERAPY_RESPONSIVE_DN 90 -1.13 0.39 -1.34 0.17 -1.62 0.02 IVANOVA_HEMATOPOIESIS_INTERMEDIATE_PROGENITOR 31 -1.25 0.25 -1.31 0.19 -1.62 0.02 AMIT_SERUM_RESPONSE_240_MCF10A 57 -1.07 0.50 -1.58 0.05 -1.62 0.02 REACTOME_BRANCHED_CHAIN_AMINO_ACID_CATABOLISM 17 -0.65 0.97 -0.92 0.67 -1.62 0.02 BIOCARTA_41BB_PATHWAY 17 0.86 0.78 -0.82 0.81 -1.62 0.02 MYLLYKANGAS_AMPLIFICATION_HOT_SPOT_23 17 -1.09 0.45 -1.25 0.24 -1.62 0.02 REACTOME_FRS2MEDIATED_CASCADE 27 -0.96 0.65 -0.73 0.91 -1.62 0.02 KEGG_CHRONIC_MYELOID_LEUKEMIA 72 -1.30 0.20 -1.01 0.55 -1.62 0.02 THEILGAARD_NEUTROPHIL_AT_SKIN_WOUND_DN 221 -1.42 0.10 -1.17 0.34 -1.60 0.03 BERNARD_PPAPDC1B_TARGETS_UP 35 1.16 0.30 -0.89 0.72 -1.60 0.03 SIG_PIP3_SIGNALING_IN_CARDIAC_MYOCTES 63 -1.33 0.17 -1.22 0.28 -1.60 0.03 WELCSH_BRCA1_TARGETS_1_UP 169 1.68 0.04 -1.39 0.13 -1.60 0.03 KEGG_PROTEIN_EXPORT 23 -1.22 0.28 -1.44 0.10 -1.60 0.03 CHARAFE_BREAST_CANCER_LUMINAL_VS_MESENCHYMAL_DN 448 1.80 0.02 -1.32 0.18 -1.60 0.03 LIANG_HEMATOPOIESIS_STEM_CELL_NUMBER_LARGE_VS_TINY_DN 42 -1.15 0.37 -1.42 0.11 -1.60 0.03 NIKOLSKY_BREAST_CANCER_8P12_P11_AMPLICON 57 -1.30 0.20 -1.37 0.15 -1.59 0.03 CHOI_ATL_STAGE_PREDICTOR 36 -1.29 0.21 -1.36 0.15 -1.59 0.03 ZHANG_PROLIFERATING_VS_QUIESCENT 49 1.27 0.20 -1.12 0.40 -1.59 0.03 BONOME_OVARIAN_CANCER_POOR_SURVIVAL_UP 31 -0.73 0.93 -0.82 0.81 -1.58 0.03 TURASHVILI_BREAST_NORMAL_DUCTAL_VS_LOBULAR_UP 63 -1.15 0.37 -1.34 0.16 -1.58 0.03 THUM_SYSTOLIC_HEART_FAILURE_DN 221 -1.12 0.42 -1.02 0.53 -1.58 0.03 WANG_LMO4_TARGETS_UP 350 -1.24 0.26 -1.16 0.34 -1.58 0.03 REACTOME_GOLGI_ASSOCIATED_VESICLE_BIOGENESIS 54 0.90 0.70 1.11 0.38 -1.58 0.03 IVANOVA_HEMATOPOIESIS_EARLY_PROGENITOR 101 -1.34 0.16 -1.13 0.39 -1.58 0.03

303

REACTOME_CONVERSION_FROM_APC_CDC20_TO_APC_CDH1_IN_LATE_ANAPHASE 17 -1.14 0.39 -1.13 0.39 -1.58 0.03 CHANDRAN_METASTASIS_TOP50_UP 16 -1.17 0.34 -1.05 0.48 -1.57 0.03 TIEN_INTESTINE_PROBIOTICS_2HR_DN 86 1.41 0.12 -1.15 0.36 -1.57 0.03 RICKMAN_METASTASIS_UP 330 -1.26 0.23 -1.41 0.12 -1.57 0.03 REACTOME_PEROXISOMAL_LIPID_METABOLISM 20 1.04 0.46 -0.84 0.79 -1.57 0.04 AMIT_EGF_RESPONSE_240_HELA 60 -0.96 0.65 -1.41 0.12 -1.57 0.04 MORI_PLASMA_CELL_UP 36 1.14 0.33 -1.40 0.13 -1.56 0.04 SASSON_RESPONSE_TO_GONADOTROPHINS_DN 71 1.25 0.22 -1.02 0.53 -1.56 0.04 AMIT_EGF_RESPONSE_60_HELA 43 -1.04 0.53 -1.43 0.11 -1.56 0.04 KEGG_VALINE_LEUCINE_AND_ISOLEUCINE_DEGRADATION 44 1.03 0.48 -1.10 0.42 -1.56 0.04 DAZARD_RESPONSE_TO_UV_SCC_UP 83 1.57 0.06 -1.37 0.14 -1.56 0.04 HASLINGER_B_CLL_WITH_CHROMOSOME_12_TRISOMY 17 -0.76 0.92 -0.82 0.81 -1.56 0.04 MULLIGHAN_NPM1_SIGNATURE_3_UP 332 -1.42 0.10 -1.35 0.16 -1.55 0.04 BIOCARTA_MAL_PATHWAY 19 -1.04 0.53 1.10 0.39 -1.55 0.04 SIG_INSULIN_RECEPTOR_PATHWAY_IN_CARDIAC_MYOCYTES 49 -0.85 0.81 -1.02 0.52 -1.55 0.04 REACTOME_PI3K_CASCADE 38 -1.02 0.55 -0.91 0.69 -1.55 0.04 BLALOCK_ALZHEIMERS_DISEASE_INCIPIENT_DN 165 1.26 0.21 -1.17 0.33 -1.55 0.04 REACTOME_CLATHRIN_DERIVED_VESICLE_BUDDING 61 1.14 0.33 1.26 0.26 -1.55 0.04 SMITH_LIVER_CANCER 31 -1.35 0.15 -1.35 0.16 -1.55 0.04 LANDIS_ERBB2_BREAST_TUMORS_324_UP 133 1.39 0.13 -1.10 0.42 -1.54 0.04 AMUNDSON_POOR_SURVIVAL_AFTER_GAMMA_RADIATION_2G 154 -1.37 0.14 -1.30 0.20 -1.54 0.05 WONG_MITOCHONDRIA_GENE_MODULE 209 -1.21 0.30 -1.41 0.12 -1.54 0.05 SETLUR_PROSTATE_CANCER_TMPRSS2_ERG_FUSION_UP 62 -1.27 0.23 -0.99 0.57 -1.54 0.05 REACTOME_UNFOLDED_PROTEIN_RESPONSE 19 -1.23 0.27 -1.40 0.12 -1.54 0.05 BERENJENO_TRANSFORMED_BY_RHOA_REVERSIBLY_DN 26 -1.02 0.56 -1.22 0.27 -1.54 0.05 CHEN_NEUROBLASTOMA_COPY_NUMBER_GAINS 46 -1.04 0.53 -1.16 0.35 -1.54 0.05 LAIHO_COLORECTAL_CANCER_SERRATED_UP 108 1.63 0.05 -1.37 0.14 -1.53 0.05 BARRIER_CANCER_RELAPSE_NORMAL_SAMPLE_UP 33 -0.84 0.82 -0.89 0.71 -1.53 0.05 BROWNE_HCMV_INFECTION_20HR_DN 109 2.02 0.00 -1.17 0.33 -1.53 0.05 WANG_RECURRENT_LIVER_CANCER_UP 16 -1.42 0.11 -1.24 0.26 -1.52 0.05 CHEOK_RESPONSE_TO_HD_MTX_DN 24 -0.98 0.62 -0.78 0.86 -1.52 0.05 LIANG_HEMATOPOIESIS_STEM_CELL_NUMBER_QTL 15 -0.83 0.84 -1.24 0.26 -1.52 0.05 LOPEZ_MESOTHELIOMA_SURVIVAL_OVERALL_DN 15 -1.23 0.27 -1.33 0.17 -1.52 0.05 KEGG_PATHWAYS_IN_CANCER 326 -1.41 0.11 -1.21 0.28 -1.52 0.05 KEGG_PEROXISOME 77 1.19 0.27 -1.05 0.50 -1.51 0.05 SENESE_HDAC3_TARGETS_UP 480 1.84 0.01 -1.41 0.12 -1.51 0.05 TAYLOR_METHYLATED_IN_ACUTE_LYMPHOBLASTIC_LEUKEMIA 63 -1.11 0.43 -1.38 0.13 -1.51 0.06 SA_PTEN_PATHWAY 16 -0.87 0.79 -0.90 0.70 -1.51 0.06 REACTOME_RNA_POLYMERASE_I_PROMOTER_ESCAPE 20 -1.31 0.19 -1.40 0.13 -1.51 0.06 GARGALOVIC_RESPONSE_TO_OXIDIZED_PHOSPHOLIPIDS_GREEN_DN 25 -1.00 0.58 -0.99 0.57 -1.51 0.06 Notes: Gene sets corresponding to pathways labelled T1-11 are indicated in Table 6.7. NES Normalised enrichment scores, FDR-False Discovery Rate.

304

Table A.4 Additional gene sets correlating with gene expression changes induced by repression of dyskerin in immortal and/or tumorigenic cells (Top 100) Gene sets correlating to the repression of dyskerin cont. Tumorigenic Immortal Normal Unique to immortal cells only (Positive NES n=80/551 gene sets) Pathways Gene set size NES FDR NES FDR NES FDR REACTOME_NUCLEAR_EVENTS_KINASE_AND_TRANSCRIPTION_FACTOR_ACTIVATION 24 1.51 0.21 2.03 0.00 1.42 0.12 KEGG_HUNTINGTONS_DISEASE 177 -1.61 0.04 2.03 0.00 -1.07 0.55 REACTOME_ELECTRON_TRANSPORT_CHAIN D7 63 -1.69 0.02 2.03 0.00 -1.40 0.25 AGUIRRE_PANCREATIC_CANCER_COPY_NUMBER_UP D8 289 -1.22 0.31 2.03 0.00 1.31 0.18 REACTOME_RNA_POLYMERASE_III_TRANSCRIPTION D9 34 -0.77 0.92 2.03 0.00 1.06 0.45 MOOTHA_HUMAN_MITODB_6_2002 421 -1.89 0.00 2.03 0.00 -1.36 0.26 KEGG_REGULATION_OF_ACTIN_CYTOSKELETON 214 -0.84 0.85 2.03 0.00 1.28 0.20 REACTOME_INTEGRATION_OF_ENERGY_METABOLISM D8 214 -1.70 0.02 2.02 0.00 -1.35 0.26 MOOTHA_MITOCHONDRIA D7 443 -1.76 0.01 2.02 0.00 -1.37 0.26 BIOCARTA_RAS_PATHWAY D7 23 1.18 0.42 2.02 0.00 1.33 0.17 HSIAO_HOUSEKEEPING_GENES D1 384 -1.82 0.01 2.01 0.00 1.30 0.19 REACTOME_DIABETES_PATHWAYS D7 369 -1.91 0.00 2.00 0.00 -1.20 0.40 FAELT_B_CLL_WITH_VH_REARRANGEMENTS_DN 48 -1.49 0.08 1.99 0.00 0.99 0.55 WONG_MITOCHONDRIA_GENE_MODULE D7 209 -1.75 0.01 1.99 0.00 -1.32 0.29 MOOTHA_PGC D7 330 -1.49 0.08 1.98 0.00 1.26 0.22 REACTOME_METABOLISM_OF_PROTEINS D9 215 -2.24 0.00 1.98 0.00 -1.40 0.25 REACTOME_INSULIN_SYNTHESIS_AND_SECRETION D7 129 -2.25 0.00 1.97 0.00 -1.13 0.49 REACTOME_CENTROSOME_MATURATION 68 -2.29 0.00 1.97 0.00 0.94 0.63 KEGG_COLORECTAL_CANCER 62 -1.47 0.09 1.95 0.00 1.08 0.41 JAZAERI_BREAST_CANCER_BRCA1_VS_BRCA2_UP D8 45 -1.26 0.26 1.95 0.00 1.37 0.14 HOSHIDA_LIVER_CANCER_SURVIVAL_DN 112 -1.02 0.58 1.95 0.00 1.23 0.25 REACTOME_MAPK_TARGETS_NUCLEAR_EVENTS_MEDIATED_BY_MAP_KINASES 30 1.32 0.33 1.95 0.00 1.32 0.17 OUYANG_PROSTATE_CANCER_PROGRESSION_DN D8 18 -1.11 0.46 1.95 0.00 1.05 0.46 BERNARD_PPAPDC1B_TARGETS_UP 35 -1.05 0.53 1.95 0.00 1.34 0.16 SIG_REGULATION_OF_THE_ACTIN_CYTOSKELETON_BY_RHO_GTPASES 35 -1.30 0.22 1.94 0.00 1.10 0.38 KEGG_ENDOMETRIAL_CANCER 52 -0.99 0.63 1.94 0.00 1.15 0.32 ZHAN_MULTIPLE_MYELOMA_HP_DN 45 -1.10 0.46 1.94 0.00 1.46 0.10 KEGG_PARKINSONS_DISEASE 118 -1.67 0.03 1.94 0.00 -1.30 0.31 MOHANKUMAR_TLX1_TARGETS_UP 390 -1.83 0.01 1.94 0.00 1.42 0.12 REACTOME_ERK_MAPK_TARGETS 21 1.28 0.35 1.93 0.00 1.43 0.12 KEGG_ALZHEIMERS_DISEASE D1 161 -1.35 0.17 1.92 0.00 1.35 0.16 BARIS_THYROID_CANCER_UP 20 -1.13 0.43 1.92 0.00 1.42 0.12 RASHI_RESPONSE_TO_IONIZING_RADIATION_3 44 -1.13 0.42 1.92 0.00 -0.94 0.72 IVANOVA_HEMATOPOIESIS_EARLY_PROGENITOR 101 -1.74 0.02 1.92 0.00 1.28 0.20 KEGG_CITRATE_CYCLE_TCA_CYCLE D7 32 -1.53 0.06 1.91 0.00 -1.14 0.48 KEGG_PROTEIN_EXPORT 23 -1.65 0.03 1.91 0.00 1.41 0.13 REACTOME_SEMA4D_INDUCED_CELL_MIGRATION_AND_GROWTH_CONE_COLLAPSE D8 23 1.00 0.63 1.91 0.00 1.35 0.16 REACTOME_INFLUENZA_VIRAL_RNA_TRANSCRIPTION_AND_REPLICATION 100 -2.42 0.00 1.91 0.00 -1.32 0.29 305

Gene sets correlating to the repression of dyskerin cont. Tumorigenic Immortal Normal BIOCARTA_PAR1_PATHWAY 37 -0.62 0.99 1.91 0.00 1.11 0.37 YAO_TEMPORAL_RESPONSE_TO_PROGESTERONE_CLUSTER_14 132 -1.67 0.03 1.91 0.00 1.06 0.44 KEGG_LONG_TERM_POTENTIATION 70 -1.24 0.28 1.91 0.00 1.26 0.22 PAL_PRMT5_TARGETS_UP 182 -1.97 0.00 1.90 0.00 1.11 0.37 REACTOME_TRANSLATION_INITIATION_COMPLEX_FORMATION D9 56 -2.07 0.00 1.90 0.00 -1.34 0.27 RAMALHO_STEMNESS_UP 193 -2.02 0.00 1.90 0.00 1.11 0.37 REACTOME_RNA_POLYMERASE_III_TRANSCRIPTION_INITIATION 29 -1.11 0.46 1.89 0.00 0.67 0.96 WEST_ADRENOCORTICAL_TUMOR_UP 290 -2.15 0.00 1.89 0.00 1.37 0.15 ZHAN_MULTIPLE_MYELOMA_CD1_VS_CD2_DN 50 1.22 0.39 1.89 0.00 1.46 0.10 REACTOME_OPIOID_SIGNALLING 83 -0.91 0.75 1.89 0.00 1.13 0.35 REACTOME_SEMA4D_IN_SEMAPHORIN_SIGNALING 28 1.07 0.53 1.89 0.00 1.37 0.14 DAIRKEE_TERT_TARGETS_UP 327 -1.62 0.04 1.88 0.00 1.22 0.26 BERTUCCI_INVASIVE_CARCINOMA_DUCTAL_VS_LOBULAR_UP D8 24 1.14 0.47 1.88 0.00 1.27 0.21 KEGG_PYRUVATE_METABOLISM D7 40 -1.68 0.03 1.88 0.00 -0.80 0.89 REACTOME_SIGNALLING_TO_RAS D1 26 1.50 0.22 1.88 0.00 1.38 0.14 LIU_SOX4_TARGETS_DN 303 -1.79 0.01 1.88 0.00 1.31 0.18 ST_JNK_MAPK_PATHWAY D1 38 -0.86 0.82 1.88 0.00 1.34 0.16 KAAB_HEART_ATRIUM_VS_VENTRICLE_DN 260 -1.39 0.15 1.88 0.00 1.30 0.19 DACOSTA_UV_RESPONSE_VIA_ERCC3_COMMON_DN 415 -2.21 0.00 1.87 0.00 1.34 0.17 REACTOME_SIGNALING_BY_ROBO_RECEPTOR 31 1.20 0.40 1.87 0.00 1.31 0.18 REACTOME_BRANCHED_CHAIN_AMINO_ACID_CATABOLISM D7 17 -1.11 0.45 1.86 0.00 1.04 0.48 REACTOME_GTP_HYDROLYSIS_AND_JOINING_OF_THE_60S_RIBOSOMAL_SUBUNIT D9 106 -2.50 0.00 1.86 0.00 -1.73 0.09 REACTOME_RNA_POLYMERASE_III_TRANSCRIPTION_TERMINATION D9 17 -0.98 0.64 1.86 0.00 1.20 0.27 SHEDDEN_LUNG_CANCER_GOOD_SURVIVAL_A5 66 -1.12 0.44 1.86 0.00 1.27 0.21 REACTOME_REGULATION_OF_INSULIN_SECRETION_BY_ACETYLCHOLINE D7 22 0.69 0.97 1.86 0.00 0.85 0.78 REACTOME_LOSS_OF_NLP_FROM_MITOTIC_CENTROSOMES 60 -2.22 0.00 1.86 0.00 -0.88 0.81 AMUNDSON_POOR_SURVIVAL_AFTER_GAMMA_RADIATION_2G 154 -1.82 0.01 1.85 0.00 1.28 0.20 CHOI_ATL_STAGE_PREDICTOR 36 -1.88 0.00 1.86 0.00 -1.06 0.56 REACTOME_INFLUENZA_LIFE_CYCLE 137 -2.45 0.00 1.86 0.00 -1.33 0.29 BIOCARTA_TFF_PATHWAY 21 -1.06 0.52 1.86 0.00 -1.17 0.44 REACTOME_SEMA3A_PAK_DEPENDENT_AXON_REPULSION 15 1.44 0.25 1.85 0.00 1.46 0.10 SIG_CD40PATHWAYMAP 33 1.07 0.53 1.85 0.00 1.18 0.29 REACTOME_DUAL_INCISION_REACTION_IN_TC_NER 28 -1.35 0.17 1.85 0.00 0.65 0.97 KIM_GASTRIC_CANCER_CHEMOSENSITIVITY 96 1.05 0.56 1.85 0.00 1.37 0.14 BIOCARTA_GH_PATHWAY 28 0.94 0.72 1.85 0.00 1.00 0.54 NIKOLSKY_BREAST_CANCER_6P24_P22_AMPLICON 22 -0.98 0.64 1.85 0.00 0.65 0.97 KEGG_ALDOSTERONE_REGULATED_SODIUM_REABSORPTION 42 0.90 0.78 1.84 0.00 1.29 0.20 REACTOME_RHO_GTPASE_CYCLE D1 119 -1.05 0.54 1.84 0.00 1.34 0.16 BIOCARTA_NDKDYNAMIN_PATHWAY 18 -1.40 0.13 1.84 0.00 1.26 0.21 NIKOLSKY_BREAST_CANCER_16P13_AMPLICON 116 0.98 0.68 1.84 0.00 0.84 0.80 CHAUHAN_RESPONSE_TO_METHOXYESTRADIOL_DN 99 -1.35 0.17 1.83 0.00 1.19 0.29 SIG_CHEMOTAXIS 44 -1.24 0.29 1.83 0.00 1.02 0.50 Notes: Gene sets corresponding to pathways labelled D1-11 are indicated in Table 6.7. NES Normalised enrichment scores, FDR-False Discovery Rate.

306

Gene sets correlating to the repression of dyskerin cont. Tumorigenic Immortal Normal Unique to tumorigenic cells only (Negative NES n=80/458 gene sets) Pathways Gene set size NES FDR NES FDR NES FDR MARKEY_RB1_ACUTE_LOF_DN D10 205 -2.49 0.00 1.06 0.47 -1.32 0.29 REACTOME_CYCLIN_E_ASSOCIATED_EVENTS_DURING_G1_S_TRANSITION_ D2, D10 58 -2.48 0.00 -1.25 0.26 -0.79 0.91 REACTOME_ELONGATION_AND_PROCESSING_OF_CAPPED_TRANSCRIPTS D9 133 -2.48 0.00 1.53 0.05 -1.50 0.20 KEGG_SPLICEOSOME D9 126 -2.48 0.00 1.50 0.06 -1.27 0.33 REACTOME_METABOLISM_OF_MRNA D9 46 -2.47 0.00 1.25 0.22 -1.62 0.12 BOYAULT_LIVER_CANCER_SUBCLASS_G3_UP 184 -2.47 0.00 1.72 0.01 -1.56 0.18 KEGG_RIBOSOME D9 87 -2.47 0.00 1.70 0.01 -1.54 0.18 MUELLER_PLURINET 294 -2.47 0.00 1.19 0.29 1.00 0.53 ZHAN_MULTIPLE_MYELOMA_PR_UP 44 -2.46 0.00 0.95 0.64 -1.32 0.29 FUJII_YBX1_TARGETS_DN 139 -2.43 0.00 0.89 0.74 1.07 0.43 REACTOME_INFLUENZA_VIRAL_RNA_TRANSCRIPTION_AND_REPLICATION 100 -2.42 0.00 1.91 0.00 -1.32 0.29 SHEN_SMARCA2_TARGETS_UP 417 -2.42 0.00 2.22 0.00 1.35 0.16 FOURNIER_ACINAR_DEVELOPMENT_LATE_2 273 -2.42 0.00 1.59 0.03 -1.12 0.49 REACTOME_REGULATION_OF_APC_ACTIVATORS_BETWEEN_G1_S_AND_EARLY_ANAPHA SE D2 70 -2.41 0.00 -1.13 0.37 -1.06 0.55 PUJANA_BREAST_CANCER_WITH_BRCA1_MUTATED_UP 54 -2.39 0.00 -1.17 0.35 -1.30 0.30 REACTOME_FORMATION_OF_A_POOL_OF_FREE_40S_SUBUNITS D9 95 -2.39 0.00 1.88 0.00 -1.44 0.23 REACTOME_PEPTIDE_CHAIN_ELONGATION 84 -2.38 0.00 1.76 0.01 -1.60 0.14 REACTOME_SCF_SKP2_MEDIATED_DEGRADATION_OF_P27_P21 D2,D10 52 -2.38 0.00 -1.33 0.22 -0.85 0.84 REACTOME_SYNTHESIS_OF_DNA D2 88 -2.38 0.00 -1.15 0.35 -0.93 0.73 KEGG_RNA_DEGRADATION D9 55 -2.38 0.00 1.05 0.49 -0.95 0.70 OUELLET_OVARIAN_CANCER_INVASIVE_VS_LMP_UP D8 116 -2.37 0.00 -1.55 0.10 -1.57 0.16 MOREAUX_MULTIPLE_MYELOMA_BY_TACI_DN 131 -2.34 0.00 1.54 0.05 -1.49 0.20 REACTOME_CDC20_PHOSPHO_APC_MEDIATED_DEGRADATION_OF_CYCLIN_A D2 62 -2.33 0.00 -1.12 0.38 -1.00 0.64 REACTOME_VIRAL_MRNA_TRANSLATION 84 -2.33 0.00 1.82 0.00 -1.39 0.25 SCHLOSSER_MYC_TARGETS_REPRESSED_BY_SERUM 158 -2.33 0.00 1.60 0.03 -1.65 0.12 GARY_CD5_TARGETS_DN 422 -2.33 0.00 1.36 0.12 1.26 0.22 REACTOME_DNA_REPLICATION_PRE_INITIATION D2, D9 75 -2.32 0.00 -1.28 0.24 0.75 0.91 RHEIN_ALL_GLUCOCORTICOID_THERAPY_DN 358 -2.30 0.00 1.73 0.01 -1.53 0.18 MARTINEZ_RESPONSE_TO_TRABECTEDIN_DN D11 218 -2.30 0.00 1.40 0.10 -1.36 0.26 WHITEFORD_PEDIATRIC_CANCER_MARKERS 92 -2.29 0.00 1.09 0.42 -1.11 0.49 BERENJENO_TRANSFORMED_BY_RHOA_UP D1 489 -2.29 0.00 1.20 0.28 1.25 0.22 MORI_IMMATURE_B_LYMPHOCYTE_DN 53 -2.29 0.00 0.80 0.84 -1.24 0.36 NAKAMURA_CANCER_MICROENVIRONMENT_DN D8 46 -2.29 0.00 1.38 0.11 1.00 0.53 LEE_LIVER_CANCER_SURVIVAL_DN 123 -2.28 0.00 1.11 0.39 -1.64 0.12 ENK_UV_RESPONSE_KERATINOCYTE_DN 477 -2.27 0.00 1.68 0.02 -1.48 0.21 REACTOME_AUTODEGRADATION_OF_CDH1_BY_CDH1_APC D9 57 -2.26 0.00 1.05 0.49 -0.75 0.93 PUJANA_BREAST_CANCER_LIT_INT_NETWORK 100 -2.26 0.00 -1.22 0.30 -1.22 0.37 REACTOME_INSULIN_SYNTHESIS_AND_SECRETION D7 129 -2.25 0.00 1.97 0.00 -1.13 0.48 REACTOME_RNA_POLYMERASE_II_TRANSCRIPTION D9 91 -2.24 0.00 1.40 0.10 -1.12 0.49 REACTOME_P53_INDEPENDENT_DNA_DAMAGE_RESPONSE 43 -2.24 0.00 -1.29 0.24 -0.83 0.86 REACTOME_METABOLISM_OF_PROTEINS D9 215 -2.24 0.00 1.98 0.00 -1.40 0.24 REACTOME_TRANSPORT_OF_MATURE_MRNA_DERIVED_FROM_AN_INTRON_CONTAININ G_TRANSCRIPT D9 51 -2.24 0.00 1.25 0.21 -1.67 0.11 307

Gene sets correlating to the repression of dyskerin cont. Tumorigenic Immortal Normal REACTOME_SNRNP_ASSEMBLY D9 50 -2.24 0.00 0.91 0.70 -1.52 0.19 TOYOTA_TARGETS_OF_MIR34B_AND_MIR34C D9 447 -2.24 0.00 1.61 0.03 1.48 0.09 LEE_EARLY_T_LYMPHOCYTE_UP 82 -2.23 0.00 1.12 0.38 -1.16 0.46 LINDGREN_BLADDER_CANCER_CLUSTER_3_UP 318 -2.23 0.00 1.34 0.14 1.23 0.25 REACTOME_MRNA_SPLICING_MINOR_PATHWAY D9 42 -2.23 0.00 1.74 0.01 -1.48 0.21 REACTOME_ORC1_REMOVAL_FROM_CHROMATIN 63 -2.23 0.00 -1.24 0.28 -0.64 0.98 KAUFFMANN_DNA_REPAIR_GENES 204 -2.22 0.00 1.38 0.11 -1.46 0.22 DACOSTA_UV_RESPONSE_VIA_ERCC3_COMMON_DN 415 -2.21 0.00 1.87 0.00 1.34 0.16 REACTOME_M_G1_TRANSITION D2 61 -2.21 0.00 -1.31 0.23 -0.68 0.96 CHEN_HOXA5_TARGETS_9HR_UP 219 -2.21 0.00 -1.10 0.40 -1.60 0.14 REACTOME_REGULATION_OF_GENE_EXPRESSION_IN_BETA_CELLS 101 -2.20 0.00 1.61 0.03 -1.43 0.24 REACTOME_SCF_BETA_TRCP_MEDIATED_DEGRADATION_OF_EMI1 48 -2.20 0.00 -1.16 0.35 -0.70 0.95 GAZDA_DIAMOND_BLACKFAN_ANEMIA_PROGENITOR_DN 64 -2.19 0.00 1.74 0.01 -1.07 0.54 RHODES_UNDIFFERENTIATED_CANCER 60 -2.19 0.00 -1.13 0.38 -1.24 0.36 PENG_GLUTAMINE_DEPRIVATION_DN 83 -2.19 0.00 -1.21 0.30 -1.57 0.16 SCIAN_CELL_CYCLE_TARGETS_OF_TP53_AND_TP73_DN 22 -2.19 0.00 -0.93 0.66 0.79 0.86 REACTOME_DNA_REPAIR 104 -2.19 0.00 1.40 0.10 -1.09 0.53 BORCZUK_MALIGNANT_MESOTHELIOMA_UP 298 -2.17 0.00 2.16 0.00 1.46 0.10 RICKMAN_TUMOR_DIFFERENTIATED_WELL_VS_POORLY_UP 226 -2.17 0.00 1.77 0.01 -1.37 0.25 REACTOME_CDT1_ASSOCIATION_WITH_THE_CDC6_ORC_ORIGIN_COMPLEX 52 -2.17 0.00 -1.31 0.23 -0.83 0.86 REACTOME_STABILIZATION_OF_P53 46 -2.16 0.00 -1.29 0.24 -0.91 0.77 REACTOME_HIV_LIFE_CYCLE 103 -2.16 0.00 1.60 0.03 -0.95 0.70 CHEMNITZ_RESPONSE_TO_PROSTAGLANDIN_E2_UP 140 -2.16 0.00 1.53 0.05 -1.15 0.46 WEST_ADRENOCORTICAL_TUMOR_UP 290 -2.15 0.00 1.89 0.00 1.37 0.15 MORI_LARGE_PRE_BII_LYMPHOCYTE_UP 52 -2.15 0.00 0.85 0.78 -1.30 0.30 HOSHIDA_LIVER_CANCER_SUBCLASS_S2 115 -2.15 0.00 1.49 0.06 -1.62 0.13 BIOCARTA_PROTEASOME_PATHWAY D9 19 -2.15 0.00 0.93 0.67 -0.88 0.81 SCHLOSSER_MYC_TARGETS_AND_SERUM_RESPONSE_DN 47 -2.15 0.00 -1.14 0.37 -1.39 0.25 KEGG_PROTEASOME D9 48 -2.14 0.00 -1.22 0.30 -0.69 0.96 REACTOME_REGULATION_OF_ORNITHINE_DECARBOXYLASE 47 -2.13 0.00 -1.16 0.35 -0.78 0.91 FINETTI_BREAST_CANCER_BASAL_VS_LUMINAL 16 -2.13 0.00 -1.13 0.38 -1.39 0.25 REACTOME_VIF_MEDIATED_DEGRADATION_OF_APOBEC3G 47 -2.12 0.00 -1.45 0.14 -0.73 0.94 MANALO_HYPOXIA_DN 284 -2.12 0.00 1.61 0.03 1.19 0.28 SAKAI_CHRONIC_HEPATITIS_VS_LIVER_CANCER_UP 81 -2.11 0.00 1.48 0.07 1.20 0.27 GEORGES_CELL_CYCLE_MIR192_TARGETS D2, D9 59 -2.11 0.00 1.44 0.08 1.18 0.29 REACTOME_REGULATION_OF_BETA_CELL_DEVELOPMENT 114 -2.11 0.00 1.54 0.05 -1.43 0.24 LY_AGING_OLD_DN 47 -2.11 0.00 1.03 0.51 -1.06 0.55 AMUNDSON_GAMMA_RADIATION_RESPONSE 35 -2.10 0.00 -0.98 0.58 -1.29 0.32 Notes: Gene sets corresponding to pathways labelled D1-11 are indicated in Table 6.7. NES Normalised enrichment scores, FDR-False Discovery Rate.

308

Table A.5 Additional gene sets correlating with gene expression changes induced by repression of hTERT in immortal and/or tumorigenic cells (Top 100) Gene sets correlating to the repression of hTERT cont. Tumorigenic Immortal Normal Unique to immortal cells only (Negative NES 14/34 gene sets) Pathway Gene set size NES FDR NES FDR NES FDR ODONNELL_METASTASIS_DN T9 23 -1.03 0.55 -1.53 0.06 -1.04 0.52 GOTTWEIN_TARGETS_OF_KSHV_MIR_K12_11 62 1.09 0.38 -1.53 0.06 -1.15 0.36 CHUNG_BLISTER_CYTOTOXICITY_DN 42 1.45 0.10 -1.50 0.07 -1.41 0.11 WATANABE_COLON_CANCER_MSI_VS_MSS_UP T13, T14 29 -1.13 0.39 -1.50 0.08 -0.68 0.95 LIU_VMYB_TARGETS_UP T11 125 -1.38 0.13 -1.50 0.08 -1.35 0.15 STEIN_ESRRA_TARGETS_RESPONSIVE_TO_ESTROGEN_UP 29 -1.26 0.24 -1.49 0.08 -1.42 0.10 CROONQUIST_NRAS_VS_STROMAL_STIMULATION_UP 35 1.01 0.50 -1.49 0.08 -0.96 0.64 BASAKI_YBX1_TARGETS_DN 361 1.77 0.02 -1.48 0.08 -1.30 0.20 BOYAULT_LIVER_CANCER_SUBCLASS_G1_DN T13 40 -1.14 0.38 -1.48 0.08 -1.03 0.53 SESTO_RESPONSE_TO_UV_C7 T13 69 -1.34 0.17 -1.47 0.09 -1.34 0.16 BIOCARTA_LAIR_PATHWAY 17 1.65 0.04 -1.47 0.09 -1.22 0.28 BONOME_OVARIAN_CANCER_SURVIVAL_OPTIMAL_DEBULKING 239 1.35 0.15 -1.46 0.09 -1.27 0.22 BARRIER_CANCER_RELAPSE_TUMOR_SAMPLE_UP 16 1.10 0.37 -1.45 0.10 -1.27 0.22 AMIT_SERUM_RESPONSE_20_MCF10A 20 -0.78 0.90 -1.45 0.10 -1.19 0.31 Notes: Gene sets corresponding to pathways labelled T1-15 are indicated in Table 6.7. NES Normalised enrichment scores, FDR-False Discovery Rate.

309

Gene sets correlating to the repression of hTERT cont. Tumorigenic Immortal Normal Unique to tumorigenic cells only (Positive NES n=80/413 gene sets) Pathway Gene set size NES FDR NES FDR NES FDR GARGALOVIC_RESPONSE_TO_OXIDIZED_PHOSPHOLIPIDS_TURQUOISE_UP 73 2.05 0.00 1.39 0.19 1.47 0.13 MCLACHLAN_DENTAL_CARIES_DN 229 2.05 0.00 -1.46 0.09 1.48 0.13 CERVERA_SDHB_TARGETS_1_UP 112 2.05 0.00 1.09 0.41 1.16 0.32 BASSO_CD40_SIGNALING_UP 101 2.05 0.00 -1.36 0.15 1.54 0.11 ODONNELL_TARGETS_OF_MYC_AND_TFRC_UP T15 78 2.05 0.00 -1.36 0.15 1.04 0.47 ONDER_CDH1_TARGETS_2_DN T11 456 2.05 0.00 -1.39 0.13 1.50 0.12 OSADA_ASCL1_TARGETS_DN 24 2.03 0.00 1.33 0.21 1.24 0.25 BROWNE_HCMV_INFECTION_20HR_DN T3 109 2.02 0.00 -1.17 0.33 -1.53 0.05 ALCALAY_AML_BY_NPM1_LOCALIZATION_UP 137 2.02 0.00 -1.18 0.32 1.13 0.35 KANG_CISPLATIN_RESISTANCE_UP T13, T15 19 2.02 0.00 -1.11 0.40 1.42 0.15 RICKMAN_TUMOR_DIFFERENTIATED_WELL_VS_POORLY_DN 369 2.00 0.00 1.27 0.26 -1.16 0.35 BOYLAN_MULTIPLE_MYELOMA_C_DN 37 2.00 0.00 0.94 0.65 -0.69 0.94 LI_WILMS_TUMOR_VS_FETAL_KIDNEY_1_UP 179 2.00 0.00 -1.09 0.43 1.23 0.26 LENAOUR_DENDRITIC_CELL_MATURATION_UP 92 1.99 0.00 1.19 0.31 1.41 0.15 CHIARADONNA_NEOPLASTIC_TRANSFORMATION_KRAS_CDC25_DN 50 1.99 0.00 1.37 0.20 1.06 0.44 BOYLAN_MULTIPLE_MYELOMA_C_CLUSTER_DN 30 1.98 0.00 -1.37 0.14 -0.80 0.84 BLUM_RESPONSE_TO_SALIRASIB_UP T15 244 1.97 0.01 -1.13 0.39 1.41 0.15 DAZARD_RESPONSE_TO_UV_NHEK_UP T13 155 1.97 0.01 -1.11 0.41 1.32 0.20 KEGG_SNARE_INTERACTIONS_IN_VESICULAR_TRANSPORT 38 1.97 0.01 1.03 0.48 1.15 0.33 WANG_CISPLATIN_RESPONSE_AND_XPC_DN T13, T15 141 1.97 0.01 1.56 0.12 -1.11 0.42 TSENG_ADIPOGENIC_POTENTIAL_DN 43 1.97 0.01 1.47 0.15 1.31 0.21 JAATINEN_HEMATOPOIETIC_STEM_CELL_DN T8 216 1.96 0.01 -1.09 0.44 1.46 0.13 BERNARD_PPAPDC1B_TARGETS_DN T9 55 1.96 0.01 1.55 0.12 -0.95 0.65 LINDGREN_BLADDER_CANCER_CLUSTER_2B 383 1.96 0.01 1.36 0.21 1.45 0.14 SU_PLACENTA 28 1.95 0.01 1.21 0.30 1.49 0.13 RADMACHER_AML_PROGNOSIS 78 1.95 0.01 1.24 0.27 1.30 0.22 MORI_EMU_MYC_LYMPHOMA_BY_ONSET_TIME_DN T11 16 1.93 0.01 0.91 0.70 0.85 0.78 GOLDRATH_IMMUNE_MEMORY 57 1.93 0.01 1.60 0.10 1.42 0.15 FRASOR_RESPONSE_TO_ESTRADIOL_DN 63 1.93 0.01 1.55 0.12 1.44 0.14 REACTOME_BASIGIN_INTERACTIONS 25 1.92 0.01 1.34 0.21 1.50 0.12 BROWNE_HCMV_INFECTION_24HR_DN T3 145 1.92 0.01 1.40 0.18 -1.08 0.46 TIAN_TNF_SIGNALING_VIA_NFKB T12 21 1.92 0.01 -1.32 0.18 1.14 0.35 HOFFMANN_LARGE_TO_SMALL_PRE_BII_LYMPHOCYTE_DN 35 1.91 0.01 1.35 0.21 1.28 0.22 TOMLINS_PROSTATE_CANCER_DN T14 39 1.91 0.01 1.62 0.10 1.23 0.26 KOYAMA_SEMA3B_TARGETS_UP 219 1.91 0.01 -1.30 0.20 -1.12 0.40 LEE_EARLY_T_LYMPHOCYTE_DN 44 1.91 0.01 -1.84 0.00 0.91 0.70 BOHN_PRIMARY_IMMUNODEFICIENCY_SYNDROM_DN 25 1.90 0.01 1.36 0.21 1.29 0.22 MARKEY_RB1_ACUTE_LOF_UP 202 1.90 0.01 -2.44 0.00 0.96 0.59 HOSHIDA_LIVER_CANCER_SUBCLASS_S3 265 1.90 0.01 1.24 0.27 1.31 0.21 BOYAULT_LIVER_CANCER_SUBCLASS_G2 27 1.90 0.01 1.04 0.47 1.31 0.21 PRAMOONJAGO_SOX4_TARGETS_UP 48 1.90 0.01 -1.36 0.15 1.35 0.19 MARKEY_RB1_CHRONIC_LOF_DN 110 1.89 0.01 -1.42 0.11 1.53 0.11 ZHOU_INFLAMMATORY_RESPONSE_LIVE_UP T3 452 1.89 0.01 -1.04 0.50 1.33 0.20 KEGG_O_GLYCAN_BIOSYNTHESIS 30 1.88 0.01 1.62 0.10 1.29 0.22 GAJATE_RESPONSE_TO_TRABECTEDIN_UP T13, T15 53 1.88 0.01 -1.52 0.07 1.08 0.42 GRAHAM_NORMAL_QUIESCENT_VS_NORMAL_DIVIDING_UP 64 1.87 0.01 -2.12 0.00 -1.13 0.39 310

Gene sets correlating to the repression of hTERT cont. Tumorigenic Immortal Normal GRAHAM_CML_DIVIDING_VS_NORMAL_QUIESCENT_DN 93 1.87 0.01 -1.87 0.00 -0.86 0.77 HAHTOLA_MYCOSIS_FUNGOIDES_UP 16 1.87 0.01 -1.80 0.01 1.22 0.27 NAKAYAMA_SOFT_TISSUE_TUMORS_PCA1_UP 71 1.87 0.01 -2.08 0.00 1.34 0.19 ONDER_CDH1_TARGETS_3_DN T13, T14 54 1.87 0.01 -1.30 0.20 -0.56 0.99 NEWMAN_ERCC6_TARGETS_DN T13 36 1.86 0.01 -0.98 0.58 -0.89 0.74 LANDIS_ERBB2_BREAST_TUMORS_324_DN 136 1.86 0.01 1.59 0.11 1.53 0.11 GRAHAM_CML_QUIESCENT_VS_NORMAL_DIVIDING_UP 55 1.86 0.01 -2.29 0.00 1.01 0.52 BOYLAN_MULTIPLE_MYELOMA_C_D_DN 230 1.85 0.01 -1.45 0.10 1.29 0.22 ROZANOV_MMP14_TARGETS_DN T9 35 1.85 0.01 -1.17 0.34 1.50 0.12 REACTOME_RNA_POLYMERASE_I_PROMOTER_OPENING 58 1.85 0.01 -2.00 0.00 1.00 0.53 PICCALUGA_ANGIOIMMUNOBLASTIC_LYMPHOMA_UP 202 1.85 0.01 -1.11 0.40 1.42 0.15 NAGASHIMA_EGF_SIGNALING_UP T1 57 1.85 0.01 -1.38 0.14 -1.07 0.48 TSENG_IRS1_TARGETS_DN 123 1.85 0.01 1.45 0.16 1.51 0.11 THEILGAARD_NEUTROPHIL_AT_SKIN_WOUND_UP 76 1.84 0.01 -0.95 0.64 1.12 0.36 CREIGHTON_ENDOCRINE_THERAPY_RESISTANCE_5 458 1.84 0.01 1.51 0.13 -1.18 0.32 SENESE_HDAC3_TARGETS_UP T14 480 1.84 0.01 -1.41 0.12 -1.51 0.05 HINATA_NFKB_TARGETS_FIBROBLAST_UP T12 65 1.84 0.01 -1.77 0.01 -1.18 0.33 YAO_TEMPORAL_RESPONSE_TO_PROGESTERONE_CLUSTER_0 69 1.84 0.01 -0.97 0.60 1.26 0.24 GALE_APL_WITH_FLT3_MUTATED_DN 16 1.83 0.02 -1.37 0.14 1.22 0.26 PASQUALUCCI_LYMPHOMA_BY_GC_STAGE_UP 256 1.83 0.02 1.40 0.18 -0.99 0.59 KHETCHOUMIAN_TRIM24_TARGETS_UP 45 1.82 0.02 -0.91 0.69 1.30 0.22 NAGASHIMA_NRG1_SIGNALING_UP 171 1.82 0.02 -1.27 0.22 -1.23 0.27 SENESE_HDAC1_TARGETS_UP T14 435 1.82 0.02 -1.45 0.10 -1.50 0.06 AMIT_EGF_RESPONSE_120_HELA T1 65 1.82 0.02 -1.15 0.36 -1.09 0.44 NADERI_BREAST_CANCER_PROGNOSIS_DN 17 1.81 0.02 -0.78 0.86 1.11 0.38 ELVIDGE_HIF1A_AND_HIF2A_TARGETS_DN 100 1.81 0.02 1.17 0.33 1.41 0.16 ELVIDGE_HIF1A_TARGETS_DN 88 1.80 0.02 1.21 0.29 1.49 0.13 CHARAFE_BREAST_CANCER_LUMINAL_VS_MESENCHYMAL_DN 448 1.80 0.02 -1.32 0.18 -1.60 0.03 REACTOME_EFFECTS_OF_PIP2_HYDROLYSIS 16 1.80 0.02 1.19 0.31 -0.74 0.90 SENESE_HDAC1_AND_HDAC2_TARGETS_UP T14 227 1.80 0.02 -1.34 0.16 -1.19 0.31 DAVICIONI_TARGETS_OF_PAX_FOXO1_FUSIONS_UP T4 252 1.79 0.02 1.27 0.25 -1.15 0.36 CASORELLI_ACUTE_PROMYELOCYTIC_LEUKEMIA_UP 168 1.79 0.02 -1.10 0.42 -1.26 0.24 RUTELLA_RESPONSE_TO_CSF2RB_AND_IL4_UP 337 1.79 0.02 1.31 0.23 -1.22 0.28 XU_HGF_SIGNALING_NOT_VIA_AKT1_6HR T1 24 1.78 0.02 -0.73 0.91 -1.03 0.54 Unique to immortal and tumorigenic cells only ( Positive NES 7/27 gene sets) Pathway Gene set size NES FDR NES FDR NES FDR VERRECCHIA_EARLY_RESPONSE_TO_TGFB1 T1 51 1.64 0.04 1.99 0.02 1.53 0.11 HUANG_DASATINIB_RESISTANCE_DN T15 58 1.63 0.05 1.96 0.02 1.28 0.23 BIOCARTA_CARDIACEGF_PATHWAY T10 18 1.62 0.05 1.69 0.08 1.27 0.23 KEGG_SPHINGOLIPID_METABOLISM 40 1.58 0.06 1.64 0.09 1.36 0.18 SOTIRIOU_BREAST_CANCER_GRADE_1_VS_3_DN T9 51 1.57 0.06 1.76 0.05 1.13 0.35 KEGG_HYPERTROPHIC_CARDIOMYOPATHY_HCM T10 85 1.53 0.07 2.02 0.01 1.48 0.13 Notes: Gene sets corresponding to pathways labelled T1-15 are indicated in Table 6.7. NES Normalised enrichment scores, FDR-False Discovery Rate.

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