Elucidating the Gene Regulatory Network of the Sonic Hedgehog Subgroup of Medulloblastoma

Shiao Yuan Huang

A thesis submitted in conformity with the requirements for the degree of Master of Science Department of Molecular Genetics University of Toronto

Elucidating the Gene Regulatory Network of the Sonic Hedgehog Subgroup of Medulloblastoma

Shiao Yuan Huang

Master of Science

Department of Molecular Genetics University of Toronto

2019 Abstract

Sonic Hedgehog subgroup medulloblastoma (SHH MB) is classified by the molecular signature of overactive Hedgehog (Hh) signaling pathway. GLI2, the major transcriptional activator of Hh pathway, is often amplified or overexpressed in SHH MB.

My project examines two molecular components of Hh signaling pathway in SHH MB. The first part focuses on SUFU, a key regulator of GLI2. SUFU mutations are commonly found in SHH

MB patients, however the molecular actions of SUFU are poorly characterized. Through

CRISPR/Cas9-mediated knockout of SUFU, I unveiled that SUFU is required for stabilization of

GLI2 in human SHH MB. The second part focuses on a candidate GLI2 partner, OLIG2. My results suggested that OLIG2 has a tumor-promoting role in SHH MB, and it possesses the ability to transcriptionally repress as well as activate GLI2 target genes.

Together, my study allowed the elucidation of the molecular actions of human SUFU, and unveiled a novel player, OLIG2, in SHH MB.

Acknowledgments

I want to address my deepest gratitude to my project supervisor, Dr. Chi-chung Hui, for the opportunity to take on this project which is greatly tailored to my interest. I also thank him for the tremendous support and mentorship he has provided throughout the pursuit of my Master’s degree. Through his supervision, my knowledge, research ability, as well as intrapersonal skills, have highly advanced during these few years.

I also want to thank my supervisory committee, Dr. Sean Egan and Dr. Jason Moffat, for the countless valuable insights they have provided me during the course of my degree. Their genuine advice has helped me steer my project in the right direction and allow me to gain focus on the proper and most important work.

Thank you to all the members in the Hui lab, including the past members. I am very lucky to have the opportunity to work with all of you. I want to express my appreciation especially to the following members: Dr. Wenchi Yin, who made the amazing discoveries which led me to my project and provided a lot of support and expertise during my study; Mary Zhang, the mother figure in the lab whom I turn to with all the questions on almost a daily basis, but she guided me through all of them with patience and kindness.

Last but not least, I want to thank my family. Thank you for always being there for me when I needed support.

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Table of Contents

Acknowledgments ...... iii

Table of Contents ...... iv

List of Figures ...... vii

List of Abbreviations...... ix

Chapter 1 Introduction ...... 1

1.1 Overview of medulloblastoma ...... 1

1.2 SHH subgroup of MB ...... 3

1.2.1 Overview ...... 3

1.2.2 Cerebellum development and Hh signaling pathway ...... 3

1.2.3 Hedgehog signaling pathway...... 4

1.2.4 Mutations in SHH MB ...... 7

1.2.5 In vivo mouse models of SHH MB ...... 8

1.2.6 In vitro models of SHH MB ...... 10

1.3 Cerebellum development and basic helix-loop-helix factors ...... 10

1.4 Thesis rationale and outline ...... 12

Chapter 2 Material and Methods ...... 14

2.1 Experimental procedures ...... 14

2.1.1 Cell culture ...... 14

2.1.2 siRNA knockdown ...... 14

2.1.3 CRISPR/Cas9-mediated knockout ...... 14

2.1.4 Western blot ...... 15

2.1.5 RNA extraction and reverse transcription ...... 15

2.1.6 Quantitative real-time PCR ...... 15

2.1.7 MTS assay ...... 16

2.1.8 ChIP-seq ...... 16 iv

2.1.9 Luciferase reporter assay ...... 16

2.1.10 Site-directed mutagenesis (SDM) ...... 17

2.2 Bioinformatic analyses ...... 17

2.2.1 Kaplan-Meier survival analysis ...... 17

2.2.2 ChIP-seq analysis ...... 17

2.2.3 RNA-seq analysis...... 18

2.2.4 Functional pathway enrichment analysis ...... 18

Chapter 3 Elucidating the mechanism of SUFU in the context of human SHH MB...... 19

3.1 Introduction ...... 19

3.2 Results ...... 21

3.2.1 SUFU is successfully deleted by CRISPR/Cas9 system ...... 21

3.2.2 Generation of monoclonal SUFU KO populations ...... 22

3.2.3 SUFU is required to maintain high GLI2 expression ...... 24

3.2.4 Deletion of SUFU did not result in significant cellular phenotype ...... 26

3.3 Discussion ...... 27

3.4 Future directions ...... 29

3.4.1 Transcriptomic analysis of SUFU KO SHH MB cell lines ...... 29

3.4.2 Utility of SUFU KO SHH MB cell lines...... 29

Chapter 4 Characterizing the role of OLIG2 in SHH MB ...... 31

4.1 Introduction ...... 31

4.2 Results ...... 34

4.2.1 High expression of OLIG TFs correlates with poor patient prognosis ...... 34

4.2.2 OLIG2 ChIP-seq in GFAP-Cre-Sufuf/f;Spopf/f MB revealed OLIG2 binding to over 70% of GLI2 bound regions ...... 36

4.2.3 OLIG2 is a potent repressor of GLI2 and ATOH1-dependent activation of Ptch1 and Cxcr4...... 39

4.2.4 Transcriptional activity of OLIG2 is dependent on its bHLH domain ...... 42 v

4.2.5 OLIG3 shares similar transcriptional activity as OLIG2, while OLIG1 has a different transcriptional mechanism ...... 44

4.2.6 OLIG2 knockdown resulted in significantly reduced cell metabolic activity ...... 45

4.3 Discussion ...... 47

4.3.1 OLIG2 is a potential therapeutic target in SHH MB ...... 47

4.3.2 OLIG2 binds to large number of GLI2 binding regions in SHH MB ...... 47

4.3.3 Transcriptional repressor and activator functions of OLIG2 ...... 48

4.3.4 OLIG2 and OLIG3 have similar transcriptional roles in SHH MB ...... 50

4.4 Future directions ...... 51

4.4.1 Decipher OLIG2’s tumor-promoting role in SHH MB ...... 51

4.4.2 Unravel the transcriptional regulatory functions of OLIG2 ...... 51

4.4.3 Study overlapping roles between OLIG TFs ...... 52

Chapter 5 Conclusion ...... 54

References ...... 55

List of Figures

Figure 1-1. Clinical and genomic features of the molecular subgroups of medulloblastoma...... 2

Figure 1-2. Schematic of cerebellum development under normal and excessive SHH signaling. ..4

Figure 1-3. Schematic of mammalian Hh pathway...... 5

Figure 3-1. SUFU exerts dual function in SHH MB...... 20

Figure 3-2. SUFU is successfully targeted by CRISPR/Cas9-mediated gene editing...... 22

Figure 3-3. Screenshots of ICE analysis revealing genotype of Vandy-MB-11 SUFU CRISPR clones...... 23

Figure 3-4. Single clone isolation of Vandy-MB-11 and DAOY SUFU KO clones...... 25

Figure 3-5. MTS assay revealed no significant cell phenotype upon SUFU deletion...... 26

Figure 4-1. Previous studies done by Dr. Wenchi Yin discovered bHLH factors as promising candidate partners of GLI2 in SHH MB...... 33

Figure 4-2. Kaplan-Meier survival analysis of human SHH MB patients grouped according to OLIG TFs expression status...... 35

Figure 4-3. OLIG2 ChIP-seq in GFAP-Cre;Sufuf/f;Spopf/f revealed that OLIG2 is binding to a large number of sites in tumor cerebellum...... 36

Figure 4-4. Comparative analysis of GLI2 and OLIG2 ChIP-seq in GFAP-Cre;Sufuf/f;Spopf/f revealed that OLIG2 is binding to > 70% of GLI2 binding sites...... 39

Figure 4-5. OLIG2 is a potent repressor of GLI2-dependent activation of Ptch1 and Cxcr4 reporters...... 42

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Figure 4-6. Transcriptional regulation by OLIG2 is dependent on its bHLH domain...... 44

Figure 4-8. OLIG2 siRNA treatment resulted in significantly decreased cell metabolic activity. 46

Figure 4-9. Olig3 is associated with an MB-specific super-enhancer region...... 53

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List of Abbreviations bHLH Basic helix-loop-helix

ChIP Chromatin immunoprecipitation

EGL External granule layer

GNP Granule neuron precursor gRNA Guide RNA

GSC Glioblastoma stem cell

Hh Hedgehog

IGL Internal granule layer

IP-MS Immunoprecipitation coupled by mass spectrometry

KD Knockdown

KO Knockout

LOH Loss of heterozygosity

MB Medulloblastoma

NSC Neural stem cell qRT-PCR Quantitative real-time PCR

RNAi RNA interference

SD Standard deviation

SHH Sonic Hedgehog

TF Transcription factor

VGCC Voltage gated calcium channel

VZ Ventricular zone

WT Wildtype

Chapter 1 Introduction

1.1 Overview of medulloblastoma

Medulloblastoma (MB) represents the most common brain malignancy in childhood. Tumor originates in the cerebellar vermis, and later spreads to the ventricles through cerebrospinal fluid (Roussel & Hatten 2011). Current treatment of MB includes surgical resection, craniospinal irradiation, and chemotherapy. Although MB patients have a 5-year overall survival rate of approximately 60%, the surviving patients often suffer from long-term adverse side effects from intense irradiation including cognitive impairment, retardation, and psychiatric disorders (Polkinghorn & Tarbell 2007). Thus, to minimize the undesired consequences, researchers are now in search of better treatment options for MB.

Years of studying the underlying biology of the disease have led to the discovery that MB has a heterogeneous nature, and current consensus is that MB can be classified into four major molecular subgroups, namely WNT, SHH, Group 3 and Group 4. The four subgroups each have different mutational profiles, transcriptomic signatures, as well as epigenetic signatures (Figure 1-1). Tumor histology and clinical outcomes of each subgroup can also vary greatly. Furthermore, studies have revealed that the subgroups of MB have different cell-of-origin (Gibson et al. 2010). Together, these observations suggest that although termed MB as a whole, the molecular events which cause the disease are distinct and thus may require different therapeutic approaches. My thesis project focuses on exploiting the Sonic Hedgehog (SHH) subgroup of MB.

Figure 1-1. Clinical and genomic features of the molecular subgroups of medulloblastoma. CGNPs, cerebellar granule neuron precursors; EGL, external granule cell layer; LCA, large cell and anaplastic; MBEN, medulloblastoma with extensive nodularity; SVZ, subventricular zone. (Northcott et al. 2012)

1.2 SHH subgroup of MB

1.2.1 Overview

SHH subgroup is the second largest subgroup of MB, accounting for around 30% of the overall MB cases. Notably, SHH MB has the highest incidence of infant patients, which represent the most difficult to treat age group as their developing brain is especially vulnerable to irradiation (Kool et al. 2012). A recent study by Cavalli et al. (2017) discovered that SHH MB can be further stratified into 4 subtypes, SHH ⍺, SHH β, SHH ", and SHH #. Each subtype has variable genomic signatures and clinical outcome, with SHH ⍺ and SHH β having higher metastases rate and worse prognosis (Cavalli et al. 2017). Overall, SHH MB has a unified signature of overactive hedgehog (Hh) signaling pathway.

1.2.2 Cerebellum development and Hh signaling pathway

Development of cerebellum, the tissue of origin of MB, is tightly controlled by Hh signaling in both humans and mice. Despite taking up only 10% of total brain volume, the cerebellum is packed with numerous neurons, with granule cell being the most abundant. Shortly after birth, around P5-P8 in mice, granule neuron precursors (GNPs) undergo massive proliferation in the external granule layer (EGL) upon receiving SHH signal from Purkinje cells (Figure 1-2). GNPs then start to mature and migrate inwards through the molecular layer to form the internal granule layer (IGL), where they complete their final differentiation into granule cells (around P20 in mice). Overactive Hh signaling results in uncontrollable proliferation of GNPs in the EGL and ultimately MB.

Figure 1-2. Schematic of cerebellum development under normal and excessive SHH signaling. EGL, external granule layer; IGL, internal granule layer; PCL, Purkinje cell layer. (Polkinghorn & Tarbell 2007)

1.2.3 Hedgehog signaling pathway

Hedgehog (Hh) signaling pathway is an essential developmental pathway highly conserved in mammals. It has critical roles in embryonic patterning and adult tissue homeostasis. In mammals, there are three Hh ligands, including Sonic Hedgehog (SHH), Indian Hedgehog (IHH), and Desert Hedgehog (DHH). SHH is most widely expressed and has well-characterized roles in neural cell specification and limb patterning (Carballo et al. 2018).

Figure 1-3. Schematic of mammalian Hh pathway.

The pathway is activated when the Hh ligand binds to the membrane receptor Patched (PTCH1), releasing the inhibition on the signal transducer Smoothened (SMO) by PTCH1. SMO then goes on to initiate downstream signaling cascade, resulting in the activation of GLI transcription factors (TFs). There are three GLI TFs in mammals, GLI1, GLI2, and GLI3, and together they mediate the transcriptional output of the Hh pathway. GLI proteins belong to the family of C2H2 zinc finger TFs, and they recognize the DNA binding motif 5′-GACCACCCA-3′ (Hui & Angers 2011). GLI2 and GLI3 contain a C-terminal activation domain and an N-terminal repressor domain, therefore can function as activator and repressor when processed accordingly. Nonetheless, it has been established that GLI2 mainly functions as an activator and GLI3 as a repressor, with the reciprocal roles poorly characterized (Hui & Angers 2011). On the other hand, GLI1 contains only the activation domain, and functions downstream of GLI2 as part of positive feedback loop of the pathway.

SUFU plays a role as the key intracellular regulator of the pathway. When the Hh pathway is off, SUFU acts to sequester GLI proteins in the cytoplasm, and thus prevents them from translocating in the nucleus to activate transcription. Additionally, SUFU promotes the truncation of GLI TFs into their repressor forms. Upon Hh ligand binding and the activation of SMO, SUFU/GLI complexes dissociate, allowing the modification of GLI TFs into their activator forms to turn on transcription of Hh target genes.

Besides SUFU, other factors also contribute to regulation of GLI TFs. KIF7, like SUFU, is an evolutionarily conserved regulator of GLI proteins (Cheung et al. 2009). Studies have suggested that KIF7 could promote Hh pathway activity through mediating the dissociation of SUFU-GLI2 complex, but it could also repress GLI2 transcriptional activity in the absence of SUFU (Z. J. Li et al. 2012). On the other hand, β-TrCP, a component in the SCF-type E3 ubiquitin protein ligase complex, has been demonstrated to promote proteolysis of GLI2 and GLI3 into their repressor forms, and degradation of GLI2 (Hui & Angers 2011; Bhatia et al. 2006). Similarly, SPOP (speckle-type PDZ protein), a Cul3-based E3 ubiquitin ligase adaptor, has also been shown to promote the ubiquitin-mediated proteasomal degradation of GLI2 (Chen et al. 2009; Zhang et al. 2009). Transcriptional output of Hh pathway depends on the interplay between these regulators.

Direct targets of GLI TFs include Hh pathway components to exert feedback mechanisms, namely PTCH1 and HHIP for negative feedback, and GLI1 for positive feedback (Hui & Angers 2011). Besides these, downstream targets of the Hh pathway include genes involved in cell proliferation and tissue self-renewal, and many of them have been identified to contribute to tumor formation and progression. Specifically, Hh pathway targets MYCN and CCND1 have been shown to promote cell proliferation, and inhibiting them in MB models slowed tumor growth (Kenney et al. 2003). CXCR4 also functions downstream of SHH signaling and as a chemokine receptor, it signals to induce progrowth transcriptional responses such as increasing expression of MYCN and CCND1 (Kieran 2014; Sengupta et al. 2012). Additionally, studies showed that SHH signaling increases expression of genes associated with progenitor or stem cell maintenance including SOX2 and BMI1, and they are demonstrated to play important roles in MB tumor initiating cells (Ahlfeld et al. 2013; Wang et al. 2012; Subkhankulova et al. 2010). Altogether, excessive Hh signaling results in an upregulation of Hh target genes which are collectively contributing to tumor formation and progression. 6

1.2.4 Mutations in SHH MB

Driver mutations in SHH MB are mostly known Hh pathway genes with PTCH1 being the most frequently mutated gene (Kool et al. 2014). PTCH1 mutations are found in approximately equal distributions among different age groups (infants, children, adults), and they are loss-of-function mutations, most commonly somatic loss of chromosome 9q, where PTCH1 gene is located (Kool et al. 2014; Pugh et al. 2012). SMO mutations are also frequently observed, but almost exclusively in adult patients, and are commonly missense mutations resulting in a gain of SMO activity (Northcott et al. 2017). Together, mutations in these two genes account for around 60% of all mutations in SHH MB patients. For this reason, they pose as attractive therapeutic targets for treatment of SHH MB. As PTCH1 functions upstream of SMO, SMO inhibitors have been the primary approach in treating PTCH1/SMO mutations driven SHH MB. In fact, several drugs have shown promising results and have advanced to final stages of clinical trials, such as vismodegib, erismodegib, and saridegib (Kieran 2014; Rimkus et al. 2016; Rubin & de Sauvage 2006).

SUFU mutations are also highly recurrent in SHH MB patients. Importantly, SUFU mutations are almost exclusively found in infant patients, and germline mutational events are commonly observed (Kool et al. 2014). Frameshift mutations and splice site mutations make up the majority of SUFU mutations, and since they are predicted to result in a truncated protein product, SUFU is widely regarded as a tumor-suppressor gene in SHH MB (Taylor et al. 2002; Guerrini- Rousseau et al. 2018). Additionally, truncating SUFU mutations are often accompanied by chromosome 10q loss-of-heterozygosity (LOH). Clinical outcome for patients harboring germline SUFU mutations are worse than expected for SHH MB patients, and incidence of relapse is high, with a 5-year progression-free survival rate of 42% (Guerrini-Rousseau et al. 2018).

GLI2 and MYCN focal amplifications also represent commonly identified mutations in SHH MB patients, suggesting that they have key oncogenic roles in MB. Notably, amplifications of both GLI2 and MYCN are often cooccurring in patients, along with germline mutation of the tumor suppressor gene TP53 (Kool et al. 2014). This group of SHH MB patients with GLI2 and MYCN amplifications compromise a specific subtype of SHH MB subgroup, the SHH ⍺ subtype, and

they mostly affect children. A recent WHO classification has identified tumors with SHH- activation and TP53 mutations as very high risk, with a dismal prognosis (Cavalli et al. 2017).

With all the different mutations found in SHH MB patients, it is important to note that only patients carrying PTCH1 and SMO mutations are ideal for targeted therapy with SMO inhibitors. On the other hand, MB driven by mutations downstream of SMO will not respond to SMO inhibitors, which constitute a great portion of infant MB patients (SUFU mutated), and high risk children MB patients (GLI2, MYCN amplified). In vivo xenograft testing of the SMO inhibitor drugs has confirmed that SUFU and MYCN mutated MBs display no rescue of tumor burden nor improvement of lifespan after treatment with NVP-LDE225 (erismodegib) (Kool et al. 2014). The lack of effective therapeutics to treat these patients is of great concern in the clinical field of SHH MB.

1.2.5 In vivo mouse models of SHH MB

SHH MB has been extensively studied with mouse models due to the high evolutionary conservation of Hh pathway in mammals. SHH MB mouse models carry perturbations in the Hh signaling pathway and mostly mimic mutations in found in human patients. The most commonly used SHH MB models include the inactivation of Ptch1, such as Ptch1+/- mice, or the constitutive activation of Smo, such as Smoa1 or SmoM2 mice (Hallahan et al. 2004; Northcott et al. 2012). Ptch1+/- mouse is the first reported sporadic mouse model of MB, and the tumors that developed in these mice largely resembled histology of human medulloblastomas, with dark elongated nuclei and little cytoplasm (Goodrich et al. 1997). However, penetrance is low with only 14-20% mice developing tumors in the first 15-25 weeks, making it less ideal for research purposes.

Advances in research techniques in the next following years allow genetic perturbations of Ptch1 and Smo genes to be coupled with tissue-specific promoters to achieve targeted inactivation/expression. In particular, under control of the glial fibrillary acidic protein (GFAP) promoter, GFAP-Cre mice drives Cre recombinase activity specifically in a population of ventricular zone (VZ) neural stem cells (NSCs) that later differentiates into granule neurons as well as other types of neurons and glia in the cerebellar cortex (Yang et al. 2008; Casper & McCarthy 2006; Zhuo et al. 2001). In a similar manner, Math1-Cre transgenic mice express Cre 8

recombinase in a more restricted lineage of GNPs under control of the Math1 (also termed Atoh1) promoter. Conditional knockout (KO) of Ptch1 (Ptch1f/f) using both GFAP-Cre and Math1-Cre systems result in aggressive MB formation with 100% penetrance (Yang et al. 2008). GFAP-Cre;Ptch1f/f mice suffer from MB by around 2 weeks of age, while Math1-Cre;Ptch1f/f mice show MB symptoms a little slower at around 8 weeks of age. Similarly, conditional expression of a constitutively activated Smo (SmoM2) allele using GFAP-Cre and Math1-Cre drivers also resulted in MB formation in all cases (Schüller et al. 2008).

Generating faithful mouse models affecting components of the Hh pathway downstream of SMO has been a more difficult challenge. Previous efforts in generating MB mouse models with Sufu alterations have revealed that Sufu+/- mice do not develop tumor, and also the mutation has no effect on lifespan of the mice, despite its widely predicted tumor-suppressing role in MB (Y. Lee et al. 2007). Studies in our lab also unveiled that conditional KO of Sufu in GFAP or Math1 lineage does not result in MB formation (Yin et al. 2019). Only when SUFU level is reduced simultaneously with p53 (Sufu+/-;Trp53−/−) , the mice went on to develop MB (Y. Lee et al. 2007). However, the Sufu+/-;Trp53−/− mice do not accurately capture human MB cases, as no patient has been found to carry cooccurring mutations of SUFU and TP53. Only until recently, our lab successfully generated clinically relevant mouse model involving Sufu mutation, the Sufu;Spop conditional double knockout (DKO) mice, which deletes Sufu together with Spop, the E3 ubiquitin ligase adaptor which targets GLI2 for proteosomal degradation, under either GFAP- Cre or Math1-Cre (Yin et al. 2019). Sufu;Spop DKO mice have been demonstrated to be highly tumor-prone, with 100% penetrance of early-onset aggressive MB.

Surprisingly, given the major role of GLI2 in activating the Hh pathway and its oncogenic role in MB, forced expression of constitutively active GLI2 in mouse cerebellum under GFAP-Cre could not induce MB formation (Han et al. 2009). It is speculated that Hh pathway exerts several negative regulatory mechanisms, through SUFU, SPOP, or the transcriptional repressor GLI3, to prevent overactivation of Hh targets by GLI2, however the exact underlying reason of the absence of tumor in these mice remains unclear.

1.2.6 In vitro models of SHH MB

In addition to in vivo models, in vitro models also serve as useful and rather inexpensive tool to study SHH MB development. Due to its cost-effectiveness and rapid turnover, in vitro studies have greatly stimulated MB research and play important roles in clinical testing of potential drug targets. Furthermore, cell lines which are derived from human patients represent as human conditions, therefore could greatly increase clinical utility when combined with in vivo studies. There are several established cell lines that are classified as SHH subgroup, and here I will highlight the cell lines that I have utilized in my study.

DAOY is the most used SHH MB cell line, accounting for almost 50% of the citations of overall cited MB cell lines (Ivanov et al. 2016). The cell line is a tetraploid cell line first established in 1985 from a 4-year-old male patient, and later classified as SHH subgroup through transcriptomic profiling (Jacobsen et al. 1985; Triscott et al. 2013).

Vandy-MB-11, on the other hand, is a diploid cell line, and it represents a comparatively low passaged cell line that is more recently established in 2012 by Huang et al. from an 11-year-old male patient. Similarly through microarray analysis and subsequent transcriptomic clustering, Vandy-MB-11 is classified as SHH subgroup (Huang et al. 2015; Huang et al. 2012).

1.3 Cerebellum development and basic helix-loop-helix factors

Besides SHH signaling, precise spatial and temporal control of basic helix-loop-helix (bHLH) TFs also play an important role in the development and patterning of cerebellum. bHLH TFs constitute one of the largest families of dimerizing TFs. They are characterized by the protein structural motif bHLH, which consists of a basic domain which is responsible for DNA-binding, and the helix-loop-helix domain, which is responsible for dimerization of the TFs. A small group of bHLH TFs has been termed proneural proteins for their prominent role in converting progenitor cells into a neural (neuronal + glial) or neuronal fate in the developing nervous system (Guillemot & Hassan 2017).

Expression of bHLH TFs appears early in cerebellum development, particularly in embryonic stages. PTF1A together with ASCL1, mark the VZ of the cerebellum, and give rise to the vast majority of GABAergic interneurons derived from VZ (Butts et al. 2014). Additionally, OLIG2 10

expression is also detected in VZ, and it contributes to the generation of Purkinje cells (Ju et al. 2016). ATOH1, on the other hand, marks cells in the upper rhombic lip or EGL. It is well characterized that ATOH1 responds to SHH ligand and collaborates with other SHH targets, such as CXCR4, to mediate proliferation of the GNPs in the outer EGL (Butts et al. 2014). Subsequently, the expression of NEUROD1 in the ATOH1-expressing cells triggers downregulation of ATOH1, followed by the onset of granule cell differentiation and migration to inner EGL.

Figure 1-4. Schematic of a sagittal section through midbrain (mb), cerebellum (cb), and roof plate (rp), marking the expression of TFs which contribute to different cell populations and progenitor zones. Besides Lmx1a, all other TFs are bHLH TFs. egl, external granule layer; vz, cerebellar ventricular zone; rl, rhombic lip; cbn, cerebellar nuclei. (Butts et al. 2014)

Many of these bHLH TFs have been implicated in the development of brain tumor. Specifically, ATOH1 has been shown to collaborate with Hh pathway components, including GLI2 and GLI1, to promote SHH MB tumorigenesis (Yin et al. 2019; Grausam et al. 2017; Ayrault et al. 2010; Flora et al. 2009). On the other hand, high ASCL1 expression has been detected in glioblastoma stem cells (GSCs), and studies in GSCs suggested that ASCL1 could function as a pioneer factor, opening up closed chromatin and promote neuronal differentiation (Park et al. 2017). OLIG2 expression has also been linked with glioma stem cells, and several studies have suggested its oncogenic functions in glioma (Ligon et al. 2007; F. Lu et al. 2016; J. L. Anderson et al. 2017).

1.4 Thesis rationale and outline

Hh signaling pathway has a profound role in SHH subgroup of MB, however despite decades of studying the pathway and disease, there are still many unanswered questions in the field. Particularly, molecular actions of the components of Hh pathway downstream of SMO, including SUFU and GLI2 and its transcriptional targets, remain poorly understood, resulting in a great portion of SHH MB patients unable to receive targeted treatment options. My thesis project aims to gain mechanistic insights of SUFU and understand the transcriptional regulation of Hh pathway targets in SHH MB.

In a recently published work by our lab, we reported dual roles of SUFU in SHH MB discovered in in vivo mouse studies: a tumor-suppressing role in MB driven by Sufu/Spop loss-of-function mutations, and a tumor-promoting role in MB driven by Ptch1/Smo mutations (Yin et al. 2019). In the same study, we also highlighted that GLI2 is the major driver of SHH MB, and that bHLH factors, namely ATOH1 and OLIG TFs, likely cooperate with GLI2 to regulate tumorigenesis. Building on these observations, my project aims to (i) establish the role of SUFU in human SHH MB cell lines, and (ii) dissect the role of the OLIG2 TF in SHH MB.

In Chapter 3, to further elucidate the mechanisms of SUFU and establish the role of SUFU in the context of human SHH MB, I generated stable CRISPR/Cas9-mediated SUFU KO cell lines using 2 human SHH MB cell lines, Vandy-MB-11 and DAOY. I performed expression analysis to study the role of SUFU in regulating GLI2, and examined cell phenotype in the SUFU KO cell lines.

In Chapter 4, I focused on addressing the role of bHLH TF OLIG2 in SHH MB. My hypothesis is that OLIG2 cooperates with GLI2 to regulate Hh target genes in SHH MB. Therefore, to test the hypothesis, I performed ChIP-seq of OLIG2 in mouse SHH MB model, followed by comparative analysis of OLIG2 and GLI2 ChIP-seq to examine their binding profiles. I then performed a series of luciferase reporter assays to understand the transcriptional activity of OLIG2 in regulating the shared targets between OLIG2 and GLI2 in the absence or presence of GLI2. Additionally, transcriptional activity of OLIG1, OLIG2, and OLIG3 were compared to determine whether the 3 OLIG TFs could be exerting similar roles in MB. Finally, to characterize OLIG2’s functional role in SHH MB, I performed OLIG2 siRNA knockdown (KD) 12

studies in human SHH MB cell lines and analyzed cell phenotype as well as the expression of Hh target genes upon KD.

My study targets downstream components of Hh pathway with the ultimate goal to elucidate the gene regulatory network of SHH MB and deliver target treatment options for more SHH MB patients. Together, this study unveils SUFU’s molecular action in the context of human SHH MB (Chapter 3) and identifies a novel potential molecular target, OLIG2, for the treatment of SHH MB (Chapter 4).

Chapter 2 Material and Methods

2.1 Experimental procedures

2.1.1 Cell culture

Vandy-MB-11, DAOY, and C3H10T1/2 cells were maintained in DMEM with 10% fetal bovine serum (FBS) at 37°C and 5% carbon dioxide, and propagated using standard tissue culture protocol.

2.1.2 siRNA knockdown

Knockdown (KD) cells were transfected with ON-TARGETplus SMARTpool Human GLI2 or OLIG2 siRNA (Dharmacon), whereas negative control cells were transfected with ON- TARGETplus Non-targeting Control Pool (Dharmacon). Lipofectamine 3000 (Thermo Fisher Scientific) was used as transfection reagent following protocol for siRNA transfection. Cells for RNA expression analysis were harvested 48 hours post transfection, whereas cells for protein expression analysis were harvested 72 hours post transfection.

2.1.3 CRISPR/Cas9-mediated knockout

The 2 CRISPR/Cas9 guide RNAs (gRNAs) targeting SUFU were designed with crispr.mit.edu (Zhang lab, MIT): gRNA1: 5’-GGTTACCGCTATCGTCAAGT-3’, gRNA2: 5’- AAGGGGTTAACGGAGCCCGT-3’. gRNA1 was cloned into CRISPR/Cas9 vector with GFP selection marker (PX458), while gRNA2 was cloned into CRISPR/Cas9 vector with puromycin selection marker (PX459). Following verification of plasmid constructs by Sanger sequencing, the 2 gRNAs were transfected simultaneously into Vandy-MB-11 and DAOY cells using Lipofectamine 3000 (Thermo Fisher Scientific). Double selection was performed to enrich for cell populations with both gRNAs: puromycin selection was performed 24 hours post transfection; GFP selection was performed ~48 hours post transfection by fluorescence-activated cell sorting.

Monoclonal cell populations were obtained through limiting dilution method. Individual cells were seeded in 96-well plates, and continuously monitored for growth and expansion.

Genotyping of cells were performed by extracting cell DNA with DNeasy Blood & Tissue Kit (QIAGEN), followed by amplification of CRISPR targeted region by polymerase chain reaction (PCR) with the primer sets: Vandy-MB-11: (F) 5’-GAGTCTCACCCACCGAGTCC-3’, (R) 5’- ACGTTTACAGTTCCCTCGCT-3’; DAOY: (F) 5’-TCGTTTGCCCTCTCCAGTTCC-3’, (R) 5’- GCACGGAGAAAGAGAACAGTGC-3’. PCR products were subjected to Sanger sequencing and analysis with Inference with CRISPR Edits (ICE) tool (Hsiau et al. 2018).

2.1.4 Western blot

Cell lysates were prepared as follows: Trypsinization then lysing of cells with HEPES lysis buffer supplemented with cOmplete mini EDTA-free protease inhibitor cocktail (Roche), followed by brief sonication using Bioruptor Plus (Diagenode).

Proteins were separated by 10% SDS-PAGE and transferred to a nitrocellulose membrane for blotting at 4 °C with the following primary antibodies: GLI2 (1:1000, Cheung et al. 2009), SUFU (1:1000, Meng et al. 2001), OLIG2 (1:1000, MilliporeSigma, AB15328), β-actin (1:10000, Cell Signaling Technology, 8H10D10).

2.1.5 RNA extraction and reverse transcription

RNA was extracted with TRIzol reagent (Thermo Fisher Scientific) followed by purification and DNase treatment with RNeasy Plus Micro Kit (QIAGEN). cDNA was synthesized from 1 µg RNA with M-MLV reverse transcriptase (Thermo Fisher Scientific), 10 mM dNTP mix (Thermo Fisher Scientific), and Oligo (dT) primer (Thermo Fisher Scientific).

2.1.6 Quantitative real-time PCR

Quantitative real-time PCR (qRT-PCR) was performed with Power SYBR Green Master Mix (Thermo Fisher Scientific) on the Applied Biosystems ViiA 7 real-time PCR system (Thermo

Fisher Scientific), and analyzed using Viia 7 software (Thermo Fisher Scientific). The following primer pairs were used for each gene (sequences are presented 5’ to 3’):

β-actin TCCCTGGAGAAGAGCTACGA AGCACTGTGTTGGCGTACAG GLI1 CCTCTGAGACGCCATGTTCA AGACAGTCCTTCTGTCCCCA GLI2 CTCCGAGAAGCAAGAAGCCA GATGCTGCGGCACTCCTT OLIG2 ATAGATCGACGCGACACCAG ACCCGAAAATCTGGATGCGA PTCH1 CCCCTGTACGAAGTGGACACTCTC AAGGAAGATCACCACTACCTTGGCT

2.1.7 MTS assay

Cells were counted and seeded in 96-well plates with equal number of cells in each well. Upon ~90% confluency of fastest-growing cell lines for Vandy-MB-11 and DAOY control/SUFU KO lines or 72-hours after treatment with siRNA, cells were treated with CellTiter 96 AQueous One solution (Promega) and cell metabolic activity was measured by reading absorbance at 490 nm.

2.1.8 ChIP-seq

ChIP was performed as described in Schmidt et al. (2009) using OLIG2 antibody (MilliporeSigma, AB15328). Libraries were prepared using the NEBNext® DNA Library Prep Master Mix Set for Illumina (New England Biolabs, E6040L) and NEBNext® Multiplex Oligos for Illumina (New England Biolabs, E7335L) following manufacturer’s instructions. Libraries were size-selected for 150–350 bp fragments on a Pippin Prep system, and sequenced on an Illumina HiSeq 2500 sequencing system.

2.1.9 Luciferase reporter assay

Cells were cotransfected with luciferase reporter plasmids (Ptch1 or Cxcr4) and pRL-TK control plasmid (Promega), with or without gene expression plasmids (OLIG1, OLIG2, OLIG2–Δb, OLIG2–ΔHLH, OLIG3, GLI2, ATOH1) into either Vandy-MB-11 or C3H10T1/2 cells. Lipofectamine 3000 (Thermo Fisher Scientific) was used as the transfection reagent. Cells were harvested ~40-48 hours post transfection, and luciferase activity was determined using the Dual Luciferase Reporter Assay System (Promega) and Lumat LB 9507 luminometer. Statistical significance is determined using one-way ANOVA. 16

2.1.10 Site-directed mutagenesis (SDM)

OLIG2 mutants (OLIG2–Δb, OLIG2–ΔHLH) were generated using QuikChange II Site-Directed Mutagenesis Kit (Agilent Technologies) and validated with Sanger sequencing.

2.2 Bioinformatic analyses

2.2.1 Kaplan-Meier survival analysis

Patient and gene expression data was obtained from Cavalli et al. (2017). Expression data was stratified by the median. Overall survival was analyzed with the Kaplan-Meier method using the R packages “survival” and “survminer” (Therneau et al. 2013; Kassambara et al. 2018), and p- value was calculated using the log-rank test.

2.2.2 ChIP-seq analysis

Paired-end reads were trimmed for sequencing quality and adaptor sequences using Trimmomatic 0.33 (Bolger et al. 2014), and aligned to mm9 genome using BWA-MEM with default parameters (H. Li 2013). The alignment was then filtered for map quality and paired mapping using BamTools (Barnett et al. 2011). Uniquely mapped reads for the biological replicates were merged and used for peak calling with MACS2 (Feng et al. 2012). GLI2, H3K36me3, and H3K27ac ChIP-seq data were obtained from Yin et al. (2019).

Genomic distribution analysis was performed using CEAS (Cis-regulatory Element Annotation System) (Shin et al. 2009). De novo motif analysis was performed using HOMER software (Heinz et al. 2010). Co-binding of GLI2 and OLIG2 peaks was analyzed with BEDTools (Quinlan & Hall 2010) and statistical significance was determined using GAT (Heger et al. 2013). ChIP-seq signal heatmap was generated and visualized with deepTools (Ramírez et al. 2016). Peak-associated genes were identified using GREAT with basal plus extension association method (McLean et al. 2010). Mapped ChIP-Seq reads were converted to BigWig format and visualized on UCSC Genome Browser.

2.2.3 RNA-seq analysis

RNA-seq data was obtained from Yin et al. (2019). Differential expression and significance were determined using DESeq2 (Love et al. 2014).

2.2.4 Functional pathway enrichment analysis

Functional pathway enrichment analysis of the GLI2 and OLIG2 shared target genes was performed using g:Profiler (Reimand et al. 2007).

Chapter 3 Elucidating the mechanism of SUFU in the context of human SHH MB

3.1 Introduction

Mutations in SUFU are frequently found in MB and are almost exclusive in infant patients. SUFU, being the key intracellular regulator of the Hh pathway, exerts several mechanisms to keep GLI TFs in check and prevent them from misregulating Hh target genes. Nonetheless, the molecular actions of SUFU are not well understood.

Our lab has focused on dissecting the role of SUFU in SHH MB and more specifically Hh pathway regulation. First, despite its widely suspected tumor suppressor role, mouse models with conditional KO of Sufu in the cerebellum (GFAP-Cre;Sufuf/f and Math1-Cre;Sufuf/f) did not result in MB tumor formation (Yin et al. 2019). Molecular analysis revealed that in comparison to GFAP-Cre;Ptch1f/f, which has aggressive MB development and clear signature of overactive Hh pathway, Sufu KO models displayed a rather low expression of Hh pathway genes (Gli2, Gli1, Ptch1, and Hhip), suggesting low Hh pathway activity. Although Gli2 expression is moderately upregulated when compared to WT, the expression of its downstream targets (Gli1, Ptch1, Hhip) is downregulated, and moreover, GLI2 protein expression drops significantly to a level comparable to WT at P13. These data suggested that there are other negative regulators of GLI2 alongside of SUFU. Following this observation, we generated double KO mice, GFAP- Cre;Sufuf/f;Spopf/f (conditional KO of both Sufu and Spop), which resulted in full-blown aggressive MB, and clear activation of Hh pathway similar to GFAP-Cre;Ptch1f/f mice. In addition, our lab investigated SUFU’s role in GLI2 regulation in MBs with SMO activation. Strikingly, deletion of Sufu in mice with Smo activation (GFAP-Cre;Ptch1f/f;Sufuf/f and Math1- Cre;SmoM2;Sufuf/f) resulted in downregulation of GLI2 expression, and better prognosis for the mice. Taken together, our lab has discovered that SUFU has 2 opposing roles in regulating GLI2 in SHH MB: (1) cooperation with SPOP to negatively regulate GLI2 level, and (2) stabilize

GLI2 levels as it protects GLI2 from proteosomal degradation when SPOP is present (Figure 3- 1).

Figure 3-1. SUFU exerts dual function in SHH MB. (Yin et al. 2019)

Given the multiple roles of SUFU in regulation of GLI2, the aim of my project is to determine the molecular action of SUFU in human SHH MB. In this chapter, I explore SUFU’s role through generation of CRISPR/Cas9 SUFU KO Vandy-MB-11 and DAOY cells. The use of CRISPR/Cas9 system allows the generation of stable SUFU KO cell clones that can be maintained indefinitely. Following the characterization of SUFU KO cell lines, I determined the levels of GLI2 through Western blot analysis and examined SUFU’s role in MB through MTS assays.

The study in this chapter enables functional molecular studies of SUFU in a human context, and importantly, it brings our group’s previous findings in mouse models to human cells to assess their clinical relevance.

3.2 Results

3.2.1 SUFU is successfully deleted by CRISPR/Cas9 system

To elucidate the role of SUFU in human SHH MB, I performed SUFU KO experiments using the CRISPR/Cas9 system. Two guide RNAs (gRNAs) were designed to target exon 1 of SUFU, closely downstream of SUFU’s translation start site (Figure 3-2A). The gRNAs were designed by Dr. Wenchi Yin, and I cloned them into CRISPR/Cas9 plasmid vectors with different selection markers. The first gRNA (gRNA1) was cloned into vector with GFP selection marker, while gRNA2 was cloned into vector containing puromycin selection marker. Both gRNAs were transfected simultaneously into Vandy-MB-11 and DAOY cells. Following double selection with puromycin and GFP, I studied the CRISPR KO efficiency by Western blot analysis of the polyclonal population of both cell lines. Results suggested that SUFU is successfully targeted by CRISPR/Cas9 system with high efficiency, as protein levels are largely diminished following KO (Figure 3-2B). Additionally, GLI2 protein levels are found to be downregulated in SUFU KO cell populations (Figure 3-2B).

Figure 3-2. SUFU is successfully targeted by CRISPR/Cas9-mediated gene editing. (A) Schematic of the CRISPR/Cas9 gRNA design. 2 gRNAs are designed to target exon 1 of SUFU gene. (B) Western blot showing high efficiency KO of SUFU upon CRISPR/Cas9 gene editing. GLI2 levels are greatly reduced in SUFU KO populations compared to control. Numbers beneath blot represent relative quantification of band signal normalized to β-actin levels.

3.2.2 Generation of monoclonal SUFU KO populations

Following validation of the KO efficiency, I then proceeded to isolate single clones of SUFU KO cells by limiting dilution method to obtain stable monoclonal KO clones. For Vandy-MB-11 cells, 7 individual clones were obtained. To characterize the clones, PCR primers were designed to amplify the CRISPR targeted region. DNA gel electrophoresis of the PCR products showed one clone (C2) with a single band with the same size as a wildtype (WT) SUFU allele (570 bp)

(Figure 3-4A). 4 clones (C1, C3, C6, C8) showed single band which indicated homozygous deletion of ~200 bp, which is the predicted size of deletion when both gRNAs introduced cuts. 2 of the clones (C5, C7) showed double bands which has one band corresponding to the size of a WT band, and another to the size of ~200 bp deletion. Further analysis of the genotypes of the clones through Sanger sequencing followed by ICE (Inference of CRISPR Edits) analysis unveiled that C2 is an unedited clone which has WT SUFU sequence, while C5 and C7 clones both contained one allele with ~200 bp deletion, and the other allele with short indels (1 bp insertions or deletions), thus appearing as roughly the same size as WT band on DNA gel (Figure 3-3).

Figure 3-3. Screenshots of ICE analysis revealing genotype of Vandy-MB-11 SUFU CRISPR clones.

The indels are predicted to cause a frameshift mutation which will result in truncated SUFU protein. Additionally, ICE analysis also revealed that C3 and C6 contained alleles with different edits by CRISPR, one with an 1 bp insertion along with the ~200 bp deletion, and another with only the ~200 bp deletion. Subsequent Western blot analysis confirmed the absence of SUFU protein from C1, C3, C5, C6, C7 and C8 (Figure 3-4B). Together, PCR amplification of the CRISPR/Cas9 targeted region followed by sequencing and Western blot revealed that out of the 23

7 Vandy-MB-11 clones, 6 of them are SUFU KO clones, while the remaining clone is SUFU WT. This SUFU WT clone (C2) was later used as control for cell proliferation studies.

6 individual clones were obtained for DAOY, and the clones were analyzed in a similar manner. DNA gel of PCR showed 1 clone (C1) with single WT band, 2 clones (C2, C3) with double bands, and 3 clones (C5, C6, C7) with single band indicating deletion around ~200 bp (Figure 3- 4A). However, due to the tetraploidy of DAOY, the sequencing quality of the clones did not meet the criteria of the ICE tool therefore detailed genotypes of DAOY clones were not obtained. Nonetheless, Western blot analysis demonstrated the complete absence of SUFU protein of clones C2, C3, C5, C6 and C7, suggesting they are SUFU KO clones (Figure 3-4B). C1 showed expression of SUFU protein, indicating unsuccessful deletion.

3.2.3 SUFU is required to maintain high GLI2 expression

To study how SUFU is regulating GLI2 in human MB cells, I blotted for GLI2 protein expression in all SUFU KO populations obtained. Strikingly, in every SUFU KO clone, a clear reduction of GLI2 expression is observed (Figure 3-4B), suggesting that SUFU is required to maintain high GLI2 protein level.

Figure 3-4. Single clone isolation of Vandy-MB-11 and DAOY SUFU KO clones. (A) DNA gel electrophoresis image showing genotyping information of the single clones. (B) Western blot confirming the absence of SUFU in the individual clones. GLI2 levels are reduced with KO of SUFU. Numbers beneath blot represent relative quantification of band signal normalized to β-actin levels.

3.2.4 Deletion of SUFU did not result in significant cellular phenotype

To gain insight on how KO of SUFU is affecting growth of human SHH MB cells, I performed MTS assay on SUFU KO monoclonal cell lines along with cell lines with no SUFU deletion. The assay was performed with varying concentrations (0%, 1%, 2.5%, 10%) of fetal bovine serum (FBS) to sensitize the cells, as normal cell culturing conditions might be optimized to support rapid cell growth. No significant cell metabolic differences were observed between SUFU expressing clones and SUFU KO clones for both Vandy-MB-11 and DAOY cells under conditions tested, suggesting that the deletion of SUFU is not compromising growth of cells (Figure 3-5).

FBS

Figure 3-5. MTS assay revealed no significant cell phenotype upon SUFU deletion. Red boxes indicate SUFU KO clones.

3.3 Discussion

As a key regulator in Hh signaling pathway, the molecular mechanisms of SUFU remain largely unelucidated. Prior studies have mostly emphasized SUFU’s role as a negative regulator of GLI2 and tumor suppressor in SHH MB but have failed to provide direct mechanism of SUFU’s contribution to SHH MB, as mice harboring deletion of Sufu are not prone to MB (Y. Lee et al. 2007; Svärd et al. 2006). In our mouse studies, we unveiled that SUFU can function to protect from SHH MB through negatively regulating GLI2 protein expression together with SPOP. We further discovered that SUFU can function to maintain high GLI2 expression in MB driven by Ptch1 or Smo mutations and promote tumor formation.

To understand SUFU’s role in a human context, I generated SUFU KO clones using human MB cell lines Vandy-MB-11 and DAOY. By introduction of 2 gRNAs, I demonstrated that SUFU is successfully deleted using the CRISPR/Cas9 system (Figure 3-2). Furthermore, through isolation of monoclonal cell population, I obtained stable SUFU KO cell lines with different CRISPR edits, as revealed by gel electrophoresis and further validated through sequencing (Figure 3-3, 3- 4A).

Importantly, in this chapter, I showed that the deletion of SUFU lead to reduction of GLI2 protein, suggesting SUFU’s positive role in maintaining high GLI2 level (Figure 3-2B, Figure 3- 4B). It has been established that GLI2 is targeted for protein degradation by proteasomes through several mechanisms, including the ubiquitination mediated by SPOP, therefore the decrease in GLI2 level following SUFU depletion is suggesting that SUFU functions to protect GLI2 from proteosomal degradation (Chen et al. 2009; Zhang et al. 2009). Importantly, this role of SUFU has been highlighted in our lab’s publication, thus my study presented in this chapter confirms our previous study of SUFU in SHH MB mouse models in a human context and increases its clinical utility (Yin et al. 2019).

A clear cell phenotype was not detected following deletion of SUFU under normal cell culturing condition, as well as under reduced serum conditions, though MTS assay (Figure 3-5). This is unexpected as the SUFU KO cells show greatly reduced GLI2 levels, and SHH MB cell lines are dependent on GLI2 for growth (Buczkowicz et al. 2011). One possible reason for this observation is that due to the absence of SUFU, GLI2 is no longer sequestered in cytoplasm, 27

therefore there might be an increased fraction of nuclear GLI2 in SUFU KO cells. Thus, although the amount of GLI2 protein in the SUFU KO cells is largely reduced, it could be sufficient to maintain aberrant cell proliferation. Additionally, it is possible that the cell lines Vandy-MB-11 and DAOY contain downstream mutations which caused the cells to be less sensitive to reduction of GLI2. Notably, I did observe slight proliferation differences between individual DAOY SUFU KO clones (Figure 3-5). This could be a result of the different SUFU mutations present in the cell lines, or other mutations occurring in the cells. Further experiments and more detailed characterization of each cell line will be needed to understand the cellular events which are occurring in the SUFU KO SHH MB cell lines.

3.4 Future directions

3.4.1 Transcriptomic analysis of SUFU KO SHH MB cell lines

SUFU is a key regulator of the Hh signaling pathway, and it plays an important role during SHH MB tumorigenesis. In this chapter, although I have demonstrated that the deletion of SUFU resulted in a clear reduction in GLI2 protein levels, it remains unclear how the expression of other genes is affected in the cells. To fully characterize and understand the role of SUFU in the context of human SHH MB, first we would like to test the hypothesis that the absence of SUFU leads to an increased nuclear fraction of GLI2. To do so, we would perform immunofluorescence to examine the ratio of nuclear versus cytoplasmic distribution of GLI2 in control and SUFU KO cells. Western blot analysis of subcellular fractionated samples could also be performed. This would unveil whether SUFU can simultaneously exert a positive role (preventing GLI2 from degradation) and a negative role (preventing GLI2 from translocating into nucleus) in regulating the Hh pathway transcriptional effector GLI2. Next, we would study the transcriptome of control and SUFU KO Vandy-MB-11 and DAOY cells through RNA-sequencing. Transcriptomic analysis of SUFU KO cells would unmask SUFU’s role in regulation of downstream Hh pathway genes in MB, and also reveal whether genes are downregulated/upregulated to compensate for the loss of SUFU. Additionally, as we have not yet fully characterized Vandy-MB-11 and DAOY cells and the mutations that they might have accumulated along passaging, data from RNA-seq would reveal this crucial information and provide insight into the lack of cellular phenotype upon SUFU KO. Furthermore, we would perform a detailed analysis to determine if there are transcriptomic differences between each individual SUFU KO clones with different SUFU edits created by CRISPR/Cas9 system. As SHH MB patients harbor varying mutations of SUFU, this data could help us understand whether distinct molecular events are occurring in patients with different SUFU mutations.

3.4.2 Utility of SUFU KO SHH MB cell lines

In this chapter, I generated human SUFU KO MB cell lines, which could in many means aid the study of molecular mechanisms of SUFU, a focus of study in our lab. Currently, a graduate student in our lab, Jessica Parker, is performing immunoprecipitation coupled by mass spectrometry (IP-MS) experiments aiming to characterize the SUFU interactome in SHH MB.

The SUFU KO cell lines that I have generated in this study serves as excellent negative controls. Importantly, this IP-MS study will reveal the molecular partners of SUFU in a tumor setting, which in turn allows us to gain insight into the mechanisms in which SUFU exerts its tumor- suppressing functions versus tumor-promoting functions. A study by D’Amico et al. (2015) revealed that SUFU can bind to and stabilize CNBP, an RNA-binding protein involved in regulating translation of ornithine decarboxylase, to promote MB tumorigenesis (D’Amico et al. 2015). Assuredly, the characterization of SUFU interactome would unveil additional partners of SUFU which will help us obtain information to deduce the molecular actions of SUFU in MB.

Chapter 4 Characterizing the role of OLIG2 in SHH MB

4.1 Introduction

GLI2 is the major effector of Hh signaling pathway, and amplifications of which have been frequently found in high risk SHH MB patients. To provide better and targeted therapeutic options to this group of patients, our lab has been focusing on characterizing downstream targets of GLI2 which contribute to MB tumorigenesis, as well as TF partners that likely cooperate with GLI2 to drive maximal activation of oncogenes in SHH MB.

From previous studies in our lab, we found bHLH TFs to be candidate GLI2 binding partners as their motif is highly enriched around GLI2 binding sites identified by GLI2 ChIP-seq (Fig 4-1A). The bHLH TFs which recognize the matching motif included many neural specific factors, ASCL1, ATOH1, OLIG1, OLIG2, OLIG3, as well as more ubiquitously expressed factors, TCF3, TCF4, TCF12. Upon studying their expression in SHH MB mouse models, we found Atoh1, Olig2 and Olig3 to be upregulated in both tumor samples when compared to WT counterparts (Fig 4-1B). Further studies revealed that ATOH1 indeed is able to synergistically activate Hh target genes together with GLI2, unveiling its role in SHH MB (Yin et al. 2019). Having characterized ATOH1 in SHH MB, we next raised the question if OLIG TFs are also contributing to SHH MB development, and specifically if OLIG TFs are cooperating with GLI2 to regulate Hh targets.

The OLIG family of bHLH TFs contains 3 members, OLIG1, OLIG2, and OLIG3. All 3 TFs share high amino acid sequence homology, especially within the bHLH domain. OLIG1 and OLIG2 have been reported to have partially redundant functions in neuron lineage specification (Zhou & D. J. Anderson 2002; Q. R. Lu et al. 2002). Function of OLIG3 is less characterized, however its expression pattern in normal development diverge from OLIG1 and OLIG2 (Takebayashi et al. 2002; Müller et al. 2005).

With respect to tumor development, the role of OLIG TFs in MB remains uncharacterized. However, studies in glioma have demonstrated that expression of OLIG2 is highly correlated

with cycling glioma progenitor cells (Ligon et al. 2007). Additionally, although during development OLIG1 and OLIG2 have overlapping roles, studies have demonstrated that OLIG2 specifically, but not OLIG1, is required for glioma formation (Ligon et al. 2007). For this reason, and the observation that OLIG2 expression is highly correlated with patient prognosis presented later in this chapter, I mainly centered on OLIG2 in my study.

In this chapter, I explored the role of OLIG2 in SHH MB, focusing on its transcriptional activity with GLI2, through multiple approaches, including ChIP-seq and luciferase reporter assays. I also performed RNAi experiment to characterize its functional role in SHH MB. Furthermore, I assayed the transcriptional activity of OLIG1, OLIG2, OLIG3 all together to study if they exert redundant functions in SHH MB. The study in this chapter provides evidence of a novel player, OLIG2, in SHH MB, and further reveals an unreported transcriptional regulatory role of OLIG2.

Figure 4-1. Previous studies done by Dr. Wenchi Yin discovered bHLH factors as promising candidate partners of GLI2 in SHH MB. (A) De novo motif discovery of regions flanking GLI2 peaks. (B) RNA-seq data showing the expression of bHLH TFs in mouse tumor versus wildtype samples. TPM, transcripts per million.

4.2 Results

4.2.1 High expression of OLIG TFs correlates with poor patient prognosis

To explore if OLIG TFs are playing a role in SHH MB, I first performed Kaplan-Meier survival analysis on human patient data according to gene expression of OLIG TFs. Notably, all 3 OLIG TFs demonstrated poor prognosis when they are highly expressed, with OLIG2 showing the most significant negative correlation between expression and survival (Figure 4-2A). As OLIG TFs have high amino acid homology and OLIG1 and OLIG2 have been previously demonstrated to share overlapping roles in neuron differentiation, I next further stratified the patient data to study if combination of 2 out of the 3 OLIG TFs would result in additive phenotype, which would suggest an overlapping tumor-promoting role. Indeed, the combined high expression of OLIG1 and OLIG2, as well as OLIG2 and OLIG3, showed highest correlation with poor prognosis (Fig 4-2B).

A B OLIG1 OLIG2 and OLIG1 100 + + ++++ 100 ++++ + + ++++ + + ++++ +++++ 90 + ++++++ +++++ + + ++++ + + +++++ +++++++++++++++ + + + + + + ++ + + 80 ++ + + ++ ++++++ + + +++ + +++++ + +++ 80 ++ 70 +++ + + + +++ +++++++++++ ++++ + + +++ + + Overall Survival (%) Overall 60 +++++ ++ + + ++ +++ +++++ + 50 p = 0.033 + +++ ++ +++ + + +++ + + + + +++ + + 0 5 10 15 20 25 60 Time (years) Survival (%) Overall

Expression + High (65) + Low (107) + + + +

OLIG2 40 p = 0.032 100 + +++++++ 0 5 10 15 20 25 +++++++ Time (years) + + 90 ++++++++++ Expression + High (56) + Low (81) + OLIG2 High OLIG1 Low (26) + OLIG2 Low OLIG1 High (9) + + ++ +++++ + 80 ++ +++ OLIG2 and OLIG3 ++++++ + 70 100 + +++ ++++++ ++++++++++ + ++ +++ + + + ++ +

Overall Survival (%) Overall +++++++++ + 60 ++++ +++ +++++ + ++ +++++ ++ ++++++++++ +++ + ++ 50 p = 0.0055 + 0 5 10 15 20 25 +++ +++ + 80 ++ ++++ + Time (years) ++

Expression + High (82) + Low (90) + ++++ + + + + + + + +

+++ + +++ + + ++++ + OLIG3 60 100 + Survival (%) Overall ++++ + +++ + + ++ + ++ ++ ++ + + ++++++++ 90 + ++ +++ + +++ + + 80 +++ +++++++ 40 p = 0.0072 ++++++++ ++ ++++++ 70 0 5 10 15 20 25 +++++ Time (years)

Overall Survival (%) Overall +++++++++ + 60 + ++ ++ + Expression + High (40) + Low (53) + OLIG2 High OLIG3 Low (42) + OLIG2 Low OLIG3 High (37)

50 p = 0.1 0 5 10 15 20 25 Time (years)

Expression + High (77) + Low (95)

Figure 4-2. Kaplan-Meier survival analysis of human SHH MB patients grouped according to OLIG TFs expression status. (A) High expression of OLIG TFs are correlated with poor prognosis of SHH MB patients. (B) Combined high expression of OLIG1+ OLIG2, and OLIG2 + OLIG3, are predictors of poor prognosis of SHH MB patients.

4.2.2 OLIG2 ChIP-seq in GFAP-Cre-Sufuf/f;Spopf/f MB revealed OLIG2 binding to over 70% of GLI2 bound regions

To characterize OLIG2’s role and its binding targets in SHH MB, I performed OLIG2 ChIP-seq in GFAP-Cre;Sufuf/f;Spopf/f together with Dr. Wenchi Yin. ChIP-seq identified over 30,000 OLIG2 peaks in mouse MB. Annotation of genomic distribution of OLIG2 peaks revealed that OLIG2 is binding widely across the genome, with 10.1% of the peaks located in promoter regions, and 41% located in introns (Figure 4-3A). Known OLIG2 targets, including Nkx2.2, Olig1, and Olig2 itself, are identified in this study.

B C G G C GC TT

G T G T C T CG C T G GT ACA C ATA G ATA GA CTA CA A CANNTG

Figure 4-3. OLIG2 ChIP-seq in GFAP-Cre;Sufuf/f;Spopf/f revealed that OLIG2 is binding to a large number of sites in tumor cerebellum. (A) Genomic distribution of OLIG2 binding sites. (B) De novo motif analysis of OLIG2 binding sites identified canonical E-box motif (CANNTG).

To investigate whether OLIG2 is binding to specific DNA motifs in SHH MB, I performed de novo motif discovery of OLIG2 binding regions. Analysis revealed that the motif (C/A)CA(G/T)(C/A)TG(G/T)(T/C) is significantly over-represented in OLIG2 binding sites (Figure 4-3B). Importantly, this motif is largely similar with the motif identified in OLIG2 ChIP- seq experiments in various mouse tissues, including adult spinal cord, embryonic stem cells, as well as neural progenitor cells, reported in previous studies (Meijer et al. 2014; Darr et al. 2017; Mazzoni et al. 2011). The identified motif contained a canonical E-box binding motif, CANNTG, which is the generic binding motif for bHLH TFs.

To study whether OLIG2 and GLI2 have cooperative functions to regulate downstream GLI2 target genes in SHH MB, I next performed comparative analysis of OLIG2 and GLI2 ChIP-seq. Strikingly, the analysis revealed that OLIG2 binding signal can be detected in over 70% of GLI2 binding sites, which is ~1000 of GLI2 peaks (Figure 4-4A,B). Associating the shared binding sites with their nearby genes through basal plus extension method with GREAT software identified 1190 shared targets between OLIG2 and GLI2, which includes many MB signature genes such as Ptch1, Sufu and Cxcr4.

Following identification of the shared targets between the 2 TFs, I then examined if these genes are upregulated or downregulated in tumor samples to gain insight on whether OLIG2 is contributing to the activation or repression of Hh pathway genes. Integrating ChIP-seq analysis with RNA-seq analysis revealed that 377 genes are significantly upregulated in GFAP- Cre;Sufuf/f;Spopf/f, whereas 357 genes are significantly downregulated (Figure 4-4C). Functional pathway enrichment analysis identified that upregulated genes are enriched with TFs, including Myod1, Sox18, Atoh1, and genes associated with cancer development, such as Cdk6, Ccnd1, and E2f1 (Figure 4-4D). Importantly, SHH MB associated genes are all among the upregulated genes, including Gli2, Ptch1, Cxcr4, and Atoh1 (Figure 4-4C). On the other hand, downregulated genes are enriched with genes associated with voltage-gated calcium channel activity, including Tpcn2, Gas6, Cacng2/5, and Nalcn (Figure 4-4D).

Figure 4-4. Comparative analysis of GLI2 and OLIG2 ChIP-seq in GFAP- Cre;Sufuf/f;Spopf/f revealed that OLIG2 is binding to > 70% of GLI2 binding sites. (A) Venn diagram schematic showing number of GLI2/OLIG2 peaks which overlap. P-value reports the probability to obtain the observed overlap between two segment sets by chance, calculated with GAT. (B) GLI2 and OLIG2 ChIP-seq signal at GLI2 peak regions. (C) Heatmap showing expression pattern of ~1000 GLI2 and OLIG2 shared target genes in P13 WT and GFAP-Cre;Sufuf/f;Spopf/f cerebellum. Labeled are genes implicated in Hh signaling and MB/cancer. (D) Separate functional enrichment analysis of significantly upregulated and downregulated GLI2 and OLIG2 shared target genes. Plotted are the top 4 terms from the following data sources, KEGG biological pathway (KEGG), GO – Molecular Functions (GO:MF), GO – Biological Process (GO:BP).

4.2.3 OLIG2 is a potent repressor of GLI2 and ATOH1-dependent activation of Ptch1 and Cxcr4

To study if OLIG2 cooperate with GLI2 to regulate Hh target genes and SHH MB-associated genes, I performed reporter gene assay to investigate the transcriptional activity of the genes associated with the binding of GLI2 and OLIG2 identified from ChIP-seq analyses, including Ptch1 and Cxcr4 (Figure 4-5A). Regulatory regions of Ptch1 and Cxcr4 containing ChIP-seq binding signals of both GLI2 and OLIG2 were cloned into separate luciferase reporter constructs, and later were transfected into Vandy-MB-11 cells or C3H10T1/2 cells in the presence of GLI2 and/or OLIG2 (Figure 4-5B). Data from reporter gene assay suggested that GLI2 alone activated both the Ptch1 and Cxcr4 reporters to a great extent, while OLIG2 on the other hand, demonstrated a strong repressor activity on the GLI2-dependent activation of Ptch1 and Cxcr4 reporters (Figure 4-5C). As mentioned in the introduction of this chapter, ATOH1 is also highly expressed in SHH MB and has been found to synergize with GLI2 to activate Ptch1 and Cxcr4 reporters. Therefore, to understand OLIG2’s activity in the presence of GLI2 and ATOH1, I transfected all three TFs along with the reporter. Surprisingly, OLIG2 is able to strongly repress the synergistic activation on Ptch1 and Cxcr4 reporter by GLI2 and ATOH1.

To further characterize OLIG2’s transcriptional activity, I performed reporter gene assay with the same reporter constructs but with mutated E-box motif (Figure 4-5B). The mutated motif contained 2 nucleotide substitutions in well conserved positions of the E-box motif, and therefore should abolish the binding of bHLH factors (CATCTG to CTTCAG). Surprisingly, OLIG2’s repression persisted even when the E-box motif is mutated, whereas ATOH1’s activation was no 39

longer seen with the mutated Ptch1 and Cxcr4 reporter (Figure 4-5C, Yin et al. 2019). This result suggested that unlike ATOH1, OLIG2’s repressor activity could be independent of a canonical E-box motif.

Figure 4-5. OLIG2 is a potent repressor of GLI2-dependent activation of Ptch1 and Cxcr4 reporters. (A) ChIP-seq signals of GLI2, OLIG2, H3K27ac, and H3K36me3 in GFAP-Cre;Sufuf/f;Spopf/f, along with ATOH1 ChIP-seq peaks in P5 normal cerebellum obtained from Klisch et al. (2011), showing binding of GLI2, OLIG2, and ATOH1 on Ptch1 and Cxcr4 regulatory elements. (Klisch et al. 2011) (B) Schematic of luciferase reporter constructs derived from Ptch1 (i, ii) and Cxcr4 (iii, iv) elements containing either WT (i, iii) or mutated (ii, iv) E-box motifs. (C) Reporter gene assays revealed that OLIG2 is a strong repressor of GLI2-dependent activation of Ptch1 and Cxcr4 reporters, even when E-box motif is mutated. Numbers above bars indicate statistical significance of pairwise comparisons; for example, (1,5): significant difference from bars 1 and 5. *: significant difference from all. n.s.: non-significant difference from all. n = 2 for Ptch1 reporter, n = 3 for Cxcr4 reporter. Error bar: SD.

4.2.4 Transcriptional activity of OLIG2 is dependent on its bHLH domain

The ability for OLIG2 to repress Ptch1 and Cxcr4 reporters with mutated E-box motif suggested that OLIG2 might act through either non-canonical E-box motif or a DNA-independent mechanism. To investigate the mechanism which OLIG2 exerts to carry out its repression activity, I generated 2 mutants of OLIG2 by site-directed mutagenesis. The first mutant, termed the basic domain mutant (OLIG2–Δb), contains 2 amino acid substitutions (R118Q, R120Q) in the basic domain of OLIG2. The second mutant, termed the helix-loop-helix (HLH) domain mutant (OLIG2–ΔHLH), contains one amino acid substitution (D129P) in the first helix in the HLH domain of OLIG2. The basic domain is essential for DNA-binding ability of bHLH TFs, whereas the HLH domain is critical for oligomerization of the TFs. The mutations were created in sites that have been validated to ablate the corresponding basic and HLH domain functions in previous studies (Davis et al. 1990). Following the generation of the 2 mutants by SDM and confirmation of their mutations through Sanger sequencing, I proceeded to perform reporter gene assay with the OLIG2 mutants. Interestingly, OLIG2–Δb completely lost its ability to repress the GLI2-dependent activation of the Ptch1 reporter, while OLIG2–ΔHLH partially lost its repressor activity (Figure 8B). This result revealed that both the DNA-binding domain and the HLH domain are required for OLIG2’s repressor activity, suggesting that OLIG2 likely regulates its downstream targets through the binding to DNA.

Intriguingly, luciferase reporter assay also revealed that OLIG2 is capable of synergistically activating an 8X-Gli reporter with GLI2 (Figure 4-6D). The 8X-Gli reporter consists of multimerized Gli motifs but importantly, no E-box motif (Figure 4-6C, Sasaki et al. 1997). To understand the activator function of OLIG2, I performed reporter gene assay with the OLIG2 mutants, along with WT OLIG2 on 8X-Gli reporter. Similar to its repressor activity, the mutant forms of OLIG2 lost their ability to activate the 8X-Gli reporter synergistically with GLI2. Interestingly, while OLIG2–Δb had no activity, OLIG2–ΔHLH appeared to slightly repress the GLI2-dependent activation of the 8X-Gli reporter, suggesting that the dimerization partner of OLIG2 could be mediating the activator activity of OLIG2.

Figure 4-6. Transcriptional regulation by OLIG2 is dependent on its bHLH domain. (A) Schematic of the 2 OLIG2 bHLH domain mutants, OLIG2–Δb (left) and OLIG2–ΔHLH (right), created by site-directed mutagenesis. (B) Reporter gene assay with OLIG2 mutants revealed that OLIG2’s repressor activity on Ptch1 reporter is dependent on its bHLH domain. Numbers above bars indicate statistical significance of pairwise comparisons; for example, (1,5): significant difference from bars 1 and 5. *: significant difference from all. n = 3. Error bar: SD. (C) Schematic of 8X-Gli reporter from Sasaki et al. (1997). (D) Reporter gene assay revealed that OLIG2 can synergize with GLI2 to activate 8X-Gli reporter, and the activation is dependent on bHLH domain of OLIG2. n = 3. Error bar: SD.

4.2.5 OLIG3 shares similar transcriptional activity as OLIG2, while OLIG1 has a different transcriptional mechanism

The OLIG TF family consists of 3 members, OLIG1, OLIG2, and OLIG3. Previous studies have shown that OLIG1 and OLIG2 share overlapping functions in specifying motor neurons and the maturation of oligodendrocytes (Zhou & D. J. Anderson 2002). OLIG3 function is less well understood, however it shares high amino acid sequence homology with OLIG2, particularly within the bHLH domain. Additionally, all 3 OLIG TFs are highly expressed in SHH MB mouse models as revealed by RNA-seq analysis (Figure 4-1B). To investigate whether OLIG1/2/3 have similar transcriptional activities, our lab technician Mary Zhang and I performed reporter gene assays with OLIG1 and OLIG3 on the Ptch1 reporter (Figure 4-7). Results suggested that the transcriptional activity of OLIG3 is strikingly similar with OLIG2, both capable of strong repression of the GLI2-dependent activation of Ptch1. On the other hand, OLIG1 showed little effect on GLI2 activity, suggesting a divergence in transcriptional regulation.

Figure 4-7. Reporter gene assay revealed that transcriptional activity of OLIG3 is strikingly similar with OLIG2, while OLIG1 demonstrates a different transcriptional regulation of Ptch1 reporter. Numbers above bars indicate statistical significance of pairwise comparisons; for example, (1,3): significant difference from bars 1 and 3. *: significant difference from all. n = 3. Error bar: SD.

4.2.6 OLIG2 knockdown resulted in significantly reduced cell metabolic activity

To characterize the functional role of OLIG2 in SHH MB, I performed siRNA KD of OLIG2 in Vandy-MB-11. OLIG2 siRNA concentrations of 25, 50, and 75 nM all resulted in efficient KD of OLIG2 when compared to treatment of non-targeting control siRNAs as revealed by Western blot (Figure 4-8A). To understand OLIG2’s role in regulating Hh genes, I performed qRT-PCR of GLI2, GLI1, and PTCH1 and compared their expression levels upon transient knockdown of OLIG2. 25 nM of OLIG2 siRNA resulted in a slight upregulation of GLI1 and PTCH1, although non-significant (Figure 4-8B).

Next, I examined cell phenotype upon the KD of OLIG2 in Vandy-MB-11. MTS assay revealed that 25 nM of OLIG2 siRNA resulted in significant reduction of cell metabolic activity when compared to controls, which could indicate decreased cell proliferation or viability (Figure 4- 45

8C). The cell phenotype suggested that OLIG2 has a tumor-promoting role in SHH MB, which is consistent with clinical data (Figure 4-2A).

B C

Figure 4-8. OLIG2 siRNA treatment resulted in significantly decreased cell metabolic activity. (A) Western blot showed that 25 nM, 50 nM, 75 nM of OLIG2 siRNA in Vandy-MB-11 cells all resulted in efficient KD of OLIG2. (B) qRT-PCR comparing expression levels of OLIG2, GLI2, GLI1 and PTCH1 between OLIG2 KD and control (n = 3). GLI2, GLI1, and PTCH1 levels were not significantly affected upon KD of OLIG2. Error bar: SD. (C) 25 nM of OLIG2 siRNA treatment resulted in significantly reduced cell metabolic activity as revealed by MTS assay. n=16.

4.3 Discussion

OLIG2 belongs to the bHLH family of TFs and has well characterized roles in specification of motor neurons and oligodendrocytes (Q. R. Lu et al. 2002; Zhou & D. J. Anderson 2002; S.-K. Lee et al. 2005). Studies have reported that OLIG2 is associated with oncogenic pathways in glioma, and that its expression is required for tumor formation and malignant cell growth (Appolloni et al. 2012; Ligon et al. 2004; Ligon et al. 2007). However, its role in MB is unexplored. In this chapter, I characterized the role of OLIG2 in SHH MB, with a major focus on OLIG2’s transcriptional activity.

4.3.1 OLIG2 is a potential therapeutic target in SHH MB

Our results indicated that OLIG2 has a tumor-promoting function in SHH MB. First of all, OLIG2 is identified as candidate partner of GLI2 in ChIP-seq analysis (Figure 4-1A). Consecutive RNA-seq analysis revealed that Olig2 is upregulated in mouse SHH MB models when compared to WT controls (Figure 4-1B). Moreover, survival analysis of human patient data suggested that high OLIG2 expression is significantly correlated with poor prognosis of SHH MB patients (Figure 4-2A). In agreement with clinical data, RNA interference (RNAi) experiment knocking down OLIG2 in Vandy-MB-11 cells showed a clear reduction of cell metabolic activity upon transient loss of OLIG2 expression, which could be indicative of reduced cell proliferation or cell viability, suggesting that OLIG2 is responsible for promoting cell growth in SHH MB (Figure 4-8C).

4.3.2 OLIG2 binds to large number of GLI2 binding regions in SHH MB

In comparison to GLI2 binding to ~1500 sites in SHH MB (Yin et al. 2019), we discovered that OLIG2 binds to a large number of sites (~30,000) in SHH MB. This difference could reveal the different binding properties of TFs, as ChIP-seq of ATOH1, another bHLH TF, in normal cerebellum identified ~20,000 Atoh1 binding sites (Klisch et al. 2011). Furthermore, the wide association of OLIG2 to chromatin, with great enrichment in intronic and intergenic regions, could indicate that OLIG2 participates in epigenetic regulation, in addition to direct transcriptional regulation of its target genes (Figure 4-3A). In fact, a study by Yu et al. (2013) in immature oligodendrocytes have demonstrated that OLIG2 shares a vast majority of binding sites with BRG1, an ATP-dependent chromatin remodeler that is associated with transcriptional 47

activation, and their physical interaction could be detected with co-immunoprecipitation (Yu et al. 2013). Another study in neural progenitor cells also showed that OLIG2 could interact with HDAC1 and other members in the nucleosome remodeling and histone deacetylase (NuRD) complex (Meijer et al. 2014).

The comparative analysis of OLIG2 and GLI2 ChIP-seq revealed that OLIG2 binds to a significant portion (> 70%) of GLI2 binding sites, which associated with ~1200 shared target genes, highly suggesting that OLIG2 could cooperate with GLI2 to regulate Hh target genes in SHH MB (Figure 4-4A,B). Integrating the above information with RNA-seq data suggested that among the shared targets between OLIG2 and GLI2, 377 are significantly upregulated, while 357 are significantly downregulated in mouse MB samples. Consecutive functional enrichment analysis, as well as manual literature search, suggested that the upregulated genes are enriched with cancer-associated and MB-associated genes, including Gli2, Ptch1, Cxcr4, Atoh1, Cdk6, and Ccnd1 (Figure 4-4D). On the other hand, downregulated genes are enriched with voltage gated calcium channel activity. Voltage gated ion channels have been demonstrated in many studies to play a role in controlling tumor microenvironment, cell cycle progression, and metastasis. Specifically, voltage gated calcium channels (VGCCs) have been linked with reduction of proliferation and increase in apoptosis (Phan et al. 2017). Notably, many VGCCs exhibit low expression in brain tumors. Taken together, it is likely that OLIG2 might be activating cancer-promoting GLI2 target genes, while repressing GLI2 targets that might contribute to apoptotic or anti-proliferative functions.

4.3.3 Transcriptional repressor and activator functions of OLIG2

My study revealed that unlike ATOH1, OLIG2 demonstrated strong repressor activity on Ptch1 and Cxcr4 luciferase reporters (Figure 4-5C). Importantly, OLIG2 could potently repress the GLI2 and ATOH1-dependent activation of the 2 reporters, and to our surprise, the repressing function of OLIG2 persisted even when the E-box motif is mutated. This finding of OLIG2 is again different from ATOH1, which losses its activating activity upon E-box mutation. An E- box-independent transcriptional regulation of OLIG2 has never been reported before. Studies that have investigated the transcriptional mechanism of OLIG2 during normal development reported loss of DNA-binding and transcriptional activity upon mutation of canonical E-box

motif (S.-K. Lee et al. 2005; Meijer et al. 2014). Following this observation, we discovered that OLIG2 is capable of activating the 8X-Gli luciferase reporter synergistically with GLI2 (Figure 4-6D). This finding is especially intriguing as the 8X-Gli reporter does not contain any E-box motif, further assuring that OLIG2 is able to function in an E-box-independent manner. It is also important to note that the repressor function of OLIG2 is much more well-characterized than its activator function. Particularly, studies have demonstrated that key developmental roles of OLIG2 in specifying motor neurons and oligodendrocytes are channeled through its function as a repressor (Mizuguchi et al. 2001; Novitch et al. 2001; Zhou & D. J. Anderson 2002; S.-K. Lee et al. 2005). However, activator functions of OLIG2 have mostly been postulated through the downregulation of its target genes following OLIG2 KO or KD (Meijer et al. 2014; F. Lu et al. 2016). My study in this chapter serves as one of the few direct evidence of OLIG2’s activator function through luciferase reporter assay.

Further investigation of the mechanism of transcriptional regulation of OLIG2 through generation of OLIG2–Δb and OLIG2–ΔHLH mutants suggested that the transcriptional activity of OLIG2 relies on its binding to DNA, as OLIG2–Δb demonstrated no activity on the Ptch1 reporter (Figure 4-6B). On the other hand, OLIG2–ΔHLH mutant did repress the GLI2- dependent activation of Ptch1, although the repression was much weaker than WT OLIG2, which suggested that the oligomerization partner of OLIG2 contributes partly, but not fully, to the repressor activity of OLIG2. Similarly, when investigating the activator function of OLIG2, I discovered that the mutation in the basic domain resulted in a completely abolished transcriptional activity of OLIG2 (Figure 4-6D). Mutation in the HLH domain of OLIG2 also resulted in the ablation of synergistic activation with GLI2, however a slight repression of the GLI2-dependent activation of the 8X-Gli reporter could be observed. Together, these results suggested that OLIG2 requires DNA-binding to exert its transcriptional activity, while its dimerizing partner plays a prominent role in mediating its activator activity. Further experiments are needed to understand the switch between the repressor or activator activity of OLIG2, and how OLIG2 is able recognize a binding motif other than the canonical E-box.

4.3.4 OLIG2 and OLIG3 have similar transcriptional roles in SHH MB

OLIG family TFs share high amino acid homology, particularly within the bHLH domain. OLIG1 and OLIG2 have been reported to share overlapping expression and developmental roles, however, studies have also suggested that they might contain more divergent roles than shared functions (Kitada & Rowitch 2006; Arnett et al. 2004; Meijer et al. 2012). Specifically in terms of brain tumor development, OLIG1 expression is dispensable, whereas OLIG2 expression is required in certain subtypes of glioma (Ligon et al. 2007; Meijer et al. 2012). On the other hand, during developmental stages, OLIG3 showed a non-overlapping expression pattern when compared to OLIG1 and OLIG2, suggesting a divergent role (Takebayashi et al. 2002). Nonetheless, in terms of amino acid identity, OLIG2 is more closely related to OLIG3 than OLIG1 (Meijer et al. 2012). Survival analysis presented in this chapter demonstrated that the combined high expression of OLIG1 + OLIG2 as well as OLIG2 + OLIG3, resulted in the worst prognosis of patients, therefore suggesting that the TFs might have collaborative or redundant roles in SHH MB (Figure 4-2B). It is important to note that, due to the close proximity (< 40 kb apart) of OLIG1 and OLIG2 on human chromosome 21, they mostly share similar transcriptional profiles.

To investigate whether OLIG TFs could have redundant roles in SHH MB, I studied the transcriptional activity of all 3 TFs together. Results suggested that the transcriptional activity of OLIG3 is remarkably similar as OLIG2, with the ability to strongly repress the GLI2-dependent activation of Ptch1 reporter (Figure 4-7). However, OLIG1 demonstrated comparably weak activity, suggesting a divergent role in regulating Hh target genes. Thus, data from luciferase reporter assays suggested that OLIG2 and OLIG3 are more likely to share similar functions in SHH MB.

4.4 Future directions

4.4.1 Decipher OLIG2’s tumor-promoting role in SHH MB

Data from MTS assay showed that KD of OLIG2 resulted in significantly decreased cell metabolic activity, which could be indicative of reduced cell proliferation or increased cell death. To understand how OLIG2 is affecting cell growth, we would like to perform immunofluorescence staining of Ki-67, a proliferation marker, and Caspase-3, an apoptotic marker, of OLIG2 KD or KO cells compared to untreated and negative controls. BrdU staining could also be performed. Furthermore, to explore the oncogenic potential of OLIG2, we would overexpress OLIG2 in mouse neural progenitor cells, and examine cell phenotype upon enforced expression of OLIG2. Together, these studies will reveal whether OLIG2 is driving cell growth and has oncogenic functions in SHH MB.

4.4.2 Unravel the transcriptional regulatory functions of OLIG2

My data suggested that the DNA-binding ability of OLIG2 is essential for the transcriptional regulation of its downstream targets. To validate the finding, we would perform chromatin immunoprecipitation (ChIP) coupled to detection by quantitative real-time PCR (ChIP-qPCR) experiments to investigate if OLIG2 is exerting its transcriptional repressor and activator functions through directly binding to DNA.

Additionally, as my data from OLIG2 siRNA experiment demonstrated a non-significant change in GLI2, GLI1 and PTCH1 expression upon OLIG2 KD, it is possible that the expression of these Hh pathway genes are not mainly mediated by OLIG2, but by other factors such as GLI2 or ATOH1. However, this does not rule out the possibility of OLIG2 directly mediating the expression of other Hh and MB-associated genes. Moreover, my data suggested that OLIG2 is capable of both activating and repressing GLI2 target genes. Although some insight is provided through OLIG2 ChIP-seq and transcriptomic data of SHH MB mouse model, we cannot apply a direct relationship between OLIG2 and the upregulated/downregulated genes. To understand which downstream targets are dependent on the transcriptional regulation by OLIG2, we would perform OLIG2 KO experiments and consecutive RNA-seq profiling to compare the expression changes of OLIG2 target genes of OLIG2 WT and OLIG2 KO populations. Through this

proposed experiment, we could identify the target genes that require regulation by OLIG2 in SHH MB, and the findings could be further validated by ChIP-qPCR.

My data from the luciferase reporter assays of OLIG2–ΔHLH mutant suggested that the dimerizing partner of OLIG2 is important in mediating OLIG2’s transcriptional output. Moreover, given the wide association with chromatin in SHH MB discovered in the OLIG2 ChIP-seq experiment presented in this chapter, we speculate that OLIG2 could function in modifying chromatin structure and regulating gene expression in an epigenetic manner in SHH MB. As mentioned previously, OLIG2 has been identified to interact with histone modifying complex NuRD and chromatin remodeler BRG1 (Meijer et al. 2014; Yu et al. 2013). To test the hypothesis of OLIG2 participating in epigenetic regulation and characterize the oligomerization partners of OLIG2 to understand how it is exerting its transcriptional activity in MB setting, we would perform OLIG2 IP-MS in GFAP-Cre;Sufuf/f;Spopf/f mice to identify the OLIG2 partners in SHH MB. The protein interaction network of OLIG2 will shed light on the mechanism of OLIG2’s regulation of its downstream targets. Furthermore, the partners of OLIG2 could be key to deciphering the switch between its activating and repressing roles.

4.4.3 Study overlapping roles between OLIG TFs

In this chapter, I showed that OLIG2 and OLIG3 displayed remarkably similar transcriptional activities, therefore we hypothesize that OLIG2 and OLIG3 share overlapping roles in SHH MB. Additionally, I have discovered that Olig3 is associated with a super-enhancer region that is unique in SHH MB but not found in WT, and super-enhancers are known to be important in driving aberrant expression of oncogenes (Figure 4-9). To unmask the role of OLIG3, we would perform OLIG3 siRNA experiments to first study if OLIG3, like OLIG2, exerts a tumor- promoting role in SHH MB. Following the single siRNA KD, we would perform double siRNA KD of OLIG2 and OLIG3 to study whether there are any additive phenotypes upon the KD of both genes. This study could reveal whether OLIG3 is also participating in SHH MB, and if OLIG2 and OLIG3 are functioning redundantly in the tumor.

Figure 4-9. Olig3 is associated with an MB-specific super-enhancer region. UCSC genome browser screenshot showing H3K36me3, marker of actively transcribed regions, and H3K27ac, marker of promoter and enhancer regions, in genomic region encompassing Olig3 from ChIP-seq in GFAP-Cre;Sufuf/f;Spopf/f.

Chapter 5 Conclusion

My project investigated the molecular actions of downstream components of Hh pathway, aiming to address the lack of targeted therapeutics of SHH MB driven by mutations downstream of SMO. In the first half of my study, I generated CRISPR/Cas9-mediated SUFU KO cells using 2 different human SHH MB cell lines, Vandy-MB-11 and DAOY. The SUFU KO cell lines allow us to study the molecular mechanisms of SUFU in a human context, and I demonstrated that the deletion of SUFU resulted in great reduction of GLI2 protein, suggesting that SUFU functions to maintain high GLI2 expression in SHH MB. This finding supported our observations in SHH MB mouse models, in which we reported that SUFU protects GLI2 from proteosomal degradation and thus contributes a tumor-promoting function in SHH MB (Yin et al. 2019). The SUFU KO cell lines generated in this study serve as a useful model to further characterize the molecular actions of human SUFU in SHH MB. In the second half of my study, I focused on the role of OLIG2. Prior to my study, OLIG2 has only been extensively characterized in glioma with regards to tumor development (Ligon et al. 2007; F. Lu et al. 2016; Suvà et al. 2014). Here, I reported that OLIG2 has a tumor-promoting role in SHH MB through survival analysis and siRNA KD studies. Additionally, I characterized the genome-wide binding profile of OLIG2 in SHH MB by ChIP-seq, and through comparative analysis discovered that OLIG2 and GLI2 share a large number of target genes (~1200 genes). Through reporter gene assays, I demonstrated that OLIG2 can both activate and repress GLI2 target genes, and these actions depend on the binding of DNA through its basic domain. Although the molecular mechanisms of OLIG2 contributing to the tumorigenesis of SHH MB are not yet fully characterized, my studies provided promising data suggesting that OLIG2 is a novel molecular target in SHH MB.

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