Regulation of BTRC by Gain-Of-Function TP53 Mutation(S) in Cancer Cells

Regulation of BTRC by gain-of-function TP53 mutation(s) in cancer cells

Peter Xu

A thesis submitted in conformity with the requirements for the degree of Masters of Science (M.Sc) Graduate Department of Molecular Genetics University of Toronto

Regulation of BTRC by gain-of-function TP53 mutation(s) in cancer cells

Peter Xu

Masters of Science

Department of Molecular Genetics University of Toronto

2015

ABSTRACT

Regulation of BTRC by gain-of-function TP53 mutation Peter Xu, Masters of Science (2015), Department of Molecular Genetics, University of Toronto

Mutation or loss of TP53 has been detected in more than 50% of all human tumours. Gain-of-function (GOF) TP53 mutations have been linked to metastasis, altered metabolism, and drug resistance. Cancer patients carrying GOF TP53 mutations in their tumours respond poorly to standard of care treatments and have a worse prognosis. In my Master’s thesis, I have focused on understanding the relationship between a frequently observed mutation of TP53 (TP53 R248W ), and BTRC , an E3 ubiquitin ligase with functions impinging on cell cycle, morphology and metabolism. I observed a correlation between BTRC protein expression and TP53 genotype across a panel cancer cell lines. Additionally, I observed that TP53 R248W -mediated regulation of BTRC expression is dependent on a post-transcriptional mechanism, likely involving more than one micro-RNA. In conclusion, I present a model describing the regulation of BTRC by TP53 , which may have implications for targeted strategies in cancers harboring GOF TP53 mutations.

ii ACKNOWLEDGEMENTS

Foremost, I would like to express my sincere gratitude to my advisor Prof. Jason Moffat for his continuous support of my M.Sc study and research. In particular, I want to thank him for his patience, motivation, and immense knowledge during both high and especially low stages of my graduate career. In addition, his guidance propelled me to persevere through my research and thesis writing.

Besides my advisor, I want to take this opportunity to thank the rest of my committee members: Prof. Leonardo Salmena, Prof. Mark Minden and Prof. Freda Miller for their time, encouragement, and insightful comments. I want to thank Prof. Leonardo Salmena especially for his time and our lively and fun discussion both research and non-research related.

I want to thank all my fellow Moffat labmates for their guidance and support throughout my graduate studies. In particular, I want to thank my fellow friend and labmate, Sachin Kumar for the endless nights of research and stimulating discussions. I want to thank Dr. Taras Makhnevych setting up an example of research vigor and scientific standard for me to follow closely. I want to thank my friends in the Moffat lab past and present, Hayoung Yoo, Eun Jee Koh, Megha Chandrashekhar, Rashida Williams, Esther Lau, Satra Nim, Andrea Uetrecht, Hong Han, and Michael Aregger.

Last but not least, I would like to thank my family and especially my mother and father for their endless support for the past quarter century.

iii TABLE OF CONTENTS

Chapter Title Page Number ABSTRACT ...... ii ACKNOWLEDGEMENTS...... iii TABLE OF CONTENTS...... iv LIST OF TABLES ...... v LIST OF FIGURES ...... v LIST OF ABBREVIATIONS ...... vi

Chapter 1 – Introduction ...... 1 1.1 – TP53 Mutation and Cancer ...... 1 1.2 – Molecular Role of TP53 as a Tumour Suppressor ...... 2 1.3 – Metabolic Role of TP53 in Cancer ...... 2 1.4 - Molecular Role of BTRC ...... 3 1.5 - TP53 and BTRC ...... 6 1.6 – Hypothesis ...... 6

Chapter 2 – Experimental Methods ...... 7 2.1 - Cell Culture ...... 7 2.2 - Western Blot ...... 7 2.3 - Preparation of protein lysates ...... 8 2.4 - Lentiviral Infection ...... 8 2.5 - Cellular RNA Isolation ...... 9 2.6 - nCounter Nanostring Analysis ...... 9 2.7 – CRISPR sgRNA Constructs ...... 9 2.9 – HCT116 Cell Transfections ...... 10 2.10 - Intracellular Flow Cytometry ...... 10

Chapter 3 -Results ...... 11 3.1 – Gain-of-function TP53 mutant cells express low levels of BTRC ...... 11 3.2 – Survey of TP53 and BTRC protein levels across a panel of cancer cell lines ...... 12 3.3 – An inducible system to examine relationship between TP53 R248W and BTRC ...... 14 3.4 – BTRC levels are not regulated by the proteasome, cytoskeleton remodeling, or cell cycle ...... 15 3.5 - An inducible system to examine relationship between TP53 R248W , BTRC , and DICER1 ...... 16 3.6 – Examining BTRC in miR-195 knockout cells ...... 18 3.7 – A model for TP53 regulation of BTRC levels ...... 20

Chapter 4 – Conclusion and Future Direction ...... 22 4.1 – Conclusion ...... 22 4.2 - Future Directions ...... 23

Chapter 5 - References ...... 26

iv LIST OF TABLES

Chapter 2.1 - Table 1. Culture method of cancer cell lines Chapter 2.7 - Table 2. Sequences of sgRNA against hsa-mir-195 and BTRC

LIST OF FIGURES

Chapter 1.1 - Fig 1. The distribution of missense mutations in TP53 : the hotspot region. Chapter 1.4 - Fig 2. Schematic of basic ubiquitin pathway. Chapter 1.4 - Fig 3. List of proteins regulated by ubiquitin through BTRC . Chapter 1.4 - Fig 4. Regulation of AKT signaling by BTRC . Chapter 1.4 - Fig 5. TP53 R248W genotype influences AKT phosphorylation. Chapter 3.1 - Fig 6. Gain-of-function TP53 mutant cells express low levels of BTRC. Chapter 3.2 - Fig 7. BTRC and GOF TP53 protein levels across a panel of cancer cell lines. Chapter 3.3 - Fig 8. Induction of wild-type and GOF TP53 reduces BTRC expression. Chapter 3.4 - Fig 9. BTRC protein levels were not dependent on proteasome, cytoskeleton remodeling or cell cycle. Chapter 3.5 - Fig 10. Induction of wild-type and TP53 R248W in DICER1 deficient cells rescues BTRC expression. Chapter 3.6 - Fig 11. Schematic of sgRNA targets locations at the hsa-mir-195 genomic locus. Chapter 3.6 - Fig 12. miR-195 knockout modestly induces BTRC expression. Chapter 3.7 - Fig 13. Overexpressed miRNAs in TP53 GOF cancer cells. Chapter 3.7 - Fig 14. Schematic model for the metabolic TP53 mediated regulation of BTRC .

v LIST OF ABBREVIATIONS

TERM MEANING AKT Protein kinase B AMPK 5' Adenosine monophosphate-activated protein kinase ATP Adenosine triphosphate BTRC Beta-transducin repeat containing E3 ubiquitin protein ligase CRISPR Clustered regularly interspaced short palindromic repeats Cas9 CRISPR associated protein 9 CTNNB1 Catenin beta-1 DBD DNA-binding domain DEPC Diethylpyrocarbonate DMEM Dulbecco’s modified eagle medium DNA Deoxyribonucleic Acid DTT Dithiothreitol E1 Ubiquitin-activating enzyme E2 Ubiquitin-conjugating enzyme E3 Ubiquitin protein ligase ECL Enhanced chemiluminescence EDTA Ethylenediaminetetraacetic acid FBS Fetal bovine serum GOF Gain of function HCC1143 Breast carcinoma cell line HCT116 Colon adenocarcinoma cell line MDA-MB-468 Breast Adenocarcinoma Cell Line MG132 Proteasome inhibitor miRNA Micro ribonucleic acid mRNA Messenger ribonucleic acid mTOR Mammalian target of Rapamycin NADPH Nicotinamide adenine dinucleotide phosphate NCI-H1299 Lung adenocarcinoma cell line OVCAR3 Human ovarian carcinoma cell line PANC Human pancreatic carcinoma, epithelial-like cell line PBS Phosphate buffered saline PR Proline-rich PHLPP1 PH domain and leucine rich repeat protein phosphatases 1 PTEN Phosphatase and tensin homolog REG Carboxy-terminal regulatory domain RIPA Buffer Radio immuno-precipitation assay buffer RNA Ribonucleic acid RNAi Ribonucleic acid interference RPMI Roswell Park Memorial Institute RT-qPCR Reverse transcription quantitative polymerase chain reaction rtTA Reverse tetracycline-controlled transactivator SDS Sodium dodecyl sulphate sgRNA Short guide RNA SILAC Stable isotope labeling by amino acids in cells

vi TA Transactivation domain TBST Tris-Buffered saline and Tween 20 TET Tetramerization domain TP53 Tumor protein p53 TP63 Tumor protein p63 TP73 Tumor protein p73 TRE Tet-response element WNT Drosophila melanogaster wingless gene protein

Amino Acids MEANING G Glycine H Histidine Q Glutamine R Arginine S Serine W Tryptophan

vii Chapter 1 – Introduction 1.1 – TP53 Mutation and Cancer The mutation or loss of TP53 has been detected in more than 50% of all human tumours worldwide (e.g. colon, breast, and lung)1. Tumor suppressor genes are typically characterized by loss- of-function mutations (e.g. PTEN ); however, the TP53 gene often contains point mutations that result in the expression of a full-length TP53 protein with a single amino acid substitution 2. These mutations tend to be clustered at various hot spots within the central sequence-specific DNA-binding region of the protein and result in the loss or alteration of DNA recognition 3. Most TP53 mutations can be classified into two categories: DNA contact mutants or conformational mutants. Contact mutations (e.g. R248, R273) result in altered TP53 DNA binding activity and conformational mutations (e.g. R249, G245, R175, R282) cause conformational distortion of the TP53 protein and impair the thermodynamic stability of the TP53 protein (Fig.1). For my thesis, I have explored one particular TP53 GOF mutation in depth, TP53 R248W , where amino acid residue 248 is mutated from an arginine to a tryptophan. Of all the TP53 mutations observed across different cancer types, this mutation is the most frequent TP53 mutation observed in cancer 4. Some mutant TP53 proteins, such as the contact mutant R248W, have been shown to possess cell transforming activity and GOF activities that are independent of wild type TP53 5. In fact, genetically engineered mice that express mutant TP53 present with a broad spectrum of tumor types in comparison with TP53 +/+ and TP53 -/- animals 6. Some oncogenic functions of mutant TP53 have been characterized in various cell culture models, including the ability to promote cellular invasion, migration, angiogenesis, proliferation, survival, and remodeling of the tumour microenvironment 7,8.

Fig 1. The distribution of missense mutations in TP53: the hotspot region. The distribution of six most common hotspot mutations along the 393 amino-acid sequence of TP53 protein are highlighted above. Contact mutations (R248, and R273) are represented in orange, while conformational mutations (R175, G245, R249, and R282) are shown in blue. Regions of TP53 amino- acid sequences are divided into transactivation domain (TA), proline-rich domain (PR), tetramerization domain (TET), and carboxy-terminal regulatory domain (REG).

1 1.2 – Molecular Role of TP53 as a Tumour Suppressor The transcription factor TP53 is best known for its role as a tumour suppressor, and a wealth of evidence highlights its importance in inhibiting cancer development 9. The TP53 signaling pathway is activated in response to a variety of extrinsic and intrinsic stress signals, allowing TP53 to coordinate downstream transcriptional activity and molecular checkpoints, which contribute to tumour suppression 9. Appropriate responses to stressors including DNA damage, oncogene activation, and nutrient deprivation, are essential for normal cell and organismal growth 10 . TP53 is considered to be the ‘Guardian of the Genome’, and plays a major role in orchestrating appropriate responses to cellular stress 11 . For example, severe or sustained stress such as DNA damage or oncogene activation triggers TP53 -dependent cellular death or senescence to block malignant transformation. Milder stress such as fluctuations in nutrient or oxygen availability induce a weaker adaptive response, in which TP53 decreases cellular growth via metabolic remodeling and induction of catabolism 8,12,13 . Consequently, the role of TP53 in metabolic regulation is emerging as an important component of cellular homeostasis to control tumour development 14 .

1.3 – Metabolic Role of TP53 in Cancer Alterations in cellular metabolism are increasingly regarded as critical for tumor progression and have been considered to be a crucial hallmark of cancer (ie. ‘deregulated cellular energetics’) 15 . Evidence suggests that tumour cells become dependent on metabolic remodeling for their growth and survival 16,17 Cancer cells shift their metabolism towards glycolysis to provide rapid production of metabolic intermediates that can be used for anabolism even under aerobic conditions in a phenomenon known as the Warburg Effect 18 . As a key tumour suppressor, normal cellular homeostasis is maintained through TP53 as it controls a range of metabolic processes, including glycolysis, oxidative phosphorylation, glutaminolysis, and anti-oxidant response 19-24 . Furthermore, TP53 interacts with mammalian target of rapamycin ( mTOR ) and 5' adenosine monophosphate-activated protein kinase (AMPK ), two master regulators of cellular metabolism, to direct downstream metabolic pathways and metabolic changes in cancer cells 25-28 . In addition, metabolic roles have been implicated in TP53 family members, TP63 and TP73 29-32 . It is well established that the inhibition of TP63 and TP73 function are considered key mechanisms for mutant TP53 gain-of-function 6,33 . The impact of mutant TP53 on cancer cell metabolism is only recently being appreciated 34 .

2 1.4 - Molecular Role of BTRC β -Transducin repeat-containing protein 1 (also known as BTRC , b -TRCP1 , FBXW1 , FWD1 , FBW1A , b TrCP , BETA-TRCP ) is an E3 ubiquitin ligase. E3 ubiquitin ligases catalyze the final step of ubiquitination and function as the substrate recognition component to target specific protein for degradation by the proteasome (Fig.2). Through degradation of target proteins, BTRC has been implicated in the regulation of several critical cellular processes such as cell cycle checkpoint, cellular development, and DNA damage response (Fig.3). Specifically, BTRC plays an important role in cellular development as a negative regulator of the WNT signaling pathway upon binding directly to CTNNB1 (β-catenin ) for its destruction. Similarly, BTRC promotes AKT activation through direct degradation of PHLPP1 through ubiquitination (PH domain and Leucine rich repeat Protein Phosphatases 1), which is normally required for de-phosphorylating the Ser473 residue in AKT 35 . AKT is a survival kinase that regulates cellular metabolism through modulation of AMPK (5' adenosine monophosphate-activated protein kinase), a protein capable of sensing nutrient stress (Fig.4) 36 . Through a series of pooled RNA interference screens in isogenic HCT116 colorectal cancer cells, a large-scale experiment designed to identify genetic interactions or genetic dependencies due to TP53 genotype (see Vizeacoumar et al, 2013 and unpublished data from the Moffat lab), BTRC was identified as a synthetic lethal interaction in TP53 R248W -expressing cells compared with TP53 -null cells or wild-type TP53 cells. To elaborate, HCT116 TP53 R248W/- cells were more sensitive to BTRC knockdown compared to HCT116 TP53 -/- isogenic cells in proliferation screens. Consistent with this observation, in HCT116 TP53 R248W/- cells, CTNNB1 protein was not localized in the nucleus, and the plasma membrane in the same magnitude as HCT116 TP53 +/+ cells suggesting CTNNB1 degradation and disruption of WNT signaling (T. Makhnevych & J. Moffat, personal communication). To summarize, BTRC regulates both AKT and CTNNB1 expression and is also synthetically lethal with the TP53 R248W/- mutant genotype relative to the TP53 -/- genotype.

Fig 2. Schematic of basic ubiquitin pathway Activation of free ubiquitin (Ub) through ubiquitin-activating enzyme (E1) utilizes ATP (adenosine triphosphate) to form a complex with ubiquitin. E1-ubiquitin is transferred on to ubiquitin-conjugating enzyme (E2s). Moreover, E2 complex is bound to E3, the ubiquitin protein ligase that promotes multi- ubiquitin chain formation on a substrate protein. In general, multi-ubiquitination labels the substrate protein for degradation by the proteasome.

Fig 3. List of proteins regulated by ubiquitin through BTRC List of different protein substrates of BTRC dependent ubiquitination is indicated. Multi-ubiquitination labels theses substrate proteins for degradation by the proteasome 35,37-43 .

Fig 4. Regulation of AKT signaling by BTRC BTRC dependent regulation of AKT signaling through the degradation of PHLPP1 with ubiquitination mediated degradation pathway. PHLPP1 is thought to de-phosphorylate the Ser473 residue in AKT 35 . AKT is a survival kinase that regulates cellular metabolism through modulation of AMPK , a protein capable of sensing nutrient stress as depicted in the model above 36 .

5 1.5 - TP53 and BTRC High levels of AKT phosphorylation on Ser473 have previously been shown to occur in HCT116 TP53 -/-, but not HCT116 TP53 R248W/- cells (T. Makhnevych, P. Xu, and J. Moffat, unpublished; see Fig.5). In other words, HCT116 TP53 -/- cells maintain an elevated level of active AKT in comparison to HCT116 TP53 R248W/- cells. In addition, PHLPP1 is a negative regulator of AKT phosphorylation on Ser473 residue, while BTRC expression is inversely related to PHLPP1 protein levels through BTRC ubiquitin dependent degradation 35,38 . The difference in BTRC expression may provide a molecular basis for different levels of AKT activation in HCT116 TP53 isogenic cell lines through PHLPP1 . HCT116 TP53 isogenic cell lines ( TP53 +/+ , TP53 +/-, TP53 -/-, TP53 R248W/+ , and TP53 R248W/-) were utilized to identify TP53 R248W mutant specific phenotypes under identical genetic cancer cell line background. In addition, AKT regulates cellular metabolism through modulation of AMPK, a protein capable of sensing nutrient stress. Therefore, the role of BTRC in metabolism and its genetic relationship with TP53 may be related. Differential expression of BTRC with different TP53 genotypes may explain the differences in AKT phosphorylation between HCT116 TP53 -/- and TP53 R248W/- cell lines.

1.6 – Hypothesis The metabolic phenotypes associated with mutant TP53 R248W in cancer cells compared to TP53 - loss or wild-type TP53 have not been explored. In this study, I aim to clarify the relationship between TP53 R248W and BTRC , an E3 ubiquitin ligase heavily implicated in cellular morphology through regulation of CTNNB1 stability, and metabolism through regulation of AKT activity. I hypothesize that differential expression of BTRC is regulated by TP53 through a post-transcriptional mechanism that can be co-opted to maintain cellular plasticity.

6 Chapter 2 – Experimental Methods 2.1 - Cell Culture Standard tissue culture conditions and protocols were followed DMEM (Dulbecco’s Modified Eagle Medium), RPMI (RPMI-1640), and McCoy’s 5A modified media] for the cell lines used in this study and outlined in Table 1. Cell culture media was supplemented with 5% penicillin/streptomycin and 10% FBS (fetal bovine serum).

Table 1. Culture method of cancer cell lines.

Name of Cancer Cell Cell Culture Media TP53 Status Cell Line Origin HCT116 TP53 +/+ McCoy’s 5A Modified Media + 10% FBS Wild-type Colon HCT116 TP53 +/ - McCoy’s 5A Modified Media + 10% FBS Heterozygous (+/-) Colon HCT116 TP53 -/- McCoy’s 5A Modified Media + 10% FBS Null Colon HCT116 TP53 R284W /+ McCoy’s 5A Modified Media + 10% FBS R284W/+ Colon HCT116 TP53 R248W/ - McCoy’s 5A Modified Media + 10% FBS R248W/- Colon Mia-PaCa-2 DMEM + 10% FBS R248W Pancreas PANC02.03 DMEM + 10% FBS R248Q Pancreas SU86.86 RPMI + 10% FBS G245S Pancreas OVCAR3 RPMI + 10% FBS R248Q Ovary HCC1143 RPMI + 10% FBS R248Q Breast MDA-MB-468 DMEM + 10% FBS R273H Breast NCI-H1299 RPMI + 10% FBS Null Lung

Cancer cells were treated with the following inhibitors: latrunculin A (Sigma-Aldaich, Cat. No. L5163), MG-132 (Sigma-Aldaich, Cat. No. M7449), or nocodazole (Sigma-Aldaich, Cat. No. M1404) for a final concentration of 1µM, 10µM, or 0.1µg/mL respectively. Duration of treatment was 6 hours and 18 hours before protein extraction. Latrunculin A treatments were for 30 or 120 minutes prior to protein extraction.

2.2 - Western Blot Western blotting was performed with SDS-PAGE (sodium dodecyl sulfate-polyacrylamide gel electrophoresis). 20 µg protein samples were loaded in to a 10% Tris gel with Tris running buffer at 100V for 2 hours. Proteins were then transferred to PVDF (polyvinylidene fluoride) membranes and sequentially blocked with 2.5% BSA (bovine serum albumin) in 0.1% TBST (Tris-Buffered Saline with Tween 20) for one hour. Primary antibodies (1:1000) in 0.1% TBST were incubated overnight and membranes were washed three times five minutes with 0.1% TBST. Horseradish peroxidase- conjugated goat anti-rabbit or anti-mouse antibodies (1:5000) were incubated for one hour following 7 the primary incubations then the membranes were washed three times five minutes with 0.1% TBST. Enhanced chemiluminescence (ECL) was used to visualize the band.

2.3 - Preparation of protein lysates Protein lysates were prepared from cells at a confluency of approximately 80% in 100mm dishes. Cell culture dishes were placed on ice and ice cold PBS (phosphate buffered saline) solution was used to wash the cells twice. Following aspiration of the cold PBS, 600 µL of lysis RIPA buffer (Radio Immunoprepcipitation Assay Buffer) was added to the plate in a dropwise fashion. Adherent cells were scraped off the dish using a plastic cell scraper, and the cell suspension was transferred gently into a pre-cooled microfuge tube. Constant agitation was applied to the sample for 30 minutes at 4°C. Protein samples were centrifuged in a microcentrifuge at 4 °C for 10 minutes at 12,000 rpm. The supernatant was transferred to a fresh pre-cooled microfuge tube and pellet was discarded.

Protein samples were standardized to 2 µg/ µL in RIPA buffer with NuPAGE LDS sample buffer (4X) with the addition of DTT (dithiothreitol) and boiled at 95 °C for 5 minutes prior to loading. A volume of 10-15 µL of protein samples were loaded in 4-12% or 10% Bis-Tris Plus Gels (Life Technology). Bradford assay was utilized to determine protein concentration. Protein lysate samples were stored in -20 °C for long-term storage. Recipe for RIPA buffer includes the following components: 150 mM sodium chloride, 1.0%Triton X-100, 0.5% sodium deoxycholate, 0.1% SDS (sodium dodecyl sulphate), and 50 mM Tris maintained at pH 8.0. Protease and phosophatase inhibitors were supplemented in RIPA buffer at a final concentration of 2µg/ mL.

2.4 - Lentiviral Infection Lentiviral infections were conducted in 6 well plates (60mm) with an initial seeding of 2.0x10 5 cells per well with 2mL of media in each well. Lentiviruses were removed from the -80 °C freezer and thawed at room temperature, then 200µL of lentivirus and 4µL of polybrene (2 µg/µL) were added to each well of the 6-well dishes. After addition of virus, plates were swirled gently and then incubated at 37ºC and 5% C02 overnight. 24 hours post infection, blasticidin (10µg/ml) and puromycin (2µg/ml) were added to select stable integrants.

8 2.5 - Cellular RNA Isolation RNA was harvested from cells grown on 15cm plates at a cell density of approximately 80% confluency. Cells were first washed with ice cold PBS twice, then 4mL of TRIzol® Reagent (Life Technology, Cat. No. 15596-026) was added and cells were moved with a cell scraper. The cell lysate was collected and 0.8mL of chloroform was added to this mixture, which was then mixed vigorously for 15 seconds. The samples were incubated for 2 minutes at room temperature and then centrifuged at 12,000g for 10 minutes in 4 °C. The colorless aqueous phase, which contains nucleic acid including RNA, was isolated from the other phases. 2mLs of isopropyl alcohol was added to the isolated sample to precipitate the RNA. The sample was incubated at room temperature for 10 minutes and centrifuged at 12,000g at 4 °C. The supernatant was removed after centrifugation, and the RNA pellet was washed with 1ml of 75% ethanol. The samples were mixed and centrifuged at 7,500g for 5 minutes at 4 °C. The ethanol is removed from the sample and dried for 5 minutes. The RNA pellet was dissolved in DEPC (Diethylpyrocarbonate)-treated water and the RNA concentration was measured using a Nanodrop.

2.6 - nCounter Nanostring Analysis Total RNA lysates were collected as described above for HCT116 TP53 +/+ , HCT116 TP53 +/-, HCT116 TP53 -/-, HCT116 TP53 R248W/+, HCT116 TP53 R248W/-, HCT116 DICER1 +/+ , and HCT116 DICER1 ex5/ex5 cancer cells. Total RNA lysates were standardized to a concentration 66ng/µL. In total, 200ng of total RNA were provided per sample to Nanostring service facility to perform nCounter © miRNA Expression Assay on human miRNAs.

2.7 – CRISPR sgRNA Constructs The CRISPR-Cas9 short guide construct designs were taken from an in-house algorithm that can be found at http://moffatlab.ccbr.utoronto.ca/intranet/crispr/ . The single guide RNA sequences against the miR-195 and BTRC were depicted above along with orientation and genomic coordinates within the human genome.

Table 2. Sequences of sgRNA against hsa-mir-195.

sgRNA GENOMIC Orientation Sequence COORDINATES sgRNA #1 Chr 17:6920974- Forward CACCGGGAAGCGAGTCTGCCAATAT hsa-miR-195 6920993 Reverse AAACATATTGGCAGACTCGCTTCCC sgRNA #2 Chr17: 6920994- Forward CACCGTTTCTGTGCTGCTAGAGCCA hsa-miR-195 6921013 Reverse AAACTGGCTCTAGCAGCACAGAAAC

9 2.9 – HCT116 Cell Transfections HCT116 isogenic cell lines ( TP53 -/-, TP53 R248W/-, DICER1 +/+ , DICER1 ex5/ex5 ) were transfected in a 6-well format. Cells were trypsinized and counted the day prior to transfection and seeded at a density of 250,000 cells per well in 0.5mL of McCoy 5A complete media. On the day of transfection, 2µg of DNA was diluted in 200uL of Opti-MEM® I Reduced Serum Media along with 6µl of FuGene® 6 (Promega, Cat. No. E2691). The solution was mixed gently and incubated at room temperature for 30 minutes. After 30 minutes incubation, 200µl of DNA-FuGene reagent mixture was directly added to wells containing cells and mixed gently. Cultured cells were incubated at 37 °C in a CO 2 incubators for 24 hours post-transfection before additional assays were performed.

2.10 - Intracellular Flow Cytometry HCT116 cancer cells were treated with 3mL of EDTA-PBS solution (10mM EDTA in PBS) to facilitate removal of cells from 100mm plates. Cells were transferred to a 5mL polystyrene tube designed for flow cytometry. Cells were pelleted by centrifugation for 5mins at 500rpm. Supernatants were removed and cell pellets were resuspended in 250 µL of Cytofix/Cytoperm TM solution from BD Biosciences (Cat. No. 555028) for 15 minutes at 4 °C and cells were washed twice in 1X Perm/Wash TM solution from BD Biosciences (Cat. No. 554723). BTRC expression levels were probed with primary anti- BTRC rabbit antibody (Abcam, Cat. No. ab71753) at a 1:250 dilution for an hour at 4°C and washed two times with 1X Perm/Wash TM solution. Secondary goat anti-rabbit AlexaFluor 647 antibody (Life Technologies, Cat. No. A-21244) was added at a dilution of 1:1000 and washed two times with 1X Perm/Wash TM solution . 10,000 cells/events in each condition were analyzed by flow cytometry.

10 Chapter 3 -Results 3.1 – Gain-of-function TP53 mutant cells express low levels of BTRC Based on pooled shRNA screening data from five HCT116 isogenic colorectal cancer cell lines with different TP53 genotypes including TP53 wild-type ( TP53 +/+ ), heterozygous ( TP53 +/-), null (TP53 -/-), and gain-of-function mutant (TP53 R248W/+ , TP53 R248W/-), we identified BTRC as a putative negative genetic interaction in the GOF genotypes. To understand the potential molecular mechanism behind this observation, I examined BTRC protein levels in all five HCT116 TP53 isogenic colorectal cancer cell lines. As described in the introduction, the level of BTRC regulates PHLPP1 expression through ubiquitination and degradation via proteasome. PHLPP1 dephosphorylates the Ser473 residue of AKT, which has been shown to control AKT activation 38 . I hypothesized that a difference in BTRC protein levels could contribute to the lack of AKT Ser473 phosphorylation in TP53 R248W mutants. Consistent with this, I observed that the level of BTRC was significantly lower in HCT116 cells expressing TP53 R248W mutants compared to TP53 +/+ , TP53 +/- and TP53 -/- cells (Fig.6). This observation suggests that BTRC protein expression is dependent on TP53 mutant status. In other words, TP53 R248W expression leads to lower BTRC protein levels across a series of TP53 isogenic cancer cells.

Fig 5. TP53 R248W genotype influences AKT phosphorylation Isogenic HCT116 cell lines of wild-type( TP53 +/+ ), null( TP53 -/-), and GOF (TP53 R248W ) TP53 status were harvested for preparation of whole-cell protein lysates and subsequent Western blot analysis on AKT , and AKT phosphorylation (Ser473) protein levels. Short hairpin RNA (shRNA) on a lentiviral vector directed at TP53 gene was infected and protein lysates were collected prior (day 0) to infection and on 4,8,12,16 and 20 days post-infection. Western blot analysis on TP53 and AKT phosphorylation (Ser473) protein levels was performed. β-actin protein level was used as a loading control.

Fig 6. Gain-of-function TP53 mutant cells express low levels of BTRC. Isogenic HCT116 cell lines including wild-type( TP53 +/+ ), null( TP53 -/-), and GOF (TP53 R248W ) TP53 status were harvested for preparation of whole-cell protein lysates and Western blot was performed to assess BTRC protein levels. β-actin protein level was used as a loading control.

3.2 – Survey of TP53 and BTRC protein levels across a panel of cancer cell lines To test the hypothesis that decreased expression of BTRC is correlated with TP53 GOF mutations across a panel of cancer cells, and not exclusive to HCT116 cell lines, I examined six other cancer cells lines with TP53 GOF mutations derived from different tissues such as breast, pancreas, and ovary and measured BTRC protein expression. Cells expressing mutant versions of TP53 had consistently lower levels of BTRC in breast, pancreatic, and ovarian cancer cell lines (Fig.7). A lung cancer cell line entirely lacking TP53 expression (NCI-1299), along with HCT116 TP53 wild-type (TP53 +/+ ) and null ( TP53 -/-) cell lines were used as controls and showed higher BTRC protein levels compared with cancer cell lines harbouring mutant versions of TP53 (Fig.7). BTRC and GOF TP53 status were examined in an array of cancer cell lines with different tissue origins and GOF TP53 mutations including R248W, R248Q, R273H, and G245S. These results demonstrate an inverse correlation between TP53 mutation status and the level of TP53 protein across various cancer cell lines with the expression of BTRC (Fig. 7). To summarize, the anti-correlation between BTRC and GOF TP53 protein levels were consistent across a range of cell lines with varying TP53 genotypes (ie. R248W, R248Q, R273H, G245S).

Fig 7. BTRC and GOF TP53 protein levels across a panel of cancer cell lines. Western blot analysis of the BTRC protein in GOF TP53 mutated breast (HCC1143, MDA-M3-468), pancreatic (Mia-Paca 2.0, PANC 02.03, SU86.86), and ovarian (OVCAR3) human cancer cell lines. Isogenic HCT116 cell lines of wild-type (TP53 +/+ ), null (TP53 -/-), and GOF (TP53 R248W ) TP53 status were utilized as controls for varying BTRC levels. The lung cancer cell line NCI-H1299 was TP53 null ( TP53 -/-). TP53 wild-type (green), null (red), or GOF mutant (blue) cell lysates are described above each lane. β-actin protein level was used as a loading control.

13 3.3 – An inducible system to examine relationship between TP53 R248W and BTRC Regulation of BTRC protein level has not previously been correlated to TP53 mutational status. To test the idea that GOF TP53 can regulate the level of BTRC protein, a Tet-on expression system (tetracycline controlled transcriptional activation) was used to induce the expression of TP53 or TP53 R248W in HCT116 TP53 -/- cells. As expected, the protein level of BTRC declined significantly upon induction of mutant TP53 R248W protein (Fig.8). Interestingly, the introduction of wild-type TP53 protein expression was equally effective in decreasing protein level of BTRC (Fig.8). Evidently, both wild-type and GOF TP53 R248W possess the capacity to regulate the expression of BTRC.

Fig 8. Induction of wild-type and GOF TP53 reduces BTRC expression. Lentiviral infection of a reverse tetracycline-controlled transactivator (rtTA) plasmid in HCT116 TP53 null ( TP53 -/-) isogenic cells was carried out. Selection of transduced cells with blasticidin (10µg/ml) was carried out for 7 days. TP53 -/- rtTA expressing cells were infected with tet-responsive wild-type TP53 or TP53 R248W lentiviruses and selected with puromycin (2µg/mL) for 48 hours. Whole-cell protein lysates were harvested prior to doxycycline (1µg/mL) induction, as well as on days 3,4, and 5 post-induction. Lysates from cells following recovery in tetracycline-free media for 24 hours after 5 days of treatment are labeled ‘Rec’. Western blot analysis of BTRC and TP53 protein levels were carried out for each condition. β-actin protein levels were used as loading controls.

14 3.4 – BTRC levels are not regulated by the proteasome, cytoskeleton remodeling, or cell cycle My observations from surveying different cancer cell types from different tissue origins suggest that BTRC expression is dependent on TP53 genotype. Therefore, I speculated that BTRC expression is negatively regulated by TP53 . However, the exact molecular mechanism of GOF TP53 R248W mutation dependent regulation of BTRC expression remains unclear. I hypothesized that this could happen through one of several different mechanisms either at the transcriptional, post-transcriptional, translational or post-translational level. Initially, TP53 R248W or TP53 -/- cancer cells were treated with latrunculin A, which was an actin filament inhibitor to test whether disruption of actin filament of the cytoskeleton would impede on BTRC expression. BTRC protein levels remained unaltered upon treatment with latrunculin A (Fig.9). This observation suggested that BTRC expression does depend on actin filament polymerization. Similarly, treatment with an inhibitor of microtubule polymerization, nocodazole, was utilized on TP53 R248W and TP53 -/- cancer cells to determine whether regulation of BTRC protein levels were cell cycle dependent. BTRC protein levels remained constant and unaltered upon treatment with nocodazole (Fig 9), which suggested that BTRC regulation is independent of cell cycle effect (Fig.9). Lastly, BTRC protein levels in TP53 R248W or TP53 -/- cancer cells remain constant after treatment with the proteasome inhibitor (MG132), suggesting BTRC protein regulation is not through proteasome regulation (Fig.9). Therefore, TP53 -dependent regulation of BTRC did not occur through the proteasome, cytoskeletal remodeling, or cell cycle.

Fig 9. BTRC protein levels were not dependent on proteasome, cytoskeleton remodeling or cell cycle. Western blot analysis of the BTRC protein in HCT116 TP53R24W and TP53 -/- cancer cells upon treatment with Lat A (Latrunculin A, 1µM) for 30 minutes and 120 minutes along with treatment of nocodazole (Noc, 0.1µg/mL), and MG132 (10µM) for 6 hour and 18 hours, respectively. β-actin protein level was used as a loading control.

3.5 - An inducible system to examine relationship between TP53 R248W , BTRC , and DICER1 To further explore the model of TP53 -dependent regulation of BTRC , I set out to look at whether this occurred through a post-transcriptional mechanism. One method to regulate gene expression post-transcriptionally is through microRNA (miRNA)-mediated pathways that silence translation off specific mRNA sequences. To test the hypothesis that miRNAs regulate BTRC levels post-transcriptionally, I induced wild-type TP53 and GOF mutant TP53 R248W in HCT116 isogenic DICER1 wild-type ( DICER1 +/+ ) and DICER1 loss-of-function ( DICER1 ex5/ex5 ) cancer cell line (Fig.10B) to look at whether BTRC protein levels become restored. The loss of BTRC protein expression was evident upon induction of TP53 wild-type and TP53 R248W protein with doxycycline, reaffirming that TP53 expression decreases BTRC expression (Fig.10B). When the same experiment was conducted in a DICER1 deficient HCT116 colon cell line, the loss of BTRC phenotype was rescued (Fig.10B). These observations support the idea that TP53 -specific regulation of BTRC expression/stability is regulated in a miRNA dependent manner.

Fig 10. Induction of wild-type and TP53 R248W in DICER1 deficient cells rescues BTRC exprssion. (A). Western blot analysis of DICER1 protein level was conducted in HCT116 DICER1 wild-type (DICER1 +/+ ) and loss-of-function ( DICER1 ex5/ex5 ) isogenic cell lines. β-actin protein level was analyzed as loading control. (B). Lentiviral infections of reverse tetracycline-controlled transactivator (rtTA) plasmid in HCT116 DICER1 wild-type ( DICER1 +/+ ) and loss-of-function ( DICER1 ex5/ex5 ) isogenic cell lines were conducted and selected with blasticidin (10µg/ml) for 7 days. HCT116 DICER1 +/+ and DICER1 ex5/ex5 rtTA expressing cells were infected with tet-response element (TRE) coupled with TP53 wild-type and TP53 R248W plasmids and selected with puromycin (2µg/mL) for 48 hours. Whole-cell protein lysates were harvested prior to doxycycline (1µg/mL) induction, as well as on days 3, 4, and 5 post-induction. Lysates from cells following recovery in tetracycline-free media for 24 hours after 5 days of treatment are labeled ‘Rec’. Western blot analysis of BTRC and TP53 protein level was conducted during each condition. β-actin protein level was used as a loading control.

17 3.6 – Examining BTRC in miR-195 knockout cells BTRC levels were unchanged upon TP53 induction in a DICER1 deficient cell line, which implies that TP53 specific regulation of BTRC is a miRNA-mediated process. Notably, BTRC has previously been implicated as a direct target of miR-195 44 . Furthermore, one would expect that changes in miR-195 would alter BTRC protein levels. To test whether miR-195 de-stabilizes or influences BTRC protein expression, I designed two distinct short guide RNAs (sgRNAs) targeting miR-195 to leverage CRISPR (clustered regularly interspaced short palindromic repeats construct) genome editing technology to generate miR-195 knockout cells in order to investigate its effect on BTRC protein levels. The CRISPR-Cas9 system facilitates gene editing through strand-specific cleavage to induce double strand breaks (DSB) 45 . The error prone non-homologous end joining pathway is activated upon DSBs, resulting in random insertions and deletions (indels) at the cleavage leading to loss-of-function gene mutations. In this experiment, the sgRNA targeted against miR-195 was employed to direct insertion or deletions in order to miR-195 expression. Intracellular flow cytometry was performed on miR-195 knockout HCT116 TP53 R248W cells to measure the level of BTRC protein at a greater sensitivity. A slight increase in mean fluorescent intensity on BTRC protein levels was observed consistently across experimental replicates between miR-195 knockout and control HCT116 TP53 R248W cells (Fig.12). This observation suggests that regulation of BTRC mRNA transcripts might be mediated partially through TP53 R248W -specific miR-195 expression. The modest effect of increasing BTRC levels from miR-195 knockout cells might suggest alternative mechanism to regulate BTRC levels synergistically.

Fig 11. Schematic of sgRNA target locations at the hsa-mir-195 genomic locus. Hsa-miR-195 genomic DNA sequence was labeled and depicted in grey. Blue rectangles indicate the location of short-guide guide RNA targets in hsa-miR-195.

Fig 12. miR-195 knockout modestly induces BTRC expression. Flow cytometric analysis was conducted on the HCT116 GOF (TP53 R248W ) TP53 cell line transfected with two CRISPR (clustered regularly interspaced short palindromic repeats) single guide RNA (sgRNA) in triplicates. Transfected cells were passaged 48 hours post-transfection and cells were fixed and permeabilized for intracellular staining after 7 days. BTRC expression levels were visualized with primary anti-BTRC and secondary goat anti-rabbit AlexaFluor 647 antibodies. Depicted above is a histogram analysis from FlowJo (Treestar) detailing the mean fluorescent intensity (a.u) of 10,000 events per condition. Treatment conditions of unstained cells (red), secondary only (blue), transfection control (green), miR-195 sgRNAs (orange) are represented above.

19 3.7 – A model for TP53 regulation of BTRC levels To summarize, BTRC protein levels were reduced when GOF mutant TP53 protein was expressed in cancer cells. Cancer cells harboring GOF TP53 mutations including R248W, R248Q, R273H, and G245S contained lower overall BTRC protein compared to cancer cells with null TP53 status. In other words, TP53 status and expression level dictates the level of BTRC. Furthermore, BTRC regulation was observed to be independent of the proteasome or cell cycle. The hypothesis that TP53 specific regulation of BTRC levels occurs in a miRNA-dependent manner was confirmed through the rescue of BTRC protein levels when GOF TP53 induction did not alter BTRC protein levels in a DICER1 deficient cell line. BTRC is a known target of miR-195 44 . However, I observed that miR-195 expression re only modestly regulated BTRC protein levels in TP53 R248W/- miR-195 knockout cells (Fig.12). The modest effect of increasing BTRC levels from miR-195 knockout indicates that other miRNAs are important for BTRC regulation. A total of 4 miRNAs that are commonly overexpressed in TP53 R248W/- and TP53 R248W/+ with at least 1.5-fold difference compared to expressions identified in TP53 -/- cell line were identified and listed (Fig.13). These TP53 GOF specific miRNAs are potential candidates to validate in controlling BTRC expression post-transcriptionally.

Fig 13. Overexpressed miRNAs in TP53 GOF cancer cells. Whole genome miRNA analysis (nanostring) was performed on TP53 R248W/-, TP53 R248W/+ and TP53 -/- isogenic cancer cell lines. Overexpressed miRNAs by 1.5-fold difference in transcript levels in TP53 R248W/- and TP53 R248W/+ compared to TP53 -/- cell line were visualized in the venn diagram above. A total of 4 overexpressing miRNAs that correspond with TP53 GOF genotype were identified and listed above.

Fig 14. Schematic model for the metabolic TP53 mediated regulation of BTRC. Diagram of the altered cellular metabolism acquired in GOF TP53 R248W mutation cell lines through the activity of AKT was shown above. The level of PHLPP1 and AKT were regulated through the level of BTRC . The four overexpressed miRNA in GOF TP53 R248W are listed above.

21 Chapter 4 – Conclusion and Future Direction 4.1 – Conclusion Mutation or loss of TP53 has been detected in more than 50% of all human tumours worldwide. GOF TP53 mutations have been linked to cellular invasion, altered metabolism, and increased drug resistance. Patients with GOF TP53 mutations respond poorly to standard of care treatments and have a worse prognosis. I have focused on understanding the relationship between a frequently observed mutation of TP53 (TP53 R248W ), and BTRC , an E3 ubiquitin ligase with functions impinging on cell cycle, morphology and metabolism.

In summary, I have observed a difference in BTRC protein levels in cancer cells harbouring different alleles of TP53 including the GOF TP53 R248W mutation, wild-type TP53 and TP53 -null. The level of BTRC protein was significantly lower in cells with the GOF TP53 R248W mutation. Similarly, the same correlation between BTRC protein expression and TP53 genotype was observed across cancer cell lines harboring different TP53 mutations, such as the R248Q, R273H, and G245S mutations. TP53 status and expression appears to regulate the level of BTRC in cancer cells. Furthermore, through the induction of both wild-type and GOF TP53R248W in a TP53 -null cell line, BTRC levels decreased significantly. Therefore, negative regulation of BTRC is a TP53 -dependent phenotype.

The mechanism by which TP53 regulates BTRC was deduced by examining BTRC -dependent changes following the treatment of latrunculin A, nocodazole, and MG-132. It was evident that BTRC is not regulated by the proteasome, through cytoskeletal remodeling or in a cell cycle fashion. BTRC regulation was then examined at the transcriptional and post-transcriptional levels. The induction of wild-type TP53 and GOF mutant TP53 R248W in HCT116 isogenic DICER1 wild-type ( DICER1 +/+ ) and DICER1 loss-of-function ( DICER1 ex5/ex5 ) cancer cell line was utilized to test if BTRC is regulated post- transcriptionally. The loss of BTRC protein level was clearly observed upon induction of TP53 wild- type or TP53 R248W protein with doxycycline (Fig.8). When the same experiment was conducted in a DICER1 -deficient HCT116 colon cell line, the loss of BTRC was restored (Fig.10). These observations support the idea that TP53 -specific regulation of BTRC is regulated in post-transcriptional manner.

One microRNA, miR-195 has been implicated in targeting BTRC mRNA for translational silencing. A modest effect on BTRC levels was observed following knockout of miR-195 using CRISPR-Cas9 gene editing. This observation suggests multiple miRNAs might be involved for

22 miRNA-mediated regulation of BTRC . The common overexpressed miRNAs in GOF TP53 R248W cells were identified using NanoString technology (See Method). In addition, a total of 4 miRNAs were identified as potential candidates for TP53 specific miRNA mediated BTRC expression. To summarize, a novel GOF TP53 R248W mutant dependent regulation of BTRC partially through miR-195 and list of miRNA candidates targets were identified. With a close link between AMPK and AKT signaling both functioning to manage nutrient stress, BTRC expression can potentially act as a metabolic switch in GOF TP53 R248W cancer cells to survive under energy stress. Further studies on the role of BTRC and TP53 GOF in cellular metabolism can provide insights to metabolic adaptations in cancer cells.

4.2 - Future Direction In my Master’s thesis, I have focused on elucidating the relationship between a frequently observed mutation of TP53 (TP53 R248W ), and BTRC , an E3 ubiquitin ligase with functions in cell cycle, morphology and metabolism of the cell. I observed that TP53 R248W -mediated regulation of BTRC expression is dependent in part on a post-transcriptional mechanism, likely involving more than one miRNA. A list of potential candidate miRNAs involved in the regulation of BTRC was identified. A model describing the regulation of BTRC by TP53 was presented in this thesis. These results have implications for targeted strategies in cancers harboring different GOF TP53 mutations. The key outstanding question is the identification of miRNA(s) responsible for the regulation of BTRC. I hypothesize that the miRNA(s) responsible for the regulation of BTRC in a TP53 specific manner should be differentially expressed in TP53 R248W/- cells compared to TP53 -/- cells.

It has been well established that cancer cells shift their metabolism towards glycolysis to provide rapid production of metabolic intermediates that can be used for anabolism even under aerobic conditions in a phenomenon known as the Warburg Effect 18 . As a key tumour suppressor, TP53 controls a large range of metabolic processes, including glycolysis, oxidative phosphorylation, glutaminolysis, and anti-oxidant response 19-24 . Furthermore, TP53 also interacts with mammalian target of rapamycin ( mTOR ) and AMPK , two master regulators of cellular metabolism, to direct downstream metabolic pathways and responses in cancer cells 25-28 . In addition, metabolic roles have been heavily implicated within the TP53 family members, TP63 and TP73 29-32 . In addition, the inhibition of TP63 and TP73 function are key mechanisms for mutant TP53 gain-of-function 6,33 . The impact of GOF TP53 on cancer cell metabolism is only beginning to be understood. Mechanistic insight into the role of GOF TP53 in cancer may provide novel and improved strategies in targeting cancers harboring different

23 GOF TP53 mutations 29 . The main question I would want to address next is to define the unique characteristics in metabolism of GOF TP53 cancer cells, and how do these alterations in metabolism induce cancer transformation. I hypothesize that GOF TP53 cancer cells exist in a transformed metabolic state that imparts a fitness advantage and promotes tumourigenesis.

Identification of BTRC targeted miRNAs Previously, a list of 4 potential candidates were identified that might regulate BTRC in a miRNA-mediated manner. To narrow these possibilities down, I would propose to perform a RT-qPCR (reverse transcription quantitative polymerase chain reaction) on all 4 potential targets in HCT116 TP53 R248W/- and HCT116 TP53 -/- isogenic cells to confirm the results from the wide genome miRNA analysis. The RT-qPCR results would identify miRNA transcript levels and the expected result of this experiment is some of the true observation from the list of 4 would provide high transcript levels in TP53 R248W/- compared HCT116 TP53 -/- cells.

The validated miRNA candidates would then subjected to further investigation. If the number of miRNA candidates is two or less than two, due to the immense amount of labour involved, one approach that could be utilized would be to generate miRNA specific CRISPR mediated knockout cell lines and then analyze of BTRC protein levels through intracellular flow cytometry. With this approach, knockdown of specific miRNAs expressed in the GOF TP53 mutant cell line that target BTRC will increase BTRC protein expression as the miRNA mediated repression is no longer present. If no one specific miRNA knockout can increase BTRC protein expression fully, multiple miRNA CRIPSR knockouts will be utilized to generate knockout of all miRNAs. The specific combination required for recovery of BTRC protein levels can be assessed systemically. On the other hand, if the number of miRNA candidates validated from RT-qPCR is three or more, one strategy would be to only select the top five most promising candidates with the most increase in miRNA expression comparatively.

BTRC has shown to degrade pleckstrin homology domain leucine-rich repeat protein phosphatases 1 ( PHLPP1 ), which is a regulator of growth factor signaling by directly dephosphorylating and inactivate serine/threonine protein kinase (AKT )35,38 . The identification of miRNAs that target BTRC for translational repression in a TP53 -dependent manner can provide insight into the metabolic differences in GOF TP53 cancer cells.

24 Metabolic differences in isogenic HCT116 TP53 R248W/- and HCT116 TP53 -/- cancer cells

To identify differences in metabolism of GOF TP53 cancer cells specifically, I employed isogenic HCT116 TP53 R248W/- and TP53 -/- cancer cells to generate metabolic profiles associated with TP53 gain-of-function mutation, in collaboration with Dr. Andrew Emili and with a research associate at the Moffat lab, Taras Makhnevych. SILAC (Stable isotope labeling by amion acids in cell culture) was performed in the isogenic colon cancer cell line pair, HCT116 TP53 R248W/- and TP53 -/- to generate protein complex landscape between the GOF TP53 and null TP53 cell line to identify specific complexes that are associated with the GOF TP53 instead of the loss-of-function TP53 phenotypes. The SILAC approach to identify protein complexes were picked, as metabolic enzymes are extremely abundant in cell and easy to quantify with this method. Furthermore, an overall metabolic protein landscape will be identified and sorted. The activity of various metabolic pathways such as glycolysis, pentose phosphate pathway, citric acid cycle, fatty acid oxidation, and oxidative phosphorylation can be inferred through the protein level of rate limiting enzymes in each metabolic pathway. The measurement of metabolic intermediates such as lactate, adenosine triphosphate (ATP) and NADPH will be used as validation to SILAC results. If the SILAC approach does not generate hits and difficult to conduct, whole genome RNA-Seq will be utilized as a backup strategies to generate a transcriptomic profiling of metabolic pathways between HCT116 TP53 R248W/- and TP53 -/-. In this case, the same comparison will be looked at the mRNA transcript level instead of protein. Key metabolic pathways and enzymes required by the GOF TP53 mutations will be confirmed through different metabolic inhibitors to identify metabolic dependency of GOF TP53 cells. In summary, transcriptomic and proteomic profiling from RNA-Seq and SILAC can reveal metabolic differences between HCT116 TP53 R248W/- and TP53 -/- cells. One example of an important protein to further investigate might be BTRC , and determine if TP53 R248W mutation is dependent on the low expression of BTRC to combat metabolic stress. Furthermore, essential metabolic pathways and enzymes in GOF TP53 cells can be tested in other cancer cells of origin with different GOF TP53 mutations with RNAi mediated knockdown to identify the dependency of particular metabolic pathway is uniform across all GOF TP53. The identification of essential metabolic genes might serve as potential therapeutic targets in cancer harbouring different GOF TP53 mutations.

25 Chapter 5 - References

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