MASTER of SCIENCE (2014) Hamilton, Ontario (Biochemistry and Biomedical Sciences)

NICOTINIC ACETYLCHOLINE RECEPTORS IN BREAST CANCER

IDENTIFICATION OF NICOTINIC ACETYLCHOLINE RECEPTORS

REQUIRED FOR BREAST TUMOURIGENESIS

BY NATALLIA KASMACHOVA, B.Sc.

A Thesis Submitted to the School of Graduate Studies in Partial Fulfillment of the

Requirements for the Degree Master of Science

McMaster University ©Copyright by Natallia Kasmachova, September 2014

MSc Thesis – N. Kasmachova; McMaster – Biochemistry and Biomedical Sciences

McMaster University MASTER OF SCIENCE (2014) Hamilton, Ontario (Biochemistry and Biomedical Sciences)

TITLE: Identification of Nicotinic Acetylcholine Receptors Required for Breast

Tumorogenisis AUTHOR: Natallia Kasmachova, B.Sc. (ISEU) SUPERVISOR: Dr. John

A. Hassell NUMBER OF PAGES: xi, 71

MSc Thesis – N. Kasmachova; McMaster – Biochemistry and Biomedical Sciences

ABSTRACT

Breast cancer continues to be the most common diagnosed cancer among women, and radiation or chemotherapy generally fails to provide durable cure, especially in the context of advanced or metastatic disease. Tumours recurrence is believed to be driven by cancer stem cells, which resist anti-cancer therapy and survive to seed relapse after remission in breast cancer patients. Small molecules inhibitors of nicotinic acetylcholine receptors (nAChR) target cancer stem cells, however, the precise nAChR required for breast cancer stem cell activity is unknown. Hence, we propose to test the capacity of shRNAs that target each individual nAChR to inhibit breast cancer stem cell activity.

Briefly, we performed a cancer stem cell based pooled lenti-vector shRNA screen, to identify receptors required for the propagation of breast cancer stem cell enriched cultures.

Our results demonstrate that the suppression of multiple receptors can be detected and corresponding genes are essential for TIC viability and survival. We anticipate our approach will identify the relevant nAChR receptor required for breast cancer stem cell activity. Such receptors may represent useful drug targets for the development of anti- breast cancer stem cell therapeutics.

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MSc Thesis – N. Kasmachova; McMaster – Biochemistry and Biomedical Sciences

ACKNOWLEDGMENTS

It is my pleasure to express my sincere gratitude to the people who contributed to my academic research career and emotional being.

I am expressing my special appreciation and thanks to my thesis supervisor Dr.

John A. Hassell who introduced me the world of cancer stem cells and whose immense knowledge, enthusiasm and support has helped to make my research project exciting. He has provided me an outstanding opportunity to gain skills and knowledge as a scientific researcher. Without his expertise, motivation and guidance this thesis would not have been possible.

A special gratitude I give to my committee members Drs. Kristin Hope and Andre

Bedard for their time and consideration to reading my reports and providing brilliant comments and suggestions. I am thankful for their incredible contribution to my scientific progress.

I wish to thank Bonnie Bojovic who has been a tremendous mentor to me during this long two-year journey. Her professional expertise, patience, and sense of humour made everyday of my lab work easy-going. To Adele Girgis-Gabardo, being a tissue culture guru, she showed me all tricks and tips to maintain sphere-cultures.

I am indebted to many lab members for their interest and involvement into my project. I am especially thankful to Robin Hallett for assisting me in so many different ways, to Jennifer Seager for her tremendous organizational skills. I am grateful to Anna

Dvorkin-Gheva, who has become my genuine friend, for her warm personality, amazing mind, and outstanding knowledge of bioinformatics.

MSc Thesis – N. Kasmachova; McMaster – Biochemistry and Biomedical Sciences

Many thanks to Biochemistry Department, especially to Lisa Kush, who kindly provided all necessary academic information, helped and supported me from the very first day of enrolment.

Special thanks to my friend and my soul mate Alex for providing a supportive environment for me. I cannot overstate his involvement and contribution to my emotional well-being. I am thankful for your wise words, sincere attitude and empathic nature. Your adventurous personality motivated and taught me to think outside of the box. I will be grateful forever for your love and support.

Lastly, and most importantly, my dearest thanks go to my beloved family, my parents and my sister Anna. I am expressing my appreciation to you for being with me throughout the entire process, for sharing stressful situations and happy moments, for playing as a team, for sacrifices that have been made on my behalf. Words can never explain how truly grateful and proud I am to have you three in my life. To you I dedicate this thesis.

MSc Thesis – N. Kasmachova; McMaster – Biochemistry and Biomedical Sciences

TABLE OF CONTENTS

1 INTORDUCTION

1.1 Breast cancer overview ………………………………...……………... 12 1.2 Breast cancer types…………………………………………………….. 12 1.3 Breast cancer treatments……………………………………………….. 14 1.4 Molecular classification of breast cancer………………………………. 15 1.5 History of CSC and breast CSCs………………………………………. 16 1.6 Acetylcholine receptors………………………………………………… 18 1.7 nAChRs and CSC……………………………………………………..... 21 1.8 Experimental Rationale………………………………………………… 22

2 EXPERIMENTAL PROCEDURES

2.1 Cell culture…………………………………………………………… 23 2.2 Lentivius Production………………………………………………… 24 2.3 Lentiviral Infection………………………………………………….. 26 2.4 gDNA extraction…………………………………………………….. 26 2.5 gDNA PCR amplification…………………………………………… 28 2.6 Illumina primer design and high-throughput DNA sequencing…….. 29 2.7 Statistical methods………………………………………………….. 30 2.7.1 Data visualization…………………………………………………… 30 2.7.2 Data filtering………………………………………………………… 30 2.7.3 Examining changes in shRNA………………………………………. 31

3 RESULTS

3.1 shRNA dropout screen revealed multiple nAChRs that are essential for breast CSC

viability and proliferation, and required for breast tumourogenisis……. 32

MSc Thesis – N. Kasmachova; McMaster – Biochemistry and Biomedical Sciences

4 DISCUSSION

4.1 Role of nAChR with antiproliferative effect…………………………. 49 4.2 Role of nAChR co-regulation in inhibition of breast tumourgenisis… 54 4.3 Role of nAChR that promote breast cancer stem cell proliferation…. 55 4.4 Screen limitations……………………………………………………. 55 4.5 Conclusions………………………………………………………….. 57

5 APPENDIX

6 REFERENCES

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MSc Thesis – N. Kasmachova; McMaster – Biochemistry and Biomedical Sciences

LIST OF FIGURES

Figure 1. Heterogeneity of nAChR …………………………… 20

Figure 2. Dropout viability screen experimental plan………… 33

Figure 3. Percentage distribution of single shRNA within the control samples (p0A, p0B, p0C) and tumours (T1, T2, T3, T4, T7, T8)...... 35

Figure 4. Removing low readings from p0 data by using EM (Expectation Maximalization) algorithm……………………………………………. 37

Figure 5. Hierarchical clustering of the control samples (p0A, p0B, p0C) and tumours (T1, T2, T3, T4, T7, T8)...... 38

Figure 6. Principal Coordinate Analysis (PCoA) 2D plots indicating similarities/differences between the control samples (p0A, p0B, p0C) and tumours (T1, T2, T3, T4, T7, T8)……………... 39

Figure 7. shRNA targeting β-galactasidase and luciferase…… 40

Figure 8. shRNA that were statistically significant different in the tumour samples compared to the control……………………………………….. 42

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MSc Thesis – N. Kasmachova; McMaster – Biochemistry and Biomedical Sciences

LIST OF TABLES

Table 1. Number of reads of single shRNA, presented in the control samples (p0A, p0B, p0C) and tumours (T1, T2, T3, T4, T7, T8)………...... 46 Supplementary Table 1. shRNA plasmids sequences ……………………. 58

Supplementary Table 2. Titers of shRNA-encoding lentiviruses ………… 60

Supplementary Table 3. Reverse and forward Illumina fusion

primer templates ………………………………… 61

Supplementary Table 4. shRNA sequences found to be not presented in the control and in the tumour samples……….. 63

Supplementary Table 5. Pair-wise comparison performed for 79 sequences……………………………………… 63

MSc Thesis – N. Kasmachova; McMaster – Biochemistry and Biomedical Sciences

LIST OF ABBREVIATIONS

cAMP Cyclic Adenosine Monophosphate

CK Cytokeratin

CREB cAMP Response Element-Binding Protein

CSC Cancer Stem Cell

ER Estrogen Receptor

FDR False Discovery Rate

gDNA Genomic DNA

GRB Growth Factor Receptor-Bound Protein

HER2 Human Epidermal Growth Factor Receptor 2

MAPK Mitogen-Activated Protein Kinases

MOI Multiplicity of Infection

nAChR Nicotinic Acetylcholine Receptor

PR Progesteron Receptor

TIC Tumour Initiating Cell

TM Transmembrane

TRC The RNAi Consortium

MSc Thesis – N. Kasmachova; McMaster – Biochemistry and Biomedical Sciences

CONTRIBUTION BY OTHERS

I would like to thank Jason Moffat from Donnelly Centre for Cellular and Biomolecular Research at University of Toronto for providing the TRC library shRNA-sequences library.

I would also like to thank Christine King from Francombe Metagenomics Facility at McMaster University for sequencing the samples and generating the data, Fiona

Whelan from Dr. Surrette laboratory at McMaster University for processing the sequences data.

I would also like to thank the following Hassell lab members who contributed to the data presented herein: Robin Hallett for accurate planning of the experiment, Bonnie

Bojovic for her assistance with gDNA preparation and Anna Dvorkin-Gheva for the analysis of the sequences data.

MSc Thesis – N. Kasmachova; McMaster – Biochemistry and Biomedical Sciences

1 INTRODUCTION

1.1 Breast cancer overview

Breast cancer is one of the leading cancer diseases among women worldwide.

Breast cancer is one of the most frequently diagnosed malignancies, and characterized with an 88% survival rate according to the Canadian Cancer Society statistics (2012).

Relative to other tumours the disease is distinguished by a high survival rate, but subtypes resistant to current treatments still exist (Jemal et al 2011). In the majority of cases, anticancer treatment comprises endocrine therapy and chemotherapy. Multiple regimens may reduce tumour size and restrain its growth, but tumours still recur (Fisher et al. 1997).

In addition, cytotoxic chemotherapy causes side-effects affecting patient quality of life

(Herbst et al. 2006). Therefore, there is a significant need to discover new therapeutic agents that will improve breast cancer patient outcome and disease-free survival.

1.2 Breast cancer types

According to the Canadian Cancer Society (2014), breast cancer is classified by the abnormal cells localization, behaviour and migration from their common place of origin.

The human breast is made of glandular, fatty and connective tissues. Glandular tissue is organized in lobes and smaller structures, lobules, glands that produce and store milk. Milk is released through the ducts and nipple. Adipose tissue is composed of collagen and elastin, and together with connective tissue, protects and supports the glands

MSc Thesis – N. Kasmachova; McMaster – Biochemistry and Biomedical Sciences

(Halim 2009). Atypical cells commonly develop in the lobules or in the ducts. The earliest stage of breast cancer cell development is called ductal carcinoma in situ (DCIS) and lobular carcinoma in situ (LCIS). Both types are characterized as non-invasive cancer, when abnormal cells develop either in the lobules or in the ducts, and do not spread into the surrounded breast tissue (Habel et al. 1997). When the abnormal cells start developing and carrying the potential to invade and metastasis, the tumours are classified as invasive ductal and invasive lobular carcinoma. The difference between these types is that ductal carcinoma cells start growing and forming small-sized tumours (Cowell et al. 2013), whereas invasive lobular carcinoma cells spread into nearby lymph glands (Jolly et al.

2006). When uncontrolled growing cells start to migrate from the primary tumour to the other tissues, they are recognized as metastatic. Metastatic breast cancer cells spread through lymph or blood to the common sites of breast cancer metastasis: bone, brain, liver and lungs (Minn et al. 2005). There are a number of risk factors that increase the chances of developing breast cancer in women: age, having children, family cancer history, diet, and smoking. It had been found that breast cancer is more common among women aged

45 and older (Anders et al. 2009). Moreover, genetic susceptibility to cancer plays an integral role in individual breast cancer development. Thus, the risk factors, such as genetic mutations including mutations BRCA1 and BRCA2 genes, can be inherited

(Serova et al. 1997). There are a number of epidemiological factors that affect breast cancer survival rate, such as regular and/or long term alcohol consumption and cigarette smoking (Sopori et al. 1998). Tobacco smoking is a known risk factor associated not only with a lung cancer, but also with other cancer types, such as gastric, urinary tract and

MSc Thesis – N. Kasmachova; McMaster – Biochemistry and Biomedical Sciences

colon cancers (Veveis et al. 2004). Recent studies provide evidence suggesting a correlation between smoking and breast cancer development. Moreover, it has been shown that having children before age 30 and breastfeeding significantly reduce breast cancer risk. Among other factors that are beneficial in breast cancer prevention are physical activity and low fat diet (Kroenke et al. 2013).

1.3 Breast cancer treatments

Breast cancer treatment aims to eliminate tumour cells and thus control cancer progression. The main methods of breast cancer treatment include surgery, radiation therapy, chemotherapy, and hormonal therapy. Cancer therapy can be generally categorized into two groups, which are local and systemic therapies. Local therapy involves radiotherapy as a method to shrink the tumour before the surgery. Local therapy also comprises conserving surgery that is aimed of removing the tumour, while preserving normal breast tissue. In some instances, when the tumour is massive, mastectomy (breast removal) is another option of treatment. Systemic therapy, comprising chemotherapy, and hormonal therapy, targets cancer cells throughought the body, prevents cancer cells from spreading and seeding the growth of a new tumour

(Fisher et al. 2001). The most common method of systematic treatment are endocrine therapy and chemotherapy, comprising a combination of anthracyclines (doxorubicin/Adriamycin®) and taxanes (docetaxel/Taxotere®) with hormones. Treatment can be given to a patient before surgery (neoadjuvant therapy) to shrink large tumours or after (adjuvant therapy) to prevent tumour relapse (Charfare et al.

MSc Thesis – N. Kasmachova; McMaster – Biochemistry and Biomedical Sciences

2005) and prolong disease-free survival (Meuller et al. 1993). Even though the success rate of breast cancer treatment is high, patients struggle with side effects caused by anti- cancer therapy. Treatment-related side effects usually comprise chemotherapy-induced peripheral neuropathy, pain, body weight fluctuation, fatigue (Binkley et al. 2012).

1.4 Molecular classification of breast cancer

It had been observed, that patients diagnosed with histologically similar breast tumours have differential response to neoadjuvant chemotherapy. Breast cancer can be arranged into subtypes based on hormone receptor expression, lymph molecular status, and morphological distinctions. Perou et al classified breast cancer into 6 subtypes

(luminal A, luminal B, basal, HER2, claudin-low and normal-like type) based on their gene expression profiles (Herschkowitz et al. 2007; Perou et al. 2000).

Basal-like and triple-negative subtype tumours do not express estrogen receptor

(ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), but expresses cytokeratin 5 (CK 5) and cytokeratin 17 (CK 17), which are associated with the basal epithelial cell lineage, and characterized by high levels of epidermal growth factor receptor (EGFR) (Sotiriou et al. 2003; Sorlie et al. 2004; Nalwoga et al. 2008; Nielson et al. 2004). Patients with basal-like tumours have poor prognosis. The fact that basal-like breast tumours do not express hormone receptors make them hard to target, thus, they are treated with chemotherapy alone (Reddy et al. 2011). In contrast, the luminal A and B molecular subtypes are characterized by estrogen and progesterone receptor expression and characterized by a better prognosis (Loi et al. 2007; Parker et al. 2009). HER2+

MSc Thesis – N. Kasmachova; McMaster – Biochemistry and Biomedical Sciences

molecular subtype tumours overexpress HER2 receptor. Patients with HER2 tumours are treated with hormonal therapy targeting HER2-receptor (Herschkowitz et al. 2007). The normal-like subtype has characteristics and similar gene expression profile to normal breast tissue. With the development of gene expression studies, claudin-low, a new breast cancer subtype has been identified recently (Prat et al. 2010; Peddi et al. 2012). Claudin- low tumours are characterized by a reduced expression of claudin protein that plays a role in cellular junction, suggesting higher potential of the tumours to metastasize. Claudin- low tumours are identified as common to triple-negative tumours (Perou 2010).

1.5 History of CSC and breast CSCs

There is significant evidence that the cellular organization of tumours follows a hierarchical model, where CSCs, or tumour-initiating cells (TIC), sit at the apex of the hierarchy, and drive tumour growth, metastsis, and relapse. CSC were first observed by

Ernest A. McCulloch and James E. Till, who in 1960 transplanted normal bone marrow cells into mice, that gave rise to nodules in the spleen (McCulloh et al. 1960). Later they confirmed that the minor population of injected cancer cells called “stem cells” initiated tumour growth (Becker et al. 1963). Furthermore, they discovered the “self-renewal” properties of stem cells by performing a colony assay, that demonstrated the formation of additional colonies during colony replating (Siminovitch et al. 1963).

Later, in 1971, McCulloh et al demonstrated that leukemic cells are able to grow in vitro as colonies, proposing the fact that cancer stem cells are clonogenic (Park et al.

1971). In 1997, Bonnet and Dick discovered that only cells expressing CD34+/CD38-

MSc Thesis – N. Kasmachova; McMaster – Biochemistry and Biomedical Sciences

specific surface markers are able to initiate acute myeloid leukemia (AML) in immunodefficient mice (Bonnet et al. 1997).

In 1988, Petersen et al observed that breast luminal epithelial cells convert into basal-like cells under the exposure to cholera toxin (Petersen et al. 1988). This observation was interpreted as evidence of breast stem cells. In 2003, Al-Hajj et al discovered that breast cancer epithelium express the surface markers CD44+/CD24-. It has been shown that the breast tumour cells comprising a CD44+CD24-/lowLin- phenotype had high tumourigenic activity in immunodefficient mice, rather than cells comprising other phenotypes, such as CD44+CD24high (Al-Hajj et al. 2003). Moreover, cell populations expressing CD133+/Nestin+/Lineage– from brain tumours were able to grow and form tumourspheres in vitro (Singh et al. 2004). Together these findings suggest that

CSCs have self-renewal properties and are able to differentiate to stable cell population

(Ke et al. 2013).

A number of mechanisms of chemoresistance and radiation resistance have also been reported for CSC, further supporting their capacity to survive cancer therapy and seed relapse after remission in cancer patients (Abdullah et al. 2013). Although, the molecular mechanisms of resistance are not known with certainty, many reports suggest they might be related to the activity of various signalling pathways such as Notch, hedgehog, β-catenin, WNT that contribute to TIC maintenance (Ke Wang et al. 2013).

Activation of signal transduction pathways plays an important role in TIC differentiation and self-renewal. Known regulators of TIC self-renewal are components of the WNT pathway. Expression of WNT pathway components, such as β-catenin, upregulation of

MSc Thesis – N. Kasmachova; McMaster – Biochemistry and Biomedical Sciences

Notch1 and HoxB4 all promote cell proliferation (Reya et al. 2003). Altered hedgehog signals have been determined in many pathological processes, including cancer initiation and progression (Shen et al. 2010). Identification of agents that selectively target cancer stem cells, affect self-renewal and/or induce sensitivity to chemotherapy might aid in the development of anti-cancer therapies that provide durable cure for breast cancer patients.

1.6 Acetylcholine receptors

Cholinergic receptors are membrane proteins that bind the neurotransmitter acetylcholine. In general, acetylcholine receptors are classified as nicotinic or muscarinic receptors depending on their response to nicotine or muscarin, respectively (Akk et al.

1999). These two subtypes have different structures and function. Muscarinic receptors are G-coupled proteins and respond best to muscarin, whereas nAChRs are ligand-gated ion channels. In response to acetylcholine binding nAChRs allows the influx of K+, Na+,

Ca2+ ions when the receptor is activated. Even though, nAChRs are ion-gated channels, muscle-type and neuronal-type nAChR have different permeability to ions. The permeability depends on amino acid composition and the selectivity located in transmembrane domain (TM2) domain.

Having a ligand-binding structure, nAChR can bind acetylcholine as their physiological agonist and, and also a number of compounds such as nicotine, cytisine, anatoxin A, epibatidine, anabasine (Kem et al. 1997). nAChR are classified by sensitivity to nicotine and α-bungarotoxin (α-Bgtx), a natural neurotoxin derived from snakes, which binds with high affinity to muscle-type nAChR binding sites (Young et al. 2003).

MSc Thesis – N. Kasmachova; McMaster – Biochemistry and Biomedical Sciences

Usually nAChRs are expressed in both central and peripheral nerves. They form pentamers, which consist of combinations of α1-10, β1-4, γ, δ and ε monomers (Figure 1).

The muscle-specific subfamily includes α1, β1, γ δ and ε; whereas, the neuronal-specific type composes α2-10, β2-4 subunits. Subunits can be assembled in either heteropentamers or homopentamers. Homopentamers are composed of homologous subunits, whereas heteropentamers comprise a combination of various subunits (Figure 1).

Each subunit of the pentamer composed of four transmembrane domains, TM1-

TM4. These domains are organized in an antiparallel manner, and connected by amino acids chains. Thus the TM2 region forms a central hydrophilic ionic pore (Heidelberger et al. 2009). The muscle-type nAChR can form heteropentamers composed of αβγδ or ε subunits, and the neuronal-type nAChR form functional pentamers based on 2α+3β stoichiometry. Moreover, α and β subunits form multiple heteropentamers. Thus, pentamers have distinct physiological and pharmacological properties (Albuquerque et al.

2009). Individually only neuronal-type α7, α8 and α9 subunits can assemble into homopentameric conglomerates. However, α8 nAChRsubunit has only been found in birds. α7, α8 and α9 nAchR are classified as α-Bgtx sensitive, and are mainly expressed in the central nervous system (Figure 1). Interestingly, α9 nAChR can also form heteropentamer with α10, which is inactive by itself and requires the α9 subunit for activation (Baker et al. 2004). Moreover, there is evidence that α9 and α10 nAChRs are involved in cell proliferation and/or tumour growth maintenance (Schuller et al. 2009).

MSc Thesis – N. Kasmachova; McMaster – Biochemistry and Biomedical Sciences

Figure 1. Heterogeneity of nAChR. Phylogenetic tree illustrates all possible subunit combinations of 16 human nAChR, which are classified to tribes and subfamilies. Scheme of composition for homopentamers and heteropentamers shows the rules of subunits assembly and location of the agonist binding sites (arrows). Figure contains information where certain nAChR can be found in mammals. Taken from Figure 1 (Changeux et al. 2001).

MSc Thesis – N. Kasmachova; McMaster – Biochemistry and Biomedical Sciences

1.7 nAChRs and CSC

In 1989, Shuller et al discovered subsequently the involvement of nAChR in lung cancer (Schuller et al. 1989). Moreover nAChR activity was found to be involved in cancer cell migration (Wei et al. 2009), angiogenesis (Cooke et al. 2007), and apoptosis

(Paliwal et al. 2010).

nAChR mediates a number of cellular signalling cascades that promote cancer cell proliferation (Schuller et al. 2009). Various nAChR subunit assemblies and their over expression in the breast or lung tissues (Lee et al. 2003; Lam et al. 2007). High expression of the well-studied α7 nAChR is correlated with lung cancer cell promotion through Akt signal activation (Davis et al. 2009). Heteropentameric α4β2 modulates dopamine release in brain tissues leads to nicotine-addiction and plays a role in lung cancer progression (Wullner et al. 2008).

As a membrane protein, homopentamer α9 is mainly expressed in cochlea hair cells. Moreover, α9 nAChR expression was found in both normal and cancerous breast epithelium, but it is more highly expressed in the tumour (Lee et al. 2010). If signaling through α9 nAChR is required for CSC activity, we will identify a specific nAChR(s) that are essential for TIC growth and proliferation by performing a dropout screen.

Interestingly, α9 nAChR has features of both muscarinic and nicotinic receptors. In that respect, a number of muscarinic and nicotinic receptor antagonists were tested for their capacity to sensitize chemo-resistant cell lines to chemotherapy (Hallett, unpublished).

The majority of inhibitors targeted nicotinic receptors but only one inhibited muscarinic.

As α9 has properties of both receptors, thus, the anti-cancer stem cell or chemo-

MSc Thesis – N. Kasmachova; McMaster – Biochemistry and Biomedical Sciences

sensitizing activity of inhibitor might be via inhibition of the α9 AChR (R.M.Hallett, unpublished).

Angiogenesis is another pathological processes regulated by nAChRs. nAChRs regulate vascular endothelial growth factor (VEGF) which makes endothelial cells grow and expand, allowing them to form new channels (Carmeliet et al. 2000). Additionally, nAChR-induced angiogenesis machinery enhances tumour cell growth and proliferation due to activation of the MAPK signalling cascade (Mousa et al. 2006).

In summary, reported findings and clinical results indicate the involvements of nAChR in a number of malignancies suggesting that nAChRs may be valuable targets for drug discovery.

1.8 Experimental Rationale

According to the CSC model, genomic alterations result in cancer cell populations with stem cell properties. The cellular heterogeneity of tumours arises from the aberrant differentiation of cancer stem cells. Previous Hassell laboratory findings revealed inhibitors of nAChR increase breast tumour cell sensitivity to chemotherapeutic compounds, and reduce sphere formation of human breast tumour cell lines propagated in vitro. (Hallett, unpublished). My present project centered on determining which of the various nAChR is required to target breast cancer stem cells. Any findings will be important to indentify inhibitors of the specific nAChRs.

To identify nAChR tumorigenesis whose expression might be required for breast

CSC proliferation and viability, we performed a shRNA dropout screen. Our expectation

MSc Thesis – N. Kasmachova; McMaster – Biochemistry and Biomedical Sciences

was that shRNA targeting one or more nAChR transcripts required for tumorigenesis or the serial propagation of tumorspheres would be lost during this process.

2 EXPERIMENTAL PROCEDURES

2.1 Cell culture

293FT cells were grown in high-glucose D-MEM, supplemented with 10% fetal bovine serum (FBS), 0.1 mM MEM non-essential amino acids (NEAA), 6 mM L- glutamine, 1 mM MEM sodium pyruvate, 1% pen-strep, 500 μg/mL Geneticin (G418).

Cell lines were incubated at 37°C in 5% CO2 atmosphere.

Adherent HCC1954 cells were cultured by using RPMI, supplemented with 10%

FBS, penicillin streptomycin, and fungizone. Cells were incubated at 37°C in 5% CO2 atmosphere.

Non-adherent HCC1954 cells were cultured in stem cell media (SCM), composing of low-glucose DMEM:Ham's F-12 (3:1), supplemented with 1 mg/mL fungizone, 1% pen-strep, 4 μg/mL B-27, 20 ng/mL EGF, 40 ng/mL FGF-2 and 4 ng/mL Heparin

(Invitrogen).

The number of viable cells cultured in vitro were counted with hemocytometer after Trypan Blue (5%) staining. 2.4x107 viable cells were placed in complete serum-free low-glucose DMEM, and incubated at 37°C in 5% CO2 atmosphere.

Growing spheres were propagated for 3 passages by centrifugation at 1000rpm

(10,285g) for 5 min, and mechanical dissociation in low-glucose serum-free DMEM.

MSc Thesis – N. Kasmachova; McMaster – Biochemistry and Biomedical Sciences

After that, tumourspheres were dissociated, plated in T150 flasks at a density of 60,000 cells/mL, and incubated at 37°C in 5% CO2 atmosphere.

2.2 Lentivius production

The TRC2 library containing shRNA constructs targeting human nAChR was obtained from Moffat’s lab. The ViraPower Lentiviral Expression System was used for delivery of the gene of interest in dividing cells. The system is based on the pLKO-vector, which contains packaging plasmids (pLP1, pLP2, pLP/VSV-G) and expression constructs

(5’ and 3’ LTR’s) for effective lentivirus production.

shRNA-encoding lentiviruses were generated by transfection of 293FT cells with a four-plasmid combination as follows: 5 x 106 293FT cells were seeded in 100mm plates containing of 10 mL complete DMEM media. Cells were transfected using pLP1 4 ug/uL, pLP2 1.88 ug/uL, pLP/VSVG 2.62 ug/uL, destination vector 6.34 ug/uL (Supplementary

Table 1). Total DNA concentration was 15 ug.

The day before transfection 293FT cells were plated in 100mm diameter plates at a density of 5 x 106 cells/10 mL of complete growth DMEM medium containing 10% serum. On the day of transfection medium was replaced to 5 ml complete DMEM growth media without antibiotics. For efficient transfection, Lipofectamine 2000 (Invitrogen) was used to create DNA-Lipofectamine 2000 complexes (1 ug DNA : 3 uL LF2000) as follows: packaging plasmids (pLP1, pLP2, pLP/VSV-G) at known concentrations were mixed with expression plasmid DNA in 1.5 mL of Opti-MEM without serum. Separately,

Lipofectamine 2000 was diluted in 1.5 mL of Opti-MEM, mixed and incubated for 5 min

MSc Thesis – N. Kasmachova; McMaster – Biochemistry and Biomedical Sciences

at room temperature. After that, DNAs were combined with Lipofectamine 2000

(LP2000) and incubated for 20 min at room temperature. Therefore, the DNA-LP2000 complexes were added drop-by-drop to the cells, and were later incubated overnight at

37°C in a 5% CO2 atmosphere. 24h later media containing LP2000 was replaced with 10 mL complete DMEM with antibiotics and incubated overnight at 37°C in 5% CO2 atmosphere. The following day virus-containing supernatants were harvested and filtered through a Millex-HV 0.45um low-protein binding filter. Supernatants were mixed with

Lenti-X concentrator (1 volume Lenti-X : 3 volume clarified supernatant) and incubated at 4°C overnight. Samples were centrifuged at 4,500rpm (5,000g) for 45 min at 4°C, and the supernatants were carefully removed taking care not to disturb pellets. The pellet were resuspended in 1/10th volume using complete DMEM.

HCC1954 adherent cells were plated in 6-well tissue culture plates at a density of

1x105 cells/2 mL of complete RPMI medium and were allowed to adhere by incubating overnight at 37°C in a 5% CO2 atmosphere. The next day 10-fold serial dilutions of the virus ranging from 10-3 to 10-7 were prepared and incubated for 24h. The following day, the medium was replenished with 2ml of a complete RPMI, and the cells incubated overnight at 37°C in a 5% CO2 atmosphere. The following day medium was replaced to a complete RPMI medium containing puromycin (1 ug/mL). The complete RPMI medium containing puromycin was replenished every 3 days. After 12-14 days after addition of the antibiotic, the colonies were stained with a 1% crystal violet solution (Sigma), washed twice with 2xPBS, and puromycin-resistant colonies at each dilution were counted

(Supplementary Table 2).

MSc Thesis – N. Kasmachova; McMaster – Biochemistry and Biomedical Sciences

2.3 Lentiviral Infection

A pool containing single constitutively expressed shRNA-encoding lentiviruses was prepared by mixing 79 lentiviruses in such a manner that the pool MOI was 0.3 to introduce one viral copy in the single cell. shRNA-encoding lentiviruses targeting human nAChR, β-galactosidase and firefly luciferase were included in the lintiviral pool as negative controls. 24h the infected cells were selected with puromycin (0.5 ug/mL). To eliminate uninfected cells, after 48h of growing in puromycin-containing medium, sphere-culture was divided into 3 independent pools that would represent technical replicates (T=0). 10 NOD-SCID-mice were injected with an equal number of infected and selected cells, harvested at initial time T=0. Tumours were isolated and cell suspension was used for gDNA extraction.

2.4 gDNA extraction

DNA Blood Maxi Kit (QIAgen) was used to isolate genomic DNA (gDNA) extraction. gDNA were resuspended in 2xPBS to a total volume of 7 mL. Fresh 500 uL protease was added and samples were briefly mixed. After that, 6 ml of buffer AL was added, the samples were vortexed thoroughly and incubated at 70°C for 15 min. The samples were cooled to ~40°C, 5 mL of 100% ethanol was added and mixed by inversion for 2 min. Half of the solution was transferred to the QIAGEN maxi columns, the columns were placed into 50ml conical plastic tubes and centrifuged at 3,000rpm for 3 min. The filtrates were discarded and the other half was added to the same columns and centrifuged under the same conditions. A 5 ml buffer AW1 was added to the columns that

MSc Thesis – N. Kasmachova; McMaster – Biochemistry and Biomedical Sciences

were placed into 50 mL conical plastic tubes and centrifuged at 5,000rpm (4,500g) for 2 min. 5 mL of buffer AW2 was added to the columns, columns were placed into 50ml conical plastic tubes and centrifuged at 5,000rpm (4,500g) for 15 min. After that, the filtrates were discarded and the columns were placed at room temperature. 600 ul of room temperature buffer AE was pipetted on the columns membranes, incubated for 5 min at room temperature, and centrifuged at 5,000rpm (4,500g) for 5 min. An additional 200 uL of room temperature buffer AE was dispensed on the membrane, incubated for 5 min at room temperature, and centrifuged at 5,000rpm (4,500g) for 5 min. The concentration of gDNA was measured with NanoDrop.

500uL of sample gDNA was used for precipitation. 5M NaCl was added to the sample, to a final concentration 0.2M, in addition to 1 ml of -20°C 100% ethanol. The tubes were inverted 5 times, and centrifuged at 13,000rpm (13,800g) at 4°C for 15 min. supernatants were carefully removed, and the sample gDNA pellets were washed with

500ul of -20°C 70% ethanol, by inverting and centrifuging it at 13,000rpm (13,800g) at

4°C for 15 min. The supernatants were carefully removed and residual liquid was air- dried for ~10min. The DNA pellet was resuspended in buffer EB (10mM Tris-HCl pH7.5) to a final concentration of 450ng/uL. The samples were subsequently incubated at

50°C for 60 min. The concentration of solubilised DNA was measured with NanoDrop and adjusted to 400ng/uL.

MSc Thesis – N. Kasmachova; McMaster – Biochemistry and Biomedical Sciences

2.5 gDNA PCR amplification

200 ng of template was PCR amplified by using the Platinum Pfx polymerase kit

(Invitrogen). The following components were added to sterile microcentrifuge tubes on ice: 10X Pfx amplification buffer (5 uL), 10 mM dNTP mixture (1.5 uL - final concentration 0.3 mM each; 50 mM MgSO4 (1 uL - final concentration 1 mM); primer mix (0.5 uL – final concentration 1 uM); template DNA (200 ng); ≥1 uL, Platinum® Pfx

DNA polymerase (0.5 uL - final concentration 1 unit), and distilled water to 50 uL. The microcentrifuge tubes were placed into the thermocycler and 40 cycles of PCR amplification were performed as follows: initialization step at 95ºC (5min), denaturation step at 95ºC (45sec), annealing step at 52ºC (45sec), extension/elongation step at 72ºC

(30sec), final elongation step at 72ºC (5min), final hold at 4ºC. Reactions were maintained at 4ºC after cycling and samples were stored at -20ºC until use.

2% agarose preparative gels were prepared and placed into the apparatus filled with 1xTBE buffer. PCR amplified product was stained with EZ-Vision DNA Dye

(Amresco), and was added to 200ng, 1 ug of 100bp ladder standard was loaded into the wells. Electrical current of 100A was applied to separate the amplified DNA products.

The expected DNA fragment 320bp was excised from the gel with a clean razor blade.

Slices from preparative gels were collected and purified with PureLink PCR Purification

Quick Kit (Invitrogen, Cat. No. K3100-01). 1 volume of DNA containing agarose slices were dissolved in 3 volumes of gel solubilization buffer L3. The tubes were incubated in a 50ºC water bath for 15-20 min until the agarose had fully dissolved. Thereafter, 1 volume of iso-propanol was added to the mixture and pipetted onto the membrane of the

MSc Thesis – N. Kasmachova; McMaster – Biochemistry and Biomedical Sciences

PureLink Clean-Up Spin Column and centrifuged at 13,000rpm (13,800g) for 1 min. 700 uL of wash buffer W1 containing ethanol was added to the membrane and centrifuged at

13,000rpm for 1 min. The tubes were centrifuged at the 13,000rpm (13,800g) for 3 min to remove residual buffer from the membrane, and the column was placed into a clean elution tube. 30 ul of elution buffer E1 was added to the membrane, incubated at room temperature for 1 min and centrifuged at 13,000rpm (13,800g) for 1min. The concentration of the purified DNA was measured with the NanoDrop instrument and the samples were aliquoted and stored at -20 ºC.

2.6 Illumina primer design and high-throughput DNA sequencing

We designed 6 forward P5 (1-6) and 6 reverse primers P7 (7-12) in such a manner that they assembled in 36 unique combinations. Each primer contained the following sequences: Illumina binding sequence, 6bp barcode, Illumina adapter sequence, DNA insert (Supplementary Table 3). 4 nmole of the oligonucleotides were received and diluted in distilled water to a final concentration of 100 uM. The samples were diluted to

10nM and sequenced with MiSeq Illumina sequencer. Paired-end high-throughput sequencing was performed to improve the quality of the data.

2.7 Statistical methods

2.7.1 Data visualization

Each individual gDNA sample was PCR amplified using individual primers, which contained a unique barcode. All gDNA samples were pooled for paired-end

MSc Thesis – N. Kasmachova; McMaster – Biochemistry and Biomedical Sciences

sequencing using the Illumina MiSeq platform. The resulting FASTQ files were then processed and analyzed using custom software developed by Fiona J Whelan in the

Surette laboratory at McMaster University. Briefly, sequences were trimmed to the shRNA sequence, removing any sequenced primer products. Paired-end sequences were aligned using PANDAseq and any sequences with no overlap or insufficient quality were removed (Masella et al. 2012). Sequences sharing > 97% identity (i.e. unique shRNAs) were clustered using AbundantOTU+ (Ye et al. 2011). The QIIME workflow was then used to split samples by use of the unique barcoded primers, and to assign each sequence cluster to the list of shRNA input sequences (Caporaso et al. 2010). QIIME was also used to produce plots to summarize the number of each shRNA per sample, as well as the similarities among samples based on the abundance and type of shRNAs present.

2.7.2 Data filtering

We assumed that if a shRNA had low number of readings in the P0 samples, then such a result was unreliable and should not be used for further analysis. Therefore, by performing EM algorithm (Everitt et al. 1981) on the distribution of pooled readings for all 3 P0 samples, we obtained a cutoff, that filtered out all readings lower than 886. If only one out of three P0 samples had a number of readings lower than 886 for a certain shRNA, it was excluded from further analysis, but the shRNA was still analyzed with the remaining 2 P0 samples. If more than one P0 samples had a number of readings lower than 886 for a certain shRNA, the shRNA was excluded from further analysis. We then proceeded by excluding outliers from the readings obtained for tumour samples. For

MSc Thesis – N. Kasmachova; McMaster – Biochemistry and Biomedical Sciences

each shRNA the outliers were computed by using the following formula: values higher than Quartile3+1.5IQR were considered to be outliers. This procedure did not result in exclusion of any more shRNAs from the final analysis.

2.7.3 Examining changes in shRNA

For each shRNA a t-test was performed comparing P0 to the tumour samples.

Multiple testing correction was performed by FDR (Benjamini et al.

1995). shRNA showing significant changes (corrected p-value<0.05) were shown by boxplots. All analyses and visualizations were performed in Matlab R2008b

MSc Thesis – N. Kasmachova; McMaster – Biochemistry and Biomedical Sciences

3 RESULTS

3.1 shRNA dropout screen revealed multiple nAChRs that are essential for breast CSC viability and proliferation, and required for breast tumourigenesis.

To identify regulators of breast CSC proliferation and viability, we performed a shRNA dropout viability screen. The assay was done with our RNA interference library, which was composed of 79 individual shRNA-encoding lentiviruses that constitutively express shRNAs against all 16 human nAChRs (Supplementary Table 1). We introduced this pooled lentiviral library into dissociated established HCC1954 cells with an average representation of 10,000 integration per shRNA, at a multiplicity of infection (MOI) of

0.3. A low MOI minimizes the chance that greater than one viral particle will infect a single cell, thus the dynamic dropout ranges can be monitored through the behaviour of the infected cells that carry only one provirus. Infected cells were selected with puromycin, and divided into three independent pools (p0A, p0B, p0C), representing technical replicates. Infected cells were injected into NOD-SCID-mice, tumours were allowed to form and gDNA was isolated from the tumours. Collected tumour gDNA was

PCR amplified with Illumina primers containing particular barcodes, and PCR products were sequenced (Figure 2). We anticipated that if a minimum of one out of five shRNA was depleted during tumourigenesis, the target transcript be essential for this process.

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 Establish HCC1954 spheres

 Infect Cells with a Pool of 79 shRNA-encoding lentiviruses

 Infect dispersed tumor cells from dissociated spheres at an 24h MOI of ~0.3 Selection  Select for Infected Cells in 48h Puromycin (0.5 ug/ml) for 2 days

Figure 2. Dropout viability screen experimental plan

Flow-chart illustrates the successive steps to perform pre-sequencing cell culturing. Steps include sphere-culture establishing, infection, selection and propagation. MOI – multiplicity of infection; T – time

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A dropout screen for mouse serotonin receptors previously performed in the lab revealed that the effect of shRNA was more pronounced in tumours than in the cells growing in vitro (Hallett, unpublished).

High-throughput DNA sequencing data was received as a table of proportions of shRNA sequences in controls (p0A, p0B, p0C) and tumours (T1, T2, T3, T4, T7, T8).

The proportions were calculated for each sample separately, meaning that a total of all values for each sample is 1. We anticipated that the representation of every single shRNA should be equal in the control samples (p0A, p0B, p0C), as each virus was equally represented within the pool. However, examination of the shRNA distribution in the control samples revealed that the shRNAs were distributed unevenly within the control samples (p0A, p0B, p0C) (Figure 3).

The analysis revealed that a number of sequencing reads varied in the control samples (Table 1). Moreover, 7 out of 79 sequences were not present either in the control samples or in the tumours (Supplementary Table 4). Data filtering was done based on the assumption that if control samples exhibited low values, readings for the specific shRNA would not be reliable. In that respect, the number of readings equal to 0 were removed from the analysis, and the rest of the reads from all 3 control samples were pooled together. EM algorithm was performed for these reads revealed a threshold of 886 number of reads, leading to removal of all reads lower than 886. If only 1 out of 3 reads for a specific shRNA was to be removed, shRNA was still analyzed, after the low value was excluded from the analysis. However, if 2 or 3 out of 3 reads for a specific shRNA were to be removed, the whole shRNA was excluded from further analysis.

MSc Thesis – N. Kasmachova; McMaster – Biochemistry and Biomedical Sciences

Figure 3. Percentage distribution of single shRNA within the control samples (p0A, p0B, p0C) and tumours (T1, T2, T3, T4, T7, T8)

Color blocks represent the abundance of individual shRNA in a particular control and tumour samples. Each color block represents abundance of the same shRNA across all the samples.

MSc Thesis – N. Kasmachova; McMaster – Biochemistry and Biomedical Sciences

The sequences that were scored higher than the threshold of 886 stayed in the analysis

(Table 1, dark grey pattern) and (Figure 4). The next step involved excluding statistically significant outliers from the tumour samples data. Outlier values were computed for each shRNA separately in the following way:

Upper Outliers = Quartile3 + 2.5*IQR

IQR = Quartile3 – Quartile1

The sequences that were scored lower than the individually calculated threshold remained in the analysis (Table 1, light grey pattern).

We performed hierarchical clustering of the samples based on shRNA abundance to reveal how similar or different the samples were. There analysis showed that the control samples were highly similar, and clustered separately from the tumour samples.

However, tumours T4 and T8 were identified as outliers among all the tumour samples

(Figure 5). These findings were confirmed by Principal Component Analysis performed on reads from all gDNA samples (Figure 6). Control samples (p0A, p0B, p0C) all grouped together, suggesting their similarity. By contrast, tumour samples T1, T2, T3, T7 were distinct from T4 and T8, suggesting that latter samples were outliers.

We measured the fold change dropout of individual shRNA within each sample with Student’s test using multiple testing correction. Control sequences (β-galactasidase and luciferase) were also analyzed for differences between controls and tumours. The difference in abundance of the control gene β-galactasidase shRNA (LacZ) was not found to be statistically significant compared to the control samples. However, one of the shRNAs targeting luciferase dropped out during tumourigenesis (Figure 7).

MSc Thesis – N. Kasmachova; McMaster – Biochemistry and Biomedical Sciences

Figure 4. Removing low readings from p0 data by using EM (Expectation Maximalization) algorithm

Left panel: BIC (Bayesian Information Criterion) parameter. E – Equal variance (univariate data model); V – Variable variance (univariate data model). Right panel: data classification. All values used for the classification are shown at the bottom of the plot (black bars). Color bars show data separated into 6 different groups based on the classification. Values belonging to the group with lowest values (dark blue) have been removed.

MSc Thesis – N. Kasmachova; McMaster – Biochemistry and Biomedical Sciences

shRNAsequences Normalized raw reads Normalized raw reads

Figure 5. Hierarchical clustering of the control samples (p0A, p0B, p0C) and tumours (T1, T2, T3, T4, T7, T8)

Heat map illustrates sets of the sample clustered by the similar shRNA abundance. Red – high scored values, green – low scored values.

MSc Thesis – N. Kasmachova; McMaster – Biochemistry and Biomedical Sciences

Figure 6. Principal Component Analysis (PCA) 2D plots indicating similarities/differences between the control samples (p0A, p0B, p0C) and tumours (T1, T2, T3, T4, T7, T8)

Plot shows the similarities/differences of shRNA abundance in the samples. Each color dot represents a sample. Principle coordinates X-axis explains 37.01% of the total variance, Y-axis explains 26.36% of the total variance.

MSc Thesis – N. Kasmachova; McMaster – Biochemistry and Biomedical Sciences

Figure 7. shRNA targeting β-galactasidase and luciferase

Box-plots show regulation of shRNA targeting negative controls LacZ and luciferase in the tumour samples compared to the control samples. Upper panel for each graph indicates a particular sequence, targeted gene, and fold change difference compared to the controls; FC – fold change. X-axis shows samples, Y-axis shows number of reads.

MSc Thesis – N. Kasmachova; McMaster – Biochemistry and Biomedical Sciences

Even though, most shRNA did not affect tumour growth, t-tests revealed that shRNA targeting multiple transcripts were lost during tumorigenesis. shRNAs targeting

CHRNA1, CHRNA3, CHRNA4, CHRNA5, CHRNA6, CHRNA7, CHRNA9, CHRNA10,

CHRNB1, CHRNB3, CHRNB4, CHRND, CHRNG genes met the criteria of statistical significance (Figure 8). Moreover, some shRNA against CHRNA1 and CHRNA10 genes were found to be both downregulated and upregulated during tumourigenesis.

We wanted to check that the sequences used in the analysis were significantly different from each other and, therefore, there would not be any technical artefacts attributed to a high level of similarity between several sequences. To do so we performed pair-wise comparisons for all sequences. The highest level of similarity found was 83.3%.

It has been observed between 2 pairs of sequences:

Sequence 54 (CHRNB4) and Sequence 5 (CHRNA10)

Sequence 44 (CHRNB2) and Sequence 27 (CHRNA4) (Supplementary Table 5)

Sequence 54 was found not to be significantly different between P0 and tumour samples. In the second pair of sequences, Sequence 44 was excluded from the analysis, because the P0 samples had very low readings (step 1 of the filtering procedure) and

Sequence 27 was not found to be significantly different between the P0 and tumour samples.

Loss-of-function analysis revealed both muscle-specific and neuronal-specific nAChR genes whose expression was inhibited, suggesting their involvement in cell proliferation and survival.

MSc Thesis – N. Kasmachova; McMaster – Biochemistry and Biomedical Sciences

Figure 8. shRNA that were statistically significant different in the tumour samples compared to the control

Box-plots show multiple shRNA that were downregulated or upregulated in the tumour samples compared to the control samples. Upper panel for each graph indicates a particular sequence, targeted nAChR, and fold change difference compared to the controls; FC – fold change. X-axis shows samples, Y-axis shows number of reads. Fold change is computed by dividing mean(P0) by mean(Tumours). Therefore, when mean(Tumours) < mean(P0), FC is higher than 1; otherwise, FC is lower than 1.

MSc Thesis – N. Kasmachova; McMaster – Biochemistry and Biomedical Sciences

The highest fold-change expression difference (infinite) compared to the control was a shRNA targeting CHRNA10 gene transcripts, suggesting that this gene has the strongest effect on cancer cell viability. Previously investigated CHRNA9 gene was shown to be knocked down with single shRNA with a 2.1-fold difference compared to the control

(Figure 8).

MSc Thesis – N. Kasmachova; McMaster – Biochemistry and Biomedical Sciences

Receptor Seq. ID pOA pOB pOC T1 T2 T3 T4 T7 T8 CHRNA1 10 37550 48183 47564 48678 41211 31238 267 30645 12165 CHRNA1 11 15205 18607 17630 16131 14531 36458 5621 21746 7595 CHRNA1 12 28878 35552 35417 65948 65714 80930 103300 86106 55497 CHRNA1 13 24511 20193 18716 4104 4187 5091 1705 4230 5721 CHRNA1 14 9762 8443 10003 90213 45573 13549 20222 29346 19682 CHRNA2 15 226 352 357 1550 1212 9431 12 552 48 CHRNA2 16 2576 4779 5696 31753 13145 18730 2201 18064 25861 CHRNA2 17 1229 354 885 10184 724 2553 4178 4160 2034 CHRNA2 18 306 421 783 2073 3153 2292 0 750 150 CHRNA2 19 3566 2614 3052 13979 4406 3537 2560 6798 1370 CHRNA3 20 12899 8943 14667 14961 7295 18717 42531 15216 28305 CHRNA3 21 3367 3059 2184 0 1167 1064 997 562 1767 CHRNA3 22 38613 37008 34412 21511 53548 30981 50290 27664 36670 CHRNA3 23 764 2375 2531 2549 3806 2859 2 3757 865 CHRNA4 25 3893 4343 5812 1182 588 735 22 1702 1303 CHRNA4 26 4570 5372 8371 3032 10551 7462 4190 3299 1729 CHRNA4 27 1370 3545 2943 3030 1040 808 5 1341 399 CHRNA4 28 202 576 617 567 2015 599 39 300 0 CHRNA4 29 8054 9044 10838 8846 6292 14385 7092 5142 5117 CHRNA5 30 8156 11070 10102 16652 8334 10984 1864 16104 14490 CHRNA5 31 14191 13443 15752 8682 8702 9282 39468 11124 4374 CHRNA5 32 2855 3191 1200 0 130 6343 0 783 795 CHRNA5 33 208 545 100 0 0 7 921 21242 13 CHRNA6 0 15992 13696 11773 10710 28807 14142 58081 13544 50830 CHRNA6 1 4986 5257 5379 3897 1086 34174 215 1924 1211 CHRNA6 2 18198 15387 16473 15470 2451 3942 10269 2323 1411 CHRNA6 3 62251 65785 66633 101392 68032 73147 66157 105291 82388 CHRNA6 4 6118 7635 7097 8526 8997 11043 1111 6518 1573 CHRNA7 35 17683 9762 11062 5028 6221 4914 7439 21081 33564 CHRNA7 36 19776 20894 23216 16665 30762 32704 12548 33010 42660 CHRNA7 37 8535 9217 7281 29965 7600 18608 14609 14284 4503 CHRNA7 38 2411 3093 2718 300 587 841 63 444 1435 CHRNA9 73 5935 8377 10687 4627 1846 6551 3936 3601 2452 CHRNA9 74 1363 717 395 1663 6574 0 5184 0 3 CHRNA10 5 43560 35904 39677 122403 81133 89754 159013 73333 145087 CHRNA10 6 1075 871 1925 0 1122 0 0 0 0

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Receptor Seq. ID pOA pOB pOC T1 T2 T3 T4 T7 T8 CHRNA10 7 7514 10270 7834 12607 4549 9849 1367 12726 2158 CHRNA10 8 9975 12201 14362 10573 13134 16102 37051 20759 13898 CHRNA10 9 998 2956 2181 3709 12716 9265 23 3091 10133 13025 CHRNB1 39 130843 116381 3 96734 44888 95463 105216 76609 92643 CHRNB1 40 998 648 700 370 180 1614 4960 2735 286 CHRNB1 41 713 702 576 0 0 445 21 378 0 CHRNB1 42 3776 5525 5009 0 412 5248 3331 1768 2407 CHRNB1 43 2049 1871 2867 1684 3700 1337 0 6478 2364 CHRNB2 44 569 1054 534 3256 0 1290 197 2136 309 CHRNB2 45 5961 3181 4782 0 7133 1266 8392 1123 1299 CHRNB2 46 2268 1680 552 0 0 2063 0 3 3710 CHRNB2 47 4937 7471 9409 23576 63340 17166 1282 16562 9429 CHRNB3 48 4660 3092 5670 4139 1904 293 926 29431 1022 CHRNB3 49 3595 5488 6618 8184 9256 19009 8439 8694 7730 CHRNB3 50 1620 4050 3973 0 792 7146 10 1490 26541 CHRNB3 51 21044 20016 23795 9574 979 6883 39815 32975 14317 CHRNB3 52 7342 5956 7296 2 779 2084 4609 2507 3817 CHRNB4 53 2363 1445 2709 437 263 472 344 598 0 CHRNB4 54 6347 8297 9161 3815 2 363 106 6136 63 CHRNB4 56 2083 1523 1629 3637 3530 2089 5841 2199 7 CHRND 57 14687 6281 5642 2 1517 1582 0 2302 1268 CHRND 58 11403 11689 11643 1510 2541 11111 593 1788 2753 CHRND 59 5949 5755 3788 3312 266 3636 4294 6649 3682 CHRND 60 29640 27210 27166 35616 22643 12104 8290 26918 28758 CHRND 61 0 0 142 161 0 161 0 105 52 CHRNE 62 92 143 37 0 0 0 0 0 0 CHRNE 63 425 290 588 0 0 26298 134 910 557 CHRNE 64 200 181 229 0 0 0 0 0 3 CHRNE 65 204 786 352 342 264 1919 0 835 9 CHRNG 66 23851 23399 23442 23912 24079 41503 2645 38444 22925 CHRNG 68 14 167 173 110 0 281 0 301 2 CHRNG 69 2432 2976 3249 0 721 0 74 545 3846 CHRNG 70 3780 3104 6747 290 2721 44120 208 2937 2500 LACZ 81 1503 1932 1639 2270 987 2057 136 3821 2870 LACZ 82 1948 3449 3787 3876 1081 3594 207 6396 7569 LUC 83 2341 669 2038 2 9084 0 670 588 473 LUC 84 2451 4066 3274 5228 12600 4877 109 6878 6348

MSc Thesis – N. Kasmachova; McMaster – Biochemistry and Biomedical Sciences

Table 1. Number of reads of single shRNA, presented in the control samples (p0A, p0B, p0C) and tumours (T1, T2, T3, T4, T7, T8)

Dark grey patterns represent reads for particular shRNA in the control samples that were left in the analysis after applying 885 cutoff threshold. Light grey patterns illustrate reads for particular shRNA in the tumour samples that were left after applying formula: Upper Outliers = Quartile3 + 2.5*IQR IQR = Quartile3 – Quartile1 White patterns indicate shRNA in the control and tumour samples that were found to be outliers and were excluded from the analysis. Black patters shows the shRNA sequences and corresponded genes that were analyzed.

MSc Thesis – N. Kasmachova; McMaster – Biochemistry and Biomedical Sciences

4 DISCUSSION

4.1 Role of nAChR with antiproliferative effect

By performing a shRNA dropout screen, we found that shRNA targeting multiple nAChRs were lost during breast tumourigenesis.

It has been shown previously that certain nAChR were overexpressed in breast tumour tissue (Lee et al. 2010), suggesting that the specific nAChR(s) are required for breast CSC activity. We set out to investigate the importance of individual nAChRs on breast tumourigenesis by monitoring the abundance of individual shRNA sequences after tumour development in mice using high-throughput DNA sequencing. We found that the provirus integrated into the genomic DNA shortly after lentivirus infection were not equally represented in the total tumour cell population despite the fact that the equal number of individual lentiviruses was used to infect the tumour cells at an MOI of 0.3.

Toxic response can arise when shRNA-encoding lentiviruses are introduced into the cells (Martin et al. 2011). 7 shRNA were not found in the control samples, which were collected shortly after retroviral infection (T=0). Absent sequences comprise individual shRNA targeting CHRNA3, CHRNA5, CHRNB4, CHRNG and CHRNA9. Dropout of multiple shRNA targeting CHRNA9 transcript might occurred perhaps because virus infection caused rapid deleterious effects on the tumour cell viability. Correspondingly, a number of shRNA-encoding proviruses were found to be much more abundant at the initial time point following infection. However, consistent shRNA fractional representation in T=0 control triplicates eliminates the possibility of spontaneous depletion. Together these findings led us to the conclusion that the shRNA-encoding

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lentiviruses introduced into the breast cancer cells had different ability to infect the cells; could be lost from the study, be toxic or lethal to the tumour cells; this fact explains the uneven level of pro-virus representation in the control samples.

Hierarchical clustering analysis of sequencing data revealed the phenotypic similarities among all the tumours by their provirus abundance, confirming that controls samples are distinct from tumour samples.

We anticipate that shRNA with an antiproliferative effect would be depleted during breast tumour development. CHRNA4, CHRNA5, CHRNA6, CHRNB3, CHRNB4,

CHRND were knocked-down with 2 shRNA targeting the same transcript, suggesting that these genes are the most essential for tumourigenesis. Additionally, shRNAs targeting

CHRNA1, CHRNA3, CHRNA7, CHRNA9, CHRNA10, CHRNB1, CHRNG gene transcripts were also lost from the population, however, CHRNA1 and CHRNA10 were found to be upreulated at the same time. Together these findings indicate that multiple nAChR are required for CSC activity during tumourigenesis.

Even though tumours were found to be similar to each other, a hierarchical clustering algorithm identified some tumour samples enriched variously with the introduced pro-viruses. These findings indicate that tumours with the same origin may comprise different phenotypes.

Quantitative analysis of reads identified one shRNA against luciferase control gene included in the viral pool was depleted with statistically significant difference. Even though, luciferase was used in the assay as a non-targeting gene, it was found to be suppressed in the tumours. Despite this fact, many shRNA from our library did not affect

MSc Thesis – N. Kasmachova; McMaster – Biochemistry and Biomedical Sciences

tumourigenesis and quantitative analysis of reads revealed numerous shRNA that were consistently depleted in the various tumours. Dropped shRNA corresponded to the multiple suppressed genes during tumourigenesis. In 1998 Wessler et al discovered expression of neuronal acetylcholine receptors in non-neuronal cells (Wessler et al. 1998).

In our experiment, both muscle-specific and neuronal-specific nAChR were observed to be inhibited, providing us evidence that both types are required for breast tumourigenesis.

It was discovered previously that not all shRNA targeting the same transcript have similar capacities to reduce the level of their target (Sledz et al. 2004). Therefore, depletion of only one of five shRNAs targeting the same transcript was taken to represent a requirement for the transcript-encoded protein in tumourigenesis. Our data showed depletion of shRNA targeting CHRNA7 gene at statistically significantly high frequency during tumourigenesis, suggesting involvement of the CHRNA7 gene in tumourigenesis.

The occurrence of homomeric CHRNA7 transcript in malignant breast epithelium and the effect of overexpression on MCF7 breast tumour cell propagation in vitro suggest that

CHRNA7 plays a role in this process (Lin et al. 2012). CHRNA7 induces expression of cAMP response element binding protein (CREB), through the mediation of Ca2+- dependent activation of mitogen-activated protein kinase (MAPK) signalling pathway

(Gubbins et al. 2008), PKC, c-myc, Raf-1 (Ho et al. 2011) which leads to tumour cell proliferation. Therefore, our results are consistent with previous data demonstrating that

CHRNA7 plays an important role in cancer biology.

Similar findings were observed for CHRNB4, whose transcription is regulated by the transcription factor Sox10. Recent studies revealed high-level expression of Sox10 in

MSc Thesis – N. Kasmachova; McMaster – Biochemistry and Biomedical Sciences

breast carcinomas, and in triple-negative cancers in particular (Cimino-Mathews et al.

2013). The CHRNA1 gene was observed to regulate calpain-1 (Zhang et al. 2011). The calpain family was discovered to be expressed in breast cancer tissue, affecting cell migration and adhesion. Therefore, our experiment confirmed that inhibition of CHRNA1 gene plays a significant role in breast tumour progression. Together these results indicate that the dropout of one shRNA targeting a specific transcript may be sufficient to identify genes that encode protein important for tumorigenesis.

Depletion of multiple shRNA targeting specific nAChR transcripts might provide stronger evidence for their contribution to breast tumour development. The shRNA dropout screen identified additional genes encoding nAChRs that were targeted by 2 shRNA including CHRNA3, CHRNA5, CHRNA6, CHRNB3, CHRNB4, CHRND and

CHRNG. The dropouts of multiple shRNA that target the same transcript suggest that the downregulation is unlikely due to off-target effect (Schlabach et al. 2008).

In addition, we identified a suppressed gene CNRNA9, the role of which was previously discovered in human breast epithelium (Lee et al. 2003). In our experiment one shRNA against CHNRA9 dropped with a 2 fold difference, however, 3 out of 5 shRNA targeting CHRNA9 dropped out immediately after the lentivirus infection and were not present in the viral pool. One explanation is that this event occurred because of the toxic effects of most shRNA targeting CHRNA9 gene.

Moreover, CHRNA9 transcript upregulation can be due to enhanced estrogen activity. E2 stimulation results in activation of MAPK signalling, and therefore increases

ER transcriptional activity (Importa-Brears et al. 1999). Estrogen-dependent activation

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occurs on Ser118 and Ser167 phosphorylation sites on Alpha9 protein (Kato et al. 1995); such signalling cascade had been shown to promote breast tumour cell proliferation and migration (Huderson et al. 2012). Interestingly, our dropout screen revealed significant suppression of the CHRNA10 expression, which was completely lost in the tumour cell the population. CHRNA10 is unable to form active homopentamer in nature (Elgoyhen et al. 2001). However, Lips et al findings suggest that CHRNA10 is co-expressed with

CHRNA9, and the heteropentamers form active channels (Lips et al. 2006). In 2004 Baker et al group observed an increased level of co-expressed CHRNA9 and CHRNA10 expression, thus confirming our findings (Baker et al. 2004).

All α subunits with the exception of α5 assemble into heteropentameric nAChR with β subunits. Moreover, subunit combinations can also be composed of single and multiple α and β subunits, making the functional receptors more complex (Brady et al.

2012). The heterogeneity of nAChR is illustrated in Figure 1. This provided us evidence that not only individual homomeric nAChR are involved in tumour cell maintenance but perhaps that there is a number of nAChR heteropentamer combinations that affect tumour cell viability and proliferation and can be valuable molecular targets.

4.2 Role of nAChR co-regulation in inhibition of breast tumorigenesis

CHRNA3, CHRNA5, CHRNB4 genes are located on the same chromosome 15q24, and found to be expressed in the same cell types including the epithelium and endothelium cells found in small-cell lung carcinoma (SCLC) (Wang et al. 2001).

Moreover, their transcription is believed to be linked to each other (Boulter et al. 1990;

MSc Thesis – N. Kasmachova; McMaster – Biochemistry and Biomedical Sciences

Scofield et al. 2008). The gene encoding human α3 nAChR subunit is co-expressed with

β4; however, α5 subunit plays a accessory role in functional receptor assembly (Conroy et al. 1992; Conroy et al. 1995; Vernallis et al. 1993). The common transcription machinery of these genes explains co-expression of the CHRNA3, CHRNA5, CHRNB4 genes.

Promoters of these genes are composed of binding sites for the transcriptional factors SP1 and SP3, whose overexpression induces tumourigenesis (Bigger et al. 1997; Yang et al.

1995). Moreover, CHRNA3, CHRNA5, CHRNB4 genes promoters are activated and regulated by Sox10 and SCIP/Tst-1/Oct6 factors (Fyodorov et al. 1996; Liu et al. 1999), whose high-level expression was reported in malignant breast tissues (Lee et al. 2010).

Therefore, the depletion of the transcripts of these genes is likely to reflect their requirement for tumorigenesis.

shRNA targeting muscle-specific nAChR δ and γ transcripts were observed to be lost during tumourigenesis. The muscle-specific nAChR comprises four subunits with the stoichiometry α2βγδ (Hammond et al. 2001). The loss of 2 shRNA targeting the γ subunit in our screen suggested that the α2βγδ heteropentamer plays a role in breast cancer development.

4.3 Role of nAChR that promote breast cancer stem cell proliferation

Dropout screen is aimed to identify genes essential for tumour cell death; however, our screen revealed genes acting in opposite fashion. Interestingly, despite the gene suppression, we also assessed gain-of-function of these genes during tumourigenesis.

MSc Thesis – N. Kasmachova; McMaster – Biochemistry and Biomedical Sciences

Even though, high-throughput screen discovered inhibition of CHRNA1 gene during tumour development, we also observed recovered shRNA that promoted cell survival and proliferation during tumourigenesis.

Quantitative analysis of readings for shRNA upregulating CHRNA10 gene revealed high correlation of the DNA sequence with other sequences of the lentiviral library.

Methods used for the analysis of the dropout screen data were employing parameters that were supposed to perform classifications correctly based on the 83.3% similarity.

However, since only one sequence out of these 4 was observed to be significantly changed and it was conflicting with the other result obtained for that receptor, we hypothesize that the obtained level of similarity was significant enough to introduce a technical artifact to the outcome.

Taken together, these observations suggest that dual regulation of gene expression discovered in our screen less likely occurred due to biological reason but is an experimental artifact.

4.4 Screen limitations

RNA dropout screen can be a useful tool in discover cancer biology. However, this tool has several limitations and disadvantages. Dropout screens rely on shRNA depletion from the cell population during the finite periods compared to the control samples (Kittler et al. 2005). However, properties of new generation shRNA sequences such as cell toxicity and specificity have not been fully described. (Ngo et al. 2006; Schlabach et al.

2008; Kim et al. 2010). Another limitation of this approach is the PCR amplification step,

MSc Thesis – N. Kasmachova; McMaster – Biochemistry and Biomedical Sciences

which is required to construct the DNA library for sequencing. The PCR reactions are usually very sensitive to PCR reagents concentrations. Additionally, unique combinations of Illumina primer sequences require PCR components optimization for each sample. The

PCR reactions are also limited to the size of amplified DNA, which affects PCR amplification accuracy.

One of the major obstacles of high-throughput screens is the off-target effects that can provide false results. Off-target effects can occur when the target shRNA-sequences are complemented (Johnson et al. 2004). However, this issue can be overcome by careful choice of the shRNA library. The second problem that can arise is that shRNA are able to activate interferon-regulatory factor 3 (IRF3). Interferon response caused by viral infection triggers the non-specific side effects, such as cell death, that are considered false positive (Sledz et al. 2004).

High-throughput screens are a very powerful tool in investigating cancer biology; however, they require special bioinformatics analysis skills to perform accurate data interpretation. Even though Illumina sequencing platforms fully process the data, obtained results require further analysis. Therefore, well-established bioinformatics approaches are required for interpreting data properly.

It would be beneficial to validate the obtained results by monitoring the effects of shRNA-mediated knockdown in different models. Thus, the in vitro model will confirm the true positive hits identified in in vivo RNAi-based screen.

Despite the fact that there are some disadvantages and limitations of the screens, which are based on the shRNA-mediated knockdown, they remain widely used. However,

MSc Thesis – N. Kasmachova; McMaster – Biochemistry and Biomedical Sciences

recently discovered CRISPR/Cas system overcomes many limitations and is designed to mediate genome alterations precisely by knocking out the genes of interest. This highly specific approach minimizes possibility of the off-target effects.

4.5 Conclusions

We investigated the role of nAChR in breast tumourigenesis by performing a shRNA dropout screen in vivo. Our study demonstrates the downregulation effect of multiple nAChR on tumour formation and its growth. We pursued the hypothesis that not only homopentameric receptors involved in tumourigenesis but also heteropentameric nAChR may be required for tumour cell viability and proliferation. Considering all observations, it is possible that nAChR subunits assembling regulate tumour formation and its development in such a fashion that the nAChR co-regulate each other and promote different pathological processes. Further studies are necessary to validate the mechanism of action of co-expressed nAChR and their role in breast TIC activity and maintenance.

In summary, high-throughput screen strategy to identify specific nAChR(s) seems to be a promising and beneficial tool for further drug discovery.

MSc Thesis – N. Kasmachova; McMaster – Biochemistry and Biomedical Sciences

5. APPENDIX

Supplementary Table 1. shRNA plasmids sequences

Gene TRC Clone Name Target Seq TRC Clone ID Symbol CHRNA6 NM_004198.2-341s1c1 CCAATGGAATATGATGGCATT TRCN0000060418 CHRNA6 NM_004198.2-904s1c1 GCTGGTCATCACAGAAACCAT TRCN0000060419 CHRNA6 NM_004198.2-1259s1c1 GCCACAAGCAAGAGAAGATTA TRCN0000060420 CHRNA6 NM_004198.2-751s1c1 CATTAGAAGATTGCCGATGTT TRCN0000060421 CHRNA6 NM_004198.2-1230s1c1 GCTTCCATTGTCACAAATCAA TRCN0000060422 CHRNA10 NM_020402.2-411s1c1 GCCAGACATCGTACTCTATAA TRCN0000060773 CHRNA10 NM_020402.2-1023s1c1 CCTTATCATGAACCTGCATTA TRCN0000060774 CHRNA10 NM_020402.2-298s1c1 CTGACCCTGTATCTGTGGATA TRCN0000060775 CHRNA10 NM_020402.2-969s1c1 GAAGTACTACATGGCCACTAT TRCN0000060776 CHRNA10 NM_020402.2-174s1c1 CCGTGACCTCTTTGCCAACTA TRCN0000060777 CHRNA1 NM_000079.1-785s1c1 CCTTCTTAACTGGCCTGGTAT TRCN0000060988 CHRNA1 NM_000079.1-368s1c1 GCCCAGACCTTGTTCTCTATA TRCN0000060989 CHRNA1 NM_000079.1-1059s1c1 CGACACTATCCCAAATATCAT TRCN0000060990 CHRNA1 NM_000079.1-1315s1c1 GCAATGGTGATGGACCACATA TRCN0000060991 CHRNA1 NM_000079.1-259s1c1 ACAACCAATGTGCGTCTGAAA TRCN0000060992 CHRNA2 NM_000742.1-1130s1c1 CGACCAGCAGAACTGCAAGAT TRCN0000061043 CHRNA2 NM_000742.1-2122s1c1 CGTTCCTAGCTGGAATGATCT TRCN0000061044 CHRNA2 NM_000742.1-1962s1c1 CTGGAAGGTGTGCACTACATT TRCN0000061045 CHRNA2 NM_000742.1-785s1c1 GCCCAACACTTCAGACGTGGT TRCN0000061046 CHRNA2 NM_000742.1-938s1c1 CAACATCACATCTCTCAGGGT TRCN0000061047 CHRNA3 NM_000743.2-307s1c1 CGGCTGTTTGAAGATTACAAT TRCN0000061068 CHRNA3 NM_000743.2-873s1c1 CGACATCACATACTCGCTGTA TRCN0000061069 CHRNA3 NM_000743.2-1601s1c1 CCATGGTGATTGATCGTATTT TRCN0000061070 CHRNA3 NM_000743.2-295s1c1 CGTCTATTTGAGCGGCTGTTT TRCN0000061071 CHRNA3 NM_000743.2-1309s1c1 GCCGAGCTCTCAAATCTGAAT TRCN0000061072 CHRNA4 NM_000744.2-1341s1c1 GCTCATCGAGTCCATGCATAA TRCN0000061103 CHRNA4 NM_000744.2-357s1c1 GAAACTCTTCTCCGGTTACAA TRCN0000061104 CHRNA4 NM_000744.2-921s1c1 CCCGGACATCACCTATGCCTT TRCN0000061105 CHRNA4 NM_000744.2-1814s1c1 CGTGCAAATGCACATGCAAGA TRCN0000061106 CHRNA4 NM_000744.2-472s1c1 CAGATGATGACCACGAACGTA TRCN0000061107 CHRNA5 NM_000745.2-1350s1c1 GCTCGATTCTATTCGCTACAT TRCN0000061133 CHRNA5 NM_000745.2-496s1c1 CCTGATGACTATGGTGGAATA TRCN0000061134 CHRNA5 NM_000745.2-702s1c1 CCTTCAGAACTGTTCCATGAA TRCN0000061135 CHRNA5 NM_000745.2-973s1c1 GTCTTCTATCTTCCTTCAAAT TRCN0000061136 CHRNA5 NM_000745.2-1434s1c1 CCAGGTTCTTGATCGGATGTT TRCN0000061137 CHRNA7 NM_000746.2-322s1c1 GATCACTATTTACAGTGGAAT TRCN0000061169 CHRNA7 NM_000746.2-141s1c1 CGAGTTCCAGAGGAAGCTTTA TRCN0000061170 CHRNA7 NM_000746.2-396s1c1 GAAACCAGACATTCTTCTCTA TRCN0000061171 CHRNA7 NM_000746.2-216s1c1 GCAACCACTCACCGTCTACTT TRCN0000061172 CHRNB1 NM_000747.2-943s1c1 CCTCACTATCAGTACCCATTA TRCN0000061198 58

MSc Thesis – N. Kasmachova; McMaster – Biochemistry and Biomedical Sciences

CHRNB1 NM_000747.2-527s1c1 GCAGAATTGCACTATGGTGTT TRCN0000061199 CHRNB1 NM_000747.2-1349s1c1 CGTCTCCTCTATCAGCTACAT TRCN0000061200 CHRNB1 NM_000747.2-659s1c1 CCAGTGGGAGATTATCCACAA TRCN0000061201 CHRNB1 NM_000747.2-257s1c1 GATGAGCACAAAGGTGTACTT TRCN0000061202 CHRNB2 NM_000748.1-927s1c1 CGTGGACATCACGTATGACTT TRCN0000061223 CHRNB2 NM_000748.1-723s1c1 CGCATGCAAGATTGAAGTAAA TRCN0000061224 CHRNB2 NM_000748.1-556s1c1 GAGTTTGACAACATGAAGAAA TRCN0000061225 CHRNB2 NM_000748.1-1647s1c1 CTTCCTCTGGATCTTTGTCTT TRCN0000061226 CHRNB3 NM_000749.2-824s1c1 GCCTTTATTCTATACCCTCTT TRCN0000061258 CHRNB3 NM_000749.2-1473s1c1 GCTTTGAAGATGTGGCTACAT TRCN0000061259 CHRNB3 NM_000749.2-261s1c1 CGCCCTGTATTACATTCTAAT TRCN0000061260 CHRNB3 NM_000749.2-1314s1c1 GCTGATTCCATTAGATACATT TRCN0000061261 CHRNB3 NM_000749.2-784s1c1 CCTATCCCTTTATCACGTATT TRCN0000061262 CHRNB4 NM_000750.1-783s1c1 CGTGACTTACGACTTCATCAT TRCN0000061308 CHRNB4 NM_000750.1-456s1c1 GCCTGACATCGTGCTTTACAA TRCN0000061310 CHRNB4 NM_000750.1-1259s1c1 CCAACTTCTATGGGAACTCCA TRCN0000061311 CHRNB4 NM_000750.1-1428s1c1 GCACATGAAGAATGACGATGA TRCN0000061312 CHRND NM_000751.1-502s1c1 CCTCAAGTTCAGTTCCCTCAA TRCN0000061348 CHRND NM_000751.1-97s1c1 CCTGTTTCAAGAGAAGGGCTA TRCN0000061349 CHRND NM_000751.1-1192s1c1 CAGTGACCTCATGTTCGAGAA TRCN0000061350 CHRND NM_000751.1-587s1c1 GTGGAGTGGATCATCATTGAT TRCN0000061351 CHRND NM_000751.1-175s1c1 CCTCACACTCTCCAACCTCAT TRCN0000061352 CHRNE NM_000080.2-229s1c1 GCGTCTGGATTGGAATCGATT TRCN0000061389 CHRNE NM_000080.2-566s1c1 CGGCAAGACCATCAACAAGAT TRCN0000061390 CHRNE NM_000080.2-251s1c1 GCAGGATTACCGACTCAACTA TRCN0000061391 CHRNE NM_000080.2-1440s1c1 GCCTACTTCAACCGAGTGCCT TRCN0000061392 CHRNG NM_005199.3-1368s1c1 CCAGCAGAGTCACTTTGACAA TRCN0000061428 CHRNG NM_005199.3-456s1c1 CTGCTCTATCTCAGTCACCTA TRCN0000061429 CHRNG NM_005199.3-955s1c1 ACCATCCTCATTGTCGTGAAT TRCN0000061430 CHRNG NM_005199.3-519s1c1 CCAGACTTACAGCACCAATGA TRCN0000061431 CHRNG NM_005199.3-171s1c1 CCTGAAGCTAACCCTCACCAA TRCN0000061432 CHRNA9 NM_017581.1-907s1c1 CCTGATAGGTAAATACTACAT TRCN0000063393 CHRNA9 NM_017581.1-1258s1c1 CGAGAGTTACTGTGCACAGTA TRCN0000063394 CHRNA9 NM_017581.1-204s1c1 CGCTCTCTCAGATTAAGGATA TRCN0000063395 CHRNA9 NM_017581.1-848s1c1 GCCATGACTGTATTTCAGCTA TRCN0000063396 CHRNA9 NM_017581.1-1355s1c1 GAATGGAAGAAGGTGGCGAAA TRCN0000063397 LACZ lacZ_1935s1c1 CCGTCATAGCGATAACGAGTT TRCN0000072235 LACZ lacZ_29s1c1 TCGTATTACAACGTCGTGACT TRCN0000072240 LUC promegaLuc_221s1c1 CAAATCACAGAATCGTCGTAT TRCN0000072246 LUC promegaLuc_158s1c1 ACGCTGAGTACTTCGAAATGT TRCN0000072256

Table illustrates TRC library content, that comprises individual shRNA name (Ref.: TRC Clone Name), its nomenclature (Ref.: Clone ID) and corresponded unique sequences (Ref.: Target Sequence). All clones are grouped by the particular target (Ref.: Gene Symbol).

MSc Thesis – N. Kasmachova; McMaster – Biochemistry and Biomedical Sciences

Supplementary Table 2. Titers of shRNA-encoding lentiviruses

TRC Titer TRC Titer TRC Titer Clone ID (CFU/ml) Clone ID (CFU/ml) Clone ID (CFU/ml) TRCN0000060418 1.40E+07 TRCN0000061200 4.00E+06 TRCN0000061133 1.10E+07 TRCN0000060419 9.00E+06 TRCN0000061201 2.80E+07 TRCN0000061134 1.00E+07 TRCN0000060420 1.00E+07 TRCN0000061202 1.50E+07 TRCN0000061135 1.20E+07 TRCN0000060421 2.00E+05 TRCN0000061223 3.70E+07 TRCN0000061136 4.00E+06 TRCN0000060422 1.20E+07 TRCN0000061224 1.20E+07 TRCN0000061169 2.20E+07 TRCN0000060773 3.50E+06 TRCN0000061225 3.30E+07 TRCN0000061170 1.70E+05 TRCN0000060774 2.20E+07 TRCN0000061226 1.30E+07 TRCN0000061171 1.60E+07 TRCN0000060775 1.50E+06 TRCN0000061258 1.20E+07 TRCN0000061172 1.00E+07 TRCN0000060776 1.30E+07 TRCN0000061259 6.00E+06 TRCN0000061198 3.00E+05 TRCN0000060777 7.00E+06 TRCN0000061260 1.30E+07 TRCN0000061199 2.30E+07 TRCN0000060988 1.20E+06 TRCN0000061261 1.30E+07 TRCN0000063393 1.20E+07 TRCN0000060989 2.80E+06 TRCN0000061262 1.00E+07 TRCN0000063394 5.00E+07 TRCN0000060990 1.10E+06 TRCN0000061308 1.40E+07 TRCN0000063395 2.00E+07 TRCN0000060991 5.50E+06 TRCN0000061310 6.00E+06 TRCN0000063396 6.00E+04 TRCN0000060992 1.30E+07 TRCN0000061311 5.00E+06 TRCN0000063397 5.00E+07 TRCN0000061043 8.00E+06 TRCN0000061312 2.10E+07 TRCN0000072235 1.50E+07 TRCN0000061044 1.80E+07 TRCN0000061348 4.00E+06 TRCN0000072240 1.00E+07 TRCN0000061045 6.00E+06 TRCN0000061349 1.30E+07 TRCN0000072246 1.00E+07 TRCN0000061046 1.00E+07 TRCN0000061350 1.60E+07 TRCN0000072256 5.50E+06 TRCN0000061047 1.70E+07 TRCN0000061351 5.60E+06 TRCN0000061068 8.00E+06 TRCN0000061352 2.70E+07 TRCN0000061069 1.00E+07 TRCN0000061389 3.40E+07 TRCN0000061070 4.00E+06 TRCN0000061390 2.60E+07 TRCN0000061071 2.00E+06 TRCN0000061391 4.90E+07 TRCN0000061072 6.00E+05 TRCN0000061392 8.50E+07 TRCN0000061103 5.00E+06 TRCN0000061428 8.00E+06 TRCN0000061104 1.10E+07 TRCN0000061429 5.00E+06 TRCN0000061105 6.50E+06 TRCN0000061430 2.90E+07 TRCN0000061106 3.00E+06 TRCN0000061431 2.70E+07 TRCN0000061107 4.00E+06 TRCN0000061432 1.10E+07 TRCN0000061133 1.10E+07 TRCN0000063393 1.20E+07

Table represents the viral titers calculated with individual colony-forming units count from viral dilution. TRC Clone ID – name of the individual shRNA-encoding lentivirus.

MSc Thesis – N. Kasmachova; McMaster – Biochemistry and Biomedical Sciences

Supplementary Table 3. Reverse and forward Illumina fusion primer templates

AAACACCGG

TGGATGAATACTGCCATTTG

TTGTGGATGAATACTGCCATTTG

CACTCTTTCCCTACACGACGCTCTTCCGATCTGGCTTTATATATCTTGTGGAAAGGACGAAACACCGG

AGTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTCCTTTTCTTTTAAAATTGTGGATGAATACTGCCATTTG

AATGATACGGCGACCACCGAGATCTACACCGTGATACACTCTTTCCCTACACGACGCTCTTCCGATCTGGCTTTATATATCTTGTGGAAAGGACGAAACACCGG AATGATACGGCGACCACCGAGATCTACACACATCGACACTCTTTCCCTACACGACGCTCTTCCGATCTGGCTTTATATATCTTGTGGAAAGGACG AATGATACGGCGACCACCGAGATCTACACGCCTAAACACTCTTTCCCTACACGACGCTCTTCCGATCTGGCTTTATATATCTTGTGGAAAGGACGAAACACCGG AATGATACGGCGACCACCGAGATCTACACTGGTCAACACTCTTTCCCTACACGACGCTCTTCCGATCTGGCTTTATATATCTTGTGGAAAGGACGAAACACCGG AATGATACGGCGACCACCGAGATCTACACCACTGTA AATGATACGGCGACCACCGAGATCTACACATTGGCACACTCTTTCCCTACACGACGCTCTTCCGATCTGGCTTTATATATCTTGTGGAAAGGACGAAACACCGG CAAGCAGAAGACGGCATACGAGATGATCTGGTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTCCTTTTCTTTTAAAATTG CAAGCAGAAGACGGCATACGAGATTCAAGTGTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTCCTTTTCTTTTAAAATTGTGGATGAATACTGCCATTTG CAAGCAGAAGACGGCATACGAGATCTGATCGTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTCCTTTTCTTTTAAAATTGTGGATGAATACTGCCATTTG CAAGCAGAAGACGGCATACGAGATAAGCT CAAGCAGAAGACGGCATACGAGATGTAGCCGTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTCCTTTTCTTTTAAAATTGTGGATGAATACTGCCATTTG CAAGCAGAAGACGGCATACGAGATTACAAGGTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTCCTTTTCTTTTAAAA

1 2 3 4 5 6 7 8 9

10 11 12

------

- - -

P5 P5 P5 P5 P5 P5 P7 P7 P7

P7 P7 P7

MSc Thesis – N. Kasmachova; McMaster – Biochemistry and Biomedical Sciences

Table shows the sequences of reverse and forward Illumina primers. P7 (7-12) – reverse primer sequences; P5 (1-6) – forward primer sequences.

MSc Thesis – N. Kasmachova; McMaster – Biochemistry and Biomedical Sciences

Supplementary Table 4. shRNA sequences found to be not presented in the control and in the tumour samples

Gene Symbol TRC Clone ID CHRNA3 TRCN0000061071 CHRNA5 TRCN0000061136 CHRNB4 TRCN0000061311 CHRNG TRCN0000061429 CHRNA9 TRCN0000063393 CHRNA9 TRCN0000063394 CHRNA9 TRCN0000063396

Table illustrates a shRNA nomenclature name (Ref.: TRC Clone ID) and corresponded target receptors (Ref.: Gene Symbol) that had not been identified in the control and tumour populations.

Supplementary Table 5. Pair-wise comparison performed for 79 sequences

CHRNA10 (sequence 5) GCCAGACATCGTACTCTATAACTCGAGTTATAGAGTACGATGTCTGGC CHRNB4 (sequence 54) GCCTGACATCGTGCTTTACAACTCGAGTTGTAAAGCACGATGTCAGGC

CHRNB2 (sequence 44) CGTGGACATCACGTATGACTTCTCGAGAAGTCATACGTGATGTCCACG CHRNA4 (sequence 27) CCCGGACATCACCTATGCCTTCTCGAGAAGGCATAGGTGATGTCCGGG

Tables reveal overlapping between sequence 54 and sequence 5, sequence 44 and sequence 27 (differences are marked in Bold).

MSc Thesis – N. Kasmachova; McMaster – Biochemistry and Biomedical Sciences

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