Identification and characterization of
microRNAs that play novel functional roles in
MYCN-driven neuroblastoma
Chi Yan Ooi
A thesis in fulfilment of the requirements for the degree of Doctor of Philosophy
(Research)
Children’s Cancer Institute
School of Women’s and Children’s Health
Faculty of Medicine
UNSW Sydney
November 2017
http://doi.org/10.4225/53/5a1b3688156e3
Thesis/Dissertation Sheet
Thesis/Dissertation Sheet
Chi Yan Ooi i Originality Statement
Chi Yan Ooi ii Copyright and Authenticity Statements
Chi Yan Ooi iii Table of Contents
Table of Contents
Thesis/Dissertation Sheet ...... i
Originality Statement ...... ii
Copyright and Authenticity Statements ...... iii
Table of Contents ...... iv
List of Figures ...... xi
List of Tables ...... xv
Abstract ...... xvi
Abbreviations ...... xviii
Publication in preparation from this thesis ...... xxiii
Conference presentations arising from this thesis ...... xxiv
Acknowledgements ...... xxvi
Introduction ...... 1
Cancer...... 2
1.1.1 Tumorigenesis – Initiation, Promotion and Progression of Cancer ...... 2
1.1.2 Oncogenes, Tumour Suppressors and the Hallmarks of Cancer ...... 3
Neuroblastoma ...... 6
1.2.1 Clinical Stages, Features and Management ...... 9
1.2.2 MYCN: A major proto-oncogene of neuroblastoma ...... 14
MYCN and neuroblastoma initiation and progression ...... 17
MYCN and the dysregulation of microRNA in neuroblastoma ...... 17
Chi Yan Ooi iv Table of Contents
MicroRNA ...... 19
1.3.1 The biogenesis of microRNAs ...... 19
1.3.2 miRNA binding sites on mRNAs ...... 24
1.3.3 MicroRNA stability ...... 29
1.3.4 MicroRNA’s non-canonical function in positive regulation of
translation ...... 30
1.3.5 MicroRNA’s non-canonical functions in the nucleus ...... 31
MicroRNA and cancer: Acting as tumour suppressor or oncogene or both ...33
1.4.1 MicroRNA in neuroblastoma biology ...... 34
The miR-17-92 cluster, its paralogs and target genes ...... 35
1.4.1.1.1 MYCN-E2F1 negative feedback loop ...... 37
1.4.1.1.2 Regulation of TGFβ pathway by the miR-17-92 cluster ...... 40
1.4.1.1.3 Regulation of Cell cycle by the miR-17-92 cluster ...... 42
1.4.1.1.4 Regulation of estrogen receptor-α (ESR1) and neuroblast
differentiation by the miR-17-92 cluster ...... 44
The LIN28B-let-7-MYCN axis and neuroblastoma initiation ...... 45
The roles of miR-34a in neuroblastoma...... 50
The role of miR-9 in MYCN driven neuroblastoma ...... 54
miR-204 as a potential tumour suppressor of neuroblastoma ...... 57
miR-375’s interactions with MYCN ...... 60
miRNAs that directly repress MYCN expression ...... 61
1.4.2 MicroRNAs as therapeutic targets in neuroblastoma ...... 61
Chi Yan Ooi v Table of Contents
1.4.3 MicroRNAs as biomarkers for neuroblastoma ...... 64
Summary and prospective ...... 66
Materials and Methods ...... 69
Bioinformatical analyses ...... 70
2.1.1 Enrichment Analysis with GeneCodis3 ...... 70
2.1.2 Compute overlaps with gene sets from MSigDB ...... 70
2.1.3 miRNA targeting predictions...... 71
2.1.4 R2 Kaplan Meier by gene expression analysis ...... 72
2.1.5 Peer-reviewed high-throughput miRNA expression profiling studies of
human neuroblastoma samples...... 73
Cell biology techniques ...... 74
2.2.1 Cell lines and culture conditions ...... 74
2.2.2 Cell viability counts ...... 74
2.2.3 Doxycycline-induced induction of MYCN and miR-204 over-
expression ...... 75
2.2.4 Panobinostat and 5-Aza-2’-deoxycytidine treatments ...... 75
Transfection of neuroblastoma cells ...... 76
2.3.1 Transient transfection of siRNA or miRNA mimic / inhibitor ...... 76
2.3.2 Stable transfection of lentiviral shMIMIC miRNA ...... 79
Molecular biological techniques ...... 82
2.4.1 miRNA extraction...... 82
2.4.2 TaqMan microRNA reverse transcription and assays ...... 83
Chi Yan Ooi vi Table of Contents
2.4.3 mRNA extraction ...... 86
2.4.4 cDNA synthesis and qPCR ...... 86
2.4.5 Whole cell protein extraction ...... 89
2.4.6 Protein quantification ...... 89
2.4.7 Western blotting ...... 90
2.4.8 Chromatin Immunoprecipitation Assay...... 91
2.4.9 Biotin-labelled miRNA mimic pulldown of mRNAs ...... 95
2.4.10 Microarray ...... 97
Cell phenotype and functional assays ...... 98
2.5.1 Cell viability and cell proliferation assays ...... 98
2.5.2 Colony forming assay ...... 99
2.5.3 Fluorescence microscopy ...... 100
Mouse models of neuroblastoma...... 101
2.6.1 Th-MYCN mouse neuroblastoma tumorigenesis model ...... 101
Ganglia dissection ...... 101
2.6.2 Stable cells subcutaneous xenografts ...... 102
Engraftment and monitoring ...... 102
Doxycycline administration and in vivo fluorescence imaging ...... 103
Tumour tissue preparation ...... 104
Statistical analysis ...... 105
Identification of candidate miRNAs & mRNAs for roles in neuroblastoma tumorigenesis ...... 106
Chi Yan Ooi vii Table of Contents
Introduction ...... 107
Results ...... 110
+/+ 3.2.1 Th-MYCN ganglia tissue profiling and computational miRNA-mRNA network prediction ...... 110
3.2.2 Selection of miRNA candidates ...... 116
3.2.3 Correlations of the top 5 miRNA candidates expression in primary
neuroblastoma tumours with MYCN amplification and survival ...... 125
3.2.4 Gene enrichment analyses of predicted target genes to identify likely
functions of miRNA candidates ...... 130
+/+ 3.2.5 Differential expression of the miRNA candidates in mouse Th-MYCN sympathetic ganglia tissues ...... 139
3.2.6 The expression of the miRNA candidates are regulated by MYCN ...... 141
Discussion ...... 147
miR-204 is a novel tumour suppressor of neuroblastoma ...... 156
Introduction ...... 157
Results ...... 160
4.2.1 miR-204 mimic reduced mRNA levels of MYCN and cell cycle enriched
targets 160
4.2.2 miR-204 mimic reduced MYCN protein levels ...... 166
4.2.3 miR-204 mimic reduced neuroblastoma cell proliferation but not
viability 168
4.2.4 miR-204 mimic reduced neuroblastoma colony forming capability...... 171
Chi Yan Ooi viii Table of Contents
4.2.5 Lentivirally transfected neuroblastoma stable cell lines expressed green
fluorescence and over-expressed miR-204 when induced by doxycycline ...... 173
4.2.6 Over-expression of miR-204 in neuroblastoma stable cells reduced MYCN
protein levels ...... 176
4.2.7 Over-expression of miR-204 in neuroblastoma stable cells reduced their
colony forming capability ...... 178
4.2.8 miR-204 over-expression in neuroblastoma stable cells subcutaneous
xenografts prolonged survivals of the xenografted mice ...... 180
4.2.9 miR-148a mimic and miR-375 inhibitor reduced neuroblastoma cell
proliferation ...... 186
Discussion ...... 190
Mechanism of bi-directional suppression between miR-204 and MYCN and
the genome-wide identification of miR-204 targets...... 197
Introduction ...... 198
Results ...... 200
5.2.1 ChIP assay revealed MYCN binding to pre-miR-204 genomic region .. 200
5.2.2 HDACi panobinostat and DNA methyltransferase inhibitor 5-Aza-2’-
deoxycytidine increased miR-204 expression in BE(2)-C cells in vitro ...... 205
5.2.3 miR-204 is predicted to target CDS of MYCN mRNA ...... 207
5.2.4 Biotin-labelled miR-204 mimic retained its ability to reduce target
levels 210
5.2.5 Biotin-labelled miR-204 mimic pulldown assay confirmed direct
interaction between miR-204 and MYCN mRNA ...... 213
Chi Yan Ooi ix Table of Contents
5.2.6 Microarray analysis of inducible miR-204 over-expression stable cell line
BE(2)-C-miR-204...... 217
Discussion ...... 227
Concluding remarks ...... 238
General discussion...... 239
Conclusions and future perspectives ...... 245
References ...... 249
Appendix A. 21 predicted network miRNAs sequences conservation ...... 305
Appendix B. miRStart aggregated data of CAGE and TSS tags and H3K4me3 signature up to 50,000 bp upstream of pre-miR-204 ...... 307
Chi Yan Ooi x List of Figures
List of Figures
Figure 1.1 The Hallmarks and Enabling Characteristics of Cancer ...... 5
Figure 1.2 The development of normal sympathetic ganglia and neuroblastoma tumourigenesis ...... 8
Figure 1.3 The structure of MYCN proto-oncogene ...... 16
Figure 1.4 The biogenesis of miRNAs ...... 23
Figure 1.5 The miR-17-92 cluster and its paralogs ...... 36
Figure 1.6 The MYCN-E2F1 negative feedback loop ...... 39
Figure 1.7 The miR-17-92 cluster and TGFβ pathway ...... 41
Figure 1.8 The miR-17-92 cluster, cell cycle and differentiation ...... 43
Figure 1.9 The LIN28B-let-7-MYCN axis ...... 49
Figure 1.10 miR-34a, p53 and MYCN ...... 53
Figure 1.11 miR-9 and MYCN in neuroblastoma ...... 56
Figure 2.1 Structure of the inducible miR-204 lentiviral construct from GE Healthcare
Dharmacon ...... 81
Figure 3.1 Overview of workflow from ganglia tissues profiling to network prediction
112
Figure 3.2 The π-value quantification of expression divergence over time ...... 113
Figure 3.3 Selection of miRNA candidates ...... 119
Figure 3.4 Top 5 miRNA candidates and miR-574-3p subnetworks ...... 124
Figure 3.5 Kaplan–Meier curves for overall and progression free survivals of the top 5 miRNA candidates ...... 127
Figure 3.6 Top 5 miRNA candidates expression in MYCN-amplified vs non-amplified primary tumours ...... 129
Chi Yan Ooi xi List of Figures
Figure 3.7 Fold changes of the top 5 miRNA candidates and miR-574-3p in week 2 postnatal sympathetic ganglia tissues ...... 140
Figure 3.8 Top 5 miRNA candidates and miR-574-3p expression in SHEP MYCN-3 cells with doxycycline-induced MYCN over-expression ...... 143
Figure 3.9 Top 5 miRNA candidates and miR-574-3p expression in BE(2)-C cells after siRNA knockdown of MYCN ...... 145
Figure 3.10 Top 5 miRNA candidates and miR-574-3p expression in Kelly cells after siRNA knockdown of MYCN ...... 146
Figure 4.1 Top 10 MSigDB Canonical pathways gene sets enriched for miR-204 ...... 162
Figure 4.2 miR-204 KEGG Cell Cycle predicted target genes mice expression heat map
163
Figure 4.3 MYCN and KEGG Cell Cycle predicted targets mRNA levels are reduced in
BE(2)-C cells after transfection of miR-204 mimic ...... 164
Figure 4.4 MYCN and KEGG Cell Cycle predicted targets mRNA levels are reduced in
Kelly cells after transfection of miR-204 mimic ...... 165
Figure 4.5 MYCN protein levels are reduced in BE(2)-C and Kelly cells after transfection of miR-204 mimic ...... 167
Figure 4.6 miR-204 mimic did not affect BE(2)-C and Kelly cell viability ...... 169
Figure 4.7 miR-204 mimic reduced BE(2)-C and Kelly cell proliferation ...... 170
Figure 4.8 miR-204 mimic reduced BE(2)-C and Kelly colony forming capability ....172
Figure 4.9 Inducible miR-204 stable cell lines expressed green fluorescence and over- expressed miR-204 when induced by doxycycline ...... 175
Figure 4.10 Doxycycline-induced miR-204 over-expression reduced MYCN protein expression ...... 177
Chi Yan Ooi xii List of Figures
Figure 4.11 Doxycycline-induced miR-204 over-expression reduced stable cells colony forming capability ...... 179
Figure 4.12 turboGFP is over-expressed in subcutaneous xenografts 7 days after beginning doxycycline treatment compared to negative control treatment ...... 183
Figure 4.13 Prolonged survivals of xenografted mice treated with doxycycline compared to control ...... 184
Figure 4.14 miR-204 is over-expressed in doxycycline-treated subcutaneous xenografts at the experimental end-point compared to negative control treatment ...... 185
Figure 4.15 miR-148a mimic and miR-375 inhibitor did not alter cell viability in both
BE(2)-C and Kelly cell lines ...... 188
Figure 4.16 miR-148a mimic and miR-375 inhibitor suppressed neuroblastoma cell proliferation ...... 189
Figure 4.17 miR-204 suppresses MYCN and cell cycle genes ...... 193
Figure 5.1 MYCN binds to the pre-miR-204 DNA sequence in BE(2)-C ...... 204
Figure 5.2 miR-204 expression is elevated by HDACi panobinostat and DNA methyltransferase inhibitor 5-Aza-2’-deoxycytidine ...... 206
Figure 5.3 miR-204 is predicted to bind to MYCN CDS ...... 209
Figure 5.4 Biotin-labelled miR-204 mimic is able to suppress MEIS2 and MYCN mRNA expression ...... 211
Figure 5.5 Biotin-labelled miR-204 mimic is able to suppress MYCN protein expression
212
Figure 5.6 Confirmation of direct interaction between miR-204 and MYCN mRNA by biotin-labelled miR-204 mimic pulldown ...... 216
Figure 5.7 Differential expression of genes in stable cell line BE(2)-C-miR-204 after doxycycline-induced miR-204 over-expression...... 221
Chi Yan Ooi xiii List of Figures
Figure 5.8 SFN, CYP3A5 and MAGEB1 are high confidence DNA methylation-related targets up-regulated by miR-204 ...... 226
Figure 6.1 The miR-204-MYCN Double-Negative Regulatory Feedback Loop...... 244
Chi Yan Ooi xiv List of Tables
List of Tables
Table 1.1 The International Neuroblastoma Staging System (INSS) ...... 12
Table 1.2 The International Neuroblastoma Risk Group Staging System (INRGSS) ....13
Table 1.3 Canonical and noncanonical miRNA target site motifs ...... 28
Table 2.1 siRNA and miRNA mimics and inhibitors used in this thesis ...... 78
Table 2.2 Applied Biosystems TaqMan MicroRNA Assays used in this thesis ...... 85
Table 2.3 mRNA qRT-PCR primers used in this thesis ...... 88
Table 2.4 ChIP assay primers ...... 94
Table 3.1 Expression and π-value of the 21 miRNAs in the predicted interaction network
115
Table 3.2 Selection of the top 5 miRNA candidates ...... 121
Table 3.3 The correlations of miRNA candidates with survival, MYCN amplification and
MYCN overexpression in published human high-throughput expression profiling studies
123
Table 3.4 miR-204 Predicted Target Genes Enrichment Analyses ...... 133
Table 3.5 miR-574-3p Predicted Target Genes Enrichment Analyses ...... 134
Table 3.6 miR-148a Predicted Target Genes Enrichment Analyses ...... 136
Table 3.7 miR-375 Predicted Target Genes Enrichment Analyses ...... 137
Table 3.8 miR-135a Predicted Target Genes Enrichment Analyses ...... 138
Table 3.9 Integration of findings to select top 3 miRNAs for further experimental studies
155
Table 5.1 Top 10 enriched gene sets from down- and up-regulated genes in stable cell line
BE(2)-C-miR-204 after doxycycline-induced miR-204 over-expression ...... 224
Chi Yan Ooi xv Abstract
Abstract
Neuroblastoma is a childhood cancer derived from embryonic neural crest cells of the peripheral sympathetic nervous system, and is the most common extracranial solid tumours in children. The MYCN proto-oncogene is amplified in around a quarter of primary neuroblastoma tumours and this remains a major factor that predicts poor prognosis. MYCN is also a major oncogenic driver through pleiotropic effects and is regulated by multiple protein coding genes and non-coding RNAs such as microRNAs (miRNAs) in neuroblastoma. However, the complete miRNA regulatory network that contributes to
MYCN-driven neuroblastoma has not been fully explored. To explore this regulatory network, we investigated the global miRNA and mRNA expression profiles of the Th–
MYCN neuroblastoma mouse model during early tumorigenesis and inferred a statistically significant interaction network between miRNAs and mRNAs. Of these miRNAs, miR-204 expression is significantly reduced in ganglia of Th–MYCN homozygotes compared to wild- type mice. High miR-204 expression is strongly associated with better overall and event- free survival and lack of MYCN amplification in neuroblastoma patients. In addition, miR-
204 expression is increased with MYCN siRNA knockdown in MYCN-amplified human neuroblastoma cells, suggesting MYCN may regulate miR-204 expression. Indeed, chromatin immunoprecipitation assay revealed MYCN binding to DNA encoding the miR-
204 precursor sequence, showing MYCN can transcriptionally regulates miR-204 expression in neuroblastoma. Moreover, miR-204 over-expression significantly reduced
MYCN mRNA and protein expression. A biotin pulldown assay confirmed miR-204 binding to MYCN mRNA. Importantly, miR-204 over-expression significantly reduced neuroblastoma cell proliferation and colony forming capacity. Doxycycline-induced miR-
204 over-expression in two subcutaneous xenografts models of stable neuroblastoma cell lines also prolonged mouse survival. For
Chi Yan Ooi xvi Abstract
the first time, our data demonstrate that miR-204 inhibits neuroblastoma growth in vitro and in vivo and directly represses MYCN, while MYCN transcriptionally represses miR-204 to form a double-negative regulatory feedback loop. Together these findings describe miR-204 as a tumour suppressor microRNA that functions in regulation of
MYCN expression in neuroblastoma tumorigenesis.
Chi Yan Ooi xvii Abbreviations
Abbreviations
× g relative centrifugal force
3’UTR Three Prime Untranslated Region
5’UTR Five Prime Untranslated Region
Ago Argonaute protein(s)
AGO2 Argonaute 2, RISC catalytic component
BDNF Brain-Derived Neurotrophic Factor
BNSA Bayesian Network with Split-Averaging
bp Base pair(s)
CAGE Cap Analysis of Gene Expression
CDC25A Cell Division Cycle 25A
CDC25B Cell Division Cycle 25B
CDS Coding DNA Sequence or Protein Coding Sequence
ChIP Chromatin Immunoprecipitation
ChIP-chip ChIP on DNA microarray
ChIP-seq ChIP on DNA sequencing
CMGG Center for Medical Genetics
c-MYC MYC proto-oncogene, bHLH transcription factor
CYP3A5 Cytochrome P450 family 3 subfamily A member 5
DMEM Dulbecco’s Modified Eagle’s Medium
DMSO Dimethyl Sulfoxide
DNA Deoxyribonucleic Acid
Chi Yan Ooi xviii Abbreviations
DNMT DNA methyltransferase
E2F1 E2F transcription factor 1
FDR False Discovery Rate
GAPDH glyceraldehyde-3-phosphate dehydrogenase
GD2 disialoganglioside
GUSB Glucuronidase Beta
h Hour(s)
H3K27Ac Histone 3 Lysine 27 acetylation
H3K4Me1 Histone 3 Lysine 4 mono-methylation
H3K4me3 Histone 3 Lysine 4 tri-methylation
H3K9/14Ac Histone 3 Lysine 9/14 acetylation
HDAC Histone Deacetylase
HDACi Histone Deacetylase inhibitor
HRPT1 Hyperparathyroidism 1
INRGSS International Neuroblastoma Risk Group Staging System
INSS International Neuroblastoma Staging System
IRF1 Interferon Regulatory Factor 1
isomiR Isomeric miRNA
JUND JunD proto-oncogene, AP-1 transcription factor subunit
KEGG Kyoto Encyclopedia of Genes and Genomes
LEF1 Lymphoid Enhancer Binding Factor 1
LNA Locked Nucleic Acid
Chi Yan Ooi xix Abbreviations
7 m G 7-methylguanosine
MAGEB1 MAGE family member B1
MCM7 Minichromosome Maintenance Complex Component 7
MEIS2 Meis homeobox 2
min Minute(s)
miRISC miRNA-associated RNA-Induced Silencing Complex
miRNA microRNA
MITF Melanogenesis Associated Transcription Factor
MSigDB Molecular Signatures Database
MYCN MYCN proto-oncogene, bHLH transcription factor, or v-myc avian
myelocytomatosis viral oncogene neuroblastoma derived homolog
NCBI National Center for Biotechnology Information
NCI National Cancer Institute
PBS Phosphate-Buffered Saline
PCR Polymerase Chain Reaction
PRC2 Polycomb Repressive Complex 2
Pre-miRNA Precursor microRNA
Pre-miRNA Precursor miRNA
Pri-miRNA Primary microRNA transcript
Pri-miRNA Primary miRNA transcript
qRT-PCR quantitative Reverse Transcription Polymerase Chain Reaction
RBL1 RB transcriptional corepressor Like 1
Ref. Reference
Chi Yan Ooi xx Abbreviations
RISC RNA-Induced Silencing Complexa
RLM-RACE RNA Ligase-Mediated Rapid Amplification of cDNA Ends
RNA Ribonucleic Acid
RNAi RNA interference
RNase Ribonuclease
rpm revolutions per minute
RPMI Roswell Park Memorial Institute
s Second(s)
SFN Stratifin or 14-3-3σ
siMYCN siRNA targeting MYCN
siRNA small interfering RNA
snoRNA small nucleolar RNA
snoRNA Small nucleolar RNA
snoRNA small nucleolar RNA
snRNA small nuclear RNA
STAT3 Signal Transducer and Activator of Transcription 3
TBST Tris-Buffered Saline with 0.1% Tween 20
TCF3 Transcription Factor 3
TFDP1 Transcription Factor Dp-1
Th tyrosine hydroxylase promoter
TrkB Tropomyosin receptor kinase B, also known as Neurotrophic
Receptor Tyrosine Kinase 2 (NRTK2)
tRNA transfer RNA
Chi Yan Ooi xxi Abbreviations
tRNA transfer RNA
TRPM3 Transient Receptor Potential cation channel subfamily M member 3
TSS Transcription Start Site
UCSC University of California Santa Cruz
WEE1 WEE1 G2 checkpoint kinase
β2M beta-2-microglobulin
Chi Yan Ooi xxii Publication in preparation from this thesis
Publication in preparation from this thesis
Chi Yan Ooi †, Daniel R. Carter †, Bing Liu, Chelsea Mayoh, Anneleen Beckers, Sara
De Brouwer, Tzong-Tyng Hung, Murray D. Norris, Michelle Haber, Tao Liu, Katleen
De Preter, Frank Speleman, Belamy B. Cheung ‡, Glenn M. Marshall ‡. miR-204 acts as a tumor suppressor in neuroblastoma tumorigenesis by inhibition of MYCN, Manuscript in final preparation, † Equal first authors, ‡ Equal corresponding authors
Chi Yan Ooi xxiii Presentations arising from this thesis
Conference presentations arising from this thesis
1. Chi Yan Ooi, Daniel R. Carter, Bing Liu, Chelsea Mayoh, Anneleen Beckers,
Sara De Brouwer, Murray D. Norris, Michelle Haber, Tao Liu, Katleen De
Preter, Frank Speleman, Belamy B. Cheung and Glenn M. Marshall.
MicroRNA-204 is a tumour suppressor that down-regulates MYCN oncogene
and suppresses tumour growth in neuroblastoma, The 2016 Annual Scientific
Meeting of the Australian Association of Biomedical Scientists, Sydney,
Australia, 2016. (Awarded Second Place in the CanCare Research Awards)
2. Chi Yan Ooi, Daniel R. Carter, Bing Liu, Anneleen Beckers, Sara De Brouwer,
Katleen De Preter, Murray D. Norris, Michelle Haber, Frank Speleman, Tao Liu,
Belamy B. Cheung, Glenn M. Marshall. MicroRNA-204 suppresses neuroblastoma
tumour growth through down-regulation of MYCN oncogene, Advances in
Neuroblastoma Research Congress 2016, Cairns, Australia, 2016.
3. Chi Yan Ooi, Daniel R. Carter, Bing Liu, Anneleen Beckers, Sara De Brouwer,
Katleen De Preter, Murray D. Norris, Michelle Haber, Frank Speleman, Glenn
M. Marshall and Belamy B. Cheung. MicroRNA-204 down-regulates MYCN
oncogene and suppresses neuroblastoma cell growth, The 2015 Annual
Scientific Meeting of the Australian Association of Biomedical Scientists,
Sydney, Australia, 2015. (Awarded Third Place in the Postgraduate Student
Speaker Awards)
4. Chi Yan Ooi, Daniel R. Carter, Bing Liu, Anneleen Beckers, Sara De Brouwer,
Katleen De Preter, Murray D. Norris, Michelle Haber, Frank Speleman, Glenn
M. Marshall and Belamy B. Cheung. MicroRNA-204 down-regulates MYCN
oncogene and suppresses neuroblastoma cell growth, 43rd Annual Tow Research
Awards Day, Sydney, Australia 2015.
Chi Yan Ooi xxiv Presentations arising from this thesis
5. Chi Yan Ooi, Daniel R. Carter, Bing Liu, Anneleen Beckers, Sara De Brouwer,
Katleen De Preter, Murray D. Norris, Michelle Haber, Frank Speleman, Glenn
M. Marshall and Belamy B. Cheung. miR-204 is a novel target of MYCN and
potential tumour suppressor in neuroblastoma tumourigenesis, 27th Lorne
Cancer Conference, Lorne, Australia, 2015.
6. Chi Yan Ooi, Daniel R. Carter, Bing Liu, Anneleen Beckers, Sara De Brouwer,
Katleen De Preter, Murray D. Norris, Michelle Haber, Frank Speleman, Glenn
M. Marshall and Belamy B Cheung. miR-204 is a novel target of MYCN and
potential tumour suppressor in neuroblastoma, The 2014 Annual Scientific
Meeting of the Australian Association of Biomedical Scientists, Sydney,
Australia, 2014.
Chi Yan Ooi xxv Acknowledgements
Acknowledgements
“I want to be a scientist”- A boy’s dream in an UNICEF
advertisement on board of Cathay Pacific flights
The first time I realized that I have become a scientist is when I rewatched this advertisement for the first time since I started my PhD degree. I feel privileged to be able to do a PhD scientific research degree and make novel and significant contribution to science. Not a lot of people can afford to do a scientific research degree. First of all, I want to thank my supervisor Dr Belamy Cheung for giving me the opportunity to study at the Children’s Cancer Institute. I also want to thank my co-supervisors Dr Dan Carter and Dr Bing Liu, and Head of Program Professor Glenn Marshall for their guidance.
Also thank you to Professor Frank Speleman and his team for their collaborations that form a part of the foundation for this research project. I would also like to thank UNSW
Sydney and the Children’s Cancer Institute for the scholarships, and research funding bodies and the Institute’s donors that have made this research possible.
In addition, I want to thank the past members of the Molecular Carcinogenesis Program, in particular Dr Jessica Koach, Dr Selina Sutton, Dr Adam Carroll and Owen Tan, for their technical assistances. I would also thank Associate Professor Tao Liu for designing the chromatin immunoprecipitation primers, Dr Tzong-Tyng Hung for technical assistance in in vivo imaging, and Dr Chelsea Mayoh for bioinformatical assistances. I also want to thank the scientific services and information technology teams at the
Institute for their supportive roles in the research operations within the Institute.
Most importantly, I would like to thank my parents, especially my mother, for their nurture and support.
Chi Yan Ooi xxvi
Introduction
Chi Yan Ooi 1 Introduction
Cancer
Cancer is a collection of complex genetic diseases caused by aberration in multiple genes [1-5]. Despite many years of research and improved patient survival over the years, cancer is still a major cause of illness in Australia that accounts for 3 in 10 deaths in 2010, the second most common cause of death behind cardiovascular diseases
[6]. A lot more research is still required to better understand and treat cancer. One of the approaches is to understand how cancer begins and to halt the processes in order to prevent the formation of cancer.
1.1.1 Tumorigenesis – Initiation, Promotion and Progression of Cancer
Tumorigenesis, the formation of tumour, is encapsulated in a classical model where tumour forms by a multistage process [4, 7]. The exact stages are subjected to different interpretations and constantly evolving, but the basic framework of initiation, promotion and progression applied generally [8, 9]. Cancer first begins when a single normal cell or cells acquire alteration that provides a survival advantage over other normal cells [4, 8].
This initiating change, such as mutations and amplifications, is often considered to be in a gene that is either a proto-oncogene or a tumour suppressor [4, 5]. The initiated cells further sequentially acquire multiple alterations, undergo stepwise clonal expansion and selection of different evolutionary advantageous cells during promotion to form benign tumour that is heterogeneous in nature [3, 4, 9-11]. Additional aberration(s) occurs during progression to gain malignancy [4, 9]. Ongoing research over the years have shown that the alterations during tumorigenesis are not just genetic in nature, but can also be epigenetic, and the epigenetic changes are even suggested to occur before the initiating event [12]. The microenvironment of initiated cells is also proposed to be vital to tumorigenesis that facilitates evolutionary selection, expanding and co-evolving with initiated cells in a manner parallel to tumorigenesis [13]. In addition, the
Chi Yan Ooi 2 Introduction
theory of cancer stem cell has been proposed in recent years, where there is a stem cell- like subset within a tumour population that generates and perpetually sustains the more differentiated bulk of the tumour [14-16]. This hypothesis seems to extend the concept of multistage tumorigenesis in which normal stem cell or their stem cell-like progenitor cells are proposed to undergo initiation and clonal evolution to form malignant cancer stem cells [16].
1.1.2 Oncogenes, Tumour Suppressors and the Hallmarks of Cancer
As mentioned above, cancer is a group of diseases with aberrant genes, genes that are classified as proto-oncogenes or tumour suppressors. It is important to note that multiple genes have to be altered to form cancer due to the presence of multiple safeguards against alterations, and hence multiple genes are altered during tumorigenesis [2, 4, 9]. Proto- oncogenes are aberrantly activated in caner by 'gain-of-function' alterations like mutations, chromosomal rearrangements that enhance transcription or produce activated gene fusions, and gene amplifications [1, 2, 5]. These genes (called oncogenes when in abnormal states) traditionally encode proteins that are transcription factors, chromatin remodelers of epigenetic codes, growth factors, growth factor receptors, signal transducers, and apoptosis regulators [2, 5]. These proteins control aspects like cell proliferation, apoptosis and nutrients supply through angiogenesis [2, 5]. On the other hand, tumour suppressors are aberrantly inactivated in cancer by 'loss-of-function' alterations like mutations, inactivating chromosomal deletions or insertions and epigenetic silencing [1, 2]. These genes mainly suppress tumours by suppressing cell division, inducing apoptosis, facilitating DNA damage repair and inhibiting metastasis
[17]. Alterations in these genes during tumorigenesis contribute to the 6 hallmarks of cancer as proposed by Hanahan and Weinberg [18], [19] (Figure 1.1), namely proliferative signalling sustainment, growth suppressors evasion, invasion and metastasis
Chi Yan Ooi 3 Introduction
activation, replicative immortality enablement, angiogenesis induction, and cell death resistance, also the emerging hallmarks of cellular energetics deregulation, immune destruction avoidance, the enabling characteristics of genome instability/mutation and tumour-promoting inflammation.
Chi Yan Ooi 4 Introduction
Figure 1.1 The Hallmarks and Enabling Characteristics of Cancer
During tumorigenesis, cells acquire the six cancer hallmarks of sustaining proliferative signalling, evading growth suppressors, enabling replicative immortality, activating invasion and metastasis, inducing angiogenesis and resisting cell death originally proposed in 2000 [18]. Later research suggested two additional ‘emerging hallmarks’ of deregulating cellular energetics to support cancer proliferation and avoiding immune destruction that are involved in at least some if not all cancer. In addition, the enabling characteristics of genome instability and mutation, and tumour promoting inflammation from the consequences of tumorigenesis facilitated the acquisition of the above hallmarks. Consequently, drugs are being developed to target these hallmarks and characteristics to combat cancer. Figure adapted from ref. [19].
Reprinted from Cell, 144, Douglas Hanahan, Robert A. Weinberg, Hallmarks of Cancer: The Next Generation, p. 646-674, Copyright 2011, with permission from Elsevier.
Chi Yan Ooi 5 Introduction
Neuroblastoma
Neuroblastoma is the most common solid tumour in infants of the age of 1 or less
[20]. Although only contributing to more than 7% of cancer incidence in children aged 15 or below, neuroblastoma accounts for around 15% of all childhood cancer death [21]. This highlights the need for more research in this type of childhood cancer. In particular, although a subset of tumours spontaneously regresses, around half of all patients are classified as high-risk with survival rates of less than 50% [22-27]. In addition, urine test for early detection of neuroblastoma usually detects only tumours that spontaneously regress
[22-25, 28], disabling the early detection of high-risk patients that needs it most.
Neuroblastoma originates from embryonic neural crest cells that develop into the adrenal medulla and paravertebral sympathetic ganglia tissues of the sympathetic nervous system [28-30]. Consequently, tumours can be found mainly in the abdomen and also in the chest, neck and pelvis [25, 28, 30]. Neural crest cells normally migrate out from the neural crest of the developing embryo and differentiate into sympathoadrenal progenitor cells [31]
(Figure 1.2). MYCN, a proto-oncogene, is important in the normal regulation of migration and expansion of neural crest cells and maintenance of progenitor cell state by blocking neuronal differentiation during development [32-34]. MYCN expression gradually reduces as tissues mature to allow for differentiation of neuronal progenitor cells [32, 34].
Alterations in MYCN is considered as the ‘first hit’ or initiating event of neural crest cells in malignant transformation [29, 35, 36]. Alterations in other genes such as PHOX2B, ALK
(anaplastic lymphoma kinase) and LIN28B that are activated and involved in neural crest cells development and/or proliferation are also able to initiate neuroblastoma [31, 37-47]. In addition, cooperation between these genes and MYCN may be involved during initiation and progression [42, 47-49]. In order to persist/survive after birth, the initiating pre-cancer cells then need to acquire a second modification that
Chi Yan Ooi 6 Introduction
confers resistance to apoptotic cell death induced by nerve growth factor (NGF) withdrawal at the end of organogenesis [29, 50]. Persisting/surviving cells acquire the third hit after birth to further promote transformation into neuroblastoma [29].
Unsurprisingly, with the embryonic and stem cell-like nature of neural crest cells, cancer stem cells have been identified in neuroblastoma development [51, 52].
Chi Yan Ooi 7 Introduction
Figure 1.2 The development of normal sympathetic ganglia and neuroblastoma tumourigenesis
Inside the developing embryo, neural crest cells migrate ventrally from the neural tube towards the dorsal aorta to become primary sympathetic ganglia (PSG). These are controlled by MYCN and morphogenetic proteins (BMPs). In the presence of nerve growth factor (NGF), these cells further mature into terminal sympathetic ganglion cells. Alterations in the developmental regulator genes, such as MYCN, PHOX2B and ALK in immature cells serve as the first hit initiating event for neuroblastoma tumourigenesis. Withdrawal of NGF at the end of organogenesis eliminates immature cells through apoptotic cell death. Immature pre-cancer cells need to acquire death resistance (the second hit) in order to persist beyond birth. Transformation of pre-cancer cells into neuroblastoma occurs after a third hit event. Figure from ref. [29].
Reprinted by permission from Macmillan Publishers Ltd: NATURE REVIEWS CANCER, copyright 2014.
Chi Yan Ooi 8 Introduction
1.2.1 Clinical Stages, Features and Management
The initial international staging system for neuroblastoma, called International
Neuroblastoma Staging System (INSS), was developed in 1988 and revised in 1993 [53,
54]. The INSS is a postsurgical staging system that classify patients into six different stages
[54] (see Table 1.1). Patients in stage 4 have worse 5-year event-free and overall survival rates compared to patients in other stages [55]. In addition, patients can be classified into three different risk groups (low, intermediate, high) based on INSS stage, age at diagnosis,
MYCN gene amplification status, histopathology and ploidy/DNA index
[56]. Patients diagnosed before the age of 12 or 18 months have better 5-year event-free and overall survival rates compared to those diagnosed at an older age [55]. On the other hand, MYCN amplification correlates to poor patient event-free and overall survivals compared to without MYCN amplification [55]. Moreover, patients with diploid or hypodiploid (DNA index ≤ 1) tumours have poorer 5-year event-free and overall survival rates compared to those with hyperdiploid (DNA index > 1) tumours [55].
Because the INSS is dependent on surgical resection, a new pre-treatment staging system relying on image-defined risk factors based on radiographs has been proposed
[57]. The proposed staging system is called the International Neuroblastoma Risk Group
Staging System (INRGSS) and stratifies patients into four different stages (Table 1.2). A new pre-treatment risk classification system based on the INRGSS instead of the INSS and consists of sixteen different risk groups designated to four risk categories (very low, low, intermediate, high) has also been proposed [55]. This proposed system takes into account of the grade of tumour differentiation and the presence or absence of chromosome 11q aberration in addition to the before mentioned factors for risk stratifications [55]. In the
International Neuroblastoma Risk Group study cohort, more than 80% of patients have undifferentiated tumours, which is associated with poorer 5-year event-free and
Chi Yan Ooi 9 Introduction
overall survival rates compared to those with differentiated tumours [55]. On the other hand, loss of heterozygosity in 11q and other aberrations occurs in at least 21% of patients, frequently in non-MYCN-amplified tumours, and correlates to reduced patient survival [55, 58-60].
In addition to the above mentioned genetic aberrations, there are other genetic aberrations being reported and correlated to patient survival. Loss of heterozygosity in chromosome 1p occurs in around 23 to 33% of patients and correlates to MYCN amplification and poor patient survival [55, 61, 62]. On the other hand, loss of heterozygosity in chromosome 3p occurs in around 15% of patients and correlates to loss of heterozygosity in 11q and reduced patient survival [58]. Moreover, loss of heterozygosity in chromosome 9p has been reported in 32% of patients and is correlated to poor patient survival [63]. Other losses of heterozygosity were also reported in chromosomes 4p, 14q, and 19q [64-66]. In contrast, homozygous deletions are rare and have been detected in chromosome locus 9p21 [67]. For chromosomal gain, 17q gain has been reported in around half of the patients and is correlated with MYCN amplification, chromosome 1p deletion and poor patient survival [55, 68]. Finally, additional non-genetic clinical features include high serum levels of ferritin and lactate dehydrogenase in some patients which correlates to poorer patient survival [55, 69-71].
Clinical management of the disease varies according to the risk stratification. For very low-risk and low-risk patients, they are mainly observed and monitored for progression of the disease, with some requiring surgical resection or chemotherapy depending on clinical evaluations [72]. For intermediate-risk patients, they are prescribed with two to eight cycles of chemotherapy with surgical resection whenever possible, and radiotherapy for some with unfavourable unresectable disease [72]. For high-risk patients, several sequential stages of therapy are prescribed. These patients are first administered
Chi Yan Ooi 10 Introduction
with four to five cycles of induction chemotherapy consisting of a combination of four to six drugs, such as vincristine, cisplatin, carboplatin, etoposide, cyclophosphamide, ifosfamide, doxorubicin and topotecan [72]. Surgical resection is then performed, followed by myeloablative therapy, autologous haematopoietic stem cell transplantation(s) and radiotherapy to the vasculature and connective tissues surrounding the site of the resected primary tumour [72]. Finally, maintenance therapy is prescribed, which consists of differentiation agent isotretinoin, anti-GD2 antibody, granulocyte– macrophage colony-stimulating factor and interleukin 2 [72]. If the disease relapses, topotecan plus cyclophosphamide, topotecan plus temozolomide, irinotecan plus temozolomide, or 131I-metaiodobenzylguanidine therapy can be used [72].
Chi Yan Ooi 11 Introduction
Stage Definition
1 Localized tumour with complete gross excision, with or without microscopic residual disease; representative ipsilateral lymph nodes negative for tumour microscopically (nodes attached to and removed with the primary tumour may be positive)
2A Localized tumour with incomplete gross excision; representative
ipsilateral non-adherent lymph nodes negative for tumour microscopically
2B Localized tumour with or without complete gross excision, with ipsilateral
non-adherent lymph nodes positive for tumour. Enlarged contralateral lymph nodes must be negative microscopically
3 Unresectable unilateral tumour infiltrating across the midline (vertebral column), with or without regional lymph node involvement; or localized unilateral tumour with contralateral regional lymph node involvement; or midline tumour with bilateral extension by infiltration (unresectable) or by lymph node involvement
4 Any primary tumour with dissemination to distant lymph nodes, bone, bone marrow, liver, skin and/or other organs (except as defined
for stage 4S)
4S Localized primary tumour (as defined for stage 1, 2A or 2B), with dissemination limited to skin, liver, and/or bone marrow (< 10% of total
nucleated cells identified as malignant on bone marrow biopsy or on marrow aspirate. The iodine-123-meta-iodobenzylguanidine scintigraphy scan, if performed, should be negative in the marrow.) Limited to infants < 1 year of age
Table 1.1 The International Neuroblastoma Staging System (INSS)
Adapted from ref. [54].
Brodeur, G M et al, Revisions of the international criteria for neuroblastoma diagnosis, staging, and response to treatment, Journal of Clinical Oncology, 11, 8, p. 1466-1477. Reprinted with permission. © 1993 American Society of Clinical Oncology. All rights reserved.
Chi Yan Ooi 12 Introduction
Stage Description
L1 Localized tumour not involving image-defined risk factors and confined to one
body compartment
L2 Locoregional tumour with presence of one or more image-defined risk factors
M Distant metastatic disease (except stage MS)
MS Metastatic disease in children younger than 18 months with metastases confined to skin, liver, and/or bone marrow
Table 1.2 The International Neuroblastoma Risk Group Staging System (INRGSS)
Adapted from ref. [57].
Monclair, T et al, The International Neuroblastoma Risk Group (INRG) Staging System: An INRG Task Force Report, Journal of Clinical Oncology, 27, 2, p. 298-303. Reprinted with permission. © 2009 American Society of Clinical Oncology. All rights reserved.
Chi Yan Ooi 13 Introduction
1.2.2 MYCN: A major proto-oncogene of neuroblastoma
The MYCN gene is amplified in around 20-30% of primary neuroblastoma tumours, and is a major classifier of high-risk disease and predictor of poor survival [25, 55, 56, 73,
74]. MYCN belongs to the Myc transcription factor family comprising of MYCN, c-MYC and L-MYC that share structural similarities [75]. The N-terminal of MYCN is composed of
2 Myc boxes that act as transactivation domain, and contains a basic helix–loop– helix/leucine zipper (bHLH-Zip) domain at the C-terminal for DNA binding and dimerization with another bHLH-Zip protein Max [74-78] (Figure 1.3). MYCN binds to
DNA sequence motifs called E-boxes, mainly of the classical sequence CACGTG or the main non-canonical sequence CATGTG, but also other non-canonical sequences CATTTG,
CATCTG and CAACTG under MYCN-amplified conditions in neuroblastoma [74, 75, 79].
This binding commonly leads to transcriptional activation, but transcriptional repression at
E-boxes has also been demonstrated [74, 75, 80, 81]. For example, MYCN binds to a non- canonical E box and recruits histone modifier polycomb repressive complex 2 to repress clusterin expression in neuroblastoma [82]. In addition, c-MYC can bind to the initiator consensus sequence YYCAYYYYY, where Y representing a pyrimidine base, to repress transcription [75, 83]. It is possible that MYCN may do the same. Furthermore, it has been shown that c-MYC can induce transcriptional repression by indirect binding to DNA through interaction with other transcription factors even without E-box sequences [84, 85].
Indeed, MYCN has also been shown to bind sequence-specific transcription factor Sp1 or
MIZ1 and recruit histone deacetylases (HDACs) to epigenetically repress gene transcription in neuroblastoma [86-89]. Moreover, MYCN transcriptionally represses the microRNA miR-183 through recruitment of HDAC2, seemingly without any E-box or Sp1 as co-factor
[90]. On the other hand, a large amount of co-localizations were found in neuroblastoma cell lines
Chi Yan Ooi 14 Introduction
between MYCN genomic binding sites and DNA hypermethylation sites that mark repressed transcription [79]. MYCN were later shown to be able to interact with DNA methyltransferases DNMT1 and DNMT3A, and recruit them to the promoter of protein coding gene RASSF1A [91]. These suggest that MYCN-mediated transcriptional repression could occur with multiple mechanisms. Through its transcriptional functions,
MYCN is an important proto-oncogene that regulates wide range of biological processes such as cell cycle and proliferation, differentiation, apoptosis, DNA damage response, cell invasion, angiogenesis and metabolism [75, 92, 93].
Chi Yan Ooi 15 Introduction
Figure 1.3 The structure of MYCN proto-oncogene
The bold lines denote the autoregulation sites and the exons of MYCN on the DNA level. MYCN has three exons but the first exon does not code for any protein sequence. The N- terminal of MYCN has two Myc boxes (MB I, MB II) for transcriptional activation. EX2/EX3 denotes the exon boundary. The C-terminal contains the basic (BR) helix (H1)
–loop (L) –helix (H2) /leucine zipper (Zip) domain for DNA binding and dimerization with Max protein as indicated by the green line. The MYCN protein also interacts with other proteins and the interacting amino acids are indicated by the green lines. (ref. [74- 78]) Figure from ref. [74].
Reprinted from The International Journal of Biochemistry & Cell Biology, 36, Wayne D Thomas, Anna Raif, Loen Hansford, Glenn Marshall, N-myc transcription molecule and oncoprotein, p. 771-775, Copyright 2004, with permission from Elsevier.
Chi Yan Ooi 16 Introduction
MYCN and neuroblastoma initiation and progression
As mentioned above, MYCN plays a critical role in the initiation of neuroblastoma. This was demonstrated by the Th-MYCN-transgenic mouse model, and later further supported by a mouse neural crest progenitor cell line called JoMa1 that could be transformed by MYCN [35, 36, 45]. By over-expressing the human MYCN gene specifically in perinatal mouse neural crest cells in vivo through the use of rat tyrosine hydroxylase promoter (Th), these transgenic mice are able to undergo neuroblastoma tumorigenesis and develop tumours that recapitulate human neuroblastoma [35, 36]. It has been shown that developmental neuroblast hyperplasias are present in paravertebral ganglia tissues of both normal and transgenic mouse at equivalent frequency at time of birth [36]. However, hyperplasias spontaneously regress by apoptosis in normal mouse by 2 weeks after birth that echoes the spontaneous regression of a subset of human tumours, while transgenic hyperplasias grow in size and frequency before more gradual, delayed and incomplete regression [36]. It has been suggested that MYCN promotes the proliferation and expansion of neuroblasts, mainly in Phox2B-expressing neuronal progenitors, while inhibiting their terminal differentiation [48]. Eventually tumours developed at around 6-7 weeks on average after birth for homozygous transgenic mice [94, 95]. During tumour progression, cells from transgenic tissues exhibit increased death resistance to NGF withdrawal that is accompanied by loss of NGF receptor expression, and undergone MYCN transgene amplification [36]. Despite the current knowledge, the mechanism of MYCN-driven neuroblastoma initiation and tumorigenesis is still not fully understood.
MYCN and the dysregulation of microRNA in neuroblastoma
MYCN has been demonstrated in a number of studies that it can dysregulate a wide number of microRNAs (miRNAs), which are short (~22 nucleotides), non-protein-
Chi Yan Ooi 17 Introduction
coding, single-stranded RNAs, in neuroblastoma [81, 96-100]. A number of miRNAs has been confirmed to be regulated by MYCN or targeting MYCN in neuroblastoma and act as important regulators of neuroblastoma genes expression [97, 101], and it will be discussed later. Furthermore, miRNA expression have also demonstrated prognostic values in human neuroblastoma [96, 97, 102-104].
Chi Yan Ooi 18 Introduction
MicroRNA
In 1993, Rosalind Lee, Rhonda Feinbaum and Victor Ambros discovered that lin-4, a gene that controls the timing of events in larval development of roundworm
Caenorhabditis elegans, temporally and negatively regulates LIN-14 expression [105]. At the time, this was unexpected as lin-4 does not encode a protein [105]. Instead, the lin-4 gene is lying within an intron of another gene and codes for two RNA transcripts that are around 22 and 61 nucleotides in length that share the same 5’ ends [105]. 7 elements on the
3’ untranslated region (3’UTR) of the LIN14 messenger RNA (mRNA) were discovered to be complimentary to the 5’ end of the lin-4 RNA transcripts, and were essential for the temporal down-regulation of LIN14 through RNA duplexes formation [105-107]. It is not until more than 6 years later that another such short regulatory RNA, let-7, was discovered
[108]. The let-7 gene was also originally identified in C. elegans and controls the transition into adult stage [108]. The temporally regulated let-7 RNA transcript is 21 nucleotides in length and down-regulates adult specification regulator LIN-41 through 2 elements in the
LIN-41 mRNA 3’UTR that are complementary to let-7 [108, 109]. lin-4 and let-7, also known as small temporal RNAs (stRNAs), lead to a class of small regulatory RNAs called microRNAs (miRNAs) [110-112]. Hundreds of miRNAs have now been identified in human through microarray, cloning, sequencing, homology to miRNAs in other species and computational strategies [113].
1.3.1 The biogenesis of microRNAs
Canonically, the generation of mature miRNAs begins with transcription of long primary transcripts (pri-miRNAs) by RNA Polymerase II or III from genomic DNA encoding the miRNA precursors (pre-miRNAs) [114-119] (Figure 1.4). miRNA genes are arranged within the introns and exons of longer non-coding genes and protein coding genes
(host genes) [114]. They are also arranged in single units without other miRNA
Chi Yan Ooi 19 Introduction
genes nearby or as clusters of miRNA genes, often co-transcribed into a single polycistronic primary transcript for members of the same cluster [120, 121]. In addition, intronic miRNAs can be transcribed from promoters independent from their host genes to generate shorter pri- miRNAs, which can be subjected to alternative splicing to remove some of the pre-miRNA members within miRNA clusters [122]. Pri-miRNAs are then processed by the microprocessor complex consisting of the RNase III endonuclease Dorsha and the double- stranded RNA binding protein Pasha/DGCR8 to crop the ~70-nucleotide hairpin-shaped pre-miRNAs out from the primary transcripts [114-119]. Cleavages by Dorsha do not appear to be completely precise and can generate pre-miRNAs at slightly different lengths from identical pri-miRNAs for some miRNAs, which lead to isomeric mature miRNAs or isomiRs [123-125]. For intronic miRNAs, the cropping of pre-miRNAs from introns by the microprocessor complex proceeds after commitment to splicing of the introns from primary transcripts but before intron excision [114, 126]. An alternative non-canonical pathway for intronic miRNAs bypasses the Dorsha-dependent cropping step, completes the intron splicing event instead and the excised intron is further processed into pre-miRNA through debranching and trimming [114, 115, 117, 127]. Pre-miRNAs generated by both pathways have a double-stranded stem with 2-nulcetode 3’ overhang that is recognised by Exportin-5 for exportation from the nucleus in association with RAN–GTP [114, 128, 129].
Alternatively, there have been suggestions of direct transcription of pre-miRNA as endogenous short hairpin RNA molecules, with confirmation of RNA Polymerase II- mediated transcription of pre-miR-320a with a 7-methylguanosine (m7G) 5’ cap [130, 131].
The m7G-capped pre-miRNA is exported from the nucleus by the PHAX-dependent
Exportin-1 [130].
Exported pre-miRNAs, including m7G-capped pre-miRNAs, are processed by another RNase III endonuclease called Dicer to generate miRNA duplexes [130, 132,
Chi Yan Ooi 20 Introduction
133]. The efficiency of Dicer processing varies by variations in the hairpin structure of different pre-miRNAs [134]. Like Dorsha, Dicer can generate isomiRs by cleaving some pre-miRNAs at different positions [123, 135]. Small nucleolar RNAs (snoRNAs) and transfer RNAs (tRNAs) also serve as alternative sources of pre-miRNAs for Dicer processing, as they can be processed by an unknown nuclease and RNaseZ respectively to become pre-miRNAs [131, 136-141]. The miRNA duplexes generated are loaded into
Argonaute proteins (Ago) which assembles with other proteins to form the miRNA- associated RNA-Induced Silencing Complex (miRISC) [114, 115, 142]. There are four
Argonaute proteins, Ago 1-4, in human and mouse that execute the gene silencing function of miRNAs [143, 144]. The protein TRBP (transactivating response (TAR)
RNA-binding protein) and PACT (protein activator of PKR) independently recruit Dicer to Ago2 in facilitating the loading of miRNA duplex and regulating Dicer processing and Ago2 miRNA duplex strand selection [145-149]. While either strand of a miRNA duplex can be selected for retainment in Ago proteins, particular strand of a duplex is favoured over the other depending on the relative thermodynamic stability of both ends of that particular duplex [115, 150-152]. For example, the strand derived from the 5’ end of an m7G-capped pre-miRNA, which is denoted as the 5p strand, has a m7G cap and is preferentially removed and degraded over the other 3p strand [130]. The strand that remains with Ago is the mature miRNA, which is around 22 nucleotides in length
[114, 115, 117, 127, 153].
Nevertheless, it has been shown that pre-miRNAs with shorter hairpin structure bypass Dicer processing and instead processed by Ago2 to become mature miRNA [154-
156]. Ago2-dependent processing of pre-miRNA requires cleavage within the 3p strand and optional trimming of the product by poly(A)-specific ribonuclease [157]. As only Ago2 but not the other three Ago proteins has functional endonuclease catalytic activity
Chi Yan Ooi 21 Introduction
[158, 159], only Ago2 is capable of generating mature miRNA from pre-miRNA. The multistep nature and alternative pathways of miRNA biogenesis provide additional platforms for regulating miRNA expression [114, 115, 117].
With mature miRNAs bound inside Ago proteins to assemble miRISCs, the miRNAs can be directed to its target mRNAs to inhibit their translation into proteins through several Ago-mediated mechanisms. First, miRNAs’ predominant mechanism of action is direct repression of translation [127]. This can be achieved through inhibition of translation initiation by interfering with the m7G-cap-binding protein eukaryotic initiation factor 4E [160-162]. Alternatively, translational repression is achieved through the inhibition of translation elongation by a complex consisting of Ago proteins, pumilio
RNA binding family member 2 and eukaryotic translation elongation factor 1 alpha 1
[163-165]. Second, miRNAs can accelerate the deadenylation of mRNA poly(A) tail, leading to accelerated mRNA degradation [166-168]. Third, miRNAs can cleave its target mRNAs through the functional endonuclease catalytic activity that is exclusive to
Ago2 but not the other three Ago proteins, similar to that for small interfering RNAs
(siRNAs) [159, 169, 170]. The specifics of miRNA-mRNA physical interactions will be discussed in the next section.
Chi Yan Ooi 22 Introduction
Exportin 1 Nucleus Cytoplasm
7 m G-capped RAN-GTP Mature pre-miRNA Exportin-5 miRNA Ago2- Pre-miRNA mediated Intronic Splicing, Dicer Debranching & Cropping by Trimming Dorsha + Pasha / DGCR8 miRISC ? / RNaseZ Primary snoRNA + Dicer Transcripts /tRNA (Pri-miRNAs) poly(A) Target mRNA or promoter promoter miRNA cluster Translational repression Single miRNA And/Or enhanced gene (intronic / exonic in non-coding / coding genes) mRNA degradation Transcription
Figure 1.4 The biogenesis of miRNAs miRNAs are organised in the genome as individual miRNAs or as miRNA clusters containing more than one miRNA, and can be located within the introns of protein coding genes or as an independent unit. In the canonical biogenesis of miRNA (coloured blue), transcription of the genome by RNA Polymerase II or III generates the primary RNA transcripts containing the mature miRNAs sequences (in red). The ribonuclease Drosha and its cofactor Pasha/DGCR8 process the primary transcripts to release the pre- miRNA precursor molecules each containing ~70 nucleotides. Pre-miRNAs are exported from the nucleus into the cytoplasm by RAN–GTP and Exportin-5. Pre- miRNAs are further processed by Dicer to generate miRNA duplexes of ~22 nucleotides long. The duplex is then loaded into a protein complex to form the miRISC (miRNA- associated RNA-Induced Silencing Complex), with only the mature strand ultimately being retained. Mature miRNAs in miRISCs then bind to target mRNAs at their binding sites and induce translation repression and/or enhance mRNA degradation. Non- canonical biogenesis pathways are coloured in green. Adapted from ref. [118] with additional information from ref. [114, 115, 127, 153].
Chi Yan Ooi 23 Introduction
1.3.2 miRNA binding sites on mRNAs
MicroRNAs are canonically considered to be molecules that target the 3’UTRs of mRNAs [171, 172]. The development of anti-Ago immunoprecipitation techniques such as high-throughput sequencing of RNA isolated by crosslinking immunoprecipitation (HITS-
CLIP), and photoactivatable ribonucleoside-enhanced crosslinking and immunoprecipitation
(PAR-CLIP), which allow indirect genome-wide identification of targets, confirmed the
3’UTRs of mRNAs as the main targets of miRNAs [173-175]. However, 3’UTRs only account for 40% and 39% of Ago-bound sequences in mouse brain and human embryonic kidney stable cell lines respectively [174, 175]. Moreover, 25% and 42% of those sequences are distributed to protein coding sequences (CDSs), and 1% and 3% are distributed to 5’ untranslated regions (5’UTRs) for the above samples respectively. The distribution of Ago- bound sequences seems to at least vary by cell types
[173]. Generally, the abundance of CDS targets is second only to that of 3’UTR targets, and approximately equal in some cases [173-175]. Importantly, miRNA repressive targeting of CDSs and 5’UTRs has been confirmed for several miRNAs using dual luciferase reporter assays [176-179]. Although actively translating ribosomes seem to have a negative effect on miRISC association with mRNAs, miRNA target sites on
CDSs appear to be more potent in inducing direct translational repression (but less effective in mRNA degradation) than those on 3’UTRs [180, 181]. Regardless, miRNA targeting of CDSs and 5’UTRs further enhance miRNA-mediated repression at 3’UTRs
[179, 181, 182].
MicroRNA target sites (or binding sites) on mRNAs complementary to miRNA sequences allow miRNA-guided miRISC binding to targets. However, perfect/complete complementarity to the whole miRNA sequence is not necessary for miRNA targeting.
The most basic miRNA canonical binding sites involve 6 nucleotides on the mRNA (the
Chi Yan Ooi 24 Introduction
seed match site) that can form perfect Watson–Crick base pairing with 6 nucleotides at position 2 – 7 on the 5’ end of the miRNA (the 6mer seed sequence) (Table 1.3) [171,
172]. The 6mer seed match site can be offset by one nucleotide to match position 3 – 8 of the miRNA instead, which is termed as offset 6mer site [171, 172]. In addition, there are 8mer sites base pairing miRNA position 1-8, and 7mer sites that are different from the 6mer sites by an additional A-U paring at position 1 (7mer-A1) or an additional
Watson– Crick base pairing at position 8 (7mer-m8) [171, 172]. Perfect Watson-Crick paring sites at the 3’ end of miRNAs that are at least several base pairs in length can supplement perfect seed match sites or compensate seed match sites with single nucleotide mismatch or bulge (further discussed below) [171, 172]. As an extreme case of 3’ compensation, the HOXB8-miR-196a non-canonical binding site is almost perfectly complementary (see near perfect site in Table 1.3) that it triggers siRNA-like cleavage of HOXB8 mRNA [183, 184].
Over the recent years, researchers began to realize that non-canonical miRNA binding sites also significantly contribute to miRNA targeting [127, 185-189]. Functional miRNA binding sites appear to frequently tolerate single nucleotide mismatch in the seed match region, albeit likely less potent than those with perfect complementary in the seed match sites [188, 190]. Single nucleotide mismatch commonly but not exclusively occur at positions 2, 5 and 7 [188, 190]. G and U nucleotides can base pair with each other through two hydrogen bonds and is a common type of single nucleotide mismatch in seed match sites [188, 191, 192]. G:U wobble base pairings have comparable thermodynamic stability to Watson–Crick pairing and are frequently present in RNA secondary structures
[192]. Unsurprisingly, miRNA bindings sites can still be functional with two non- consecutive G:U wobbles in the seed match sites [191]. Another type of non-canonical miRNA binding sites involves a single nucleotide bulge on the seed match site, where the
Chi Yan Ooi 25 Introduction
site would be perfectly complementary except for a single nucleotide on one strand of the site that does not form a base pair to the other strand [189]. G-bulged seed match sites seem to be most prevalent, at least for miR-124, and involve a ‘G’ nucleotide bulge on the mRNA between positions 5 and 6 of the miRNA [189]. However, an A-bulged seed match site with an ‘A’ nucleotide bulge on the mRNA has also been validated
[193]. The miRNA binding sites that do not rely on complementarity with miRNA seed sequences have also been identified. Centred sites containing at least 11 consecutive nucleotides perfectly complementary to miRNA position 4 – 14 or 5 – 15 are found to be functional and lead to cleavage of the mRNAs [183]. Centred sites with imperfect complementary were later discovered by pull-down assays of synthetic biotin-labelled miRNAs and validated by luciferase assays [194]. Imperfect centred sites typically trigger direct translational repression, and may allow isomiRs with seed sequences from their respective canonical versions shifted towards the centre to share a subset of targets with their respective canonical miRNAs [194]. Binding sites that are only complementary to the 3’ end of miRNAs were also discovered by an anti-Ago1 immunoprecipitation experiment using the crosslinking, ligation, and sequencing of hybrids (CLASH) technique and validated by dual luciferase assays [195]. That anti-
Ago1 CLASH experiment also identified potential binding sites that do not have any binding motif and displayed distributed base pairings instead [195].
The diversity and complexity of miRNA binding sites make it difficult to predict functional miRNA target sites in silico. Existing prediction algorithms showed high rates of false positives and false negatives [196, 197]. mRNA sequences matching to 6mer, 7mer or
8mer miRNA seed sequences do not necessary confer functional miRNA binding, leading to high rates of false positives even when additional stringency factors were used [196]. Some algorithms such as TargetScan [198, 199] do not allow for any non-canonical
Chi Yan Ooi 26 Introduction
binding, while other algorithms like miRanda [200] only allow for one or two non- canonical features in seed match sites. In addition, some algorithms such as the ones above only considers 3’UTR sequences. These lead to omissions of miRNA binding sites that could be biologically important. For more comprehensive details, references
[187] and [171] reviewed a number of existing miRNA targeting prediction algorithms.
Chi Yan Ooi 27 Introduction
Canonical Sites Base pairing examples Ref.
6mer seed 5’ NNNNNNNNNNNNNNN CUACCUN mRNA [171, 172] |||||| 3’ GGGUUGUUGUACUUUGAUGGAU miR-196a 87654321 Offset 6mer seed 5’ NNNNNNNNNNNNNN ACUACCNN mRNA [171, 172] | |||||_ 3’ GGGUUGUUGUACUU UGAUGGAU miR-196a 7mer-A1 seed 5’ NNNNNNNNNNNNNNN CUACCU A mRNA [171, 172] |||||| | 3’ GGGUUGUUGUACUUU GAUGGAU miR-196a 7mer-m8 seed 5’ NNNNNNNNNNNNNN ACUACCUN mRNA [171, 172] | ||||||
3’ GGGUUGUUGUACUU UGAUGGAU miR-196a 8mer seed 5’ NNNNNNNNNNNNNN ACUACCUA mRNA [171, 172] | |||||| | 3’ GGGUUGUUGUACUU UGAUGGAU miR-196a 3’ supplementary 5ʹ NNNNNN AACANNNNACUACCUN mRNA [171, 172] |||| | |||||| 3ʹ GGGUUG UUGUACUUUGAUGGAU miR-196a
3’ compensatory 5ʹ NNNNN CAACANNNNACUNCCUN mRNA [171] ||||| | || ||| 3ʹ GGGUU GUUGUACUUUGAUGGAU miR-196a Noncanonical Sites Base pairing examples Ref.
Single mismatch in 5’ NNNNNNNNNNNNNNNNUACCUN mRNA [190] _ ||||| seed 3’ GGGUUGUUGUACUUUCAUGGAU miRNA G:U wobble in seed 5’ GUCUGAUUCAG- AAGGGCUCA Nanog [201] :: |||| || ||||: | 3’ UGUCCUAACUCCCCCCCGGGA miR-296 G-bulge in seed 5’ NNNNNNNNNNNNNNNUGGCCUUN mRNA [189] || |||| 3’ GGGUUGUUGUACUUCAC - GGAAU miR-124 A-bulge in seed 5’ UUUUAUACAACCGUUCUACACUCA lin-41 [193] :||||||||| | ||| ||| | 3’ UUGAUAUGUUGG-AUGAUG - GAGU let-7 Centred site 5’ NNNNNNNACAUGAAACUACNNN mRNA [183, 194] ||||||||||||__ (Perfect/Imperfect) 3’ GGGUUGUUGUACUUUGAUGGAU miR-196a 3’ motif 5ʹ ACCAACCACANNNNNNNNNNNN mRNA [195] |||||||||| 3ʹ UGUCCGGCCCUGUUCACGUUAU miR-92a
Near perfect site 5ʹ CCCAACAACAUGAAACUGCCUA HOXB8 [183, 184] |||||||||||||||||:||| | 3ʹ GGGUUGUUGUACUUUGAUGGAU miR-196a
Table 1.3 Canonical and noncanonical miRNA target site motifs
| = Watson-Crick base pairing, : = G:U wobble, 6mer seed regions in yellow, characteristic motifs in green. Adapted from ref. [186, 187]. Licenced under CC BY-NC-SA 3.0 and CC BY 4.0 respectively. This table is distributed under CC BY-NC-SA 3.0.
Chi Yan Ooi 28 Introduction
1.3.3 MicroRNA stability
The stability of miRNAs can vary greatly. Ablation of mouse Dicer1 RNase III domain showed miRNAs’ half-lives varied from ~28 h to a maximum of 225 h between individual miRNAs, with the average being 119 h [202]. For comparison, the median half-life of human mRNA is ~10 h [203]. In an extreme case, rat miR-208 had a half-life of >12 days and 30 % of the mature miRNA persisted 21 days after blockage of transcription [204]. However, a number of miRNAs was also found to have much shorter half-lives. The half-life of miR-503 in mouse fibroblast NIH 3T3 cells was 3.6 h, allowing a rapid down-regulation of its expression during re-entry of cell cycle from the
G0 phase to the G1 phase [205]. Human miR-29b has been reported to have a half-life of ~4 h in actively cycling HeLa cells but stabilized to >12 h in those arrested in mitosis
[206]. Many miRNAs also degraded more rapidly in mammalian neuronal cells compared to non-neuronal cells, allowing the expression of the miR-183/96/182 cluster, miR-204 and miR-211 in mouse retina to drop to roughly half, in ~90 min after the mice were transferred from light to dark environment [207]. Certain short nucleotide sequences on miRNAs appear to be necessary for accelerated degradation [205, 208,
209]. In addition, 3’ adenylation and uridylation of mammalian miRNAs influence stability for certain miRNAs in certain cellular context [210-213].
Moreover, target transcripts can also regulate miRNA stability. Artificial targets that are highly complementary to miRNA mature sequences promoted degradation of those miRNAs [213, 214]. Viral non-coding RNA and 3’UTR of viral mRNA have also been shown to induce degradation of miR-27 from mammalian hosts [215-217]. Lastly, miR-107 can bind to and destabilize let-7, suggesting a regulatory role for miRNA- miRNA interactions [218, 219]. In summary, miRNA expression can be further regulated through its stability after its biogenesis.
Chi Yan Ooi 29 Introduction
1.3.4 MicroRNA’s non-canonical function in positive regulation of translation
Several publications have reported that miRNAs can translationally activate mRNAs in addition to their classical translational repression function. Vasudevan, Tong
& Steitz [220] reported that at least some miRNAs can oscillate between translational repression and translation activation depending on the cell cycle. Using 3’UTR firefly luciferase reporter assays where experimental 3’UTR sequences are cloned downstream of a luciferase reporter, they have found that miR369-3 can enhance translation with the
TNFα (tumour necrosis factor-α) 3’UTR in a sequence-dependent manner, only in HEK293 human embryonic kidney cells grown in serum-starved condition, but has no effect when with serum present [220]. Using a firefly luciferase reporter with four artificial 3′UTR target sites, they have also shown that the corresponding artificial miRNA miRcxcr4 enhances translation in HeLa human cancer cells grown in serum-starved condition, but did not reduced translation in serum present condition as would have normally expected [220].
Translational repression by miRcxcr4 on the artificial 3’UTR is only observed when the cells are synchronized to late S/G2 phase by serum starvation and subsequently grown in serum condition [220]. Similarly, let-7 was able to enhance translation of HMGA2 3’UTR reporters in serum-starved quiescent HeLa cells but reduce translation in synchronized proliferating HeLa cells [220]. Translational activation can be similarly achieved for miRcxcr4 and let-7 on the artificial 3’UTR and HMGA2 3’UTR reporters respectively in
NIH3T3 mouse fibroblast cells arrested in G0 phase by contact inhibition [221]. In addition, translational activation of the TNFα 3’UTR and artificial 3′UTR reporters by miR369-3 and miRcxcr4 respectively can be observed in immature folliculated Xenopus laevis oocytes, which are induced quiescent cells, and repression is restored for the 3′UTR reporters by progesterone-induced maturation [222]. Furthermore, Myt1 mRNA from Xenopus laevis is also translationally activated by
Chi Yan Ooi 30 Introduction
xlmiR16 in a sequence-dependent manner in the immature oocytes [222]. These demonstrated at least some miRNAs switch to translational activation in quiescent cells but return to translational repression in proliferating cells. However, the cell cycle dependency of miRNA function does not seems to apply to siRNAs [221].
Translational activation by miRNAs is dependent on Ago2, the core component of miRISC, and FXR1 isoform a (FXR1-iso-a), which is only physically associated with miRISC during translational activation and is not required for translational repression
[220, 222, 223]. Moreover, Ago2-FXR1-iso-a complex preferentially localized to the nucleus, and miRNAs and mRNAs are required to be in the nucleus for translational activation while repressed mRNAs do not associate with nuclear Ago2 [224]. These suggested that miRNA-mediated translational activation and repression occurs in two different cellular compartments: the nucleus and the cytoplasm respectively.
1.3.5 MicroRNA’s non-canonical functions in the nucleus
In addition to translational activation in the nucleus of quiescent cells, there are other non-canonical functions discovered for miRNAs in the nucleus. At least two proteins has been shown to import mature miRNAs into the nucleus. Exportin-1 can import miRISCs that assembled from Ago1 and/or Ago2 loaded with mature miRNA into the nucleus [225].
In addition, Importin-8 can import mature miRNAs via associating with and trafficking
Ago2 into the nucleus [226, 227]. Once inside the nucleus, miRNAs can target complementary sites on long non-coding RNAs and circular antisense RNAs for cleavage of these RNAs through Ago2 [228-230]. Furthermore, nuclear miRISCs may also activate or silence transcription by repressing long non-coding RNA-mediated transcriptional silencing, or binding to promoter associated RNAs and recruit transcriptional activators or epigenetic modifiers [153, 231]. Moreover, nuclear miRISCs can also bind to complementary sites on pri-miRNAs to prevent or promote the processing
Chi Yan Ooi 31 Introduction
of the bound pri-miRNAs into pre-miRNAs [232, 233]. Finally, it is hypothesized that nuclear miRISCs can influence alternative transcripts splicing through binding to complementary sites on nascent transcripts to block splicing, or modifying chromatin structures to modulate RNA Polymerase II procession rate which affects splicing efficiency [153, 231].
Chi Yan Ooi 32 Introduction
MicroRNA and cancer: Acting as tumour suppressor or oncogene or both
MicroRNAs have been associated with cancer and played multiple functional roles in cancer biology. Many miRNAs are located in chromosomal fragile sites, and genetic loci that are deleted, amplified or translocation breakpoints in cancer [234]. In addition, knockdown of miRNA processing proteins Drosha, DGCR8 or Dicer lead to reduced mature miRNA levels and enhanced tumorigenicity in vitro and in vivo [235]. Loss-of-function
Exportin-5 mutation is present in a subset of human cancer and led to accumulation of pre- miRNAs in the nucleus [236]. Re-expression of wild-type Exportin-
5 reduced tumorigenicity while knockdown of wild-type Exportin-5 enhanced tumorigenicity in vitro and in vivo [236]. These highlight the general importance of miRNAs in cancer. In cancer, miRNAs have been shown to be involved in regulating biological processes related to cancer hallmarks and characteristics (see Figure 1.1), such as cell proliferation, growth suppression, cell death, invasion, metastasis, angiogenesis and
DNA damage response [237, 238]. miRNAs can be tumour suppressors while some can be oncogenes or oncomiRs [118, 239]. For example, miR-15 and miR-16 act as tumour suppressors in chronic lymphocytic leukaemia by repressing anti-apoptotic BCL2 expression and inducing apoptosis [240]. In contrast, miR-21 acts as an oncomiR in non- small cell lung cancer through repressing tumour suppressor PTEN and enhancing cell growth and invasive [241]. However, it is possible for miRNAs to be tumour suppressors and oncomiRs depending on the cellular context. For example, miR-26a act as an oncomiR in lung cancer by stimulating metastasis via PTEN repression [242]. On the other hand, miR-26a act as a tumour suppressor in liver cancer through repression of cyclin D2, cyclin
E2 and cell proliferation, and induction of apoptosis [243]. In the
Chi Yan Ooi 33 Introduction
following sections, the roles of miRNAs in cancer in the context of neuroblastoma biology, therapies and biomarkers will be reviewed.
1.4.1 MicroRNA in neuroblastoma biology
Research articles investigating the roles of miRNAs in neuroblastoma are not rare but miRNAs is still a relatively new area of research in neuroblastoma. Several papers investigated miRNAs in neuroblastoma at a transcriptome-wide level using RNA sequencing [244], TaqMan miRNA qRT-PCR arrays [103] and microarrays using special probes such as Locked Nucleic Acid (LNA)-based probes [98] due to the short lengths of miRNAs. Several papers have investigated the expression of miRNAs in neuroblastoma patients’ tumour samples and discovered a large number of miRNAs is differentially expressed between high-risk and low-risk tumours [104], and also between
MYCN-amplified and non-MYCN-amplified tumours [96-98, 103, 244]. A large number of miRNAs predicts patient prognosis in overall and/or event-free survival as individual miRNAs and/or as a part of a multi-miRNA signature [96, 103, 104]. The regulation of miRNAs on the transcriptome-wide level by MYCN has also be studied with inducible modulation of MYCN expression in human genetically modified neuroblastoma stable cell lines SHEP MYCN-ER and SHEP Tet21N [96, 98, 245]. MYCN binding to transcriptional promoters of miRNAs has also been studied [81, 97]. Transcriptome- wide miRNAs differential expression has also been studied in Th-MYCN+/- mice and
TH-MYCN/p53ERTAM mutant p53 hemizygous mice [99]. However, none of these took into account of the importance for the miRNAs during neuroblastoma initiation.
Individual miRNAs and miRNA cluster (a collection of miRNAs in close proximity) have been studied in neuroblastoma, some of which extended from other cancer research related to c-MYC possibly due to the structural and functional similarities to MYCN [246].
Chi Yan Ooi 34 Introduction
The miR-17-92 cluster, its paralogs and target genes
The miR-17-92 cluster is initially studied as a c-MYC activated oncogenic miRNA cluster in c-MYC-related cancer such as B-cell lymphoma [247-249]. The miR-17-92 cluster consists of six miRNAs (miR-17, miR-18a, miR-19a, miR-20a, miR-19b-1, miR-92a-1) encoded within an 800 base-pair region in the third intron of the C13orf25 host gene in human chromosome 13 [250, 251] (Figure 1.5A). Due to ancient gene duplications, mutations, and loss of individual miRNAs during early vertebrates evolution, there are two miR-17-92 cluster paralogs miR-106b-25 cluster and miR-106a-363 cluster [247, 250]
(Figure 1.5A). Five miRNAs in the miR-17-92 cluster (miR-17, miR-18a, miR-19a, miR-
20a, miR-19b which is the mature form of miR-19b-1 and miR-92a from miR-92a-1), two miRNAs in the miR-106b-25 cluster (miR-93 and miR-25) and four miRNAs in the miR-
106a-363 (miR-106a, miR-20b, miR-19b from miR-19b-2 and miR-92a from miR-92a-2) have been shown to predict poor neuroblastoma patient prognosis [96, 103, 104, 244]. The miR-17-92 cluster is the most studied cluster among the three paralogous clusters, but they are known to share common targets due to the conservation of miRNA seed sequences across the cluster paralogs [247, 250, 252] (Figure 1.5B).
Chi Yan Ooi 35 Introduction
92 Cluster in Development and Development in Cluster 92
-
17
-
from Elsevier. from
Roles for the miR the for Roles
Copyright 2008, with with 2008, Copyright permission
133, Joshua T. Mendell, miRiad Mendell, T. Joshua 133,
-
Disease, p. 217 p. Disease, 222, Reprinted from Cell, from Reprinted
Figure 1.5 The miR-17-92 cluster and its paralogs
A) The miR-17-92 cluster consists of six miRNAs and is located in chromosomal locus 13q31.3 within intron 3 of the C13orf25 gene. The miR-106a-363 and miR-106b-25 clusters are paralogs of the miR-17-92 cluster, consisting of six and three miRNAs and is located in locus Xq26.2 and 7q22.1 respectively. miR-106b-25 is encoded in intron 13 of the MCM7 gene, while the primary transcript for miR-106a-363 is uncharacterized. B) miRNAs in the three clusters share their seed sequences (highlighted), which are major gene targeting determinants, with other miRNAs across and/or within the clusters. (Figure from ref. [250])
Chi Yan Ooi 36 Introduction
1.4.1.1.1 MYCN-E2F1 negative feedback loop
Several miRNA members in the miR-17-92 cluster and its two cluster paralogs were detected to be higher expressed when MYCN expression is high in human neuroblastoma stable cell lines SHEP MYCN-ER and SHEP Tet21N with inducible MYCN expression [96,
98, 245]. MYCN, in addition to c-MYC, has later been shown to transcriptionally activate the miR-17-92 cluster through the promoter of its host gene C13orf25, and also its other two cluster paralogs, in neuroblastoma [245, 253]. It has also been shown that miR-17, miR-19a and miR-19b can directly target MYCN mRNA 3’UTR [254, 255]. In addition, E2F1 also transcriptionally activate the miR-17-92 and miR-106b-25 cluster and itself translationally suppressed by miR-17 and miR-20a of the miR-17-92 cluster and miR-106b of the miR-
106b-25 cluster, although not demonstrated in neuroblastoma cells [249, 256-259]. This showed that E2F1 activates the miR-17-92 and miR-106b-25 cluster to supress its own expression in an auto-regulatory feedback loop. Furthermore, MYCN promotes E2F1 expression and E2F1 transcriptionally activates MYCN [260, 261]. This represents a complicated negative feedback loop where MYCN activates the miR-17-92 and miR-106b-
25 cluster directly and indirectly through E2F1 to both activate and suppress E2F1 expression and supress MYCN expression (Figure 1.6). The feedback loop through E2F1 also applies to c-MYC [259]. These potentially provides one of the mechanisms to escape oncogene-induced apoptosis by preventing over-accumulation of E2F1 and MYCN [247] or to prevent uncontrolled up-regulation of E2F1 and MYCN in normal cells. In fact, miR-17-
92 has been suggested to act as tumour suppressors in yet to be identified cellular context depending on the overall effect on global gene expression [254]. In spite of that, miR-17-92 acts as oncogenes or oncomiRs in various cancers, as evident by their functions in promoting cell cycle
Chi Yan Ooi 37 Introduction
progression, resisting TGFβ-dependent cell-cycle arrest and apoptosis, and accelerating lymphomagenesis in c-MYC transgenic mice [248, 257, 258, 262].
Chi Yan Ooi 38 Introduction
MYCN
E2F1
miR-17 miR-18a miR-19a miR-20a miR-19b-1 miR-92a-1
miR-106b miR-93 miR-25
Figure 1.6 The MYCN-E2F1 negative feedback loop
MYCN transcriptionally activates the miR-17-92 and miR-106b-25 clusters and upregulates E2F1 expression. E2F1 also transcriptionally activates the miR-17-92 and miR-106b-25 clusters and MYCN. In turn, miR-17, miR-20a and miR-106b, the products of miR-17-92 and miR-106b-25, directly supress E2F1 translation. As a result, E2F1 expression is reduced, which reduces E2F1-mediated transcription of MYCN. miR-17, miR-19a and miR-19b also target MYCN, further suppressing MYCN expression. The reduced MYCN and E2F1 expression would also reduce miR-17-92 and miR-106b-25 activation, reliving suppression on E2F1. miRNAs sharing the same seed sequence are marked in the same colour. (ref. [245, 249, 254, 256-261, 263])
Chi Yan Ooi 39 Introduction
1.4.1.1.2 Regulation of TGFβ pathway by the miR-17-92 cluster
The miR-17-92 cluster was initially identified as suppressor of the Transforming
Growth Factor β (TGFβ) pathway in c-MYC-related cancers [247, 258]. miR-17 and miR-
20a from the miR-17-92 cluster, and miR-106b and miR-93 from the miR-106b-25 cluster suppress p21, which is required for TGFβ-induced cell cycle arrest by disrupting the G1/S checkpoint [258, 264]. BIM gene expression, which is required for TGFβ-dependent apoptosis, is also suppressed by miR-92a-1 from the miR-17-92 cluster and miR-25 from the miR-106b-25 cluster [247, 258]. The role of miR-17 in suppressing the TGFβ pathway through p21 and BIM in neuroblastoma has been confirmed by dual luciferase assays, and the in vivo delivery of chemically modified oligonucleotide antisense to miR-17 can inhibit tumorigenesis of MYCN-amplified neuroblastoma subcutaneous xenografts [253]. The roles of the miR-17-92 cluster in suppressing the TGFβ signalling pathway in neuroblastoma has later been further reported, with the identification of additional target genes TGFBR2,
SMAD2 and SMAD4 involved in the TGFβ pathway [265] (Figure 1.7).
Chi Yan Ooi 40 Introduction
miR-17 miR-18a miR-19a miR-20a miR-19b-1 miR-92a-1
p21, TGFBR2 SMAD2, SMAD4 BIM
TGFβ signalling pathway
Cell cycle arrest, Apoptosis
Figure 1.7 The miR-17-92 cluster and TGFβ pathway
The miR-17-92 cluster has been identified as a suppressor of the TGFβ signalling pathway. Mature miR-17 and miR-20a can directly target p21, an important cell cycle regulator, and TGFBR2 via the 3’UTR on their mRNAs. Mature miR-18a can directly target SMAD2 and SMAD4, and mature miR-92a (from miR-92a-1) can directly target BIM. These target genes contribute to the signalling of the TGFβ pathway and the pathway’s downstream effects such as cell cycle arrest and apoptosis. miRNAs sharing the same seed sequence are marked in the same colour. (ref. [247, 253, 258, 264, 265])
Chi Yan Ooi 41 Introduction
1.4.1.1.3 Regulation of Cell cycle by the miR-17-92 cluster
As mentioned above that miR-17 and miR-20a of the miR-17-92 cluster suppress
E2F1. Inhibition of miR-17 and miR-20a reduced cell proliferation and caused cell cycle arrest at the G1 checkpoint due to premature temporal accumulation of E2F1 that lead to
DNA double-strand breaks and DNA damage repair response in normal human fibroblast
[257]. Cloonan et al [254] discovered that the expression of both the pri-miRNA and mature form of miR-17 is different in different phases of the cell cycle, with the expression lowest during S phase and highest during the G2/M phase. The cell cycle- regulated expression of the miR-17-92 cluster provide further evidence for its role in cell cycle, and suggest the miR-17-92 cluster may act to facilitate the prevention of premature re-entry to S phase and premature DNA replication. In fact, Cloonan et al
[254] investigated the list of predicted targets for the miR-17-92 cluster from the miRNA-mRNA interaction prediction database PicTar, and found an enrichment in genes associated with cell cycle progression, arrest in G1 phase and entry into S phase for miR-17 and miR-20a. From these, one would have thought that the miR-17-92 cluster would act as tumour suppressor. However, the miR-17-92 cluster can directly target both pro-and anti-proliferative genes via their mRNAs’ 3’UTRs (Figure 1.8), and overall promote entry into S phase and cell proliferation in cancer cells by preferentially up-regulating pro-proliferative genes through indirect effects likely due to the dominance of pro-proliferative genes in cancer cells [252, 254, 266]. The promotion of
S phase entry and cell proliferation by the miR-17-92 cluster is also observed in neuroblastoma [253]. These demonstrate the ability for the miR-17-92 cluster to act as either tumour suppressor or oncogene/oncomiR dependent on the cellular context.
Chi Yan Ooi 42 Introduction
miR-17 miR-18a miR-19a miR-20a miR-19b-1 miR-92a-1
ESR1
Anti-proliferative Pro-proliferative e.g. p21, RB1, RBL1, e.g. E2F1, MYCN, RBL2, PTEN, CCND1 TP53INP1
Reduced cell proliferation, Induction of neuroblastoma Cell cycle regulation differentiation
Figure 1.8 The miR-17-92 cluster, cell cycle and differentiation
The miR-17-92 cluster has been shown to regulate cell cycle progression and neuroblastoma differentiation. 3’UTR luciferase assays have shown that mature miR-17 and miR-20a can suppress both anti- and pro-proliferative genes via their mRNA 3’UTR and the overall effect in cancer cells, including neuroblastoma, is increased proliferation. In addition, mature miR-18a directly targets mRNA 3’UTR of ESR1, which has been shown to reduce proliferation and induce differentiation of neuroblastoma cells. miRNAs sharing the same seed sequence are marked in the same colour. (ref. [245, 252-254, 256, 267])
Chi Yan Ooi 43 Introduction
1.4.1.1.4 Regulation of estrogen receptor-α (ESR1) and neuroblast differentiation by the miR-17-92 cluster
Estrogen receptor-α (ESR1) is an estrogen-inducible transcription factor that reduces cell proliferation as measured by EdU incorporation into DNA and induces extensive neurite outgrowth after prolonged cell propagation when lentivirally expressed in MYCN-amplified neuroblastoma SK-N-BE(2) cells, indicating roles in cell cycle progression and neuronal differentiation [245]. Two binding sites for each of miR-
18a and miR-19a from the miR-17-92 cluster were predicted for the ESR1 mRNA
3’UTR from using TargetScanHuman 5.1, PicTar and EIMMo prediction databases
[245]. Transfection of miR-18a or miR-19a precursors and co-transfection of both precursors into HEK-293 human embryonic kidney cells were able to reduce luciferase activities from a luciferase reporter construct containing the ESR1 3’UTR sequence when compared to scramble control precursors [245]. On the other hand, transfection of miR-18a precursors did not reduce luciferase activities from the same luciferase reporter construct with miR-18a seed sequence binding sites mutated, proving miR-18a interacts with ESR1 3’UTR in a sequence-dependent manner [245]. Furthermore, transfection of miR-18a or miR-19a precursors into MCF-7 breast cancer cells caused time-dependent suppression of ESR1 protein and mRNA expression, suggesting mRNA destabilization by these miRNAs [245]. Since ESR1 is expressed in 9-weeks-old human fetal sympathetic ganglia and its expression is negatively correlated with MYCN expression in human neuroblastoma tumours and positively correlated with patient event-free survival, it is suggested that MYCN amplification may supress ESR1 through miR-18 and miR-19a to disrupt estrogen signalling and prevent normal neuroblast differentiation induction [245]. This suggests miRNAs may have important roles in neuroblastoma tumorigenesis.
Chi Yan Ooi 44 Introduction
The LIN28B-let-7-MYCN axis and neuroblastoma initiation
lin-28, like the earliest miRNAs discovered, was first discovered by studying the temporal regulation of larva development of roundworm Caenorhabditis elegans [268]. lin-
28 has three exons and encodes a RNA-binding protein with two RNA-binding domains; a cold shock domain and a pair of retroviral-type CCHC zinc finger motifs [269]. lin-28 is predominately expressed in the cytoplasm, and in the first two stages of the four-stage larva development [269]. lin-28 supresses genes and events specific to stage 3 development, and loss-of-function mutations led to skipping of stage 2 development and resulting in a total of only three instead of four developmental stages [268, 269]. The stRNA/miRNA lin-4 mentioned earlier in the introduction to miRNA negatively regulates lin-28 expression through a binding site at the 3’UTR of lin-28 mRNA [269]. The C. elegans lin-28 mRNA
3’UTR also contains a predicted binding site for let-7 [270].
There are two lin-28 mammalian homologs LIN28A (or just LIN28) and LIN28B, both having the two RNA-binding domains in the encoded proteins [271, 272]. LIN28A and
LIN28B are highly expressed in embryonic and undifferentiated cells and adult developing tissues, and down-regulated after differentiation [46, 271-274]. lin-4’s homologs miR-125a and/or miR-125b, and let-7’s homologs are predicted to bind to LIN28A mRNA 3’UTR in mouse and human, and LIN28B mRNA in human [47, 271, 272, 275]. The let-7 human homologs, or the let-7 family, actually consist of 13 members (let-7a-1, a-2, a-3, let-7b, c, d, e, let-7f-1, f-2, let-7g, i, miR-98 and miR-202) spread across 9 different loci in 9 different chromosomes [276]. let-7b has been shown to suppress LIN28A and LIN28B expression through direct binding to the predicted binding sites on LIN28A and LIN28B mRNA
3’UTR in human [272, 277]. In addition, miR-125a and miR-125b were shown to suppress
LIN28A expression while miR-125b can also suppress LIN28B expression through their predicted binding sites on LIN28A and
Chi Yan Ooi 45 Introduction
LIN28B mRNA 3’UTR respectively in human [47, 168, 278]. According to the
DIANA-TarBase v7.0 curated database of experimentally observed miRNA-mRNA interactions [279], Ago cross-linking immunoprecipitation (CLIP) followed by high- throughput sequencing (CLIP-seq or HITS-CLIP) experiments have identified human let-7b, other human let-7 family members let-7a, let-7c, let-7d, let-7e, let-7f, let-7g, let-
7i and miR-98, human miR-125a and miR-125b as binding partners of human LIN28B mRNA [175, 280-283].
Both LIN28A and LIN28B showed predominately cytoplasmic localization [272,
284]. LIN28A is physically associated with translationally-competent mRNAs-proteins complexes (or mRNPs), polyribosomes, Processing bodies (or P-bodies) and stress granules
[284]. In addition, nuclear accumulation of LIN28A occurs when both RNA-binding domains of LIN28A are mutated, while nuclear accumulation of LIN28B occurs at certain phases of the cell cycle [272, 284]. Therefore, it is thought that LIN28 proteins have to be trafficked into the nucleus after they were translated in the cytoplasm, and then bind to mRNAs for exportation into the cytoplasm where they regulate the translation and stability of mRNAs [272, 284]. In fact, LIN28A can bind to mRNA of IGF-2, a crucial growth and differentiation factor for muscle tissue and a neurogenesis promoter, and enhance the translational efficiency of IGF-2 mRNA [46, 285]. Importantly, both LIN28A and LIN28B can bind to the primary transcripts and precursor miRNAs of multiple members in the let-7 family through the interactions between both of their RNA-binding domains and the loop structure in the pri-let-7 and pre-let-7 [46, 286-289]. The binding of pri-let-7 by LIN28A and LIN28B in the nucleus prevents processing of pri-let-7 by the ribonuclease enzyme
Drosha into pre-let-7 for nuclear exportation, while LIN28A and LIN28B block Dicer enzyme cleavage sites access and recruit TUT4 and TUT7 for 3’ terminal oligo-uridylation of their bound pre-let-7 to prevent further
Chi Yan Ooi 46 Introduction
processing by Dicer into mature miRNA and enhance pre-let-7 degradation [286-293].
However, it is suggested that at least in some cellular context, LIN28B localized to the nucleus due to its nuclear localization signals which are absent in LIN28A and acts through pri-let-7 preferentially over pre-let-7 [294]. These all work together to reduce mature let-7 expression. Since the let-7 family can inhibit LIN28A and LIN28B expression through the mRNA 3’UTR, this forms a double-negative feedback loop between LIN28A/LIN28B and let-7 where both inhibit each other’s expression that in turn enhance their own expression [295, 296].
Moreover, let-7a and let-7e can target MYCN mRNA 3’UTR to suppress MYCN expression and inhibit cell viability, proliferation and clonogenic growth in MYCN- amplified human neuroblastoma Kelly cells [47, 255]. This forms an axis where
LIN28A/LIN28B is able to upregulate MYCN expression via reducing let-7 suppression on
MYCN [47]. Indeed, silencing LIN28B up-regulated multiple members of the let-7 family and reduced MYCN protein expression and over-expressing LIN28B enhanced MYCN protein levels in human neuroblastoma cells [47]. In addition, overexpression of LIN28B in non-malignant mouse JoMa1 neuroblasts also repressed expression of let-7 family members and induced N-myc expression, and was able to maintain their proliferation [47]. Moreover, shRNA silencing of LIN28B in human neuroblastoma cell lines induced neuronal differentiation, reduced cell viability and cell cycle arrest to G0/G1 phase [47]. Importantly, human neuroblastoma patients with high LIN28B expression have poor overall survival and the neural crest–specific over-expression of mouse Lin28b in transgenic mouse lead to palpable abdominal neuroblastoma tumours in 4 out of 16 mice [47]. These tumours had lower expression of let-7 family members and high expression of N-myc protein in addition to highly expressed Lin28b protein levels compared to normal tissue derived from the transgenic mice [47]. MYCN-inhibitor JQ1
Chi Yan Ooi 47 Introduction
can induce regression of these Lin28b-driven tumours and MYCN without its 3’UTR sequence (thus immunes to let-7 targeting) can rescue the phenotypic effect of LIN28B knockdown in human and mouse neuroblastoma cell lines [47]. This demonstrated the ability of LIN28B to initiate neuroblastoma mainly through the LIN28B-let-7-MYCN axis.
Furthermore, the oncogenic function of LIN28B-let-7-MYCN axis can be used as a therapeutic target. Difluoromethylornithine (DFMO) is a drug under clinical trials in children neuroblastoma patients, which inhibits polyamine biosynthesis by binding ornithine decarboxylase (ODC) and inhibits biosynthesis of spermidine that is needed for post-transcriptional modification of eIF-5A, a translation initiation factor and positive regulator of LIN28A [297-299]. In vitro treatments of human neuroblastoma cell lines with DFMO reduced LIN28B and MYCN protein expression, increased let-7 expression and inhibited neurospheres formation [299]. Interestingly, the cell lines’ drug sensitivity is positively correlated to LIN28B and MYCN mRNA expression [299].
These demonstrate the potential to exploit miRNA networks in treating neuroblastoma.
For a review of the roles of the LIN28A/LIN28B-let-7 double-negative feedback loop in the hallmarks of other cancers, see Wang et al. [295].
Chi Yan Ooi 48 Introduction
let-7 LIN28B MYCN miRNA family
miRNA post- transcriptional processing Promote cell cycle progression, inhibit differentiation, and initiate neuroblastoma tumorigenesis
Figure 1.9 The LIN28B-let-7-MYCN axis
The LIN28B protein can bind to immature forms of miRNA members in the let-7 family and inhibit their processing into mature miRNAs. LIN28B can also promote the degradation of bound pre-let-7 when acting in the cytoplasm. The let-7 family can directly targets LIN28B and MYCN expression via their mRNA 3’UTR. This forms a loop where LIN28B and let-7 inhibit each other’s expression, and increased LIN28B expression can increase MYCN and LIN28B expression through suppression of let-7 regulation. In neuroblastoma, the over-expression of LIN28B promotes cell cycle progression, inhibits differentiation, and initiates tumorigenesis in mouse mainly through MYCN up-regulation. (ref. [46, 47, 255, 286-296])
Chi Yan Ooi 49 Introduction
The roles of miR-34a in neuroblastoma
miR-34a is situated at chromosomal locus 1p36, a region frequently deleted hemizygously in high-risk neuroblastoma patients and correlates with MYCN amplification [300-302]. miR-34a expression from qRT-PCR analysis is generally reduced in neuroblastoma primary tumours, with a reduction of 30% in tumours with 1p loss, and in several human neuroblastoma cell lines compared to normal adrenal gland
[303]. Over-expression of miR-34a by transfection of miR-34a precursors into neuroblastoma cell lines reduced cell viability and cell number in both MYCN- and non-
MYCN-amplified cells as measured by MTT metabolic cell viability assay and Beckman
Coulter Cell counter [303, 304], while Cole et al. [305] only observed cell growth inhibition in neuroblastoma cells with 1p36 deletion as measured by the RT-CES microelectronic cell sensor system. Over-expression of miR-34a also caused cell cycle arrest in G0/G1 phase and reduction of cells in S phase as measured by propidium iodide (PI) staining, and followed by apoptosis as measured by Annexin-V staining and Caspase 3/7 assay [303-
305]. An orthotopic xenograft model of one MYCN-amplified and one non-MYCN-amplified neuroblastoma cell line stably expressing firefly luciferase and injected behind the left adrenal gland of 4 week old CB-17/SCID mice was used to evaluate the role of miR-34a in vivo [304]. The mice with cells transfected with pre-miR-34a before injection had reduced tumour volume up to 21 days post injection as measured by bioluminescence, and better overall survival compared to negative control pre-miRNA [304]. This result suggested that miR-34a is a tumour suppressor of neuroblastoma.
Wild-type p53, a tumour suppressor and activator of apoptosis, has been shown to transcriptionally activate miR-34a expression by binding to a p53 binding site in exon
1 of miR-34a’s host gene ~30kb upstream of miR-34a in human non-small lung cancer cells [306]. Inactivation of miR-34a by a locked nucleic acid (LNA) oligonucleotide
Chi Yan Ooi 50 Introduction
strongly reduced p53-induced apoptosis, showing miR-34a significantly contribute to p53’s apoptotic function [306]. Rihani et al. [307] confirmed that up-regulation of miR-
34a by p53 is also present in neuroblastoma cells using nutlin-3 to activate p53 by disrupting p53 inhibitor MDM2 in a human neuroblastoma cell line with wild-type p53.
Feinberg-Gorenshtein et al. also showed that the p53 binding site upstream of miR-34a was not mutated in neuroblastoma patients with reduced miR-34a expression, suggesting transcriptional regulation of miR-34a by wild-type p53 is intact in neuroblastoma [308]. On the other hand, miR-34a can directly suppress MYCN protein expression via two binding sites at the MYCN mRNA 3’UTR [309]. Other miR-34a direct targets are unsurprisingly involved in cell cycle, proliferation and apoptosis, such as E2F3, CCND1, CDK6, BCL2 and SIRT1 [303, 305, 310, 311].
Due to miR-34a’s abilities to suppress MYCN and induce apoptosis, it has been explored for its therapeutic implications in restoring apoptosis and targeting MYCN in neuroblastoma. For example, CDK1 inhibitor purvalanol A has been shown to increase miR-34a expression and reduce MYCN protein expression [312]. On the other hand, anti- miR-34a oligonucleotides were able to restore MYCN expression and restore the reduced cell viability by purvalanol A [312]. The in vivo delivery of synthetic miR-34a to neuroblastoma animal model has also been investigated, and since miR-34a can cause adverse effect on non-malignant cell lines as well, a targeted approached is needed [313].
Silica-based nanoparticles with anti-GD2 antibody conjugated to their surface and loaded with miR-34a were able to be selectively uptaken in vitro by MYCN-amplified NB1691 and non-MYCN-amplified SK-N-AS neuroblastoma cell lines both expressing GD2 on their surface to trigger apoptosis and decreased cell viability, but not by embryonic kidney
HEK293 cells with low surface GD2 expression [313]. The targeted delivery was verified in vivo by systemic intravenous administration of fluorescently-tagged anti-GD2-
Chi Yan Ooi 51 Introduction
nanoparticles in the orthotopic xenograft mouse model mentioned above, which showed
6.7 to 258 fold higher mean fluorescence intensities ex vivo in tumours compared to the livers, spleens, kidneys, lungs and hearts, and 13.9 fold higher than in tumours treated with fluorescently-tagged nanoparticles without anit-GD2 [313]. Importantly, systemic intravenous administration of anti-GD2-nanoparticles loaded with miR-34a once every three days for three times in the orthotopic xenograft mouse model and compared to that loaded with scrambled oligonucleotides, showed reduced growth of tumours derived from both non-MYCN-amplified and MYCN-amplified neuroblastoma cell lines as measured by bioluminescent imaging [313]. In addition, there were increases in apoptosis and reductions in cell proliferation and tumour vascularisation for both tumour types as assessed by TUNEL staining and immunohistochemistry [313]. miR-
34a expression was also significantly increased only in the tumours but not in livers, kidneys and lungs, further demonstrating the specificity of the delivery [313]. Moreover,
MYCN mRNA and protein, and MYCN transcriptional target NME1 mRNA was down- regulated, while anti-angiogenic factor TIMP2 mRNA and protein which is directly suppressed by MYCN-activated miR-20b was up-regulated in the MYCN-amplified tumours [313]. These findings demonstrated the potential of miR-34a delivery therapy in inducing apoptosis, and inhibiting MYCN expression and its downstream targets.
miR-34a, as a potent tumour suppressor, has great potential as a therapeutic agent for multiple cancers (see reviews [314, 315]) and miR-34a synthetic mimic became the first miRNA mimic to enter phase I clinical trial (clinical record assessable on ClinicalTrials.gov with the identifier NCT01829971) [316]. This illustrates the potential for the use of miR-34a and other tumour suppressing miRNAs as therapeutic treatment approach for neuroblastoma if appropriate targeted delivery methods are available for humans.
Chi Yan Ooi 52 Introduction
p53 miR-34a MYCN
Anti-apoptotic Cell cycle e.g. BCL2, SIRT1 e.g. E2F3, CCND1, CDK6
Figure 1.10 miR-34a, p53 and MYCN
Tumour suppressor p53 transcriptionally activates miR-34a expression, which directly suppresses anti-apoptotic genes such as BCL2 and SIRT1 to facilitate p53-induced apoptosis. miR-34a also directly supresses MYCN and cell cycle genes such as E2F3, CCND1 and CDK6. (ref. [303, 305, 306, 309-311])
Chi Yan Ooi 53 Introduction
The role of miR-9 in MYCN driven neuroblastoma
miR-9 is highly expressed in brain and neural cells, and helps maintain proliferation and supress migration of human neural progenitor cells derived from human embryonic stem cells at least in part through direct mRNA 3’UTR targeting of stathmin [317, 318]. miR-9 expression is higher in MYCN-amplified vs non-MYCN- amplified human neuroblastoma tumours [319]. In addition, miR-9 expression is increased after 4-hydroxy-tamoxifen-induced over-expression of MYCN in genetically modified human non-MYCN-amplified SHEP-MYCN-ER neuroblastoma cells [319].
Genome-wide chromatin immunoprecipitation (ChIP)-on-chip experiments on human
MYCN-amplified Kelly and c-MYC-amplified SJ-NB-12 neuroblastoma cells showed binding of endogenous MYCN and c-MYC to canonical E-boxes in the mir-9-3 locus, together with elevated H3K4me3 activated chromatin markings [319]. This result further validates that miR-9 is transcriptionally activated by MYCN and c-MYC. miR-9 directly supresses mRNA 3’UTR of CDH1 encoding cell adhesion molecule E-cadherin, which is down-regulated during neural crest development to allow neural crest cells detachment and migration away from the neural tube [319, 320]. Stable miR-9 over- expression in SUM149 non-metastatic human breast cancer cells xenografts lead to a bigger size of primary tumours, increased Ki-67 cell proliferation marker levels and angiogenesis, and increased lung micrometastases [317]. Therefore, one would expect miR-9 to be an oncomiR promoting migration and metastasis in neuroblastoma.
However, Zhang et al. [321] reported that miR-9 over-expression in human non-
MYCN-amplified SH-SY5Y and SK-N-SH neuroblastoma cells suppressed their invasion, metastasis, and angiogenesis via direct mRNA 3’UTR suppression of Matrix
Metalloproteinase 14 (MMP-14), which breaks down collagen in extracellular matrixes for invasion [322]. In addition, retinoic acid all-trans-RA-induced differentiation of
Chi Yan Ooi 54 Introduction
human MYCN-amplified SK-N-BE and non-MYCN-amplified SH-SY5Y neuroblastoma cells upregulate miR-9, which in turns directly suppresses the Helix-Loop-Helix (HLH) transcription factor ID2 via its mRNA 3’UTR [323]. ID2 is known to usually promote proliferation and prevent differentiation [324]. Over-expression of miR-9 reduced cell proliferation (DNA synthesis) in SK-N-BE and SH-SY5Y cells, promote neuronal differentiation in human non-MYCN-amplified SHEP neuroblastoma cells and reduced
MYCN protein expression by ~40% in SK-N-BE in part through ID2 suppression [323].
Since some miRNAs are known to be able to target functionally opposing targets and the overall effect can depend on the relative targets abundances and the cellular context
(see ref. [254] for an example), it appears that miR-9 acts as a tumour suppressor in neuroblastoma. It is unclear why MYCN activates a miRNA that inhibits MYCN expression at least partly via direct ID2 suppression. But this demonstrates the roles of miRNAs in retinoic acid differentiation therapy in neuroblastoma.
Chi Yan Ooi 55 Introduction
MYCN miR-9 ID2
MMP-14 promote
proliferation and prevent invasion, differentiation
metastasis, and angiogenesis
Figure 1.11 miR-9 and MYCN in neuroblastoma
MYCN transcriptionally activates miR-9, which appears to act as a tumour suppressor in neuroblastoma. miR-9 directly suppresses transcriptional factor ID2 through the 3’UTR on ID2 mRNA to reduce MYCN transcriptional activation, and suppresses the promotion of proliferation and the inhibition of differentiation by ID2. miR-9 also directly suppresses MMP-14, a protein that facilitate invasion, metastasis, and angiogenesis of neuroblastoma. (ref. [319, 321-324])
Chi Yan Ooi 56 Introduction
miR-204 as a potential tumour suppressor of neuroblastoma
miR-204 was originally identified by in situ hybridization to be expressed in the choroid plexus of the embryonic and adult brain and in the retinal pigment epithelium
(RPE) and the ciliary body of the adult eye in mouse [318]. miR-204 was also identified to be highly expressed in pulmonary arteriolar smooth muscle cells of the lung, moderately expressed in the heart, skeletal muscle and kidney, and highly expressed in the brain by qRT-PCR [325, 326]. miR-204 is encoded in one of the introns of host gene TRPM3, and was also identified to be co-expressed with TRPM3 mRNA in the choroid plexus by in situ hybridization [318, 327]. RPE plays major roles in visual function, such as the maintenance of blood-retinal barrier and supplying of nutrients to photoreceptors, and its degeneration contribute to age-related macular degeneration [328]. miR-204 is up-regulated when human parthenogenetic embryonic stem cells (hPESCs) differentiate into hPESC-derived RPE cells and miR-204 over-expression in hPESCs enhanced their morphological differentiation into hPESC-derived RPE cells and enhanced expression of RPE signature genes [329].
Transcription factor Pax6 can bind to the DNA region downstream of the second transcriptional start site of Trpm3 in mouse for transcriptional activation as evaluated by in vitro ChIP-on-chip, electrophoretic mobility shift assay and in vivo ChIP-qPCR, and this regulation is functionally conserved in Medaka fish [330]. In turn, miR-204 directly supresses pro-neuronal transcription factor Sox11 in mouse, and more importantly transcription factor MEIS2 in human which can activate Pax6 [330, 331]. These form a negative feedback loop where Pax6 up-regulates miR-204 and miR-204 supresses Pax6 expression via supressing MEIS2 [330, 331]. This Pax6-miR-204-MEIS2 loop is important in lens and retinal differentiation, maturation and development [330, 331]. The MEIS family is implicated in normal nervous system development and oncogenic activities in neuroblastoma [332]. In particular, MEIS2 is essential for
Chi Yan Ooi 57 Introduction
neuroblastoma cells M-phase progression, prevention of cell death from mitotic catastrophe, and promotion of cell proliferation, anchorage-independent growth in soft agar and subcutaneous xenograft growth by transcriptionally maintaining the expression of late cell cycle genes [333]. Therefore the repression of MEIS2 by miR-204 suggests potentially important role(s) of miR-204 to neuroblastoma and normal nervous system development.
miR-204 is frequently deleted from chromosome 9 in ovarian cancers (158/354,
44.63%), breast cancers (10/35, 28%), head and neck squamous cell carcinoma (53/79, 67%) and paediatric renal tumours (15/38, 40%) [334, 335]. miR-204 also has significantly reduced expression in breast, ovarian, prostate, adult kidney, paediatric renal, liver intrahepatic cholangiocarcinoma, gastric, non-small cell lung cancer, acute myeloid leukaemia, glioma and malignant peripheral nerve sheath tumours compared to respective normal tissues [334, 336-343]. miR-204 down-regulation is also detected in a number of cancer cell lines from ovarian, pancreatic cancer, liver intrahepatic cholangiocarcinoma, colon cancer, lung cancer, hematologic cancer and Burkitt B-cell lymphoma, brain cancer and glioma, and malignant peripheral nerve sheath tumours compared to respective normal or immortalized primary cell culture or normal tissues [336-338, 340, 344]. Additionally, expression of miR-204 is also lower in the NCI60 cancer cell lines panel compared to thirteen normal tissues [344]. These showed that miR-204 expression is generally lost in cancer cells, suggesting a possible tumour suppressor function. In particular, miR-204 over- expression caused cell death in pancreatic cancer cell lines in vitro and patient tumour subcutaneous xenografts via direct suppression of Mcl-1 [345]. In addition, miR-204 reduced cell proliferation, migration, invasion and metastasis-associated epithelial- mesenchymal transition of lung cancer cell lines in vitro and lung metastasis in vivo via direct suppression of SIX1 and NUAK1 [340, 346]. Moreover, miR-
Chi Yan Ooi 58 Introduction
204 over-expression also inhibited head and neck squamous cell carcinoma cell lines migration, adhesion and invasion in vitro, and lung metastasis in vivo [336]. Similarly, miR-204 over-expression in liver intrahepatic cholangiocarcinoma cells in vitro reduced migration, invasion and epithelial-mesenchymal transition via direct suppression of Slug
[338].
In neuroblastoma, miR-204 is not located in commonly altered genetic loci but positive correlations between miR-204 expression in tumour samples and patient survival metrics have been found [103, 104, 347]. Although miR-204 did not affect cell viability of both MYCN-amplified and non-MYCN-amplified neuroblastoma cell lines, they were sensitized by miR-204 to chemotherapeutic agent cisplatin [347]. In addition, miR-204 expression is down-regulated in one doxorubicin chemoresistant cell line model of neuroblastoma [348]. These suggest miR-204 may be a chemosensitivity factor for some chemotherapeutic agents in neuroblastoma. On the other hand, dual luciferase assays confirmed miR-204 direct targeting of mRNA 3’UTRs from anti-apoptotic protein BCL2, as well as PHOX2B and TrkB receptor, in neuroblastoma cells [347, 349]. PHOX2B is an important transcription factor in the development of neural crest cells into sympathetic ganglia [31, 37, 40] (see Figure 1.2). Germline mutations in PHOX2B lead to predisposition to rare familial neuroblastoma [39, 41]. Moreover, miR-204 directly represses expression of
BDNF, an activation ligand for the TrkB receptor, in other cancers [29, 342]. Both BDNF and TrkB expression are correlated to MYCN amplification and aggressive phenotype in neuroblastoma, and required for final migration of primary sympathetic ganglia in developing chick embryo explants [28, 350, 351]. These further suggest miR-204 may play important roles in normal sympathetic ganglia tissues development and potentially neuroblastoma tumorigenesis when dysregulated.
Chi Yan Ooi 59 Introduction
miR-375’s interactions with MYCN
miR-375 is marked as up-regulated in hepatocellular carcinoma and down- regulated in esophageal cancer inside the KEGG curated human microRNAs in cancer pathway [352]. In neuroblastoma, high miR-375 expression is correlated with worse patient event-free and overall survival [103]. In addition, miR-375 expression is higher in metastatic neuroblastoma tumours compared to primary tumours in a heterotopic transplant mice model [353]. Furthermore, miR-375 expression is up-regulated in several neuroblastoma cell lines established from relapsed patients compared to their respective cell lines established at time of diagnosis from the same patients [354]. These suggest miR-375 may act as an oncomiR and promote metastasis in neuroblastoma in vivo. On the other hand, miR-375 inhibited in vitro differentiation of neuroblastoma cells by direct repression of HuD protein expression [355, 356]. MYCN knockdown and over-expression experiments, and chromatin immunoprecipitation (ChIP) assays showed that MYCN up-regulates miR-375 expression transcriptionally in neuroblastoma cells through one of the two non-canonical E-boxes in miR-375 promoter [356]. However, it is also reported that miR-375 expression is generally higher in non-MYCN-amplified neuroblastoma cell lines compared to MYCN-amplified ones [357]. Moreover, miR-375 appears to directly repress MYCN protein expression in vitro and in vivo through targeting the 5’UTR of MYCN mRNA [357], which is rare if not a unique miRNA interaction for MYCN. Furthermore, the same study reported miR-375 reduced xenograft tumour growth in vivo, reduced cell viability and colony forming and increased sensitivity to ionizing radiation-mediated cell death in vitro for MYCN- amplified neuroblastoma cells [357]. Therefore, there are conflicting views on the roles of miR-375 in neuroblastoma.
Chi Yan Ooi 60 Introduction
miRNAs that directly repress MYCN expression
As discussed above, miR-17, miR-19a, miR-19b, let-7a, let-7e and miR-34a directly target the 3’UTR of MYCN mRNA, while miR-375 directly targets the 5’UTR instead [47,
254, 255, 309, 357]. In addition, several more miRNAs has been shown to directly target
MYCN mRNA 3’UTR, including miR-29a, miR-29b, miR-29c, miR-101, miR-34c, miR-
449 and miR-202 [263]. A high-throughput luciferase reporter screen using MYCN mRNA
3’UTR as probe additionally identified 17 miRNAs that are likely to repress MYCN expression directly [358]. Expression of several MYCN-repressing miRNAs above are negatively correlated to the degree of DNA methylation at their promoters in 18 primary neuroblastoma tumours [359]. Since MYCN DNA binding sites are often co-localized with
DNA hypermethylation sites in neuroblastoma cell lines [79], it is likely MYCN transcriptionally silences the expression of some MYCN-repressing miRNAs. On the other hand, MYCN-repressing miRNAs miR-17, miR-19a, miR-19b and miR-375 are transcriptionally activated by MYCN as discussed above [245, 253, 356].
1.4.2 MicroRNAs as therapeutic targets in neuroblastoma
MicroRNAs have potentials in real world therapeutic applications in cancer. As cancer is a collection of diseases each involving multiple genes and miRNAs are able to target a broad range of genes, miRNAs have an advantage over drugs that target only a single gene [360]. These therapeutic applications would include the replacement of tumour suppressor miRNAs and inhibition of oncomiRs [361]. A synthetic mimic of tumour suppressor miR-34a encapsulated in liposomes called MRX34 became the first miRNA mimic replacement therapy to enter phase I clinical trial in 2013 [304, 313, 316].
Nevertheless, MRX34 is designed for liver cancer and cancer metastasized to liver due to the affinity for liposomes to accumulate in the liver [316]. A second phase I clinical trial of miRNA mimic replacement therapy began in 2014 with TargomiRs, a miR-16 mimic
Chi Yan Ooi 61 Introduction
packaged in bacterially-derived non-viable minicells coated with anti-EGFR antibody for targeted delivery to recurrent malignant pleural mesothelioma and non-small cell lung cancer [362]. As discussed above, the targeted delivery of miR-34a mimic via antibody- conjugated nanoparticles to orthotopic xenograft models of human neuroblastoma to induce apoptosis and repress expression of MYCN and its downstream targets has been demonstrated [313]. Therefore, if MRX34, TargomiRs and the application of miR-34a in neuroblastoma are successful in clinical settings, it could open up further applications of replacement therapy with mimics of other tumour suppresser miRNAs in neuroblastoma.
Furthermore, replacement therapies using miRNA mimic face major challenges in vivo, such as instability, poor cellular uptake and non-specific delivery [314, 315, 363].
Alternatively, small molecules that can enhance miRNA expression and/or activity can be considered. For example, expression of MYCN-repressing miR-101 can be activated by
DNA methyltransferase inhibitor 5-Aza-2’-deoxycytidine in two out of three MYCN- amplified neuroblastoma cell lines in vitro [359]. In addition, the treatment of MYCN- amplified neuroblastoma cells and tumour spheres with histone deacetylases inhibitors
(HDACi) in vitro activates the expression of miR-183, which can induce apoptosis and inhibit colony forming in vitro, and inhibit xenograft growth in vivo [90]. However, the above agents can also activate and/or repress expression of other miRNAs [90, 359], which could be beneficial if they are also tumour suppressive but antagonistic if they are oncogenic. Small molecules that can selective enhance the expression and/or activity of specific miRNAs are more ideal. The discovery of a small molecule selective activator of miR-122 expression that induced apoptosis in liver cancer cells has demonstrated that it is possible to develop such kind of small molecules [364].
For miRNA inhibition therapy, there are three major approaches. First, chemically enhanced single- antisense oligonucleotides perfectly stranded
Chi Yan Ooi 62 Introduction
complementary to specific miRNAs can be used to block their actions against endogenous mRNA targets [365, 366]. Chemical modifications could include the substitution of the hydroxyl group at the 2’ carbon position of the ribose ring with an O-methyl group, an O- methyoxyethyl group or a fluorine, or linking the oxygen at the 2’ carbon to the 4’ carbon through a methylene bridge to create locked nucleic acid (LNA) [365, 366]. These modifications can improve serum stability by increasing resistance to serum nucleases and/or binding affinity to targeted miRNAs [365, 366]. The LNA chemistry also allows miRNA inhibition with oligonucleotides containing a mixture of LNA and DNA bases, or with short
LNA only sequence complementary to the miRNA seed sequence [365]. Cleavages by nucleases can also be blocked by sulphur substitution of an oxygen in the phosphodiester bond that does not involve in bridging two ribose rings [365, 366]. In addition, the resulting phosphorothioate bonds and conjugation with cholesterol at oligonucleotide’s 3’ end enhance the in vivo delivery of non-encapsulated oligonucleotide inhibitors [365, 366]. An oligonucleotide inhibitor of miR-17 with 2′-O-methyl modifications, two and four phosphorothioate bonds at 5’ end and 3’ end respectively, and 3’ cholesterol conjugation was successfully delivered intratumourally to subcutaneous neuroblastoma xenografts [253].
This led to increased expression of miR-17 targets p21 and BIM, increased apoptosis and reduced tumour growth in vivo. However, the in vivo model used in that study cannot accurately reflect the efficacy of systematic delivery to normal anatomical locations of neuroblastoma tumours. Strategies have been developed to improve efficiency of in vivo delivery of antisense oligonucleotides, including encapsulation in liposomes and nanoparticles, and antibody fusion proteins that bind the oligonucleotides for efficient tissue- specific delivery [365]. Alternatively, virus-based delivery of miRNA sponges that act as mRNA decoys with multiple artificial
Chi Yan Ooi 63 Introduction
miRNA binding sites, or small molecule inhibitors could be used for miRNA inhibition therapy [365, 366].
Lastly, miRNA replacement and inhibition therapies could potentially be used in combination therapies to sensitize neuroblastoma cells to existing chemotherapeutic agents. Several miRNAs have been shown to enhance the effect of cisplatin in neuroblastoma cells in vitro. For example, miR-204 enhanced apoptosis of Kelly
MYCN-amplified neuroblastoma cells treated with cisplatin but not untreated cells, in part via direct repression of the anti-apoptotic protein BCL2 [347]. In addition, miR-
520f selectively reduced the viability of cisplatin-resistant non-MYCN-amplified neuroblastoma cells derived from the non-resistant parental cell line SK-N-AS when treated with cisplatin or etoposide, without affecting non-resistant SK-N-AS cells [367].
Moreover, miR-497 alone induced apoptosis in Kelly and CHP-212 MYCN-amplified neuroblastoma cells and enhanced cisplatin-mediated apoptosis in those cells [368]. On the other hand, several neuroblastoma cell lines resistant to doxorubicin or etoposide were generated in vitro from chemosensitive cells, and identified seven miRNA candidates, four up-regulated and three down-regulated compared to parental cells, for further investigations [348]. Additionally, eight and thirty-four miRNAs were identified by small RNA sequencing to be up- and down-regulated respectively in cell lines established from neuroblastoma patients at relapse after therapy compared to their respective cell lines from the same patients at diagnosis [354]. Therefore, replacement and inhibition therapies of the above miRNAs could potentially augment conventional chemotherapies for better treatments in neuroblastoma.
1.4.3 MicroRNAs as biomarkers for neuroblastoma
As miRNAs are generally more stable than mRNAs and regulates a broad range of targets, miRNAs may have better performance than mRNAs as biomarkers [104].
Chi Yan Ooi 64 Introduction
Several studies have shown that multi-miRNA expression signatures in human neuroblastoma tumours, including fresh frozen and formalin-fixed paraffin-embedded samples, can accurately classify patients into different risk groups and predict clinical outcomes [96, 102-104]. In addition, since expression of miRNAs affects neuroblastoma chemosensitivity (see above section), miRNAs may be used as biomarkers to predict drug responses and personalize treatments [369]. Furthermore, genetic polymorphisms in pri-, pre- and mature miRNA sequences, miRNA target sites and genes involved in miRNA biogenesis are associated with improved or worsened clinical outcomes and/or drug response in cancer patients [369]. Therefore, miRNA-associated polymorphisms may potentially serve as useful biomarkers for neuroblastoma.
Recent developments focus on circulating miRNAs as neuroblastoma biomarkers. In addition to miRNAs within circulating tumour cells, miRNAs can be encapsulated within microvesicles, exosomes, prostasomes and apoptotic bodies, and secreted by tumour cells into the bloodstream [370, 371]. Neuroblastoma cells can at least secrete miRNAs within exosomes to their microenvironment for signalling functions [372], suggesting secretion of miRNAs into blood circulation that could be used as biomarkers. Indeed, the levels of five serum cell-free miRNAs can be used to classify patients into MYCN-amplified high-risk and non-MYCN-amplified low-risk neuroblastoma [373]. Moreover, increase in levels of three serum cell-free miRNAs may indicate the development of metastatic disease, at least in neuroblastoma mouse models [374]. Therefore, cell-free circulating miRNAs may also serve as biomarkers for early detection, diagnosis, prognosis and monitoring of neuroblastoma.
Chi Yan Ooi 65 Introduction
Summary and prospective
Neuroblastoma is a childhood cancer with poor clinical outcomes for the aggressive forms of the disease. MYCN amplification is a major poor prognosis marker and its over- expression has long been known and demonstrated as a major pleiotropic oncogenic initiator and driver of neuroblastoma. However, how MYCN over-expression lead to the tumorigenesis of neuroblastoma is not fully understood. MicroRNAs are also pleiotropic and regulate many biological processes and MYCN expression in cells, while abnormal
MYCN expression dysregulates miRNAs expression, several of which have demonstrated functional roles in neuroblastoma. The overall objective of this thesis is to extend these current knowledge by identifying and characterizing miRNAs that have novel functional roles in MYCN-driven neuroblastoma, especially if they may contribute to tumorigenesis.
This would be addressed in the following three results chapters:
Chapter 3 - Identification of candidate miRNAs & mRNAs for roles in neuroblastoma tumorigenesis
Hypothesis: miRNAs, and their potential target mRNAs, that have important functions in the biology of MYCN-driven neuroblastoma are dysregulated during early tumorigenesis. The aim of this chapter is to identify miRNAs that may have novel important biological functions in MYCN-driven neuroblastoma, and their potential target genes. In this chapter, we would present the gene expression profiling of the Th-
MYCN homozygous mouse model of MYCN-driven neuroblastoma tumorigenesis, and the bioinformatical inference of a statistically significant interaction network of miRNAs and mRNAs. miRNAs would be shortlisted by several selection criteria, such as the conservation of miRNA sequences between human and mice, and published human neuroblastoma profiling data on correlations with patient survival and MYCN
Chi Yan Ooi 66 Introduction
amplification. Further prioritization and functional discoveries would be performed through further bioinformatical analyses, validation of miRNA dysregulations in our mouse model and in vitro experimentations on human neuroblastoma cells.
Chapter 4 - miR-204 is a novel tumour suppressor of neuroblastoma
Hypothesis: Prioritized miRNA candidates can regulate predicted target genes expression and neuroblastoma phenotypes. The aim of this chapter is to characterize the functions of the prioritized miRNAs on expression of target genes which are predicted from our mouse model, and human neuroblastoma phenotypes. miRNA candidates would be transiently over-expressed or inhibited to assess any change to genes’ expression and phenotypes in vitro. Of these miRNAs, miR-204, a promising tumour suppressor, would be further functionally characterized in vitro and in vivo.
Chapter 5 - Mechanism of bi-directional suppression between miR-204 and MYCN and the genome-wide identification of miR-204 targets
Hypotheses: MYCN transcriptionally represses miR-204 expression. miR-204 binds to MYCN mRNA to directly repress MYCN expression. From the previous two chapters, we found that MYCN and miR-204 suppress each other’s expression. The major aim of this chapter is to investigate the mechanism of the above regulations. A
ChIP assay would be used to determine MYCN binding to the genomic region encoding miR-204. On the other hand, a pull-down assay with biotin-labelled miRNA mimics would be used to assess any association between mature miR-204 and MYCN mRNA.
Another aim of this chapter is to perform genome-wide identification of novel targets and biological functions for miR-204 in neuroblastoma through microarray.
Chi Yan Ooi 67 Introduction
Significance of this thesis
Identification and characterisation of miRNAs that may have early influence in the regulations of MYCN-driven neuroblastoma tumorigenesis would allow us to better understand how miRNAs may contribute to this MYCN-driven tumorigenesis, which could be essential to neuroblastoma biology. This in turn could lead to more and better miRNAs targets for improved therapies, biomarkers for improved diagnosis, risk stratification/prognosis, personalized medicine, disease monitoring and possibly early detection and prevention before tumour formation. Therefore, this thesis has significance in improving future neuroblastoma patient care. The knowledge generated in this thesis may also have wider applications in other MYCN-associated cancer, such as medulloblastoma and glioma [375, 376].
Chi Yan Ooi 68
Materials and Methods
Chi Yan Ooi 69 Materials and Methods
Bioinformatical analyses
2.1.1 Enrichment Analysis with GeneCodis3
The GeneCodis3 method of discovering over-represented annotations in a list of genes compared to a reference gene list is described in references [377-379] and assessed through a web-based interface on the web address http://genecodis.cnb.csic.es/.
First, “Homo sapiens” was selected from a dropdown menu under “Select the organism”. Second, the appropriate annotations categories, such as gene ontologies
(results not shown) and KEGG Pathways, were selected from the list of annotations categories. Third, the gene list of interest was pasted into the text field “Paste list of genes”. Fourth, the minimum number of genes needed to support a certain annotation was modified from the default 3 to 1 under “Advanced options”. Lastly, the submit button is pressed and the results were downloaded after the computations are completed.
2.1.2 Compute overlaps with gene sets from MSigDB
The Molecular Signatures Database (MSigDB) compute overlaps tool [380] allows discovery of over-represented MSigDB gene sets in a list of genes when compared to the genes annotated in the MSigDB. The tool was assessed through a web-based interface on the web address http://software.broadinstitute.org/gsea/msigdb/annotate.jsp. First, the gene list of interest was pasted into the text field “Gene Identifiers”. Second, the appropriate
MSigDB gene sets collection or sub-collection was selected under “Compute Overlaps”.
Third, the number of gene sets to be displayed was modified from the default top 10 to top
100 through a dropdown menu. Lastly, the compute overlaps button was pressed and the results generated were then downloaded.
Chi Yan Ooi 70 Materials and Methods
2.1.3 miRNA targeting predictions
The predicted miRNA-mRNA interaction network / interactome was generated by my co-supervisor Dr Bing Liu using the Bayesian Network with Split-Averaging (BNSA) method [381]. Briefly, differential expressed miRNAs and mRNAs (fold change
≥ 2 or ≤-2 and adjusted p-value < 0.005 for miRNA or 0.001 for mRNA) between the Th-
MYCN homozygous (Th-MYCN+/+) mouse ganglia and wild-type mouse ganglia over the time period from 1 week to 2 weeks after birth were determined from expression profiling.
Next, the expression profiles of those miRNAs and mRNAs were discretised and integrated with predicted miRNA targets binding information from TargetScanMouse
[199] through BNSA to infer statistically significant positive and negative interactions by miRNAs on mRNAs.
TargetScanHuman (Release 7.1) [198] was used to determine whether the predicted miRNA-mRNA interactions is conserved in human. It is assessed through the web-based interface on web address http://www.targetscan.org/vert_71/. Simply the miRNA of interest was selected from one of the dropdown menus and the search was performed. Next, the option “View top predicted targets, irrespective of site conservation” was selected in order to display all predicted targets of the miRNA of interest. From here, the presence of specific targets can be checked. This was also used to determine whether MYCN mRNA 3’UTR is a predicted target of miR-204.
miRanda [200] and PITA [382] were also used to determine whether MYCN mRNA
3’UTR is a predicted target of miR-204. miRanda was assessed through the web-based interface at the web address http://www.microrna.org/microrna/home.do. First, the “Target mRNA” tab was clicked to bring up the “Target mRNA Search”. Second, “MYCN” was entered with the species selected as Homo sapiens from a dropdown menu before submitting the search using the “Go” button. On the search results page, the
Chi Yan Ooi 71 Materials and Methods
“alignment details” link for the sole result of transcript NM_005378 was clicked to reveal details of predictions. Under “Display Options”, the option “View target sites of all miRNAs with all mirSVR scores” was selected from a dropdown menu to reveal all predictions. Finally, miR-204 was searched for using a web browser’s built-in find function (Ctrl+F). For PITA, it was assessed from the web address https://genie.weizmann.ac.il/pubs/mir07/mir07_dyn_data.html. “miR-204” was searched with organism set to human, minimum seed size set to 6, allow single G:U and single mismatch, minimum seed conservation set to 0.0 and flank settings set to no flank. Next, the outputted results Microsoft Excel files were downloaded and MYCN was searched for using Microsoft Excel’s find function (Ctrl+F).
RNA22 [383] was used to analyse the full length MYCN mRNA for miR-204 binding site. The pre-computed predictions version of RNA22 was used and assessed through the web-based interface at web address https://cm.jefferson.edu/rna22/Precomputed/. First, species was set to human, target
RNA was set to mRNA, databases was set to “miRBase 21 (Jun. 2014), ENSEMBL 78
(Dec. 2014) and RNA22v2.0”, and submitted using the “Submit Choices” button. On the next page, “MYCN” was entered into the text field under “Common Gene Name” and then the submit button was clicked. On the results page, the “View Predictions as cDNA map” link for the sole result for transcript ENST00000281043 was clicked to view the predictions. Lastly, a miR-204 binding site was found through a “Choose miR/location” dropdown menu.
2.1.4 R2 Kaplan Meier by gene expression analysis
The R2: Genomics Analysis and Visualization Platform (http://r2.amc.nl) was used for Kaplan Meier analysis of patient survival by mRNA expression in patient tumour samples. To perform the analysis, first the “Change Dataset” link was clicked to bring up
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a dropdown menu where the “Tumour Neuroblastoma – SEQC – 498 – RPM – seqcnb1” dataset was selected. The select button was pressed to confirm selection.
Second, “Kaplan Meier by gene expression” was selected from a dropdown menu under
“Select type of analysis” and then the “Next” button was pressed to submit the analysis conditions. On the next page, the gene symbol of interest was entered into the text field for “Gene/PS:” and “cutoff_modus:” was set to median before pressing the “Next” button. Finally, the appropriate transcript and type of survival was selected before generating the desired Kaplan Meier analysis through the “Next” button.
2.1.5 Peer-reviewed high-throughput miRNA expression profiling studies of human neuroblastoma samples
The findings from previous high-throughput miRNA expression profiling of human neuroblastoma samples in 7 peer-reviewed publications [96-98, 103, 104, 244,
245] were extracted and pooled together in an Microsoft Excel file, where the miRNAs of interests can be cross-referenced with findings from multiple profiling studies. These samples include human neuroblastoma tumours and human neuroblastoma cell lines genetically modified to over-express human MYCN in the absence of doxycycline or in the presence of 4-OHT. Expression profiling techniques used include RNA-seq, microarray and qRT-PCR. These findings include correlations between expression and patient survivals, and differential expression between MYCN-amplified and non-MYCN- amplified tumours, between unfavourable and favourable tumours, and between cells with and without MYCN induction.
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Cell biology techniques
2.2.1 Cell lines and culture conditions
MYCN-amplified human neuroblastoma cell lines BE(2)-C and Kelly was kindly supplied by Dr J Biedler (Memorial Sloan-Kettering Cancer Centre, New York, USA) and Dr T Littlewood (Imperial Cancer Research Fund, London, UK) respectively. Non-
MYCN-amplified human neuroblastoma cell line SHEP MYCN-3, which is genetically modified to over-express human MYCN cDNA when exposed to doxycycline [384], was generously supplied by Associate Professor Jason Shohet (Texas Children's Cancer
Center, Houston, USA) and obtained from Dr Jamie Fletcher (Children’s Cancer
Institute, Sydney, Australia) with permission. BE(2)-C, SHEP MYCN-3 cells and their stably transfected cell lines if any were grown and cultured in vitro as an adherent monolayer (except during colony forming assays) in Dulbecco’s Modified Eagle’s
Medium (DMEM), supplemented with L-glutamine and tetracycline-free 10 % foetal bovine serum unless otherwise specified. Kelly cells and its stably transfected cell lines were grown and cultured in vitro as an adherent monolayer (except during colony forming assays) in Roswell Park Memorial Institute (RPMI) 1640 supplemented with L- glutamine and tetracycline-free 10 % foetal bovine serum unless otherwise specified.
All cell lines were grown at 37 °C with 5 % atmospheric CO2 and regularly tested to be mycoplasma-free.
2.2.2 Cell viability counts
To determine the number of viable cells, 50 µL of cells thoroughly suspended in solution were mixed with equal volume of trypan blue (Gibco) and added to a haemocytometer. Under a microscope, cells in the 16 squares grids at each corner that were unstained by the blue dye were considered viable and counted. Cell numbers were
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calculated as follow: (number in grid 1 + number in grid 2 + number in grid 3 + number in grid 4) ÷ 2 × 10^4 cells/mL.
2.2.3 Doxycycline-induced induction of MYCN and miR-204 over-expression
100 mg/mL doxycycline hydrochloride in dimethyl sulfoxide (DMSO) (Sigma-
Aldrich) were diluted to 1 mg/mL with DMSO (Sigma-Aldrich) and store at -20 °C in aliquots. After cells were seeded onto cell culture vessels, the 1 mg/mL doxycycline solution was thawed and added to the culturing media at 1:1000 ratio to yield a final concentration of 1 μg/mL. The same volume of DMSO was added to negative controls.
The media were replaced with fresh media and doxycycline after three days.
2.2.4 Panobinostat and 5-Aza-2’-deoxycytidine treatments
BE(2)-C and Kelly cells were seeded at a density of 1 million and 3.2 million cells per T25 flask respectively for later panobinostat treatment, and 0.4 million and 1.2 million cells per T25 flask respectively for later 5-Aza-2’-deoxycytidine treatment. On the next day, 20 µM panobinostat (Jomar Life Research) or 2 mg/mL 5-Aza-2’- deoxycytidine (Sigma-Aldrich) dissolved in DMSO was added to culturing media at
1:1000 ratio to give a final concertation of 20 nM or 2 μg/mL respectively. The same volume of DMSO was added to negative controls. After 24 h, the cells treated with panobinostat were harvested and miRNAs were extracted from the cells (see Section
2.4.1). After 48 h, the media of the cells treated with 5-Aza-2’-deoxycytidine were replaced with fresh media and 5-Aza-2’-deoxycytidine. These cells were harvested and their miRNAs extracted at 72 h after first exposure to 5-Aza-2’-deoxycytidine.
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Transfection of neuroblastoma cells
2.3.1 Transient transfection of siRNA or miRNA mimic / inhibitor
siRNAs, miRNA mimics and inhibitors were commercially purchased from Qiagen or GE Healthcare Dharmacon (Table 2.1). miRNA mimics with a biotin conjugated to the 3’ end of the mature strand were custom made by GE Healthcare Dharmacon based on the mimics listed in Table 2.1. They were dissolved in nuclease-free water at 20 µM concentration and stored in aliquots at -80 °C. Cells were transfected by reverse transfection. The final transfection solution contained the cells required to be transfected,
Opti-MEM reduced serum medium with GlutaMAX supplement (Gibco) (hereafter referred to as Opti-MEM), 2 µL/mL Lipofectamine 2000 (Invitrogen), and 30 nM siRNA or 100 nM miRNA mimic or inhibitor. Lipofectamine 2000 was first added to an aliquot of cold Opti-
MEM to generate Solution I. This solution was incubated at room temperature for at least 5 min. 20 µM of siRNAs, miRNA mimics or miRNA inhibitors were then added to another aliquot of cold Opti-MEM to generate Solution II. At the end of the 5 min incubation, equal volumes of Solution I and Solution II were mixed and allowed to incubate at room temperature for at least 20 min.
Cells were trypsinized and washed with warm sterile culturing media. Cells were then spun down at 12,000 rpm for 3 min and resuspended in warm Opti-MEM. The concentration of the cells were determined by trypan blue counting (see Section 2.2.2).
After the above 20 min incubation, an appropriate volumes of the cell suspension and warm
Opti-MEM were added to the mixture of Solution I and Solution II to yield the final transfection solution. The final transfection solution was transferred to a Corning cell culturing vessel and incubated at 37 °C with 5 % CO2 for ~6 h. An equal volume of warm culturing media with 20 % instead of 10 % of foetal bovine serum was added to the final
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transfection solution after the 6 h incubation and the cells were continued to be grown at
37 °C with 5 % CO2.
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Oligonucleotide Product name Manufacturer Catalogue no. name
siControl AllStars Negative Control Qiagen SI03650318 siRNA
siMYCN #5 Hs_MYCN_5 FlexiTube Qiagen SI03078222 siRNA
siMYCN #6 Hs_MYCN_6 FlexiTube Qiagen SI03087518 siRNA
siMYCN #7 Hs_MYCN_7 FlexiTube Qiagen SI03113670 siRNA
Negative miRIDIAN microRNA Mimic GE Healthcare CN-001000-01 Control Mimic Negative Control #1 Dharmacon #1
hsa-miR-204 miRIDIAN microRNA Human GE Healthcare C-300563-05 Mimic hsa-miR-204-5p – Mimic Dharmacon
hsa-miR-148a miRIDIAN microRNA Human GE Healthcare C-300540-05 Mimic hsa-miR-148a-3p – Mimic Dharmacon
Negative miRIDIAN microRNA Hairpin GE Healthcare IN-001005-01 Control Inhibitor Negative Control #1 Dharmacon Inhibitor #1
hsa-miR-375 miRIDIAN microRNA Human GE Healthcare IH-300682-07 Inhibitor hsa-miR-375 - Hairpin Inhibitor Dharmacon
Table 2.1 siRNA and miRNA mimics and inhibitors used in this thesis
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2.3.2 Stable transfection of lentiviral shMIMIC miRNA
SMARTchoice shMIMIC human inducible lentiviral microRNA construct
(Figure 2.1) for human miR-204 were commercially purchased as lentiviral particles from GE Healthcare Dharmacon (catalogue no. VSH6906-224648868). The construct allows the options to choose between green florescent protein turboGFP and red fluorescence protein turboRFP as florescent reporter of doxycycline induction, and between four RNA Polymerase II promoters for constitutive expression of puromycin resistance gene and doxycycline-activated Tet-On 3G transactivator protein. For this construct, turboGFP and the mCMV (murine cytomegalovirus) promoter were selected.
BE(2)-C and Kelly cells were seeded at a density of 0.6 and 3.2 million cells per T25 flasks respectively for the cells to achieve ~40 % confluence for the next day. The culturing media were then removed from BE(2)-C and Kelly cells and replaced with 8 μg/mL polybrene in 2.5 mL warm serum free DMEM or 6 μg/mL polybrene in 2.5 mL warm serum free 1:1 DMEM + RPMI 1640 media mixture, respectively. 1.55 µL or 8.28 µL of the 1.16 x
10^8 TU/mL lentiviral particles were then added to the BE(2)-C and Kelly cells respectively, and incubated at 37 °C with 5 % CO2 for 6 h. After the incubation, 2.5 mL DMEM or RPMI
1640 media with 20 % instead of 10 % foetal bovine serum were added to the BE(2)-C and
Kelly cells respectively. The cells were continued to be grown at 37 °C with 5 % CO2. 24 h after the lentiviral particles were added to the cells, the culturing media were replaced with fresh normal culturing media. Another 24 h later, the BE(2)-C and Kelly cells were transferred to a T75 flask, and puromycin selection was performed at a concentration of 2
μg/mL or 0.7 μg/mL respectively for 4 days. The culturing media were replaced with fresh normal culturing media at the end of the puromycin selection and the cells were allowed to recover. Successful lentiviral transductions were confirmed by inducing a passage of each resulting cell lines with
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doxycycline, and then confirming turboGFP expression with florescence microscopy and miR-204 over-expression with TaqMan qRT-PCR.
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Figure 2.1 Structure of the inducible miR-204 lentiviral construct from GE Healthcare Dharmacon
The mCMV promoter was chosen for the constitutive expression of the puromycin resistance gene PuroR and the doxycycline-activated transactivator protein Tet-On 3G. Other RNA Polymerase II promoters available for selection are PGK (phosphoglycerate kinase) promoter, and mEF1a or hEF1a (murine or human elongation factor 1-alpha) promoter. TurboGFP was chosen instead of turboRFP as florescence reporter that co- expresses with miR-204 shMIMIC when the PTRE3G promoter is activated by Tet-On 3G. 5’ LTR = 5' Long Terminal Repeat, Ψ = psi packaging sequence, RRE = Rev Response
Element, PTRE3G = inducible promoter with Tetracycline Response Elements, tGFP = turboGFP, tRFP = turboRFP, 2a = self-cleaving peptide, WPRE = Woodchuck Hepatitis Post-transcriptional Regulatory Element, 3' SIN LTR = 3' Self-inactivating Long Terminal Repeat. Adapted from reference [385].
The above figure has been replaced with the original, unmodified version due to copyright restrictions. This figure is property of Dharmacon Inc. and is used with their permission. This figure cannot be reproduced and/or used for commercial purposes.
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Molecular biological techniques
Unless otherwise specified, cultured cell samples were trypsinized, washed with cold sterile culturing media, spun down at 12,000 rpm for 5 min and then washed with cold phosphate-buffered saline (PBS). Cells were pelleted at 300 × g for 3 min and the supernatants were removed as much as possible.
2.4.1 miRNA extraction
miRNAs were extracted together with total RNA using the Qiagen miRNeasy Mini
Kit similarly to the manufacturer’s protocol. 700 µL of QIAzol Lysis Reagent was added to non-liquid samples regardless of the type of samples. Cell pellets and ganglia tissues were lysed and homogenized by pipetting and vortexing for 1 min at room temperature. Some homogenized cell lysates were stored at -80 °C for delayed processing within three months.
Frozen tumour samples were lysed by vortexing for several hours at room temperature and briefly spun down to isolate the supernatants. The lysates were incubated at room temperature for at least 5 min before 140 µL molecular biology grade chloroform (Sigma-
Aldrich) was added. The solutions were mixed by shaking vigorously for 15 s and then incubated at room temperature for at least 2 min. The samples were then centrifuged at
12,000 × g for 15 min at 4 °C. The upper aqueous phase was collected from each sample and mixed with 1.5 volumes of 100 % molecular biology grade ethanol (Sigma-Aldrich).
The mixtures were added to RNeasy Mini spin columns in 2 mL collection tubes and spun for 30 s at ≥10,000 × g. The flow-throughs were discarded and the spin columns were washed with 700 µL Buffer RWT at ≥10,000 × g for 30 s. The flow-throughs were discarded again and the spin columns were washed twice with 500 µL Buffer RPE at
≥10,000 × g for 30 s the first wash and then 2 min for the second wash. The spin columns were then placed into new 2 mL collection tubes and centrifuged for 1 min at maximum speed. The spin columns were transferred to new 1.5 mL collection
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tubes and the RNAs were eluted with 30-50 µL RNase-free water, which was supplied with the kit, for 1 min at ≥10,000 × g. RNA samples were quantified using ND-1000
Spectrophotometer (NanoDrop) and stored at -80 °C.
2.4.2 TaqMan microRNA reverse transcription and assays
Quantitative reverse transcription polymerase chain reaction (qRT-PCR) was used to quantify miRNAs expression. qRT-PCR was performed using the TaqMan MicroRNA
Reverse Transcription Kit and TaqMan MicroRNA Assays (Table 2.2) (Applied
Biosystems) according to the manufacturer’s protocol. Briefly, a reverse transcription master mix was prepared from nuclease-free water (Invitrogen), 100 mM dNTPs (with dTTP), 50 U/μL MultiScribe Reverse Transcriptase, 10 × Reverse Transcription Buffer and
20 U/μL RNase Inhibitor from the reverse transcription kit at manufacturer’s recommended ratio. RNA samples extracted with the Qiagen miRNeasy Mini Kit (see above) were diluted to 2 ng/µL with nuclease-free water and mixed with the reverse transcription master mix at
5:7 ratio. 12.0 μL aliquots of the resulting solution was added to 0.2 mL polypropylene reaction tubes and 3 μL of 5 × reverse transcription primers from the microRNA assays were mixed with the aliquots. The reaction mixtures were spun down briefly and reverse transcription was performed in a thermal cycler
(Applied Biosystems) at 16 °C for 30 min, 42 °C for 30 min, 85 °C for 5 min and hold at 4 °C.
For the (quantitative) polymerase chain reaction, a miRNA PCR master mix was prepared from nuclease-free water, 20 × TaqMan PCR primers and probes from the microRNA assays and 2 × TaqMan Universal PCR Master Mix II with no UNG (Applied
Biosystems) at manufacturer’s recommended ratio for triplicates reactions. 4.8 µL of the reverse transcription products was mixed with 67.2 μL of the miRNA PCR master mix,
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spun down and three 20 µL aliquots were added to the wells of a MicroAmp Optical 96-
Well Reaction Plate (Applied Biosystems). PCR was performed in the Applied
Biosystems 7900HT Fast Real-Time PCR System at 95 °C for 10 min and then 40 cycles of 95 °C for 15 s and 60 °C for 1 min. The Ct (cycle threshold) values were determined for each well with a threshold cut-off of ≥ 35 and averaged between three replicates. The average Ct values were normalized to the endogenous controls snoRNA202 and U6 snRNA for mouse samples or U6 snRNA and RNU19 for human samples. Expression relative to negative control were then calculated.
Applied Biosystems’ TaqMan-based miRNA expression profiling of ganglia and tumour tissues from transgenic mice were performed by Prof Frank Speleman’s lab at the Center for Medical Genetics in Belgium. These were performed twice with two slightly different methods. The first profiling was performed in ‘Megaplex’ multiplex reverse transcription format with DNA pre-amplification and subsequent PCR on 384- well plates as described in reference [386] and covered 587 mature miRNAs in miRBase v10. The second profiling was performed using TaqMan Array Rodent
MicroRNA A+B microfluidic Cards Sets v3 instead of 384-well plates and with updated
Megaplex reverse transcription primers and pre-amplification primers, covering 649 mature miRNAs in miRBase v20.
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Assay Name Species Assay ID
Endogenous Controls
snoRNA202 Mouse 001232
U6 snRNA Human, Mouse, Rat 001973
RNU19 Human 001003
miRNAs
hsa-miR-135a Human, Mouse, Rat, Anolis carolinensis, Ateles geoffroyi, 000460 Bovine, Chicken, Dog, Goat, Gorilla gorilla, Horse, Ictalurus punctatus, Lagothrix lagotricha, Monodelphis domestica, Ophiophagus hannah, Ornithorhynchus anatinus, Pan paniscus, Pan troglodytes, Petromyzon marinus, Pig, Pongo pygmaeus, Rhesus monkey, Taeniopygia guttata, Tupaia chinensis, Xenopus tropicalis, Zebrafish
hsa-miR-135b Human, Mouse, Rat, Bovine, Dog, Goat, Gorilla gorilla, 002261 Horse, Pan troglodytes, Pongo pygmaeus, Rhesus monkey, Tupaia chinensis
hsa-miR-148a Human, Mouse, Anolis carolinensis, Bovine, Chicken, Dog, 000470 Goat, Horse, Monodelphis domestica, Ornithorhynchus anatinus, Pan troglodytes, Pig, Pongo pygmaeus, Rhesus monkey, Sheep, Taeniopygia guttata, Tupaia chinensis, Xenopus tropicalis
hsa-miR-204 Human, Mouse, Rat, Anolis carolinensis, Bovine, Chicken, 000508 Cricetulus griseus, Dog, Fugu rubripes, Goat, Gorilla gorilla, Horse, Ictalurus punctatus, Macaca nemestrina, Monodelphis domestica, Ophiophagus hannah, Ornithorhynchus anatinus, Pan paniscus, Pan troglodytes, Petromyzon marinus, Pig, Pongo pygmaeus, Rhesus monkey, Saguinus labiatus, Salmo salar, Taeniopygia guttata, Tetraodon nigroviridis, Xenopus tropicalis, Zebrafish
hsa-miR-375 Human, Mouse, Rat, Dog, Gorilla gorilla, Pan troglodytes, 000564 Pongo pygmaeus, Rhesus monkey
hsa-miR-574-3p Human, Mouse, Dog, Gorilla gorilla, Pig 002349
Table 2.2 Applied Biosystems TaqMan MicroRNA Assays used in this thesis
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2.4.3 mRNA extraction
mRNAs were extracted as part of total RNA extraction using PureLink RNA
Mini Kit (Invitrogen) according to the manufacturer’s protocol. Briefly, β- mercaptoethanol (Sigma-Aldrich) was first added to Lysis Buffer at 1:100 ratio. 300-
600 µL of the Lysis Buffer containing β-mercaptoethanol was added to each cell pellet and vortexed until the samples appeared lysed and homogenised. One volume of molecular biology grade 70 % ethanol was added to each volume of the cell lysates and mixed thoroughly. 600 µL of each sample was transferred to a spin cartridge inside a collection tube and centrifuged at 12,000 × g for 1 min. The flow-through was discarded and the spin cartridge was washed with 700 µL Wash Buffer I at 12,000 × g for 1 min.
The flow-through was again discarded and the spin cartridge was washed twice with
500 µL Wash Buffer II plus ethanol at 12,000 × g for 1 min for the first wash and 2 min for the second wash. The spin cartridge was transferred to a recovery tube and 30 µL
RNase-free water was added to the spin cartridge. The spin cartridge was incubated at room temperature for at least 1 min and RNAs were eluted at 12,000 × g for 2 minutes.
RNA samples were quantified using ND-1000 Spectrophotometer and stored at -80 °C.
2.4.4 cDNA synthesis and qPCR
cDNA synthesis from mRNAs were performed using the Tetro cDNA Synthesis Kit
(Bioline) under conditions recommended by the manufacturer with minor adjustments. First, a reverse transcription master mix was prepared from Oligo (dT)18 Primers, 10 mM dNTP mix, 5 x RT Buffer, RiboSafe RNase Inhibitor and 200 u/μL Tetro Reverse Transcriptase from the Tetro cDNA Synthesis Kit at 1:1:4:1:1 ratio. Next, 8 µL aliquots of the reverse transcription master mix were added to 1.5m L Eppendorf tubes. Up to 5 µg of each RNA sample was then added to each 1.5 mL Eppendorf tube and nuclease-free water was also added to bring up the volume to 20 µL. The solution in each tube was
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gently mixed, briefly spun down and incubated in 45 °C water bath for 30-50 min. The solutions were then diluted to a RNA concentration of 20 ng/µL, briefly spun down and heat inactivated at 85 °C for 5 mins. cDNA samples were stored at -20 °C.
For the quantitative PCR (qPCR) of cDNAs, the gene-specific master mixes were prepared from Power SYBR Green PCR Master Mix (Applied Biosystems), nuclease-free water, forward primers and reverser primers (Table 2.3) (Sigma-Aldrich) at 12:10:1:1 ratio. 1 µL of cDNA samples were added in duplicates to each well of a
MicroAmp Optical 96-Well Reaction Plate. 24 µL of the gene-specific master mixes were then added to the wells. The plate was sealed and spun down. qPCR was performed in the Applied Biosystems 7900HT Fast Real-Time PCR System at 95 °C for
10 min and then 40 cycles of 95 °C for 15 s and 60 °C for 1 min. The Ct values were determined for each well with a threshold cut-off of ≥ 35 and averaged between two replicates. The average Ct values were normalized to the endogenous control β2M and the mRNA expression relative to negative control were then calculated.
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Primer Name Sequence
Human β2M Forward 5’-ACTGGTCTTTCTATCTCTTGTACTACACTGA-3’
Human β2M Reverse 5-TGATGCTGCTTACATGTCTCGAT-3’
Human MYCN 5’-CGACCACAAGGCCCTCAGTA-3’ Forward
Human MYCN Reverse 5’-CAGCCTTGGTGTTGGAGGAG-3’
Human GUSB Forward 5’-TGGTTGGAGAGCTCATTTGGA-3’
Human GUSB Reverse 5’-GCACTCTCGTCGGTGACTGTT-3’
Sigma-Aldrich Predesigned SYBR Green Primers
FH1_E2F1 5’-CTGATGAATATCTGTACTACGC-3’
RH1_E2F1 5’-CTTTGATCACCATAACCATCTG-3’
FH1_WEE1 5’-TCAATGGCATGAAATCAGAC-3’
RH1_WEE1 5’-CTGGATCTGGATGAATCATAAC-3’
FH1_MEIS2 5’-ACCGATACATTAGCTGTTTG-3’ 5’-
RH1_MEIS2 AGAAGAAGGGTTATGGTCAG-3’ 5’-
FH2_RBL1 TGAATCTTGTGTGCGTAATC-3’ 5’-
RH2_RBL1 GTGTTTCCTGAACCATTACAG-3’ 5’-
FH3_CDC25A GACTCTTCATCAGTCTTTATCC-3’ 5’-
RH3_CDC25A GAAGTCTCCTATAAGGTCCC-3’ 5’-
FH3_CDC25B CCTGAGATGTATATCCTGAAAG-3’
RH3_CDC25B 5’-AGGTCTTTAGCTCATCCTTG-3’ 5’-
FH1_HRPT1 ATAAGCCAGACTTTGTTGG-3’ 5’-
RH1_HRPT1 ATAGGACTCCAGATGTTTCC-3’
Table 2.3 mRNA qRT-PCR primers used in this thesis
Human β2M, MYCN and GUSB primer sequences were previously used in my research group, the Molecular Carcinogenesis Program at the Children’s Cancer Institute, and synthesized by Sigma-Aldrich. Other primers were pre-designed and synthesized by Sigma-Aldrich.
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2.4.5 Whole cell protein extraction
A cold lysis buffer was first prepared by adding Protease Inhibitor Cocktail in
DMSO (Sigma-Aldrich) into cold RIPA Buffer (Sigma-Aldrich) at 1:100 ratio on ice.
The cold lysis buffer was added to cell pellets and the cells were resuspended by pipetting. The cells were vortexed three times, with the cells kept on ice in between vortexing steps. The samples were then incubated on ice for around 30 min and vortexed again for three times. The samples were centrifuged at 16,000 × g and 4 °C for
20 mins. The protein supernatants were collected and stored at -80 °C.
2.4.6 Protein quantification
Protein quantifications of protein extracts were performed using the Pierce BCA
Protein Assay Kit (Thermo Scientific) according to manufacturer’s protocol for microplate. Briefly, bovine serum albumin standards were prepared by diluting the 2 mg/mL standard included in the kit with Milli-Q water and 25 µL of each was added into a clear Pierce 96-Well Polystyrene Plate (Thermo Scientific) in duplicates. 20 µL
Milli-Q water was added to the sample wells and 5 µL protein extracts were added to those wells in duplicates. 50 parts of BCA Reagent A was mixed with 1 part of BCA
Reagent B and 200 µL of the mixture was added to each well. The plate was then covered with a clear plastic film and incubated at 37 °C for ~20 min. The plate was cooled to room temperature afterwards. Next, the plastic film was removed and the absorbance was measured using a Bio-Rad microplate reader at or near a wavelength of
562 nm depending on the capacity of the reader. The standard curve was generated and the protein concentrations of the samples were calculated by the Microplate Manager software (Bio-Rad).
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2.4.7 Western blotting
To prepare a gel loading buffer, 1 part of XT Reducing Agent 20 x (Bio-Rad) was added to 19 part of XT Sample Buffer 4 x (Bio-Rad). For each well on a gel, 20 µg protein extracts were added to 6 µL gel loading buffer and were made up to 24 µL with
RIPA Buffer. The samples were incubated at 95 °C for at least 5 min and placed on ice.
24 µL samples and 4 μL Precision Plus Protein Dual Color Standards (Bio-Rad) were loaded onto each well of the 12+2-well 10.5–14 % Criterion Tris-HCl or 18-well 10 %
Criterion Tris-HCl gels (Bio-Rad) in Tris/Glycine/SDS Running Buffer inside a
Criterion Cell (Bio-Rad). Electrophoreses were performed at 70 V constant voltage for
20 min, and then at 120 V constant voltage for 150 min or 150 V constant voltage for 85 min. Proteins were transferred from the gels to nitrocellulose membranes (Bio-Rad) in
10 % methanol Tris/Glycine Transfer Buffer using Criterion Blotter (Bio-Rad) at 250 mA constant current for 2 h. The membranes were stained with Ponceau S solution
(Sigma-Aldrich) and trimmed for the expected protein size ranges. The membrane strips were washed with Tris-Buffered Saline with 0.1 % Tween 20 (TBST) before blotting.
The membrane strips were blocked with 10 % (w/v) skim milk in TBST for 1 h at room temperature. The membrane strips were then washed three times with TBST for 10 min each, and incubated with primary antibodies diluted in 0.05 % skim milk in TBST at 4
°C overnight. Anti-MYCN antibody sc-53993 (Santa Cruz Biotechnology) were used at a dilution of 1:2,000. Anti-GAPDH antibody ab8245 (Abcam) or sc-365062 (Santa Cruz
Biotechnology) were used at a dilution of 1:10,000 or 1:3000 respectively. After overnight incubation, the membrane strips were washed three time with TBST for 10 min each. The membrane strips were then incubated for 3-4 h at room temperature with horseradish peroxidase-conjugated anti-mouse secondary antibody (Thermo Scientific) diluted in 0.05
% skim milk in TBST, at the same dilutions as the primary antibody used
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for the particular strips. The membrane strips were again washed three times with TBST for
10 min each and chemiluminescence detection was performed with Immobilon Western
Chemiluminescent HRP Substrate (Merck Millipore). The membrane strips were incubated with 1:1 mixture of peroxide solution and luminol for ~2 min at room temperature. Next, the membranes were sealed in plastic films and exposed to X-ray films in the dark. X-ray films were processed by a tabletop X-ray film processor (Konica Minolta) and scanned into digital image files for protein expression quantification through Quantity One software
(Bio-Rad). GAPDH was used as a loading control.
2.4.8 Chromatin Immunoprecipitation Assay
The Chromatin Immunoprecipitation (ChIP) Assay Kit (Merck Millipore) was used to perform the ChIP assay. BE(2)-C cells at ~80 % confluency in four T75 flasks
(~40-50 million cells in total) were trypsinized and the culturing medium was added to made up the volume of the cell suspension to 40 mL. 1.5 mL of 37 % formaldehyde
(Sigma-Aldrich) was mixed with the cell suspension and incubated for 10 min at room temperature with rotation. Next, 5 mL of 1.25 M cold Glycine was mixed with the mixture and incubated for 5 min at room temperature with rotation. The mixture was then centrifuged at 1,000 rpm and 4 °C for 10 min. The supernatant was removed and the cell pellet was washed with ~45 mL cold PBS. The solution was centrifuged again at
1,000 rpm and 4 °C for 10 min. The supernatant was discarded and the cell pellet was resuspended in 1 mL cold PBS. The cell suspension was transferred to a 1.5 mL
Eppendorf tube, and centrifuged at 8,000 rpm at 4 °C for 4 min to remove the PBS. The cells were lysed by vortexing and then incubating on ice for 10 min with 500 µL of the assay kit’s SDS Lysis Buffer supplemented with 2 % (v/v) Protease Inhibitor Cocktail.
The DNAs from the lysed cells were sheared by the Bioruptor Pico sonication system
(Diagenode), which was set to run for 45 cycles of 30 s on and 30 s off at 4 °C. The
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solution was centrifuged at 13,000 rpm at 4° C for 10 min and the supernatant was collected to a 2 mL Eppendorf tube.
1.5 mL ChIP Dilution Buffer supplemented with 2 % (v/v) Protease Inhibitor
Cocktail was mixed with the ~500 µL sheared DNA supernatant. 70 µL of the mixture was set aside as DNA input and stored at -20 °C. 70 µL Protein A Agarose/Salmon Sperm DNA was mixed and rotated with the sheared DNA mixture at 4 °C for 30 min to reduce non- specific binding. The sheared DNA mixture was then centrifuged at 2,000 rpm at 4 °C for 2 min to sediment the agarose beads. The supernatant was collected and split into two halves into two 1.5 mL Eppendorf tubes. The solutions were then incubated with either 10 µg anti-
MYCN antibody sc-53993 (Santa Cruz Biotechnology) or 10 µg Mouse IgG (Control
Antibody) I-2000 (Vector Laboratories) at 4 °C overnight with rotation. After the overnight incubation, the samples were incubated with 50 µL Protein A Agarose/Salmon Sperm DNA at 4 °C with rotation for at least 3 h. The mixtures were then centrifuged at 1,000 rpm at 4
°C for 1 min and the supernatants were discarded. The agarose beads were washed once with 1 mL Low Salt Wash Buffer, once with 1 mL High Salt Wash Buffer, once with 1 mL
LiCl Immune Complex Wash Buffer and twice with 1 mL TE Buffer. Each wash was performed at 4 °C with rotation for 4min and centrifuged at 1,000 rpm at 4 °C for 1 min to remove the supernatants. To elute the DNA-protein complexes from the beads, 260 µL
100mM NaHCO3 + 1% SDS Elution Buffer was added to each tube and vortexed for 30 min at room temperature. The samples were centrifuged at 1,000 rpm for 2 min and the supernatants (the eluates) were collected. During the elution, the 70 µL DNA input collected from the previous day was thawed and 190 µL Elution Buffer was added to make up the volume to 260 µL. The diluted DNA input and the eluates collected were incubated with 10
µL 5 M NaCl at 65 °C overnight. The samples were then briefly spun down and stored at -
20 °C for 24 h.
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After the 24 h storage, the samples were thawed at 37 °C and then incubated with
2.5 µL RNase A at 37 °C for 30 min. Next, the samples were mixed by vortexing with 5 µL
0.5 M EDTA, 10 µL 1 M Tris-HCl and 1 µL 20 mg/mL Proteinase K (Invitrogen). The samples were briefly spun down and incubated at 45 °C for 1.5 h. To purify the DNAs from the samples, the MinElute PCR Purification Kit (Qiagen) was used. 1200 µL PB Buffer was mixed with each sample by vortexing. 750 µL of each mixture was added into a MinElute spin column and spun at 12,000 rpm for 1 min. The flow-throughs were discarded and the spin was repeated with the remainder of each mixture. The spin columns were then washed with 750 µL PE Buffer at 12,000 rpm for 1 min. The flow-throughs were discarded and the spin columns were spun again at 12,000 rpm for 1 min. Next, the spin columns were transferred into collection tubes and incubated with 40 µL nuclease-free water at room temperature for 1 min. The spin columns were spun at 12,000 rpm for 2 min to elute the
DNAs. qPCR was performed as described in Section 2.4.4 using the primers listed in Table
2.4 and the primers for ODC1 promoter from reference [387].
Chi Yan Ooi 93 Materials and Methods
Primer Sequence
Negative region forward primer 5’-GCCTGGGGAGTATGTGCTTA-3’
Negative region reverse primer 5’-AGGGTTGGTTCCTCTGGATT-3’
Amplicon No. 1 forward primer 5’-AAGCACCATGCTAGGCATTT-3’
Amplicon No. 1 reverse primer 5’-CAGCTGTGCCACTAATTGGA-3’
Amplicon No. 2 forward primer 5’-GGCTCCCCTAAAGTTTGGTT-3’
Amplicon No. 2 reverse primer 5’-CCACCATCTTCTTCCCAAAG-3’
Amplicon No. 3 forward primer 5’-TGACTCGTGGACTTCCCTTT-3’
Amplicon No. 3 reverse primer 5’-GCATTTGATGATGGTGCAAT-3’
Amplicon No. 4 forward primer 5’-AGGCAACTGTAACACGATGG-3’
Amplicon No. 4 reverse primer 5’-CAGTGACTCACCCAGAGCAA-3’
Table 2.4 ChIP assay primers
These primers for the ChIP assays performed in this thesis were kindly designed by Associate Professor Tao Liu at the Children’s Cancer Institute. These primers were synthesized by Sigma-Aldrich.
Chi Yan Ooi 94 Materials and Methods
2.4.9 Biotin-labelled miRNA mimic pulldown of mRNAs
This biotin assay was adapted from reference [388]. BE(2)-C cells were reverse transfected with miRNA mimics conjugated with biotin at the 3’ end of the mature strand
(see Section 2.3.1 for reverse transfection). On the next day after transfection, magnetic
Dynabeads M-280 Streptavidin (Invitrogen) was activated by washing three times with 1 mL
50 mM Tris-HCl pH 7.5 + 0.5 mM EDTA + 1 M NaCl Binding and Washing Buffer, twice with 1 mL 100 mM NaOH + 50 mM NaCl Solution A and once with 1 mL 100 mM NaCl
Solution B. The beads were rotated for at least 30 min at 4 °C with cold 1.4 mL 20 mM Tris-
HCl + 100 mM KCl + 5 mM MgCl2 + 0.3 % (v/v) IGEPAL CA-630 Lysis Buffer with 0.5 %
(v/v) RNaseOUT (Invitrogen) and 1 % (v/v) Protease Inhibitor Cocktail, 40 μL 10 mg/mL yeast tRNA (Invitrogen) and 8 μL 50 mg/mL UltraPure BSA (Invitrogen). The beads were then washed twice with 1 mL Lysis Buffer.
24 h after transfection, the cells were washed twice with PBS before being trypsinized and washed with culturing medium and PBS. The cells were then pelleted at
300 × g for 5 min and the supernatants were removed as much as possible. The cell pellets were lysed by pipetting 20 times with 700 µL Lysis Buffer supplemented with
0.5 % (v/v) RNaseOUT and 1 % (v/v) Protease Inhibitor Cocktail, and incubated on ice for 20 min. The lysates were centrifuged at 10,000 × g at 4 °C for 15 min to collect the cytoplasmic supernatants. 50 μL aliquots of each supernatant were set aside as inputs and mixed with 200 μL Lysis Buffer and 750 μL Trizol LS (Invitrogen) before being stored at -80 °C. The remainder of the supernatants (~600 μL) were added to the beads and incubated at 4 °C for 4 h with rotation.
The supernatants were then separated from the magnetic beads and the beads were washed five times with 1 mL Lysis Buffer and once with 600 µL Lysis Buffer supplemented with 0.5 % (v/v) RNaseOUT. To eluate the pulldowns, the beads were
Chi Yan Ooi 95 Materials and Methods
vortexed and incubated at 55 °C for 20 min with 100 μL Lysis Buffer supplemented with 0.5 % (v/v) RNaseOUT, 2.5 μL 20 mg/mL Proteinase K and 1 μL 10 % molecular biology grade SDS (Invitrogen). The eluates were separated and collected from the magnetic beads. The magnetic beads were vortexed again with 200 µL Lysis Buffer supplemented with 0.5 % (v/v) RNaseOUT. The eluates were again separated and collected from the magnetic beads, and then combined with their respective first eluates.
300 μL Acid-Phenol:Chloroform:Isoamyl Alcohol 125:24:1 pH 4.5 (Invitrogen) was added to the eluates and vortexed for 1 min. The mixtures were spun at 10,000 × g at room temperature for 1 min and 250 μL of upper layer was collected from each sample.
5 μL Glycoblue (Invitrogen), 25 μL 3 M sodium acetate pH 5.2 (Thermo Scientific) and
625 μL 100 % pre-chilled ethanol (Sigma-Aldrich) were mixed with the collected solutions and incubated at -20 °C for 16 h.
After 16 h, the pulldowns were centrifuged at 16,000 × g at 4 °C for 30 min and the supernatants were removed. The RNA pellets were washed with 750 μL 70 % chilled ethanol and then centrifuged at 7,500 × g at 4 °C for 5 min in order to remove the ethanol.
The RNA pellets were air dried at room temperature for 5 min. 40 μL nuclease-free water was added to the dried RNA pellets and incubated on ice for at least 5 min. For the inputs,
250 μL nuclease-free water and 200 μL chloroform were added to the thawed lysates in
Trizol LS and vortexed for 1 min. The inputs were spun at 12,500 × g for 15 min and 400
μL of the upper layer was collected from each sample. 400 μL isopropanol (Sigma-Aldrich) and 5 μL Glycoblue was mixed with the collected solutions, and incubated at room temperature for 10 min. The mixtures were spun at 12,500 × g at 4 °C for 15 min to remove the supernatants. The RNA pellets were washed with 1 mL 70 % chilled ethanol and then spun at 7,500 × g at 4°C for 10 min in order to remove the ethanol. The RNA pellets were air dried at room temperature for 5 min. 40 μL nuclease-free water was
Chi Yan Ooi 96 Materials and Methods
added to the dried RNA pellets and incubated on ice for at least 5 min. Both the pulldowns and inputs RNAs were quantified using ND-1000 Spectrophotometer and stored at -80 °C. The RNA samples were reverse transcribed as described in Section
2.4.4. The qPCR of the input cDNA samples were performed as in Section 2.4.4. For the pulldowns cDNA samples, the gene-specific master mixes were prepared at 12:1:1:1 ratio and 10 µL of the samples without technical replicates were used instead.
2.4.10 Microarray
RNAs were extracted from BE(2)-C-miR-204 stable cells treated with doxycycline or just DMSO only for 48 h (see Section 2.2.3), using the PureLink RNA
Mini Kit and miRNeasy Mini Kit (see Sections 2.4.1 and 2.4.3). miR-204 over- expression in doxycycline-induced samples compared to DMSO-treated samples were verified by TaqMan qRT-PCR (see Section 2.4.2) on RNAs extracted by the miRNeasy
Mini Kit. The matching RNA samples extracted by the PureLink RNA Mini Kit were diluted to 50-100 ng/µL and more than 500 ng were sent to Ramaciotti Centre for
Genomics, UNSW Sydney for processing. RNA integrity was analysed by an Agilent
Bioanalyzer RNA Nano 6000 chip before performing the microarray on an Agilent
SurePrint G3 Human Gene Expression v3 8x60K Microarray chip. Three biological replicates were used. The raw data of the microarray were analysed by Dr Chelsea
Mayoh at the Children’s Cancer Institute.
Chi Yan Ooi 97 Materials and Methods
Cell phenotype and functional assays
2.5.1 Cell viability and cell proliferation assays
Cells were reverse transfected (see Section 2.3.1) with 100 nM miRNA mimics or inhibitors and the final transfection solutions were added at 100 µL per well to
Corning 96-well tissue culture-treated flat bottom clear polystyrene plates. The plates were incubated at 37 °C with 5 % CO2 for ~6 h before 100 µL of culturing media with
20 % instead of 10 % foetal bovine serum was added to each well. The cells were allowed to grow at 37 °C with 5 % CO2 until measurements of cell viability or proliferation was required.
To measure cell viability, Alamar Blue was added at 22 μL per well at the desired time points and florescence were measured by a Wallac VICTOR3 Multilabel
Plate Reader (PerkinElmer) at an excitation wavelength of 560 nm, an emission wavelength of 590 nm and measurement time of 0.1 s per well. The cells were allowed to be incubated with the Alamar Blue for 5-8 h at 37 °C with 5 % CO2. Florescence were measured again to calculate the change in florescence. Changes in florescence were averaged between 6 wells. Viabilities were calculated as percentages of the florescence relative to the respective negative controls.
To measure cell proliferation, the Cell Proliferation ELISA, BrdU (colorimetric)
Kit (Roche) was used. At the desired time points, BrdU labelling reagent (bottle 1) was diluted with culturing media at 1:100 ratio. 20 μL of the diluted BrdU labelling reagent was added to each well. The plates were covered loosely with aluminium foil and incubated at 37 °C with 5 % CO2 for 4 h. After the 4 h incubation, the media were discarded. FixDenat (bottle 2) was added at 200 μL per well and incubated at room temperature for 30 min. After the 30 min incubation, the FixDenat was discarded from
Chi Yan Ooi 98 Materials and Methods
the plates as before. Next, anti-BrdU-POD was diluted in antibody dilution solution
(bottle 4) at 1:100 ratio. The antibody was added to the plates at 100 μL per well and incubated at room temperature for 90 min. After the 90 min incubation, the antibody was discarded as before. The wells were rinsed three times with PBS at 300 μL per well.
After the final PBS wash was discarded from the plates, the substrate solution was added at 100 μL per well. Colours were allowed to develop at room temperature before absorbance were measured by a Bio-Rad Benchmark Microplate Reader at 370 nm with reference wavelength at 490 nm. Absorbance were averaged between 6 wells and cell proliferation/BrdU incorporations were calculated as percentages of the absorbance relative to the respective negative controls.
2.5.2 Colony forming assay
BE(2)-C and Kelly cells were trypsinized, thoroughly resuspended into single cells and plated onto Corning Costar tissue culture-treated 6-well plates at 500 cells/well and 250 cells/well respectively at 24 h after reverse transfection of 100 nM miR-204 mimic or negative control mimic #1. BE(2)-C and Kelly-miR-204 stable cells were similarly trypsinized, resuspended and plated after 72 h treatment with doxycycline or just DMSO only. Those BE(2)-C and Kelly cells were cultured at 37 °C with 5 % CO2 for 7 and 12 days respectively with media refresh every 3-4 days. For BE(2)-C-miR-204 and Kelly-miR-204 cells, they were cultured in culturing media with doxycycline or
DMSO only at 37 °C with 5 % CO2 for 7 and 14 days respectively with media refresh every 3-4 days. Colonies formed were washed with PBS, stained by 0.5 % crystal violet for 5-20 min, washed twice with PBS and once with Milli-Q water. The plates were air dried upside down for at least 1h before scanning the plates with the Gel Doc XR+ Gel
Documentation System (Bio-Rad). Colonies were counted using the ImageJ software
(Wayne Rasband, National Institute of Health, USA).
Chi Yan Ooi 99 Materials and Methods
2.5.3 Fluorescence microscopy
BE(2)-C-miR-204 and Kelly-miR-204 stable cells cultured with doxycycline or
DMSO only were placed on a CKX41 Inverted Microscope Reflected Fluorescence
System (Olympus) using the 10 x objective. The reflected fluorescent mirror slider and the light bulb’s power was adjusted to switch between bright-field view and fluorescence view. Bright-field images and fluorescence images were captured by a
MicroPublisher 3.3 camera (QImaging) attached to the microscope using the QCapture
Pro software (QImaging). Bright-field images were captured with the auto-set setting of the software while fluorescence images were captured with exposure set at 25.5 s, gain set at 1.5 and white balance set at R=1, G=1, B=1.
Chi Yan Ooi 100 Materials and Methods
Mouse models of neuroblastoma
2.6.1 Th-MYCN mouse neuroblastoma tumorigenesis model
The Th-MYCN transgenic mouse model is described in references [36, 389].
Germline genetic modification using a construct containing a human MYCN cDNA under the control of rat tyrosine hydroxylase promoter (Th) enables neural crest-specific expression of human MYCN proteins in these mice. For this thesis, the homozygous version of the mouse model was used, where 100% of the mice develop neuroblastoma tumours at 6 – 7 weeks after birth. The divergence of phenotype from wild-type mice begin 1 week after birth where normal developmental hyperplasias in postnatal sympathetic ganglia tissues begin to regress while the Th-MYCN homozygous developmental hyperplasias grow in size and frequency. By 2 weeks after birth, normal developmental hyperplasias regress completely while the Th-MYCN homozygous developmental hyperplasias proceed with a more gradual, delayed and incomplete regression.
Ganglia dissection
To dissect ganglia tissues from 1- and 2-weeks-old mice, they were first wrapped in paper towel and euthanized by another paper towel wetted with halothane inside a euthanasia chamber in a cytotoxic safety cabinet for at least 10 min. ½ cap of halothane was used for one mouse and additional ¼ cap for each additional mouse. After that, the death of the animals was verified before being pinned onto a pin board through each of its foot and mouth and disinfected with 70 % ethanol. A vertical incision in the middle of the neck was made using a scalpel, and then fat at the side of the neck were teared away using forceps.
Tissues were further separated and teared to expose the carotid artery and vagus nerve. The nerve was lifted upwards without breaking it to expose the white and opalescent superior cervical ganglia. The ganglia was removed and placed in a petri dish
Chi Yan Ooi 101 Materials and Methods
of cold Hank’s buffer. These were repeated at the other side of the neck. Next, a long incision was made at the midline of the abdomen through into the peritoneal cavity.
Skins from both sides were removed and then the lower rib cage was also removed but leaving diaphragm intact. Stomach and intestines were pushed to the left using a cotton bud exposing the right kidney and then removed by severing the blood vessel above the left kidney. The kidneys were then also removed but leaving the adrenal glands intact.
The tissue between where the kidneys were was teared away using forceps and placed in a petri dish of cold Hank’s buffer. Any non-neuronal tissue was removed from the tissues extracted under the microscope using forceps. Placed the tissues into 15 mL falcon tubes with 12 mL Hank’s buffer on ice. These were repeated on other mice.
Tissues from the same genotype were pooled together.
Tissues were spun for 10 min at 2000 rpm. Hank’s buffer was removed as much as possible without disturbing the pellets. RNA were extracted from the tissues with
Qiagen miRNeasy Mini extraction kit immediately (see Section 2.4.1) or the tissues were stored in -80 °C overnight for RNA extraction the next day.
2.6.2 Stable cells subcutaneous xenografts
Stably transfected human MYCN-amplified BE(2)-C and Kelly neuroblastoma cell lines were generated by lentiviral transduction (see Section 2.3.2) for establishing in vivo model of miR-204 over-expression. These cell lines were denoted as BE(2)-C-miR-
204 and Kelly-miR-204.
Engraftment and monitoring
Cells grown under normal doxycycline-free culturing conditions (see Section
2.2.1) were harvested by trypsin and washed with fresh culturing media. Cells from the same cell line were pooled together and washed with 1 x PBS. 50 µL of cells were used
Chi Yan Ooi 102 Materials and Methods
in cell counting (see Section 2.2.2) to determine the cell concentrations, and the volumes that would provide sufficient amounts of cells required for the injection of mice. The calculated volumes were spun down at 12,000 rpm for 5 min and thoroughly resuspended in sufficient amount of 1x PBS to give 2 million cells per 100 µL for
BE(2)-C-miR-204 and 10 million cells per 100 µL for Kelly-miR-204. Cells were put on ice until injection. Before injection, cells were thoroughly resuspended with p200 pipette. 100 µL of the cell resuspensions was subcutaneously injected into the left flank each BALB/c nude (BALB/c---Fox1nu/Ausb) mouse.
Mice were monitored at least twice a week for the appearance of tumour and any tumour formed was measured at least twice a week using digital callipers. Tumour
2 volumes were calculated as ½ x Length x [Width] and mice were euthanized by CO2 asphyxiation when either tumour volume equalled or exceeded 1000mm3 or after 12 weeks had passed since injection.
Doxycycline administration and in vivo fluorescence imaging
When tumour diameter reached 4-5 mm, the mice were transferred to another cage with 100 mL drinking water supplemented with either 5 % sucrose alone (negative control) or 5 % sucrose plus 2 mg/mL doxycycline hyclate. The supplemented drinking water was replaced at least three times a week with freshly prepared supplemented drinking water until all the mice in the cage were euthanized.
7 days after first exposure to supplemented drinking water, anaesthesia was performed using an isoflurane anaesthetic machine. The mice were placed in an anaesthetic induction chamber with the vaporizer set to 3.5 % isoflurane and oxygen supplied at 1 litre per minute. Once anaesthesia was achieved, the mice were moved onto a heated mat inside an IVIS SpectrumCT in vivo imaging machine. Anaesthesia of the
Chi Yan Ooi 103 Materials and Methods
mice was maintained inside the IVIS SpectrumCT with the vaporizer retained between 2
– 3 %. In vivo fluorescence imaging of the mice was performed with the Living Imaging software connected to the IVIS SpectrumCT using its default settings for turboGFP. The mice were replaced into their cages as soon as the imaging procedure was confirmed to be completed and allowed to be recovered from anaesthesia. The intensities of fluorescence were analysed by the Living Imaging software.
Tumour tissue preparation
After a mouse was euthanized, it was pinned to a board through each of its foot. The skin was cut opened at midline from the abdomen to the chest using a scissor. The tumour was separated from the mouse using scissor and forceps. The tumour was then cut into smaller pieces and placed into 10 % neutral buffered formalin or on dry ice before moving into -80 °C storage. Next, the abdomen and the chest were cut open without damaging the internal organs. The kidney and lung were removed and cut into halves and each halves was placed into either 10 % neutral buffered formalin or on dry ice before moving into -80 °C storage. RNA extraction on the tumour samples were performed at later dates using the
Qiagen miRNeasy Mini extraction kit (see Section 2.4.1).
Chi Yan Ooi 104 Materials and Methods
Statistical analysis
Statistical significances were assessed by GraphPad Prism 6 / 7 (GraphPad
Software) using two-tailed t-test or log-rank test where appropriate for the particular analysis. A p-value of less than 0.05 was considered statistically significant.
Chi Yan Ooi 105
Identification of candidate miRNAs &
mRNAs for roles in neuroblastoma tumorigenesis
Chi Yan Ooi 106 Identification of candidate miRNAs & mRNAs for roles in neuroblastoma tumorigenesis
Introduction
MYCN, as a major pleiotropic oncogenic driver, directly and indirectly influences the expression of numerous protein coding genes as well as non-coding miRNAs both positively and negatively in neuroblastoma [75, 79, 81, 96-98, 101, 390, 391]. MYCN itself has also been shown to be regulated by protein coding genes, long non-coding RNAs and miRNAs [101, 263, 358, 392]. While the MYCN regulatory network that contributes to neuroblastoma biology is progressively being approached on the system biology level, the discoveries were mainly done with primary neuroblastoma tumours and neuroblastoma cell lines in vitro (with verification in vivo for some) [358, 393, 394]. Studying the MYCN regulatory network in a transgenic mouse model of neuroblastoma would offer additional information in a more controlled, homogenous experimental condition while being more clinically relevant compared to cell lines. A transgenic mouse model with neural crest- specific over-expression of Lin28b gene was able to recapitulate the LIN28B-let-7-MYCN regulatory axis previously shown in vitro and induced development of neuroblastoma much like the Th-MYCN transgenic mouse model with neural crest-specific over-expression of human MYCN [47]. In addition, miRNA expression profiling of neuroblastoma tumours from Th-MYCN hemizygous mice compared to wildtype and Th-MYCN hemizygous adrenals showed differential expression of a number of evolutionary conserved miRNAs in concordance with that in human MYCN-amplified neuroblastoma tumours when compared to human non-MYCN-amplified neuroblastoma tumours [99]. However, inconsistency in differential expression of some miRNAs were also observed although this could be at least partially due to the use of imperfect control tissues since tumours arise from sympathetic ganglia tissues rather than adrenals. On the other hand, the generation of a transgenic zebrafish model of neuroblastoma from the sympathoadrenal-specific over-expression of human MYCN in
Chi Yan Ooi 107 Identification of candidate miRNAs & mRNAs for roles in neuroblastoma tumorigenesis
2012 offers a potential alternative animal model for studying the MYCN regulatory network
[395]. Besides easier visualization due to zebrafish’s transparency, neuroblastoma originates from the interrenal gland in the transgenic zebrafish in contrast to ganglia tissues in Th-
MYCN mouse, mirroring the adrenal medullary origin of some human neuroblastoma [395].
However, zebrafish genome is evolutionary more distant to human genome compared to mouse genome, and shares less protein-coding genes with human compared to mouse [396,
397]. These limit the applicability of sequence-specific miRNA-mRNA interactions from zebrafish to human, compared to mouse.
The focus of this thesis is to extend the knowledge in the human MYCN regulatory interaction network or interactome in relationship to miRNAs that may play functional roles in neuroblastoma tumorigenesis and/or neuroblastoma cancer cell biology. This would be conducted through the use of the Th-MYCN transgenic mouse model. My co-supervisor Dr
Dan Carter had previously dissected sympathetic ganglia tissues from Th-MYCN homozygous and wild-type mice at 1 and 2 weeks after birth and tumours from Th-MYCN homozygous mice at 6 weeks after birth. These were frozen and sent to our collaborator
Prof Frank Speleman and his lab at the Center for Medical Genetics (CMGG), Ghent
University, Ghent, Belgium for expression profiling of mRNAs and miRNAs. My other co- supervisor Dr Bing Liu then performed a bioinformatics analysis called Bayesian Network with Split-Averaging (BNSA) [381]. This method splits expression profiles according to the different sample categories and determines whether a particular miRNA down- or up- regulates a particular mRNA based on the dependency in their differential expression in each category. A miRNA target prediction database is used to constraint the search for interactions to known predictions, in order to reduce false discovery and the computation required. A statistical technique called bootstrapping is used to estimate the confidence of each predicted interaction. In our
Chi Yan Ooi 108 Identification of candidate miRNAs & mRNAs for roles in neuroblastoma tumorigenesis
analysis, the mRNA and miRNA expression profiles from 1 and 2 weeks after birth of both wild-type and Th-MYCN homozygous mice were integrated with interaction predictions from the mouse miRNA target prediction database TargetScanMouse [199] to infer a statistically significant interaction network between differentially expressed mRNAs and miRNAs. The use of pre-tumour tissue profiles (1 and 2 weeks after birth) has allowed us to capture early changes in the ganglia tissues before the formation of tumours (at 6 weeks after birth) that could provide information in the transformation of normal neuroblast cells into cancer cells and neuroblastoma cell biology.
The aim of this chapter is to identify miRNAs and their predicted mRNA targets in this interaction network prediction which may have important and novel biological functions in MYCN-driven neuroblastoma. Literature analyses, further bioinformatics analyses and experimental investigations would be used to eliminate and prioritize miRNA candidates and their predicted targets that should be further investigated and validated in both in vitro and in vivo.
Chi Yan Ooi 109 Identification of candidate miRNAs & mRNAs for roles in neuroblastoma tumorigenesis
Results
+/+ 3.2.1 Th-MYCN ganglia tissue profiling and computational miRNA-mRNA network prediction
Sympathetic ganglia tissues from Th-MYCN homozygous (Th-MYCN+/+) and wild-type mice at 1 and 2 weeks after birth and tumours from Th-MYCN+/+ mice at 6 weeks after birth were dissected. These were frozen and sent to CMGG in Belgium for
RNA extraction using the Qiagen miRNeasy Mini extraction kit for expression profiling of mRNAs and miRNAs. mRNA expression profiling was performed using a microarray approach, on the Agilent SurePrint G3 Mouse GE 8x60K Microarray platform, which is the original strategy for transcriptome profiling in miRNA research and continues to be widely used for cancer despite now inferior to RNA-seq [187]. On the other hand, miRNA expression profiling was performed using the Applied Biosystems Megaplex high-throughput TaqMan MicroRNA Assays with pre-amplification on 384-well plate format, as this is a speciality of Prof Frank Speleman’s lab and offers real-time PCR the gold standard of nucleic acid quantification combined with TaqMan stem–loop qRT-
PCR chemistry for highly specific, sensitive (low input RNA requirement) and replicable miRNA quantification in a high-throughput format [386, 398, 399]. 587 mature miRNAs were covered by the qRT-PCR array based on the miRNA sequence database miRBase v10, covering ~81 % of the 721 high confidence mature miRNAs out of 1915 known mature miRNA sequences in mouse on miRBase v21 [400]. The mRNA and miRNA expression profiles from 1 and 2 weeks after birth of both wild-type and
Th-MYCN homozygous mice were integrated with interaction predictions from a miRNA target prediction database using the BNSA method [381] to infer a statistically significant interaction network between differentially expressed mRNAs and miRNAs.
This predicted interaction network consists of 21 miRNAs interacting with 759 mRNAs
Chi Yan Ooi 110 Identification of candidate miRNAs & mRNAs for roles in neuroblastoma tumorigenesis
forming 1322 interactions, as some mRNA targets were predicted to be shared between more than one miRNA (see Figure 3.1).
Sympathetic ganglia tissues from wild-type mice at 6 weeks after birth, which is the time tumours first appear in the Th-MYCN+/+ model, were also dissected. The mRNA and miRNA expression profiling were also performed on these samples and repeated on some of the above samples (n=4 per genotype per time point). However, the
TaqMan Array Rodent MicroRNA A+B microfluidic Cards Sets v3 covering 649 miRNAs based on miRBase v20 were used for miRNA expression profiling instead.
The expression profiles of both Th-MYCN homozygous and wild-type mice from 1, 2 and 6 weeks after birth have allowed us to quantify the divergences in expression of both mRNAs and miRNAs from neuroblastoma initiation to progression and disease onset, using the π-value (or sometimes π-score) as described in reference [358]. The π- value quantifies the degree of divergence in expression between Th-MYCN homozygous and wild-type mice by measuring the difference in the slopes from the linear regression of the expression line graphs for the two genotype, taking into account the statistical significance of this difference (see Figure 3.2A). A negative π-value indicates a downward trending expression in the Th-MYCN+/+ mice compared to wild-type, while a positive π-value indicates an upward trending expression (see Figure 3.2A and B).
Although a zero π-value indicates an overall similarity in expression between the two genotypes over the time period from 1 week to 6 weeks after birth, the π-value cannot capture the differential expression during tumorigenesis at 2 weeks after birth (see miR-
135a in Figure 3.2B). The π-values and expression of the 21 miRNAs in the predicted interaction network are listed in Table 3.1.
Chi Yan Ooi 111 Identification of candidate miRNAs & mRNAs for roles in neuroblastoma tumorigenesis
Postnatal:Week 1 Week 2 Sympathetic Ganglia Expression Profiling: Wild-type mRNA – microarray n=5 n=7 miRNA – TaqMan qRT-PCR array
Th-MYCN+/+ n=5 n=6 TargetScanMouse miRNA Target Prediction Database Bioinformatics
Analysis: Bayesian Network 21 miRNAs with Splitting- 759 mRNAs Averaging 1322 interactions
Figure 3.1 Overview of workflow from ganglia tissues profiling to network prediction
Sympathetic ganglia tissues were dissected from wild-type and Th-MYCN+/+ mice and mRNA and miRNA expression profiles were obtained. A bioinformatics analysis called Bayesian Network with Split-Averaging (BNSA) [381] was conducted to integrate the expression profiles with pre-determined miRNA targeting predictions to generate a statistically significant interaction network. Up-regulated mRNAs and miRNAs are coloured red while those down-regulated are coloured green.
Chi Yan Ooi 112 Identification of candidate miRNAs & mRNAs for roles in neuroblastoma tumorigenesis
Figure 3.2 A)
Difference in the slopes of each
genotype from linear regression
= slope
-log10 (p-value of slope) x slope =
π-value
n = 4 per genotype per time point π-value=-16.72 Expression = mean with 95 % confidence interval
Figure 3.2 B) Figure 3.2 C)
π-value=0.00 π-value=16.21
Figure 3.2 The π-value quantification of expression divergence over time
The π-value quantifies the divergence in expression over time by measuring the difference in the slopes from the linear regression of each genotype, taking into account the statistical significance of this difference [358]. A-C) Examples of how the π-value correlates to the expression of miRNAs in the predicted interaction network from actual profiling data. Mean expression were plotted with error bars representing the 95 % confidence intervals calculated by GraphPad Prism 6. n=4 per genotype per time point.
Chi Yan Ooi 113 Identification of candidate miRNAs & mRNAs for roles in neuroblastoma tumorigenesis
Table 3.1
+/+ Fold Change (Th-MYCN vs wild-type) st nd 1 Round (384-well Plates) / 2 Round (Microfluidic
Cards) π-
miRNA value
6 Weeks (Tumour 1 Week 2 Weeks onset in Th- +/+ MYCN mice)
mmu-miR-204 -1.22 / -1.43 -3.22 / -2.79 N/A / -590.53 -16.72
mmu-miR-574-3p -1.79 / -1.37 -2.77 / -3.85 N/A / -173.42 -13.81
mmu-miR-676 -1.72 / -1.35 -2.57 / -2.71 N/A / -10.26 -2.07
mmu-miR-135a 1.29 / 1.39 5.71 / 5.99 N/A / 2.16 0.00
mmu-miR-344 1.00 / 1.05 2.91 / 2.96 N/A / 1.71 0.01
mmu-miR-130b 1.31 / 1.30 6.24 / 6.49 N/A / 3.80 0.05
mmu-miR-93 1.20 / 1.51 3.59 / 3.30 N/A / 3.79 0.39
mmu-miR-18a 1.29 / 1.83 7.48 / 11.56 N/A / 10.14 0.43
mmu-miR-148a 1.47 / 1.52 10.00 / 9.36 N/A / 7.72 0.48
mmu-miR-706 -1.24 / 1.82 3.02 / -1.82 N/A / 9.91 0.73
mmu-miR-135b 1.97 / 2.51 17.54 / 11.96 N/A / 14.65 0.93
mmu-miR-19b -1.11 / 1.55 2.60 / 2.70 N/A / 5.83 1.60
mmu-miR-20a 1.22 / 1.47 4.06 / 3.72 N/A / 7.03 2.05
mmu-miR-17 -1.05 / 1.26 2.56 / 2.28 N/A / 5.40 2.11
mmu-miR-106b 1.37 / 1.56 3.96 / 3.76 N/A / 9.17 2.76
mmu-miR-106a 1.39 / 1.30 3.06 / 2.26 N/A / 6.19 2.90
mmu-miR-298 -1.02 / 1.16 4.08 / 3.08 N/A / 15.18 2.97
mmu-miR-25 1.18 / 1.00 3.86 / 2.52 N/A / 10.16 3.06
mmu-miR-19a -1.13 / 1.53 3.21 / 2.87 N/A / 10.42 3.21
mmu-miR-375 -1.11 / 1.12 5.69 / 3.17 N/A / 155.89 16.21
mmu-miR-709 -1.01 / N/A 3.47 / N/A N/A / N/A N/A
Chi Yan Ooi 114 Identification of candidate miRNAs & mRNAs for roles in neuroblastoma tumorigenesis
Table 3.1 Expression and π-value of the 21 miRNAs in the predicted interaction network
The miRNA expression profiling of sympathetic ganglia tissues from wild-type mice 1 and 2 weeks after birth (n=5, n=7 respectively) and from Th-MYCN+/+ mice at 1 and 2 weeks after birth (n=5, n=6 respectively) and tumours from Th-MYCN+/+ mice at 6 weeks after birth (n=5) were performed using the Applied Biosystems Megaplex TaqMan MicroRNA Assays with pre-amplification on 384-well plate format. The fold changes at each age in Th-MYCN+/+ mice compared to wild-type for the 21 miRNAs in the predicted interaction network are listed as the first value in each cell. The fold changes at 6 weeks after birth are not available due to the absence of wild-type expression data. The miRNA expression profiling using the TaqMan Array Rodent MicroRNA A+B microfluidic Cards Sets v3 was later performed on wild-type sympathetic ganglia tissues at 6 weeks after birth and repeated on the other samples (n=4 per genotype at each age). The fold changes from this profiling are listed as the second value in each cell. miR-709 was not covered by the second profiling due to the increase in the number of known miRNAs in miRBase and the limited number of miRNAs that can be covered by two microfluidic cards per cards set.
Chi Yan Ooi 115 Identification of candidate miRNAs & mRNAs for roles in neuroblastoma tumorigenesis
3.2.2 Selection of miRNA candidates
To identify miRNAs and their predicted mRNA targets from this massive interaction network prediction that may have important and novel biological functions in neuroblastoma, I have sought to systematically eliminate the miRNA candidates according to several criteria (Figure 3.3). Since there are less miRNAs than mRNAs and a single miRNA is predicted to target multiple mRNAs, many predicted target mRNAs can be eliminated by just eliminating one miRNA. The first eligibility criterion is that the miRNA has to have at least one negatively correlated predicted target. The BNSA algorithm predicts interactions with either positive or negative (inverse) correlation between the expression of the miRNA and the mRNA in each interaction pair. Since miRNAs’ canonical function is to suppress the expression of their direct targets, eliminating those interaction that are positively correlated would give higher chance of identifying direct miRNA targets. Therefore I eliminated any miRNA that does not have a negatively correlated interaction (Table 3.2) and only negatively correlated interactions would be considered later on in the selection of predicted targets.
Since I sought to discover miRNAs that are novel in our understanding of the biology of neuroblastoma, I should eliminate those that are already well-studied in neuroblastoma. On investigation of the miRNA candidates, I’ve identified that the list of miRNAs contains 5 (miR-17, miR-18a, miR-19a, miR-20a and miR-19b) out of the 6 miRNA members of the miR-17-92 cluster, 1 (miR-106a) out of the 6 miRNA members of the miR-106a-363 cluster and 3 (miR-106b, miR-93, miR-25) out of 3 miRNA members of the miR-106b-25 cluster. The miR-17-92 cluster is well-studied in neuroblastoma and other cancers and pathological and physiological conditions (>1000 publications in 2012 alone), and its paralogs miR-106a-363 and miR-106b-25 clusters target similar mRNAs due to seed sequences conservation [101, 247, 250, 252, 262] (see
Chi Yan Ooi 116 Identification of candidate miRNAs & mRNAs for roles in neuroblastoma tumorigenesis
Chapter 1.4.1.1). Therefore, the members from these three clusters are eliminated from those that have satisfied the first criterion (Table 3.2). In addition, for a miRNA to have a potential role in human neuroblastoma (or to any human condition at all), it has to have a known human homolog. Two (miR-706 and miR-344) of the 21 miRNAs in the interaction network prediction did not have a known human homolog and therefore eliminated from my remaining candidates (Table 3.2). Furthermore, the seed sequence of a miRNA is the main determinant of mRNA targeting and is the basis of the
TargetScanMouse predictions and thus a part of our network target predictions. For evolutionary conserved miRNAs, the miRNA seed match sites on their target mRNAs are usually conserved [401-403]. Therefore, conservation of the miRNA seed sequences likely leads to conservation of mRNA targets in human. Such conservations also mean conserved miRNAs likely perform similar functions in mouse and human.
Consequently, the forth eligibility criterion is that the miRNA should have its 6mer seed sequence identical between its mouse and human homologs, and those that don’t should be eliminated (Table 3.2, refer to Appendix A for miRNA sequences information).
Finally to determine whether there is any biological significance to human neuroblastoma for our miRNA candidates, I have reviewed the findings of human neuroblastoma publications with high-throughput miRNA expression profiling of human neuroblastoma tumours and/or human neuroblastoma cell line(s) with artificially altered
MYCN expression (ref. [96-98, 103, 104, 244, 245]). As expected, almost all of the miRNA members in the miR-17-92, miR-106a-363 and miR-106b-25 clusters except miR-106b are over-represented in these studies compared to the rest of the 21 miRNA candidates (Table
3.3). Besides these clustered miRNAs, I have also found these five miRNA candidates, miR-135b, miR-148a, miR-135a, miR-375 and miR-204, that were found to be significant prognostic markers correlated to patient survival, differentially
Chi Yan Ooi 117 Identification of candidate miRNAs & mRNAs for roles in neuroblastoma tumorigenesis
expressed in MYCN-amplified compared to non-MYCN-amplified tumours, or differentially expressed in SHEP-TET21 human neuroblastoma cell line overexpressing
MYCN in the absence of doxycycline compared to when there was doxycycline present
(Table 3.3). These five miRNAs have also satisfied the first four eligibility criteria
(Table 3.2) and therefore become the top 5 miRNA candidates that should be investigated further. I have also initially selected miR-574-3p for further investigations only within the scope of this Chapter. It was selected because its mature miRNA sequences between the mouse and human homologs are 100 % identical just like those candidates in the miR-17-92, miR-106a-363 and miR-106b-25 clusters and the top 5 candidates, but unlike the other eliminated candidates.
As a result of the miRNA selection and exclusion of positively correlated predicted mRNA targets, I have reduced the predicted interaction network into smaller subnetworks that may have important functional roles in neuroblastoma (Figure 3.4).
Chi Yan Ooi 118 Identification of candidate miRNAs & mRNAs for roles in neuroblastoma tumorigenesis
21 miRNAs
Has Negatively Correlated Interaction
Doesn’t belong to miR-17-92 / miR-106a-363 / miR-106b-25 clusters
Existence of Human Homolog
Conserved miRNA 6mer Seed Sequence Between Mouse and Human
Implications from Human Neuroblastoma Expression Profiling Studies (prognosis, MYCN status, MYCN over-expression)
5 miRNAs
Correlations of expression in primary neuroblastoma tumours with MYCN amplification and survival (Section 3.2.3)
Gene enrichment analyses of miRNA predicted target genes (Section 3.2.4)
Confirm differential expression in mouse Th-MYCN+/+ sympathetic ganglia tissues (Section 3.2.5)
Expression regulation by MYCN (Section 3.2.6)
3 miRNAs (Section 3.3)
Functional characterizations (Chapter 4)
Figure 3.3 Selection of miRNA candidates
5 miRNA candidates were selected from the original 21 in the predicted interaction network, based on the satisfaction of 5 criteria. They were further investigated in the following sections with 3 selected for functional characterizations in the next chapter.
Chi Yan Ooi 119 Identification of candidate miRNAs & mRNAs for roles in neuroblastoma tumorigenesis
Table 3.2
Not miR- Conserved Human 17-92 / miRNA Expression Negatively miR- Human 6mer Seed Profiling Studies miRNA Correlated 106a-363 / Homolog Sequence (prognosis, Interaction miR- Exists Between MYCN status, 106b-25 Mouse and MYCN over- clusters Human expression)
miR-135b ✔ ✔ ✔ ✔ ✔
miR-148a ✔ ✔ ✔ ✔ ✔
miR-135a ✔ ✔ ✔ ✔ ✔
miR-375 ✔ ✔ ✔ ✔ ✔
miR-204 ✔ ✔ ✔ ✔ ✔
miR-18a ✔ ✔ ✔ ✔
miR-20a ✔ ✔ ✔ ✔
miR-106b ✔ ✔ ✔ ✔
miR-25 ✔ ✔ ✔ ✔
miR-93 ✔ ✔ ✔ ✔
miR-19a ✔ ✔ ✔ ✔
miR-106a ✔ ✔ ✔ ✔
miR-19b ✔ ✔ ✔ ✔
miR-17 ✔ ✔ ✔ ✔
miR-709 / ✔ ✔ ✔ ✔ miR-1827
miR-574-3p ✔ ✔ ✔ ✔
miR-130b ✔ ✔ ✔
Chi Yan Ooi 120 Identification of candidate miRNAs & mRNAs for roles in neuroblastoma tumorigenesis
Not miR- Conserved Human 17-92 / miRNA Expression Negatively miR- Human 6mer Seed Profiling Studies miRNA Correlated 106a-363 / Homolog Sequence (prognosis, Interaction miR- Exists Between MYCN status, 106b-25 Mouse and MYCN over- clusters Human expression)
miR-298 ✔ ✔ ✔
miR-676 ✔ ✔ ✔
miR-706 ✔ ✔
miR-344 ✔ ✔
Table 3.2 continued
Table 3.2 Selection of the top 5 miRNA candidates
Five selection criteria were used to eliminate miRNA candidates and selected five top miRNA candidates (miR-135b, miR-148a, miR-135a, miR-375 and miR-204) to be further investigated in silico and experimentally. miR-574-3p was also selected to be investigated only within the scope of this chapter due to it being the only eliminated miRNA besides the members of the miR-17-92, miR-106a-363 and miR-106b-25 clusters that has identical mature miRNA sequences between its mouse and human homologs. The top 5 miRNA candidates and those miRNAs that belong to the well-studied miR-17-
92 cluster and the miR-106a-363 and miR-106b-25 paralog clusters all have 100 % mature sequence identity between their mouse and human homologs (see Appendix A). The miRNAs are sorted by number of criteria satisfied. The human homolog of mouse miR-709 is miR-1827.
Chi Yan Ooi 121 Identification of candidate miRNAs & mRNAs for roles in neuroblastoma tumorigenesis
Table 3.3
Differential Expression (Up- or down-regulated)
Correlations MYCN MYCN- MYCN between Unfavourable induction miRNA vs non- induction expression vs favourable in SHEP- MYCN in SHEP and survival neuroblastoma TET21 / amplified MYCN- tumours SHEP tumours ER Tet21N
Down miR-135b [96]
Down miR-148a [97, 103]
High = Poor Up miR-18a Up [97] [103, 104] [96, 245]
miR-130b
High = Better miR-135a [103]
High = Poor miR-375 [103]
miR-298
High = Poor Up [96, Up miR-20a [96, 103, 104] 97, 103] [96, 245]
miR-106b Up [245]
High = Poor Up miR-25 Up [244] Up [96] [96, 104] [96, 98]
High = Poor Up miR-93 Up [98] [104] [96, 245]
miR-709 / miR-1827
High = Poor Up Up miR-19a [96, 103, 104] [96, 97] [96, 245]
High = Poor Up miR-106a Up [245] Up [98] [96] [96, 98]
miR-706
Chi Yan Ooi 122 Identification of candidate miRNAs & mRNAs for roles in neuroblastoma tumorigenesis
Differential Expression (Up- or down-regulated)
Correlations MYCN MYCN- MYCN between Unfavourable induction miRNA vs non- induction expression vs favourable in SHEP- MYCN in SHEP and survival neuroblastoma TET21 / amplified MYCN- tumours SHEP tumours ER Tet21N
miR-344
High = Poor miR-19b Up [96] Up [96] [96]
High = Poor Up Up miR-17 Up [244] Up [98] [96, 104] [96, 98] [96, 245]
miR-676
miR-574- 3p
High = Better miR-204 [103, 104]
Table 3.3 continued
Table 3.3 The correlations of miRNA candidates with survival, MYCN amplification and MYCN overexpression in published human high-throughput expression profiling studies
The 21 miRNA candidates from the interaction network prediction were cross- referenced with results from 7 publications with high-throughput expression profiling studies of miRNAs in human neuroblastoma tumours and/or human neuroblastoma cell lines (ref. [96-98, 103, 104, 244, 245]). SHEP-TET21 and SHEP Tet21N cell lines overexpress MYCN in the absence of antibiotic doxycycline. SHEP MYCN-ER cell line overexpresses MYCN protein fused with the mutant hormone binding domain of the murine oestrogen receptor that can be functionally activated by 4-OHT.
Chi Yan Ooi 123 Identification of candidate miRNAs & mRNAs for roles in neuroblastoma tumorigenesis
Fold Change
-2 2
Figure 3.4 Top 5 miRNA candidates and miR-574-3p subnetworks
By eliminating positively correlated interactions and 15 miRNAs (Table 3.2) and their predicted mRNA targets, I have reduced the interaction network to 3 subnetworks involving 6 miRNAs and 148 predicted mRNA targets. The miRNAs are marked in diamonds and the mRNAs are marked in circles, and coloured according to their fold change at 2 weeks after birth in Th-MYCN+/+ compared to wild-type mice from the first expression profiling on the TaqMan 384-well plates format. Figure adapted from the original interaction network map Cytoscape file (see the green/red map in Figure 3.1)
Chi Yan Ooi 124 Identification of candidate miRNAs & mRNAs for roles in neuroblastoma tumorigenesis
3.2.3 Correlations of the top 5 miRNA candidates expression in primary neuroblastoma tumours with MYCN amplification and survival
To validate the selection of the miRNA candidates, we analysed unpublished miRNA expression profiling data from CMGG on 364 neuroblastoma primary tumours
(note: survival and MYCN amplification status data not available for all the tumours).
We found high expression of miR-204, miR-135a and miR-135b correlated with both better overall and progression free survivals, while high miR-148a expression only correlated with better overall survival with statistical significance (see Figure 3.5, median cut-offs). The high expression of miR-375 is significantly correlated with poor overall and progression free survivals compared to those with low miR-375 expression.
In summary, the expression of all the selected miRNA candidates with expression data available in human primary neuroblastoma tumours are correlated with patient survival.
We also examined whether there are any difference in miRNA candidates expression between primary neuroblastoma tumours with and without MYCN amplification. The expression of miR-204 is significantly lower in MYCN-amplified tumours (see Figure 3.6), similar to its reduced expression in Th-MYCN+/+ ganglia tissues and tumours compared to wild-type ganglia tissues at 2 and 6 weeks after birth
(see Table 3.1). The expression of miR-148a and miR-135a are also significantly lower in MYCN-amplified tumours, oppose to their increased expression in Th-MYCN+/+ ganglia tissues and tumours compared to wild-type ganglia tissues at 2 weeks after birth.
However, the expression of miR-375 and miR-135b are not significantly different between the two types of tumours.
Chi Yan Ooi 125 Identification of candidate miRNAs & mRNAs for roles in neuroblastoma tumorigenesis
Figure 3.5 A)
Overall Survival – miR-204 Progression Free Survival – miR-204
1 1
bab ilit y Pro High Expression, n=165
Probabili ty
High Expression, n=164
Low Expression, n=164
Survival Low Expression, n=164
Survival p=4.1e-10
0 p=7.2e-11 6 1 2 0
0 6 1 2
0 Follow up in months Follow up in months
Figure 3.5 B)
Overall Survival – miR-375 Progression Free Survival – miR-
1 375 1
Low Expression, n=164 Low Expression, n=164
Probability High Expression, n=164
High Expression, n=165 Probability
l Surviva p=1.3e-04
p=8.2e-04 Survival 0 21 0 6
6 1 2
0
0
Follow up in months Follow up in months
Figure 3.5 C)
Overall Survival – miR-148a
Progression Free Survival – miR-148a
o ba bi lit y Pr 1 Low Expression, n=164 1
High Expression, n=165
y Probabilit High Expression, n=164
Low Expression, n=164
p=0.011 Survival Survival p=0.084
0 6 1 2 0
6 1 2
0
0
Follow up in months Follow up in months
Chi Yan Ooi 126 Identification of candidate miRNAs & mRNAs for roles in neuroblastoma tumorigenesis
Figure 3.5 D)
Overall Survival – miR-135a Progression Free Survival – miR-135a
1 1
r o b a b i l i t y High Expression, n=165 P High Expression, n=164
Low Expression, n=164 ty Probabili Low Expression, n=164
Survival
p=1.0e-03 val Survi p=6.8e-04
21
6 1 2 0
0 6
0 0
Follow up in months Follow up in months
Figure 3.5 E)
Overall Survival – miR-135b Progression Free Survival – miR-135b 1
1 bility High Expression, n=165 Proba High Expression, n=164
Low Expression, n=164
Low Expression, n=164
Probabil ity
p=0.032
Survival val Survi p=0.027
6 1 2 0 0
6 1 2
0
0
Follow up in months Follow up in months
Figure 3.5 Kaplan–Meier curves for overall and progression free survivals of the top 5 miRNA candidates
Analysis of the expression of the top 5 miRNA candidates in an unpublished 364 primary neuroblastoma primary tumours dataset from Prof Frank Speleman’s lab revealed statistically significant correlations to overall and progression free survivals, except for miR-148a expression with progression free survival. Median expression cut- offs were used. Expression data for the extra candidate miR -574-3p are not available. Survival data not available for all tumours in dataset. Figures modified from those generated by Prof Frank Speleman’s lab.
Chi Yan Ooi 127 Identification of candidate miRNAs & mRNAs for roles in neuroblastoma tumorigenesis
Figure 3.6 A) Figure 3.6 B)
miR-204 Expression miR-375 Expression 12 12
(log2)
(log2)
p=1.8e-07 p=0.74
ion
Express Expression 0 n=265 n=64 0
n=265 n=64 -6 -4
Figure 3.6 C) Figure 3.6 D)
miR-148a Expression miR-135a Expression 5 8
log2) ( p=5.2e-12
(log2)
p=0.03
Expression 0
Expression n=265
n=64 n=64 n=265 0 -3 -1
Figure 3.6 E)
miR-135b Expression 10 p=0.07
(log2)
n=265 n=64 0 Expression Expression -2
Chi Yan Ooi 128 Identification of candidate miRNAs & mRNAs for roles in neuroblastoma tumorigenesis
Figure 3.6 Top 5 miRNA candidates expression in MYCN-amplified vs non- amplified primary tumours
Analysis of the expression of the top 5 miRNA candidates in an unpublished 364 primary neuroblastoma primary tumours dataset from Prof Frank Speleman’s lab revealed reduced expression of A) miR-204, C) miR-148a and E) miR-135b in MYCN-amplified tumours compared to non-MYCN-amplified ones. Others showed no statistically significant difference. Expression data for the extra candidate miR-574-3p are not available. Figures modified from those generated by Prof Frank Speleman’s lab. Values plotted represent the minimum, first quartile, median, third quartile and maximum expression measured.
Chi Yan Ooi 129 Identification of candidate miRNAs & mRNAs for roles in neuroblastoma tumorigenesis
3.2.4 Gene enrichment analyses of predicted target genes to identify likely functions of miRNA candidates
To predict the biological functions of the miRNA candidates in neuroblastoma, we investigated which biological features are enriched in the list of negatively correlated predicted target genes for each miRNA by using the GeneCodis3 [377-379] and
MSigDB Compute Overlap [380] tools. Enrichment of KEGG (Release 58.0, April 1,
2011) and Panther (version 2.1, December 2007) curated pathways was analysed by using GeneCodis3. Enrichment of MSigDB (Release 4.0, May 2013) CP: Canonical pathways under the C2: curated gene sets and TFT: transcription factor targets gene sets under the C3: motif gene sets was analysed using MSigDB Compute Overlap. The TFT: transcription factor targets gene sets contain annotations for genes that contain certain transcription factor binding motifs in their DNA sequences from the TRANSFAC
(version 7.4) database.
miR-204 negatively correlated predicted targets showed enrichment of mainly cell cycle associated items, particularly with the MSigDB CP: Canonical pathways under the C2: curated gene sets, and E2F / E2F1 / TFDP1 transcription factors binding targets (based on presence of binding motifs) (Table 3.4) that are generally involved in cell cycle progression [404]. These suggest miR-204 suppresses cell cycle processes in neuroblastoma, consistent with that identified from expression profiling, cancer genetics and bioinformatics analysis of head and neck tumours [336]. On the other hand, negatively correlated predicted targets of miR-574-3p which is also down-regulated in
Th-MYCN+/+ mouse ganglia compared to wild-type showed enrichment of items related to DNA damage response and repair, Wnt signalling, early embryogenesis and neurogenesis (Table 3.5). Since Wnt signalling is involved in neural crest development
Chi Yan Ooi 130 Identification of candidate miRNAs & mRNAs for roles in neuroblastoma tumorigenesis
and specification [405, 406], miR-574-3p likely regulates normal development of neural crest and their differentiation that are oppose to the processes of tumorigenesis.
For those miRNAs up-regulated in Th-MYCN+/+ mouse ganglia compared to wild- type at 2 weeks after birth (see Figure 3.4), miR-148a negatively correlated predicted targets have the highest amount of enriched items. The enrichment of angiogenesis pathway genes and TCF3 transcriptional targets (Table 3.6) suggests miR-148a regulates the Wnt signalling pathway as both are related to Wnt signalling [407-410]. In addition, transcriptional targets for transcription factors involved in oncogenic NF-kB signalling, which also participates in cross regulation with Wnt signalling [411], are also enriched in miR-148a negatively correlated predicted targets. The enrichment of interferon-gamma signalling pathway and transcriptional targets of IRF1, a transcriptional regulator of interferon and interferon-inducible genes also suggests miR-148a may regulate interferon anti-tumour functions and potentially have implications to immunotherapy. On the other hand, JUN transcriptional targets are enriched in miR-375 negatively correlated targets
(Table 3.7), suggesting potential roles of miR-375 in p53 signalling and/or metastasis that are regulated by JUN [412-416]. In contrast, there is enrichment of transcriptional targets of transcription factors involved the embryonic development and neuronal development and differentiation in miR-135a negatively correlated predicted targets (Table 3.8). Together with the enrichment of integrin signalling, which is long known to be involved in cell differentiation [417], these suggest miR-135a may regulate neural crest development and differentiation. Unfortunately, no significant enrichment was found for miR-135b negatively correlated predicted targets.
Chi Yan Ooi 131 Identification of candidate miRNAs & mRNAs for roles in neuroblastoma tumorigenesis
Table 3.4 A)
Pathway ID Pathway Name Adjusted p- Genes value Kegg:04110 Cell cycle 0.000705788 RBL1, E2F1, CDC25B, CDC25A, WEE1 Kegg:05223 Non-small cell lung cancer 0.00897601 PLCG1, E2F1, PIK3R1 Kegg:05214 Glioma 0.00900056 PLCG1, E2F1, PIK3R1 Kegg:04012 ErbB signaling pathway 0.0145047 ERBB4, PLCG1, PIK3R1 Kegg:00601 Glycosphingolipid 0.016506 B3GNT5, FUT9 biosynthesis - lacto and neolacto series Kegg:04914 Progesterone-mediated 0.0175354 CDC25B, CDC25A, oocyte maturation PIK3R1 Kegg:04650 Natural killer cell mediated 0.0265706 SH3BP2, PLCG1, cytotoxicity PIK3R1
Table 3.4 B)
Pathway ID Pathway Name Adjusted p-value Genes Panther:P00047 PDGF signaling 0.000705788 PIK3R1, NIN, pathway PLCG1 Panther:P00009 Axon guidance 0.00897601 PIK3R1, PLCG1 mediated by netrin Panther:P04398 p53 pathway 0.00900056 PIK3R1, RBL1 feedback loops 2 Panther:P00059 p53 pathway 0.0145047 SUMO2, PIK3R1 Panther:P00053 T cell activation 0.016506 PIK3R1, PLCG1 Panther:P00056 VEGF signaling 0.0175354 PIK3R1, PLCG1 pathway
Table 3.4 C)
Gene Set Name Transcription # Genes in p-value FDRq- Factor Overlap value (k) V$E2F_Q4_01 E2F / TFDP1 12 4.47E-14 3.74E-11 V$E2F1_Q4_01 E2F / TFDP1 11 9.15E-13 2.13E-10 V$E2F_Q6 Unknown 11 1.11E-12 2.13E-10 V$E2F_Q4 Unknown 11 1.21E-12 2.13E-10 V$E2F_Q3_01 E2F / TFDP1 11 1.27E-12 2.13E-10 V$E2F_Q6_01 E2F / TFDP1 11 1.60E-12 2.23E-10 V$E2F_Q3 Unknown 10 2.56E-11 2.97E-09 V$E2F1DP1RB_01 E2F1 / TFDP1 10 3.04E-11 2.97E-09 / RB1 GGGCGGR_V$SP1_Q6 SP1 26 3.20E-11 2.97E-09 V$E2F_03 Unknown 10 5.42E-11 4.53E-09
Chi Yan Ooi 132 Identification of candidate miRNAs & mRNAs for roles in neuroblastoma tumorigenesis
Table 3.4 D)
Gene Set Name p-value FDR q- Genes value SA_G2_AND_M_PHASES 3.90E-07 5.15E-04 CDC25A, WEE1, CDC25B REACTOME_CYCLIN_A_B1_AS 3.14E-06 2.07E-03 CDC25A, WEE1, SOCIATED_EVENTS_DURING_ CDC25B G2_M_TRANSITION KEGG_CELL_CYCLE 5.33E-06 2.35E-03 CDC25A, WEE1, CDC25B, E2F1, RBL1 BIOCARTA_CELLCYCLE_PAT 1.21E-05 3.44E-03 CDC25A, E2F1, HWAY RBL1 BIOCARTA_G2_PATHWAY 1.38E-05 3.44E-03 CDC25A, WEE1, CDC25B REACTOME_G0_AND_EARLY_ 1.57E-05 3.44E-03 CDC25A, E2F1, G1 RBL1 REACTOME_MITOTIC_G2_G2_ 1.95E-05 3.68E-03 CDC25A, WEE1, M_PHASES CDC25B, E2F1 BIOCARTA_VEGF_PATHWAY 2.47E-05 4.08E-03 PIK3R1, PLCG1, ELAVL1 PID_NCADHERINPATHWAY 3.67E-05 5.39E-03 PIK3R1, PLCG1, CNR1 REACTOME_CELL_CYCLE_MI 4.24E-05 5.60E-03 CDC25A, WEE1, TOTIC CDC25B, E2F1, RBL1, CENPP
Table 3.4 miR-204 Predicted Target Genes Enrichment Analyses
Enrichment analyses were performed on the 89 negatively correlated predicted targets of miR-204 using GeneCodis3 against the A) KEGG (Release 58.0, April 1, 2011) and B)
Panther (version 2.1, December 2007) curated pathways and using MSigDB Compute
Overlap against the C) TFT: transcription factor targets under the C3: motif gene sets and
D) CP: Canonical pathways under the C2: curated gene sets (Release 4.0, May 2013). Only the top 10 enriched items are shown for each category. Enriched genes were not shown for C) due to the extensive length of the list. Cell cycle related pathways and transcription factors E2F, TFDP1 and RB1 were predominately enriched from the 89 negatively correlated predicted targets of miR-204, suggesting miR-204’s main function is to regulate cell cycle.
Chi Yan Ooi 133 Identification of candidate miRNAs & mRNAs for roles in neuroblastoma tumorigenesis
Table 3.5 A)
Pathway ID Pathway Name Adjusted p-value Genes Kegg:05322 Systemic lupus erythematosus 0.0430571 H2AFJ Kegg:03410 Base excision repair 0.0483637 TDG
Table 3.5 B)
Pathway ID Pathway Name Adjusted p-value Genes Panther:P00029 Huntington disease 0.0494537 KALRN
Table 3.5 C)
Gene Set Name Transcription p-value FDR q- Genes Factor value CTTTGT_V$L LEF1 7.36E-05 2.16E-02 KCTD15, PHOX2B, EF1_Q2 ELAVL1, SFPQ, TDG, WDR3 V$CHOP_01 DDIT3 1.06E-04 2.16E-02 KCTD15, PHOX2B, CREBZF V$ZF5_01 ZFP161 1.06E-04 2.16E-02 KCTD15, ELAVL1, SENP1 V$TST1_01 POU3F1 1.40E-04 2.16E-02 KCTD15, PHOX2B, CREBZF
Table 3.5 miR-574-3p Predicted Target Genes Enrichment Analyses
Enrichment analyses were performed on the 18 negatively correlated predicted targets of miR-574-3p using GeneCodis3 against the A) KEGG (Release 58.0, April 1, 2011) and
B) Panther (version 2.1, December 2007) curated pathways and using MSigDB Compute Overlap against the CP: Canonical pathways under the C2: curated gene sets (no significant enrichment found) and C) TFT: transcription factor targets under the C3: motif gene sets (Release 4.0, May 2013). Enrichment of base excision repair and DDIT3 (DNA Damage Inducible Transcript 3) transcriptional target genes suggests miR-574-3p plays a role in DNA damage response and repair. According to UniProt [418], LEF1 is involved in the Wnt signalling pathway while DDIT3 inhibits Wnt signalling suggesting miR-574-3p is involved in Wnt signalling pathway regulation. UniProt also describe ZFP161 as a transcriptional repressor of c-MYC and POU3F1 is considered to have a role in early embryogenesis and neurogenesis, suggesting miR-574-3p potentially regulates the development of neural crest cells.
Chi Yan Ooi 134 Identification of candidate miRNAs & mRNAs for roles in neuroblastoma tumorigenesis
Table 3.6 A)
Pathway ID Pathway Name Adjusted Genes p-value Panther:P00005 Angiogenesis 0.015599 FGF1,WNT10A Panther:P00035 Interferon-gamma signaling 0.046713 SOCS6 pathway
Table 3.6 B)
Gene Set Name p-value FDR q- Genes value REACTOME_REGULATION_OF_KIT_ 2.96E-05 3.91E-02 SH2B3, SIGNALING SOCS6
Table 3.6 C)
Gene Set Name Transcription p-value FDR q- Genes Factor value GGGNNTTTCC_ Unknown 3.58E-05 1.18E-02 WNT10A, AFGF1, V$NFKB_Q6_01 SH2B3 CAGGTG_V$E12 TCF3 1.11E-04 1.18E-02 WNT10A, FGF1, _Q6 PCDH7, RPH3A, CASZ1, PHYHIP, PDE11A TGACCTY_V$ER ESRRA 1.14E-04 1.18E-02 WNT10A, SH2B3, R1_Q2 PCDH7, RPH3A, TNXB V$NFKB_Q6_01 Unknown 1.82E-04 1.18E-02 WNT10A, FGF1, SH2B3 V$NFKAPPAB65_ RELA (p65) 1.94E-04 1.18E-02 WNT10A, FGF1, 01 SH2B3 V$IRF_Q6 IRF1 2.06E-04 1.18E-02 SH2B3, PCDH7, CASZ1 V$GATA1_02 GATA1 2.11E-04 1.18E-02 SH2B3, PCDH7, CASZ1 V$GATA1_03 GATA1 2.14E-04 1.18E-02 PCDH7, CASZ1, TNXB V$GATA1_04 GATA1 2.14E-04 1.18E-02 PCDH7, CASZ1, TNXB V$NFKAPPAB_01 NFKB / RELA 2.30E-04 1.18E-02 WNT10A, FGF1, SH2B3
Chi Yan Ooi 135 Identification of candidate miRNAs & mRNAs for roles in neuroblastoma tumorigenesis
Table 3.6 miR-148a Predicted Target Genes Enrichment Analyses
Enrichment analyses were performed on the 22 negatively correlated predicted targets of miR-148a using GeneCodis3 against the KEGG (Release 58.0, April 1, 2011) and A) Panther (version 2.1, December 2007) curated pathways and using MSigDB Compute Overlap against the B) CP: Canonical pathways under the C2: curated gene sets and C) TFT: transcription factor targets under the C3: motif gene sets (Release 4.0, May 2013). No enrichment is obtained from KEGG. Only the top 10 enriched items are shown for each category. Angiogenesis pathway is regulated by Wnt signalling [407, 408] while TCF3 transcription factor suppresses Wnt signalling [409, 410]. The enrichment of angiogenesis pathway and TCF3 suggests miR-148a acts as a tumour suppressor to suppress Wnt signalling, which plays a role in the induction, differentiation and apoptosis of neural crest cells and initiates tumorigenesis in a number of cancer [405, 406, 411]. The enrichment of RELA transcription factor also suggests miR-148a potentially regulates oncogenic NF-kB signalling and the cross regulation between NF-kB and Wnt signalling [411]. The enrichment of IRF1, a transcriptional regulator of interferon and interferon-inducible genes, and interferon-gamma signalling pathway suggests miR-148a may also potentially be involved in interferon anti-tumour functions [418, 419].
Chi Yan Ooi 136 Identification of candidate miRNAs & mRNAs for roles in neuroblastoma tumorigenesis
Gene Set Transcription p-value FDR q- Genes Name Factor value AACTTT_UN Unknown 4.02E-05 2.47E-02 MRGPRF, SHC3, KNOWN CASZ1, LSAMP, CREB3L2, CDH10 V$AP1_01 JUN 1.24E-04 3.82E-02 MRGPRF, SHC3, PLBD2
Table 3.7 miR-375 Predicted Target Genes Enrichment Analyses
Enrichment analyses were performed on the 17 negatively correlated predicted targets of miR-375 using GeneCodis3 against the KEGG (Release 58.0, April 1, 2011) and Panther (version 2.1, December 2007) curated pathways and using MSigDB Compute Overlap against the CP: Canonical pathways under the C2: curated gene sets and TFT: transcription factor targets under the C3: motif gene sets (Release 4.0, May 2013). Only the TFT: transcription factor targets category showed significant enrichments as shown. JUN is an oncogene that transcriptionally suppress p53 and promote metastasis [412-416]. The enrichment of JUN suggests miR-375 may be involved in JUN-related functions.
Chi Yan Ooi 137 Identification of candidate miRNAs & mRNAs for roles in neuroblastoma tumorigenesis
Table 3.8 A)
Pathway ID Pathway Name Adjusted Genes p-value Panther:P00034 Integrin signalling pathway 0.007882 COL4A3,PARVA Panther:P00035 Interferon-gamma signaling 0.025821 SOCS6 pathway
Table 3.8 B)
Gene Set Transcription p-value FDR q- Genes Name Factor value V$ZIC3_01 ZIC3 2.08E-06 1.28E-03 PURA, MRGPRF, CASZ1, ATP2B4 AACTTT_UN Unknown 4.02E-05 7.88E-03 PURA, MRGPRF, CASZ1, KNOWN ATP2B4, NETO1, MAGT1 V$HEN1_01 NHLH1 4.97E-05 7.88E-03 PURA, MRGPRF, CAPN5 V$HEN1_02 NHLH1 5.12E-05 7.88E-03 PURA, MRGPRF, CAPN5 V$CEBP_Q2 CEBPA 8.41E-05 1.03E-02 PURA, COL4A3, CLU CTAWWWA MEF2A 3.01E-04 3.09E-02 PURA, CASZ1, ATP2B4 TA_V$RSRF C4_Q2 AACYNNNN Unknown 5.64E-04 4.95E-02 PURA, CASZ1 TTCCS_UNK NOWN
Table 3.8 miR-135a Predicted Target Genes Enrichment Analyses
Enrichment analyses were performed on the 17 negatively correlated predicted targets of miR-135a using GeneCodis3 against the KEGG (Release 58.0, April 1, 2011) (no significant enrichment found) and A) Panther (version 2.1, December 2007) curated pathways and using MSigDB Compute Overlap against the CP: Canonical pathways under the C2: curated gene sets (no significant enrichment found) and B) TFT: transcription factor targets under the C3: motif gene sets (Release 4.0, May 2013). According to UniProt [418], transcription factor NHLH1 may be involved in cell-type determination control in the developing nervous system and MEF2A mediates neuronal differentiation and survival. In addition, CEBPA controls differentiation of several cell types in early embryogenesis and after birth while ZIC3 is required for axial midline development and left-right asymmetry specification [418]. Moreover, integrin signalling is long known to be involved in cell differentiation [417]. Therefore, miR-135a might be involved in neural crest development and differentiation.
Chi Yan Ooi 138 Identification of candidate miRNAs & mRNAs for roles in neuroblastoma tumorigenesis
3.2.5 Differential expression of the miRNA candidates in mouse Th-MYCN+/+ sympathetic ganglia tissues
To confirm the differential expression of the 5 miRNA candidates and miR-574-3p in the Th-MYCN+/+ mouse ganglia compared to wild-type at 2 weeks after birth, ganglia tissues from each genotype were dissected and total RNA (including miRNAs) were extracted using the Qiagen miRNeasy Mini Kit. Expression of the miRNAs were quantify using TaqMan qRT-PCR miRNA assays for the specific miRNAs and U6 snRNA and snoRNA202 were used as endogenous RNA controls. The results showed significant up- regulation of miR-135a, miR-135b, miR-148a and miR-375, and significant down- regulation of miR-204 and miR-574-3p in 2-weeks-old Th-MYCN+/+ mouse ganglia compared to wild-type (Figure 3.7). These are consistent with both expression profiling of mouse ganglia at two weeks after birth (Table 3.1). The fold changes for most of the miRNAs are also comparable to both expression profiling, except for miR-135b and miR-
148a which are lower. The results showed the 5 miRNA candidates and miR-574-3p are indeed differentially expressed in the Th-MYCN+/+ mouse ganglia compared to wild-type at
2 weeks after birth, at the early stage of neuroblastoma tumorigenesis. miR-135a, miR-
135b, miR-148a and miR-375 were significantly up-regulated while miR-204 and miR-574-
3p were significantly down-regulated.
Chi Yan Ooi 139 Identification of candidate miRNAs & mRNAs for roles in neuroblastoma tumorigenesis
Figure 3.7 Fold changes of the top 5 miRNA candidates and miR-574-3p in week 2 postnatal sympathetic ganglia tissues
Sympathetic ganglia tissues were dissected form Th-MYCN+/+ and wild-type mice at 2 weeks after birth and total RNA extracted using Qiagen miRNeasy Mini kit. miRNAs expression were quantified using TaqMan qRT-PCR miRNA assays specific for each miRNA, and U6 snRNA and mouse-specific snoRNA202 as two endogenous short RNA controls. All other TaqMan assays are compatible for both mouse and human due to identical sequences across species. miRNAs expression were normalized to the two endogenous controls and fold changes were calculated for Th-MYCN+/+ ganglia compared to wild-type.
Chi Yan Ooi 140 Identification of candidate miRNAs & mRNAs for roles in neuroblastoma tumorigenesis
3.2.6 The expression of the miRNA candidates are regulated by MYCN
To determine whether expression of the selected miRNAs can be regulated by
MYCN in human cells, MYCN was over-expressed in non-MYCN-amplified human neuroblastoma cells using the SHEP MYCN-3 doxycycline-inducible system [384].
Incubation of these cells in doxycycline for 72 h (in this case in the form of doxycycline hydrochloride dissolved in DMSO) causes over-expression of MYCN proteins (Figure
3.8A). Expression of the miRNAs were analysed and quantified using TaqMan qRT-
PCR miRNA assays. miR-135b and miR-375 expression were significantly reduced after 72 h incubation with doxycycline (Figure 3.8B), oppose to their up-regulation in 2- weeks-old Th-MYCN+/+ ganglia. miR-574-3p expression was also significantly reduced by almost 40 % after 72 h incubation with doxycycline, and consistent with its down- regulation in 2-weeks-old Th-MYCN+/+ ganglia. No significant change was detected for miR-135a. Lastly, miR-148a and miR-204 expression were below the detection limit, suggesting SHEP MYCN-3 may not be a suitable system for these miRNAs in the context of MYCN regulation.
In addition, MYCN expression was also knocked down in MYCN-amplified human neuroblastoma cells using siRNAs to evaluate any relationship between expression of MYCN and the selected miRNAs. In general, miRNAs have very long life span / stability, with average half-life at around 20-fold of that of typical mRNA [202,
420]. Therefore, miRNAs expression were initially evaluated at 96 h after transfection of siRNA against MYCN (siMYCN) in BE(2)-C cells, where MYCN protein expression knockdown were still achievable (Figure 3.9A). This was further expanded to the earlier
24 h and 48 h time points with an additional siRNA (siMYCN #7) (see Figure 3.9A).
Down-regulation of miR-148a expression was only detected at 96 h post-transfection
(Figure 3.9B), consistent with its up-regulation in 2-weeks-old Th-MYCN+/+ ganglia.
Chi Yan Ooi 141 Identification of candidate miRNAs & mRNAs for roles in neuroblastoma tumorigenesis
miR-135a and miR-135b expression were up-regulated consistently with at least two different siRNAs at all time points assessed, oppose to their up-regulation in 2-weeks- old Th-MYCN+/+ ganglia. miR-375 expression was up-regulated 24 h and 48 h post- transfection of two different siRNAs, oppose to its up-regulation in 2-weeks-old Th-
MYCN+/+ ganglia. Expression of miR-204 and miR-574-3p were up-regulated 24 h and both 24 h and 48 h post-transfection respectively of at least two different siRNAs, consistent with their down-regulation in 2-weeks-old Th-MYCN+/+ ganglia. These were also repeated on the MYCN-amplified human neuroblastoma cell line called Kelly using siRNAs siMYCN #6 and siMYCN #7 at 48 h post-transfection to determine whether some of these results can be replicated (Figure 3.10A). The up-regulation of miR-204, miR-375 and miR-574-3p expression were successfully replicated in Kelly cells 48 h after transfection, further supporting their suppression by MYCN.
Chi Yan Ooi 142 Identification of candidate miRNAs & mRNAs for roles in neuroblastoma tumorigenesis
Figure 3.8 A)
MYCN
GAPDH
Figure 3.8 B)
Figure 3.8 Top 5 miRNA candidates and miR-574-3p expression in SHEP MYCN-3 cells with doxycycline-induced MYCN over-expression
To determine the relationships between the expression of MYCN and the selected miRNAs in human, genetically modified non-MYCN-amplified human neuroblastoma SHEP MYCN-3 cells were induced for 72 h by doxycycline hydrochloride (Dox) to over-express MYCN. DMSO was used as solvent control. A) Protein were extracted to confirm over-expression of MYCN via western blotting. B) miRNAs expression were quantify using TaqMan qRT-PCR miRNA assays and normalized to the average of endogenous controls U6 snRNA and human-specific RNU19 and then normalized to the DMSO solvent negative control. miR-148a and miR-204 expression were below the detection limit. All p-values are comparisons to negative control.
Chi Yan Ooi 143 Identification of candidate miRNAs & mRNAs for roles in neuroblastoma tumorigenesis
Figure 3.9 A)
24 h 48 h 96 h
MYCN
GAPDH
Figure 3.9 B)
Chi Yan Ooi 144 Identification of candidate miRNAs & mRNAs for roles in neuroblastoma tumorigenesis
Figure 3.9 C)
Figure 3.9 D)
Figure 3.9 Top 5 miRNA candidates and miR-574-3p expression in BE(2)-C cells after siRNA knockdown of MYCN
A) To evaluate the relationships between miRNAs expression and MYCN expression in human, BE(2)-C MYCN-amplified human neuroblastoma cells were transfected with siRNAs against MYCN (siMYCN) to knockdown MYCN protein expression at several time points after transfection. Representative western blot images are shown. Expression were quantified at B) 24 h, C) 48 h and D) 72 h after transfection using TaqMan qRT- PCR miRNA assays with U6 snRNA and RNU19 as endogenous controls. All p-values are comparisons to siControl.
Chi Yan Ooi 145 Identification of candidate miRNAs & mRNAs for roles in neuroblastoma tumorigenesis
Figure 3.9 A)
48 h
MYCN
GAPDH
Figure 3.9 B)
Figure 3.10 Top 5 miRNA candidates and miR-574-3p expression in Kelly cells after siRNA knockdown of MYCN
A) siRNAs against MYCN siMYCN #6 and siMYCN #7 were transfected into Kelly
MYCN-amplified human neuroblastoma cells to knockdown MYCN protein expression and evaluate whether some of the results obtained from BE(2)-C were replicable (see Figure 3.9). Representative western blot images are shown. B) Expression were quantified using TaqMan qRT-PCR miRNA assays with U6 snRNA and RNU19 as endogenous controls. All p-values are comparisons to siControl.
Chi Yan Ooi 146 Identification of candidate miRNAs & mRNAs for roles in neuroblastoma tumorigenesis
Discussion
miRNAs have long been known to act as tumour suppressors and oncogenes in regulating multiple cancer-related cellular processes [118, 119, 421-423]. In neuroblastoma, the major oncogenic driver MYCN is also known to regulate expression of multiple miRNAs and in turn regulated by several miRNAs via binding sites in the 3’UTR of MYCN mRNA [101, 424]. To further extend this body of knowledge in MYCN-driven neuroblastoma, particularly concerning early tumorigenesis, this project started with a regulatory interaction network of miRNAs and mRNAs predicted from bioinformatics analysis of expression profiling data from the Th-MYCN homozygous neuroblastoma mouse model during early tumorigenesis integrated with a database of miRNA targeting predictions. However, it was not possible to investigate every miRNA and mRNA in this network consisting of 1322 interactions. In addition, not every element in this network can be transferrable from mouse to human. Therefore, several steps were taken to reduce the amount of nodes to investigate. The first step was to remove positively correlated interactions since miRNAs in most circumstances negative impact expression of their direct targets. While this eliminated those interactions that are likely indirect, it can also eliminate those true interactions where both the miRNA and the mRNA target were dysregulated towards the same direction (i.e. both up-regulated or both down-regulated). However, it’s hard to distinguish between those two cases and those that were dysregulated in opposite directions are the low-hanging fruits - much simpler and straightforward to investigate. In addition, examination of the list of miRNAs in this interaction network revealed many of those came from the miR-17-92 cluster, which is extensively studied not just in neuroblastoma or cancer in general but also in other pathological and physiological conditions, and its paralogs miR-106a-363 and miR-106b-25 clusters [101, 247, 250, 252,
262]. While this means members of these clusters will
Chi Yan Ooi 147 Identification of candidate miRNAs & mRNAs for roles in neuroblastoma tumorigenesis
likely be over-represented in any cancer dataset, it nevertheless provide some assurance that our methodology is sound. These miRNAs and their interactions were also removed from further consideration as we would like to investigate those that are relatively novel.
To further narrow down on the number of miRNAs to investigate, I then looked into the transferability of our network predictions in mouse into human. The first logical step was to determine whether human homologs exist for these miRNAs but having a human homolog doesn’t mean all of its interactions in mouse are conserved in human
[401]. miRNA seed sequence is a major determinant of miRNA targeting, which was also used in our network prediction in the form of the TargetScanMouse database, and miRNA seed sequence matching target sites on mRNAs are usually evolutionary conserved for conserved miRNAs [199, 401-403, 425]. Therefore, we checked for conservation of the miRNA seed sequences as defined by the shortest definition of the seed site – a 6mer comprising of 6 nucleotides starting from the 2nd nucleotide of the 5’ end [171, 172] (see
Appendix A). Those miRNAs with conserved miRNA seed sequences should have a higher chance of conserving their predicted interactions in human. I have also checked for any indication of biological relevance of the miRNAs to human neuroblastoma in published high-throughput expression profiling studies of human neuroblastoma tumours and cell lines (Table 3.3). These included whether there was any correlation between miRNA expression in tumours and patients’ chance of survival, and any differential miRNAs expression in MYCN-amplified vs non-MYCN-amplified tumours, in unfavourable vs favourable tumours, and after MYCN over-expression in cell lines. Most of the conserved miRNAs were indicated to be biologically relevant to human neuroblastoma while none of those non-conserved were similarly indicated (see Table 3.2), highlighting the importance of sequence conservation. Moreover, all miRNAs that were considered biologically relevant are 100 % identical in their full mature sequences
Chi Yan Ooi 148 Identification of candidate miRNAs & mRNAs for roles in neuroblastoma tumorigenesis
between mouse and human, suggesting high neuroblastoma-related functional conservation and knowledge transferability of fully conserved mature miRNAs across these species. Therefore besides the five miRNAs that ticked all the boxes (see Table
3.2), I have also selected miR-574-3p, which has 100 % conserved mature sequence and ticked all but one box, to be further studied in the scope of this chapter.
To verify my selections, we’ve asked our collaborators Prof Frank Speleman and his lab to look into their unpublished miRNA expression profiling data on 364 neuroblastoma primary tumour samples to determine whether there were any correlation between expression of the selected miRNAs and survival of the patients. Expression of four of the top five miRNA candidates (miR-204, miR-375, miR-135a, miR-135b) had significant correlations with both patient overall and progression free survivals. On the other hand, expression of the other top candidate miR-148a was only correlated to overall survival (see
Figure 3.5, summarized in Table 3.9). High expression of all but one of the top five miRNA candidates were correlated with better survival compared to low expression, which suggest they could be tumour suppressors in human neuroblastoma. Only the high expression of miR-375 is correlated with poor survival compared to low expression (see Table 3.9), suggesting miR-375 maybe acting as an oncogene in human neuroblastoma. Unfortunately miR-574-3p expression data are not available from this dataset. Thus we cannot evaluate whether miR-574-3p may have any biological significance in human neuroblastoma. We also evaluated whether there is any correlation between MYCN amplification and expression of selected miRNA. Only the expression of miR-204, miR-148a and miR-135a were significantly altered in MYCN-amplified compared to non-MYCN-amplified tumours
(Figure 3.6). Expression of these three miRNAs were lower in MYCN-amplified tumours which suggest the reduced survival for
Chi Yan Ooi 149 Identification of candidate miRNAs & mRNAs for roles in neuroblastoma tumorigenesis
patients with lower expression of miR-204, miR-148a or miR-135a could be at least partially the result of association with oncogenic MYCN amplification.
I have also utilized enrichment analyses to discover the biological functions that were implied by our interaction network predictions to provide additional information for further narrowing down on three miRNAs for more extensive experimental investigations, and to help prioritize the predicted mRNA targets to study with these experimental investigations. Only negatively correlated predicted target genes of each selected miRNA were investigated. I have used two bioinformatics tools available as web portals, which were the GeneCodis3 [377-379] and MSigDB Compute Overlap [380], to perform the enrichment analyses for increased chances of discovery compared to just using one. Multiple datasets were available to choose from for the enrichments to be performed against but it is not practical to investigate all due to the huge volume of outputs that would be generated. Since miRNAs are known to target multiple genes within the same pathway, I have chosen to enriched against known curated pathways, which several publications published during the years this project was conducted had similarly done [118, 426-429]. The pathways datasets investigated included the KEGG and Panther datasets available in GeneCodis3 and the CP:
Canonical pathways gene sets of the MSigDB C2: curated gene sets that included datasets from the BioCarta, KEGG and Reactome pathway databases. I have also chosen to complement those enrichments by enriching against gene sets that contain genes sharing the same transcription factor binding motifs present in their promoters (the MSigDB TFT: transcription factor targets gene sets under the C3: motif gene sets), which I’m not aware to be commonly utilized. The most interesting enrichments are those of miR-204, where cell cycle pathways are the top enriched pathways from both KEGG and in particularly
MSigDB’s CP: Canonical pathways gene sets and supported by top enrichments of cell cycle related E2F / E2F1 /
Chi Yan Ooi 150 Identification of candidate miRNAs & mRNAs for roles in neuroblastoma tumorigenesis
TFDP1 transcription factors binding motifs sets (Table 3.4). These are consistent with the discovery of miR-204 in suppressing expression of mRNAs involved in cell cycle and proliferation of head and neck tumours from network modelling using expression profiling, cancer genetics and bioinformatics analysis [336], further validating our prediction methodology. Moreover, we only shared one target gene CDC25B with them in terms of cell cycle and proliferation, enlarging the scope of potential cell cycle genes regulated by miR-204. On the other hand, I have found that miR-148a and miR-574-3p potentially regulate Wnt signalling (summarized in Table 3.9), which the former is consistent with the known targeting of miR-148a against WNT10B in cancer-associated fibroblast [430]. I have also found that miR-135a potentially regulates neural crest development and differentiation (Table 3.8), while miR-375 potentially regulates JUN transcriptional target genes and enrichment analyses with pathways sets failed (Table
3.7). Unfortunately, the number of negatively correlated predicted targets for miR-135b is too low for any enrichment to be significant.
Lastly, I performed several experiments to confirm the differential expression of the selected miRNAs in 2-weeks-old Th-MYCN+/+ mouse ganglia compared to wild-type and evaluate any regulation on the expression of these miRNAs by MYCN in human. I have confirmed all of the six miRNAs were indeed differentially expressed in 2-weeks-old Th-
MYCN+/+ mouse ganglia compared to wild-type (Figure 3.7, summarized in Table 3.9), most with fold changes comparable to the two different rounds of expression profiling. This demonstrated the robustness of the TaqMan qRT-PCR miRNA expression profiling platform although it cannot cover all known miRNAs like other high-throughput platforms can. The relative expression of the miRNAs were also quantified after over-expression of
MYCN in SHEP MYCN-3 cells and knockdown of MYCN in BE(2)-C and Kelly cells compared to negative controls (summarized in Table 3.9). miR-204 expression
Chi Yan Ooi 151 Identification of candidate miRNAs & mRNAs for roles in neuroblastoma tumorigenesis
was up-regulated while miR-148a expression was down-regulated after knockdown of
MYCN in human cells, consistent with their down-regulation and up-regulation respectively in 2-weeks-old Th-MYCN+/+ mouse ganglia compared to wild-type. In addition, they are predicted to be tumour suppressors targeting respectively cell cycle, a major well known function regulated by MYCN, and oncogenic Wnt signalling that can activates MYCN [75, 390, 431-433] (Table 3.9). The consistency in expression between mouse and human and their targeting of MYCN-related pathways make them attractive candidates, making them top 2 of three miRNA selections for further experimental investigations. Furthermore, miR-204 is a better candidate than miR-148a since high expression of miR-204 correlates to both better overall and progression free survivals while high miR-148a expression only correlates with better overall survival.
The differential expression of miR-574-3p after MYCN over-expression and knockdown in human are consistent with its down-regulation in 2-weeks-old Th-MYCN+/+ mouse ganglia compared to wild-type, and it is predicted to target Wnt signalling. However, we do not have any patient survival datum to determine whether the differential expression of miR-574-3p could have biological significance in human neuroblastoma (Table 3.9). This makes miR-574-3p a relatively high risk candidate that we chose not to take for further investigations for this project. However, miR-574-3p would be great for any other future research project as it is highly novel scientifically – it was not investigated in any neuroblastoma publication and limited publications implicating it as tumour suppressor in several cancer types [434-436]. Nevertheless, we’ve demonstrated for the first time that miR-574-3p expression can be reduced by MYCN and potentially acting as a tumour suppressor in neuroblastoma by targeting Wnt signalling.
In contrast, the up-regulation of miR-375, miR-135b and miR-135a expression after MYCN knockdown is not consistent with their up-regulation in 2-weeks-old Th-
Chi Yan Ooi 152 Identification of candidate miRNAs & mRNAs for roles in neuroblastoma tumorigenesis
MYCN+/+ mouse ganglia compared to wild-type (Table 3.9). However, those are consistent with their down-regulation after MYCN over-expression for miR-375 and miR-135b. In addition, gene sets enrichments were obtained for miR-375 and miR-375 is listed in KEGG MicroRNAs in cancer curated pathway [352], but not for miR-135b.
This makes miR-375 a safer choice as our third miRNA candidate for further investigations. My results also suggested miR-375 is suppressed by MYCN, in contrast to claim by Samaraweera et al. [356] that MYCN transcriptionally activates miR-375 in neuroblastoma.
In conclusion, I have identified miR-204 as potential tumour suppressor suppressed by MYCN and as our top candidate for further experimental investigation and will be the main miRNA discussed in the rest of this thesis. I have also chosen miR-
148a, a potential MYCN-activated tumour suppressor, and miR-375, a potential oncogene suppressed by MYCN, for further experimental investigations with less promising results than miR-204. Moreover, we’ve demonstrated for the first time the negative effect of MYCN expression on miR-574-3p expression and for miR-574-3p potentially acting as a tumour suppressor in neuroblastoma by targeting Wnt signalling.
Chi Yan Ooi 153 Identification of candidate miRNAs & mRNAs for roles in neuroblastoma tumorigenesis
Table 3.9
Prognosis 2-weeks- MYCN MYCN Main Potential with high oldTh- Over- knock- Predicted Role expression MYCN+/+ expression down Function miRNA ganglia
Better Down- N/A Up- Cell Cycle Tumour
204 regulated regulated Suppressor,
-
suppress by
R mi MYCN
Better Up- N/A Down- Wnt signalling Tumour
regulated regulated Suppressor, 148a
- activate by MYCN
miR
Worse Up- Down- Up- JUN Oncogene,
375 regulated regulated regulated transcriptional suppress by
-
target genes MYCN
R mi
Up- Down- Up- N/A Tumour Better regulated regulated regulated Suppressor,
suppress by 135b - MYCN
Better Up- No change Up- Neuralcrest Tumour regulated regulated development Suppressor,
135a 135a miR and suppress by - differentiation MYCN
Down- Down- Up- Wnt signalling Tumour
N/A 3pmiR
- regulated regulated regulated Suppressor,
suppress by 574 - MYCN
miR
Chi Yan Ooi 154 Identification of candidate miRNAs & mRNAs for roles in neuroblastoma tumorigenesis
Table 3.9 Integration of findings to select top 3 miRNAs for further experimental studies
Brief summary of the findings in this chapter on the top 5 miRNA candidates and miR- 574-3p. Differential expressions consistent between 2-weeks-old Th-MYCN+/+ mouse ganglia compared to wild-type and after MYCN over-expression and knockdown in human cells are underlined. No expression data on miR-574-3p were available from our collaborators’ human primary tumours dataset, preventing us from evaluating the potential biological significance of its dysregulation in human neuroblastoma tumours and thus not selected for further studies. Conclusion on what the potential roles of these miRNAs are included. miR-204 is the top candidate due to consistent differential expression between mouse and human and predicted function in regulating cell cycle, a major well known function regulated by MYCN [390, 431]. miR-148a is the second candidate also with consistent differential expression between mouse and human, and predicted to target Wnt signalling that can activate MYCN expression [432, 433]. Finally miR-375 is the third candidate with consistent differential expression after MYCN over-expression and knockdown, predicted targeting of JUN transcriptional targets and a miRNA in KEGG MicroRNAs in cancer curated pathway [352].
Chi Yan Ooi 155
miR-204 is a novel tumour
suppressor of neuroblastoma
Chi Yan Ooi 156 miR-204 is a novel tumour suppressor of neuroblastoma
Introduction
Three miRNAs, miR-204, miR-148a and miR-375, were selected in Chapter 3 for further experimental studies. Located at human chromosome 9q21.12, my top candidate miR-204 is frequently lost from the genomes of a number of human cancer, including ovarian cancer (44.63 %), breast cancer (28 %), paediatric renal tumour (40 %), squamous cell carcinoma of the head and neck (67 %) [340, 437]. In addition, miR-204 expression is down-regulated in a number of human cancer compared to the respective normal counterparts, such as pancreatic cancer, adult acute myeloid leukaemia, B-cell lymphoma, non-small-cell lung carcinoma, clear cell renal cell carcinoma, intrahepatic cholangiocarcinoma (liver cancer), gastric cancer, colorectal cancer, papillary thyroid carcinoma, and most importantly central nervous system cancer glioma [337, 338, 341-343,
345, 346, 438-441]. Unsurprisingly, the above studies showed that miR-204 is a tumour suppressor in most of the above mentioned cancer types. Furthermore, miR-204 expression is also lower in the NCI60 cancer cell lines panel when compared to thirteen normal tissues
[344]. These suggest that miR-204 is likely a universal tumour suppressor.
In neuroblastoma, miR-204 is not located in commonly altered genetic loci but we and others have shown positive correlations between miR-204 expression in neuroblastoma tumour and patient survival (see Figure 3.5 and Table 3.3) [103, 104,
347]. Moreover, miR-204 has only been shown to sensitize neuroblastoma cell lines to cisplatin in vitro via direct suppression of anti-apoptotic factor BCL2 and directly suppress chemotherapy resistance factor TrkB (NTRK2) and neural crest cells development gene PHOX2B via the 3’UTR of their mRNAs [29, 347, 349]. The only additional information is that miR-204 expression is down-regulated in only one doxorubicin chemoresistant cell line model of neuroblastoma [348]. Therefore, the notion of miR-204 as tumour suppressor of neuroblastoma is still relatively novel.
Chi Yan Ooi 157 miR-204 is a novel tumour suppressor of neuroblastoma
On the other hand, the other two candidates, miR-148a and miR-375, have also been associated with cancer. miR-148a expression is silenced by hypermethylation of its promoter in lymph node metastatic cancer cell lines compared to a variety of normal tissues, and hepatocellular carcinoma cell lines compared to a normal liver cell line [442, 443]. miR-148a is also down-regulated in pancreatic ductal adenocarcinomas tumours compared to normal pancreatic epithelia, and gastric cancer tumours compared to matched non- tumorous gastric tissues [444, 445]. miR-148a has been associated with suppression of Wnt signalling/metastasis in lymph node metastatic cancer, medulloblastoma, endometrial cancer and gastric cancer [430, 442, 444, 446]. miR-148a has also been shown to suppress cell growth in ovarian cancer, pancreatic ductal adenocarcinomas and hepatocellular carcinoma, and directly suppress cell cycle regulator CDC25B [443, 445, 447]. We and others have shown that miR-148a expression is lower in MYCN-amplified neuroblastoma tumours compared to the non-MYCN-amplified ones (see Figure 3.5 and Table 3.3) [97,
103]. We also showed a positive correlation between miR-148a expression in neuroblastoma tumour and overall patient survival (Figure 3.5), suggesting miR-148a is a tumour suppressor in neuroblastoma.
In contrast, miR-375 is up-regulated in hepatocellular carcinoma and down- regulated in esophageal cancer, and was also shown to act as tumour suppressor by repressing c-MYC expression via direct suppression of CIP2A [352, 448]. In addition, miR-
375 directly supresses MYCN expression via the 5’UTR of MYCN mRNA in neuroblastoma cells [357]. Moreover, it has been reported that miR-375 is transcriptionally activated by MYCN but also that miR-375 expression is generally lower in MYCN- amplified neuroblastoma cell lines compared to those that are non-MYCN-amplified [356,
357]. On the other hand, miR-375 expression is higher in cell lines derived from neuroblastoma patients at time of relapse compared to the corresponding
Chi Yan Ooi 158 miR-204 is a novel tumour suppressor of neuroblastoma
cell lines derived at time of diagnosis [354]. Phenotypically, miR-375 has been shown to inhibit neuroblastoma cell differentiation in vitro by directly suppressing HuD [355,
356]. However, miR-375 has also been reported to reduce MYCN-amplified neuroblastoma cell viability and colony forming in vitro and xenograft tumour growth in vivo [357]. Nevertheless, we and others have showed negative correlations between miR-375 expression and patient survival (see Figure 3.5 and Table 3.3) [103], suggesting miR-375 acts as oncogene in neuroblastoma.
The major aims of this chapter are to determine whether some of the miR-204 predicted targets are suppressed by miR-204 and characterize the functional roles of miR-204 on neuroblastoma. This chapter would also briefly investigate experimentally the functions of miR-148a and miR-375.
Chi Yan Ooi 159 miR-204 is a novel tumour suppressor of neuroblastoma
Results
4.2.1 miR-204 mimic reduced mRNA levels of MYCN and cell cycle enriched targets
First, I sought to validate the targeting of cell cycle genes by miR-204 as predicted in Section 3.2.4 (also see Table 3.4 and Table 3.9). I have decided to validate the five genes enriched for the KEGG Cell Cycle pathway, which is the top pathway enriched using
GeneCodis3 against the KEGG dataset and the third most significantly enriched using
MSigDB compute overlap tool against the MSigDB CP: Canonical pathways gene sets
(Figure 4.1). These five predicted target genes, E2F1, WEE1, RBL1, CDC25A and
CDC25B, also cover all the genes enriched for the top seven cell cycle related gene sets (see
Table 3.4C). The miRNAs targets prediction database TargetScanHuman (Release 7.1)
[198] predicted that there are still miR-204 binding sites predicted in the 3’UTR of human
E2F1, WEE1, RBL1 and CDC25B mRNA but not of human CDC25A mRNA.
Regardless, the mRNA expression of all the five predicted target genes were exanimated in MYCN-amplified human BE(2)-C and Kelly neuroblastoma cells after transfection of miR-204 mimic, which are chemically enhanced double-stranded oligonucleotides with mature miR-204 sequence on the active strand, and negative control mimic #1 carrying the mature cel-miR-67 sequence that should not specifically target any human, mouse and rat genes. At 48 h after transfection of 30 nM miR-204 mimic, mRNA expression of E2F1, WEE1 and CDC25B were reduced in both BE(2)-C and Kelly cells compared to negative control mimic #1 (Figure 4.3A and Figure 4.4A). mRNA expression of positive control MEIS2, a confirmed target of miR-204 [331, 449] and also a predicted target in our miR-204 interaction network (Figure 3.4), were also reduced while unrelated negative control mRNAs GUSB and HRPT1 were not significantly affected. These negative control mRNAs were also used by Lee et al. [336] for the same
Chi Yan Ooi 160 miR-204 is a novel tumour suppressor of neuroblastoma
mimics in a squamous cell carcinoma of the head and neck cell line. I later discovered that a higher mimic concentration of 100 nM and longer timeframes are required for observable phenotypic changes. This concentration is still within the range of concentrations frequently used in the literature, with the highest being 200 nM (e.g.
[336, 450-452]). mRNA expression of all five cell cycle predicted targets were reduced in both cell lines 72 h post-transfection of 100 nM miR-204 mimic compared to negative control mimic #1 (Figure 4.3B and Figure 4.4B). No significant change in mRNAs expression was observed 96 h after transfection (not shown).
Furthermore, one of MYCN’s major function in neuroblastoma is to regulate the cell cycle and proliferation [261, 390, 431]. Therefore, I also examined the mRNA expression of MYCN after transfection of miR-204 mimic. MYCN mRNA expression was reduced in both cell lines 48 h and 72 h after transfection of 30 nM and 100 nM miR-204 mimic respectively compared to negative control mimic #1 (Figure 4.3 and
Figure 4.4).
Chi Yan Ooi 161 miR-204 is a novel tumour suppressor of neuroblastoma
Figure 4.1 Top 10 MSigDB Canonical pathways gene sets enriched for miR-204
The top ten MSigDB Canonical pathways enriched from negatively correlated predicted targets of miR-204 using the MSigDB Compute Overlap tool (see Table 3.4C), ranked by the log10 of their adjusted p-values (FDR q-values). The longer the bar, the more statistically significant for the corresponding enriched gene set. Eight out of the top ten enriched gene sets are cell cycle related.
Chi Yan Ooi 162 miR-204 is a novel tumour suppressor of neuroblastoma
Colour Key And Density Plot
0.2
Density 0
6 7 89 10 11 12 Value
Cdc25b
Rbl1
Wee1
Cdc25a
E2F1
Figure 4.2 miR-204 KEGG Cell Cycle predicted target genes mice expression heat map
This heat map and clustering was generated by Dr Chelsea Mayoh with expression data from microarray expression profiling of total RNA extracted from wild-type mouse ganglia 1, 2 and 6 weeks after birth (wtW1, wtW2, wtW6), and Th-MYCN+/+ mouse ganglia 1 and 2 weeks after birth (mycnW1, mycnW2) and tumour 6 weeks after birth (mycnW6) that were also used in the 2nd round of TaqMan qRT-PCR miRNA expression profiling (see Section 3.2.1). The mRNA expression of the five miR-204 negatively correlated predicted target genes enriched for the KEGG Cell Cycle pathway are shown. Their expression are higher in Th-MYCN+/+ samples compared to wild-type, as opposed to the down-regulation of miR-204 observed in the same samples.
Chi Yan Ooi 163 miR-204 is a novel tumour suppressor of neuroblastoma
Figure 4.3 A)
Figure 4.3 B)
Figure 4.3 MYCN and KEGG Cell Cycle predicted targets mRNA levels are reduced in BE(2)-C cells after transfection of miR-204 mimic
A) Human MYCN-amplified neuroblastoma BE(2)-C cells were transfected with 30 nM miR-204 mimic (chemically modified double-stranded oligonucleotides) or negative control mimic #1 (cel-miR-67). 48 h after transfection, total RNA samples were harvested and SYBR Green qRT-PCR were performed to quantify mRNA expression using β2M as endogenous control. MEIS2, a confirmed target of miR-204 [331, 449] also predicted in our miR-204 interaction network (see Figure 3.4), was used as positive control. GUSB and HRPT1 were used as negative controls. These are replicated in B) with a higher mimic concentration of 100 nM, 72 h after transfection due to a higher concentration and duration requirement for observable phenotypic changes. * = p<0.05, ** = p<0.005, ***
= p<0.0005, **** = p<0.0001, ns = p≥0.05. All p-values are comparisons between miR- 204 mimic and negative control mimic #1. Plots represent mean + standard error mean, n=3.
Chi Yan Ooi 164 miR-204 is a novel tumour suppressor of neuroblastoma
Figure 4.4 A)
Figure 4.4 B)
Figure 4.4 MYCN and KEGG Cell Cycle predicted targets mRNA levels are reduced in Kelly cells after transfection of miR-204 mimic
A) Human MYCN-amplified neuroblastoma Kelly cells were transfected with 30 nM miR- 204 mimic or negative control mimic #1 (cel-miR-67). 48 h after transfection, total RNA samples were harvested and SYBR Green qRT-PCR were performed to quantify mRNA expression using β2M as endogenous control. MEIS2, a confirmed target of miR-204 [331, 449] also predicted in our miR-204 interaction network (see Figure 3.4), was used as positive control. GUSB and HRPT1 were used as negative controls. B) With a higher mimic concentration of 100 nM and a longer 72 h after transfection, mRNA levels of all five of the predicted targets are reduced. * = p<0.05, ** = p<0.005, *** = p<0.0005,
**** = p<0.0001, ns = p≥0.05. All p-values are comparisons between miR-204 mimic and negative control mimic #1. Plots represent mean + standard error mean, n=3.
Chi Yan Ooi 165 miR-204 is a novel tumour suppressor of neuroblastoma
4.2.2 miR-204 mimic reduced MYCN protein levels
To confirm miR-204’s suppressive effect on MYCN expression, proteins were extracted 48 h after transfection of BE(2)-C and Kelly cells with 100 nM miR-204 mimic or negative control mimic #1. Western blotting were performed, and MYCN and housekeeping gene GAPDH (loading control) protein expression were quantified by densitometry. There were less of MYCN in miR-204 mimic samples compared to negative control mimic #1 for both cell lines in the western blots (Figure 4.5A).
Densitometry confirmed that MYCN protein expression is significantly reduced for miR-204 mimic compared to negative control mimic #1 in both cell lines (Figure 4.5B).
Therefore, miR-204 mimic can also reduce MYCN protein expression. I also attempted to determine whether miR-204 mimic can reduce E2F1 protein expression but the results were inconclusive (not shown).
Chi Yan Ooi 166 miR-204 is a novel tumour suppressor of neuroblastoma
Figure 4.5 A)
BE(2)-C Kelly 48 h 48 h - + - + - Negative Control Mimic #1 MYCN + hsa-miR-204 Mimic
GAPDH
Figure 4.5 B)
Figure 4.5 MYCN protein levels are reduced in BE(2)-C and Kelly cells after transfection of miR-204 mimic
MYCN-amplified human BE(2)-C and Kelly neuroblastoma cells were transfected with
100 nM miR-204 mimic or negative control mimic #1 (cel-miR-67) and proteins were extracted 48 h after transfection. A) Western blot of the protein extracts. Representative western blot images are shown. B) Densitometry of more than three western blot biological replicates confirmed MYCN protein expression was significantly reduced 48 h after transfection of miR-204 mimic compared to negative control mimic #1. * = p<0.05. Plots represent mean + standard error mean with n=3 for all plots.
Chi Yan Ooi 167 miR-204 is a novel tumour suppressor of neuroblastoma
4.2.3 miR-204 mimic reduced neuroblastoma cell proliferation but not viability
I performed Alamar Blue metabolic activity and BrdU DNA synthesis assays on
BE(2)-C and Kelly cells to measure their viability and proliferation after transfection of miR-204 mimic compared to negative control mimic #1 in 96-well plates. Even at 100 nM mimic concentration, no significant difference in cell viability was detected at 72 h and 96 h after transfection in both cell lines (Figure 4.6). However, cell proliferation was reduced at
72 h after transfection of 100 nM miR-204 mimic compared to negative control mimic #1 in both cell lines (Figure 4.7). Significant reduction in cell proliferation remained at 96 h after transfection of BE(2)-C cells but not of Kelly cells. Therefore, miR-204 mimic can reduce the proliferation (DNA synthesis) of BE(2)-C and Kelly cells.
Chi Yan Ooi 168 miR-204 is a novel tumour suppressor of neuroblastoma
Figure 4.6 miR-204 mimic did not affect BE(2)-C and Kelly cell viability
MYCN-amplified human BE(2)-C and Kelly neuroblastoma cells were transfected with 100 nM miR-204 mimic or negative control mimic #1 and plated on 96-well plate. Alamar Blue metabolic activity assays were performed to measure the cell viability of the transfected cells. ns = p≥0.05. Plots represent mean + standard error mean, n≥3.
Chi Yan Ooi 169 miR-204 is a novel tumour suppressor of neuroblastoma
Figure 4.7 miR-204 mimic reduced BE(2)-C and Kelly cell proliferation
MYCN-amplified human BE(2)-C and Kelly neuroblastoma cells were transfected with 100 nM miR-204 mimic or negative control mimic #1 and plated on 96-well plate. BrdU DNA synthesis assays were performed to measure the cell proliferation of the transfected cells. * = p<0.05, ** = p<0.005, ns = p≥0.05. Plots represent mean + standard error mean, n≥3.
Chi Yan Ooi 170 miR-204 is a novel tumour suppressor of neuroblastoma
4.2.4 miR-204 mimic reduced neuroblastoma colony forming capability
To evaluate the effect of miR-204 on the colony forming capability of MYCN- amplified human neuroblastoma cells, I transfected BE(2)-C and Kelly cells with miR-204 mimic or negative control mimic #1 and 24 h later trypsinized and thoroughly resuspended into single cells for performing colony forming assays. These BE(2)-C and Kelly cells were plated onto 6-well plates and incubated at 37 °C and 5 % CO2 for 7 days and 12 days respectively with media refresh every 3-4 days. At the end of the assays, colonies formed were stained by crystal violet and counted (Figure 4.8). The numbers of colonies formed by the cells transfected with miR-204 mimic were lower compared to that with negative control mimic #1 in both cell lines. These showed that miR-204 mimic can reduce the colony forming capability of MYCN-amplified human neuroblastoma cells.
Chi Yan Ooi 171 miR-204 is a novel tumour suppressor of neuroblastoma
Figure 4.8 A)
Negative Control hsa-miR-204 Mimic #1 Mimic
BE(2)-C
Kelly
Figure 4.8 B)
Figure 4.8 miR-204 mimic reduced BE(2)-C and Kelly colony forming capability
A) MYCN-amplified human BE(2)-C and Kelly neuroblastoma cells were trypsinized, thoroughly resuspended into single cells and plated onto 6 well plates at 24 h after transfection of 100 nM miR-204 mimic or negative control mimic #1. Those cells were then incubated at 37 °C and 5 % CO2 for 7 and 12 days respectively with media refresh every 3-4 days and colonies formed were stained by crystal violet. Representative well images are shown. B) The numbers of colonies were quantified and normalized to negative control mimic #1 as percentages. * = p<0.05. Plots represent mean + standard error mean with n=4 for all plots.
Chi Yan Ooi 172 miR-204 is a novel tumour suppressor of neuroblastoma
4.2.5 Lentivirally transfected neuroblastoma stable cell lines expressed green fluorescence and over-expressed miR-204 when induced by doxycycline
To generate doxycycline-inducible stable cell lines for evaluating the effect of miR-204 expression in vivo, SMARTchoice Inducible shMIMIC microRNA lentivirus was purchased from Dharmacon. The lentivirus contains the Tet-On 3G doxycycline- inducible expression system and puromycin resistance gene in its construct, and mature miR-204 sequence and turboGFP for doxycycline-induced co-expression from the same promoter (see Figure 2.1). BE(2)-C-miR-204 and Kelly-miR-204 stable cell lines were generated by pooling all BE(2)-C and Kelly cells respectively that were transduced by the lentivirus and survived puromycin selection. As expected, incubation of these stable cells with doxycycline but not DMSO solvent negative control activated turboGFP expression that was detected by fluorescence microscopy (Figure 4.9). TaqMan qRT-
PCR miRNA assays confirmed miR-204 was highly over-expressed after 72 h of doxycycline induction. These confirmed that the BE(2)-C-miR-204 and Kelly-miR-204 stable cell lines can indeed over-express miR-204 in a doxycycline-inducible manner.
Chi Yan Ooi 173 miR-204 is a novel tumour suppressor of neuroblastoma
Figure 4.9
BE(2)-C-miR-204 Kelly-miR-204
+DMSO
24 h
+Dox
+DMSO
48 h
+Dox
+DMSO
72 h
+Dox
Bright-field turboGFP Bright-field turboGFP
Chi Yan Ooi 174 miR-204 is a novel tumour suppressor of neuroblastoma
Figure 4.9 Inducible miR-204 stable cell lines expressed green fluorescence and over-expressed miR-204 when induced by doxycycline
BE(2)-C-miR-204 and Kelly-miR-204 stable cell lines were generated by transduction and puromycin selection of MYCN-amplified human BE(2)-C and Kelly neuroblastoma cells with lentivirus carrying the Tet-On 3G doxycycline-inducible expression system embedded with mature miR-204 sequence and turboGFP for co-expression from the same promoter (see Figure 2.1). Representative bright-field and fluorescence microscope images with 10 x objective are shown. U6 snRNA and RNU19 were used as endogenous controls for TaqMan qRT-PCR to quantify miR-204 expression. ** = p<0.005. Plots represent mean + standard error mean with n=4 for all plots.
Chi Yan Ooi 175 miR-204 is a novel tumour suppressor of neuroblastoma
4.2.6 Over-expression of miR-204 in neuroblastoma stable cells reduced MYCN protein levels
To further validate the BE(2)-C-miR-204 and Kelly-miR-204 cell lines for use in vivo, I attempted to determine whether the doxycycline-induced over-expression of natural mature miR-204 can reduce MYCN protein expression similarly to chemically enhanced miR-204 mimic. MYCN protein expression was reduced after 72 h and 48 h incubation with doxycycline compared to DMSO negative control for BE(2)-C-miR-204 and Kelly-miR-204 cells respectively (Figure 4.10A). Quantification by densitometry confirmed significant reductions in MYCN protein expression after doxycycline induction in BE(2)-C-miR-204 and Kelly-miR-204 cells compared to DMSO negative control. Therefore, doxycycline-induced over-expression of miR-204 can also reduce
MYCN protein expression. This further shows that miR-204 suppresses MYCN expression.
Chi Yan Ooi 176 miR-204 is a novel tumour suppressor of neuroblastoma
Figure 4.10 A)
BE(2)-C-miR-204 Kelly-miR-204 72 h 48 h
MYCN
GAPDH
Figure 4.10 B)
Figure 4.10 Doxycycline-induced miR-204 over-expression reduced MYCN protein expression
A) Reductions in MYCN protein expression were detected by western blotting after 72 h and 48 h of incubation with doxycycline hydrochloride (Dox) compare to DMSO solvent negative control for BE(2)-C-miR-204 and Kelly-miR-204 cells respectively. Representative western blot images are shown. B) Densitometry of the western blots confirmed significant reduction of MYCN protein expression in BE(2)-C-miR-204 and Kelly-miR-204 cells treated with doxycycline compared to DMSO negative control. * = p<0.05, ** = p<0.005. Plots represent mean + standard error mean, n≥3.
Chi Yan Ooi 177 miR-204 is a novel tumour suppressor of neuroblastoma
4.2.7 Over-expression of miR-204 in neuroblastoma stable cells reduced their colony forming capability
Next, colony forming assays were performed for the BE(2)-C-miR-204 and Kelly- miR-204 stable cell lines to determine whether phenotypic effects of the chemically enhanced miR-204 mimic can be replicated with doxycycline-induced over-expression of natural mature miR-204. BE(2)-C-miR-204 and Kelly-miR-204 cells were first incubated with doxycycline or DMSO for 72 h before performing the colony forming assays. These
BE(2)-C-miR-204 and Kelly-miR-204 cells were then plated as single cells onto 6-well plates and incubated with doxycycline or DMSO for 7 and 14 days respectively, with media refresh every 3-4 days. Colonies formed at the end were stained with crystal violet and counted (Figure 4.11). The colony counts were reduced significantly for cells from both cell lines incubated with doxycycline compared to DMSO negative control. This is consistent with the colony forming assays results for BE(2)-C and Kelly cells transfected with miR-
204 mimic compared to negative control mimic #1 (see Section 4.2.4). Thus the phenotypic effect of miR-204 mimic can be replicated with doxycycline-induced miR-204 over- expression in the stable cell lines. This also further supports that miR-204 is a potential tumour suppressor in neuroblastoma.
Chi Yan Ooi 178 miR-204 is a novel tumour suppressor of neuroblastoma
Figure 4.11 A)
+DMSO +Dox
BE(2)-C-miR-204
Kelly-miR-204
Figure 4.11 B)
Figure 4.11 Doxycycline-induced miR-204 over-expression reduced stable cells colony forming capability
A) BE(2)-C-miR-204 and Kelly-miR-204 cells were first treated with doxycycline hydrochloride (Dox) or DMSO for 72 h before being trypsinized, thoroughly resuspended into single cells and plated onto 6 well plates. Those cells were then incubated at 37 °C and
5 % CO2 with either Dox or DMSO for 7 and 14 days respectively with media refresh every 3-4 days. Colonies formed were stained by crystal violet. Representative well images are shown. B) The numbers of colonies were quantified and normalized to the DMSO solvent negative control as percentages. * = p<0.05, ** = p<0.005. Plots represent mean + standard error mean with n=3 for all plots.
Chi Yan Ooi 179 miR-204 is a novel tumour suppressor of neuroblastoma
4.2.8 miR-204 over-expression in neuroblastoma stable cells subcutaneous xenografts prolonged survivals of the xenografted mice
To determine the function of miR-204 in vivo, BE(2)-C-miR-204 and Kelly- miR-204 inducible stable cells were injected subcutaneously into the left flank of
BALB/c nude (BALB/c---Fox1nu/Ausb) mice. When the cells developed into a small tumour of 4-5 mm in diameter, sucrose alone or sucrose plus doxycycline were added into their drinking water until reaching the experimental end-point. The experimental end-point was defined as reaching a tumour volume of 1000 mm3 (an event/non- survival) or surviving at 12 weeks after injection of the stable cells. Doxycycline induction was verified by detecting turboGFP activation 7 days after beginning treatment through measurement of green fluorescence emission from the tumour region of the mice under in vivo fluorescence imaging (Figure 4.12). Those doxycycline-treated tumours that did not exhibit green fluorescence emission above the maximum background emission measured for the sucrose negative control treatment group were omitted from this and further analyses.
Continuing sucrose plus doxycycline treatments prolonged the survivals of xenografted mice, as defined by having a tumour volume of less than 1000 mm3, compared to sucrose only treatments (Figure 4.13). This showed that miR-204 over- expression delayed the time required for human MYCN-amplified neuroblastoma tumours to grow to a volume of 1000 mm3 in vivo. Thus miR-204 acts as a tumour suppressor in vivo in neuroblastoma.
Furthermore, total RNA samples were extracted from tumours archived from the xenografted mice when they were euthanized at the experimental end-point. I were able to determine from TaqMan qRT-PCR of the total RNA samples that miR-204 was still over-expressed in doxycycline-treated tumours compared to those treated with sucrose
Chi Yan Ooi 180 miR-204 is a novel tumour suppressor of neuroblastoma
only at the experimental end-point for both stable cell lines (Figure 4.14). This further validates the in vivo survival results.
Chi Yan Ooi 181 miR-204 is a novel tumour suppressor of neuroblastoma
Figure 4.12 A)
BE(2)-C-miR-204 Epi- Kelly-miR-204 Epi- fluorescence +Sucrose fluorescence +Sucrose +Sucrose +Doxycycline 5.0 +Sucrose +Doxycycline 2.5 4.0 2.0 3.0 9 x10 2.0 x107 1.5 1.0 0.0 Radiant Efficiency Radiant Efficiency
/ / 3/ / / 3/
( )
) µ / 3 µ / 3 (
Figure 4.12 B)
Figure 4.12 C)
Chi Yan Ooi 182 miR-204 is a novel tumour suppressor of neuroblastoma
Figure 4.12 turboGFP is over-expressed in subcutaneous xenografts 7 days after beginning doxycycline treatment compared to negative control treatment
BE(2)-C-miR-204 and Kelly-miR-204 cells were subcutaneously injected into BALB/c nude (BALB/c---Fox1nu/Ausb) mice and the mice began treatment of drinking water supplemented with sucrose only or with sucrose and doxycycline when these cells developed into tumours 4-5 mm in diameter. A) In order to verify that sufficient amount of doxycycline was successfully delivered to activate the Tet-On 3G doxycycline- inducible expression system in the BE(2)-C-miR-204 and Kelly-miR-204 cells, the mice were anaesthetized and in vivo fluorescence imaging were performed on whole living mice 7 days after the start of treatment. Representative images are shown. B-C) Green fluorescence emissions from the tumours were measured as average radiant efficiency [p/sec/cm²/sr] / [μW/cm²] and the average emission from each group are listed near the bottom.
Chi Yan Ooi 183 miR-204 is a novel tumour suppressor of neuroblastoma
Figure 4.13 Prolonged survivals of xenografted mice treated with doxycycline compared to control
Xenografted BALB/c nude mice were treated with drinking water supplemented with sucrose or sucrose and doxycycline from the day a small subcutaneous tumour of 4-5 mm in diameter was detected until the experimental end-point was reached. The experimental end-point is defined as the subcutaneous tumour reaching a volume of 1000 mm3, which counted as an event/non-survival, or otherwise after 12 weeks post- injection of stable cells, which counted as survived.
Chi Yan Ooi 184 miR-204 is a novel tumour suppressor of neuroblastoma
Figure 4.14 miR-204 is over-expressed in doxycycline-treated subcutaneous xenografts at the experimental end-point compared to negative control treatment
Xenografted BALB/c nude mice were euthanized when they reached the experimental end-point of reaching a tumour volume of 1000 mm3 or after 12 weeks post-injection of stable cells. Total RNA samples were extracted from the tumours for quantification of miR-204 expression by TaqMan qRT-PCR. U6 snRNA and RNU19 were used as endogenous controls for qRT-PCR quantification.
Chi Yan Ooi 185 miR-204 is a novel tumour suppressor of neuroblastoma
4.2.9 miR-148a mimic and miR-375 inhibitor reduced neuroblastoma cell proliferation
Checking the predicted target genes of miR-148a and miR-375 enriched in Section
3.2.4 with miRNA targeting prediction database TargetScanHuman (Release 7.1)
[198] revealed that the predicted miRNA binding sites of almost all those genes were not conserved in human. The only genes enriched in Section 3.2.4 with conserved miRNA binding sites were CASZ1 and SH2B3 for miR-148a and only CASZ1 for miR-
375. These suggested that the enrichment analyses for miR-148a and miR-375 were likely not applicable for human studies. Nevertheless, total RNA samples were extracted from BE(2)-C and Kelly cells at 72 h after transfection of 100 nM miR-148a mimic or negative control mimic #1, or 100 nM miR-375 inhibitor (chemically-modified RNA single-stranded oligonucleotides that binds miR-375) or negative control inhibitor #1
(for cel-miR-67). The mRNA expression of miR-148a predicted targets WNT10A and
FGF1 enriched for angiogenesis, predicted conserved target CASZ1 and MYCN were investigated for miR-148a mimic. On the other hand, mRNA expression of miR-375 predicted target SHC3 enriched for JUN transcription factor binding motif gene set and important for neuroblastoma differentiation [453], predicted conserved target CASZ1 and MYCN were investigated for miR-375 inhibitor. However, the results were inconclusive (not shown).
Despite the absence of promising mRNA targets, Alamar Blue cell viability and
BrdU proliferation assays were performed for miR-148a mimic and miR-375 inhibitor. 100 nM miR-148a mimic was able to significantly enhance Kelly cell viability only at 96 h but not 72 h after transfection while having no significant effect on BE(2)-C cell viability, compared to negative control mimic #1 (Figure 4.15). On the other hand, miR-375 inhibitor reduced Kelly cell viability only at 96 h but not 72 h after transfection while
Chi Yan Ooi 186 miR-204 is a novel tumour suppressor of neuroblastoma
having no significant effect on BE(2)-C cell viability, compared to negative control inhibitor #1. It is inconclusive whether miR-148a or miR-375 affects MYCN-amplified neuroblastoma cell viability.
However, cell proliferation was significantly reduced at 72 h after transfection of
100 nM miR-148a mimic compared to negative control mimic #1 in both BE(2)-C and
Kelly cells, and continued to remain significantly reduced at 96 h after transfection for
BE(2)-C cells (Figure 4.16). In addition, both BE(2)-C and Kelly cell proliferation were significantly reduced at both 72 h and 96 h after transfection of 100 nM miR-375 inhibitor compared to negative control inhibitor #1. These suggested that miR-148a can suppress
BE(2)-C and Kelly cell proliferation while miR-375 can enhance their proliferation.
Chi Yan Ooi 187 miR-204 is a novel tumour suppressor of neuroblastoma
Figure 4.15 miR-148a mimic and miR-375 inhibitor did not alter cell viability in both BE(2)-C and Kelly cell lines
BE(2)-C and Kelly cells were transfected with 100 nM negative control mimic #1, miR- 148a mimic, negative control inhibitor #1 or miR-375 inhibitor on 96-well plates. Alamar Blue metabolic activity assays were performed at 72 h and 96 h after transfection to measure cell viability. * = p<0.05, ns = p≥0.05. Plots represent mean + standard error mean with n=3 for all plots.
Chi Yan Ooi 188 miR-204 is a novel tumour suppressor of neuroblastoma
Figure 4.16 miR-148a mimic and miR-375 inhibitor suppressed neuroblastoma cell proliferation
BE(2)-C and Kelly cells were transfected with 100 nM negative control mimic #1, miR- 148a mimic, negative control inhibitor #1 or miR-375 inhibitor on 96-well plates. BrdU DNA synthesis assays were performed at 72 h and 96 h after transfection to measure cell proliferation. * = p<0.05, ** = p<0.005, ns = p≥0.05. Plots represent mean + standard error mean with n=3 for all plots.
Chi Yan Ooi 189 miR-204 is a novel tumour suppressor of neuroblastoma
Discussion
In this chapter, I have found that miR-204 mimic suppressed mRNA expression of cell cycle genes E2F1, WEE1, RBL1, CDC25A and CDC25B, as well as MYCN. In addition, both miR-204 mimic and over-expression suppressed MYCN protein expression. Furthermore, miR-204 mimic inhibited cell proliferation but not viability while both miR-204 mimic and over-expression inhibited colony forming in vitro.
Importantly, induced over-expression of miR-204 in subcutaneous xenograft models prolonged survivals of xenografted mice. On the other hand, I was unable to validate any of the predicted targets of miR-148a and miR-375. However, miR-148a mimic and miR-
375 inhibitor were able to inhibit cell proliferation.
The multiple enrichment of cell cycle related pathways and sets of genes having the
E2F / E2F1 / TFDP1 transcription factors binding motifs from miR-204 negatively correlated predicted targets provided high confidence that miR-204 suppresses the cell cycle or proliferation of neuroblastoma. This is evident in that transfection of miR-204 mimic in two MYCN-amplified human neuroblastoma cell lines can reduce the mRNA expression of the five predicted targets E2F1, WEE1, RBL1, CDC25A and CDC25B present in the
KEGG Cell Cycle pathway, compared to negative control mimic #1 (see Figure 4.3 and
Figure 4.4). That is despite not all miR-204 predicted targets, specifically CDC25A, that were predicted based from TargetScanMouse (and expression profiles) were conserved in human when cross-checked with TargetScanHuman. This also suggests that CDC25A is not a direct target of miR-204, unless miR-204 binding site(s) exists elsewhere besides the
3’UTR of CDC25A mRNA as TargetScan only predicts binding sites on 3’UTRs. In fact, the miRNA targeting prediction database RNA22, which considers full mRNAs instead of only 3’UTRs, predicts one miR-204 binding site on the
Chi Yan Ooi 190 miR-204 is a novel tumour suppressor of neuroblastoma
CDS of human CDC25A mRNA (p=0.0385) [383]. Therefore, it is still possible that
CDC25A is a direct target of miR-204.
My observation for the reduction of CDC25B mRNA expression by miR-204 mimic was also consistent with that observed for CDC25B mRNA in the JSQ3 squamous cell carcinoma of the head and neck cell line, using mimics from the same manufacturer
[336]. My results provided additional independent evidence for supporting the suppression of CDC25B by miR-204. In addition, for the first time, my results showed that miR-204 mimic can reduce E2F1, WEE1, RBL1 and CDC25A mRNA expression.
miR-204 mimic also reduced MYCN mRNA expression in MYCN-amplified BE(2)-
C and Kelly human neuroblastoma cell lines. I was also able to show that miR-204 mimic can reduced MYCN protein expression in both cell lines (see Figure 4.5). Doxycycline- induced over-expression of miR-204 in stable cell lines generated from BE(2)-C and Kelly cells also reduced MYCN protein expression (see Figure 4.10). The magnitude of reductions observed is modest, which is usually the case for direct targets of miRNA [454,
455]. These provided stronger evidence for miR-204 to down-regulate MYCN expression than the other predicted targets. However, these are not evidence that MYCN is a direct target of miR-204. Although miR-204 was not identified both in silico (for example
TargetScanHuman [198]) and experimentally in a luciferase reporter screen
[358] as a miRNA targeting the 3’UTR of MYCN mRNA, a miR-204 binding site was predicted to be within the CDS of MYCN mRNA by database RNA22 (p=0.000961) (see
Section 5.2.3) [383]. However, MYCN is also a regulator of cell cycle and proliferation, and
MYCN and E2F1 are known to up-regulate each other’s expression in neuroblastoma
[260, 261, 390, 431]. Therefore, it is possible that MYCN is suppressed indirectly through miR-204’s repression of cell cycle, for example through direct or indirect suppression of
Chi Yan Ooi 191 miR-204 is a novel tumour suppressor of neuroblastoma
E2F1, and/or MYCN is directly repressed by miR-204 (see Figure 4.17). The direct targeting of miR-204 would be addressed in the next chapter.
Furthermore, as I have showed that miR-204 can suppress MYCN expression, it may synergize with other MYCN-repressing miRNAs, particularly those silenced by
DNA hypermethylation in neuroblastoma (as discussed in Section 1.4.1.7), to suppress post-natal MYCN expression in ganglia tissues. With the transcriptional up-regulation of MYCN-repressing miRNAs miR-17, miR-19a, miR-19b and miR-375 by MYCN
[245, 253, 356], the down-regulation of those epigenetically silenced MYCN-repressing miRNAs and miR-204 may be required to reduce the downward pressures on MYCN expression in MYCN-driven neuroblastoma. In addition, miR-204 might synergize with miR-34a’s function in repressing cell cycle in neuroblastoma as discussed in Section
1.4.1.3, by reducing the expression of cell cycle genes E2F1, WEE1, CDC25A and
CDC25B.
Chi Yan Ooi 192 miR-204 is a novel tumour suppressor of neuroblastoma
Cell Cycle
MYCN E2F1
CDC25A
miR-204 CDC25B
RBL1
WEE1
Figure 4.17 miR-204 suppresses MYCN and cell cycle genes
It is found that synthetic miR-204 mimic can reduce mRNA expression of cell cycle genes E2F1, CDC25A, CDC25B, RBL1 and WEE1, and MYCN in human MYCN- amplified neuroblastoma cell lines. Both synthetic miR-204 mimic and the inducible stable over-expression of miR-204 also reduce MYCN protein expression. MYCN is known to regulate the expression of a number of genes involved in cell cycle, including the up-regulation of E2F1 expression [261, 390, 431]. In turn, E2F1 transcriptionally activates the expression of MYCN [260]. E2F1 can also transcriptionally activate CDC25A expression [456].
Chi Yan Ooi 193 miR-204 is a novel tumour suppressor of neuroblastoma
On the other hand, I have also investigated the functional roles of miR-204 in several phenotypic assays. Transfection of miR-204 mimic did not affect the cell viability of BE(2)-C and Kelly cells compared to negative control mimic #1, as measured by the Alamar Blue metabolic activity assays (see Figure 4.6). This is consistent with the findings by Ryan et al. [347], which found miR-204 mimic from another manufacturer had no effect on the cell viability of both MYCN-amplified (Kelly and NB1691) and non-MYCN-amplified (SK-N-AS and SH-SY5Y) human neuroblastoma cell lines as measured by MTS metabolic activity assays. However, for the first time I have shown that miR-204 mimic can reduce neuroblastoma cell proliferation, specifically in MYCN-amplified human neuroblastoma cells, by measuring thymidine analogue BrdU incorporation into DNA during DNA synthesis (see Figure
4.7). The discrepancy between the two types of assay is probably due to that a BrdU assay measures the amount of DNA synthesized over the time cells were incubated with the thymidine analogue – the rate of proliferation, which is likely to be more sensitive than inferring the total amount of cells from volume of metabolic activities. In addition, the reductions in cell proliferation but not cell viability suggest miR-204 does not trigger apoptosis but instead slows down how fast neuroblastoma cells grow. Indeed,
Ryan et al. [347] did not observe any increase in apoptosis by miR-204 mimic in Kelly cells, as measured by caspase 3/7 activation and annexin V/propidium iodide staining.
In addition, I also measured the effect of miR-204 on the colony forming capability of BE(2)-C and Kelly cells, which measures how well single cells attach and grow on a plastic plate to form colonies. This phenotypic assay is experimentally closer to in vivo conditions compared to the Alamar Blue cell viability and BrdU cell proliferation assays.
The percentages of colonies formed at the end of the colony forming assays are reduced for miR-204 mimic in both BE(2)-C and Kelly cells, compared to
Chi Yan Ooi 194 miR-204 is a novel tumour suppressor of neuroblastoma
negative control mimic #1 (see Figure 4.8). These are also observed for the doxycycline-induced over-expression of miR-204 in the two stable cell lines derived from BE(2)-C and Kelly, compared to DMSO solvent control (see Figure 4.11). These further support that miR-204’s role in neuroblastoma is to reduce cell growth.
After confirming that the BE(2)-C-miR-204 and Kelly-miR-204 cell lines do over- express miR-204 when induced by doxycycline (Figure 4.9) and performed similarly with miR-204 mimic in terms of suppression of MYCN protein expression and colony forming capability, these cells were subcutaneously injected into immunodeficient BALB/c nude mice to evaluate the in vivo functional role of miR-204. Induction of miR-204 over- expression by doxycycline began once the subcutaneous tumours reached 4-5 mm in diameter and continued until the end of the experiment. Thanks to the co- expression of turboGFP with miR-204 from the same promoter (Figure 2.1, Figure 4.9 for in vitro images), I was able to verify activation 7 days later using in vivo fluorescence imaging and exclude non-induced samples with fluorescence emissions no higher than negative controls (see Figure 4.12). My results showed for the first time that continued miR-204 over-expression can prolong the survival of mice carrying neuroblastoma tumour compared to negative control treatment, as measured by the time until reaching tumour volume of 1000 mm3 (Figure 4.13). I have also confirmed that miR-204 expression remained elevated in the doxycycline-induced tumours at the end of the experiment compared to non-induced negative control tumours, through TaqMan qRT-PCR (Figure 4.14). This means miR-204 can reduce overall growth and aggressiveness of neuroblastoma tumours in vivo. Therefore, I have showed that miR-
204 is a novel tumour suppressor of neuroblastoma. This is consistent with its tumour suppressive functions in other cancer [334, 336-342, 345, 346].
Chi Yan Ooi 195 miR-204 is a novel tumour suppressor of neuroblastoma
For miR-148a and miR-375, I found that almost all of the enriched predicted targets from Section 3.2.4, which have miRNA binding sites in TargetScanMouse, were not conserved in human when cross-checked with TargetScanHuman. This suggests the results from the enrichment analyses of miR-148a and miR-375 negatively correlated predicted target genes in Section 3.2.4 may not be applicable in the human context.
Unfortunately, I was not able to confirm miRNA targeting of any of those genes, conserved or not (not shown). Nevertheless, this indicates that we might need to take miRNA binding site conservation across different species into account when generating miRNA-mRNA interaction network predictions for cross-species research. Regardless, I have shown for the first time that miR-148a mimic and miR-375 inhibitor reduced
BE(2)-C and Kelly cell proliferation in vitro compared to their respective negative controls (Figure 4.16), consistent with the hypotheses that miR-148a is a tumour suppressor and miR-375 is an oncogene of neuroblastoma (see Table 3.9). In addition, this is in agreement with miR-148a’s role in inhibition of cell growth in a number of cancer [443, 445, 447]. However, this is oppose to those findings by Zhang et al. [357] where miR-375 inhibited MYCN-amplified neuroblastoma cell viability and colony forming in vitro and xenograft tumour growth in vivo.
In conclusion, I found that mR-204 is a novel tumour suppressor of neuroblastoma. It can suppress neuroblastoma cell growth in vitro and in vivo. It can also suppress protein expression of MYCN, which is an important regulator of cell cycle and proliferation in neuroblastoma [261, 390, 431]. miR-204 mimic also reduced mRNA expression of E2F1, WEE1, RBL1, CDC25A and CDC25B, suggesting miR-
204 targets multiple genes in the cell cycle pathway.
Chi Yan Ooi 196
Mechanism of bi-directional
suppression between miR-204 and MYCN and the
genome-wide identification of miR-204 targets
Chi Yan Ooi 197 Mechanism of bi-directional suppression between miR-204 and MYCN and the genome-wide identification of miR-204 targets
Introduction
I have previously showed that MYCN expression knockdown up-regulated miR-
204 expression while miR-204 expression was reduced in 2-weeks-old Th-MYCN+/+ mouse ganglia compare to wild-type in Sections 3.2.5 and 3.2.6 respectively. I have also showed that miR-204 suppresses MYCN expression in the previous chapter. For this chapter, we focus on exploring the mechanism of the bi-directional suppression between miR-204 and MYCN. MYCN is known to bind to promoters of both oncogenic and tumour suppressive miRNAs in neuroblastoma cells [81, 245]. In addition, there are significant co-localizations between MYCN genomic binding sites and DNA hypermethylation in neuroblastoma cell lines [79]. Furthermore, the methylation scores of 67 miRNAs were inversely correlated with their expression in primary neuroblastoma tumours in one study, with 30 of those can be up-regulated by DNA methyltransferase inhibitor 5’-Aza-2 deoxycytidine in more than one human neuroblastoma cell lines
[359]. Moreover, MYCN recruits histone deacetylases (HDACs) and polycomb repressive complex 2 to cause deacetylation and methylation of histones in suppressing gene expression in neuroblastoma [82, 86-89, 457]. These suggest MYCN could suppress miRNA expression through inducing DNA methylation, histone deacetylation and/or histone methylation. Indeed, the structurally similar protein c-MYC has been shown to utilize any of these three types of modification in supressing transcription of several miRNAs in B-cell lymphomas, while MYCN has been shown to recruit HDAC2 to suppress miR-183 expression in neuroblastoma [90, 458]. Lastly, some of those 67 methylation-sensitive miRNAs mentioned above are also known to directly repress
MYCN protein expression through the 3’UTR of MYCN mRNA [263].
Therefore, the main aim of this chapter is to elucidate the mechanism of how miR-
204 and MYCN could interact with each other to suppression each other’s expression. In
Chi Yan Ooi 198 Mechanism of bi-directional suppression between miR-204 and MYCN and the genome-wide identification of miR-204 targets
addition, we also want to explore further what are the broader range of targets for miR-204 and thus what would be the additional functional roles of miR-204 in neuroblastoma.
Chi Yan Ooi 199 Mechanism of bi-directional suppression between miR-204 and MYCN and the genome-wide identification of miR-204 targets
Results
5.2.1 ChIP assay revealed MYCN binding to pre-miR-204 genomic region
Since we observed the dysregulation of miR-204 in Th-MYCN+/+ mouse ganglia compared to wild-type (see Figure 3.7) and after MYCN knockdown in human neuroblastoma cell lines (see Figure 3.9 and Figure 3.10), we attempted to determine whether MYCN can transcriptionally regulates miR-204. The classic MYCN binding E- box sequence CACGTG 1000 bp up- and down-stream of miR-204 precursor sequence
(pre-miR-204) was not found using the NCBI Genome Data Viewer
(https://www.ncbi.nlm.nih.gov/genome/gdv/, GRCh38.p7 Human Assembly) [459].
However, the other MYCN binding E-box sequence CATGTG and non-canonical E-box sequences of CATTTG and CAACTG that MYCN could bind to [74, 75, 79] were found in this region. One E-box (CATGTG, E-box 4) is a part of the pre-miR-204 sequence, beginning from the 17th nucleotide of pre-miR-204, while another E-box
(CATTTG, E-box 5) is near the end of pre-miR-204 sequence on the antisense strand
(see Figure 5.1A). One CATGTG E-box (E-box 6) and one CAACTG E-box (E-box 7) are further downstream of pre-miR-204 on the sense strand, while two CAACTG E- boxes (E-box 1 and E-box 3) and one CATGTG E-box (E-box 2) are upstream of pre- miR-204 on the antisense strand.
We also analysed the human genomic DNA sequence which is surrounding the pre-miR-204 using the UCSC Genome Browser (http://genome.ucsc.edu/, Human Dec.
2013 (GRCh38/hg38) Assembly) [460, 461]. No H3K4Me1, H3K4Me3 or H3K27Ac marks or CpG islands that are associated with transcription promoters, regulatory elements or transcription start sites were found in 1000 bp up- and down-stream of pre- miR-204. However, a JUND proto-oncogene transcription factor binding site is present at upstream of pre-miR-204 (see Figure 5.1B), according to the ORegAnno track in the
Chi Yan Ooi 200 Mechanism of bi-directional suppression between miR-204 and MYCN and the genome-wide identification of miR-204 targets
browser [462, 463]. In addition, a DNaseI hypersensitivity site was detected further upstream of pre-miR-204 in the human cell line, WERI-Rb-1, of childhood cancer retinoblastoma, which might indicate the presence of a transcriptional start site nearby
[464, 465]. E-box 1 and E-box 2 upstream of pre-miR-204 are also within this DNaseI hypersensitivity site.
We designed SYBR Green qPCR primers and the corresponding five amplicons to scan for MYCN binding to the genomic DNA region of pre-miR-204. Amplicon number 1 overlaps with part of the DNaseI hypersensitivity site and covers E-box 1 within the DNaseI hypersensitivity site. On the other hand, amplicon number 2 covers the JUND transcription factor binding site and E-box 3 upstream of pre-miR-204. In addition, amplicon number 3 begins at the second last nucleotide of E-box 4 within pre-miR-204, overlapping with most of the pre-miR-204 sequence and covering E-box 5 on the antisense strand that is near the end of pre-miR-204. Amplicon number 4 is downstream of pre-miR-204, covering E-box 7.
Lastly, a ‘negative region’ amplicon was also designed >2000 bp upstream of pre-miR-204 as endogenous control to normalize the qPCR quantification of each sample before normalization to the negative control IgG.
A significant enrichment of amplicon number 3 was found, but not the other amplicons in ChIP assays pulldowns with anti-MYCN antibody compared to with negative control IgG. A positive control amplicon of the ODC1 promoter was included as described by Bello-Fernandez et al. [387] and used in other ChIP assays previously conducted in our lab, as ODC1 is a confirmed transcriptional target of MYCN [466].
Significant enrichment of the ODC1 promoter in the anti-MYCN ChIP pulldowns compared to negative control IgG was also achieved and in similar magnitude to that of amplicon number 3. Therefore, our data suggested that MYCN selectively binds to the pre-miR-204 region, possibly through E-box 4 embedded in the pre-miR-204 sequence
Chi Yan Ooi 201 Mechanism of bi-directional suppression between miR-204 and MYCN and the genome-wide identification of miR-204 targets
and E-box 5 just outside the 3’ end of pre-miR-204, where it can assert its suppressive effect on transcription.
Chi Yan Ooi 202 Mechanism of bi-directional suppression between miR-204 and MYCN and the genome-wide identification of miR-204 targets
Figure 5.1 A)
Negative region No. 1 No. 2 Pre-miR-204 No. 3 No. 4 (136 bp) (155 bp) (150 bp) (110 bp) (123 bp) (154 bp)
-2362 -676 -443 1 313 560
-736 -619 -483 21 E-box 1 E-box 6 CAACTG CATGTG E-box 3 +17 E-box 4 CAACTG CATGTG E-box 2 +563 E-box 7 CATGTG CAACTG +137 E-box 5 CATTTG
Figure 5.1 B)
Negative region No. 1 No. 2 No. 3 No. 4 (136 bp) (155 bp) (150 bp) (123 bp) (154 bp)
-2362 -676 -443 1 560
-617 -471 21 DNaseI hypersensitivity site JUND Binding Site Pre-miR-204 (From WERI-Rb-1, 150 bp) (11 bp) (110 bp)
Figure 5.1 C)
Chi Yan Ooi 203 Mechanism of bi-directional suppression between miR-204 and MYCN and the genome-wide identification of miR-204 targets
Figure 5.1 C) continued
Figure 5.1 MYCN binds to the pre-miR-204 DNA sequence in BE(2)-C
A) An overview of the design of amplicons used in ChIP assays for the assessment of MYCN binding to genomic DNA and MYCN E-box locations. The first 5’ nucleotide of pre-miR-204 is designated as 1. The classic MYCN binding E-box sequence CACGTG was not detected. The other MYCN binding E-box sequence CATGTG (coloured red), and the two non-canonical E-box sequence CATTTG (coloured purple) and CAACTG (coloured orange) that MYCN utilizes in MYCN-amplified neuroblastoma [74, 75, 79] were detected within 1000 bp up- and down-stream of pre-miR-204.
B) H3K4Me1, H3K4Me3, H3K27Ac mark, CpG island, ORegAnno transcription factor binding [462, 463] and ENCODE DNaseI Hypersensitivity sites [464, 465] information from the UCSC Genome Browser (http://genome.ucsc.edu/) on the Human Dec. 2013 (GRCh38/hg38) Assembly [460, 461].
C) DNA from ChIP assay. The DNA pulldowns of human MYCN-amplified neuroblastoma cell line BE(2)-C with negative control mouse IgG or anti-MYCN mouse antibody were quantified by qPCR. A positive control amplicon from ODC1 promoter was included for the ChIP assay, as ODC1 is a confirmed transcriptional target of MYCN [466]. * = p<0.05, ** = p<0.005, *** = p<0.0005, **** = p<0.0001, ns = p≥0.05. Plots represent mean + standard error mean, n=4.
Chi Yan Ooi 204 Mechanism of bi-directional suppression between miR-204 and MYCN and the genome-wide identification of miR-204 targets
5.2.2 HDACi panobinostat and DNA methyltransferase inhibitor 5-Aza-2’- deoxycytidine increased miR-204 expression in BE(2)-C cells in vitro
We hypothesized that miR-204 expression may be suppressed by DNA methylation, histone acetylation or histone methylation, which are common mechanisms of transcriptional suppression by MYCN and c-MYC [82, 86-90, 457, 458]. In addition, miRNA expression profiling of MYCN-amplified neuroblastoma BE(2)-C cells treated with the histone deacetylation inhibitor (HDACi) HC-toxin for 24 h showed up- regulation of miR-204 expression compared to methanol solvent negative control [90].
Therefore, BE(2)-C cells were treated with DNA methyltransferase inhibitor 5-Aza-2’- deoxycytidine and another HDACi panobinostat to determine whether loss of DNA methylation or acetylation can reactivate miR-204 expression. Indeed, miR-204 expression was up-regulated by 2.1 fold (p=0.0447) and 2.6 fold (p=0.0016) in BE(2)-C cells after treatment with 8.8 µM 5-Aza-2’-deoxycytidine for 72 h and 20 nM panobinostat for 24 h respectively compared to DMSO solvent control (Figure 5.2).
These suggest that miR-204 could potentially be negatively regulated by DNA methyltransferases and histone deacetylases.
Chi Yan Ooi 205 Mechanism of bi-directional suppression between miR-204 and MYCN and the genome-wide identification of miR-204 targets
Figure 5.2 miR-204 expression is elevated by HDACi panobinostat and DNA methyltransferase inhibitor 5-Aza-2’-deoxycytidine
BE(2)-C cells were treated with 20 nM panobinostat or DMSO for 24 h, or 8.8 µM 5- Aza-2’-deoxycytidine or DMSO for 72 h. miR-204 expression was quantified by TaqMan qRT-PCR using TaqMan MicroRNA Assays for miR-204 and endogenous controls U6 snRNA and RNU19. Plots represent mean + standard error mean, n=3.
Chi Yan Ooi 206 Mechanism of bi-directional suppression between miR-204 and MYCN and the genome-wide identification of miR-204 targets
5.2.3 miR-204 is predicted to target CDS of MYCN mRNA
Since I have shown that miR-204 can suppress MYCN expression in the last chapter,
I investigated whether miR-204 can directly bind to MYCN mRNA in silico. Searches on databases that only contain predictions for miRNA binding sites on 3’UTRs, such as
TargetScanHuman [198], miRanda [200] and PITA [382], did not yield any putative miR-
204 binding site on MYCN mRNA. However, predictions from the database RNA22 [383], which take the whole mRNA into consideration instead of just the 3’UTR only, suggested one miR-204 binding site at the central region of the MYCN CDS (Figure 5.3). This binding site sits on exon 3 near the boundary of exon 2 and 3, and within the sequence encoding for the Asp/Glu-rich acidic region of MYCN protein that is known to be involved in interacting with Nmi [467] (see Figure 1.3). Two serine amino acids that can be phosphorylated by casein kinase II are also encoded in this central region [468]. In addition, the predicted interaction between the miR-204 and MYCN CDS involves non-canonical bulged binding, where one of the nucleotide within the seed match site of the mRNA does not match to any nucleotide of the seed sequence on the miRNA (Figure 5.3A) [262, 469]. Without the bulge, the seed match site would be considered as a canonical 7mer-A1 site where the first 7 nucleotides of the miRNA are engaged in Watson–Crick pairing with the mRNA, and the first nucleotide being an A-U pairing [171, 187]. Additional complementarities are also predicted for the rest of miR-204.
Chi Yan Ooi 207 Mechanism of bi-directional suppression between miR-204 and MYCN and the genome-wide identification of miR-204 targets
Figure 5.3 A)
1100 1122 | | 5’-ATGAAGAGGAAGATGAAGAGGAA-3’ MYCN |||| || ||| ||||
3’-TCCGTATCCTACTGTTTC - CCTT-5’ miR-204
Figure 5.3 B)
5’- ATGCCGAGCTGCTCCACGTCCACCATGCCGGGCATGATCTGCAAGAACCCAGACCTC GAGTTTGACTCGCTACAGCCCTGCTTCTACCCGGACGAAGATGACTTCTACTTCGGC GGCCCCGACTCGACCCCCCCGGGGGAGGACATCTGGAAGAAGTTTGAGCTGCTGCCC ACGCCCCCGCTGTCGCCCAGCCGTGGCTTCGCGGAGCACAGCTCCGAGCCCCCGAGC TGGGTCACGGAGATGCTGCTTGAGAACGAGCTGTGGGGCAGCCCGGCCGAGGAGGAC GCGTTCGGCCTGGGGGGACTGGGTGGCCTCACCCCCAACCCGGTCATCCTCCAGGAC TGCATGTGGAGCGGCTTCTCCGCCCGCGAGAAGCTGGAGCGCGCCGTGAGCGAGAAG CTGCAGCACGGCCGCGGGCCGCCAACCGCCGGTTCCACCGCCCAGTCCCCGGGAGCC GGCGCCGCCAGCCCTGCGGGTCGCGGGCACGGCGGGGCTGCGGGAGCCGGCCGCGCC GGGGCCGCCCTGCCCGCCGAGCTCGCCCACCCGGCCGCCGAGTGCGTGGATCCCGCC GTGGTCTTCCCCTTTCCCGTGAACAAGCGCGAGCCAGCGCCCGTGCCCGCAGCCCCG GCCAGTGCCCCGGCGGCGGGCCCTGCGGTCGCCTCGGGGGCGGGTATTGCCGCCCCA GCCGGGGCCCCGGGGGTCGCCCCTCCGCGCCCAGGCGGCCGCCAGACCAGCGGCGGC GACCACAAGGCCCTCAGTACCTCCGGAGAGGACACCCTGAGCGATTCAGATGATGAA GATGATGAAGAGGAAGATGAAGAGGAAGAAATCGACGTGGTCACTGTGGAGAAGCGG CGTTCCTCCTCCAACACCAAGGCTGTCACCACATTCACCATCACTGTGCGTCCCAAG AACGCAGCCCTGGGTCCCGGGAGGGCTCAGTCCAGCGAGCTGATCCTCAAACGATGC CTTCCCATCCACCAGCAGCACAACTATGCCGCCCCCTCTCCCTACGTGGAGAGTGAG GATGCACCCCCACAGAAGAAGATAAAGAGCGAGGCGTCCCCACGTCCGCTCAAGAGT GTCATCCCCCCAAAGGCTAAGAGCTTGAGCCCCCGAAACTCTGACTCGGAGGACAGT GAGCGTCGCAGAAACCACAACATCCTGGAGCGCCAGCGCCGCAACGACCTTCGGTCC AGCTTTCTCACGCTCAGGGACCACGTGCCGGAGTTGGTAAAGAATGAGAAGGCCGCC AAGGTGGTCATTTTGAAAAAGGCCACTGAGTATGTCCACTCCCTCCAGGCCGAGGAG CACCAGCTTTTGCTGGAAAAGGAAAAATTGCAGGCAAGACAGCAGCAGTTGCTAAAG AAAATTGAACACGCTCGGACTTGCTAG-3’
Myc box II: aa 110 – 123; bHLH domain: aa 381 – 433; Ser-261, 263
phosphorylation sites; Asp/Glu-rich (acidic) region: aa 262 – 278
Chi Yan Ooi 208 Mechanism of bi-directional suppression between miR-204 and MYCN and the genome-wide identification of miR-204 targets
Figure 5.3 miR-204 is predicted to bind to MYCN CDS
A) The RNA22 database [383] prediction of miR-204 binding to MYCN mRNA. The sequences are expressed as their DNA sequences; thymidines are used instead uracils. Nucleotides are numbered relative to the full length MYCN mRNA, ensemble transcript ID ENSG00000134323. The p-value and predicted folding energy for the prediction is 0.000961 and -15.60 Kcal/mol respectively. B) The coding DNA sequence of MYCN from the NCBI CCDS Project [470-472] (CCDS ID CCDS1687.1) showing the relative position of the miR-204 binding site (underlined). Protein domains and features of the central region and closest adjacent features outside of the central region are highlighted and their amino acids positions indicated according to UniProt [418] and ref. [74]. The miR-204 binding site also sits on the codons for the amino acids repeat DEEE DEEE, and indicated with italic and bold fonts accordingly. Exon 2 sequence is in black, exon 3 sequence is in blue.
Chi Yan Ooi 209 Mechanism of bi-directional suppression between miR-204 and MYCN and the genome-wide identification of miR-204 targets
5.2.4 Biotin-labelled miR-204 mimic retained its ability to reduce target levels
In order to confirm direct interactions between miR-204 and the mRNAs of MYCN but also the other five KEGG Cell Cycle targets, the 3′-biotinylated miR-204 mimic were used to pulldown any mRNA bound to the miR-204 mimic in the miRISC complex inside the cell cytoplasm. This method offers the advantage of being capable of genome-wide and near unbiased identification of interacting mRNA targets of a miRNA without needing to be redesigned for each target that is required for luciferase reporter assays [194, 388]. First, the biotin-labelled miR-204 mimic was tested to determine whether it is still functional after the covalent modification, in that it can still suppress its targets. So BE(2)-C cells were transfected with 100 nM biotin-labelled miR-204 mimic or biotin-labelled negative control mimic #1, and mRNAs expression were assessed at 24 h, 48 h and 72 h after transfection by
SYBR Green qRT-PCR (Figure 5.4). Expression of MEIS2, a positive control and confirmed target of miR-204 [331, 449], was significantly reduced at 24 h and 48 h after transfection of biotin-labelled miR-204 mimic compared to biotin-labelled negative control mimic #1. Importantly, MYCN expression was significantly reduced (p<0.005) by biotin- labelled miR-204 mimic at 24 h, 48 h and 72 h after transfection, without affecting negative controls GUSB and HRPT1 mRNA expression. MYCN protein expression was also reduced by biotin-labelled miR-204 mimic at 72 h after transfection, compared to biotin-labelled negative control mimic #1 (Figure 5.5). These results were consistent with the results from the normal miR-204 mimic (see Sections 4.2.1 and 4.2.2). Therefore biotin-labelled miR-
204 mimic retained its ability to suppress its targets. It can now be used for the biotin pulldown assay.
Chi Yan Ooi 210 Mechanism of bi-directional suppression between miR-204 and MYCN and the genome-wide identification of miR-204 targets
Figure 5.4 Biotin-labelled miR-204 mimic is able to suppress MEIS2 and MYCN mRNA expression
BE(2)-C cells were transfected with 100 nM biotin-labelled miR-204 mimic and RNA samples were extracted at 24 h, 48 h and 72 h after transfection. mRNA expression were quantified by SYBR Green qRT-PCR and β2M was used as endogenous control. MEIS2 mRNA was used as positive control while GUSB mRNA and HRPT1 mRNA were used as negative controls. * = p<0.05, ** = p<0.005, *** = p<0.0005, ns = p≥0.05. Plots represent mean + standard error mean, n=3.
Chi Yan Ooi 211 Mechanism of bi-directional suppression between miR-204 and MYCN and the genome-wide identification of miR-204 targets
Figure 5.5 A)
- +
- Biotin-labelled Negative Control
MYCN Mimic #1
+ Biotin-labelled hsa-miR-204 Mimic
GAPDH
Figure 5.5 B)
Figure 5.5 Biotin-labelled miR-204 mimic is able to suppress MYCN protein expression
A) Western blot showing transfection of 100 nM biotin-labelled miR-204 mimic into BE(2)-C cells at 72 h after transfection, compared to biotin-labelled negative control mimic #1. Representative western blot images are shown. GAPDH was used as loading control. B) Densitometry of three independent western blots. * = p<0.05. Plot represents mean + standard error mean, n=3.
Chi Yan Ooi 212 Mechanism of bi-directional suppression between miR-204 and MYCN and the genome-wide identification of miR-204 targets
5.2.5 Biotin-labelled miR-204 mimic pulldown assay confirmed direct interaction between miR-204 and MYCN mRNA
3’-biotinylated miRNA mimic pulldown assays were performed to validate direct binding targets of miR-204. Figure 5.6A presents a schematic of the principle of such pulldown. Briefly, 100 nM biotin-labelled miR-204 mimic or biotin-labelled negative control mimic #1 were transfected into BE(2)-C cells and allowed to be incorporated into the RNA interference pathway and loaded into the miRISC for 24 h from the start of transfection. After the 24 h period, cell membranes were lysed to collect the cell cytoplasm (the inputs). Magnetic beads covalently coupled with a monolayer of streptavidin were used to pulldown the mimics through streptavidin binding of the biotin labels. Any mRNA physically associated with the mimics should be pulled down together with the biotin-labelled mimics. The RNAs were isolated from the inputs and pulldowns so that enrichment of any mRNA can be assessed.
Using SYBR Green qRT-PCR, MYCN mRNA expression in the cell cytoplasm
(inputs) was reduced by biotin-labelled miR-204 mimic compared to biotin-labelled negative control mimic #1 (Figure 5.6B), consistent with the previous section with total
RNA extracts. The negative control GUSB mRNA expression was not significantly affected in the input samples. Since the mRNA expression levels are not identical between the two types of inputs, the fold enrichment levels of biotin-labelled miR-204 mimic pulldowns are normalized to their inputs relative expression to take into account of the reduction in amount of mRNAs available to be pulled down. MYCN mRNA but not negative control
GUSB mRNA was significantly enriched (p=0.0335) in the biotin-labelled miR-204 mimic pulldowns, compared to that for biotin-labelled negative control mimic #1 (Figure 5.6C).
This showed that miR-204 mimic is selectively and directly interacting with MYCN mRNA, suggesting a direct targeting of MYCN by miR-204
Chi Yan Ooi 213 Mechanism of bi-directional suppression between miR-204 and MYCN and the genome-wide identification of miR-204 targets
through binding of MYCN mRNA. I also attempted to determine whether miR-204 directly target mRNAs of the five KEGG Cell Cycle pathway targets E2F1, WEE1,
RBL1, CDC25A and CDC25B, but there were insufficient RNA yields from the pulldowns and insufficient reagents to obtain conclusive results due to the time limitation in my PhD study.
Chi Yan Ooi 214 Mechanism of bi-directional suppression between miR-204 and MYCN and the genome-wide identification of miR-204 targets
Figure 5.6 A)
Magnetic Bead
Streptavidin
Biotin mRNA miRNA poly(A)
miRISC (miRNA-associated RNA- Induced Silencing Complex)
Figure 5.6 B)
Chi Yan Ooi 215 Mechanism of bi-directional suppression between miR-204 and MYCN and the genome-wide identification of miR-204 targets
Figure 5.6 C)
Figure 5.6 Confirmation of direct interaction between miR-204 and MYCN mRNA by biotin-labelled miR-204 mimic pulldown
A) Schematic representation of the principle of the 3’-biotinylated miRNA mimic pulldown assay. The miRNA exerts its effect on mRNA when loaded into the miRISC and binds to its target mRNA (see Figure 1.4). By transfecting cells with 3’-biotinylated miRNA mimic and allowing them to be loaded into miRISC and binding its targets, the bound mRNA targets along with the whole RNA-protein complex can be pulled down through the use of biotin-binding streptavidin coupled to magnetic beads. B-C) The mRNA expression of MYCN and negative control GUSB were quantified by SYBR Green qRT-PCR with β2M as endogenous control. The fold enrichments for MYCN and GUSB mRNAs in the pulldowns were normalized to their relative expression in inputs to take into account for the non-identical input levels of the mRNAs for the different mimics. * = p<0.05, ns = p≥0.05. Plots represent mean + standard error mean, n=3.
Chi Yan Ooi 216 Mechanism of bi-directional suppression between miR-204 and MYCN and the genome-wide identification of miR-204 targets
5.2.6 Microarray analysis of inducible miR-204 over-expression stable cell line
BE(2)-C-miR-204
We have also taken the more conventional microarray approach to identify potential targets of miR-204. Total RNA samples of BE(2)-C-miR-204 cells incubated with DMSO or doxycycline for 48 h were sent for microarray expression profiling after confirmation of miR-204 over-expression by TaqMan qRT-PCR. Dr Chelsea Mayoh provided the analysis of the microarray raw data to obtain the fold change and its p- value detected by each microarray probe in the doxycycline-treated samples compared to DMSO negative control samples (Figure 5.7). We did not use adjusted p-values, in which p-values are corrected for multiple comparisons, as they are all equal to 1 due to the relatively small amount of samples used (three biological replicates for each treatment). 524 probes showed a fold change of -2 or less and 566 probes showed a fold change of 2 or more. The corresponding down- and up-regulated genes were separately analysed using the MSigDB Compute Overlap tool [380] against the whole C2 curated gene sets (Release 5.2, October 2016) (Table 5.1) to determine what biological features are enriched. To allow for broader enrichment of gene sets, only biological significance
(absolute value of fold change ≥ 2) and not statistical significance was used as cut-off for selecting genes for enrichment analyses.
The top 10 most significantly enriched gene sets from the down-regulated genes are shown in Table 5.1A. The number 1 most significantly enriched gene set is
ZWANG_TRANSIENTLY_UP_BY_2ND_EGF_PULSE_ONLY. This gene set includes genes that are only transiently induced by a second pulse of EGF in immortalized human mammary epithelium 184A1 cells to enter the S phase of cell cycle, from the G1 phase after induction of cell cycle re-entry by first pulse of EGF [473]. These genes are associated with promoting cell cycle progression and inhibiting anti-proliferation
Chi Yan Ooi 217 Mechanism of bi-directional suppression between miR-204 and MYCN and the genome-wide identification of miR-204 targets
processes [473]. However, this exact gene set is also enriched from the up-regulated genes, albeit with less number of genes enriched and less statistical significance (Table 5.1B).
Moreover, two cell growth-related gene sets
TAKEDA_TARGETS_OF_NUP98_HOXA9_FUSION_10D_DN and
TAKEDA_TARGETS_OF_NUP98_HOXA9_FUSION_8D_DN, which contain genes down-regulated in CD34+ hematopoetic cells at 8 and 10 days respectively after the viral transduction of NUP98-HOXA9 fusion gene associated with acute myeloid leukemia, are enriched from the down-regulated genes. Taken together, these suggest involvements of miR-204 in cell cycle and proliferation.
The second and fourth most significantly enriched gene sets from the down- regulated genes are two gene sets, NABA_MATRISOME and
NABA_MATRISOME_ASSOCIATED, related to extracellular matrix (Table 5.1A). In addition, these two exact gene sets are also the top 2 enriched gene sets from the up- regulated genes (Table 5.1B). Moreover, the up-regulated genes are also enriched with genes from the gene set REACTOME_GPCR_DOWNSTREAM_SIGNALING for downstream signalling of G protein-coupled receptors. G protein-coupled receptors are utilized by cancer cells to modify tumour microenvironment and extracellular matrix, and promote proliferation and metastasis [474-476]. These suggest that miR-204 can influence the tumour microenvironment and metastasis.
On the other hand, the MSigDB analyses showed enrichments of genes down- regulated during the course of inflammation-induced maturation of monocyte-derived dendritic cells (gene set LINDSTEDT_DENDRITIC_CELL_MATURATION_D), genes involved in olfactory transduction conducted by specialized neurons
(KEGG_OLFACTORY_TRANSDUCTION) and target genes of Polycomb protein EED in human embryonic stem cells which are repressed in poorly differentiated tumours
Chi Yan Ooi 218 Mechanism of bi-directional suppression between miR-204 and MYCN and the genome-wide identification of miR-204 targets
(BENPORATH_EED_TARGETS) [352, 477]. These suggest that miR-204 can induce neuroblastoma differentiation.
There are also enrichments of genes in gene sets of both up- and down-regulated genes in nasopharyngeal carcinoma compared to normal tissue, and in basal subtype of breast cancer (DODD_NASOPHARYNGEAL_CARCINOMA_UP,
SENGUPTA_NASOPHARYNGEAL_CARCINOMA_DN,
SMID_BREAST_CANCER_BASAL_DN, SMID_BREAST_CANCER_BASAL_UP).
In addition, the gene set DELYS_THYROID_CANCER_UP containing genes up- regulated in papillary thyroid carcinoma compared to normal tissue is also enriched. c-
MYC is known to be involved in papillary thyroid carcinoma, nasopharyngeal carcinoma and breast cancer [478-481]. These could suggest miR-204 affects c-MYC related processes.
Last but not least, there are enrichments of genes among genes up-regulated in the microarray that are also up-regulated in pancreatic cancer cell lines only but not in an immortalized human pancreatic ductal epithelial cell line, and in glioma cell lines treated with DNA methyltransferase inhibitor 5-Aza-2'-deoxycytidine (also known as decitabine) and both 5-Aza-2'-deoxycytidine and HDACi trichostatin A respectively (gene sets
SATO_SILENCED_BY_METHYLATION_IN_PANCREATIC_CANCER_1 and
KIM_RESPONSE_TO_TSA_AND_DECITABINE_UP respectively). Three of those enriched genes, SFN, CYP3A5 and MAGEB1, are present in both gene sets (Figure 5.8A).
Using RNA sequencing expression profiling data on 498 human neuroblastoma patient tumour samples [482], I have determined whether there is any correlation between the expression of the above three genes and patient survival. High SFN expression significantly correlates with both better overall and event-free survival, while high CYP3A5 expression only significantly correlates with better overall survival (Figure 5.8B
Chi Yan Ooi 219 Mechanism of bi-directional suppression between miR-204 and MYCN and the genome-wide identification of miR-204 targets
and C). In contrast, low MAGEB1 expression correlates with better overall and event- free survival (Figure 5.8D). Moreover, 98 % of primary neuroblastoma tumours showed
DNA hypermethylation of SFN and DNA methylation of SFN can be used to distinguish much higher risk patients in high risk stage 4 neuroblastoma patients [483,
484]. In addition, MAGEB1 is known to be activated by 5-Aza-2'-deoxycytidine in several cancer cell lines of melanomas, choriocarcinoma and sarcoma and normal cells such as phytohemagglutinin-stimulated peripheral blood lymphocytes and dentritic cells
[485]. Taken together these suggest that miR-204 inhibits DNA methyltransferases and/or facilitate DNA demethylation. However, none of the DNA methyltransferases detected by the microarray has a fold change of -1.5 or lower.
Chi Yan Ooi 220 Mechanism of bi-directional suppression between miR-204 and MYCN and the genome-wide identification of miR-204 targets
Fold Change = -2 Fold Change = 2
78
2.0 66
25 19
value)
- 1.5
p
1.0
log10 (
-
0.5
458 488
0.0
-2 -1 0 1 2
log2 (Fold Change)
Figure 5.7 Differential expression of genes in stable cell line BE(2)-C-miR-204 after doxycycline-induced miR-204 over-expression
Microarray analysis of total RNA samples from BE(2)-C-miR-204 cells 48 h after induction of miR-204 expression by doxycycline compared to DMSO negative control treatment. Microarray raw data analysed by and figure adapted from Dr Chelsea Mayoh at the Children’s Cancer Institute. Each dot represents one microarray probe, coloured green for those with fold change ≥ 2 or ≤ -2 and non-adjusted p-value ≤ 0.05, orange for those with fold change ≥ 2 or ≤ -2 and non-adjusted p-value > 0.05, and red for those with fold change < 2 or > -2 and non-adjusted p-value ≤ 0.05. The numbers of dots are indicated for each colour at each region of the figure.
Chi Yan Ooi 221 Mechanism of bi-directional suppression between miR-204 and MYCN and the genome-wide identification of miR-204 targets
Table 5.1 A)
# Genes
in
Overlap FDR q- Rank Gene Set Name Brief Description (k) value
ZWANG_TRANS Genes transiently induced only by IENTLY_UP_BY the second pulse of EGF [GeneID _2ND_EGF_PUL =1950] in 184A1 cells (mammary 1 SE_ONLY epithelium). 28 2.05E-06
Ensemble of genes encoding extracellular matrix and NABA_MATRIS extracellular matrix-associated 2 OME proteins 20 2.06E-05
DODD_NASOPH Genes up-regulated in ARYNGEAL_CA nasopharyngeal carcinoma (NPC) 3 RCINOMA_UP compared to the normal tissue. 26 4.16E-05
Ensemble of genes encoding ECM-associated proteins NABA_MATRIS including ECM-affilaited proteins, OME_ASSOCIAT ECM regulators and secreted 4 ED factors 16 1.04E-04
SMID_BREAST_ CANCER_BASA Genes down-regulated in basal 5 L_DN subtype of breast cancer samles. 15 1.96E-04
Genes down-regulated in CD34+ [GeneID=947] hematopoetic cells TAKEDA_TARG by expression of NUP98-HOXA9 ETS_OF_NUP98_ fusion [GeneID=4928;3205] off a HOXA9_FUSION retroviral vector at 10 days after 6 _10D_DN transduction. 7 1.57E-03
Genes down-regulated during the LINDSTEDT_DE course of maturation of NDRITIC_CELL_ monocyte-derived dendritic cells MATURATION_ (DC) in response to inflammatory 7 D stimuli (cluster D). 5 5.89E-03
SENGUPTA_NAS OPHARYNGEAL Genes down-regulated in _CARCINOMA_ nsopharyngeal carcinoma relative 8 DN to the normal tissue. 9 7.89E-03
SMID_BREAST_ CANCER_BASA Genes up-regulated in basal 9 L_UP subtype of breast cancer samles. 12 7.89E-03
Genes down-regulated in CD34+ [GeneID=947] hematopoetic cells TAKEDA_TARG by expression of NUP98-HOXA9 ETS_OF_NUP98_ fusion [GeneID=4928;3205] off a HOXA9_FUSION retroviral vector at 8 days after 10 _8D_DN transduction. 7 1.04E-02
Chi Yan Ooi 222 Mechanism of bi-directional suppression between miR-204 and MYCN and the genome-wide identification of miR-204 targets
Table 5.1 B)
nk Ra # Genes Overlap in FDR q- Gene Set Name Brief Description (k) value
Ensemble of genes encoding NABA_MATRI extracellular matrix and extracellular 1 SOME matrix-associated proteins 20 3.27E-06
Ensemble of genes encoding ECM-
NABA_MATRI associated proteins including ECM-
SOME_ASSOCI affilaited proteins, ECM regulators 2 ATED and secreted factors 14 8.48E-04
DELYS_THYR Genes up-regulated in papillary OID_CANCER_ thyroid carcinoma (PTC) compared 3 UP to normal tissue. 11 8.48E-04
ZWANG_TRAN Genes transiently induced only by
SIENTLY_UP_ the second pulse of EGF [GeneID BY_2ND_EGF_ =1950] in 184A1 cells (mammary 4 PULSE_ONLY epithelium). 21 8.83E-04
KEGG_OLFAC TORY_TRANS 5 DUCTION Olfactory transduction 10 1.24E-03
Genes up-regulated in the pancreatic
SATO_SILENC cancer cell lines (AsPC1, Hs766T, ED_BY_METH MiaPaCa2, Panc1) but not in the YLATION_IN_P non-neoplastic cells (HPDE) by ANCREATIC_C decitabine (5-aza-2'-deoxycytidine) 6 ANCER_1 [PubChem=451668]. 10 2.01E-03
REACTOME_G
PCR_DOWNST REAM_SIGNAL Genes involved in GPCR 7 ING downstream signaling 12 2.01E-02
KIM_RESPONS Genes up-regulated in glioma cell
E_TO_TSA_AN lines treated with both decitabine
D_DECITABIN [PubChem=451668] and TSA 8 E_UP [PubChem=5562]. 5 4.30E-02
Set 'Eed targets': genes identified by
ChIP on chip as targets of the Polycomb protein EED BENPORATH_ [GeneID=8726] in human embryonic 9 EED_TARGETS stem cells. 13 4.30E-02
SMID_BREAST _CANCER_BAS Genes up-regulated in basal subtype 10 AL_UP of breast cancer samles. 10 4.30E-02
Chi Yan Ooi 223 Mechanism of bi-directional suppression between miR-204 and MYCN and the genome-wide identification of miR-204 targets
Table 5.1 Top 10 enriched gene sets from down- and up-regulated genes in stable cell line BE(2)-C-miR-204 after doxycycline-induced miR-204 over-expression
Top 10 most significantly enriched MSigDB C2 curated gene sets from A) down- regulated (fold change ≤ 2) and B) up-regulated (fold change ≥ 2) genes from our microarray analysis of BE(2)-C-miR-204 cells induced by doxycycline for 48 h to over- expressed miR-204 compared to negative control. Gene sets are ranked by lowest FDR q-value.
Chi Yan Ooi 224 Mechanism of bi-directional suppression between miR-204 and MYCN and the genome-wide identification of miR-204 targets
Figure 5.8 A)
Genes up-regulated in glioma cell lines treated with both decitabine and TSA
IL36RN CPA3 ITGA7 SFN LAMC2 CIITA CYP3A5 CPA4 EPB41L4A MAGEB1 PTX3 KIF21B
Genes up-regulated in the pancreatic cancer cell lines but not in the non-neoplastic cells by decitabine (5-aza-2'-deoxycytidine)
Figure 5.8 B)
Overall Survival – SFN Event-Free Survival – SFN 1
1 ty Probabili High Expression, n=249 High Expression, n=249
Low Expression, n=249
Low Expression, n=249
v e r a l l 0 O
Survival
21
6
Free Survival Probability FreeSurvival p=1.3e-03 - p=0.013
0 0
216 Event
0 Follow up in months Follow up in months
Chi Yan Ooi 225 Mechanism of bi-directional suppression between miR-204 and MYCN and the genome-wide identification of miR-204 targets
Figure 5.8 C)
Overall Survival – CYP3A5 Event-Free Survival – CYP3A5 1 1
Probability High Expression, n=249
High Expression, n=249
Low Expression, n=249
Probability Low Expression, n=249
FreeSurvival
-
Survival
Event Overall p=0.036 p=0.072
0 0
216
216
0 0
Follow up in months Follow up in months
Figure 5.8 D)
Overall Survival – MAGEB1 Event-Free Survival –
bility Proba 1 MAGEB1 1 Low Expression, n=249
Low Expression, n=249
High Expression, n=249
lity Probabi High Expression, n=249
FreeSurvival
-
Survival
Event Overall p=4.9e-04 p=5.1e-04
0 0
216
216
0
0 Follow up in months Follow up in months
Figure 5.8 SFN, CYP3A5 and MAGEB1 are high confidence DNA methylation- related targets up-regulated by miR-204
A) Three genes, SFN, CYP3A5 and MAGEB1, are present in both lists of genes enriched forthetwogenesets
SATO_SILENCED_BY_METHYLATION_IN_PANCREATIC_CANCER_1 and KIM_RESPONSE_TO_TSA_AND_DECITABINE_UP. B-D) Correlations between expression of the above three genes and patient survival were evaluated using RNA sequencing expression data from 498 human neuroblastoma patient tumour samples [482] through the R2: Genomics Analysis and Visualization Platform (http://r2.amc.nl).
Chi Yan Ooi 226 Mechanism of bi-directional suppression between miR-204 and MYCN and the genome-wide identification of miR-204 targets
Discussion
Genomically, the precursor sequence of miR-204 pre-miR-204 is located at intron 7 of protein coding host gene TRPM3 in relation to RefSeq transcript variants 5, 6, and 8
(NM_206946.3, NM_206947.3, NM_001007470.1) [327, 459]. It is conventionally assumed that intronic miRNAs are transcribed as part of the host gene’s primary transcript before being processed into pre-miRNA [114, 126]. For example, MYCN transcriptionally activates the miR-106b-25 cluster, located at intron 13 of MCM7, through binding the classical E-box
CACGTG at the promoter of the host gene MCM7 as the miR-106b-25 cluster is co- transcribed with the MCM7 primary transcript [245, 247]. However, it has been long suggested that intronic miRNAs can be transcribed independently from their host gene [327].
Using microarray-based nucleosome positions mapping and ChIP-chip assays for transcriptionally active chromatin markings H3K4me3 and H3K9/14Ac, RNA polymerase II and III in 20 kb up- and 1 kb down-stream regions of known miRNAs, Ozsolak et al. [486] discovered putative promoters independent from respective host genes for 32 out of 88 intronic miRNAs with putative promoters identified. In particular, they discovered that a transcriptional start site <500 bp down-stream of the c-MYC/MYCN binding site at intron 1 of miR-17-92 cluster host gene C13orf25 and verified the c-MYC-sensitive intronic promoter region through luciferase reporter assays
[486]. More recently, full-length primary transcripts of pre-miRNAs with shorter 5’end and sometimes shorter 3’end than their host genes’ primary transcripts are identified for the first time using RNA Ligase-Mediated Rapid Amplification of cDNA Ends (RLM-
RACE) after Drosha knockdown [122]. For example, primary transcripts harbouring only pre-miR-106b and pre-miR-93 of the miR-106b-25 cluster but not pre-miR-25, the last member of the cluster, and any of the MCM7 exons were identified [122].
Chi Yan Ooi 227 Mechanism of bi-directional suppression between miR-204 and MYCN and the genome-wide identification of miR-204 targets
Since the TRPM3 promoter is almost 60 kb upstream of pre-miR-204, which is not close enough to drive direct transcription of pre-miR-204, we decided to focus on the 1000 bp up- and down-stream of pre-miR-204. Although the MYCN-binding classical E-box motif CACGTG is not present in this region, other non-canonical E- boxes are present in both the sense and antisense strands (see Figure 5.1A). These include three CATGTG E-boxes, which Murphy et al. [79] reported predominant usage over the classic E-box motif CACGTG by MYCN in neuroblastoma cell lines through
ChIP-chip. Our ChIP assays only showed a significant enrichment of amplicon 3 but not the other three amplicons in the anti-MYCN antibody pulldown compared to control
IgG pulldown. This suggested that MYCN selectively binds to the pre-miR-204 sequence which overlaps with amplicon 3 and proximal surrounding region (DNA was sheared into ~1000 bp fragments). Since amplicon 3 starts at the 2nd last nucleotide of one sense CATGTG E-box and covers the full antisense CATTTG E-box, this also suggested that at least one of these E-boxes is occupied by MYCN. Mutation of these E- boxes is needed to further confirm the MYCN occupancy of these E-boxes.
While we have shown the MYCN binding to the pre-miR-204 sequence and in turn supposedly transcriptionally repress miR-204 expression as the mechanism of
MYCN suppression of miR-204, we did not determine the nature of the transcriptionally repression. The first question is whether there is a local promoter of pre-miR-204 for
MYCN to act on or MYCN is affecting the host gene TRPM3 promoter transcription, which could be for example by looping the chromatin like a distal regulatory element
[487] or acting as a roadblock to transcription elongation. The Ozsolak et al. [486] study mentioned above found that intronic miRNAs with putative independent transcription start sites identified have a median distance of 57 kb from their host gene transcription start sites, compared to a mean of 7.9 kb for those identified to share their host gene
Chi Yan Ooi 228 Mechanism of bi-directional suppression between miR-204 and MYCN and the genome-wide identification of miR-204 targets
transcription start sites. Since the TRPM3 transcription start site is almost 60 kb upstream from pre-miR-204, this would suggest an independent local promoter for pre- miR-204 is possible. While I did not find any CpG island, H3K4Me1, H3K4Me3 or
H3K27Ac transcriptional regulation marking 1000 bp upstream of pre-miR-204 through the UCSC Genome Browser [288, 289], the presence of a JUND transcription factor binding site and a DNaseI hypersensitivity site (in a childhood cancer retinoblastoma cell line) were detected in this region. In addition, while the miRStart miRNA transcription start sites prediction database did not predict any transcription start sites for pre-miR-
204, it showed several Cap Analysis of Gene Expression (CAGE) and Transcription
Start Site (TSS) tags, and H3K4Me3 marks in the same region from high-throughput sequencing and ChIP-seq experiments (Appendix B) [488]. In particular, some TSS tags are observed around the JUND binding site and some H3K4Me3 marks are observed around the DNaseI hypersensitivity site. Further experimental investigations, such as cloning of genomic regions into a luciferase transcription reporter and transcripts identification by RLM-RACE, are warranted to properly define the pre-miR-204 promoter in neuroblastoma. Nevertheless, we should not ignore the role of the TRPM3 promoter in MYCN-mediated repression of miR-204. For example, the transcription factor MITF is known to up-regulate miR-211, the miR-204 paralog, and binds to non- canonical E-boxes CATGTG in the promoter of miR-211 host gene TRPM1 [489, 490].
The second question is how MYCN modified the transcription of miR-204. As summarized in this chapter’s introduction, MYCN may utilize DNA methylation, histone deacetylation or histone methylation by recruiting other proteins to suppress miRNA expression. The possibility of MYCN transcriptional repression of miR-204 through
HDAC-mediated histone deacetylation is further supported by the observation of miR-204 expression up-regulation in the expression profiling of BE(2)-C neuroblastoma cells
Chi Yan Ooi 229 Mechanism of bi-directional suppression between miR-204 and MYCN and the genome-wide identification of miR-204 targets
treated with HDACi HC-toxin compared to control [90]. In addition, our microarray analysis of miR-204 over-expression in the BE(2)-C-miR-204 stable cell line showed up-regulation of several genes that are also up-regulated by DNA methyltransferase inhibitor 5-Aza-2’-deoxycytidine in other cancer cells, particularly SFN which is also hypermethylated in neuroblastoma and especially in very high-risk neuroblastoma [483,
484]. Therefore, it is also possible for MYCN to repress miR-204 expression through the same suppression mechanism for putative miR-204 down-stream targets – DNA methylation.
To assess the possibilities for the above scenarios, I have treated the MYCN- amplified BE(2)-C cells with HDACi panobinostat and DNA methyltransferase inhibitor 5-
Aza-2’-deoxycytidine. miR-204 expression was significantly up-regulated after 72 h treatment of 5-Aza-2’-deoxycytidine, suggesting miR-204 expression is repressed by DNA methylation and DNA methyltransferase(s). However, a CpG island envelops the transcription start site of miR-204 host gene TRPM3 and bisulfite genomic sequencing PCR showed hypermethylation of this CpG island in glioma cell lines expressing low levels of miR-204 and clinical glioma tumour samples, compared to glioma cell lines with higher miR-204 expression and primary cultured astrocytes [337]. Similarly, Shi et al.
[346] identified hypermethylation of the exact same genomic region in non-small-cell lung carcinoma cell lines and clinical tissues compared to the human bronchial epithelial cell line
BEAS-2B, using the exact same bisulfite genomic-sequencing PCR primers. In either case,
72 h treatment of 5-Aza-2’-deoxycytidine elevated miR-204 expression in these cell lines.
Therefore, it is possible that this CpG island is also hypermethylated in BE(2)-C and other
MYCN-amplified neuroblastoma cells and 5-Aza-2’-deoxycytidine is acting on either or both the TRPM3 promoter and putative pre-miR-204 local promoter, if there is any.
Bisulfite genomic sequencing would allow us to experimentally determine
Chi Yan Ooi 230 Mechanism of bi-directional suppression between miR-204 and MYCN and the genome-wide identification of miR-204 targets
the methylation status of the TRPM3 promoter and putative pre-miR-204 local promoter in neuroblastoma cells. In addition, while I showed that DNA methylation inhibited miR-204 expression, I did not show that MYCN caused the hypermethylation.
Association of MYCN with specific DNA methyltransferase(s) at the pre-miR-204 sequence or TRPM3 promoter, perhaps by further ChIP and Re-ChIP assays [491] and siRNA knockdown, would allow us to make that determination.
On the other hand, miR-204 expression was also significantly up-regulated after
24 h treatment of HDACi panobinostat on BE(2)-C cells. This verified the published expression profiling data on BE(2)-C cells treated with HC-toxin mentioned previously
[90]. While 24 h of panobinostat treatment in BE(2)-C has also been shown to down- regulate MYCN protein expression [90], the degree of miR-204 up-regulation seems a lot higher for the panobinostat treatment than the siRNA knockdown of MYCN (see
Figure 5.2 and Figure 3.9). Therefore, it seems unlikely that MYCN down-regulation can fully account for the observed up-regulation of miR-204 by panobinostat. Thus, miR-204 expression may be suppressed by histone deacetylation through HDAC.
Furthermore, HDACi can down-regulate expression of protein members in polycomb repressive complex 2 and reduce histone methylation [492, 493]. Therefore, histone methylation by polycomb repressive complex 2 may also play a role in supressing miR-
204 expression. To confirm that MYCN represses miR-204 expression through histone deacetylation and/or methylation by recruiting HDAC and/or polycomb repressive complex 2, we could perform siRNA knockdown of specific HDACs and polycomb repressive complex 2, and ChIP and Re-ChIP assays in the future study.
In terms of miR-204 suppression of MYCN expression, I investigated the possibility of direct targeting of MYCN mRNA by miR-204. Consistent with the published MYCN
3’UTR luciferase reporter screen [358], I did not identify any miR-204
Chi Yan Ooi 231 Mechanism of bi-directional suppression between miR-204 and MYCN and the genome-wide identification of miR-204 targets
binding site on MYCN 3’UTR from prediction databases. However, I was able to identify a putative miR-204 binding site on MYCN CDS, suggesting miR-204 may directly interact with MYCN mRNA. While 3’UTRs are predominant binding targets of miRNAs, many miRNA binding sites in CDSs have been identified through techniques involving genome-wide RISC complex immunoprecipitation and some confirmed by luciferase reporter assays and/or mutation of binding sites [173, 176-178, 188, 201, 494-
500]. In addition, the binding site prediction displayed non-canonical seed matching with single nucleotide bulge of the mRNA at position 5 (counting from the miRNA 5’ end) of the seed match site (see Figure 5.3A). The single nucleotide bulge of mRNAs and miRNAs within seed match sites, commonly at position 5 or 6, have been computationally (e.g. by RNA22) and experimentally identified by genome-wide RISC immunoprecipitation techniques, and some have been experimentally validated [173,
189, 193, 201, 469, 501, 502]. However, it has been suggested the predominant mode of action involves a guanine bulge (G-bulge) [189]. Nevertheless, miRNA binding site involving an A-bulge at position 5 on the mRNA, which is also the case in our miR-
204-MYCN binding site prediction, has been experimentally validated previously [193].
To validate binding between miR-204 and MYCN mRNA as predicted by RNA22
(and mRNAs of other five KEGG Cell Cycle predicted target genes), I have performed a biotin-labelled miRNA mimic pulldown experiment (see Section 5.2.5). This method was chosen as it allows direct identification of the miRNA binding partners unlike RISC immunoprecipitation techniques, until the recent introduction of very inefficient process that enables such identification in RISC immunoprecipitation [503]. Before the actual pulldown experiment, I validated the biotin-labelled miR-204 mimic is still functional in suppressing the mRNA expression of the previously verified miR-204 target MEIS2, and MYCN mRNA and protein expression (see Section 5.2.4). The MYCN mRNAs but not
Chi Yan Ooi 232 Mechanism of bi-directional suppression between miR-204 and MYCN and the genome-wide identification of miR-204 targets
GUSB mRNAs were significantly enriched in the biotin-labelled miR-204 mimic pulldowns compared to biotin-labelled negative control mimic #1. This confirmed that MYCN mRNA binds to miR-204. Unfortunately, there were not enough RNA yields from the pulldowns to generate enough replicates of the other predicted target genes for any meaningful results. In addition, the enrichment of MYCN mRNA observed in the pulldowns was not particularly high. It is observed target mRNAs that are degraded by the miRNA mimic are negatively biased against in biotin pulldown experiments [194]. I have attempted to compensate for mRNA degradation by normalizing the fold enrichment in the pulldowns to the loss of mRNAs in the cytoplasmic inputs. Nevertheless, we did not confirm the location of the miR-204 binding site on MYCN mRNA. To confirm sequence-dependent targeting by miR-
204 mimic or over-expression, a luciferase reporter assay where the wild-type and mutant predicted miR-204 binding site on the MYCN mRNA together with surrounding sequence are cloned into the 3’UTR of the firefly luciferase would need to be performed. I have already designed these luciferase reporter plasmids and the sequences are already cloned into the plasmids by Life Technologies. However, there was insufficient time to perform such experiment for my PhD study.
Finally, we wanted to perform a genome-wide identification of miR-204 targets.
Initially, we intended to use the biotin-labelled miR-204 mimic pulldown assay coupled with microarray in order to identify direct targets of miR-204 on a genome-wide scale.
However, the RNA yields from the pulldowns were too low for microarray. One possible solution to this is to crosslink the RISC proteins to the interacting RNAs, similarly to later
RISC immunoprecipitation techniques [498, 503], to recover less kinetically stable interactions. In addition, crosslinking allows the use of RNase to trim unbound portions of mRNAs and identify the sequence of the binding site through sequencing [498].
Nevertheless, we have chosen to over-express miR-204 and measure changes in gene
Chi Yan Ooi 233 Mechanism of bi-directional suppression between miR-204 and MYCN and the genome-wide identification of miR-204 targets
expression through microarray (see Section 5.2.6) due to the ease access of performing microarrays through the genomics services provided by the university. However, this method cannot distinguish between direct and indirect targets. The number 1 gene set most significantly enriched from the list of genes down-regulated by miR-204 over- expression is related to cell cycle on the regulation of S phase entry, which is consistent with our findings from the last two chapters. However, the same gene set is also one of the top 10 most enriched gene sets from the list of genes up-regulated by miR-204 expression. In addition, there were also enrichments of miR-204 down-regulated genes that are also down-regulated in cells expressing the NUP98-HOXA9 fusion gene at the start of increased proliferation. This could be due to the cells counteracting the action of miR-204 or miR-204 also indirectly up-regulates cell cycle genes. Regardless, I have already showed in the last chapter that the overall long term effect of miR-204 is to reduce cell growth in neuroblastoma. Since I have also showed that miR-204 directly repress MYCN expression, there were also enrichments of genes in several gene sets from cancers related to c-MYC.
Moreover, there were enrichments of genes from two extracellular matrix gene sets and the G protein-coupled receptors downstream signalling gene set. The extracellular matrix plays a role in cancer cell invasion, migration and metastasis, while G protein- coupled receptors regulate the extracellular matrix and metastasis [474-476, 504]. These suggest that miR-204 regulates neuroblastoma cell invasion, migration and metastasis.
Indeed, the ability of miR-204 to inhibit cell invasion, migration and/or metastasis has been demonstrated in a number of cancer such as glioma, non-small-cell lung carcinoma, squamous cell carcinoma of the head and neck, colorectal cancer, bacterial pathogen- associated gastric cancer and malignant peripheral nerve sheath tumours [336, 337, 339,
346, 440, 441]. Furthermore, miR-204 has been shown to inhibit
Chi Yan Ooi 234 Mechanism of bi-directional suppression between miR-204 and MYCN and the genome-wide identification of miR-204 targets
cancer cell migration and invasion through inhibiting the AKT/mTOR G protein Rac1 signalling pathway, by directly suppressing BDNF expression [342]. In fact, BDNF mRNA expression is also reduced by 2 fold in our microarray analysis and is one of the genes enriched for the two extracellular matrix gene sets. This further supports miR-
204’s potential role in inhibiting neuroblastoma cell invasion, migration and metastasis.
On the other hand, there were enrichments of genes for three cell specialization and differentiation gene sets that suggest miR-204 promotes neuroblastoma differentiation. This is not surprising, given miR-204 has been shown to be involved in specialization and differentiation of eye cells and is required for differentiation of cardiomyocyte progenitor cells [326, 329, 331, 505]. Therefore, it is likely miR-204 also facilitates the differentiation of normal neuroblasts and neuroblastoma.
Lastly, there were enrichments of up-regulated genes for two gene sets involving treatment of cancer cells with DNA methyltransferase inhibitor 5-Aza-2’-deoxycytidine alone or with HDACi trichostatin A. These suggest that miR-204 inhibits DNA methylation, promotes DNA demethylation, or both. Unfortunately, we did not detect a fold change of -
1.5 or lower in any DNA methyltransferase mRNA from our microarray analysis. It is possible that miR-204 only affected protein expression and not mRNA expression of DNA methyltransferase(s), or it does not act via repression of DNA methyltransferase. Since I have showed that miR-204 expression is repressed by DNA methyltransferase, it may be advantageous for miR-204 to also repress the same DNA methyltransferase to form a feedback loop, which has been shown for miR-148a in hepatocellular carcinoma [443]. In addition, three enriched genes are present in both gene sets (see Figure 5.8A), providing a higher chance that these genes could be indirect targets of miR-204 and also 5-Aza-2’- deoxycytidine in neuroblastoma. In fact, one of the three genes called SFN has been shown to be hypermethylated in neuroblastoma, particularly
Chi Yan Ooi 235 Mechanism of bi-directional suppression between miR-204 and MYCN and the genome-wide identification of miR-204 targets
higher-risk neuroblastoma [483, 484], consistent with the suggested role of miR-204 in regulating DNA methylation. Unsurprisingly, I found that high SFN expression correlates to better neuroblastoma patient overall and event-free survival (see Figure
5.8B). SFN expression can be transcriptionally activated by tumour suppressor p53 to inhibit G2/M cell cycle checkpoint progression, and also suppress p53 degradation by enhancing MDM2 self-ubiquitination [506, 507]. Therefore, miR-204 potential indirect up-regulation of SFN may serve as additional mechanism for the role of miR-204 in inhibiting neuroblastoma cell proliferation. SFN also has been shown to promote c-
MYC poly-ubiquitination and consequent degradation [508]. As a result I hypothesize that SFN could similarly promote MYCN poly-ubiquitination and consequent degradation, thereby providing an additional indirect mechanism for miR-204 repression of MYCN expression through miR-204’s potential indirect up-regulation of SFN.
CYP3A5 is also one of the three enriched genes that are present in both 5-Aza-
2’-deoxycytidine related gene sets. High CYP3A5 expression correlates with only better neuroblastoma patient overall survival (see Figure 5.8C). Similar correlation has been found previously in neuroblastoma patients, although not statistically significant [509].
CYP3A5 belongs to the CYP3A cytochrome P450 subfamily that metabolize a wide range of biological substances and drugs, such as steroids, immunosuppressive agents and antibiotics, which activates or deactivates the drugs and consequently influencing drug efficacy and toxicity [510]. The pharmacokinetics of several drugs metabolize by
CYP3A5 and use in neuroblastoma patients, including cyclophosphamide, vincristine, ifosfamide and etoposide, are shown to be affected by genetic polymorphism in
CYP3A5 [509, 511-515]. These suggest miR-204’s influence on neuroblastoma drug sensitivity is beyond just cisplatin as shown by Ryan et al. [347], via potential indirect induction of CYP3A5.
Chi Yan Ooi 236 Mechanism of bi-directional suppression between miR-204 and MYCN and the genome-wide identification of miR-204 targets
In contrast, high expression of MAGEB1, the last of the three enriched genes that are present in both gene sets, correlates to poorer neuroblastoma patient overall and event-free survival (see Figure 5.8D). This is not surprising given MAGEB1 is expressed in several cancers but not in normal tissues except testis [485]. MAGEB1 expression can also be activated by DNA methyltransferase inhibitor 5-Aza-2’- deoxycytidine in some cancer and normal cells [485], supporting the potential for miR-
204 to inhibit DNA methyltransferase(s) and/or promote DNA demethylation.
In conclusion, I showed that MYCN binds to the pre-miR-204 genomic sequence, which allows MYCN to transcriptionally regulate miR-204 expression. On the other hand, miR-204 binds to MYCN mRNA in the cytoplasm which allows miR-204 to repress MYCN through RNAi. In addition, I showed that miR-204 expression could be repressed by DNA methylation, histone deacetylation and methylation. Furthermore, we identified potential miR-204 targets on a genome-wide scale that suggest miR-204 has additional roles of regulating DNA methylation levels, cell differentiation, invasion, migration and metastasis, and pharmacokinetics of multiple drugs in neuroblastoma.
Moreover, I identified an additional hypothetical mechanism for miR-204 repression of
MYCN expression through promoting poly-ubiquitination of MYCN by indirect induction of SFN.
Chi Yan Ooi 237
Concluding remarks
Chi Yan Ooi 238 Concluding remarks
General discussion
The MYCN proto-oncogene is the main oncogenic driver that initiates neuroblastoma tumorigenesis [29, 35, 36, 45]. Given MYCN amplification is a major classifier for high-risk disease and predictor of poor patient survival [25, 73, 74], understanding how MYCN is driving neuroblastoma tumorigenesis could help to counteract its aggressiveness and develop potential strategies for early detection and prevention.
We identified miR-204 as our prime candidate to investigate its roles in MYCN- driven neuroblastoma. For the first time, we have showed that miR-204 acts as a tumour suppressor in neuroblastoma. miR-204 is able to suppress human MYCN-amplified neuroblastoma cells proliferation and colony forming capability in vitro. Importantly, sustained miR-204 over-expression prolonged survival in in vivo mouse models of human
MYCN-amplified neuroblastoma subcutaneous xenografts. Since miR-204 mimic did not affect neuroblastoma cell viability, it is unlikely that miR-204 also triggers apoptosis on its own in neuroblastoma. This is consistent with the conclusion made by Ryan et al. [347], and unlike the neuroblastoma tumour suppressor miR-34a which is used in the first ever phase 1 miRNA mimic clinical trial [304, 313, 316]. Our microarray analysis also suggests that miR-204 can suppress DNA methylation-mediated gene silencing, cell invasion, migration and metastasis, and promotes differentiation in neuroblastoma.
Nevertheless, the most important discovery of this thesis is a novel regulatory loop for MYCN. We found that MYCN represses miR-204 expression, while miR-204 also represses MYCN expression (Figure 6.1A). These form a double-negative feedback regulatory loop [516, 517], where increasing the expression of either MYCN or miR-204 will reduce the expression of the other. As a result of this MYCN-miR-204 feedback loop,
Chi Yan Ooi 239 Concluding remarks
increasing the expression of either member reduces the repression on itself, in turn further enhancing its own expression. MYCN is also known to transcriptionally activate its own expression [518], which would further amplify expression changes to the loop.
Mechanistically, miR-204 binds to MYCN mRNA, allowing miR-204 to repress the amount of MYCN protein generated by translational repression and/or increasing miRNA degradation through RNA interference (Figure 6.1B). miR-204 binding to
MYCN mRNA putatively occurs near the centre of the CDS region as predicted by the database RNA22. On the other hand, MYCN binds to the pre-miR-204 genomic sequence, which would allow MYCN to suppress transcription of pri-mir-204 transcripts. We also hypothesize that the transcriptional repression is epigenetic in nature through recruitment of DNA methyltransferase(s), histone deacetylase(s) and/or polycomb repressive complex 2 by MYCN. Further experiments into miR-204 and
MYCN repressive functions within the double-negative feedback loop will be required to confirm the exact mechanism of their interaction.
Until now, only one double-negative regulatory loop has been confirmed for miR-
204. The TrkB-STAT3-miR-204 double-negative feedback loop involves TrkB repression of miR-204 through phosphorylation-mediated activation of transcription factor STAT3 which dimerizes, binds and represses the promoter of miR-204’s host-gene TRPM3 [519]. miR-204 in turn directly represses TrkB by binding its mRNA 3’UTR and completing the double-negative feedback loop [519]. Since BDNF is an activation ligand for the TrkB receptor and also a direct target of miR-204, it has been suggested that the regulatory loop can be expanded to include BDNF (the BNDF-TrkB-STAT3-miR-204 feedback loop) [342, 520, 521]. In addition, co-expression of full-length TrkB and BDNF mRNAs in neuroblastoma is correlated to MYCN amplification and aggressive disease, and they form an autocrine signalling loop that promotes cell survival,
Chi Yan Ooi 240 Concluding remarks
angiogenesis, drug resistance and tumorigenesis and inhibits differentiation [28, 351]. These further support the importance of miR-204 in MYCN-driven neuroblastoma. It has also been recently suggested that miR-204 and Six1 form a double-negative feedback loop, but the mechanism is unknown for the negative effect of Six1 expression on miR-204 expression [522]. Therefore, we have established for the first time a two-component miR-
204 double-negative feedback loop. On the other hand, double-negative regulatory feedback loops have also been identified for c-MYC [458, 523]. For example, the c-MYC-miR-148a-
5p double-negative feedback loop involves c-MYC binding to the classical E-box CACGTG at the miR-148a-5p promoter and represses miR-148a-5p expression, while miR-148a-5p directly represses c-MYC through its mRNA 3’UTR
[524]. Therefore, it is not surprising that a double-negative feedback loop is identified here for MYCN.
Double-negative feedback loops, including those involving miRNAs, can form bistable systems that seem to be widespread in cell fate decision processes [516, 517,
525]. A bistable system has two mutually exclusive stable steady states and can switch between those two states when a certain threshold intrinsic to the feedback loop is overcome [516, 517, 525]. In addition, it should be assumed that a threshold level of miRNA relative to target mRNA for effective repression by the miRNA [526] has to be crossed to switch between states. It is thought that such bistable regulatory systems help maintain asymmetric cell fates [516, 517, 525]. To understand the implications of the above on the miR-204-MYCN double-negative feedback loop and neuroblastoma, the expression of miR-204 at the Th-MYCN+/+ transgenic mouse model may provide additional information. miR-204 expression is down-regulated in Th-MYCN+/+ ganglia samples compared to wild-type, suggesting human MYCN retains its repression effect on mouse miR-204 expression (see Figure 3.2B). Moreover, mature mouse miR-204 has
Chi Yan Ooi 241 Concluding remarks
identical sequence to the mature human miR-204 (see Appendix A) and thus should identically target human MYCN mRNA. Therefore, these data suggest that the Th-
MYCN+/+ transgenic mouse model recapitulates the miR-204-MYCN double-negative feedback loop. From Figure 3.2B, it is observed that miR-204 expression remained quite stable over time in the wild-type samples, while began to dip from 2 weeks after birth and dramatically reduced by 6 weeks in the Th-MYCN+/+ samples. It is also known that normally MYCN expression gradually reduces as tissues mature to allow for differentiation of neuronal progenitor cells [32, 34]. These suggest that miR-204 expression is in a stable state dominating over MYCN expression in wild-type ganglia tissues, while Th-MYCN+/+ ganglia tissues began to switch into a state of MYCN expression dominating over miR-204 expression at 2 weeks after birth. This is consistent with a bistable regulatory system. The timing is also consistent with disease progression in Th-MYCN+/+ ganglia tissues, where hyperplastic undifferentiated neuronal progenitor cells that would otherwise be undetectable are detected at two weeks of age [36]. Therefore, miR-204 may normally act to help prevent aberrant reactivation of MYCN after it is down-regulated to maintain a differentiated neuronal cell fate. Furthermore, the over-expression of MYCN that is already detectable at 2 weeks after birth in Th-MYCN+/+ ganglia tissues [36] may have pushed the bistable system into the MYCN expression dominant state, and definitely created a high threshold for switching back. However, we cannot rule out any additional or alternative negative influence on miR-204 expression that could have facilitated the switch. For example, other endogenous mRNA targets of miR-204 can compete against MYCN mRNA for interactions with miR-204, reducing the effectiveness of miR-204. In fact, we observed in our expression profiling data that the levels of the two miR-204 targets BDNF and MEIS2 were stably elevated in Th-MYCN+/+
Chi Yan Ooi 242 Concluding remarks
samples compared to wild-type samples, while those levels decreased over time in wild- type samples (data not shown).
Neuroblastoma is thought to have a prenatal origin linked to the development of prenatal sympathetic ganglia cells derived from embryonic neural crest cells [29]. MYCN is crucial for the normal regulation of migration and expansion of neural crest cells and maintenance of progenitor cell state by blocking neuronal terminal differentiation during development [32-34, 48]. In addition, expression of miR-204 direct target PHOX2B in prenatal sympathetic ganglia cells is necessary for the cell fate specification and regulation of terminal differentiation [29, 38, 349, 527, 528]. Germline mutations in PHOX2B lead to predisposition to rare familial neuroblastoma and Phox2b-expressing neuronal progenitor cells are the predominant founder cells of neuroblastoma in hemizygous Th-MYCN mice
[39, 41, 48]. Recently, it is found that TrkB receptor and a BDNF gradient mediate the final chemotactic migration of prenatal sympathetic ganglia cells in chick embryo explants [350].
My research adds MYCN as direct target of miR-204 to the list of confirmed targets, joining recent discoveries of other miR-204 direct targets PHOX2B and TrkB in neuroblastoma and
BDNF in other cancer [334, 347, 349]. All together, these suggest a role of miR-204 and the miR-204-MYCN feedback loop in preventing reactivation of developmental programs and maintenance of terminal neuronal differentiation from the late stage of sympathetic ganglia development to after birth. Indeed, analyses from the manipulation of miR-204 expression in Medaka fish whole embryos yielded putative miR-204 human direct targets that are enriched with genes performing biological processes of nervous system development, neurogenesis and axon guidance [529]. Moreover, it is observed that miR-204 is activated and required for differentiation and morphogenesis in lens and retinal development [330,
331], reinforcing the importance of miR-204 in tissue maturation during embryonal development.
Chi Yan Ooi 243 Concluding remarks
Figure 6.1 A)
Transcriptional Transcriptional MYCN miR-204 Post-transcriptional
Figure 6.1 B)
miRISC
Pre-miR-204 Mature miR-204
poly(A) MYCN Pri-miR-204 mRNA DNMT
HDAC PRC2
MYCN MYCN
Transcription Pre-miR-204
DNA (intronic) Nucleus Cytoplasm
Figure 6.1 The miR-204-MYCN Double-Negative Regulatory Feedback Loop
A) The simplistic view of the miR-204-MYCN double-negative feedback loop, where miR-204 and MYCN repress each other’s expression. MYCN also transcriptionally activates its expression [518]. B) The mechanistic view of the feedback loop. miR-204 binds to MYCN mRNA, potentially at the CDS region, to ultimately reduce the amount of MYCN protein translated from the mRNA through the actions of miRISC. MYCN binds to pre-miR-204 genomic sequence to repress pri-mir-204 transcription, potentially through recruitment of histone deacetylate(s) (HDAC), DNA methyltransferase(s) (DNMT) and/or polycomb repressive complex 2 (PRC2) to epigenetically silence expression.
Chi Yan Ooi 244 Concluding remarks
Conclusions and future perspectives
In conclusion, we have identified and characterized miR-204 as a novel tumour suppressor of neuroblastoma that warrants future investigation into its likely additional regulatory roles in neuroblastoma differentiation and metastasis, as well as normal sympathetic ganglia development. For the first time, we have also showed that miR-204 and MYCN form a double-negative regulatory feedback loop, where miR-204 represses
MYCN expression pots-transcriptionally while MYCN represses miR-204 expression transcriptionally. We have also confirmed a positive correlation between miR-204 expression and patient survival, opening up the possibilities of using miR-204 as a biomarker and therapeutic target.
One of the main therapeutic applications of tumour suppressor miRNA is miRNA replacement therapy, with miR-34a as the most notable example [314-316]. miRNA’s ability to target a broad range of genes is advantageous against cancer, a disease involving multiple genes [360]. miRNAs are also likely to trigger a reduced immune response compared to DNA-based gene therapy and protein-based therapeutic agents [360].
Similar to miR-204, miR-34a directly targets MYCN expression but via the 3’UTR of
MYCN mRNA [309]. Being the miRNA used for the first phase 1 clinical trial of miRNA mimic replacement therapy, miR-34a participates in the p53 regulatory network and promotes apoptosis in neuroblastoma and many other cancer types [303, 304, 313-
316, 530]. However, results from us and other researchers suggest miR-204 on its own does not trigger apoptosis in neuroblastoma and a number of other cancer types, and only do so in pancreatic and papillary thyroid carcinoma cells [341, 345, 347, 439, 440].
Therefore, miR-204 is expected to be less potent than miR-34a therapeutically. The lack of apoptotic activity and strong negative feedback from MYCN amplification and over- expression on miR-204 expression also suggest long-term sustained administration of
Chi Yan Ooi 245 Concluding remarks
miR-204 mimic or stable over-expression through genetic modification would be required. However, miR-204 did not affect and even reduced the level of apoptosis in normal human cardiomyocyte progenitor cells and pulmonary artery smooth muscle cells respectively [326]. Moreover, miR-204 expression is vastly higher in wild-type ganglia compared to Th-MYCN+/+ tumours (see Figure 3.2B) and is required for differentiation and morphogenesis in lens and retinal development [330, 331]. Thus miR-204 is likely to have lower toxicity to normal tissues compared to miR-34a. Nevertheless, miR-204 promotes adipogenesis while inhibiting osteogenesis in mesenchymal stem cells, which may lead to bone loss, and inhibits insulin production in beta-cell [531, 532]. Therefore, tissue-specific delivery of miR-204 mimic is still required and would benefit from the nanoparticle delivery system developed for specific delivery of miR-34a mimic to neuroblastoma tumour in vivo [313]. Taken together, miR-204 replacement therapy as a single agent would be more appropriate for maintenance therapy after surgical removal of neuroblastoma to inhibit the resurgence of tumours rather than to shrink them.
On the other hand, it may be more viable to combine miR-204 replacement therapy with existing chemotherapy agents. miR-204 has been shown to sensitize neuroblastoma and colorectal cancer cells in vitro to cisplatin and oxaliplatin respectively [347, 440]. miR-204 also sensitized gastric cancer cells in vitro to 5- fluorouracil and oxaliplatin [341]. In addition, miR-204 has been shown to directly target TrkB, which contributes to chemotherapy resistance in neuroblastoma [347, 533].
Moreover, our microarray analyses suggested that miR-204 may indirectly up-regulates
CYP3A5, potentially affecting pharmacokinetic of drugs being used for neuroblastoma.
Therefore, further investigations are needed to determine whether the sensitivities to neuroblastoma drugs can be altered by miR-204 in vitro and in vivo.
Chi Yan Ooi 246 Concluding remarks
An alternative to miR-204 replacement therapy is to use small molecule drugs to reactivate expression of miR-204. In vivo instability, poor cellular uptake and non- specific delivery are major universal challenges needed to be overcome when using miRNA mimic as therapeutic agent [314, 315, 363]. We have shown that DNA methyltransferase inhibitor and histone deacetylase inhibitor can increase miR-204 expression in neuroblastoma cells in vitro. Furthermore, DNA methyltransferase and histone deacetylase inhibitors also activate expression of a broad range of genes and potentially synergise with the up-regulation of miR-204 expression by these compounds. For example, histone deacetylase inhibitors can activate expression of tumour suppressor miR-183 in neuroblastoma cells [90]. In addition, a Chinese herb- derived compound called triptolide increased miR-204 expression in pancreatic cancer cells in vitro and in vivo [345]. Triptolide is a heat shock protein-70 inhibitor and can promote apoptosis of neuroblastoma cells in vitro and reduce neuroblastoma tumour growth in vivo [534, 535]. Therefore, small molecule-mediated reactivation of miR-204 expression in neuroblastoma is a viable alternative.
Lastly, miR-204 could be used as a clinical biomarker. We have showed that high miR-204 expression in primary neuroblastoma tumours correlates to better patient overall and progression free survival. It has also been shown that miR-204 expression in acute myeloid leukaemia mononuclear cells positively correlates to the chance of remission after induction chemotherapy [343]. This correlation is in addition to the association between high miR-204 expression at diagnosis of acute myeloid leukaemia and favourable clinical outcome. Therefore, miR-204 expression in neuroblastoma tumours at diagnosis and surgery after induction chemotherapy may be used to predict clinical outcome and treatment response to allow for adjustments in treatment strategies. However, miR-204 as a clinical biomarker will be more useful if changes in its expression inside
Chi Yan Ooi 247 Concluding remarks
tumours can be reflected in body fluid such as blood. Cancer cells can secrete miRNAs into blood and other body fluids encapsulated within lipid/lipoprotein complexes including microvesicles, exosomes, prostasomes, and apoptotic bodies [371]. Termed cell-free circulating miRNAs, they can be identified from whole blood, serum or plasma and have the advantage of being highly stable compared to normal RNA [370, 371].
Neuroblastoma cells are known to secrete miRNAs in exosomes and have signalling functions [372]. A panel of five serum miRNAs highly over-expressed in MYCN- amplified high-risk neuroblastoma patients has been shown to be able to distinguish between MYCN-amplified high-risk and non-MYCN-amplified low-risk neuroblastoma patients [373]. Using neuroblastoma mouse models, increase in levels of three miRNAs in serum is proposed as indicator for a switch from favourable to metastatic high-risk disease [374]. If miR-204 is secreted by neuroblastoma cells, it may be possible to use the changes in circulating miR-204 level for early detection, prognosis and monitoring of treatment responses. In addition, circulating tumour cells may offer an alternative source of circulating miRNAs [370].
On a final note, this thesis used a system biology approach to identify miR-204 as a novel tumour suppressor of MYCN–driven neuroblastoma. However, this thesis did not experimentally investigate how miR-204 may affect the functions of other miRNAs, synergizing or antagonising with these miRNAs, on a system biology level. Our predicted interaction network suggests that miR-574-3p would synergize with miR-204 as it shares 6 predicted targets with miR-204, as well as predicted to target the validated miR-204 target
PHOX2B (see Figure 3.4). It would also be expected that miR-204 may synergistically enhance the effects of miR-34a on neuroblastoma as miR-34a also directly targets MYCN, and several cell cycle genes (see Figure 1.10). These knowledge could be beneficial to formulating combination therapies and could be investigated in the future.
Chi Yan Ooi 248
References
Chi Yan Ooi 249 References
1. Giordano, A. and W.H. Lee, Preface. Oncogene, 2006. 25(38): p. 5189-5189.
2. Vogelstein, B. and K.W. Kinzler, Cancer genes and the pathways they control.
Nat Med, 2004. 10(8): p. 789-99.
3. Barbara, M., Tumour heterogeneity. Nature, 2013. 501(7467): p. 327-327.
4. Weinberg, R.A., How cancer arises. Sci Am, 1996. 275(3): p. 62-70.
5. Croce, C.M., Oncogenes and Cancer. New England Journal of Medicine, 2008.
358(5): p. 502-511.
6. Australian Institute of Health and Welfare and Australasian Association of Cancer
Registries, Cancer in Australia: an overview, 2012. 2012, AIHW: Canberra.
7. Weiss, R.A., Multistage carcinogenesis. Br J Cancer, 2004. 91(12): p. 1981-2.
8. Anisimov, V.N., Biology of aging and cancer. Cancer Control, 2007. 14(1): p.
23-31.
9. Barrett, J.C., Mechanisms of multistep carcinogenesis and carcinogen risk
assessment. Environ Health Perspect, 1993. 100: p. 9-20.
10. Nowell, P.C., The clonal evolution of tumor cell populations. Science, 1976.
194(4260): p. 23-8.
11. Sabaawy, H.E., Genetic Heterogeneity and Clonal Evolution of Tumor Cells and
their Impact on Precision Cancer Medicine. J Leuk (Los Angel), 2013. 1(4): p.
1000124.
12. Feinberg, A.P., R. Ohlsson, and S. Henikoff, The epigenetic progenitor origin of
human cancer. Nat Rev Genet, 2006. 7(1): p. 21-33.
13. Barcellos-Hoff, M.H., D. Lyden, and T.C. Wang, The evolution of the cancer niche
during multistage carcinogenesis. Nat Rev Cancer, 2013. 13(7): p. 511-518.
14. Shipitsin, M. and K. Polyak, The cancer stem cell hypothesis: in search of
definitions, markers, and relevance. Lab Invest, 2008. 88(5): p. 459-63.
Chi Yan Ooi 250 References
15. Wicha, M.S., S. Liu, and G. Dontu, Cancer Stem Cells: An Old Idea—A Paradigm
Shift. Cancer Research, 2006. 66(4): p. 1883-1890.
16. Valent, P., et al., Cancer stem cell definitions and terminology: the devil is in the
details. Nat Rev Cancer, 2012. 12(11): p. 767-775.
17. Sun, W. and J. Yang, Functional mechanisms for human tumor suppressors.
Journal of Cancer, 2010. 1: p. 136-40.
18. Hanahan, D. and R.A. Weinberg, The Hallmarks of Cancer. Cell, 2000. 100(1):
p. 57-70.
19. Hanahan, D. and Robert A. Weinberg, Hallmarks of Cancer: The Next
Generation. Cell, 2011. 144(5): p. 646-674.
20. Australian Institute of Health and Welfare and Australasian Association of Cancer
Registries, Cancer in Australia: an overview, 2008. 2008, AIHW: Canberra.
21. Howlader, N., et al., SEER Cancer Statistics Review, 1975-2010. 2013, National
Cancer Institute: Bethesda, MD.
22. Nishihira, H., et al., Natural course of neuroblastoma detected by mass
screening: A 5-year prospective study at a single institution. J Clin Oncol, 2000.
18(16): p. 3012-7.
23. Yamamoto, K., et al., Spontaneous regression of localized neuroblastoma
detected by mass screening. J Clin Oncol, 1998. 16(4): p. 1265-9.
24. Oue, T., et al., Profile of neuroblastoma detected by mass screening, resected
after observation without treatment: results of the Wait and See pilot study. J
Pediatr Surg, 2005. 40(2): p. 359-63.
25. Maris, J.M., et al., Neuroblastoma. The Lancet, 2007. 369(9579): p. 2106-2120.
26. De Bernardi, B., et al., Disseminated neuroblastoma in children older than one year
at diagnosis: comparable results with three consecutive high-dose protocols
Chi Yan Ooi 251 References
adopted by the Italian Co-Operative Group for Neuroblastoma. J Clin Oncol,
2003. 21(8): p. 1592-601.
27. Matthay, K.K., et al., Treatment of high-risk neuroblastoma with intensive
chemotherapy, radiotherapy, autologous bone marrow transplantation, and 13-
cis-retinoic acid. Children's Cancer Group. N Engl J Med, 1999. 341(16): p.
1165-73.
28. Brodeur, G.M., Neuroblastoma: biological insights into a clinical enigma. Nat
Rev Cancer, 2003. 3(3): p. 203-216.
29. Marshall, G.M., et al., The prenatal origins of cancer. Nat Rev Cancer, 2014.
14(4): p. 277-289.
30. Maris, J.M., Recent Advances in Neuroblastoma. New England Journal of
Medicine, 2010. 362(23): p. 2202-2211.
31. Huber, K., The sympathoadrenal cell lineage: Specification, diversification, and
new perspectives. Developmental Biology, 2006. 298(2): p. 335-343.
32. Zimmerman, K.A., et al., Differential expression of myc family genes during
murine development. Nature, 1986. 319(6056): p. 780-783.
33. Sawai, S., et al., Defects of embryonic organogenesis resulting from targeted
disruption of the N-myc gene in the mouse. Development, 1993. 117(4): p. 1445-
1455.
34. Knoepfler, P.S., P.F. Cheng, and R.N. Eisenman, N-myc is essential during
neurogenesis for the rapid expansion of progenitor cell populations and the
inhibition of neuronal differentiation. Genes & Development, 2002. 16(20): p.
2699-2712.
35. Weiss, W.A., et al., Targeted expression of MYCN causes neuroblastoma in
transgenic mice. EMBO J, 1997. 16(11): p. 2985-2995.
Chi Yan Ooi 252 References
36. Hansford, L.M., et al., Mechanisms of embryonal tumor initiation: Distinct roles
for MycN expression and MYCN amplification. Proceedings of the National
Academy of Sciences of the United States of America, 2004. 101(34): p. 12664-
12669.
37. Pattyn, A., C. Goridis, and J.-F. Brunet, Specification of the Central
Noradrenergic Phenotype by the Homeobox Gene Phox2b. Molecular and
Cellular Neuroscience, 2000. 15(3): p. 235-243.
38. Pei, D., et al., Distinct Neuroblastoma-associated Alterations of PHOX2B
Impair Sympathetic Neuronal Differentiation in Zebrafish Models. PLoS Genet,
2013. 9(6): p. e1003533.
39. Mosse, Y.P., et al., Germline PHOX2B Mutation in Hereditary Neuroblastoma.
The American Journal of Human Genetics, 2004. 75(4): p. 727-730.
40. Pattyn, A., et al., The homeobox gene Phox2b is essential for the development of
autonomic neural crest derivatives. Nature, 1999. 399(6734): p. 366-70.
41. Trochet, D., et al., Germline Mutations of the Paired–Like Homeobox 2B
(PHOX2B) Gene in Neuroblastoma. The American Journal of Human Genetics,
2004. 74(4): p. 761-764.
42. Reiff, T., et al., Midkine and Alk signaling in sympathetic neuron proliferation
and neuroblastoma predisposition. Development, 2011. 138(21): p. 4699-4708.
43. Cheng, L.Y., et al., Anaplastic lymphoma kinase spares organ growth during
nutrient restriction in Drosophila. Cell, 2011. 146(3): p. 435-47.
44. Heukamp, L.C., et al., Targeted Expression of Mutated ALK Induces
Neuroblastoma in Transgenic Mice. Science Translational Medicine, 2012.
4(141): p. 141ra91.
Chi Yan Ooi 253 References
45. Schulte, J.H., et al., MYCN and ALKF1174L are sufficient to drive
neuroblastoma development from neural crest progenitor cells. Oncogene, 2013.
32(8): p. 1059-1065.
46. Balzer, E., et al., LIN28 alters cell fate succession and acts independently of the
let-7 microRNA during neurogliogenesis in vitro. Development, 2010. 137(6): p.
891-900.
47. Molenaar, J.J., et al., LIN28B induces neuroblastoma and enhances MYCN
levels via let-7 suppression. Nature Genetics, 2012. 44(11): p. 1199-1206.
48. Alam, G., et al., MYCN promotes the expansion of Phox2B-positive neuronal
progenitors to drive neuroblastoma development. Am J Pathol, 2009. 175(2): p.
856-66.
49. Zhu, S., et al., Activated ALK collaborates with MYCN in neuroblastoma
pathogenesis. Cancer Cell, 2012. 21(3): p. 362-73.
50. Yuan, J. and B.A. Yankner, Apoptosis in the nervous system. Nature, 2000.
407(6805): p. 802-809.
51. Ross, R.A. and B.A. Spengler, Human neuroblastoma stem cells. Seminars in
Cancer Biology, 2007. 17(3): p. 241-247.
52. LaBonne, C. and M. Bronner-Fraser, Induction and patterning of the neural
crest, a stem cell-like precursor population. Journal of Neurobiology, 1998.
36(2): p. 175-189.
53. Brodeur, G.M., et al., International criteria for diagnosis, staging, and response
to treatment in patients with neuroblastoma. J Clin Oncol, 1988. 6(12): p. 1874-
81.
Chi Yan Ooi 254 References
54. Brodeur, G.M., et al., Revisions of the international criteria for neuroblastoma
diagnosis, staging, and response to treatment. Journal of Clinical Oncology,
1993. 11(8): p. 1466-1477.
55. Cohn, S.L., et al., The International Neuroblastoma Risk Group (INRG)
Classification System: An INRG Task Force Report. Journal of Clinical
Oncology, 2009. 27(2): p. 289-297.
56. Maris, J.M., The biologic basis for neuroblastoma heterogeneity and risk
stratification. Current Opinion in Pediatrics, 2005. 17(1): p. 7-13.
57. Monclair, T., et al., The International Neuroblastoma Risk Group (INRG)
Staging System: An INRG Task Force Report. Journal of Clinical Oncology,
2009. 27(2): p. 298-303.
58. Spitz, R., et al., Deletions in chromosome arms 3p and 11q are new prognostic
markers in localized and 4s neuroblastoma. Clin Cancer Res, 2003. 9(1): p. 52-8.
59. Plantaz, D., et al., Comparative genomic hybridization (CGH) analysis of stage
4 neuroblastoma reveals high frequency of 11q deletion in tumors lacking
MYCN amplification. Int J Cancer, 2001. 91(5): p. 680-6.
60. Guo, C., et al., Allelic deletion at 11q23 is common in MYCN single copy
neuroblastomas. Oncogene, 1999. 18(35): p. 4948-57.
61. Caron , H., et al., Allelic Loss of Chromosome 1p as a Predictor of Unfavorable
Outcome in Patients with Neuroblastoma. New England Journal of Medicine,
1996. 334(4): p. 225-230.
62. White, P.S., et al., Definition and characterization of a region of 1p36.3
consistently deleted in neuroblastoma. Oncogene, 2005. 24(16): p. 2684-94.
63. Takita, J., et al., Deletion Map of Chromosome 9 and p16 (CDKN2A) Gene
Alterations in Neuroblastoma. Cancer Research, 1997. 57(5): p. 907-912.
Chi Yan Ooi 255 References
64. Mora, J., et al., Loss of Heterozygosity at 19q13.3 Is Associated with Locally
Aggressive Neuroblastoma. Clinical Cancer Research, 2001. 7(5): p. 1358-1361.
65. Thompson, P.M., et al., Loss of heterozygosity for chromosome 14q in
neuroblastoma. Medical and Pediatric Oncology, 2001. 36(1): p. 28-31.
66. Caron, H., et al., Allelic loss of the short arm of chromosome 4 in neuroblastoma
suggests a novel tumour suppressor gene locus. Human Genetics, 1996. 97(6): p.
834-837.
67. Thompson, P.M., et al., Homozygous Deletion of CDKN2A (p16INK4a/p14ARF) but
not within 1p36 or at Other Tumor Suppressor Loci in Neuroblastoma. Cancer
Research, 2001. 61(2): p. 679-686.
68. Bown , N., et al., Gain of Chromosome Arm 17q and Adverse Outcome in
Patients with Neuroblastoma. New England Journal of Medicine, 1999. 340(25):
p. 1954-1961.
69. Shuster, J.J., et al., Serum lactate dehydrogenase in childhood neuroblastoma. A
Pediatric Oncology Group recursive partitioning study. Am J Clin Oncol, 1992.
15(4): p. 295-303.
70. Quinn, J.J., A.J. Altman, and C.N. Frantz, Serum lactic dehydrogenase, an
indicator of tumor activity in neuroblastoma. The Journal of Pediatrics, 1980.
97(1): p. 89-91.
71. Hann, H.W., et al., Prognostic importance of serum ferritin in patients with
Stages III and IV neuroblastoma: the Childrens Cancer Study Group experience.
Cancer Res, 1985. 45(6): p. 2843-8.
72. Matthay, K.K., et al., Neuroblastoma. Nat Rev Dis Primers, 2016. 2: p. 16078.
Chi Yan Ooi 256 References
73. Deyell, R.J. and E.F. Attiyeh, Advances in the understanding of constitutional
and somatic genomic alterations in neuroblastoma. Cancer Genetics, 2011.
204(3): p. 113-121.
74. Thomas, W.D., et al., N-myc transcription molecule and oncoprotein. The
International Journal of Biochemistry & Cell Biology, 2004. 36(5): p. 771-775.
75. Gherardi, S., et al., MYCN-mediated transcriptional repression in
neuroblastoma: the other side of the coin. Frontiers in Oncology, 2013. 3: p. 42.
76. Maris, J.M. and K.K. Matthay, Molecular Biology of Neuroblastoma. Journal of
Clinical Oncology, 1999. 17(7): p. 2264.
77. Blackwood, E.M. and R.N. Eisenman, Max: a helix-loop-helix zipper protein
that forms a sequence-specific DNA-binding complex with Myc. Science, 1991.
251(4998): p. 1211-7.
78. Wenzel, A., et al., The N-Myc oncoprotein is associated in vivo with the
phosphoprotein Max(p20/22) in human neuroblastoma cells. Embo j, 1991.
10(12): p. 3703-12.
79. Murphy, D.M., et al., Global MYCN Transcription Factor Binding Analysis in
Neuroblastoma Reveals Association with Distinct E-Box Motifs and Regions of
DNA Hypermethylation. PLoS ONE, 2009. 4(12): p. e8154.
80. Mao, D.Y.L., et al., Analysis of Myc Bound Loci Identified by CpG Island Arrays
Shows that Max Is Essential for Myc-Dependent Repression. Current Biology,
2003. 13(10): p. 882-886.
81. Shohet, J.M., et al., A Genome-Wide Search for Promoters That Respond to
Increased MYCN Reveals Both New Oncogenic and Tumor Suppressor
MicroRNAs Associated with Aggressive Neuroblastoma. Cancer Research, 2011.
71(11): p. 3841-3851.
Chi Yan Ooi 257 References
82. Corvetta, D., et al., Physical interaction between MYCN oncogene and polycomb
repressive complex 2 (PRC2) in neuroblastoma: functional and therapeutic
implications. J Biol Chem, 2013. 288(12): p. 8332-41.
83. Claassen, G.F. and S.R. Hann, Myc-mediated transformation: the repression
connection. Oncogene, 1999. 18(19): p. 2925-33.
84. Peukert, K., et al., An alternative pathway for gene regulation by Myc. Vol. 16.
1997. 5672-5686.
85. Gartel, A.L., et al., Myc represses the p21(WAF1/CIP1) promoter and interacts
with Sp1/Sp3. Proceedings of the National Academy of Sciences, 2001. 98(8): p.
4510-4515.
86. Marshall, G.M., et al., Transcriptional upregulation of histone deacetylase 2
promotes Myc-induced oncogenic effects. Oncogene, 2010. 29(44): p. 5957-5968.
87. Liu, T., et al., Activation of tissue transglutaminase transcription by histone
deacetylase inhibition as a therapeutic approach for Myc oncogenesis.
Proceedings of the National Academy of Sciences of the United States of
America, 2007. 104(47): p. 18682-18687.
88. Iraci, N., et al., A SP1/MIZ1/MYCN Repression Complex Recruits HDAC1 at the
TRKA and p75NTR Promoters and Affects Neuroblastoma Malignancy by
Inhibiting the Cell Response to NGF. Cancer Research, 2011. 71(2): p. 404-412.
89. Marshall, G.M., et al., SIRT1 Promotes N-Myc Oncogenesis through a Positive
Feedback Loop Involving the Effects of MKP3 and ERK on N-Myc Protein
Stability. PLoS Genetics, 2011. 7(6): p. e1002135.
90. Lodrini, M., et al., MYCN and HDAC2 cooperate to repress miR-183 signaling
in neuroblastoma. Nucleic Acids Research, 2013. 41(12): p. 6018-6033.
Chi Yan Ooi 258 References
91. Charlet, J., et al., MYCN is recruited to the RASSF1A promoter but is not critical
for DNA hypermethylation in neuroblastoma. Molecular Carcinogenesis, 2014.
53(5): p. 413-420.
92. Stallings, R.L., et al., Therapeutic targeting of miRNAs in neuroblastoma. Expert
Opinion on Therapeutic Targets, 2010. 14(9): p. 951-962.
93. Eilers, M. and R.N. Eisenman, Myc's broad reach. Genes Dev, 2008. 22(20): p.
2755-66.
94. Norris, M.D., et al., Expression of N-myc and MRP genes and their relationship
to N-myc gene dosage and tumor formation in a murine neuroblastoma model.
Medical and Pediatric Oncology, 2000. 35(6): p. 585-589.
95. Burkhart, C.A., et al., Effects of MYCN Antisense Oligonucleotide
Administration on Tumorigenesis in a Murine Model of Neuroblastoma. Journal
of the National Cancer Institute, 2003. 95(18): p. 1394-1403.
96. Bray, I., et al., Widespread Dysregulation of MiRNAs by MYCN Amplification
and Chromosomal Imbalances in Neuroblastoma: Association of miRNA
Expression with Survival. PLoS ONE, 2009. 4(11): p. e7850.
97. Mestdagh, P., et al., MYCN/c-MYC-induced microRNAs repress coding gene
networks associated with poor outcome in MYCN/c-MYC-activated tumors.
Oncogene, 2010. 29(9): p. 1394-1404.
98. Schulte, J.H., et al., MYCN regulates oncogenic MicroRNAs in neuroblastoma.
International Journal of Cancer, 2008. 122(3): p. 699-704.
99. Terrile, M., et al., miRNA Expression Profiling of the Murine TH-MYCN
Neuroblastoma Model Reveals Similarities with Human Tumors and Identifies
Novel Candidate MiRNAs. PLoS ONE, 2011. 6(12): p. e28356.
Chi Yan Ooi 259 References
100. Bartel, D.P., MicroRNAs: Genomics, Biogenesis, Mechanism, and Function. Cell,
2004. 116(2): p. 281-297.
101. Buechner, J. and C. Einvik, N-myc and Noncoding RNAs in Neuroblastoma.
Molecular Cancer Research, 2012. 10(10): p. 1243-1253.
102. Lin, R.J., et al., microRNA signature and expression of Dicer and Drosha can
predict prognosis and delineate risk groups in neuroblastoma. Cancer Res,
2010. 70(20): p. 7841-50.
103. Schulte, J.H., et al., Accurate prediction of neuroblastoma outcome based on
miRNA expression profiles. International Journal of Cancer, 2010. 127(10): p.
2374-2385.
104. De Preter, K., et al., miRNA Expression Profiling Enables Risk Stratification in
Archived and Fresh Neuroblastoma Tumor Samples. Clinical Cancer Research,
2011. 17(24): p. 7684-7692.
105. Lee, R.C., R.L. Feinbaum, and V. Ambros, The C. elegans heterochronic gene
lin-4 encodes small RNAs with antisense complementarity to lin-14. Cell, 1993.
75(5): p. 843-854.
106. Wightman, B., I. Ha, and G. Ruvkun, Posttranscriptional regulation of the
heterochronic gene lin-14 by lin-4 mediates temporal pattern formation in C.
elegans. Cell, 1993. 75(5): p. 855-862.
107. Wightman, B., et al., Negative regulatory sequences in the lin-14 3'-untranslated
region are necessary to generate a temporal switch during Caenorhabditis
elegans development. Genes & Development, 1991. 5(10): p. 1813-1824.
108. Reinhart, B.J., et al., The 21-nucleotide let-7 RNA regulates developmental
timing in Caenorhabditis elegans. Nature, 2000. 403(6772): p. 901-6.
Chi Yan Ooi 260 References
109. Slack, F.J., et al., The lin-41 RBCC Gene Acts in the C. elegans Heterochronic
Pathway between the let-7 Regulatory RNA and the LIN-29 Transcription
Factor. Molecular Cell, 2000. 5(4): p. 659-669.
110. Lagos-Quintana, M., et al., Identification of Novel Genes Coding for Small
Expressed RNAs. Science, 2001. 294(5543): p. 853-858.
111. Lau, N.C., et al., An Abundant Class of Tiny RNAs with Probable Regulatory
Roles in Caenorhabditis elegans. Science, 2001. 294(5543): p. 858-862.
112. Lee, R.C. and V. Ambros, An Extensive Class of Small RNAs in Caenorhabditis
elegans. Science, 2001. 294(5543): p. 862-864.
113. Bentwich, I., et al., Identification of hundreds of conserved and nonconserved
human microRNAs. Nat Genet, 2005. 37(7): p. 766-770.
114. Kim, V.N., J. Han, and M.C. Siomi, Biogenesis of small RNAs in animals. Nat
Rev Mol Cell Biol, 2009. 10(2): p. 126-139.
115. Ha, M. and V.N. Kim, Regulation of microRNA biogenesis. Nat Rev Mol Cell
Biol, 2014. 15(8): p. 509-524.
116. Sevignani, C., et al., Mammalian microRNAs: a small world for fine-tuning gene
expression. Mammalian Genome, 2006. 17(3): p. 189-202.
117. Winter, J., et al., Many roads to maturity: microRNA biogenesis pathways and
their regulation. Nat Cell Biol, 2009. 11(3): p. 228-234.
118. Esquela-Kerscher, A. and F.J. Slack, Oncomirs — microRNAs with a role in
cancer. Nature Review Cancer, 2006. 6(4): p. 259-269.
119. Gregory, R.I. and R. Shiekhattar, MicroRNA Biogenesis and Cancer. Cancer
Research, 2005. 65(9): p. 3509-3512.
120. Lee, Y., et al., MicroRNA maturation: stepwise processing and subcellular
localization. Embo j, 2002. 21(17): p. 4663-70.
Chi Yan Ooi 261 References
121. Altuvia, Y., et al., Clustering and conservation patterns of human microRNAs.
Nucleic Acids Research, 2005. 33(8): p. 2697-2706.
122. Ramalingam, P., et al., Biogenesis of intronic miRNAs located in clusters by
independent transcription and alternative splicing. Rna, 2014. 20(1): p. 76-87.
123. Starega-Roslan, J., et al., Structural basis of microRNA length variety. Nucleic
Acids Research, 2011. 39(1): p. 257-268.
124. Wu, H., et al., Alternative Processing of Primary microRNA Transcripts by Drosha Generates 5′ End Variation of Mature microRNA. PLOS ONE, 2009. 4(10): p. e7566.
125. Ma, H., et al., Lower and upper stem–single-stranded RNA junctions together
determine the Drosha cleavage site. Proceedings of the National Academy of
Sciences, 2013. 110(51): p. 20687-20692.
126. Kim, Y.K. and V.N. Kim, Processing of intronic microRNAs. The EMBO
Journal, 2007. 26(3): p. 775-783.
127. Cloonan, N., Re-thinking miRNA-mRNA interactions: Intertwining issues
confound target discovery. BioEssays, 2015. 37(4): p. 379-388.
128. Yi, R., et al., Exportin-5 mediates the nuclear export of pre-microRNAs and
short hairpin RNAs. Genes & Development, 2003. 17(24): p. 3011-3016.
129. Lund, E., et al., Nuclear Export of MicroRNA Precursors. Science, 2004.
303(5654): p. 95-98.
130. Xie, M., et al., Mammalian 5′-capped microRNA precursors that generate a single microRNA. Cell, 2013. 155(7): p. 1568-1580.
131. Babiarz, J.E., et al., Mouse ES cells express endogenous shRNAs, siRNAs, and
other Microprocessor-independent, Dicer-dependent small RNAs. Genes Dev,
2008. 22(20): p. 2773-85.
Chi Yan Ooi 262 References
132. Zhang, H., et al., Single Processing Center Models for Human Dicer and
Bacterial RNase III. Cell, 2004. 118(1): p. 57-68.
133. Hutvágner, G., et al., A Cellular Function for the RNA-Interference Enzyme
Dicer in the Maturation of the let-7 Small Temporal RNA. Science, 2001.
293(5531): p. 834-838.
134. Feng, Y., et al., A comprehensive analysis of precursor microRNA cleavage by
human Dicer. RNA, 2012. 18(11): p. 2083-2092.
135. Ma, H., et al., A sliding-bulge structure at the Dicer processing site of pre- miRNAs regulates alternative Dicer processing to generate 5′-isomiRs. Heliyon, 2016. 2(9): p. e00148.
136. Ender, C., et al., A Human snoRNA with MicroRNA-Like Functions. Molecular
Cell, 2008. 32(4): p. 519-528.
137. Nicole, C. and C. Nicole, Multiple paths to mature miRNAs. 2014.
138. Bogerd, H.P., et al., A mammalian herpesvirus uses noncanonical expression
and processing mechanisms to generate viral MicroRNAs. Mol Cell, 2010.
37(1): p. 135-42.
139. Pfeffer, S., et al., Identification of microRNAs of the herpesvirus family. Nat
Meth, 2005. 2(4): p. 269-276.
140. Reese, T.A., et al., Identification of novel microRNA-like molecules generated
from herpesvirus and host tRNA transcripts. J Virol, 2010. 84(19): p. 10344-53.
141. Diebel, K.W., A.L. Smith, and L.F. van Dyk, Mature and functional viral
miRNAs transcribed from novel RNA polymerase III promoters. RNA, 2010.
16(1): p. 170-185.
142. MacFarlane, L.-A. and P.R. Murphy, MicroRNA: Biogenesis, Function and Role
in Cancer. Current Genomics, 2010. 11(7): p. 537-561.
Chi Yan Ooi 263 References
143. Su, H., et al., Essential and overlapping functions for mammalian Argonautes in
microRNA silencing. Genes & Development, 2009. 23(3): p. 304-317.
144. Höck, J. and G. Meister, The Argonaute protein family. Genome Biology, 2008.
9(2): p. 210-210.
145. MacRae, I.J., et al., In vitro reconstitution of the human RISC-loading complex.
Proceedings of the National Academy of Sciences of the United States of
America, 2008. 105(2): p. 512-517.
146. Haase, A.D., et al., TRBP, a regulator of cellular PKR and HIV-1 virus
expression, interacts with Dicer and functions in RNA silencing. EMBO Reports,
2005. 6(10): p. 961-967.
147. Maniataki, E. and Z. Mourelatos, A human, ATP-independent, RISC assembly
machine fueled by pre-miRNA. Genes & Development, 2005. 19(24): p. 2979-
2990.
148. Chendrimada, T.P., et al., TRBP recruits the Dicer complex to Ago2 for
microRNA processing and gene silencing. Nature, 2005. 436(7051): p. 740-744.
149. Noland, C.L. and J.A. Doudna, Multiple sensors ensure guide strand selection in
human RNAi pathways. Rna, 2013. 19(5): p. 639-48.
150. Khvorova, A., A. Reynolds, and S.D. Jayasena, Functional siRNAs and miRNAs
Exhibit Strand Bias. Cell, 2003. 115(2): p. 209-216.
151. Schwarz, D.S., et al., Asymmetry in the Assembly of the RNAi Enzyme Complex.
Cell, 2003. 115(2): p. 199-208.
152. Huang, C.-J., et al., Frequent Co-Expression of miRNA-5p and -3p Species and
Cross-Targeting in Induced Pluripotent Stem Cells. International Journal of
Medical Sciences, 2014. 11(8): p. 824-833.
Chi Yan Ooi 264 References
153. Roberts, T.C., The MicroRNA Biology of the Mammalian Nucleus. Mol Ther
Nucleic Acids, 2014. 3: p. e188.
154. Yang, J.S., et al., Conserved vertebrate mir-451 provides a platform for Dicer-
independent, Ago2-mediated microRNA biogenesis. Proc Natl Acad Sci U S A,
2010. 107(34): p. 15163-8.
155. Cheloufi, S., et al., A dicer-independent miRNA biogenesis pathway that
requires Ago catalysis. Nature, 2010. 465(7298): p. 584-9.
156. Cifuentes, D., et al., A Novel miRNA Processing Pathway Independent of Dicer
Requires Argonaute2 Catalytic Activity. Science, 2010. 328(5986): p. 1694-1698.
157. Yoda, M., et al., Poly(A)-specific ribonuclease mediates 3'-end trimming of
Argonaute2-cleaved precursor microRNAs. Cell Rep, 2013. 5(3): p. 715-26.
158. Liu, J., et al., Argonaute2 is the catalytic engine of mammalian RNAi. Science,
2004. 305(5689): p. 1437-41.
159. Meister, G., et al., Human Argonaute2 mediates RNA cleavage targeted by
miRNAs and siRNAs. Mol Cell, 2004. 15(2): p. 185-97.
160. Pillai, R.S., et al., Inhibition of translational initiation by Let-7 MicroRNA in
human cells. Science, 2005. 309(5740): p. 1573-6.
161. Mathonnet, G., et al., MicroRNA inhibition of translation initiation in vitro by
targeting the cap-binding complex eIF4F. Science, 2007. 317(5845): p. 1764-7.
162. Humphreys, D.T., et al., MicroRNAs control translation initiation by inhibiting
eukaryotic initiation factor 4E/cap and poly(A) tail function. Proceedings of the
National Academy of Sciences of the United States of America, 2005. 102(47):
p. 16961-16966.
Chi Yan Ooi 265 References
163. Nottrott, S., M.J. Simard, and J.D. Richter, Human let-7a miRNA blocks protein
production on actively translating polyribosomes. Nat Struct Mol Biol, 2006.
13(12): p. 1108-1114.
164. Maroney, P.A., et al., Evidence that microRNAs are associated with translating
messenger RNAs in human cells. Nat Struct Mol Biol, 2006. 13(12): p. 1102-1107.
165. Friend, K., et al., A conserved PUF-Ago-eEF1A complex attenuates translation
elongation. Nat Struct Mol Biol, 2012. 19(2): p. 176-83.
166. Eulalio, A., et al., Deadenylation is a widespread effect of miRNA regulation.
RNA, 2009. 15(1): p. 21-32.
167. Giraldez, A.J., et al., Zebrafish MiR-430 promotes deadenylation and clearance
of maternal mRNAs. Science, 2006. 312(5770): p. 75-9.
168. Wu, L., J. Fan, and J.G. Belasco, MicroRNAs direct rapid deadenylation of
mRNA. Proceedings of the National Academy of Sciences of the United States of
America, 2006. 103(11): p. 4034-4039.
169. Choe, J., et al., microRNA/Argonaute 2 regulates nonsense-mediated messenger
RNA decay. EMBO Rep, 2010. 11(5): p. 380-6.
170. Borner, K., et al., Robust RNAi enhancement via human Argonaute-2
overexpression from plasmids, viral vectors and cell lines. Nucleic Acids Res,
2013. 41(21): p. e199.
171. Bartel, D.P., MicroRNAs: Target Recognition and Regulatory Functions. Cell,
2009. 136(2): p. 215-233.
172. Grimson, A., et al., MicroRNA Targeting Specificity in Mammals: Determinants
beyond Seed Pairing. Molecular Cell, 2007. 27(1): p. 91-105.
173. Clark, P.M., et al., Argonaute CLIP-Seq reveals miRNA targetome diversity
across tissue types. Scientific Reports, 2014. 4: p. 5947.
Chi Yan Ooi 266 References
174. Chi, S.W., et al., Ago HITS-CLIP decodes miRNA-mRNA interaction maps.
Nature, 2009. 460(7254): p. 479-486.
175. Hafner, M., et al., Transcriptome-wide Identification of RNA-Binding Protein
and MicroRNA Target Sites by PAR-CLIP. Cell, 2010. 141(1): p. 129-141.
176. Schnall-Levin, M., et al., Unusually effective microRNA targeting within repeat-
rich coding regions of mammalian mRNAs. Genome Research, 2011. 21(9): p.
1395-1403.
177. Easow, G., A.A. Teleman, and S.M. Cohen, Isolation of microRNA targets by
miRNP immunopurification. RNA, 2007. 13(8): p. 1198-1204.
178. Huang, S., et al., MicroRNA-181a modulates gene expression of zinc finger
family members by directly targeting their coding regions. Nucleic Acids Res,
2010. 38(20): p. 7211-8.
179. Lee, I., et al., New class of microRNA targets containing simultaneous 5′-UTR and 3′-UTR interaction sites. Genome Research, 2009. 19(7): p. 1175-1183.
180. Gu, S., et al., Biological basis for restriction of microRNA targets to the 3'
untranslated region in mammalian mRNAs. Nat Struct Mol Biol, 2009. 16(2): p.
144-50.
181. Hausser, J., et al., Analysis of CDS-located miRNA target sites suggests that they
can effectively inhibit translation. Genome Research, 2013. 23(4): p. 604-615.
182. Fang, Z. and N. Rajewsky, The Impact of miRNA Target Sites in Coding Sequences and in 3′UTRs. PLOS ONE, 2011. 6(3): p. e18067.
183. Shin, C., et al., Expanding the MicroRNA Targeting Code: Functional Sites with
Centered Pairing. Molecular cell, 2010. 38(6): p. 789-802.
184. Yekta, S., I.-h. Shih, and D.P. Bartel, MicroRNA-Directed Cleavage of HOXB8
mRNA. Science, 2004. 304(5670): p. 594-596.
Chi Yan Ooi 267 References
185. Hausser, J. and M. Zavolan, Identification and consequences of miRNA-target
interactions [mdash] beyond repression of gene expression. Nat Rev Genet,
2014. 15(9): p. 599-612.
186. Seok, H., et al., MicroRNA Target Recognition: Insights from Transcriptome-
Wide Non-Canonical Interactions. Mol Cells, 2016. 39(5): p. 375-81.
187. Steinkraus, B.R., M. Toegel, and T.A. Fulga, Tiny giants of gene regulation:
experimental strategies for microRNA functional studies. Wiley Interdisciplinary
Reviews: Developmental Biology, 2016. 5(3): p. 311-362.
188. Loeb, Gabriel B., et al., Transcriptome-wide miR-155 Binding Map Reveals
Widespread Noncanonical MicroRNA Targeting. Molecular Cell, 2012. 48(5): p.
760-770.
189. Chi, S.W., G.J. Hannon, and R.B. Darnell, An alternative mode of microRNA
target recognition. Nat Struct Mol Biol, 2012. 19(3): p. 321-7.
190. Grosswendt, S., et al., Unambiguous Identification of miRNA:Target Site
Interactions by Different Types of Ligation Reactions. Molecular Cell, 2014. 54(6):
p. 1042-1054.
191. Didiano, D. and O. Hobert, Perfect seed pairing is not a generally reliable
predictor for miRNA-target interactions. Nat Struct Mol Biol, 2006. 13(9): p.
849-851.
192. Varani, G. and W.H. McClain, The G·U wobble base pair: A fundamental
building block of RNA structure crucial to RNA function in diverse biological
systems. EMBO Reports, 2000. 1(1): p. 18-23.
193. Vella, M.C., et al., The C. elegans microRNA let-7 binds to imperfect let-7 complementary sites from the lin-41 3′UTR. Genes & Development, 2004. 18(2):
p. 132-137.
Chi Yan Ooi 268 References
194. Martin, H.C., et al., Imperfect centered miRNA binding sites are common and
can mediate repression of target mRNAs. Genome Biology, 2014. 15(3): p. 1-22.
195. Helwak, A., et al., Mapping the Human miRNA Interactome by CLASH Reveals
Frequent Noncanonical Binding. Cell, 2013. 153(3): p. 654-665.
196. Lee, T., et al., Dosage and temporal thresholds in microRNA proteomics. Mol
Cell Proteomics, 2015. 14(2): p. 289-302.
197. Rajewsky, N., microRNA target predictions in animals. Nat Genet, 2006.
198. Agarwal, V., et al., Predicting effective microRNA target sites in mammalian
mRNAs. eLife, 2015. 4: p. e05005.
199. Garcia, D.M., et al., Weak Seed-Pairing Stability and High Target-Site
Abundance Decrease the Proficiency of lsy-6 and Other miRNAs. Nature
structural & molecular biology, 2011. 18(10): p. 1139-1146.
200. Betel, D., et al., The microRNA.org resource: targets and expression. Nucleic
Acids Research, 2008. 36(suppl_1): p. D149-D153.
201. Tay, Y., et al., MicroRNAs to Nanog, Oct4 and Sox2 coding regions modulate
embryonic stem cell differentiation. Nature, 2008. 455(7216): p. 1124-8.
202. Gantier, M.P., et al., Analysis of microRNA turnover in mammalian cells
following Dicer1 ablation. Nucleic Acids Research, 2011. 39(13): p. 5692-5703.
203. Yang, E., et al., Decay Rates of Human mRNAs: Correlation With Functional
Characteristics and Sequence Attributes. Genome Research, 2003. 13(8): p.
1863-1872.
204. van Rooij, E., et al., Control of Stress-Dependent Cardiac Growth and Gene
Expression by a MicroRNA. Science, 2007. 316(5824): p. 575-579.
Chi Yan Ooi 269 References
205. Rissland, Olivia S., S.-J. Hong, and David P. Bartel, MicroRNA Destabilization
Enables Dynamic Regulation of the miR-16 Family in Response to Cell-Cycle
Changes. Molecular Cell, 2011. 43(6): p. 993-1004.
206. Hwang, H.-W., E.A. Wentzel, and J.T. Mendell, A Hexanucleotide Element
Directs MicroRNA Nuclear Import. Science, 2007. 315(5808): p. 97-100.
207. Krol, J., et al., Characterizing Light-Regulated Retinal MicroRNAs Reveals
Rapid Turnover as a Common Property of Neuronal MicroRNAs. Cell, 2010.
141(4): p. 618-631.
208. Zhang, Z., et al., Uracils at nucleotide position 9–11 are required for the rapid
turnover of miR-29 family. Nucleic Acids Research, 2011. 39(10): p. 4387-4395.
209. Bail, S., et al., Differential regulation of microRNA stability. Rna, 2010. 16(5):
p. 1032-9.
210. Katoh, T., et al., Selective stabilization of mammalian microRNAs by 3'
adenylation mediated by the cytoplasmic poly(A) polymerase GLD-2. Genes
Dev, 2009. 23(4): p. 433-8.
211. Burroughs, A.M., et al., A comprehensive survey of 3 ′ animal miRNA
modification events and a possible role for 3′ adenylation in modulating
miRNA targeting effectiveness. Genome Research, 2010. 20(10): p. 1398-1410.
212. Jones, M.R., et al., Zcchc11-dependent uridylation of microRNA directs cytokine
expression. Nature cell biology, 2009. 11(9): p. 1157-1163.
213. Baccarini, A., et al., Kinetic Analysis Reveals the Fate of a MicroRNA following
Target Regulation in Mammalian Cells. Current Biology, 2011. 21(5): p. 369-376.
214. Xie, J., et al., Long-term, efficient inhibition of microRNA function in mice using
rAAV vectors. Nat Meth, 2012. 9(4): p. 403-409.
Chi Yan Ooi 270 References
215. Cazalla, D., T. Yario, and J.A. Steitz, Down-regulation of a host microRNA by a
Herpesvirus saimiri noncoding RNA. Science, 2010. 328(5985): p. 1563-6.
216. Libri, V., et al., Murine cytomegalovirus encodes a miR-27 inhibitor disguised
as a target. Proc Natl Acad Sci U S A, 2012. 109(1): p. 279-84.
217. Marcinowski, L., et al., Degradation of Cellular miR-27 by a Novel, Highly
Abundant Viral Transcript Is Important for Efficient Virus Replication In Vivo.
PLOS Pathogens, 2012. 8(2): p. e1002510.
218. Chen, P.S., et al., miR-107 promotes tumor progression by targeting the let-7
microRNA in mice and humans. J Clin Invest, 2011. 121(9): p. 3442-55.
219. Lai, E.C., C. Wiel, and G.M. Rubin, Complementary miRNA pairs suggest a
regulatory role for miRNA:miRNA duplexes. Rna, 2004. 10(2): p. 171-5.
220. Vasudevan, S., Y. Tong, and J.A. Steitz, Switching from Repression to
Activation: MicroRNAs Can Up-Regulate Translation. Science, 2007.
318(5858): p. 1931-1934.
221. Vasudevan, S., Y. Tong, and J.A. Steitz, Cell cycle control of microRNA-mediated
translation regulation. Cell cycle (Georgetown, Tex.), 2008. 7(11): p. 1545-1549.
222. Mortensen, R.D., et al., Posttranscriptional activation of gene expression in
Xenopus laevis oocytes by microRNA–protein complexes (microRNPs).
Proceedings of the National Academy of Sciences, 2011. 108(20): p. 8281-8286.
223. Vasudevan, S. and J.A. Steitz, AU-Rich-Element-Mediated Upregulation of
Translation by FXR1 and Argonaute 2. Cell, 2007. 128(6): p. 1105-1118.
224. Truesdell, S.S., et al., MicroRNA-mediated mRNA Translation Activation in
Quiescent Cells and Oocytes Involves Recruitment of a Nuclear microRNP.
Scientific Reports, 2012. 2: p. 842.
Chi Yan Ooi 271 References
225. Castanotto, D., et al., CRM1 mediates nuclear-cytoplasmic shuttling of mature
microRNAs. Proceedings of the National Academy of Sciences of the United
States of America, 2009. 106(51): p. 21655-21659.
226. Wei, Y., et al., Importin 8 regulates the transport of mature microRNAs into the
cell nucleus. Journal of Biological Chemistry, 2014.
227. Weinmann, L., et al., Importin 8 Is a Gene Silencing Factor that Targets
Argonaute Proteins to Distinct mRNAs. Cell, 2009. 136(3): p. 496-507.
228. Hansen, T.B., et al., miRNA-dependent gene silencing involving Ago2-mediated
cleavage of a circular antisense RNA. The EMBO Journal, 2011. 30(21): p.
4414-4422.
229. Leucci, E., et al., microRNA-9 targets the long non-coding RNA MALAT1 for
degradation in the nucleus. Scientific Reports, 2013. 3: p. 2535.
230. Fasanaro, P., et al., An Integrated Approach for Experimental Target
Identification of Hypoxia-induced miR-210. The Journal of Biological
Chemistry, 2009. 284(50): p. 35134-35143.
231. Catalanotto, C., C. Cogoni, and G. Zardo, MicroRNA in Control of Gene
Expression: An Overview of Nuclear Functions. Int J Mol Sci, 2016. 17(10).
232. Zisoulis, D.G., et al., Autoregulation of microRNA biogenesis by let-7 and
Argonaute. Nature, 2012. 486(7404): p. 541-544.
233. Tang, R., et al., Mouse miRNA-709 directly regulates miRNA-15a/16-1
biogenesis at the posttranscriptional level in the nucleus: evidence for a
microRNA hierarchy system. Cell Research, 2012. 22(3): p. 504-515.
234. Calin, G.A., et al., Human microRNA genes are frequently located at fragile sites
and genomic regions involved in cancers. Proceedings of the National Academy of
Sciences of the United States of America, 2004. 101(9): p. 2999-3004.
Chi Yan Ooi 272 References
235. Kumar, M.S., et al., Impaired microRNA processing enhances cellular
transformation and tumorigenesis. Nat Genet, 2007. 39(5): p. 673-677.
236. Melo, S.A., et al., A Genetic Defect in Exportin-5 Traps Precursor MicroRNAs
in the Nucleus of Cancer Cells. Cancer Cell, 2010. 18(4): p. 303-315.
237. Jansson, M.D. and A.H. Lund, MicroRNA and cancer. Molecular Oncology,
2012. 6(6): p. 590-610.
238. Peng, Y. and C.M. Croce, The role of MicroRNAs in human cancer. Signal
Transduction And Targeted Therapy, 2016. 1: p. 15004.
239. Stahlhut Espinosa, C.E. and F.J. Slack, The Role of MicroRNAs in Cancer. The
Yale Journal of Biology and Medicine, 2006. 79(3-4): p. 131-140.
240. Cimmino, A., et al., miR-15 and miR-16 induce apoptosis by targeting BCL2.
Proc Natl Acad Sci U S A, 2005. 102(39): p. 13944-9.
241. Zhang, J.G., et al., MicroRNA-21 (miR-21) represses tumor suppressor PTEN
and promotes growth and invasion in non-small cell lung cancer (NSCLC). Clin
Chim Acta, 2010. 411(11-12): p. 846-52.
242. Liu, B., et al., MiR-26a enhances metastasis potential of lung cancer cells via
AKT pathway by targeting PTEN. Biochimica et Biophysica Acta (BBA) -
Molecular Basis of Disease, 2012. 1822(11): p. 1692-1704.
243. Kota, J., et al., Therapeutic delivery of miR-26a inhibits cancer cell proliferation
and induces tumor-specific apoptosis. Cell, 2009. 137(6): p. 1005-1017.
244. Schulte, J.H., et al., Deep sequencing reveals differential expression of
microRNAs in favorable versus unfavorable neuroblastoma. Nucleic Acids
Research, 2010. 38(17): p. 5919-5928.
Chi Yan Ooi 273 References
245. Lovén, J., et al., MYCN-regulated microRNAs repress estrogen receptor-α
(ESR1) expression and neuronal differentiation in human neuroblastoma.
Proceedings of the National Academy of Sciences, 2010. 107(4): p. 1553-1558.
246. Malynn, B.A., et al., N-myc can functionally replace c-myc in murine
development, cellular growth, and differentiation. Genes & Development, 2000.
14(11): p. 1390-1399.
247. Petrocca, F., A. Vecchione, and C.M. Croce, Emerging Role of miR-106b-
25/miR-17-92 Clusters in the Control of Transforming Growth Factor β
Signaling. Cancer Research, 2008. 68(20): p. 8191-8194.
248. He, L., et al., A microRNA polycistron as a potential human oncogene. Nature,
2005. 435(7043): p. 828-833.
249. O'Donnell, K.A., et al., c-Myc-regulated microRNAs modulate E2F1 expression.
Nature, 2005. 435(7043): p. 839-843.
250. Mendell, J.T., miRiad roles for the miR-17-92 cluster in development and
disease. Cell, 2008. 133(2): p. 217-222.
251. Ota, A., et al., Identification and Characterization of a Novel Gene, C13orf25,
as a Target for 13q31-q32 Amplification in Malignant Lymphoma. Cancer
Research, 2004. 64(9): p. 3087-3095.
252. Trompeter, H.-I., et al., MicroRNAs MiR-17, MiR-20a, and MiR-106b Act in
Concert to Modulate E2F Activity on Cell Cycle Arrest during Neuronal Lineage
Differentiation of USSC. PLoS ONE, 2011. 6(1): p. e16138.
253. Fontana, L., et al., Antagomir-17-5p Abolishes the Growth of Therapy-Resistant
Neuroblastoma through p21 and BIM. PLoS ONE, 2008. 3(5): p. e2236.
254. Cloonan, N., et al., The miR-17-5p microRNA is a key regulator of the G1/S
phase cell cycle transition. Genome Biol, 2008. 9(8): p. R127.
Chi Yan Ooi 274 References
255. Buechner, J., et al., Tumour-suppressor microRNAs let-7 and mir-101 target the
proto-oncogene MYCN and inhibit cell proliferation in MYCN-amplified
neuroblastoma. Br J Cancer, 2011. 105(2): p. 296-303.
256. Sylvestre, Y., et al., An E2F/miR-20a Autoregulatory Feedback Loop. Journal of
Biological Chemistry, 2007. 282(4): p. 2135-2143.
257. Pickering, M.T., B.M. Stadler, and T.F. Kowalik, miR-17 and miR-20a temper
an E2F1-induced G1 checkpoint to regulate cell cycle progression. Oncogene,
2009. 28(1): p. 140-5.
258. Petrocca, F., et al., E2F1-Regulated MicroRNAs Impair TGFβ-Dependent Cell-
Cycle Arrest and Apoptosis in Gastric Cancer. Cancer Cell, 2008. 13(3): p. 272-
286.
259. Coller, H.A., J.J. Forman, and A. Legesse-Miller, “Myc’ed Messages”: Myc
Induces Transcription of E2F1 while Inhibiting Its Translation via a microRNA
Polycistron. PLoS Genet, 2007. 3(8): p. e146.
260. Strieder, V. and W. Lutz, E2F Proteins Regulate MYCN Expression in
Neuroblastomas. Journal of Biological Chemistry, 2003. 278(5): p. 2983-2989.
261. Woo, C.-W., et al., Use of RNA interference to elucidate the effect of MYCN on
cell cycle in neuroblastoma. Pediatric Blood & Cancer, 2008. 50(2): p. 208-212.
262. Mogilyansky, E. and I. Rigoutsos, The miR-17/92 cluster: a comprehensive
update on its genomics, genetics, functions and increasingly important and
numerous roles in health and disease. Cell Death Differ, 2013. 20(12): p. 1603-
1614.
263. Buechner, J., et al., Tumour-suppressor microRNAs let-7 and mir-101 target the
proto-oncogene MYCN and inhibit cell proliferation in MYCN-amplified
neuroblastoma. Br J Cancer, 2011. 105(2): p. 296-303.
Chi Yan Ooi 275 References
264. Ivanovska, I., et al., MicroRNAs in the miR-106b family regulate p21/CDKN1A
and promote cell cycle progression. Mol Cell Biol, 2008. 28(7): p. 2167-74.
265. Mestdagh, P., et al., The miR-17-92 MicroRNA Cluster Regulates Multiple
Components of the TGF-β Pathway in Neuroblastoma. Molecular Cell, 2010.
40(5): p. 762-773.
266. Ma, Y., et al., Elevated oncofoetal miR-17-5p expression regulates colorectal
cancer progression by repressing its target gene P130. Nat Commun, 2012. 3: p.
1291.
267. Wang, M., et al., miR-17-5p/20a are important markers for gastric cancer and
murine double minute 2 participates in their functional regulation. European
Journal of Cancer, 2013. 49(8): p. 2010-2021.
268. Ambros, V. and H. Horvitz, Heterochronic mutants of the nematode
Caenorhabditis elegans. Science, 1984. 226(4673): p. 409-416.
269. Moss, E.G., R.C. Lee, and V. Ambros, The Cold Shock Domain Protein LIN-28
Controls Developmental Timing in C. elegans and Is Regulated by the lin-4
RNA. Cell, 1997. 88(5): p. 637-646.
270. Reinhart, B.J., et al., The 21-nucleotide let-7 RNA regulates developmental
timing in Caenorhabditis elegans. Nature, 2000. 403(6772): p. 901-906.
271. Moss, E.G. and L. Tang, Conservation of the heterochronic regulator Lin-28, its
developmental expression and microRNA complementary sites. Developmental
Biology, 2003. 258(2): p. 432-442.
272. Guo, Y., et al., Identification and characterization of lin-28 homolog B (LIN28B)
in human hepatocellular carcinoma. Gene, 2006. 384: p. 51-61.
273. Richards, M., et al., The Transcriptome Profile of Human Embryonic Stem Cells
as Defined by SAGE. STEM CELLS, 2004. 22(1): p. 51-64.
Chi Yan Ooi 276 References
274. Yokoyama, S., et al., Dynamic gene expression of Lin-28 during embryonic
development in mouse and chicken. Gene expression patterns : GEP, 2008. 8(3):
p. 155-160.
275. Zhong, X., et al., Identification of MicroRNAs Regulating Reprogramming
Factor LIN28 in Embryonic Stem Cells and Cancer Cells. Journal of Biological
Chemistry, 2010. 285(53): p. 41961-41971.
276. Kolenda, T., et al., The mystery of let-7d – a small RNA with great power.
Contemporary Oncology, 2014. 18(5): p. 293-301.
277. Kiriakidou, M., et al., A combined computational-experimental approach predicts
human microRNA targets. Genes & Development, 2004. 18(10): p. 1165-1178.
278. Wu, L. and J.G. Belasco, Micro-RNA regulation of the mammalian lin-28 gene
during neuronal differentiation of embryonal carcinoma cells. Mol Cell Biol,
2005. 25(21): p. 9198-208.
279. Vlachos, I.S., et al., DIANA-TarBase v7.0: indexing more than half a million
experimentally supported miRNA:mRNA interactions. Nucleic Acids Research,
2015. 43(D1): p. D153-D159.
280. Kishore, S., et al., A quantitative analysis of CLIP methods for identifying
binding sites of RNA-binding proteins. Nat Meth, 2011. 8(7): p. 559-564.
281. Karginov, F.V. and G.J. Hannon, Remodeling of Ago2–mRNA interactions upon
cellular stress reflects miRNA complementarity and correlates with altered
translation rates. Genes & Development, 2013. 27(14): p. 1624-1632.
282. Gottwein, E., et al., Viral MicroRNA Targetome of KSHV-Infected Primary
Effusion Lymphoma Cell Lines. Cell Host & Microbe, 2011. 10(5): p. 515-526.
Chi Yan Ooi 277 References
283. Haecker, I., et al., Ago HITS-CLIP Expands Understanding of Kaposi's
Sarcoma-associated Herpesvirus miRNA Function in Primary Effusion
Lymphomas. PLoS Pathog, 2012. 8(8): p. e1002884.
284. Balzer, E. and E.G. Moss, Localization of the Developmental Timing Regulator
Lin28 to mRNP Complexes, P-bodies and Stress Granules. RNA Biology, 2007.
4(1): p. 16-25.
285. Polesskaya, A., et al., Lin-28 binds IGF-2 mRNA and participates in skeletal
myogenesis by increasing translation efficiency. Genes & Development, 2007.
21(9): p. 1125-1138.
286. Heo, I., et al., Lin28 Mediates the Terminal Uridylation of let-7 Precursor
MicroRNA. Molecular Cell, 2008. 32(2): p. 276-284.
287. Newman, M.A., J.M. Thomson, and S.M. Hammond, Lin-28 interaction with the
Let-7 precursor loop mediates regulated microRNA processing. RNA, 2008.
14(8): p. 1539-1549.
288. Piskounova, E., et al., Determinants of MicroRNA Processing Inhibition by the
Developmentally Regulated RNA-binding Protein Lin28. Journal of Biological
Chemistry, 2008. 283(31): p. 21310-21314.
289. Viswanathan, S.R., G.Q. Daley, and R.I. Gregory, Selective Blockade of
MicroRNA Processing by Lin28. Science, 2008. 320(5872): p. 97-100.
290. Hagan, J.P., E. Piskounova, and R.I. Gregory, Lin28 recruits the TUTase
Zcchc11 to inhibit let-7 maturation in mouse embryonic stem cells. Nat Struct
Mol Biol, 2009. 16(10): p. 1021-1025.
291. Nam, Y., et al., Molecular Basis for Interaction of let-7 MicroRNAs with Lin28.
Cell, 2011. 147(5): p. 1080-1091.
Chi Yan Ooi 278 References
292. Heo, I., et al., TUT4 in Concert with Lin28 Suppresses MicroRNA Biogenesis
through Pre-MicroRNA Uridylation. Cell, 2009. 138(4): p. 696-708.
293. Thornton, J.E., et al., Lin28-mediated control of let-7 microRNA expression by
alternative TUTases Zcchc11 (TUT4) and Zcchc6 (TUT7). RNA, 2012. 18(10):
p. 1875-1885.
294. Piskounova, E., et al., Lin28A and Lin28B Inhibit let-7 MicroRNA Biogenesis by
Distinct Mechanisms. Cell, 2011. 147(5): p. 1066-1079.
295. Wang, T., et al., Aberrant regulation of the LIN28A/LIN28B and let-7 loop in
human malignant tumors and its effects on the hallmarks of cancer. Molecular
Cancer, 2015. 14(1): p. 1-13.
296. Mayr, F. and U. Heinemann, Mechanisms of Lin28-mediated miRNA and mRNA
regulation--a structural and functional perspective. Int J Mol Sci, 2013. 14(8):
p. 16532-53.
297. Landau, G., et al., The Role of Polyamines in Supporting Growth of Mammalian
Cells Is Mediated through Their Requirement for Translation Initiation and
Elongation. The Journal of Biological Chemistry, 2010. 285(17): p. 12474-12481.
298. Paz, E.A., B. LaFleur, and E.W. Gerner, Polyamines are oncometabolites that
regulate the LIN28/let-7 pathway in colorectal cancer cells. Molecular
Carcinogenesis, 2014. 53(S1): p. E96-E106.
299. Lozier, A.M., et al., Targeting ornithine decarboxylase reverses the LIN28/Let-7
axis and inhibits glycolytic metabolism in neuroblastoma. Oncotarget, 2015.
6(1): p. 196-206.
300. Mosse, Y.P., et al., High-resolution detection and mapping of genomic DNA
alterations in neuroblastoma. Genes, Chromosomes and Cancer, 2005. 43(4): p.
390-403.
Chi Yan Ooi 279 References
301. Mosse, Y.P., et al., Neuroblastomas have distinct genomic DNA profiles that
predict clinical phenotype and regional gene expression. Genes, Chromosomes
and Cancer, 2007. 46(10): p. 936-949.
302. Fong, C.T., et al., Loss of heterozygosity for the short arm of chromosome 1 in
human neuroblastomas: correlation with N-myc amplification. Proceedings of
the National Academy of Sciences, 1989. 86(10): p. 3753-3757.
303. Welch, C., Y. Chen, and R.L. Stallings, MicroRNA-34a functions as a potential
tumor suppressor by inducing apoptosis in neuroblastoma cells. Oncogene,
2007. 26(34): p. 5017-5022.
304. Tivnan, A., et al., MicroRNA-34a is a potent tumor suppressor molecule in vivo
in neuroblastoma. BMC Cancer, 2011. 11(1): p. 1-11.
305. Cole, K.A., et al., A Functional Screen Identifies miR-34a as a Candidate
Neuroblastoma Tumor Suppressor Gene. Molecular cancer research : MCR,
2008. 6(5): p. 735-742.
306. Raver-Shapira, N., et al., Transcriptional Activation of miR-34a Contributes to
p53-Mediated Apoptosis. Molecular Cell, 2007. 26(5): p. 731-743.
307. Rihani, A., et al., Genome wide expression profiling of p53 regulated miRNAs in
neuroblastoma. Sci Rep, 2015. 5: p. 9027.
308. Feinberg-Gorenshtein, G., et al., Reduced levels of miR-34a in neuroblastoma
are not caused by mutations in the TP53 binding site. Genes Chromosomes
Cancer, 2009. 48(7): p. 539-43.
309. Wei, J.S., et al., The MYCN oncogene is a direct target of miR-34a. Oncogene,
2008. 27(39): p. 5204-5213.
Chi Yan Ooi 280 References
310. Yang, F., et al., MicroRNA-34a Targets Bcl-2 and Sensitizes Human
Hepatocellular Carcinoma Cells to Sorafenib Treatment. Technology in Cancer
Research & Treatment, 2014. 13(1): p. 77-86.
311. De Antonellis, P., et al., Early Targets of miR-34a in Neuroblastoma. Molecular
& Cellular Proteomics, 2014. 13(8): p. 2114-2131.
312. Chen, Y., Y.-H. Tsai, and S.-H. Tseng, Inhibition of cyclin-dependent kinase 1–
induced cell death in neuroblastoma cells through the microRNA-34a–MYCN–
survivin pathway. Surgery, 2013. 153(1): p. 4-16.
313. Tivnan, A., et al., Inhibition of Neuroblastoma Tumor Growth by Targeted
Delivery of MicroRNA-34a Using Anti-Disialoganglioside GD2 Coated
Nanoparticles. PLoS ONE, 2012. 7(5): p. e38129.
314. Agostini, M. and R.A. Knight, miR-34: from bench to bedside. Oncotarget, 2014.
5(4): p. 872-81.
315. Misso, G., et al., Mir-34: A New Weapon Against Cancer? Mol Ther Nucleic
Acids, 2014. 3: p. e194.
316. Bouchie, A., First microRNA mimic enters clinic. Nat Biotech, 2013. 31(7): p.
577-577.
317. Delaloy, C., et al., MicroRNA-9 coordinates proliferation and migration of
human embryonic stem cell-derived neural progenitors. Cell Stem Cell, 2010.
6(4): p. 323-35.
318. Deo, M., et al., Detection of mammalian microRNA expression by in situ
hybridization with RNA oligonucleotides. Developmental Dynamics, 2006.
235(9): p. 2538-2548.
319. Ma, L., et al., miR-9, a MYC/MYCN-activated microRNA, regulates E-cadherin
and cancer metastasis. Nat Cell Biol, 2010. 12(3): p. 247-256.
Chi Yan Ooi 281 References
320. Taneyhill, L.A., To adhere or not to adhere: The role of Cadherins in neural crest
development. Cell Adhesion & Migration, 2008. 2(4): p. 223-230.
321. Zhang, H., et al., microRNA-9 Targets Matrix Metalloproteinase 14 to Inhibit
Invasion, Metastasis, and Angiogenesis of Neuroblastoma Cells. Molecular
Cancer Therapeutics, 2012. 11(7): p. 1454-1466.
322. Chun, T.-H., et al., MT1-MMP–dependent neovessel formation within the
confines of the three-dimensional extracellular matrix. The Journal of Cell
Biology, 2004. 167(4): p. 757-767.
323. Annibali, D., et al., A New Module in Neural Differentiation Control: Two
MicroRNAs Upregulated by Retinoic Acid, miR-9 and -103, Target the
Differentiation Inhibitor ID2. PLoS ONE, 2012. 7(7): p. e40269.
324. Ruzinova, M.B. and R. Benezra, Id proteins in development, cell cycle and
cancer. Trends in Cell Biology, 2003. 13(8): p. 410-418.
325. Courboulin, A., et al., Role for miR-204 in human pulmonary arterial
hypertension. The Journal of Experimental Medicine, 2011. 208(3): p. 535-548.
326. Xiao, J., et al., MicroRNA-204 is required for differentiation of human-derived
cardiomyocyte progenitor cells. Journal of Molecular and Cellular Cardiology,
2012. 53(6): p. 751-759.
327. Li, S.C., P. Tang, and W.C. Lin, Intronic microRNA: discovery and biological
implications. DNA Cell Biol, 2007. 26(4): p. 195-207.
328. Strauss, O., The Retinal Pigment Epithelium in Visual Function. Physiological
Reviews, 2005. 85(3): p. 845-881.
329. Li, W.-B., et al., Development of Retinal Pigment Epithelium from Human
Parthenogenetic Embryonic Stem Cells and MicroRNA Signature. Investigative
Ophthalmology & Visual Science, 2012. 53(9): p. 5334-5343.
Chi Yan Ooi 282 References
330. Shaham, O., et al., Pax6 Regulates Gene Expression in the Vertebrate Lens
through miR-204. PLoS Genet, 2013. 9(3): p. e1003357.
331. Conte, I., et al., miR-204 is required for lens and retinal development via Meis2
targeting. Proceedings of the National Academy of Sciences, 2010. 107(35): p.
15491-15496.
332. Geerts, D., et al., The role of the MEIS homeobox genes in neuroblastoma.
Cancer Lett, 2003. 197(1-2): p. 87-92.
333. Zha, Y., et al., MEIS2 is essential for neuroblastoma cell survival and
proliferation by transcriptional control of M-phase progression. Cell Death Dis,
2014. 5: p. e1417.
334. Imam, J.S., et al., Genomic Loss of Tumor Suppressor miRNA-204 Promotes
Cancer Cell Migration and Invasion by Activating AKT/mTOR/Rac1 Signaling
and Actin Reorganization. PLoS ONE, 2012. 7(12): p. e52397.
335. Abou-Elhamd, K.-E.A., et al., The role of genetic susceptibility in head and neck
squamous cell carcinoma. European Archives of Oto-Rhino-Laryngology, 2007.
265(2): p. 217-222.
336. Lee, Y., et al., Network modeling identifies molecular functions targeted by miR-
204 to suppress head and neck tumor metastasis. PLoS Comput Biol, 2010. 6(4):
p. e1000730.
337. Ying, Z., et al., Loss of miR-204 Expression Enhances Glioma Migration and
Stem Cell-like Phenotype. Cancer Research, 2013. 73(2): p. 990-999.
338. Qiu, Y., et al., miR-204 Inhibits Epithelial to Mesenchymal Transition by
Targeting Slug in Intrahepatic Cholangiocarcinoma Cells. Cellular Physiology
and Biochemistry, 2013. 32(5): p. 1331-1341.
Chi Yan Ooi 283 References
339. Gong, M., et al., MicroRNA-204 critically regulates carcinogenesis in malignant
peripheral nerve sheath tumors. Neuro-Oncology, 2012. 14(8): p. 1007-1017.
340. Xia, Y., et al., miR-204 functions as a tumor suppressor by regulating SIX1 in
NSCLC. FEBS Letters, 2014. 588(20): p. 3703-3712.
341. Sacconi, A., et al., miR-204 targets Bcl-2 expression and enhances
responsiveness of gastric cancer. Cell Death Dis, 2012. 3: p. e423.
342. Mikhaylova, O., et al., VHL-Regulated MiR-204 Suppresses Tumor Growth
through Inhibition of LC3B-Mediated Autophagy in Renal Clear Cell
Carcinoma. Cancer Cell, 2012. 21(4): p. 532-546.
343. Butrym, A., et al., Low expression of microRNA-204 (miR-204) is associated
with poor clinical outcome of acute myeloid leukemia (AML) patients. Journal of
Experimental & Clinical Cancer Research, 2015. 34(1): p. 1-5.
344. Wang, F.E., et al., MicroRNA-204/211 alters epithelial physiology. FASEB J,
2010. 24(5): p. 1552-71.
345. Chen, Z., et al., miR-204 mediated loss of Myeloid cell leukemia-1 results in
pancreatic cancer cell death. Molecular Cancer, 2013. 12(1): p. 105.
346. Shi, L., et al., MiR-204 inhibits human NSCLC metastasis through suppression
of NUAK1. Br J Cancer, 2014. 111(12): p. 2316-2327.
347. Ryan, J., et al., MicroRNA-204 increases sensitivity of neuroblastoma cells to
cisplatin and is associated with a favourable clinical outcome. Br J Cancer,
2012. 107(6): p. 967-976.
348. Ayers, D., et al., Identification of miRNAs contributing to neuroblastoma
chemoresistance. Computational and Structural Biotechnology Journal, 2015.
13: p. 307-319.
Chi Yan Ooi 284 References
349. Bachetti, T., et al., miR-204 mediates post-transcriptional down-regulation of
PHOX2B gene expression in neuroblastoma cells. Biochimica et Biophysica
Acta (BBA) - Gene Regulatory Mechanisms, 2015. 1849(8): p. 1057-1065.
350. Kasemeier-Kulesa, J.C., et al., TrkB/BDNF signalling patterns the sympathetic
nervous system. Nature Communications, 2015. 6: p. 8281.
351. Nakagawara, A., et al., Expression and function of TRK-B and BDNF in human
neuroblastomas. Mol Cell Biol, 1994. 14(1): p. 759-67.
352. Kanehisa, M., et al., KEGG: new perspectives on genomes, pathways, diseases
and drugs. Nucleic Acids Research, 2017. 45(D1): p. D353-D361.
353. Guo, J., et al., Identification of miRNAs that are associated with tumor metastasis in
Neuroblastoma. Cancer Biology & Therapy, 2010. 9(6): p. 446-452.
354. Roth, S.A., et al., Next generation sequencing of microRNAs from isogenic
neuroblastoma cell lines isolated before and after treatment. Cancer Letters,
2016. 372(1): p. 128-136.
355. Abdelmohsen, K., et al., miR-375 Inhibits Differentiation of Neurites by Lowering
HuD Levels. Molecular and Cellular Biology, 2010. 30(17): p. 4197-4210.
356. Samaraweera, L., et al., MicroRNAs define distinct human neuroblastoma cell
phenotypes and regulate their differentiation and tumorigenicity. BMC Cancer,
2014. 14(1): p. 309.
357. Zhang, H., et al., Targeting MYCN IRES in MYCN-amplified neuroblastoma
with miR-375 inhibits tumor growth and sensitizes tumor cells to radiation. Mol
Oncol, 2015.
358. Beckers, A., et al., MYCN-targeting miRNAs are predominantly downregulated
during MYCN‑driven neuroblastoma tumor formation. Oncotarget, 2014. 6(7):
p. 5204-16.
Chi Yan Ooi 285 References
359. Das, S., et al., Modulation of Neuroblastoma Disease Pathogenesis By An
Extensive Network of Epigenetically Regulated MicroRNAs. Oncogene, 2013.
32(24): p. 2927-2936.
360. Chen, Y., D.-Y. Gao, and L. Huang, In vivo delivery of miRNAs for cancer
therapy: Challenges and strategies. Advanced Drug Delivery Reviews, 2015.
81(0): p. 128-141.
361. Christopher, A.F., et al., MicroRNA therapeutics: Discovering novel targets and
developing specific therapy. Perspectives in Clinical Research, 2016. 7(2): p. 68-
74.
362. Reid, G., et al., Clinical development of TargomiRs, a miRNA mimic-based
treatment for patients with recurrent thoracic cancer. Epigenomics, 2016. 8(8):
p. 1079-85.
363. Zhang, Y., Z. Wang, and R.A. Gemeinhart, Progress in microRNA delivery.
Journal of Controlled Release, 2013. 172(3): p. 962-974.
364. Young, D.D., et al., Small Molecule Modifiers of MicroRNA miR-122 Function
for the Treatment of Hepatitis C Virus Infection and Hepatocellular Carcinoma.
Journal of the American Chemical Society, 2010. 132(23): p. 7976-7981.
365. Li, Z. and T.M. Rana, Therapeutic targeting of microRNAs: current status and
future challenges. Nat Rev Drug Discov, 2014. 13(8): p. 622-638.
366. Broderick, J.A. and P.D. Zamore, MicroRNA therapeutics. Gene Ther, 2011.
18(12): p. 1104-1110.
367. Harvey, H., et al., Modulation of chemotherapeutic drug resistance in
neuroblastoma SK-N-AS cells by the neural apoptosis inhibitory protein and
miR-520f. International Journal of Cancer, 2015. 136(7): p. 1579-1588.
Chi Yan Ooi 286 References
368. Creevey, L., et al., MicroRNA-497 increases apoptosis in MYCN amplified
neuroblastoma cells by targeting the key cell cycle regulator WEE1. Molecular
Cancer, 2013. 12(1): p. 23.
369. Li, M.-P., et al., MiRNAs and miRNA Polymorphisms Modify Drug Response.
International Journal of Environmental Research and Public Health, 2016.
13(11): p. 1096.
370. Mostert, B., et al., Diagnostic applications of cell-free and circulating tumor
cell-associated miRNAs in cancer patients. Expert Review of Molecular
Diagnostics, 2011. 11(3): p. 259-275.
371. Kosaka, N., H. Iguchi, and T. Ochiya, Circulating microRNA in body fluid: a
new potential biomarker for cancer diagnosis and prognosis. Cancer Science,
2010. 101(10): p. 2087-2092.
372. Challagundla, K.B., et al., Exosome-mediated transfer of microRNAs within the
tumor microenvironment and neuroblastoma resistance to chemotherapy. J Natl
Cancer Inst, 2015. 107(7).
373. Murray, M.J., et al., Solid tumors of childhood display specific serum microRNA
profiles. Cancer epidemiology, biomarkers & prevention : a publication of the
American Association for Cancer Research, cosponsored by the American
Society of Preventive Oncology, 2015. 24(2): p. 350-360.
374. Ramraj, S.K., et al., Serum-circulating miRNAs predict neuroblastoma
progression in mouse model of high-risk metastatic disease. Oncotarget, 2016.
7(14): p. 18605-19.
375. Sala, A., Editorial: Targeting MYCN in Pediatric Cancers. Frontiers in Oncology,
2014. 4: p. 330.
Chi Yan Ooi 287 References
376. Cheung, L., et al., The MYCN Oncogene, in Oncogene and Cancer - From
Bench to Clinic, Y. Siregar, Editor. 2013, InTech: Rijeka. p. Ch. 18.
377. Nogales-Cadenas, R., et al., GeneCodis: interpreting gene lists through
enrichment analysis and integration of diverse biological information. Nucleic
Acids Res, 2009. 37(Web Server issue): p. W317-22.
378. Carmona-Saez, P., et al., GENECODIS: a web-based tool for finding significant
concurrent annotations in gene lists. Genome Biol, 2007. 8(1): p. R3.
379. Tabas-Madrid, D., R. Nogales-Cadenas, and A. Pascual-Montano, GeneCodis3:
a non-redundant and modular enrichment analysis tool for functional genomics.
Nucleic Acids Res, 2012. 40(Web Server issue): p. W478-83.
380. Subramanian, A., et al., Gene set enrichment analysis: A knowledge-based
approach for interpreting genome-wide expression profiles. Proceedings of the
National Academy of Sciences, 2005. 102(43): p. 15545-15550.
381. Liu, B., et al., Exploring complex miRNA-mRNA interactions with Bayesian
networks by splitting-averaging strategy. BMC Bioinformatics, 2009. 10: p. 408.
382. Kertesz, M., et al., The role of site accessibility in microRNA target recognition.
Nat Genet, 2007. 39(10): p. 1278-1284.
383. Miranda, K.C., et al., A pattern-based method for the identification of MicroRNA
binding sites and their corresponding heteroduplexes. Cell, 2006. 126(6): p.
1203-17.
384. Slack, A., et al., The p53 regulatory gene MDM2 is a direct transcriptional target of
MYCN in neuroblastoma. Proc Natl Acad Sci U S A, 2005. 102(3): p. 731-6.
385. GE Healthcare Dharmacon Inc. shMIMIC Inducible Lentiviral microRNA. [Web
page] 2016 [cited 2017 23 January]; Available from:
Chi Yan Ooi 288 References
http://dharmacon.gelifesciences.com/microrna/inducible-lentiviral-shmimic-
microrna/.
386. Mestdagh, P., et al., High-throughput stem-loop RT-qPCR miRNA expression
profiling using minute amounts of input RNA. Nucleic Acids Research, 2008.
36(21): p. e143.
387. Bello-Fernandez, C., G. Packham, and J.L. Cleveland, The ornithine
decarboxylase gene is a transcriptional target of c-Myc. Proc Natl Acad Sci U S
A, 1993. 90(16): p. 7804-8.
388. Subramanian, M., et al., A Biochemical Approach to Identify Direct MicroRNA
Targets, in Regulatory Non-Coding RNAs, G.G. Carmichael, Editor. 2015,
Springer New York. p. 29-37.
389. Weiss, W.A., et al., Targeted expression of MYCN causes neuroblastoma in
transgenic mice. The EMBO Journal, 1997. 16(11): p. 2985-2995.
390. Alaminos, M., et al., Genome-wide analysis of gene expression associated with
MYCN in human neuroblastoma. Cancer Res, 2003. 63(15): p. 4538-46.
391. Valentijn, L.J., et al., Inhibition of a New Differentiation Pathway in
Neuroblastoma by Copy Number Defects of N-myc, Cdc42, and nm23 Genes.
Cancer Research, 2005. 65(8): p. 3136-3145.
392. Liu, P.Y., et al., Effects of a novel long noncoding RNA, lncUSMycN, on N-Myc
expression and neuroblastoma progression. J Natl Cancer Inst, 2014. 106(7).
393. Hsu, C.L., et al., Unveiling MYCN regulatory networks in neuroblastoma via
integrative analysis of heterogeneous genomics data. Oncotarget, 2016.
394. Wang, L., et al., Regulatory network analysis of microRNAs and genes in
neuroblastoma. Asian Pac J Cancer Prev, 2014. 15(18): p. 7645-52.
Chi Yan Ooi 289 References
395. Zhu, S., et al., Activated ALK Collaborates with MYCN in Neuroblastoma
Pathogenesis. Cancer Cell, 2012. 21(3): p. 362-373.
396. Howe, K., et al., The zebrafish reference genome sequence and its relationship
to the human genome. Nature, 2013. 496(7446): p. 498-503.
397. Hiller, M., et al., Computational methods to detect conserved non-genic elements
in phylogenetically isolated genomes: application to zebrafish. Nucleic Acids
Research, 2013. 41(15): p. e151-e151.
398. Schmittgen, T.D., et al., Real-time PCR quantification of precursor and mature
microRNA. Methods (San Diego, Calif.), 2008. 44(1): p. 31-38.
399. Chen, C., et al., Real-time quantification of microRNAs by stem–loop RT–PCR.
Nucleic Acids Research, 2005. 33(20): p. e179.
400. Kozomara, A. and S. Griffiths-Jones, miRBase: annotating high confidence
microRNAs using deep sequencing data. Nucleic Acids Research, 2014. 42(D1):
p. D68-D73.
401. Friedman, R.C., et al., Most mammalian mRNAs are conserved targets of
microRNAs. Genome Res, 2009. 19(1): p. 92-105.
402. Lewis, B.P., C.B. Burge, and D.P. Bartel, Conserved Seed Pairing, Often
Flanked by Adenosines, Indicates that Thousands of Human Genes are
MicroRNA Targets. Cell, 2005. 120(1): p. 15-20.
403. Marín, R.M. and J. Vaníček, Optimal Use of Conservation and Accessibility
Filters in MicroRNA Target Prediction. PLoS ONE, 2012. 7(2): p. e32208.
404. Zaragoza, K., et al., Repression of transcriptional activity of C/EBPalpha by
E2F-dimerization partner complexes. Mol Cell Biol, 2010. 30(9): p. 2293-304.
405. Pavan, W.J. and D.W. Raible, Specification of neural crest into sensory neuron
and melanocyte lineages. Developmental Biology, 2012. 366(1): p. 55-63.
Chi Yan Ooi 290 References
406. Yanfeng, W., J.-P. Saint-Jeannet, and P.S. Klein, Wnt–frizzled signaling in the
induction and differentiation of the neural crest. BioEssays, 2003. 25(4): p. 317-
325.
407. Dejana, E., The role of wnt signaling in physiological and pathological
angiogenesis. Circ Res, 2010. 107(8): p. 943-52.
408. Parmalee, N.L. and J. Kitajewski, Wnt Signaling in Angiogenesis. Current drug
targets, 2008. 9(7): p. 558-564.
409. Wu, C.I., et al., Function of Wnt/beta-catenin in counteracting Tcf3 repression
through the Tcf3-beta-catenin interaction. Development, 2012. 139(12): p.
2118-29.
410. Kuwahara, A., et al., Tcf3 Represses Wnt–β-Catenin Signaling and Maintains Neural Stem Cell Population during Neocortical Development. PLOS ONE, 2014. 9(5): p. e94408.
411. Du, Q. and D.A. Geller, Cross-Regulation Between Wnt and NF-kappaB
Signaling Pathways. For Immunopathol Dis Therap, 2010. 1(3): p. 155-181.
412. Jiao, X., et al., c-Jun induces mammary epithelial cellular invasion and breast
cancer stem cell expansion. J Biol Chem, 2010. 285(11): p. 8218-26.
413. Zhang, Y., et al., Critical role of c-Jun overexpression in liver metastasis of
human breast cancer xenograft model. BMC Cancer, 2007. 7(1): p. 145.
414. Maeda, S. and M. Karin, Oncogene at last—c-Jun promotes liver cancer in
mice. Cancer Cell, 2003. 3(2): p. 102-104.
415. Zhang, Y., et al., c-Jun, a crucial molecule in metastasis of breast cancer and
potential target for biotherapy. Oncology reports, 2007. 18(5): p. 1207-1212.
416. Eferl, R., et al., Liver Tumor Development: c-Jun Antagonizes the Proapoptotic
Activity of p53. Cell, 2003. 112(2): p. 181-192.
Chi Yan Ooi 291 References
417. Martin-Bermudo, M.D., Integrins modulate the Egfr signaling pathway to
regulate tendon cell differentiation in the Drosophila embryo. Development,
2000. 127(12): p. 2607-15.
418. Consortium, T.U., UniProt: a hub for protein information. Nucleic Acids
Research, 2015. 43(D1): p. D204-D212.
419. Parker, B.S., J. Rautela, and P.J. Hertzog, Antitumour actions of interferons:
implications for cancer therapy. Nat Rev Cancer, 2016. 16(3): p. 131-144.
420. Zhang, Z., et al., MicroRNA degradation and turnover: regulating the regulators.
Wiley Interdisciplinary Reviews: RNA, 2012. 3(4): p. 593-600.
421. Kent, O.A. and J.T. Mendell, A small piece in the cancer puzzle: microRNAs as
tumor suppressors and oncogenes. Oncogene, 2006. 25(46): p. 6188-6196.
422. Croce, C.M., Causes and consequences of microRNA dysregulation in cancer.
Nat Rev Genet, 2009. 10(10): p. 704-714.
423. Zhang, B., et al., microRNAs as oncogenes and tumor suppressors.
Developmental Biology, 2007. 302(1): p. 1-12.
424. Sidarovich, V., V. Adami, and A. Quattrone, A cell-based high-throughput
screen addressing 3'UTR-dependent regulation of the MYCN gene. Mol
Biotechnol, 2014. 56(7): p. 631-43.
425. Ellwanger, D.C., et al., The sufficient minimal set of miRNA seed types.
Bioinformatics, 2011. 27(10): p. 1346-1350.
426. Xu, F., et al., Identification of microRNA-regulated pathways using an integration
of microRNA-mRNA microarray and bioinformatics analysis in CD34+ cells of
myelodysplastic syndromes. Scientific Reports, 2016. 6: p. 32232.
Chi Yan Ooi 292 References
427. Ding, M., et al., Integrated analysis of miRNA, gene, and pathway regulatory
networks in hepatic cancer stem cells. Journal of Translational Medicine, 2015.
13(1): p. 259.
428. Tang, Y., et al., Identification of novel microRNA regulatory pathways associated
with heterogeneous prostate cancer. BMC Systems Biology, 2013. 7(3): p. S6.
429. Li, S., et al., miRNA Profiling Reveals Dysregulation of RET and RET-Regulating
Pathways in Hirschsprung's Disease. PLOS ONE, 2016. 11(3): p. e0150222.
430. Aprelikova, O., et al., Silencing of miR-148a in cancer-associated fibroblasts
results in WNT10B-mediated stimulation of tumor cell motility. Oncogene, 2013.
32(27): p. 3246-3253.
431. Huang, M. and W.A. Weiss, Neuroblastoma and MYCN. Cold Spring Harbor
perspectives in medicine, 2013. 3(10): p. a014415-a014415.
432. Shu, W., et al., Wnt/β-catenin signaling acts upstream of N-myc, BMP4, and
FGF signaling to regulate proximal–distal patterning in the lung.
Developmental Biology, 2005. 283(1): p. 226-239.
433. Kuwahara, A., et al., Wnt signaling and its downstream target N-myc regulate
basal progenitors in the developing neocortex. Development, 2010. 137(7): p.
1035-1044.
434. Chiyomaru, T., et al., Genistein Up-Regulates Tumor Suppressor MicroRNA-
574-3p in Prostate Cancer. PLOS ONE, 2013. 8(3): p. e58929.
435. Tatarano, S., et al., Novel oncogenic function of mesoderm development
candidate 1 and its regulation by MiR-574-3p in bladder cancer cell lines. Int J
Oncol, 2012. 40(4): p. 951-9.
Chi Yan Ooi 293 References
436. Su, Y., et al., Aberrant expression of microRNAs in gastric cancer and
biological significance of miR-574-3p. International Immunopharmacology,
2012. 13(4): p. 468-475.
437. Abou-Elhamd, K.E., et al., The role of genetic susceptibility in head and neck
squamous cell carcinoma. Eur Arch Otorhinolaryngol, 2008. 265(2): p. 217-22.
438. Wu, L., et al., MicroRNA-204 targets signal transducer and activator of
transcription 5 expression and inhibits proliferation of B-cell lymphoma cells.
Mol Med Rep, 2015. 11(6): p. 4567-72.
439. Liu, L., et al., MiR-204-5p suppresses cell proliferation by inhibiting IGFBP5 in
papillary thyroid carcinoma. Biochem Biophys Res Commun, 2015. 457(4): p.
621-6.
440. Yin, Y., et al., miR-204-5p inhibits proliferation and invasion and enhances
chemotherapeutic sensitivity of colorectal cancer cells by downregulating
RAB22A. Clin Cancer Res, 2014. 20(23): p. 6187-99.
441. Mao, J., et al., MicroRNA-204, a direct negative regulator of ezrin gene
expression, inhibits glioma cell migration and invasion. Mol Cell Biochem,
2014. 396(1-2): p. 117-28.
442. Lujambio, A., et al., A microRNA DNA methylation signature for human cancer
metastasis. Proceedings of the National Academy of Sciences, 2008. 105(36): p.
13556-13561.
443. Long, X.R., et al., MicroRNA-148a is silenced by hypermethylation and interacts
with DNA methyltransferase 1 in hepatocellular carcinogenesis. Int J Oncol,
2014. 44(6): p. 1915-22.
Chi Yan Ooi 294 References
444. Zheng, B., et al., MicroRNA-148a Suppresses Tumor Cell Invasion and
Metastasis by Downregulating ROCK1 in Gastric Cancer. Clinical Cancer
Research, 2011. 17(24): p. 7574-7583.
445. Liffers, S.-T., et al., MicroRNA-148a is down-regulated in human pancreatic
ductal adenocarcinomas and regulates cell survival by targeting CDC25B. Lab
Invest, 2011. 91(10): p. 1472-1479.
446. Gokhale, A., et al., Distinctive microRNA signature of medulloblastomas
associated with the WNT signaling pathway. J Cancer Res Ther, 2010. 6(4): p.
521-9.
447. Zhou, X., et al., Altered expression of miR-152 and miR-148a in ovarian cancer
is related to cell proliferation. Oncol Rep, 2012. 27(2): p. 447-54.
448. Jung, H.M., et al., Tumor suppressor miR-375 regulates MYC expression via
repression of CIP2A coding sequence through multiple miRNA–mRNA
interactions. Molecular Biology of the Cell, 2013. 24(11): p. 1638-1648.
449. Paylakhi, S.H., et al., FOXC1 in human trabecular meshwork cells is involved in
regulatory pathway that includes miR-204, MEIS2, and ITGβ1. Experimental
Eye Research, 2013. 111: p. 112-121.
450. Goldgraben, M.A., et al., Double-stranded microRNA mimics can induce length-
and passenger strand–dependent effects in a cell type–specific manner. RNA,
2016. 22(2): p. 193-203.
451. Zhang, S.-G., et al., Examination of Artificial MiRNA Mimics with Centered–Site
Complementarity for Gene Targeting. PLOS ONE, 2013. 8(8): p. e72062.
452. Fritz, H.K., et al., The Axl-Regulating Tumor Suppressor miR-34a Is Increased
in ccRCC but Does Not Correlate with Axl mRNA or Axl Protein Levels. PLOS
ONE, 2015. 10(8): p. e0135991.
Chi Yan Ooi 295 References
453. Miyake, I., et al., Distinct role of ShcC docking protein in the differentiation of
neuroblastoma. Oncogene, 2008. 28(5): p. 662-673.
454. Baek, D., et al., The impact of microRNAs on protein output. Nature, 2008.
455(7209): p. 64-71.
455. Selbach, M., et al., Widespread changes in protein synthesis induced by
microRNAs. Nature, 2008. 455(7209): p. 58-63.
456. Vigo, E., et al., CDC25A Phosphatase Is a Target of E2F and Is Required for
Efficient E2F-Induced S Phase. Molecular and Cellular Biology, 1999. 19(9): p.
6379-6395.
457. Liu, T., et al., MYCN interacts with histone deacetylase to modulate target gene
transcription. Cancer Research, 2006. 66(8 Supplement): p. 1018-1018.
458. Tao, J., X. Zhao, and J. Tao, c-MYC–miRNA circuitry: A central regulator of
aggressive B-cell malignancies. Cell Cycle, 2014. 13(2): p. 191-198.
459. O'Leary, N.A., et al., Reference sequence (RefSeq) database at NCBI: current
status, taxonomic expansion, and functional annotation. Nucleic Acids Res,
2016. 44(D1): p. D733-45.
460. Kent, W.J., et al., The Human Genome Browser at UCSC. Genome Research,
2002. 12(6): p. 996-1006.
461. Rosenbloom, K.R., et al., The UCSC Genome Browser database: 2015 update.
Nucleic Acids Research, 2015. 43(D1): p. D670-D681.
462. Montgomery, S.B., et al., ORegAnno: an open access database and curation
system for literature-derived promoters, transcription factor binding sites and
regulatory variation. Bioinformatics, 2006. 22(5): p. 637-640.
463. Griffith, O.L., et al., ORegAnno: an open-access community-driven resource for
regulatory annotation. Nucleic Acids Research, 2008. 36(suppl 1): p. D107-D113.
Chi Yan Ooi 296 References
464. Sabo, P.J., et al., Genome-scale mapping of DNase I sensitivity in vivo using
tiling DNA microarrays. Nat Methods, 2006. 3(7): p. 511-8.
465. Sabo, P.J., et al., Discovery of functional noncoding elements by digital analysis
of chromatin structure. Proc Natl Acad Sci U S A, 2004. 101(48): p. 16837-42.
466. Lu, X., A. Pearson, and J. Lunec, The MYCN oncoprotein as a drug development
target. Cancer Letters, 2003. 197(1–2): p. 125-130.
467. Bannasch, D., I. Weis, and M. Schwab, Nmi protein interacts with regions that
differ between MycN and Myc and is localized in the cytoplasm of neuroblastoma
cells in contrast to nuclear MycN. Oncogene, 1999. 18(48): p. 6810-7.
468. Hagiwara, T., et al., Specific phosphorylation of the acidic central region of the
N-myc protein by casein kinase II. European Journal of Biochemistry, 1992.
209(3): p. 945-950.
469. Brodersen, P. and O. Voinnet, Revisiting the principles of microRNA target
recognition and mode of action. Nat Rev Mol Cell Biol, 2009. 10(2): p. 141-148.
470. Harte, R.A., et al., Tracking and coordinating an international curation effort
for the CCDS Project. Database (Oxford), 2012. 2012: p. bas008.
471. Farrell, C.M., et al., Current status and new features of the Consensus Coding
Sequence database. Nucleic Acids Res, 2014. 42(Database issue): p. D865-72.
472. Pruitt, K.D., et al., The consensus coding sequence (CCDS) project: Identifying a
common protein-coding gene set for the human and mouse genomes. Genome
Res, 2009. 19(7): p. 1316-23.
473. Zwang, Y., et al., Two phases of mitogenic signaling unveil roles for p53 and
EGR1 in elimination of inconsistent growth signals. Mol Cell, 2011. 42(4): p.
524-35.
Chi Yan Ooi 297 References
474. Liu, Y., et al., G protein-coupled receptors as promising cancer targets. Cancer
Letters, 2016. 376(2): p. 226-239.
475. Bar-Shavit, R., et al., G Protein-Coupled Receptors in Cancer. International
Journal of Molecular Sciences, 2016. 17(8): p. 1320.
476. Dorsam, R.T. and J.S. Gutkind, G-protein-coupled receptors and cancer. Nat
Rev Cancer, 2007. 7(2): p. 79-94.
477. Ben-Porath, I., et al., An embryonic stem cell-like gene expression signature in
poorly differentiated aggressive human tumors. Nature genetics, 2008. 40(5): p.
499-507.
478. Chou, J., et al., NASOPHARYNGEAL CARCINOMA—REVIEW OF THE
MOLECULAR MECHANISMS OF TUMORIGENESIS. Head & neck, 2008. 30(7):
p. 946-963.
479. Zhao, H., et al., Reduced expression of N-Myc downstream-regulated gene 2 in
human thyroid cancer. BMC Cancer, 2008. 8(1): p. 303.
480. Xu, J., Y. Chen, and O.I. Olopade, MYC and Breast Cancer. Genes & cancer,
2010. 1(6): p. 629-640.
481. Romano, M.I., et al., Relationship between the level of c-myc mRNA and
histologic aggressiveness in thyroid tumors. Horm Res, 1993. 39(3-4): p. 161-5.
482. Su, Z., et al., An investigation of biomarkers derived from legacy microarray
data for their utility in the RNA-seq era. Genome Biol, 2014. 15(12): p. 523.
483. Lau, D.T., et al., Prognostic Significance of Promoter DNA Methylation in Patients
with Childhood Neuroblastoma. Clinical Cancer Research, 2012. 18(20):
p. 5690-5700.
484. Banelli, B., et al., Clinical Potentials of Methylator Phenotype in Stage 4 High-
Risk Neuroblastoma: An Open Challenge. PLoS ONE, 2013. 8(5): p. e63253.
Chi Yan Ooi 298 References
485. Lurquin, C., et al., Two members of the human MAGEB gene family located in
Xp21.3 are expressed in tumors of various histological origins. Genomics, 1997.
46(3): p. 397-408.
486. Ozsolak, F., et al., Chromatin structure analyses identify miRNA promoters.
Genes & Development, 2008. 22(22): p. 3172-3183.
487. Kadauke, S. and G.A. Blobel, Chromatin loops in gene regulation. Biochimica
et biophysica acta, 2009. 1789(1): p. 17-25.
488. Chien, C.-H., et al., Identifying transcriptional start sites of human microRNAs
based on high-throughput sequencing data. Nucleic Acids Research, 2011.
39(21): p. 9345-9356.
489. Miller, A.J., et al., Transcriptional Regulation of the Melanoma Prognostic
Marker Melastatin (TRPM1) by MITF in Melanocytes and Melanoma. Cancer
Research, 2004. 64(2): p. 509-516.
490. Mazar, J., et al., The Regulation of miRNA-211 Expression and Its Role in
Melanoma Cell Invasiveness. PLoS ONE, 2010. 5(11): p. e13779.
491. Truax, A.D. and S.F. Greer, ChIP and Re-ChIP assays: investigating
interactions between regulatory proteins, histone modifications, and the DNA
sequences to which they bind. Methods Mol Biol, 2012. 809: p. 175-88.
492. Bommi, P.V., et al., The polycomb group protein BMI1 is a transcriptional
target of HDAC inhibitors. Cell Cycle, 2010. 9(13): p. 2663-2673.
493. Fiskus, W., et al., Histone deacetylase inhibitors deplete enhancer of zeste 2 and
associated polycomb repressive complex 2 proteins in human acute leukemia
cells. Mol Cancer Ther, 2006. 5(12): p. 3096-104.
494. Duursma, A.M., et al., miR-148 targets human DNMT3b protein coding region.
Rna, 2008. 14(5): p. 872-7.
Chi Yan Ooi 299 References
495. Shen, W.-F., et al., MicroRNA-126 Regulates HOXA9 by Binding to the
Homeobox. Molecular and Cellular Biology, 2008. 28(14): p. 4609-4619.
496. Elcheva, I., et al., CRD-BP protects the coding region of βTrCP1 mRNA from
miR-183-mediated degradation. Molecular cell, 2009. 35(2): p. 240-246.
497. Forman, J.J., A. Legesse-Miller, and H.A. Coller, A search for conserved
sequences in coding regions reveals that the let-7 microRNA targets Dicer
within its coding sequence. Proceedings of the National Academy of Sciences of
the United States of America, 2008. 105(39): p. 14879-14884.
498. Hafner, M., et al., Transcriptome-wide identification of RNA-binding protein and
microRNA target sites by PAR-CLIP. Cell, 2010. 141(1): p. 129-141.
499. Leung, A.K.L., et al., Genome-wide identification of Ago2 binding sites from
mouse embryonic stem cells with and without mature microRNAs. Nature
structural & molecular biology, 2011. 18(2): p. 237-244.
500. Skalsky, R.L., et al., The Viral and Cellular MicroRNA Targetome in
Lymphoblastoid Cell Lines. PLOS Pathogens, 2012. 8(1): p. e1002484.
501. Kim, K.-K., J. Ham, and S.W. Chi, miRTCat: a comprehensive map of human
and mouse microRNA target sites including non-canonical nucleation bulges.
Bioinformatics, 2013. 29(15): p. 1898-1899.
502. Arroyo, J.D., E.N. Gallichotte, and M. Tewari, Systematic design and functional
analysis of artificial microRNAs. Nucleic Acids Research, 2014. 42(9): p. 6064-
6077.
503. Broughton, J.P. and A.E. Pasquinelli, A tale of two sequences: microRNA-target
chimeric reads. Genet Sel Evol, 2016. 48: p. 31.
504. Lu, P., V.M. Weaver, and Z. Werb, The extracellular matrix: A dynamic niche in
cancer progression. The Journal of Cell Biology, 2012. 196(4): p. 395-406.
Chi Yan Ooi 300 References
505. Agoston, Z., et al., Meis2 is a Pax6 co-factor in neurogenesis and dopaminergic
periglomerular fate specification in the adult olfactory bulb. Development,
2014. 14(1): p. 28-38.
506. Hermeking, H., et al., 14-3-3σ Is a p53-Regulated Inhibitor of G2/M
Progression. Molecular Cell, 1997. 1(1): p. 3-11.
507. Yang, H.Y., et al., Roles for negative cell regulator 14-3-3 sigma in control of
MDM2 activities. Oncogene, 2007. 26(52): p. 7355-7362.
508. Phan, L., et al., The cell cycle regulator 14-3-3sigma opposes and reverses
cancer metabolic reprogramming. Nat Commun, 2015. 6: p. 7530.
509. Darwish, M.H., et al., Association of CYP3A4/5 genotypes and expression with
the survival of patients with neuroblastoma. Mol Med Rep, 2015. 11(2): p. 1462-
8.
510. Gellner, K., et al., Genomic organization of the human CYP3A locus:
identification of a new, inducible CYP3A gene. Pharmacogenetics, 2001. 11(2):
p. 111-21.
511. Petros, W.P., et al., Associations between drug metabolism genotype,
chemotherapy pharmacokinetics, and overall survival in patients with breast
cancer. J Clin Oncol, 2005. 23(25): p. 6117-25.
512. Dennison, J.B., et al., SELECTIVE METABOLISM OF VINCRISTINE IN VITRO
BY CYP3A5. Drug Metabolism and Disposition, 2006. 34(8): p. 1317-1327.
513. Dennison, J.B., et al., Effect of CYP3A5 expression on vincristine metabolism
with human liver microsomes. J Pharmacol Exp Ther, 2007. 321(2): p. 553-63.
514. McCune, J.S., et al., Contribution of CYP3A5 to hepatic and renal ifosfamide N-
dechloroethylation. Drug Metab Dispos, 2005. 33(7): p. 1074-81.
Chi Yan Ooi 301 References
515. Kishi, S., et al., Effects of prednisone and genetic polymorphisms on etoposide
disposition in children with acute lymphoblastic leukemia. Blood, 2004. 103(1):
p. 67-72.
516. Johnston, R.J., Jr., et al., MicroRNAs acting in a double-negative feedback loop to
control a neuronal cell fate decision. Proc Natl Acad Sci U S A, 2005. 102(35):
p. 12449-54.
517. Ferrell Jr, J.E., Self-perpetuating states in signal transduction: positive feedback,
double-negative feedback and bistability. Current Opinion in Cell Biology, 2002.
14(2): p. 140-148.
518. Suenaga, Y., et al., Positive auto-regulation of MYCN in human neuroblastoma.
Biochem Biophys Res Commun, 2009. 390(1): p. 21-6.
519. Bao, W., et al., A TrkB-STAT3-miR-204-5p regulatory circuitry controls
proliferation and invasion of endometrial carcinoma cells. Mol Cancer, 2013. 12:
p. 155.
520. Li, T., H. Pan, and R. Li, The dual regulatory role of miR-204 in cancer. Tumor
Biology, 2016. 37(9): p. 11667-11677.
521. Brodeur, G.M., et al., Trk Receptor Expression and Inhibition in
Neuroblastomas. Clinical cancer research : an official journal of the American
Association for Cancer Research, 2009. 15(10): p. 3244-3250.
522. Zeng, J., et al., MiR-204-5p/Six1 feedback loop promotes epithelial–mesenchymal
transition in breast cancer. Tumor Biology, 2016. 37(2): p. 2729-2735.
523. Jackstadt, R. and H. Hermeking, MicroRNAs as regulators and mediators of c-
MYC function. Biochimica et Biophysica Acta (BBA) - Gene Regulatory
Mechanisms, 2015. 1849(5): p. 544-553.
Chi Yan Ooi 302 References
524. Han, H., et al., A c-Myc-MicroRNA functional feedback loop affects
hepatocarcinogenesis. Hepatology, 2013. 57(6): p. 2378-2389.
525. Cai, S., P. Zhou, and Z. Liu, Functional characteristics of a double negative
feedback loop mediated by microRNAs. Cognitive Neurodynamics, 2013. 7(5):
p. 417-429.
526. Mukherji, S., et al., MicroRNAs can generate thresholds in target gene
expression. Nature genetics, 2011. 43(9): p. 854-859.
527. Raabe, E.H., et al., Prevalence and functional consequence of PHOX2B
mutations in neuroblastoma. Oncogene, 2007. 27(4): p. 469-476.
528. Bhatt, S., R. Diaz, and P.A. Trainor, Signals and Switches in Mammalian Neural
Crest Cell Differentiation. Cold Spring Harbor Perspectives in Biology, 2013.
5(2): p. a008326.
529. Conte, I., et al., The combination of transcriptomics and informatics identifies
pathways targeted by miR-204 during neurogenesis and axon guidance. Nucleic
Acids Research, 2014. 42(12): p. 7793-7806.
530. Navarro, F. and J. Lieberman, miR-34 and p53: New Insights into a Complex
Functional Relationship. PLoS ONE, 2015. 10(7): p. e0132767.
531. Huang, J., et al., MicroRNA-204 Regulates Runx2 Protein Expression and
Mesenchymal Progenitor Cell Differentiation. Stem cells (Dayton, Ohio), 2010.
28(2): p. 357-364.
532. Xu, G., et al., Thioredoxin-interacting protein regulates insulin transcription
through microRNA-204. Nat Med, 2013. 19(9): p. 1141-1146.
533. Ho, R., et al., Resistance to chemotherapy mediated by TrkB in neuroblastomas.
Cancer Res, 2002. 62(22): p. 6462-6.
Chi Yan Ooi 303 References
534. Antonoff, M.B., et al., Triptolide therapy for neuroblastoma decreases cell
viability in vitro and inhibits tumor growth in vivo. Surgery, 2009. 146(2): p.
282-290.
535. Yan, X., et al., Triptolide inhibits cell proliferation and tumorigenicity of human
neuroblastoma cells. Molecular Medicine Reports, 2015. 11(2): p. 791-796.
Chi Yan Ooi 304
Sequence Conservation (mmu = mouse, hsa = human, 6mer seed miRNA sequence bolded and underlined)
mmu-miR-135b-5p 5’-UAUGGCUUUUCAUUCCUAUGUGA-3’
mmu-miR- hsa-miR-135b-5p 5’-UAUGGCUUUUCAUUCCUAUGUGA-3’
135b conservation ***********************
mmu-miR-148a-3p 5’-UCAGUGCACUACAGAACUUUGU-3’
mmu-miR- hsa-miR-148a-3p 5’-UCAGUGCACUACAGAACUUUGU-3’
148a conservation **********************
mmu-miR-18a-5p 5’-UAAGGUGCAUCUAGUGCAGAUAG-3’
mmu-miR-18a hsa-miR-18a-5p 5’-UAAGGUGCAUCUAGUGCAGAUAG-3’
conservation ***********************
mmu-miR-130b-3p 5’-CAGUGCAAUGAUGAAAGGGCAU-3’
mmu-miR- hsa-miR-130b-3p 5’-CAGUGCAAUGAUGAAAGGGCAU-3’
130b conservation **********************
mmu-miR-135a-5p 5’-UAUGGCUUUUUAUUCCUAUGUGA-3’
mmu-miR- hsa-miR-135a-5p 5’-UAUGGCUUUUUAUUCCUAUGUGA-3’
135a conservation ***********************
mmu-miR-375-3p 5’-UUUGUUCGUUCGGCUCGCGUGA-3’
mmu-miR-375 hsa-miR-375 5’-UUUGUUCGUUCGGCUCGCGUGA-3’
conservation **********************
mmu-miR-298-5p 5’-GGCAGA-GGAGGGCUGUUCUUCCC-3’
hsa-miR-298 5’-A GCAGGGAGGUUCUCCCA-3’ mmu-miR-298 GCAGAA
conservation ***** * **** ***** **
mmu-miR-20a-5p 5’-UAAAGUGCUUAUAGUGCAGGUAG-3’
mmu-miR-20a hsa-miR-20a-5p 5’-UAAAGUGCUUAUAGUGCAGGUAG-3’
conservation ***********************
mmu-miR-106b-5p 5’-UAAAGUGCUGACAGUGCAGAU-3’
mmu-miR- hsa-miR-106b-5p 5’-UAAAGUGCUGACAGUGCAGAU-3’
106b conservation *********************
mmu-miR-25-3p 5’-CAUUGCACUUGUCUCGGUCUGA-3’
mmu-miR-25 hsa-miR-25-3p 5’-CAUUGCACUUGUCUCGGUCUGA-3’
conservation **********************
mmu-miR-93-5p 5’-CAAAGUGCUGUUCGUGCAGGUAG-3’
mmu-miR-93 hsa-miR-93-5p 5’-CAAAGUGCUGUUCGUGCAGGUAG-3’
conservation ***********************
mmu-miR-709 5’-GGAGGCAGAGGCAGGAGGA-3’
mmu-miR-709 hsa-miR-1827 5’-UGAGGCAGUAGAUUGAA-U-3’
conservation ******* * **
mmu-miR-19a-3p 5’-UGUGCAAAUCUAUGCAAAACUGA-3’
mmu-miR-19a hsa-miR-19a-3p 5’-UGUGCAAAUCUAUGCAAAACUGA-3’
conservation ***********************
Chi Yan Ooi 305 Appendix A
Sequence Conservation (mmu = mouse, hsa = human, 6mer seed miRNA sequence bolded and underlined)
mmu-miR-106a-5p 5’-CAAAGUGCUAACAGUGCAGGUAG-3’
mmu-miR- hsa-miR-106a-5p 5’-AAAAGUGCUUACAGUGCAGGUAG-3’
106a conservation ******** *************
mmu-miR-706 5’-AGAGAAACCCUGUCUCAAAAAA-3’
mmu-miR-706 No known human homolog conservation Nil
mmu-miR-344-3p 5’-UGAUCUAGCCAAAGCCUGACUGU-3’ mmu-miR-344 No known human homolog conservation Nil
mmu-miR-19b-3p 5’-UGUGCAAAUCCAUGCAAAACUGA-3’
mmu-miR-19b hsa-miR-19b-3p 5’-UGUGCAAAUCCAUGCAAAACUGA-3’
conservation ***********************
mmu-miR-17-5p 5’-CAAAGUGCUUACAGUGCAGGUAG-3’
mmu-miR-17 hsa-miR-17-5p 5’-CAAAGUGCUUACAGUGCAGGUAG-3’
conservation ***********************
mmu-miR-676-3p 5’-CCGUCCUGAGGUUGUUGAGCU-3’
mmu-miR-676 hsa-miR-676-3p 5’-CUGUCCUAAGGUUGUUGAGUU-3’
conservation * ***** *********** *
mmu-miR-574-3p 5’-CACGCUCAUGCACACACCCACA-3’
mmu-miR-574- hsa-miR-574-3p 5’-CACGCUCAUGCACACACCCACA-3’
3p conservation **********************
mmu-miR-204-5p 5’-UUCCCUUUGUCAUCCUAUGCCU-3’
mmu-miR-204 hsa-miR-204-5p 5’-UUCCCUUUGUCAUCCUAUGCCU-3’
conservation **********************
mmu-miR-709 5’-GGAGGCAGAGGCAGGAGGA-3’
mmu-miR-709 hsa-miR-1827 5’-UGAGGCAGUAGAUUGAA-U-3’
conservation ******* * **
Sequences from miRBase Release 21 [400]
Chi Yan Ooi 306
Chi Yan OoiChi Yan
Diagram has been removed due to potential copyright restrictions. This diagram can instead be found on the following webpage:
http://mirstart.mbc.nctu.edu.tw/mirna.php?no=50753
307
Appendix B
Pre-miR-204 Genomic Location: 73424891-73425000 [-]
CAGE Tags within 1000 bp upstream of pre-miR-204 from 127 human RNA samples
sequences location
GATGGAGGGGAGGGTGAGGGT 73425191
1000 bp upstream of pre-miR-204 from 8 human normal tissues and 6 human cell lines
location experiment
73425470 All 11
73425458 All 1
73425549 All 1
1000 bp upstream of pre-miR-204
location score
73425537 chr9
73425737 chr9
73425937 chr9
[488] for details on the miRStart database
Chi Yan Ooi 308