Specific communication between enhancers and alternative promoters … · 2019-04-03 · Specific communication between enhancers and alternative promoters mediates finer regulation

Specific communication between enhancers and alternative promoters mediates finer regulation of transcription initiation

by

Jorge A. Bardales Mendieta

A dissertation submitted in partial satisfaction of the

requirements for the degree of Doctor of Philosophy

in

Biophysics

in the

Graduate Division

of the

University of California, Berkeley

Committee in charge:

Professor Xavier Darzacq, Chair Professor Jennifer Doudna Professor Hernan Garcia Professor Robert Tjian

Fall 2018


Copyright 2018

by Jorge A. Bardales Mendieta

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Abstract


by

Jorge A. Bardales Mendieta

Doctor of Philosophy in Biophysics

University of California, Berkeley

Professor Xavier Darzacq, Chair Enhancers are DNA regulatory elements that play an important role in the precise regulation of Pol II transcription initiation and, consequently, in the generation and maintenance of patterns of gene expression. A defining characteristic of enhancers is their capacity to regulate transcription from their target promoter, independent of the genomic distance that separates them. Importantly, in order for enhancers to exert their regulatory action, they must first relocalize into close spatial proximity to their target promoter and form long-range interactions. Although these interactions have been demonstrated to occur, the mechanisms that allow their specific and efficient formation remains largely obscure. In this thesis, I focus on the development of tools and the expansion of our understanding with regard to this important topic. In the first chapter, I begin by providing a perspective of transcription initiation as a hub of gene regulation. Then, I perform a concise overview of promoters and enhancers as DNA regulatory elements and share the most important functional and structural characteristics. I finalize the chapter by discussing the known regulatory mechanisms that allow the formation of specific interactions between enhancers and promoters to take place. In the second chapter, I present my findings on the development of imaging systems to permit the visualization of enhancer-promoter interactions in live cells. I begin by describing the limitations of current systems. Then, I discuss the importance of the development of novel, innovative systems that allow specific labelling of genomic loci. From there, I present the possibilities and limitations of a developed dCas9/Aptamer system for the visualization of repetitive and non-repetitive loci. I also present my findings on the development of a two-step integration system of operator arrays and share the principal technical limitations encountered in its development. I finish by discussing and

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providing a critical perspective of relevant published work that has been produced since these efforts were undertaken. In the third chapter, I begin by identifying and characterizing potential enhancers in the PCSK9 locus in hepatic derived cells. I identify three candidate enhancers with strong transcriptional activity by reporter assays. Then, I demonstrate how the regulatory activity of these candidate enhancers is specific to hepatic cells and their regulatory activity can be modulated by two small molecules in a similar fashion to the endogenous PCSK9 gene. From there, I dissect these DNA regulatory sequences to identify transcription factor binding sites responsible for transcriptional activity by bioinformatic analysis and fine mutagenic studies. These results point towards the presence of three DNA regulatory elements required for PKCS9 expression. In the fourth and final chapter, I investigate two alternative promoters (P1 and P2) that drive MYC expression and are regulated by enhancers. The two MYC promoters can be differentially regulated across cell-types, and their selective usage is largely mediated by distal regulatory sequences. Moreover, in colon carcinoma cells, Wnt-responsive enhancers prefer to upregulate transcription from the P1 promoter, using reporter assays in the context of the endogenous Wnt induction. In addition, multiple enhancer deletions using CRISPR/Cas9 corroborate the regulatory specificity of P1. Finally, I explore how preferential activation between Wnt-responsive enhancers and the P1 promoter is influenced by the distinct core promoter elements that are present in the MYC promoters. Taken together, these results provide new insight into how enhancers can specifically target alternative promoters and suggest that the presence of alternative promoters could create a more efficient formation of long-range interactions and a more precise combinatorial regulation of transcription initiation.

To my parents, Fany Mendieta and Jorge Bardales, your love and sacrifice has always inspired me to do more.

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Contents

CHAPTER 1 .................................................................................................................... 1 INTRODUCTION ................................................................................................................ 1 1.1 TRANSCRIPTION INITIATION, A MAJOR HUB OF GENE REGULATION .................... 2 1.2 THREE TYPES OF DNA REGULATORY ELEMENTS ARE INVOLVED IN THE REGULATION OF TRANSCRIPTION INITIATION ............................................................... 3 1.3 THE CORE PROMOTER AND ITS MOTIFS ................................................................. 6 1.3.1 The TATA box ...................................................................................................... 6 1.3.2 The BRE motifs ..................................................................................................... 7 1.3.3 The Initiator motif .................................................................................................. 7 1.3.4 The DPE motif ....................................................................................................... 8 1.3.5 The MTE motif ...................................................................................................... 8 1.3.6 The TCT motif ...................................................................................................... 8 1.4 A GENOMIC PERSPECTIVE OF THE CORE PROMOTER ............................................ 9 1.5 ENHANCERS, POSITIVE MODULATORS OF TRANSCRIPTION INITIATION ............ 11 1.6 ENHANCERS-PROMOTER COMPATIBILITY ........................................................... 14

CHAPTER 2 .................................................................................................................. 16 EFFORTS TOWARDS THE VISUALIZATION OF ENHANCER-PROMOTER INTERACTIONS IN LIVE CELLS ................................................................................................................. 16 2.1 MOTIVATION .......................................................................................................... 16 2.2 DCAS9-APTAMER SYSTEM TO VISUALIZE CHROMATIN DYNAMICS ................... 18 2.2.1 Development of a dCas9-Aptamer system ........................................................... 20 2.2.2 Labeling of telomeres with the dCas9-Aptamer system ....................................... 20 2.2.3 Labeling of a single non-repetitive sequence with the dCas9-Aptamer system .... 22 2.3 DEVELOPMENT OF A TWO-STEP PROCESS FOR DIRECTED INTEGRATION OF OPERATOR ARRAYS TO STUDY CHROMATIN DYNAMICS. ............................................. 24 2.3.1 Generation of optimized LacO and TetO arrays .................................................. 26 2.3.2 Generation of stable cell lines expressing EGFP-LacI and TetR- mCherry fusion proteins ............................................................................................................................ 28 2.3.3 Genomic integration of a mCherry cassette adjacent to MYC enhancer ............... 28 2.3.4 Optimization of RMCE step with a EGFP cassette ............................................ 28 2.3.5 Integration of the LacOx56 array by RMCE ........................................................ 30 2.3.6 In vivo imaging of a 56 repeats LacO array ........................................................... 30 2.3.7 Attempted recombination of the mCherry cassette with arrays of 112 and 224

LacO repeats .…………………………………………………………………….31 2.4 DISCUSSION ............................................................................................................ 32 2.5 METHODS ............................................................................................................... 33

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2.5.1 Cell Culture .......................................................................................................... 33 2.5.2 Transfection Conditions for Imaging Experiments .............................................. 33 2.5.3 Generation of stable cell lines ............................................................................... 34 2.5.4 Double thymidine block synchronization ............................................................. 34 2.5.5 Genome editing experiments ................................................................................ 34 2.5.6 Flow cytometry assays ........................................................................................... 34 2.5.7 RMCE of mCherry cassette ................................................................................. 35 2.5.8 Cell Imaging ......................................................................................................... 35 2.5.9 Design and expression of gRNAs ......................................................................... 35 2.6 APPENDIX ............................................................................................................... 39 2.6.1 Fusion Protein used for loci labelling and Imaging ............................................... 39 2.6.2 Operator Arrays DNA Sequences ......................................................................... 44 2.6.3 Locus where mCherry was integrated adjacent to MYC -335 kb .......................... 45

CHAPTER 3 .................................................................................................................. 46 IDENTIFICATION, CHARACTERIZATION AND DISSECTION OF PCSK9 CANDIDATE ENHANCERS .................................................................................................................... 46 3.1 MOTIVATION .......................................................................................................... 46 3.2 IDENTIFICATION AND CHARACTERIZATION OF POTENTIAL ENHANCERS IN THE PCSK9 LOCUS ................................................................................................................. 47 3.2.1 Mapping potential enhancers in the PCSK9 locus in HEPG2 cells ..................... 47 3.2.2 PCSK9 candidate enhancers activate transcription by reporter assays ................... 47 3.2.3 PCSK9 candidate enhancers transcriptional activity is tissue-specific ................... 49 3.2.4 Candidate enhancer transcriptional activity can be modulated by small molecules known to regulate PCSK9 gene expression. ..................................................................... 50 3.3 DISSECTION OF CANDIDATE ENHANCERS HSS6, HSS8 AND HSS11 AND IDENTIFICATION OF KEY TRANSCRIPTION FACTOR BINDING SITES. .......................... 52 3.3.1 Dissection of HSS6, HSS8 and HSS11 candidate enhancers ............................... 52 3.3.2 In-silicon mapping of transcription factor binding sites ....................................... 53 3.3.3 Identification of biologically important transcription factor binding sites within the HHS6-1, HSS8-2, HSS11-1 & HSS11-3 fragments ..................................................... 54 3.4 ATTEMPTED GENOME DELETION OF PCSK9 CANDIDATE ENHANCERS HSS6, HSS8 AND HSS9 ............................................................................................................ 58 3.5 DISCUSSION AND FUTURE DIRECTIONS ............................................................... 60 3.6 METHODS ............................................................................................................... 62 3.6.1 Cell culture ............................................................................................................ 62 3.6.2 Real-time qPCR ................................................................................................... 62 3.6.3 Reporter luciferase assays ...................................................................................... 62 3.6.4 Genome editing .................................................................................................... 62 3.6.5 T7E1 Assay ........................................................................................................... 63 3.7 APPENDIX ............................................................................................................... 63

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3.7.1 Candidate enhancer sequences .............................................................................. 63 3.7.2 Fragments of HSS6, HSS8 & HSS11 .................................................................. 67 3.7.3 Primers for real-time qPCR .................................................................................. 68

CHAPTER 4 .................................................................................................................. 69 SELECTIVE ACTIVATION OF ALTERNATIVE MYC CORE PROMOTERS BY WNT-RESPONSIVE ENHANCERS ............................................................................................. 69 4.1 MOTIVATION .......................................................................................................... 69 4.2 MYC ALTERNATIVE PROMOTERS CAN BE DIFFERENTIALLY REGULATED ......... 70 4.3 WNT-RESPONSIVE ENHANCERS PREFERENTIALLY ACTIVATE THE P1 PROMOTER ..................................................................................................................... 74 4.4 WNT-ENHANCER DELETIONS PREFERENTIALLY DOWNREGULATE TRANSCRIPTION FROM THE P1 PROMOTER ................................................................. 77 4.5 DISTINCT CORE PROMOTER ARCHITECTURE INFLUENCES SPECIFIC ENHANCER-PROMOTER COMMUNICATION ................................................................ 80 4.6 DISCUSSION ............................................................................................................ 82 4.7 METHODS ............................................................................................................... 84 4.7.1 CAGE data analysis .............................................................................................. 84 4.7.2 Cell culture ............................................................................................................ 84 4.7.3 Cell growth curves ................................................................................................ 84 4.7.4 Quantification of P1 and P2 promoter activities ................................................... 85 4.7.5 Reporter luciferase assay ........................................................................................ 85 4.7.6 Real-time qPCR ................................................................................................... 85 4.7.7 Genome Editing ................................................................................................... 85 4.8 APPENDIX ............................................................................................................... 86 4.8.1 Luciferase reporter sequences ................................................................................ 86 4.8.2 Sequence of core promoter mutants ...................................................................... 87 4.8.3 Primers for real-time qPCR .................................................................................. 88 REFERENCES .................................................................................................................. 89

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List of Figures Figure 1: Regulation of transcription initiation ................................................................. 4 Figure 2: The core promoter and some of its motifs ......................................................... 7 Figure 3: The core promoter in the genomic context ...................................................... 10 Figure 4: Enhancer activation drives the formation of stable long-range enhancer-promoter interactions and transcription upregulation ..................................................... 13 Figure 5: Networks of long-range enhancer-promoter interaction occur in the genome 15 Figure 6: An ideal imaging system to visualize chromatin dynamics ............................... 17 Figure 7: A dCas9-Aptamer system to visualize chromatin dynamics ............................ 19 Figure 8: Labeling of Telomeres with the dCas9-Aptamer system ................................. 21 Figure 9: Labeling of a singly-integrated EGFP locus .................................................... 23 Figure 10: Frequency distribution of nuclear foci with specific and non-specific sgRNAs......................................................................................................................................... 24 Figure 11: A schematic representation of the two-step process ideated for directed integration of operator arrays ........................................................................................... 26 Figure 12: Generation of optimized operator arrays ........................................................ 27 Figure 13: Generation of Stable cell lines expressing EGFP-LacI and TetR-mCherry .. 29 Figure 14: Genotyping the integration of the mCherry Cassette .................................... 29 Figure 15: Optimization of the RMCE step ................................................................... 30 Figure 16: Integration of the LacO x56 array by RMCE ................................................ 31 Figure 17: Possible conformations of the modified gRNA harboring MS2 loops by UNAfold ......................................................................................................................... 37 Figure 18: Possible conformations of the modified gRNA harboring PP7 loops by UNAfold ......................................................................................................................... 38 Figure 19: Mapping of DNA regulatory elements present within the PCSK9 locus ...... 48 Figure 20: Reporter activity of 11 the candidate enhancers and control sequences on HEPG2 cells ................................................................................................................... 48 Figure 21: Reporter activity of 11 the candidate enhancers and control sequences on REP-1 hTERT cells ....................................................................................................... 49 Figure 22: Relative mRNA levels of PCSK9 and DHCR24 under the presence of different concentrations of lovastatin (Lova) and berberine (Bbr) ................................... 51 Figure 23: Reporter activity of candidate enhancers under the presence of lovastatin and berberine .......................................................................................................................... 52 Figure 24: Dissection candidate enhancer HSS6, HHS8 and HSS11 ............................ 53 Figure 25: Mapping of important binding sites within fragment HHS6-1 with strong transcriptional activity ..................................................................................................... 55 Figure 26: Mapping of important binding sites within fragment HHS8-2 with strong transcriptional activity ..................................................................................................... 56 Figure 27: Mapping of important binding sites within fragment HHS11-1 with strong transcriptional activity ..................................................................................................... 57

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Figure 28: Mapping of important binding sites within fragment HHS11-3 with strong transcriptional activity ..................................................................................................... 59 Figure 29: CRISPR/Cas9 editing on HEPG2 cells is highly inefficient ......................... 60 Figure 30: The PCSK9 promoter region forms significant long-range interactions with the upstream region that harbors candidate enhancers .................................................... 61 Figure 31: MYC transcription is driven by two alternative promoters which are differentially regulated in vivo ......................................................................................... 72 Figure 32: Total MYC expression by CAGE on Adipocyte and Neuronal differentiation......................................................................................................................................... 73 Figure 33: In HCT-116 cells, activation of Wnt-responsive enhancers preferentially upregulates the P1 promoter ........................................................................................... 75 Figure 34: Activity of MYC’s promoters by luciferase assay ............................................ 75 Figure 35: Activation of Wnt-responsive enhancers preferentially upregulates the P1 promoter by reporter assays ............................................................................................. 76 Figure 36: Wnt-responsive enhancer deletions preferentially downregulate transcription from the P1 promoter ...................................................................................................... 78 Figure 37: Growth curves of single enhancer deleted HCT-116 clonal lines .................. 79

Figure 38: Promoter architecture mediates differential enhancer communication .......................................................................................... 81

Figure 39: Effect of core promoter mutants on P2 basal activity and fold activation ….. 82

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Acknowledgments

This thesis synthesizes my last five years of work in graduate school. During this time, I have had the opportunity to meet brilliant and inspiring people, many of whom I call my friends. Over the years, they have been a source of advice, guidance, and support. Without them, I would not have been able to get this far. That is why it is important to begin with my gratitude to them.

I would like to start by thanking my advisor, Professor Xavier Darzacq, who has been extremely supportive of me and my work. He has given me the freedom to explore different ideas, test them, learn from my mistakes, and forge a pathway to new knowledge. I want to thank him for his constant support, for always providing an insightful perspective and, above all, for allowing me to pursue my dreams.

I also want to thank Professor Jennifer Doudna for allowing me to initiate my graduate work in her lab. This gave me the opportunity to develop first-hand experience of the fascinating world of CRISPR, which has greatly broadened my perspective. I also want to thank her for always providing honest and precise advice.

In addition, I want to thank several key people that contributed to my scientific and professional growth. I would like to acknowledge Brett Staahl for his guidance and support, showing me the essentials of mammalian cell work. Liangqi (Frank) Xie also provided extensive advice and assistance with genome editing experiments. Hideya Kawaji and Yasuhiro Murakawa were fantastic collaborators, furthering my understanding of MYC alternative promoter usage. Likewise, Claudia Cattoglio and Sharon Torigoe offered their support with the promoter mutant reporter assays. I would also like to thank past and present members of the Tjian-Darzacq lab who created and maintained an outstanding scientific and friendly environment. I especially want to thank Alec Heckert, Wulan Deng, and David McSwiggen.

Special thanks to IGSM and the GH brothers. My time with you has complemented my graduate life, allowing me to grow and become a better person. Wesley Wu, Larry Tang, Sam Lee, and Dai Wang – I really appreciate your friendship and the time we spent together.

I also want to thank my family and parents for their constant support, for always caring about me, and for being curious about my research.

Finally, I want to thank Cintya. Thank you for being you, for always having time for me, and for being a constant source of support. I feel incredibly blessed by your friendship.

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Chapter 1

Introduction

“An important aspect of gene expression during development and cellular differentiation occurs at the level of gene transcription. Only a fraction of the total genetic potential of any cell is ever expressed, and yet this fraction must be capable of changing to accommodate the processes of development and respond to normal physiological stresses in the organism. Thus, the regulation of gene expression is basic to the living cell. Despite the central importance of this issue, little is known regarding the molecular mechanisms involved in the process”.

Bert W. O'Malley, Howard C. Towle and Robert J. Schwartz

Regulation of gene expression in eucaryotes Annual Review of Genetics, 1977

We have come a long way since these lines were originally written. Over the last four decades our understanding of transcription and its regulation has largely expanded and deepened at many different levels. For example, we now have a good comprehension of the key steps that lead to gene transcription, the protein complexes that drive each of those steps, and the principal mechanisms involved in its regulation. Yet, despite the great progress, our understanding of the transcription is still very far from complete, with many important questions remaining unanswered. One of the most important and interesting of such questions pertains to the mechanisms that promote the specific formation of interactions between enhancers and their core promoters. Despite it having been over 30 years since enhancers were first described, very little is known about the mechanisms that allow the specific and efficient formation of enhancer-promoter interactions, especially if we take into consideration that they are formed in the dynamic and complex nuclear environment.

My graduate research work has revolved around shedding some light on this important and puzzling question. Here, I present the sum of my efforts. In the first part, I begin by sharing a concise review of our current understanding of the

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regulation of transcription initiation, focused on enhancer-promoter interactions. Then, I present my efforts in the development of a system to visualize the dynamics between enhancer and promoter interactions in live cells, followed by the characterization of the regulatory regions of a medically relevant locus in human cells. Finally, I share my work on how alternative promoters can be selectively regulated by enhancers during transcription initiation. I hope the reader finds this work interesting and will further motivate them to investigate and expand our knowledge in this exciting field.

1.1 Transcription initiation, a major hub of gene regulation Transcription can be defined as the series of molecular events that lead to the synthesis of an RNA molecule from a DNA template, usually encompassing a gene sequence. In eukaryotes, transcription is carried out by three major RNA polymerases: polymerase I (Pol I), polymerase II (Pol II) and polymerase II (Pol III). Each of these polymerases is specialized in a specific subset of genes. While Pol I and Pol III are involved in the transcription of strictly non-coding genes, such as transfer RNAs and ribosomal RNAs, Pol II directs the transcription of protein-coding genes and certain types of small RNAs. Importantly, these three RNA polymerases possess similar catalytic cores in charge of RNA transcription, but have significantly diverged in their regulatory domains, mainly due to the unique mechanisms involved in their specific regulation of each of these polymerases (Carter and Drouin, 2009; Khatter et al., 2017; Sainsbury et al., 2015).

Of these three polymerases, Pol II is likely to have the most diverse and precise mechanisms of transcriptional regulation. In fact, Pol II has to be regulated in a precise and dynamic fashion to drive the expression of thousands of protein-coding genes to allow extremely important biological processes such as cellular differentiation, cell identity maintenance and adaptation to environmental changes (Carter and Drouin, 2009; Sainsbury et al., 2015). Pol II transcription is regulated by multiple and diverse mechanism that act along the whole transcriptional cycle, from initiation to termination, but are known to preferentially act during transcription initiation, making transcription initiation a major hub of gene regulation (Fuda et al., 2009; Loya and Reines, 2016; Mischo and Proudfoot, 2013).

Transcription initiation can be defined as the series of steps that lead Pol II and the basal transcription factors to bind and assemble into a transcription-competent preinitiation complex. In-vitro biochemical experiments supported by more contemporary electron microscopy studies support a stepwise model of transcription initiation (Grünberg and Hahn, 2013; He et al., 2013). Even though -if we take into consideration complementary single-molecule experimental evidence (Lim, 2018; Zhang et al., 2016b)- this model probably has to be at least

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partially reconsidered, it still provides a good perspective on the necessary molecular steps that need to occur before the initiation of transcription.

According to the step-wise model, the first event of transcription initiation occurs when the TATA-binding protein (TBP) or the full TFIID complex binds to the TATA sequence, a specific AT-rich motif that is present within many core promoters. This newly formed DNA-protein higher complex promotes the subsequent binding of TFIIB to a DNA motif adjacent to the TATA sequence, forming a larger complex. Once this larger DNA-protein complex has been formed, the Pol II-TFIIF complex can further bind to extend the DNA-protein interactions along the promoter. Subsequently, the final two basal transcription factors, TFIIE and TFIIH, can then be recruited to form a preinitiation complex. This leads to TFIIH catalyzing the unwinding of the promoter DNA in and ATP-dependent process allowing Pol II to localize its catalytic domain around the transcriptional start site to form a preinitiation complex that is ready to initiate transcription and proceed to elongation. The events that lead to the formation of the preinitiation complex are more extensively discussed in the following reviews (Conaway and Conaway, 1993; Roeder, 1996; Sainsbury et al., 2015).

The formation of the preinitiation complex is punctiliously modulated by a large number of transcriptional regulatory elements. These regulatory elements are diverse in their nature and can include DNA regulatory sequences, transcription factors, large protein complexes, non-coding RNAs and nuclear architectural proteins (Allen and Taatjes, 2015; Andersson et al., 2015; Danino et al., 2015; Engreitz et al., 2016; Mehta et al., 2013). In order for these regulatory elements to exert their regulatory action on transcription initiation they first need to come into close spatial proximity around the core promoter. Once this occurs, they can modulate the formation of the preinitiation complex and subsequent events that lead to Pol II transcription (Lenhard et al., 2012; Erokhin et al., 2015) (Figure 1.1).

1.2 Three types of DNA regulatory elements are involved in the regulation of transcription initiation

In eukaryotes, multiple DNA regulatory elements work together to mediate precise regulation of transcription initiation. These DNA regulatory elements allow the binding of the transcription machinery and other important regulatory factors, and together regulate many of the events that lead to Pol II transcription initiation. These DNA regulatory elements can be classified into three distinct types based on their functional role and their location with respect to the gene transcription start site (TSS): namely, the core promoter, the proximal promoter element and enhancers.

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Figure 1: Regulation of transcription initiation. A schematic representation of some important regulatory elements that regulate the rate of Pol II transcription initiation. The core promoter allows the binding of the basal transcription machinery and Pol II and its assembly into a preiniation complex. The assembly of the preiniation complex can be regulated by multiple DNA regulatory elements such as the proximal Promoter Element (PE) and distal Enhancers (E1 & E2). These elements when, bound by a specific transcription factor, relocate into close proximity to the core promoter and interact with a mediator, a large protein complex, and other protein complexes to promote transcription activation. Cohesin, CTCF (not shown) and non-coding RNAs (ncRNAs) are regulatory elements involved in the establishment and stability of the long-range chromatin interactions formed between the different types of DNA regulatory elements.

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The core promoter is the DNA regulatory element that allows the binding of the different components of the basal transcription machinery and their subsequent assembly into a preinitiation complex (Danino et al., 2015; Ngoc et al., 2017). The core promoter is usually regarded as the sequence that typically extends from -40 to +40 bp around the TSS, although this can vary depending on certain nuances of each specific gene (Roy and Singer, 2015). The core promoter is a highly diverse element (Danino et al., 2015; Lenhard et al., 2012). This diversity is used by the core promoter to modulate the binding of the basal transcription machinery and consequently its basal transcriptional strength (Danino et al., 2015; Juven-Gershon and Kadonaga, 2010; Ngoc et al., 2017).

The proximal promoter element is the DNA regulatory element that usually spans a few hundred base pairs and is predominantly located upstream of the core promoter (Narlikar and Ovcharenko, 2009). The proximal promoter region is enriched in transcription factor binding sites and due to its close proximity to the core promoter, it can regulate transcription initiation in a more straightforward fashion.

Enhancers are distal DNA regulatory sequences that predominately upregulate the rate of transcription initiation. They are extremely versatile regulatory elements that can mediate their effects to a large extent independently of distance, position and orientation with respect to the core promoter (Bulger and Groudine, 2011; Erokhin et al., 2015). In a similar fashion to the proximal promoter region, enhancers are usually sequences that are several hundred base pairs long and highly enriched in transcription factor binding sites. Interestingly, enhancers can be located very distally from their target promoter, even farther than one megabase in some extreme cases (Lancho and Herranz, 2018; Paleske et al., 2014; Yashiro-Ohtani et al., 2014). Multiple studies have shown that in order for enhancers to mediate their positive effects on transcription initiation, they first need to be in close spatial proximity to their target promoter (Bulger and Groudine, 2011; Erokhin et al., 2015). Finally, enhancers are highly abundant across the genome and play an essential role in the precise control of gene expression (Andersson et al., 2014; Coppola et al., 2016; Ong and Corces, 2011).

Of special interest to this thesis are the mechanisms that promote the formation of enhancer-promoter interactions. These are not only important from a functional perspective, as previously explained, but are also extremely interesting mechanistically since they have to overcome important challenges such as the long genomic distances that separate them in the congested nuclear space. To better understand the mechanisms that mediate these long-range interactions, I will first describe the most important structural properties of these two DNA regulatory elements before focusing on the most important mechanisms that have been described to mediate their specific interactions.

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1.3 The core promoter and its motifs

Early experimental work with strong viral and endogenous promoters allowed us to initially characterize the role of the core promoters in transcription initiation. The core promoter can be defined as a DNA regulatory sequence that supports the formation of the preinitiation complex and correct positioning of the Pol II to initiate transcription. Biochemical and genetic analyses have demonstrated that within the core promoter sequence certain motifs can regulate the binding of many of the components of the transcription machinery, and increase the promoter’s transcriptional strength (Danino et al., 2015; Kadonaga, 2012; Ngoc et al., 2017), and even its regulation by distal enhancers (Danino et al., 2015). These motifs facilitate the binding of the different components of the basal transcription machinery, predominately TFIID (Kadonaga, 2012; Thomas and Chiang, 2006). They include the TATA box, upstream TFIIB recognition element (BREu), downstream TFIIB recognition element (BREd), initiator (Inr), downstream promoter element (DPE), and motif ten element (MTE). (Figure 1.2). These motifs are not universal and are only present in subsets of core promoters (Butler and Kadonaga, 2002). A brief description of some of the most prevalent core promoter motifs and their importance in transcription regulation is presented next:

1.3.1 The TATA box The TATA box is the first discovered core promoter motif, and as such, its role in the formation of the preinitiation complex and on the basal promoter strength has been extensively studied in eukaryotes (Kadonaga, 2012; Ngoc et al., 2017). The TATA box is a thymine- and adenine-rich stretch of DNA with a TATAWAWR consensus sequence (Haberle and Stark, 2018). The TATA box allows the more efficient binding of the TFIID complex or its subunit, TBP, to the promoter sequence. Biochemical and genomic analyses have uncovered that the best positioned TATA boxes are usually -31 to -24 bp with respect to the TSS (Haberle and Stark, 2018). The TATA box exerts its positive effects on transcription by allowing a more efficient binding of the TATA binding protein (TBP) and consequently of the full TFIID complex to the promoter (Ngoc et al., 2017; Reeve, 2003). It is interesting to note that the TATA box and TBP are both conserved from archaebacteria to humans (Reeve, 2003). Although the TATA box is only present in 10%–15% of mammalian core promoters, its presence is also conserved throughout evolution (Carninci et al., 2006; Deng and Roberts, 2005; Haberle and Stark, 2018), strongly suggesting that the TATA box plays an important role in the regulation of a significant number of protein-coding genes.

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1.3.2 The BRE motifs The TFIIB recognition element (BRE) motifs are conserved sequences located immediately upstream (BREu) or downstream (BREd) of the TATA box element and that allow and facilitate an efficient binding of TFIIB. Of the two BRE motifs, the BREu possess the most conserved motif, having a SSRCGCC consensus sequence. In the majority of promoters, the TATA box is only surrounded by one BRE motif. The BRE motifs work in conjunction with the TATA box to regulate the basal promoter strength. Interestingly, more recent studies also suggest that the BREu can also help in the precise control of transcription regulation through the modulation of transcriptional activation by distal regulatory sequences. 1.3.3 The Initiator motif Of all the core promoter motifs, the initiator (Inr) is the most commonly occurring one and it is positioned in a way that directly overlaps with the TSS (Carninci et al., 2006; Corden et al., 1980; Smale and Baltimore, 1989). Similar to the TATA box, the presence of an Inr motif allows a more efficient binding of the TFIID and strengthens the basal transcriptional activity of the promoter (Haberle and Stark, 2018; Ngoc et al., 2017). The Inr motif is conserved throughout metazoans, having a consensus sequence of TCAGTY in Drosophila and a BBCABW or a CA in humans (Carninci et al., 2006; Ngoc et al., 2017; Smale and Baltimore, 1989). The adenine nucleotide in the Inr consensus sequence is usually designed as the “A+1”

Figure 1: The core promoter and some of its motifs. A schematic representation of the core promoter (-40 to +40 from the TSS). These motifs include the BREu and BREd (TFIIB recognition element, upstream and downstream), TATA box, Inr (Initiator), MTE (motif ten element), DPE (downstream core promoter element), TCT (polypyrimidine initiator). The locations of the motifs are drawn roughly to scale. The BREu, TATA, Inr, MTE, DPE, and TCT motifs have been found in both Drosophila and humans. These motifs are typically found in focused core promoters.

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position or TSS, whether or not most transcription actually initiates on that particular nucleotide. This convention was established because the other core promoter motifs, like the downstream core promoter element (DPE) and motif ten element (MTE), function cooperatively with the Inr in a manner that is sensitive to their distance with respect to the first adenine (Kadonaga, 2012; Lim et al., 2004).

1.3.4 The DPE motif The downstream core promoter element (DPE) motif was first identified as a TFIID recognition site downstream of the Inr present in promoters lacking a TATA box (Burke and Kadonaga, 1996). The spacing between the DPE motif and the Inr has been shown to be critical for transcriptional activity of DPE-dependent promoters, being exactly located from +28 to +33 relative to the A+1 site (Burke and Kadonaga, 1997; Kutach and Kadonaga, 2000). The DPE motif has a consensus sequence of RGWCGTG that is conserved from flies to humans and that promotes the more efficient binding of components of TFIID such as TAF6, TAF9, and possibly TAF1. DPE-dependent promoters typically contain only DPE and Inr motifs (Louder et al., 2016; Ohler et al., 2002). However, in some exceptional cases, some promoters can have both a TATA box and a DPE element.

1.3.5 The MTE motif The motif ten element (MTE) was initially described in Drosophila as an overrepresented sequence in functionally active core promoters located immediately upstream of the DPE motif (Lim et al., 2004; Ohler et al., 2002). The MTE consensus sequence consist of CSARCSSAAC from +18 to +27 relative to the A+1 (Lim et al., 2004). Importantly, more detailed analyses that have focused on distinguishing it from the DPE have shown that the MTE is an independent element, and have redefined the MTE consensus sequence to be CGANC from +18 to +22 and CGG from +27 to +29 from the A+1 (Theisen et al., 2010). In a similar fashion to DPE, the MTE facilitates the binding of the preinitiation machinery to the core promoter through direct interactions with components of the TFIID (Louder et al., 2016). Moreover, functional analyses have also shown that the MTE can work cooperatively with the Inr and independently of DPE or the TATA box (Juven-Gershon and Kadonaga, 2010; Juven-Gershon et al., 2006).

1.3.6 The TCT motif In addition to the more general motifs described above, a more specific core promoter also exists as the TCT motif, also known as the polypyrimidine initiator. The TCT motif is present in the core promoters of virtually all of the ribosomal and some associated ribosomal regulatory genes and has been shown to exist both in flies and humans (Parry et al., 2010; Wang et al., 2014). This element encompasses the

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TSS in a similar fashion to the initiator motif (Parry et al., 2010). In humans, the TCT motif has a consensus sequence of YC+1TYTYY (Kadonaga, 2012). The term “TCT” motif refers to the TCT trinucleotide, which frequently encompasses the +1 TSS. It is estimated, in humans, that ∼1% of focused transcription initiation occurs in core promoters that contain a TCT motif (Parry et al., 2010). Therefore, the TCT motif is an example of a rarer core promoter motif that also has an important biological function.

1.4 A genomic perspective of the core promoter

Before the advent of the genomic era, a large proportion of our understanding of core promoter structure and function originated from the study of a limited number of strong viral and endogenous promoters. Although these studies allowed us to gain key insights into the role of the core promoter in transcription initiation, they only provided a partial and limited perspective of the core promoter’s role in transcription initiation due to the limited number of core promoters examined. This drastically changed with the advent of high-throughput sequencing technologies (Reuter et al., 2015). Multiple high-throughput sequencing techniques can be applied to efficient map transcription and its initiation at a genome wide scale such as RNA sequencing (RNA-seq), Expressed Sequence Tag (EST), and Cap Analysis of Gene Expression (CAGE) (Carninci et al., 2006; Mortazavi et al., 2008; Parkinson and Blaxter, 2009). Over the years, these techniques have allowed us to further investigate and expand our understanding of the core promoter from a genomic perspective.

For example, the inspection of the patterns of transcription initiation has shown that two different and opposite modes of transcription initiation occur in metazoan genomes; these are focused and dispersed initiation (Figure 1.3A) (Carninci et al., 2006; Rach et al., 2011). In focused initiation, the TSS occurs at a well-defined single nucleotide or within a narrow region of a few nucleotides, while in dispersed transcription multiple weak TSS occur over a region of about 50 to 100 nucleotides. Multiple analyses have shown that focused initiation occurs in core promoters that have low GC content and that are enriched in core promoter motifs like the TATA box and the initiator. On the other hand, dispersed initiation occurs in core promoters with high GC content and that present promoter-proximal transcription factor binding sites. Importantly, core promoters with focused initiation are associated with regulated genes such as those present in liver-specific genes; while core promoters with dispersed initiation are favored in housekeeping genes (Juven-Gershon and Kadonaga, 2010). Interestingly, some promoters can exhibit mixed patterns of transcription initiation, combining certain elements of focused and dispersed initiation, suggesting that more complex mechanisms of regulation are possible and likely mediate finer regulation of transcription initiation.

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Moreover, the inspection of the patterns also showed that the use of alternative core promoters to drive gene transcription is a widespread phenomenon across metazoan genomes (Figure 1.3B) (Haberle et al., 2015; Lizio et al., 2015). Although alternative core promoters were known to exist before the genomic era, it was largely thought that they were a feature present only on a select group of genes (Davuluri et al., 2008). This perspective changed with the generation of genome-wide TSS maps, which showed that most genes are in fact driven by alternative promoters (Carninci et al., 2006; The FANTOM Consortium and the RIKEN PMI and Clst (dgt), 2014).

Figure 2: The core promoter in the genomic context. (A) Two modes of initiation occur in the genomic context, focused and dispersed initiation. In focused initiation, the core promoter mediates transcription initiation through a predominant TSS; while in dispersed initiation, a poor defined core promoter allows transcription through multiple weak TSS. (B) Alternative promoters drive gene transcription across the genome. CAGE methodologies have wildly shown that, within a gene, multiple alternative promoters usually drive transcription. In this example, three focused core promoters are shown.

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Interestingly, multiple studies have also pointed out that alternative core promoters that drive the expression of a gene can be differentially regulated (Ayoubi and Van De Ven, 1996; Batut et al., 2013; Davuluri et al., 2008). For example, the use of alternative promoters can change during development and in the presence of specific external stimuli and events (Arner et al., 2015; Haberle et al., 2015). Moreover, for certain diseases it can be observed that the use of alternative promoters has shifted (Agarwal et al., 1996; Davuluri et al., 2008; Pedersen et al., 2002; Tan et al., 2007). Taken together, these observations suggest that the use of alternative promoters is important and can promote finer regulation of transcription initiation. Although the precise mechanisms that regulate alternative promoter use are still not well understood, it has been suggested by some reports that their precise regulation may depend on the core promoter epigenetic landscape, and on the specific interaction with DNA regulatory sequences and specific transcription factors (Archey et al., 1999; Ngondo and Carbon, 2014; Pozner et al., 2007a; Turner et al., 2008). Further investigation of the specific mechanisms that regulate alternative promoter use is necessary to obtain a more precise understanding of the regulation of transcription initiation.

1.5 Enhancers, positive modulators of transcription initiation

Enhancers can be defined as versatile DNA regulatory elements that, when bound by tissue-specific transcription factors, can upregulate the rate of transcription, predominately acting during its initiation. Enhancers were first reported in the SV40 viral genome as a 72-bp long DNA regulatory sequence that could increase the rate of transcription in a manner that is position-, orientation- and distance-independent of the promoter, and have since been subject to extensive study and investigation (Banerji et al., 1981; Benoist and Chambon, 1981). Over the years we have generated and matured our understanding of the main characteristics that define enhancers and how they promote transcription activation. I will discuss them in the next paragraphs.

Enhancers are flexible DNA regulatory elements that can be located at diverse distances from their target promoters. In fact, the distance that separates an enhancer from its specific promoter can vary drastically, going from a few kilobases to over one megabase (Chepelev et al., 2012; Furlong and Levine, 2018). Two of the most exceptional examples of distant enhancers occur in the SHH and MYC loci; in these loci, extremely distant enhancers have been mapped to be present at 0.9 and at 1.7 megabases away from their target promoter (Fulco et al., 2016; Williamson et al., 2016). Moreover, enhancers can also be located in intragenic and intergenic regions of its gene or be present within other genes (Erokhin et al., 2015).

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Enhancers are usually several hundred base pair long DNA sequences enriched in clusters of transcription factor binding sites (Spitz and Furlong, 2012). The accessibility to these binding sites is facilitated by the presence of a combination of histone marks such as H3K4me1 and H3K27ac and histone variants such as H3.3 and H2A.Z, which allow a more relaxed chromatin state (Barski et al., 2007; Heintzman et al., 2009; Jin et al., 2009). The binding of tissue-specific transcription factors leads to the recruitment of other regulatory protein complexes, such as transcriptional cofactors, chromatin remodeler complexes, and Mediator (Allen and Taatjes, 2015; Ong and Corces, 2011; Spitz and Furlong, 2012). These protein complexes drastically remodel the enhancer architecture and lead the enhancer to transition into an active state capable of regulating transcription initiation. Therefore, an active enhancer is characterized by having high levels of bound transcription factors and low levels of nucleosome occupancy. These characteristics can be used to precisely map the regulatory landscape of a defined locus at a genomic scale and also to investigate how enhancer activation takes place upon regulatory cues (Bowman, 2015; Visel et al., 2009).

In order for active enhancers to mediate their positive effects on transcription initiation they first need to come into close spatial proximity to their target promoters and form so-called chromatin loops (Figure 1.4). The establishment of these chromatin loops has been observed by multiple methodologies that include fluorescence in situ hybridization (FISH), RNA tagging and recovery of associated proteins (RNA-TRAP), and chromatin conformation capture-based techniques such as 3C, 4C and Hi-C (Amano et al., 2009; Barutcu et al., 2016; Carter et al., 2002; Wit and Laat, 2012). These techniques have shown that enhancer activation leads to its relocalization into close proximity to its target promoter and to an increase in the probability of interaction of these two sequences. Importantly, the formation of chromatin loops can sometimes occur before enhancer activation, as in the case of some enhancer-regulated developmental genes (Andrey and Mundlos, 2017; Zlotorynski, 2017). The establishment of these long-range interactions is regulated by tissue-specific transcription factors as well as other regulatory elements like Cohesin, CTCF and Mediator (Dorsett and Merkenschlager, 2013; Malik and Roeder, 2010; Ong and Corces, 2014; Weintraub et al., 2017). Once these long-range interactions have been established, active enhancers, with the help of their transcription factors and of Mediator, can finally upregulate the rate of Pol II transcription initiation (Allen and Taatjes, 2015; Erokhin et al., 2015). This occurs through a series of complex regulatory events that promote the opening of chromatin, the formation of the preinitiation complex and the subsequent release of the Pol II into active elongation (Fuda et al., 2009; Kulaeva et al., 2012).

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Figure 3: Enhancer activation drives the formation of stable long-range enhancer-promoter interactions and transcription upregulation. (A) Enhancers in their basal state are distally located DNA regulatory sequences that have the potential to upregulate transcription initiation (pink dotted line represent potential of interaction and activation). (B) In order for enhancers to have regulatory potential, they first need to be bound by tissue-specific transcription factors and other cofactors. The binding of these regulatory proteins leads to a series of molecular event that finalizes with enhancer activation. (C) Most active enhancers can then scan the chromatin and form specific long-range interactions with their target promoter (black dotted line). The establishment of these long-range interactions is an essential event that needs to occur before transcription upregulation.

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1.6 Enhancers-promoter compatibility

Metazoan genomes present intricate and dynamic networks of long-range chromatin interactions that are to a large extent formed by enhancers and their target promoters (Figure 1.5) (Ong and Corces, 2014; Schmitt et al., 2016; Spielmann et al., 2018). These networks of interactions are essential for the proper generation of specific gene expression patterns, and misregulation of these interactions leads to gene misregulation and disease (Karnuta and Scacheri, 2018; Smith and Shilatifard, 2014). The scope of potential enhancer-promoter interactions is known to be limited by certain mechanisms such as the regulation of the chromatin state, the three-dimensional architecture of chromosomes and the presence of insulators (Arensbergen et al., 2014). Yet these mechanisms fail to fully explain how long-range interactions are specified and efficiently achieved in the context of the complex nuclear environment.

Interestingly, a growing body of evidence suggests that the specific formation of enhancer-promoter interactions could be achieved by compatibility mechanisms present between enhancers and their respective promoters (Danino et al., 2015). The existence of compatibility mechanisms was first observed in the Drosophila gooseberry locus. This locus presents two divergent genes, gooseberry (GSB) and gooseberry neuro (GSBN), which are regulated by two enhancers centrally located between them. Although the geometry of this locus allows potential cross-activation by ectopic enhancer-promoter interactions, these interactions do not occur. Swapping experiments have shown that the GSB or GSBN promoter can only be activated by their specific enhancer (Li and Noll, 1994). Similarly, compatibility mechanisms also seem to occur in the murine beta-globin locus. In this locus, the LCR only interacts with a single specific globin gene, which is determined by development cues (Tolhuis et al., 2002). Moreover, transgenic experiments that tested the strength of hundreds of potential enhancer-promoter combinations in Zebra fish embryos have shown that enhancers preferentially activate specific promoters with core promoter motifs (Gehrig et al., 2009). Furthermore, it has been shown that transcription activation of Hox genes in Drosophila by the tissue-specific transcription factor Caudal occurs preferentially in core promoters that possess a DPE motif over promoters that present a TATA box (Juven-Gershon et al., 2008). Next-generation sequencing techniques, such as STAR-seq, have also shown that compatibility mechanisms occur at a genome-wide level (Zabidi and Stark, 2016). Specifically, it has been shown that enhancer specificity is largely mediated by the differential presence of core promoter motifs in housekeeping or developmentally regulated genes (Zabidi and Stark, 2016). Taken together, these observations strongly suggest that compatibility mechanisms between enhancer and promoter exist, and that they are highly dependent on the core promoter motif composition.

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Figure 4: Networks of long-range enhancer-promoter interaction occur in the genome. A schematic representation of a complex network of long-range interactions. As can be observed, a locus can have active genes under the regulation of multiple enhancers. These enhancers can form long-range specific interactions only with their target promoter. In many cases, it can be observed that enhancers do not activate the closest promoter, but instead, skip them to activate more distantly located promoters.

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Chapter 2

Efforts towards the visualization of enhancer-promoter interactions in live cells

2.1 Motivation

For enhancers to exert their positive effects on the rate of Pol II transcription initiation, they first need to relocalize into close proximity to their target promoters to form chromatin loops. While the presence of these chromatin loops has been extensively documented by multiple methodologies, still very little is known about how these interactions are established in the crowded and dynamic nuclear space. This has been the case largely because of the lack of robust methodologies to label and follow the dynamics of specific chromatin sequences in live cells. At the time when I initiated this research project, the primary methodology used to investigate the dynamics of genomic loci involved the use of the operator-repressor systems. This methodology allows the visualization of randomly integrated DNA operator arrays by using the corresponding repressor protein fused to a specific fluorescent protein. Unfortunately, this system does not allow the labelling of specific genomic loci, necessitating the study of more sophisticated chromatin interactions, such as the ones formed between enhancers and promoters. An ideal imaging system to visualize more sophisticated chromatin dynamics would allow for the precise tagging of multiple specific loci and their continuous visualization in live cells without much disturbance of the innate genomic loci (Figure 2.1).

In the next paragraphs, I present a detailed explanation of the efforts that I undertook during my early doctoral years to develop a system to visualize chromatin loops in live cells, first by a dCas9-Aptamer approach, and later by a novel operator-repressor system integrated by genome editing. Finally, I discuss the limitations that I encountered developing each of these systems and compare my results to relevant literature published since these initial experiments were conducted.

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Figure 6: An ideal imaging system to visualize chromatin dynamics. (A) A schematic representation of how specific genomic loci would be observed by an ideal imaging system. This system would allow the clear and continuous visualization of multiple genomic sequences over long periods of time; (B) Therefore, this system could be used to investigate important biophysical parameters such as fluctuation on the spatial distance between two distinct loci.

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2.2 dCas9-Aptamer system to visualize chromatin dynamics Clustered regularly interspaced short palindromic repeats (CRISPR) is a bacterial adaptive defense system that protects bacteria from foreign invading DNA, such as those originating from bacteriophages and other mobile DNA elements. Multiple classes of CRISPR systems have been discovered to date (Koonin et al., 2017; Murugan et al., 2017). Of these systems, the type-II CRISPR system has been the most extensively studied, mainly because of its early discovery and simpler structural nature (Jiang and Doudna, 2017; Jinek et al., 2012). Importantly, the type-II CRISPR system can be engineered to consist only of two molecular components, a single guide RNA (sgRNA) that provides specificity against a specific DNA sequence, and a Cas9 protein that provides the catalytic activity to generate the double-stranded break.

This research project was largely motivated by what at the time was a recent publication by Chen et al. (2013), which reported the repurposing of the Cas9 from Streptococcus pyogenes to image multiple genomic loci in live cells. This system used multiple sgRNAs targeting specific locus and a catalytically inactive version of Cas9 (dCas9) fused to an enhanced green fluorescent protein (EGFP) (Chen et al., 2013). Importantly, this imaging system had two limitations that prevented its wide applicability. First, a cell line needed to be generated for each locus of interest expressing a set of sgRNAs to allow the targeting of the specific locus. Second, and most importantly, it did not allow the specific visualization of multiple loci, since only one type of fluorescent protein could be used.

To improve upon these limitations, I devised and developed a composite dCas9-Aptamer system by using RNA aptamers and their corresponding recognition proteins (Urbanek et al., 2014). This system was ideated to allow efficient and differential labeling of multiple loci in live cells. The system consisted of three molecular components: a nuclear dCas9 protein, a 3’ modified sgRNAs harboring RNA aptamers, and a fluorescent protein fused to a domain with affinity for the RNA aptamers with cytoplasmic localization. The rationale behind this approach is that the fluorescent proteins will be preferentially cytoplasmic and will only migrate to the nucleus when bound to the aptamers present in the dCas9/sgRNA complex. Once in the nucleus, this tertiary complex will scan the DNA until it finds its DNA target site, leading to the specific labeling of the locus of interest. Since the dCas9-sgRNA complex has multiple aptamers to which fluorescent proteins can bind, it allows the use of fewer sgRNAs to visualize a specific locus. Moreover, the system could be further expanded to visualize multiple loci in live cells since multiple RNA aptamers, like MS2 or PP7, could be used for specific loci labeling (Figure 2.2).

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Figure 7: A dCas9-Aptamer system to visualize chromatin dynamics. (A) A schematic representation of the three components that are part of this system, a dCas9 fusion protein, a modified sgRNA harboring multiple RNA aptamers, and an MS2 recognition protein fused to a fluorescent protein. (B) This system allows the use of multiple aptamer-recognition protein pairs to specifically target and visualize independent genomic loci in live cells.

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2.2.1 Development of a dCas9-Aptamer system In order to develop this Cas9-Aptamer system, we used a HEK 293T cell line expressing EGFP from a unique genomic location to stably integrate the first component of the system, an MS2 coat protein fused to a turbo red fluorescent protein (MS2-tRFP). This fusion protein was under the control of a tetracycline-responsive promoter (Baron and Bujard, 2000). The tetracycline-responsive promoter was selected because it allows for fine regulation of the protein levels as a function of the tetracycline concentration that can be used to fine-tune the expression levels of MS2-tRFP and therefore optimize the signal-to-noise ratio. Interestingly, we realized that leaky expression of MS2-tRFP was enough for imaging purposes and decided to use this condition for all subsequent experiments. The second component of the system was a dCas9-2xNLS-BFP fusion protein that consists of a catalytically inactive Cas9 protein fused to two nuclear localization signals and a blue fluorescent protein (BFP). This fusion protein was originally developed to regulate the rate of transcription in Eukaryotes (Gilbert et al., 2013). Importantly, thanks to the presence of the 2xNLS, this fusion protein can efficiently translocate into the nucleus, making it ideal for these imaging experiments. The last component of the system was an engineered sgRNA harboring three MS2 aptamers in the 3’ sequence of the RNA. The sequence of the a sgRNA consisted of three regions. The first region consisted of 90 nucleotides of the standard sgRNA which were in direct contact with Cas9; this sequence was followed by a 10-15 nucleotides spacer region; and the last region consisted of 80 nucleotides containing three MS2 aptamers. The last two components of the systems, the dCas9 fusion protein and the sgRNAs were transiently expressed in the cell to allow more flexibility in the optimization steps. Once this system was generated, its labeling capacity was tested in different genomic loci. The results are presented in the following paragraphs.

2.2.2 Labeling of telomeres with the dCas9-Aptamer system To test the recently developed dCas9-Aptamer system, we first decided to label telomeres in the previously developed HEK 293T cells stably expressing MS2-tRFP. We decided firstly to image the telomere regions because of their considerable length, repetitive nature, and presence of sites that could be easily targeted by dCas9 (Figure 2.3A). For this particular experiment we used a unique sgRNA. Importantly, when either dCas9-2xNLS-BFP or the sgRNA targeting telomeres were transfected into the cell, no nuclear foci could be observed (Figure 2.3B). In some of the cells transfected with the sgRNA, we could observe the formation of cytoplasmic foci suggesting that the modified sgRNA could promoter MS2-tRFP aggregation.

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Figure 8: Labeling of Telomeres with the dCas9-Aptamer system. (A) A schematic representation of how one single sgRNA allows the labelling of the telomeres in HEK 293T cells. (B) Telomeres can only be label when the three components of the dCas9-Aptamer system are present in the cell, note the sgRNA alone can cause some aggregation in the cytoplasm. (C) A representative cell with its telomeres labelled by the dCas9-Aptamer system. (D) thymidine double blockage allows more efficient labelling of telomeres by the dCas9-Aptamer system.

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Next, we transfected both the dCas9 fusion protein and the sgRNA to investigate if telomeres could be labeled with this highly engineered system. When this was done we were able to observe multiple and clearly defined nuclear foci, highly reminiscent of telomeres (Figure 2.3C). On average, a count of 40 to 50 foci could be observed per cell nucleus, from an expected number of 128, based on the number of chromosomes that HEK 293T has (Stepanenko and Dmitrenko, 2015). This observation suggested that telomere labeling was not very effective. To improve the labeling efficiency, we decided to do a thymidine double blockage to enhance telomere deprotection and facilitate the dCas9 binding (Chen and Deng, 2018). As can be seen in Figure 2.3D, the thymidine double blockage treatment allowed a more efficient labeling of telomeres, generating a count of 60 to 70 foci per cell nucleus. Overall, these results indicate that the dCas9-Aptamer system can be used to visualize specific DNA sequences in the cell nucleus. 2.2.3 Labeling of a single non-repetitive sequence with the dCas9-Aptamer

system We then tested if the dCas9-Aptamer system could be used to label non-repetitive single loci by targeting the singly-integrated EGFP cassette present in the HEK 293T genome (Figure 2,4A). We designed four specific sgRNAs targeting a ~100 base pair sequence located within the EGFP gene. One day after transfection, nuclear foci could be observed (Figure 2,4B). Importantly, loci could be visualized in approximately 10% of the cells, likely due to the low-transfection efficiency of the four sgRNAs. In the positive cells, we were expecting to observe between one and two foci per cell since only one copy of the EGFP cassette was present in the HEK 293T genome. Surprisingly, in many cases we observed more than two foci per nucleus. The presence of more than two foci per cell nucleus strongly suggested to us that the system was either targeting non-specific loci or that nuclear aggregation was happening. To test whether this was the case, we tested if four sgRNAs targeting the bacterial GAL4 gene could generate non-specific foci. Surprisingly, we observed that nuclear foci could also be observed in approximately 10% of the cells. Quantification of the frequency of nuclear foci observed in these two conditions showed that very likely most of the foci are non-specific (Figure 2.5).

These results prompted me to test different strategies to decrease the number of non-specific foci observed within the cell nucleus, which included optimizing the aptamer structure and number, and regulating the levels of both the sgRNAs and the dCas9. However, none of these strategies considerably improved the labelling specificity since more than two foci could still be observed regularly. This project had as a first and foremost goal of developing a system to visualize specific genomic sequences to study their chromatin dynamics; since this was not possible—as the off-target events did not allow me to unambiguously study the dynamics of specific genomic locations—I decided not to pursue this new system further.

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Figure 9: Labeling of a singly-integrated EGFP locus. (A) A scheme of how the dCas9-Aptamer system was used to label singly-integrated EGFP cassette. In this case, 4 different sgRNAs were cotransfected into HEK 293T cells to allow the visualization of the locus. (B) Multiple nuclear foci can be visualized when the EGFP is labeled by the dCAs9-Apatmer system, suggesting that non-specific labeling or aggregation can occur inside the nucleus.

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2.3 Development of a two-step process for directed integration

of operator arrays to study chromatin dynamics.

Due to the limitations of the dCas9-Aptamer system described above, we decided to investigate the possibility of adapting the well-established Operator-Repressor system to visualize specific genomic loci in live cells. The Operator-Repressor system was originally developed by Robinett et al. (1996) using the LacO-LacI molecular pair. It allows the visualization of an array of LacO sites integrated into the genome using the LacI protein fused to a fluorescent protein like EGFP. This approach has been expanded to include other orthologue molecular pairs such as TetO-TetR (Soutoglou et al., 2007), and therefore can permit specific and simultaneous labelling of multiple genomic loci. The Operator-Repressor system has been successfully and amply used to study important nuclear processes such as chromosome dynamics, double-stranded breaks, and chromosome translocations (Chen et al., 2016a; Lucas et al., 2014; Roukos et al., 2013; Soutoglou et al., 2007).

It is important to point out that the Operator-Repressor system possesses a big technical limitation: it does not allow targeted integration of an operator array into a locus of interest (Robinett et al., 1996). This is the case because the integration of the operator array is facilitated by a drug-resistant marker located adjacent to the array (Robinett et al., 1996). This drug-resistance marker is used to select random integrations that have spontaneously occurred across the genome.

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EGFPsgRNAs GAL4sgRNAs

Figure 10: Frequency distribution of nuclear foci with specific and non-specific sgRNAs. Frequency distribution of nuclear foci with specific and non-specific sgRNAs. The number of nuclear foci with specific (EGFP) and non-specific (GAL4) gRNAs were counted in cells that presented foci and used to calculate the frequency distribution of foci. As can be seen, the distribution with specific and non-specific gRNAs are very similar, strongly suggesting that a large proportion of the foci labelled are non-specific.

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With the exception of specific reports with Saccharomyces cerevisiae, where directed integration of the operator array is possible, very few reports exist that investigate specific genomic loci (Lucas et al., 2014; Soutoglou et al., 2007).

This important limitation can be overcome by using genome-editing techniques such as CRISPR/Cas9. Importantly, CRISPR/Cas9 can be used in conjunction with a DNA donor sequence to facilitate directed integration of DNA sequences in specific genomic locations (Jinek et al., 2013; Lin et al., 2014; Platt et al., 2014). The integration of this donor DNA occurs as follows. First, Cas9-sgRNA complex generates a double-stranded break within the locus of interest. Then this double-stranded break is recognized by the cellular repair machinery, which tries to close this double stranded break predominately by either Non-Homologous End-Joining (NHEJ) or Homology Directed Repair(HDR), depending on whether or not a donor DNA can be found(Lieber, 2010). If a DNA donor is found, the cell proceeds through a homology directed repair pathway to close the double-stranded break and in the process integrates the DNA sequence of interest. This process, although not that efficient, can be optimized by different strategies (Lin et al., 2014; Song and Stieger, 2017). Considering this technical possibility, we decided to use CRISPR/Cas9 to facilitate the integration of an operator array in a specific genomic location.

We decided to do integration in a two-step process to improve the efficiency of the overall process (Figure 2.6). This was decided since the number of operators necessary for visualization makes the DNA cassette to be integrated very long, in the range of 10 kb (Robinett et al., 1996). In the first step, CRISPR/Cas9 is used to integrate a DNA cassette containing a positive marker of selection, flanked by two heterospecific recombination sites. In the second step, the DNA cassette containing the positive marker of selection is recombined by Recombinase-Mediated Cassette Exchange (RMCE) (Turan et al., 2014; Watson et al., 2008). In order to specifically integrate the array into two different genomic loci, we used the recombination systems FLP-FRT and CRE-LoxP (Wang et al., 2011).

We believed this two-step integration system was ideal for the integration of a large operator array, since it offered three important advantages over a one-step integration system. First, this system increases the operator array integration efficiency since the operator integration is driven by recombination rather than homologous recombination, which is known to be an inefficient process. Two, it allows the optimization of the number of operator repeats needed for imaging since their number can be easily changed by recombining DNA cassettes with a larger number of operators. Third, this two-step integration system allows integration of operator arrays without leaving a foreign promoter on the integrated DNA usually driving a positive marker of selection. This is of special importance for enhancer-promoter interaction, since a foreign promoter can potentially cause ectopic

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interactions. In the next sections I will present the technical progress and the challenges associated with the implementation of this two-step system.

2.3.1 Generation of optimized LacO and TetO arrays Optimized LacO and TetO arrays with well-spaced operators and different number of repeats were constructed. We first designed and synthesized short DNA sequences harboring seven operators spaced by long and unique spacer sequences to minimize the repetitive nature of the constructs. The number of operator repeats present in the array was increased following the strategy developed by Robinett et al. (1996). In this strategy the operator array is flanked by three restriction sites that are not destroyed throughout the process, two of which generate compatible cohesive ends that allow doubling the length of the sequence of interest through successive cloning steps. By using this strategy, we were able to generate vectors with 14, 28, 56, 112 and 224 LacO and TetO repeats, and also vectors containing both LacO and TetO combinations (Figure 2.7). Importantly, the LacO and TetO arrays were designed to be flanked by heterospecific FRT and LoxP to allow specific recombination in the following steps.

Figure 11: A schematic representation of the two-step process ideated for directed integration of operator arrays. (A) CRISCPR/Cas9-mediated integration of an mCherry cassette flanked by two heterospecific FRT sites. (B) Recombinase-Mediated Cassette Exchange of the genomically-integrated mCherry cassette with a LacO array.

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Figure 12: Generation of optimized operator arrays. (A) A scheme of the plasmid-based strategy used to generate long operator arrays. The operator array is flanked by three restriction sites that allow doubling the length of the sequence of interest through successive cloning steps, this procedure was done six times to generate arrays with 224 repeats. (B) Restriction Analysis of 112X & 224 LacO and TetO arrays flanked by different recombination sites. The lower band represent the vector backbone, while the top band is the operator array.

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2.3.2 Generation of stable cell lines expressing EGFP-LacI and TetR- mCherry fusion proteins

In order to develop an optimized system, we generated lentiviral vectors coding for the EGFP-LacI and TetR-mCherry (Figure 2.8A). This fusion proteins have been previously used for single-locus imaging experiments (Roukos et al., 2014). These vectors were later used to produce lentiviruses under standard conditions, which were transduced into the colon carcinoma cell line HCT-116 cells for stable cell line generation. Fluorescence-activated cell sorting (FACS) was used to select clonal lines with low levels of expression of the fusion proteins in order to minimize the background levels of non-bound protein during future imaging experiments. As a verification step, these clonal lines were imaged using epifluorescence microscopy to corroborate that in fact they presented low levels of the fusion fluorescent protein and that they had a preferential nuclear localization (Figure 2.8B). 2.3.3 Genomic integration of a mCherry cassette adjacent to MYC enhancer A donor vector coding for mCherry fluorescent protein flanked by two FRT heterospecific sites was constructed to be used as a positive marker. This vector was then used in conjunction with a Cas9 plasmid to target a genomic sequence adjacent to a previously described MYC enhancer in HCT-116 cells (Ahmadiyeh et al., 2010, 2010; Roukos et al., 2014; Yochum et al., 2008a). Genome-edited clonal lines expressing mCherry were first selected by fluorescence-activated cell sorting. These clonal lines were screened for homozygosity by genotyping with two primers flanking the integration site (Figure 2.9A). Agarose gel analysis of the amplified PCR products shows that both heterozygous and homozygous integrations could be obtained (Figure2.9B).

2.3.4 Optimization of RMCE step with a EGFP cassette To optimize the RMCE step, we first tested to recombine the integrated mCherry cassette with a vector harboring an EGFP cassette (Figure 2.10A). We tested how different transfection conditions with vectors coding for EGFP and FLP recombinase affected the efficiency of recombination in these cells. Importantly, the cells were grown for five days after transfection in order to decrease the fluorescence levels of transiently expressed EGFP as an intermediate step before any type of flow cytometry analysis. We observed that, in general, recombination was not very efficient, with only a small percentage of cells recombining one allele and even fewer recombining two alleles (Figure 2.10B). Of all the conditions tested, we observed that the best condition was obtained when 2ug and 1ug of the EGFP and FLP vectors were used. We decided to use this condition for the following recombination experiments with the LacO array.

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Figure 13: Generation of Stable cell lines expressing EGFP-LacI and TetR-mCherry. (A) Diagram of the fusion genes coding for EGFP-LacI and TetR-mCherry. (B) Representative images that demonstrate the correct nuclear localization of the fusion proteins EGFP-LacI and TetR-mCherry.

Figure 14: Genotyping the integration of the mCherry Cassette. (A) Primers flanking the integration site were designed such as they would generate either a 1000 bp or 2500 bp PCR fragment, depending on whether integration occurred or not. (B) Genotyping of 14 clonal lines. “Ho” signifies homozygous integrations, “He” represents heterozygous integrations, and “N” represents that no bands could be observed. As can be observed, four of the 14 clonal lines had homozygous integration of the mCherry cassette.

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2.3.5 Integration of the LacO x56 array by RMCE Based on the previous results, we decided to do three rounds of the RMCE with the LacO x56 array (~2500 bp) to improve the probability of cells having both alleles recombined (Figure 2.11A). These three rounds of RMCE were spaced three days apart to allow cells to recover from transfection in between rounds, and were grown for seven days after the third transfection to allow degradation of mCherry before FACS. Cells with two alleles successfully recombined would no longer be mCherry positive and therefore would not fluoresce. After performing this experiment, we observed that only a small proportion of cells did not present fluorescence (Figure 2.11B). Cell clones from this population were genotyped by PCR to test that mCherry swapping had occurred. These cells were used for all the following imaging experiments. 2.3.6 In vivo imaging of a 56 repeats LacO array Three clonal lines were selected from the previous step and used for the imaging experiments. To visualize the LacOx56 array, cells were transiently transfected with

Figure 15: Optimization of the RMCE step. (A) Diagram of mCherry cassette swapping with the donor EGFP cassette. The swapping would lead to loss of mCherry and gain of EGFP expression. (B) Flow cytometry analysis of the condition with the best RMCE efficiency. As can be observed, a reasonable proportion of the cells recombine at least one of the alleles (top—right panel), while very few recombine both alleles (top-left panel).

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250ng or 500ng of a previously developed vector coding for EGFP-LacI (Roukos et al., 2014). Cells were imaged two and three days after transfection with a Zeiss LSM 710 confocal microscope. After multiple attempts with all three clonal lines, we were not able to observe defined foci in the nucleus of the cells. This experiment led us to conclude that an array containing 56 LacO repeats did not provide enough binding sites to generate a strong fluorescence signal to allow efficient visualization of the specific genomic locus. 2.3.7 Attempted recombination of the mCherry cassette with arrays of 112

and 224 LacO repeats The previous results prompted us to try to integrate longer arrays containing 112 and 224 LacO repeats. We followed essentially the same procedure described in the previous sections, only increasing the number of recombination rounds from three to five in the hope of improving the proportion of cells with recombined alleles. After the fifth round we analyzed the cells by FACS; sadly, we could not observe a significantly larger proportion of mCherry negative cells when compared to the control, strongly suggesting that recombination was not successful. The few cells that were mCherry negative were expanded and imaged, but no loci could be observed either. Taken together, these results show that the recombination event represents a big limitation for this two-step genomic integration approach.

Figure 16: Integration of the LacO x56 array by RMCE. (A) Diagram of mCherry cassette swapping with the donor LacOx56 array by RMCE. The swapping would lead to loss of mCherry expression. (B) Flow cytometry analysis of cells that have undergone three rounds of recombination. As can be seen, only a very small proportion of cells (lower rectangle) have loss all fluorescence after three rounds of RMCE.

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2.4 Discussion

The three-dimensional organization of the genome in the nuclear space has emerged as a particularly important mechanism of gene regulation (Chen et al., 2016a; Keung et al., 2015; Spielmann et al., 2018). Of the many types of long-range chromatin interactions formed within the genome, likely the ones connecting enhancers and promoters are among the most important, both for their ubiquitous presence and functional importance (Novo et al., 2018; Smith and Shilatifard, 2014; Zlotorynski, 2017). Yet, despite their relevance, methodologies that allow the study of how these interactions are formed in the complex nuclear environment have been largely lacking. Only recently have efforts to develop or improve upon previous methodologies materialized, but these still have not found broad use (Chen et al., 2016b; Hong et al., 2018; Takei et al., 2017; Tasan et al., 2018). Therefore, the development of new methodologies that allow the visualization of specific genomic loci would certainly bring more detailed knowledge of how enhancer-promoter interactions and other important processes take place in the complex nuclear space.

In the last sections, I presented my efforts to develop two imaging systems in live cells by two different approaches—first, by a Cas9-Aptamer system, and second, by a two-step process for directional integration of operator array. As expected, each approach had its own associated challenges and possibilities. They will be described and contrasted with recent literature in the next paragraphs.

The Cas9-Aptamer system is an approach that is conceptually attractive because of its simplicity and flexibility in imaging multiple genomic loci. As it was designed, only one dCas9 protein was necessary to mediate all binding events, while the modified sgRNA harboring specific aptamers was necessary to provide specificity for a defined locus and for recruitment of the desired fluorescent protein to visualize it. This system also allows flexibility, since all the important molecular components can be continuously expressed in the cell and only the specific modified sgRNA would have to be transiently expressed to allow specific genomic imaging. However, as we were developing the Cas9-Aptamer system, we observed that some of the nuclear foci generated by the system were non-specific. This was evident when we visualized single locus as described above, and was confirmed by using a non-specific sgRNA. Unfortunately, modifying and optimizing the aptamer structure and number within the sgRNA or regulating the sgRNA and dCas9 expression levels did not greatly reduce the number of non-specific nuclear foci. We believe that the formation of non-specific foci could be largely due to Cas9 off-target binding events and the tendency of MS2 to self-aggregate, both previously reported phenomena (Hsu et al., 2013; Langlet et al., 2007; Pattanayak et al., 2013; Peabody and Al-Bitar, 2001). Previous reports that used dCas9 to visualize specific loci have also reported non-specific foci (Chen et al., 2013, 2016c; Qin et al., 2017). More recently, papers using a similar Cas9-Aptamer approach have been published;

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however, they largely do not investigate or discuss the presence of non-specific foci when labeling non-repetitive genomic loci (Fu et al., 2016; Qin et al., 2017; Wang et al., 2016).

The Operator-Repressor system has been the most extensively used system to visualize and investigate specific chromatin sequences and their associated processes (Chen et al., 2016a; Lucas et al., 2014; Roukos et al., 2013; Soutoglou et al., 2007). However, the applicability of this system is limited due to the lack of specific approaches to mediate a targeted and efficient integration of operator arrays. In order to overcome this important limitation, I decided to develop a two-step process for directional integration of operator arrays by combining CRISPR/Cas9 genome editing and RMCE, as described before. Conceptually, this approach provides an efficient way to integrate multiple operator arrays in different genomic locations very precisely. Unfortunately, as we were developing this system we discovered that the last challenging step, RMCE, was very inefficient and did not allow the efficient integration of long operator arrays. We believed this was largely due to the long and repetitive nature of the operator array. After a series of attempts, this system was no longer pursued. Recently, the Belmont lab has developed a simpler methodology that also uses CRISPR/Cas9 to mediate the knock-in of the operator array in a single-step process (Tasan et al., 2018). A very similar approach has also been used recently in the Darzacq lab to integrate and visualize topologically associated domain boundaries. I believe these efforts and new strategies to mediate rational and efficient integration of operator arrays will open new opportunities to expand our understanding of gene regulation and other important nuclear processes.

2.5 Methods

2.5.1 Cell Culture HEK 293T and HCT-116 were cultured in accordance with ATCC guidelines. Briefly, DMEM and DMEM-F12 media was used to culture HEK 293T and HCT-116, respectively. The media was supplemented with 10% fetal bovine serum, non-essential amino acid, penicillin-streptomycin. For cell line development the cells were cultured with puromycin at 1μg/ml. The cells were maintained in a humidified incubator at 5% CO2 and were passed every time 2 days or every time cell reached ~80% confluency.

2.5.2 Transfection Conditions for Imaging Experiments Cells were plated on 35 mm diameter glass bottom Ibidi dishes at ~20% confluency one day before transfection. Vectors and gRNA cassettes were transfected with

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Lipofectamine 2000 using Invitrogen’s recommended procedures, in most cases 250-500ng of each vector was used. 2.5.3 Generation of stable cell lines For viral production, HEK 293T cells were seeded into 15cm plates 1 day before transfection. 4 μg. of pMD2.G plasmid, 6 μg of psPAX plasmid and 10 μg of the lentiviral vector were co-transfected into HEK 293T cells using Lipofectamine 2000 using the recommended protocol. Media was refreshed 12 h after transfection. Virus was collected 24 after first media refreshment, filtered through a 0.45 μm filter, flash-frozen with liquid nitrogen and stored at −80 °C. To make stable cell lines, cells were transduced with lentivirus, selected with appropriate antibiotics for 5 days and FACS-sorted into 96 wells, single colonies were picked and expanded. The expression of the different fusion protein was corroborated by fluorescence microscopy and flow cytometry.

2.5.4 Double thymidine block synchronization For the double thymidine block synchronization is important to ensure cells are maintained at <70% confluency. HEK 293T cells were seeded at low density, ~3 × 106cell density in a 10-cm culture dish 18 hr. before transfection. Thymidine block requires two sequential treatments to enrich cells arrested at the entry of S phase. Cells were treated with 5mM thymidine for 18 hr., washed with media to remove the drugs, grown for 8 hours, and treated with a second dose of drugs for an extra 18 hours. Transfection was done right after the second thymidine block.

2.5.5 Genome editing experiments To generate the Knock-in of the mCherry cassette next to the -335 kb enhancer, a sgRNA was designed using the CRISPR design tool (http://crispr.mit.edu/) targeting the sequence CAGAACAGAGGTCCCGAAAA GGG. This sgRNA was cloned into a modified px330 vector expressing Venus fluorescent protein. Cells were cotransfected with the px330 plasmid and a donor DNA plasmid containing two homology arms of 500 bp and a functional mCherry cassette in between. Cells were grown for a week before single cells were FACS-sorted into single wells. Genotyping was done to confirm successful knock-in of the mCherry cassette. 2.5.6 Flow cytometry assays For flow cytometry, cells were trypsinized and resuspended in DMEM media to a concentration of ~107cells/mL with no FBS. On average, 50K cells were analyzed on the FITC, Cy7PE and PE channels with a BD Fortessa Flow Cytometer or with an Influx Cell Sorter (2013), depending on the type of experiment. Percentages of the different sub populations were determined by comparing the positive cells to

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control cells. When cells were recovered, they were plated in 96-well plates and grown for a week before any subsequent experiments.

2.5.7 RMCE of mCherry cassette Cells were transfected with 500ng of a plasmid coding for FLP recombinase and 1ug of donor non-linearized plasmid. The recombination cassette was flanked with two heterospecific FRT sites which were also flanking the genomic integrated mCherry cassette. Cells were grown for 5 days before the recombination of the cassette was assayed. 2.5.8 Cell Imaging Confocal imaging was performed using Zeiss LSM 710 AxioObserber confocal microscopy with a × 63 1.4 numerical aperture oil immersion objective at 37 °C with 5% CO2. 405, 488 and 561 nm excitation beams were used to image BFP, EGFP and tRFP, respectively. Digital gain and offset adjustment on PMT are optimized to use the full dynamic range of the detector. The z sectioning step was set between 0.4 to 0.7 μM and the range of sectioning was adjusted manually. 2.5.9 Design and expression of gRNAs Most of the sgRNAs were designed and selected using the online CRISPR design tool (http://crispr.mit.edu/). The sgRNA targeting the telomeres was obtained from Chen et al. (2013). The 3’ of the sgRNA were extended with either MS2 and PP7 aptamers separated with a sequence between 10 to 15 nucleotides long, the spacing was optimized to enable the correct formation for the MS2 and PP7 loops (Figure 2.11 & 2.112). This was tested in-silico by using of the UNAfold web server tool (http://unafold.rna.albany.edu/). UNAfold provides algorithms that can be used to probabilistically predict the possible RNA secondary structures that a sequence can generate and, as such, can be used to optimize the spacer and aptamer sequences for correct folding.

The modified sgRNA final sequence was designed as follows: the first 20 nucleotides represent the spacer, the next 90 nucleotides correspond to the standard sgRNA sequence, and the last ~100 nucleotides encode either the MS2 or PP7 aptamers with have been spaced by unique spacer sequences. The specific sequence of the modified sgRNA is presented below:

§ gRNA+3x MS2 aptamers:

5’NNNNNNNNNNNNNNNNNNNNGTTTAAGAGCTATGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGTCCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCATTA

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CGTACACCATCAGGGTACGTAGCGTCATCCGTACACCATCAGGGTACGTAGATTCCGTACACCATCAGGGTACGTCGAAT3’ * MS2 aptamer sequences are underlined.

§ gRNA+ 4x PP7 aptamers:

5’NNNNNNNNNNNNNNNNNNNNGTTTAAGAGCTATGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGTCCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCATAGACGGAGTTTATATGGAAACCCGTCTAGCCACCAAGGAGTCGATATGGCGACCCTTGTATATCGGAGGAGTTTATATGGAAACCCTCATGGCTGATAAGGAGCCTATATGGAGGCCCTTATCAGT3’

* PP7 aptamer sequences are underlined.

§ Protospacers Sequences targeted by dCas9/Aptamer System

sgRNA ID Protospacers sequence sgTelomere UAGGGUUAGGGUUAGGGUUA

EGFP#1 GAGGGCCTATTTCCCATGAT

EGFP#2 AGGGCGAGGAGCTGTTCACC

EGFP#3 CCCATCCTGGTCGAGCTGGA

EGFP#4 GGCCACAAGTTCAGCGTGTC

GAL4#1 GCGCGACGGCTCGACTCGGC

GAL4#2 GCGCGGGTCCGGCCTGGGAG

GAL4#3 GATCCGCTAGGCGCACCGAC

GAL4#4 CCGCCGGTCGGTGCGCCTAG

EGFP#5-optimization AGGGCCTATTTCCCATGAT

EGFP#6-optimization GGGCGAGGAGCTGTTCACC

EGFP#7-optimization CCATCCTGGTCGAGCTGGA

EGFP#8-optimization GCCACAAGTTCAGCGTGTC

EGFP#9-optimization TCAAGTCCGCCATGCCCGAA

EGFP#10-optimization GGAGCGCACCATCTTCTTCA

EGFP#11-optimization CAACTACAAGACCCGCGCCG

EGFP#12-optimization CCGCGCCGAGGTGAAGTTCG

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Figure 17: Possible conformations of the modified gRNA harboring MS2 loops by UNAfold. The four most stable conformational variants are presented according the UNAfold algorithm. It can be observed that in all four scenarios the folding allows the correct formation of the three MS2 loops (shaded in yellow).

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Figure 18: Possible conformations of the modified gRNA harboring PP7 loops by UNAfold. The three most stable conformational variants are presented according to the UNAfold algorithm. It can be observed in all the scenarios that at least three out of four PP7 loops (shaded in green) are consistently formed.

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2.6 Appendix

2.6.1 Fusion Protein used for loci labelling and Imaging

§ Human optimized Ms2 Coat Protein-turboRFP sequence: ATGGCGTCAAACTTCACGCAGTTTGTCCTCGTTGACAACGGGGGGACTGGCGATGTCACAGTGGCTCCCAGTAACTTCGCGAATGGCGTCGCCGAATGGATCAGCAGTAACTCTCGGTCTCAGGCATACAAGGTCACCTGTTCCGTGCGCCAATCATCTGCCCAGAACAGGAAATACACCATTAAGGTGGAGGTGCCAAAGGTCGCCACCCAGACTGTGGGAGGGGTGGAGCTGCCTGTGGCCGCCTGGAGGTCATACCTCAACATGGAGCTCACCATACCTATATTCGCAACCAATTCCGACTGCGAATTGATCGTCAAAGCGATGCAGGGCCTCCTGAAGGATGGTAACCCAATTCCGAGTGCAATCGCAGCTAACAGCGGCATTTACGGTGGCAGCGGTGGTGGTATGAGCGAGCTGATCAAGGAGAACATGCACATGAAGCTGTACATGGAGGGCACCGTGAACAACCACCACTTCAAGTGCACATCCGAGGGCGAAGGCAAGCCCTACGAGGGCACCCAGACCATGAAGATCAAGGTGGTCGAGGGCGGCCCTCTCCCCTTCGCCTTCGACATCCTGGCTACCAGCTTCATGTACGGCAGCAAAGCCTTCATCAACCACACCCAGGGCATCCCCGACTTCTTTAAGCAGTCCTTCCCTGAGGGCTTCACATGGGAGAGAATCACCACATACGAAGACGGGGGCGTGCTGACCGCTACCCAGGACACCAGCTTCCAGAACGGCTGCATCATCTACAACGTCAAGATCAACGGGGTGAACTTCCCATCCAACGGCCCTGTGATGCAGAAGAAAACACGCGGCTGGGAGGCCAACACCGAGATGCTGTACCCCGCTGACGGCGGCCTGAGAGGCCACAGCCAGATGGCCCTGAAGCTCGTGGGCGGGGGCTACCTGCACTGCTCCTTCAAGACCACATACAGATCCAAGAAACCCGCTAAGAACCTCAAGATGCCCGGCTTCCACTTCGTGGACCACAGACTGGAAAGAATCAAGGAGGCCGACAAAGAGACCTACGTCGAGCAGCACGAGATGGCTGTGGCCAAGTACTGCGACCTCCCTAGCAAACTGGGGCACAGATAA

§ Human optimized PP7 Coat Protein-mNeonGreen sequence: ATGAGCAAGACCATCGTGCTGAGCGTGGGCGAGGCCACCAGAACCCTGACAGAGATCCAGAGCACCGCCGACCGGCAGATCTTCGAGGAAAAAGTGGGCCCCCTCGTGGGCCGGCTGAGACTGACAGCTAGCCTGAGACAGAACGGCGCCAAGACCGCCTACAGAGTGAACCTGAAGCTGGACCAGGCCGACGTGGTGGACTGTAGCACAAGCGTGTGTGGCGAGCTGCCCAAAGTGCGGTACACCCAAGTGTGGTCCCACGACGTGACAATCGTGGCCAACAGCACCGAGGCCAGCCGGAAGTCCCTGTACGACCTGACCAAAAGCCTGGTGGCCACAAGCCAGGTGGAAGATCTGGTCGTGAACCTGGTGCCCCTGGGCAGAGGTGGCAGCGGTGGTGGTATGGTGTCAAAAGGGGAAGAGGACAATATGGCTTCACTGCCCGCCACGCACGAGTTGCACATCTTTGGTTCCATAAACGGGGTGGACTTCGACATGGTGGGACAAGGGACTGGTAACCCCAATGACGGGTACGAGGAGCTTAACCTGAAGTCTACCAAAGGGGACCTGCAGTTTAGCCCGTGGATTCTTGTCCCGCATATAGGGTATGGATTCCACCAATACCTGCCCTATCCTGATGGTATGAGCCCATTTCAAGCAGCAATGGTGGACGGGTCTGGATACCAAGTGCATAGAACCATGCAATTTGAAGATGGTGCTTCACTCACTGTTAATTACCGCTACACCTATGAAGGGTCCCACATCAAGGGGGAAGCTCAGGTAAAGGGAACCGGGTTCCCAGCGGACGGGCCAGTAATGACAAATTCTCTTACGGCGGCGGATTGGTGTCGATCTAAGAAGACGTATCCTAATGATAAGACGATAATTTCCACCTTCAAATGGTCATACACCACTGGCAATGGCAAACGGTACCGGTCCACTGCTAGGACTACCTATACATTTGCCAAGCCAATGGCTGCGAACTACCTGAAGAACCAACCTATGTACGTATTCCGGAAGACCGAATTGAAACACAGCAAGACGGAGCTCAACTTCAAAGAATGGCAGAAGGCATTCACTGATGTTATGGGAATGGATGAACTGTATAAGTAG

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§ dCas9-BFP sequence: ATGGACAAGAAGTACAGCATCGGCCTGGCCATCGGCACCAACTCTGTGGGCTGGGCCGTGATCACCGACGAGTACAAGGTGCCCAGCAAGAAATTCAAGGTGCTGGGCAACACCGACCGGCACAGCATCAAGAAGAACCTGATCGGCGCCCTGCTGTTCGACAGCGGAGAAACAGCCGAGGCCACCCGGCTGAAGAGAACCGCCAGAAGAAGATACACCAGACGGAAGAACCGGATCTGCTATCTGCAAGAGATCTTCAGCAACGAGATGGCCAAGGTGGACGACAGCTTCTTCCACAGACTGGAAGAGTCCTTCCTGGTGGAAGAGGATAAGAAGCACGAGCGGCACCCCATCTTCGGCAACATCGTGGACGAGGTGGCCTACCACGAGAAGTACCCCACCATCTACCACCTGAGAAAGAAACTGGTGGACAGCACCGACAAGGCCGACCTGCGGCTGATCTATCTGGCCCTGGCCCACATGATCAAGTTCCGGGGCCACTTCCTGATCGAGGGCGACCTGAACCCCGACAACAGCGACGTGGACAAGCTGTTCATCCAGCTGGTGCAGACCTACAACCAGCTGTTCGAGGAAAACCCCATCAACGCCAGCGGCGTGGACGCCAAGGCCATCCTGTCTGCCAGACTGAGCAAGAGCAGACGGCTGGAAAATCTGATCGCCCAGCTGCCCGGCGAGAAGAAGAATGGCCTGTTCGGCAACCTGATTGCCCTGAGCCTGGGCCTGACCCCCAACTTCAAGAGCAACTTCGACCTGGCCGAGGATGCCAAACTGCAGCTGAGCAAGGACACCTACGACGACGACCTGGACAACCTGCTGGCCCAGATCGGCGACCAGTACGCCGACCTGTTTCTGGCCGCCAAGAACCTGTCCGACGCCATCCTGCTGAGCGACATCCTGAGAGTGAACACCGAGATCACCAAGGCCCCCCTGAGCGCCTCTATGATCAAGAGATACGACGAGCACCACCAGGACCTGACCCTGCTGAAAGCTCTCGTGCGGCAGCAGCTGCCTGAGAAGTACAAAGAGATTTTCTTCGACCAGAGCAAGAACGGCTACGCCGGCTACATCGATGGCGGAGCCAGCCAGGAAGAGTTCTACAAGTTCATCAAGCCCATCCTGGAAAAGATGGACGGCACCGAGGAACTGCTCGTGAAGCTGAACAGAGAGGACCTGCTGCGGAAGCAGCGGACCTTCGACAACGGCAGCATCCCCCACCAGATCCACCTGGGAGAGCTGCACGCCATTCTGCGGCGGCAGGAAGATTTTTACCCATTCCTGAAGGACAACCGGGAAAAGATCGAGAAGATCCTGACCTTCCGCATCCCCTACTACGTGGGCCCTCTGGCCAGGGGAAACAGCAGATTCGCCTGGATGACCAGAAAGAGCGAGGAAACCATCACCCCCTGGAACTTCGAGGAAGTGGTGGACAAGGGCGCCAGCGCCCAGAGCTTCATCGAGCGGATGACCAACTTCGATAAGAACCTGCCCAACGAGAAGGTGCTGCCCAAGCACAGCCTGCTGTACGAGTACTTCACCGTGTACAACGAGCTGACCAAAGTGAAATACGTGACCGAGGGAATGAGAAAGCCCGCCTTCCTGAGCGGCGAGCAGAAAAAAGCCATCGTGGACCTGCTGTTCAAGACCAACCGGAAAGTGACCGTGAAGCAGCTGAAAGAGGACTACTTCAAGAAAATCGAGTGCTTCGACTCCGTGGAAATCTCCGGCGTGGAAGATCGGTTCAACGCCTCCCTGGGCACATACCACGATCTGCTGAAAATTATCAAGGACAAGGACTTCCTGGACAATGAGGAAAACGAGGACATTCTGGAAGATATCGTGCTGACCCTGACACTGTTTGAGGACAGAGAGATGATCGAGGAACGGCTGAAAACCTATGCCCACCTGTTCGACGACAAAGTGATGAAGCAGCTGAAGCGGCGGAGATACACCGGCTGGGGCAGGCTGAGCCGGAAGCTGATCAACGGCATCCGGGACAAGCAGTCCGGCAAGACAATCCTGGATTTCCTGAAGTCCGACGGCTTCGCCAACAGAAACTTCATGCAGCTGATCCACGACGACAGCCTGACCTTTAAAGAGGACATCCAGAAAGCCCAGGTGTCCGGCCAGGGCGATAGCCTGCACGAGCACATTGCCAATCTGGCCGGCAGCCCCGCCATTAAGAAGGGCATCCTGCAGACAGTGAAGGTGGTGGACGAGCTCGTGAAAGTGATGGGCCGGCACAAGCCCGAGAACATCGTGATCGAAATGGCCAGAGAGAACCAGACCACCCAGAAGGGACAGAAGAACAGCCGCGAGAGAATGAAGCGGATCGAAGAGGGCATCAAAGAGCTGGGCAGCCAGATCCTGAAAGAACACCCCGTGGAAAACACCCAGCTGCAGAACGAGAAGCTGTACCTGTACTACCTGCAGAATGGGCGGGATATGTACGTGGACCAGGAACTGGACATCAACCGGCTGTCCGACTACGATGTGGACGCTATCGTGCCTCAGAGCTTTCTGAAGGACGACTCCATCGATAACAAAGTGCTGACTCGGAGCGACAAGAACCGGGGCA

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AGAGCGACAACGTGCCCTCCGAAGAGGTCGTGAAGAAGATGAAGAACTACTGGCGCCAGCTGCTGAATGCCAAGCTGATTACCCAGAGGAAGTTCGACAATCTGACCAAGGCCGAGAGAGGCGGCCTGAGCGAACTGGATAAGGCCGGCTTCATCAAGAGACAGCTGGTGGAAACCCGGCAGATCACAAAGCACGTGGCACAGATCCTGGACTCCCGGATGAACACTAAGTACGACGAGAACGACAAACTGATCCGGGAAGTGAAAGTGATCACCCTGAAGTCCAAGCTGGTGTCCGATTTCCGGAAGGATTTCCAGTTTTACAAAGTGCGCGAGATCAACAACTACCACCACGCCCACGACGCCTACCTGAACGCCGTCGTGGGAACCGCCCTGATCAAAAAGTACCCTAAGCTGGAAAGCGAGTTCGTGTACGGCGACTACAAGGTGTACGACGTGCGGAAGATGATCGCCAAGAGCGAGCAGGAAATCGGCAAGGCTACCGCCAAGTACTTCTTCTACAGCAACATCATGAACTTTTTCAAGACCGAGATTACCCTGGCCAACGGCGAGATCCGGAAGCGGCCTCTGATCGAGACAAACGGCGAAACAGGCGAGATCGTGTGGGATAAGGGCCGGGACTTTGCCACCGTGCGGAAAGTGCTGTCTATGCCCCAAGTGAATATCGTGAAAAAGACCGAGGTGCAGACAGGCGGCTTCAGCAAAGAGTCTATCCTGCCCAAGAGGAACAGCGACAAGCTGATCGCCAGAAAGAAGGACTGGGACCCTAAGAAGTACGGCGGCTTCGACAGCCCCACCGTGGCCTATTCTGTGCTGGTGGTGGCCAAAGTGGAAAAGGGCAAGTCCAAGAAACTGAAGAGTGTGAAAGAGCTGCTGGGGATCACCATCATGGAAAGAAGCAGCTTCGAGAAGAATCCCATCGACTTTCTGGAAGCCAAGGGCTACAAAGAAGTGAAAAAGGACCTGATCATCAAGCTGCCTAAGTACTCCCTGTTCGAGCTGGAAAACGGCCGGAAGAGAATGCTGGCCTCTGCCGGCGAACTGCAGAAGGGAAACGAACTGGCCCTGCCCTCCAAATATGTGAACTTCCTGTACCTGGCCAGCCACTATGAGAAGCTGAAGGGCTCCCCCGAGGATAATGAGCAGAAACAGCTGTTTGTGGAACAGCACAAACACTACCTGGACGAGATCATCGAGCAGATCAGCGAGTTCTCCAAGAGAGTGATCCTGGCCGACGCTAATCTGGACAAGGTGCTGAGCGCCTACAACAAGCACAGAGACAAGCCTATCAGAGAGCAGGCCGAGAATATCATCCACCTGTTTACCCTGACCAATCTGGGAGCCCCTGCCGCCTTCAAGTACTTTGACACCACCATCGACCGGAAGAGGTACACCAGCACCAAAGAGGTGCTGGACGCCACCCTGATCCACCAGAGCATCACCGGCCTGTACGAGACACGGATCGACCTGTCTCAGCTGGGAGGCGACGCCTATCCCTATGACGTGCCCGATTATGCCAGCCTGGGCAGCGGCTCCCCCAAGAAAAAACGCAAGGTGGAAGATCCTAAGAAAAAGCGGAAAGTGGACGGCATTGGTAGTGGGAGCAACGGCAGCAGCGGATCCAGCGAGCTGATTAAGGAGAACATGCACATGAAGCTGTACATGGAGGGCACCGTGGACAACCATCACTTCAAGTGCACATCCGAGGGCGAAGGCAAGCCCTACGAGGGCACCCAGACCATGAGAATCAAGGTGGTCGAGGGCGGCCCTCTCCCCTTCGCCTTCGACATCCTGGCTACTAGCTTCCTCTACGGCAGCAAGACCTTCATCAACCACACCCAGGGCATCCCCGACTTCTTCAAGCAGTCCTTCCCTGAGGGCTTCACATGGGAGAGAGTCACCACATACGAAGACGGGGGCGTGCTGACCGCTACCCAGGACACCAGCCTCCAGGACGGCTGCCTCATCTACAACGTCAAGATCAGAGGGGTGAACTTCACATCCAACGGCCCTGTGATGCAGAAGAAAACACTCGGCTGGGAGGCCTTCACCGAGACGCTGTACCCCGCTGACGGCGGCCTGGAAGGCAGAAACGACATGGCCCTGAAGCTCGTGGGCGGGAGCCATCTGATCGCAAACATCAAGACCACATATAGATCCAAGAAACCCGCTAAGAACCTCAAGATGCCTGGCGTCTACTATGTGGACTACAGACTGGAAAGAATCAAGGAGGCCAACAACGAGACCTACGTCGAGCAGCACGAGGTGGCAGTGGCCAGATACTGCGACCTCCCTAGCAAACTGGGGCACAAGCTTAATTAG

§ EGFP-LacI sequence: ATGGTGAGCAAGGGCGAGGAGCTGTTCACCGGGGTGGTGCCCATCCTGGTCGAGCTGGACGGCGACGTAAACGGCCACAAGTTCAGCGTGTCCGGCGAGGGCGAGGGCGATGCCACCTACGGCAAGCTGACCCTGAAGTTCATCTGCACCACCGGCAAGCTGCCCGTGCCCTGGCCCACCCTCGTGACCACCCTGACCTACGGCGTGCAGTGCTTCAGCCGCTACCCCGACCACAT

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GAAGCAGCACGACTTCTTCAAGTCCGCCATGCCCGAAGGCTACGTCCAGGAGCGCACCATCTTCTTCAAGGACGACGGCAACTACAAGACCCGCGCCGAGGTGAAGTTCGAGGGCGACACCCTGGTGAACCGCATCGAGCTGAAGGGCATCGACTTCAAGGAGGACGGCAACATCCTGGGGCACAAGCTGGAGTACAACTACAACAGCCACAACGTCTATATCATGGCCGACAAGCAGAAGAACGGCATCAAGGTGAACTTCAAGATCCGCCACAACATCGAGGACGGCAGCGTGCAGCTCGCCGACCACTACCAGCAGAACACCCCCATCGGCGACGGCCCCGTGCTGCTGCCCGACAACCACTACCTGAGCACCCAGTCCGCCCTGAGCAAAGACCCCAACGAGAAGCGCGATCACATGGTCCTGCTGGAGTTCGTGACCGCCGCCGGGATCACTCTCGGCATGGACGAGCTGTACAAGTCCGGACTCAGATCTCGAGGGATGGTGAAACCAGTAACGTTATACGATGTCGCAGAGTATGCCGGTGTCTCTTATCAGACCGTTTCCCGCGTGGTGAACCAGGCCAGCCACGTTTCTGCGAAAACGCGGGAAAAAGTGGAAGCGGCGATGGCGGAGCTGAATTACATTCCCAACCGCGTGGCACAACAACTGGCGGGCAAACAGTCGTTGCTGATTGGCGTTGCCACCTCCAGTCTGGCCCTGCACGCGCCGTCGCAAATTGTCGCGGCGATTAAATCTCGCGCCGATCAACTGGGTGCCAGCGTGGTGGTGTCGATGGTAGAACGAAGCGGCGTCGAAGCCTGTAAAGCGGCGGTGCACAATCTTCTCGCGCAACGCGTCAGTGGGCTGATCATTAACTATCCGCTGGATGACCAGGATGCCATTGCTGTGGAAGCTGCCTGCACTAATGTTCCGGCGTTATTTCTTGATGTCTCTGACCAGACACCCATCAACAGTATTATTTTCTCCCATGAAGACGGTACGCGACTGGGCGTGGAGCATCTGGTCGCATTGGGTCACCAGCAAATCGCGCTGTTAGCGGGCCCATTAAGTTCTGTCTCGGCGCGTCTGCGTCTGGCTGGCTGGCATAAATATCTCACTCGCAATCAAATTCAGCCGATAGCGGAACGGGAAGGCGACTGGAGTGCCATGTCCGGTTTTCAACAAACCATGCAAATGCTGAATGAGGGCATCGTTCCCACTGCGATGCTGGTTGCCAACGATCAGATGGCGCTGGGCGCAATGCGCGCCATTACCGAGTCCGGGCTGCGCGTTGGTGCGGATATCTCGGTAGTGGGATACGACGATACCGAAGACAGCTCATGTTATATCCCGCCGTTAACCACCATCAAACAGGATTTTCGCCTGCTGGGGCAAACCAGCGTGGACCGCTTGCTGCAACTCTCTCAGGGCCAGGCGGTGAAGGGCAATCAGCTGTTGCCCGTCTCACTGGTGAAAAGAAAAACCACCCTGGCGCCCAATACGCAAACCGCCTCTCCCCGCGCGTTGGCCGATTCATTAATGCAGCTGGCACGACAGGTTTCCCGAAGCAGCCTGAGGCCT

§ TetR-mCherry sequence: ATGTCCAGATTAGATAAAAGTAAAGTGATTAACAGCGCATTAGAGCTGCTTAATGAGGTCGGAATCGAAGGTTTAACAACCCGTAAACTCGCCCAGAAGCTAGGTGTAGAGCAGCCTACATTGTATTGGCATGTAAAAAATAAGCGGGCTTTGCTCGACGCCTTAGCCATTGAGATGTTAGATAGGCACCATACTCACTTTTGCCCTTTAGAAGGGGAAAGCTGGCAAGATTTTTTACGTAATAACGCTAAAAGTTTTAGATGTGCTTTACTAAGTCATCGCGATGGAGCAAAAGTACATTTAGGTACACGGCCTACAGAAAAACAGTATGAAACTCTCGAAAATCAATTAGCCTTTTTATGCCAACAAGGTTTTTCACTAGAGAATGCATTATATGCACTCAGCGCTGTGGGGCATTTTACTTTAGGTTGCGTATTGGAAGATCAAGAGCATCAAGTCGCTAAAGAAGAAAGGGAAACACCTACTACTGATAGTATGCCGCCATTATTACGACAAGCTATCGAATTATTTGATCACCAAGGTGCAGAGCCAGCCTTCTTATTCGGCCTTGAATTGATCATATGCGGATTAGAAAAACAACTTAAATGTGAAAGTGGGTCCGCGGATCCCGCAGCAAAGCGTGTAAAGCTAGACGCAGATCCCGCCACCATGGTGAGCAAGGGCGAGGAGGATAACATGGCCATCATCAAGGAGTTCATGCGCTTCAAGGTGCACATGGAGGGCTCCGTGAACGGCCACGAGTTCGAGATCGAGGGCGAGGGCGAGGGCCGCCCCTACGAGGGCACCCAGACCGCCAAGCTGAAGGTGACCAAGGGTGGCCCCCTGCCCTTCGCCTGGGACATCCTGTCCCCTCAGTTCATGTACGGCTCCAAGGCCTACGTGAAGCACCCCGCCGACATCCCCGACTACTTGAAGCTGTCCTTCCCCGAGGGCTTCAAGTGGGAGCGCGTGATGAACTTCGAGGACGGCGGCGTGGTGACCGTGACCCAGGACTCCTCCCTGCAGGACGGCGAGTTCATCTACAAGGTGAAGCTGCGCGGC

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ACCAACTTCCCCTCCGACGGCCCCGTAATGCAGAAGAAGACCATGGGCTGGGAGGCCTCCTCCGAGCGGATGTACCCCGAGGACGGCGCCCTGAAGGGCGAGATCAAGCAGAGGCTGAAGCTGAAGGACGGCGGCCACTACGACGCTGAGGTCAAGACCACCTACAAGGCCAAGAAGCCCGTGCAGCTGCCCGGCGCCTACAACGTCAACATCAAGTTGGACATCACCTCCCACAACGAGGACTACACCATCGTGGAACAGTACGAACGCGCCGAGGGCCGCCACTCCACCGGCGGCATGGACGAGCTGTACAAGTAA

§ Human optimized CRE Recombinase sequence: ATGGTGCCCAAGAAGAAGAGGAAAGTCTCCAACCTGCTGACTGTGCACCAAAACCTGCCTGCCCTCCCTGTGGATGCCACCTCTGATGAAGTCAGGAAGAACCTGATGGACATGTTCAGGGACAGGCAGGCCTTCTCTGAACACACCTGGAAGATGCTCCTGTCTGTGTGCAGATCCTGGGCTGCCTGGTGCAAGCTGAACAACAGGAAATGGTTCCCTGCTGAACCTGAGGATGTGAGGGACTACCTCCTGTACCTGCAAGCCAGAGGCCTGGCTGTGAAGACCATCCAACAGCACCTGGGCCAGCTCAACATGCTGCACAGGAGATCTGGCCTGCCTCGCCCTTCTGACTCCAATGCTGTGTCCCTGGTGATGAGGAGAATCAGAAAGGAGAATGTGGATGCTGGGGAGAGAGCCAAGCAGGCCCTGGCCTTTGAACGCACTGACTTTGACCAAGTCAGATCCCTGATGGAGAACTCTGACAGATGCCAGGACATCAGGAACCTGGCCTTCCTGGGCATTGCCTACAACACCCTGCTGCGCATTGCCGAAATTGCCAGAATCAGAGTGAAGGACATCTCCCGCACCGATGGTGGGAGAATGCTGATCCACATTGGCAGGACCAAGACCCTGGTGTCCACAGCTGGTGTGGAGAAGGCCCTGTCCCTGGGGGTTACCAAGCTGGTGGAGAGATGGATCTCTGTGTCTGGTGTGGCTGATGACCCCAACAACTACCTGTTCTGCCGGGTCAGAAAGAATGGTGTGGCTGCCCCTTCTGCCACCTCCCAACTGTCCACCCGGGCCCTGGAAGGGATCTTTGAGGCCACCCACCGCCTGATCTATGGTGCCAAGGATGACTCTGGGCAGAGATACCTGGCCTGGTCTGGCCACTCTGCCAGAGTGGGTGCTGCCAGGGACATGGCCAGGGCTGGTGTGTCCATCCCTGAAATCATGCAGGCTGGTGGCTGGACCAATGTGAACATTGTGATGAACTACATCAGAAACCTGGACTCTGAGACTGGGGCCATGGTGAGGCTGCTCGAGGATGGGGACTGA

§ Human optimized FLP Recombinase sequence: ATGAGTATTCAACATTTCCGTGTCGCCCTTATTCCCTTTTTTGCGGCATTTTGCCTTCCTGTTTTTGCTCACCCAGAAACGCTGGTGAAAGTAAAAGATGCTGAAGATCAGTTGGGTGCACGAGTGGGTTACATCGAACTGGATCTCAACAGCGGTAAGATCCTTGAGAGTTTTCGCCCCGAAGAACGTTTTCCAATGATGAGCACTTTTAAAGTTCTGCTATGTGGCGCGGTATTATCCCGTATTGACGCCGGGCAAGAGCAACTCGGTCGCCGCATACACTATTCTCAGAATGACTTGGTTGAGTACTCACCAGTCACAGAAAAGCATCTTACGGATGGCATGACAGTAAGAGAATTATGCAGTGCTGCCATAACCATGAGTGATAACACTGCGGCCAACTTACTTCTGACAACGATCGGAGGACCGAAGGAGCTAACCGCTTTTTTGCACAACATGGGGGATCATGTAACTCGCCTTGATCGTTGGGAACCGGAGCTGAATGAAGCCATACCAAACGACGAGCGTGACACCACGATGCCTGTAGCAATGGCAACAACGTTGCGCAAACTATTAACTGGCGAACTACTTACTCTAGCTTCCCGGCAACAATTAATAGACTGGATGGAGGCGGATAAAGTTGCAGGACCACTTCTGCGCTCGGCCCTTCCGGCTGGCTGGTTTATTGCTGATAAATCTGGAGCCGGTGAGCGTGGGTCTCGCGGTATCATTGCAGCACTGGGGCCAGATGGTAAGCCCTCCCGTATCGTAGTTATCTACACGACGGGGAGTCAGGCAACTATGGATGAACGAAATAGACAGATCGCTGAGATAGGTGCCTCACTGATTAAGCATTGGTAA

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2.6.2 Operator Arrays DNA Sequences

§ LacO x7 flanked by FRT sites CAGGAAACAGCTATGACATCAAGCTGACTAGATAATCTAGCTGATCGTGGACCGATCATACGTATAATGCCGTAAGATCACGGGTCGCAGCACAGCTCGCGGTCCAGTAGTGATCGACACTGCTCGATCCGCTCGCACCGTAGATATCCCGTGATGGGCACGAAGTTCCTATTCCGAAGTTCCTATTCTCTAGAAAGTATAGGAACTTCTCTCAGCTGGAGTCGACCCGATTGCTAGCCGTGCTGGCTCGTTGAGTGGAATTGTGAGCGGATAACAATTCCGTTGCCATTGTCATAGAGTGGGGCCTTGGAATTGTGAGCGGATAACAATTTGAGGTTAACCCACACGACCTTGTGTCGTGGAATTGTGAGCGGATAACAATTGAGCCAACTCCGATAGCGGTAGTCGGCTTGGAATTGTGAGCGGATAACAATTCTCCAACCCTTGCTACAGAGAATACGCTTGGAATTGTGAGCGGATAACAATTCGAACGAGCAAGCTCAGCTATGACGCAATGGAATTGTGAGCGGATAACAATTGCATCGTGTAGGCCAACAGAGAACTACGTGGAATTGTGAGCGGATAACAATTACCCTCAGAGTAAGCTTGTAGGCAGGACTCGAGCACCTCATCGGCGATAGGATCCCAGCTGGCGAAGTTCCTATTCCGAAGTTCCTATTCTTCAAATAGTATAGGAACTTCGCAACTGAGATATCACGACGGATCGACGAGAGCAGCGCGACTGGATCTGTCGCCCGTCTCAAACGCAACCCTCCGGCGGTCGCATATCATTCAGGACGAGCCTCAGACTCCAGCGTAACTGGACTGCAATCAACTCACTGGCTCACCTTCCGGTCCACGATCAGCTAGAATCAAGCTGACTAGATAAACTGGCCGTCGTTTTAC * LacO sites are underlined ** FRT sites are double underlined.

§ TetOx7 flanked by LoxP sites GTAAAACGACGGCCAGTTTATCTAGTCAGCTTGATTCTAGCTGATCGTGGACCGGAAGGTGAGCCAGTGAGTTGATTGCAGTCCAGTTACGCTGGAGTCTGAGGCTCGTCCTGAATGATATGCGACCGCCGGAGGGTTGCGTTTGAGACGGGCGACAGATCCAGTCGCGCTGCTCTCGTCGATCCGTCGTGATATCTCAGTTGAATAACTTCGTATAAAGTATCCTATACGAAGTTATCTCAGCTGGGATCCTATCGCCGATGAGGTGCTCGAGTCCGACGAAGGTGTCGGTTCCATCTCTATCACTGATAGGGAAGTGGGATTGGCGCGAGCGACCTTTCTCTATCACTGATAGGGACAGCAAGTGAACCAGCAAACCGACTCTCTATCACTGATAGGGAATCTCTGGTTCGGCATTACTACTCTCTCTATCACTGATAGGGAATCAACGGCAAGTTGCCGTGACGATCTCTATCACTGATAGGGACATGACCTATCCCGGTACCCGATCTCTCTATCACTGATAGGGATACATCCTGGATGCTCCTCATAGTTTCTCTATCACTGATAGGGAGGCGTCGTAATCGAAGCTTGGTCGACTCCAGCTGAGAATAACTTCGTATAATGTATGCTATACGAAGTTATGTGCCCATCACGGGATATCTACGGTGCGAGCGGATCGAGCAGTGTCGATCACTACTGGACCGCGAGCTGTGCTGCGACCCGTGATCTTACGGCATTATACGTATGATCGGTCCACGATCAGCTAGATTATCTAGTCAGCTTGATGTCATAGCTGTTTCCTG * TetO sites are underlined ** LoxP sites are double underlined.

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2.6.3 Locus where mCherry was integrated adjacent to MYC -335 kb GCCCCAGTTGACTGACTTCTAAGCCTTATTTGACAATTGTGCACACCTCCCTTTCTGAAACATTTTTTTCATTTGGCTCTGTGACCACACATTCTTCTAGTTCTGCTTTTGCCTGCCTGGCTACTCCTTCAGAATCTCCTTGGCTGGGTCCTCCTCTTGTTCACAACTTTTAAATGTAGTAATGTGCTTGGCTTATACGCACTCTGCACTCTCTCTGGGGAAACACATCCATGGCTTTAAATAAAATCCGTGTGCTGACTACTCTAGTTGAGAACTCTCCCTTGATTATCAGGCTTGTGTTTCCAGCTGTTTATTGAATGCCTTCATTAGCATGCCTAGTGATCTTAAATTTACTAAGGCCAGAGCAGAACTTTCCCACCTCCACTTCACATTTGCTTCTCTCCAATCTACTCAATCTCCTTTAATAATAGTACTATTCACTCAGTTGCCCAGCCATCAGCAAGTACTATTGATTCTGCCTCCAAGATACATTTTGAGTAGCAGCATTTTTGGCAACCTCCACTGCTACCAGCTGGTGCATGTTACCACCAGATTTCTCCTGGACCACTGCAAGAGCCTCCTAGCCATCTCCCTGCTTCACCTCTTTCTCCTTGACAGTTCATTCTCCATCCAGCCACAAGAGTGATCTTCTGGAAACACATTTAAATCATGTTTCTCTCTTGCTTAATATTGAACAAGCTCTCCACAGTATATTCTGAATTATATTTATCCATGCTTTTATTCCCTTTTACTTCTCCATTCTTTCCTCTCTGGCATTTTTTCTGTTCCTCCTCCATACCAAGCTTATTCCCTTTTCGGGACCTCTGTTCTGTTTATTCTTTTTAAAAAGTGTGTCCTGTACCACAGTTCTTTACCCAGCTGTCTTTTTTGCCATTCAAATGTCACTTCTTAGAGAACCTTTCTTCAGCAATTCTTTGTACAACCGGAACACTTAGCTGGTACCTCAGGAAATCTTTATCCCATAACCATATTTTATTTCCTCGGTAGCACTCTTTTAAATGATGCCATTTATTTATGTAAATATATTTGTGCTTTTCATCTTCCCCCCCACTGGAGTGTGAACAGGTACCATATCTGTCTTATTCCTTTGGTTTCAACTCATTTGCCTGTGTGTCCACAACTCTTTGATCAATATCTGGCAATAGTAAATTCTAAATAATGCGAAAGAATAAACTTGTGTCTCCCATTGCCCTTTTCATAGTAGATGTACAATAAGTTCTTAAGAAATAGTTGAATAGATAGGATGGTGGGTGGAAGAGTGGCTGGCTGGCTGGCCGGTTGTGTGGACAGAGGTAAATATGCAAAATCAACCAATTTAATATCTCCCCATTACTTGCAAATATAGGATAGTGAGCAGGTTATTTAAGTAATCTATAGCTGCATTAACAACCCGCCCAAAATGTCATAGCTTAGAAAAGTAGTTGATTCTGTCTCATAATTCCAGAAGTTCATTGGGTGATTCTTCGGGTCCACATGGTATTGGCTGGAATGCTGACATGGCTACAGTCATCCAAGGGCTAGGTTGAACAGAAACATTCAAAATGGCTCATTCATGTGGCTGTTGTGGCAGAGTTCAGCTGTTCATCTGGGACTGTCAACCAGAAAAACTACACATGTCATCTCCATGTCACTTGGGTTTCTCCTGGCATGGCAGGTGGGTTCCGAGGCTTCCCAGAAGTGAGTGTTCTAAGAGATTCAGGCAGACACTGTAAGACTTGTTATGACCTAGCCTTGAAAGTCCCAGGACGTCATTTCTGCCACATTCTATTAAACAATCACTACTGTCAGCCTAGATGTAAGACTCCACCTCTTGAAGTGAGCAGTGTTGGTGAAGCCATCTTGGAAACTGTCTACCATACCTGTAAAGAGACAGCCCACTGGGTTCTGCAGGGAAATCACATTTATAGATTTTGATTCTTCTGTTCTTTTTATTGAAACCACTTTGGGAATATGCATCTATGTGTTTTACTTTCAGCAGATCAGATCTTACTAATCACAACCGAGTAAAGTAACAGACTTTCATAACTCCAGCCTTCATAATTCAGTAGCTGCATATTCATTATCTGAAAACTCACTCTTCAGAATTCCTTGCATCTTTTTGGT * Underline sequence represent primer sequences used for genotyping ** Double underline sequence is the targeted sequence by CRISPR/Cas9 *** Bold letters represent PAM sequence

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Chapter 3

Identification, characterization and dissection of PCSK9 candidate enhancers 3.1 Motivation

Understanding the formation of enhancer-promoter interactions in the ever-changing and complex nuclear space has proven to be one of the most challenging questions of enhancer biology. In a large extent, this has been the case because of two key challenges associated with the study of these interactions. First, as has been previously discussed, there is a lack of efficient and robust methodologies to label and visualize multiple genomic loci in live cells, thereby limiting our capacity to follow their formation in real-time. Second, there are very few enhancer-promoter pairs in which the molecular machinery involved in the regulation of the process has been well characterized. More information is needed in order to generate a precise understanding of how these regulatory components modulate the formation of these long-range interactions. In order to further advance our understanding in this matter, I decided to first characterize the Proprotein convertase subtilisin/kexin type 9 (PCSK9) locus and the potential enhancers that regulate its activity in the hepatic-derived cell line HEPG2, and second, evaluate the potential use of this locus to study the dynamic formation of enhancer-promoter interactions in live cells.

Importantly, the PCSK9 gene codes for a crucial protein involved in the regulation of plasma cholesterol homeostasis (Poirier and Mayer, 2013; Stoekenbroek et al., 2018). Its misregulation is associated with multiple cardiovascular diseases (Stoekenbroek et al., 2018), and it is under current investigation as a potential target for new biological drugs (Gouni-Berthold and Berthold, 2014; Zhang, 2017). Therefore, investigating the candidate enhancers of the PCSK9 locus and their interaction with its promoter could not only expand our understanding of enhancer-promoter interactions but could also provide important insights into PCSK9 expression regulation and its role in disease.

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3.2 Identification and Characterization of potential enhancers in the PCSK9 locus

3.2.1 Mapping potential enhancers in the PCSK9 locus in HEPG2 cells Genome-wide studies have shown that active enhancers are associated with specific epigenetic features that can aid in their identification (Calo and Wysocka, 2013; Creyghton et al., 2010; Thurman et al., 2012). In order to identify potential enhancers of the PCSK9 gene in HEGP2 cells, we decided to identify DNA regions that: present an open chromatin state, are enriched with histone posttranslational modifications such as H3K27ac (preferentially associated with enhancers) & H3K4me3 (preferentially associated with promoters), and are bound by coactivator proteins, such as p300. This was specifically done in a region of 400 kb around the PCSK9 promoter. Based on the combination of these epigenetic features, we were able to identify 11 potential enhancer sequences with lengths between 500 to 800 bp. These candidate enhancers sequences were named HSS1 to HSS11 (Figure 3.1). Importantly, this list of potential enhancers included the promoters of the PCSK9 and DHCR24 genes (DHCR24 is the only other active gene within this 400 kb region). Although these sequences, by definition, do not represent enhancer sequences, we decided to study them based on scientific curiosity. 3.2.2 PCSK9 candidate enhancers activate transcription by reporter assays In order to determine whether the previously identified candidate enhancers possess biological activity, they were cloned upstream of a minimal viral SV40 promoter driving a firefly luciferase gene. Two random DNA sequences from the PCSK9 locus were also selected as negative controls and their reporter activity was used as a baseline for enhancer activity. In addition, we used a DNA regulatory element proximal to the prl30 gene, which is known to have strong transcriptional activity in many cell lines, as a positive control (C. Cattoglio, personal communication). Reporter vectors were transiently transfected into HEPG2 cells and 24 hours after luciferase activity was measured as a readout of enhancer activity.

Most candidate enhancers could increase reporter activity at least 5 fold, with three enhancers increasing reporter activity over 20-fold (HSS6, HSS8 and HSS11) (Figure 3.2A). Surprisingly, the two negative control sequences had considerable transcriptional activity, increasing reporter activity 12-fold, being on par with many of the candidate enhancers. We observed that the positive control did increase the reporter activity more than the negative control (18- vs 12-fold). Based on these results, we decided to use a level of 19-fold activation as a threshold to distinguish strong over weak candidate enhancers (red dotted line). Overall, these results showed that most of the candidate enhancers possess transcriptional activity, but only three of them have a strong transcriptional activity (HHS6, HSS8 & HSS11).

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Figure 19: Mapping of DNA regulatory elements present within the PCSK9 locus. Chromatin immunoprecipitation sequencing (ChIP-Seq) traces of H3K4me3. P300 & H3K27ac and DNase I hypersensitive sites sequencing (DNA-seq) were used to identify the location of candidate enhancers across a 400 kb genomic region around the PCSK9 promoter. We were able to identify 11 genomic regions that presented strong ChIP-Seq marks and an open chromatin state in HEPG2 cells. Interestingly, most of these regions are located upstream from the PCSK9 promoter.

Figure 20: Reporter activity of 11 the candidate enhancers and control sequences on HEPG2 cells. It can be observed that all candidate enhancers and control sequences present significant reporter activity in HEPG2 cells. Of these elements, HHS6, HHS8 & HSS11 present the strongest transcriptional activity, being both superior to the positive control and having activity that is over 20-fold of the promoter alone vector. RLA.: Relative luciferase activity. Error bars represent standard error of mean.

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3.2.3 PCSK9 candidate enhancers transcriptional activity is tissue-specific The activity of transcriptional enhancers is precisely modulated by the presence of tissue-specific transcription factors (Bowman, 2015; Ong and Corces, 2011; Visel et al., 2009). As has been previously discussed, the binding of tissue-specific transcription factors promotes the subsequent binding of coactivators and other regulatory proteins that together lead enhancers to transition into an active state capable of regulating transcription initiation (Erokhin et al., 2015; Kulaeva et al., 2012). In order to understand if the candidate enhancers’ regulatory activity was tissue-specific, we decided to test the candidate enhancer transcriptional activity by reporter assays in the RPE-1 hTERT cell line. The RPE-1 hTERT cell line is of ectoderm origin (Bharti et al., 2011; Raymond and Jackson, 1995), and as such has a drastically different biogenesis from the endoderm-derived HEPG2 cell line, making it an ideal system to test if reporter activity of the candidate enhancers is regulated by hepatic-specific transcription factors or not. For this experiment, we used the same reporter vectors as in the previous section, but in this case they were transiently transfected into RPE-1 hTERT cells. Similar to the previous experiment, luciferase activity of the reporter vectors was measured 24 hours after transfection.

We observed that most vectors showed minimal reporter activity in the RPE-1 hTERT cells (Figure 3.3). In this cell line, most of the candidate enhancers did not increase the reporter activity above the promoter vector alone. This was observed even for the strong HSS8 sequence which generated a 35-fold increase over the promoter vector alone in HEPG2 cells under basal conditions. Importantly, only the positive control vector (DNA regulatory element from the prl30 ribosomal gene) could strongly increase reporter activity, showing a 20-fold increase in reporter activity. These results strongly suggested that of the candidate enhancers, activity is most strongly regulated by hepatic-specific transcription factors.

Figure 21: Reporter activity of 11 the candidate enhancers and control sequences on REP-1 hTERT cells. Candidate enhancers present minimal reporter activity in a cell line with a drastically different transcriptional regulatory network from HEPG2. The only element that presents strong reporter activity on this cell lines is the positive control. RLA: Relative luciferase activity. Error bars represent standard error of mean.

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3.2.4 Candidate enhancer transcriptional activity can be modulated by small molecules known to regulate PCSK9 gene expression.

PCSK9 gene expression is dynamically regulated as a response to different changes in metabolic stimuli such as diet, exercise, hormones and external factors (Zhang, 2017). Interestingly, PCSK9 gene expression can also be regulated by small molecules such as cholesterol-lowering drugs and other active natural compounds (Dubuc et al., 2004; Sahebkar, 2014; Troutt et al., 2010). These small molecules regulate PCSK9 expression indirectly by regulating the protein levels of transcription factors and their capacity to bind to promoter proximal DNA regulatory sequences. Of those small molecules, statins and berberine are likely two of the best known and studied (Dong et al., 2010; Dubuc et al., 2004; Zhang, 2017). Statins are cholesterol-lowering drugs that are known to upregulate PCSK9 expression by indirectly increasing the levels of the transcription factor SREBP-2 and its capacity to bind to a promoter proximal sterol-regulatory element adjacent to the PCSK9 promoter (Dong et al., 2010; Dubuc et al., 2004). Berberine is a natural compound that is known to downregulates PCSK9 expression by mediating the reduction of the intracellular levels of HNF1alpha, an important transcription factor that regulates PCSK9 expression (Dong et al., 2015; Li et al., 2009). Importantly, although these small molecules are known to mediate their effects by regulating the binding of transcription factors in proximal promoter elements, they may as well regulate the potential activity of potential distal enhancers. In the next section, we further investigated if these compounds can modulate the transcriptional activity of the previously identified candidate enhancers.

We first validated that lovastatin (a type of statins) and berberine can regulate PCSK9 gene expression in vivo as previously shown (Dong et al., 2010, 2015; Dubuc et al., 2004; Li et al., 2009). To do so, we measured PCSK9 mRNA levels in HEPG2 cells 24 hours after incubation with different concentration of lovastatin and berberine. In line with previous results, we observed upregulation of PCSK9 mRNA levels by lovastatin and downregulation by berberine; In both cases this occurred in a dose-dependent manner (Figure 3.4). In addition, we measured DHCR24 mRNA levels to test if this gene is also regulated under these conditions. We observed that the DHCR24 mRNA levels, contrary to PCSK9, were minimally affected by either lovastatin or berberine, even at higher concentrations. These results confirmed that both, lovastatin and berberine, preferentially and differentially regulate PCSK9 gene expression.

To further explore the role of lovastatin and berberine in regulating the activity of DNA regulatory elements associated with PCSK9, we decided to test if these compounds may also influence PCSK9 gene expression by regulating the activity of these candidate enhancers. To test whether or not this was the case, we tested if the transcriptional activity of the reporter vectors was affected by incubating HEPG2 cells with lovastatin or berberine. For these experiments, we pretreated the

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cells with the highest concentration used in the previous experiment one-day prior transfection, the following day the reporter vectors were transfected and 24 hours later we measured their luciferase activity as a readout of enhancer activity.

We gladly observed that the reporter activity for most of the candidate enhancers was significantly upregulated by lovastatin, and downregulated by berberine (Figure 3.5). Importantly, the most significant changes in transcriptional activity were associated with the strong candidate enhancers HSS6, HSS8 and HSS11, being both up- and down-regulated by these small molecules. This result suggested that these two small compounds likely regulate the activity of these enhancers by modulating the levels of tissue-specific transcription factors that present binding sites within those enhancers sequences. Taken together, these results further positions the candidate enhancers HSS6, HSS8 and HSS11 as the most likely and important distal regulatory elements of PCSK9 gene expression.

0

1

2

3

Lova9uM Lova3uM Lova1uM Control Bbr3uM Bbr9uM Bbr27uM

Relativem

RNAlevels(qPCR)

PCSK9 DHCR24

Figure 22: Relative mRNA levels of PCSK9 and DHCR24 under the presence of different concentrations of lovastatin (Lova) and berberine (Bbr). It can be observed that in HEPG2 cells the expression of PCSK9 is predominately regulated by both of these compounds, while DHCR24 is only mildly regulated by berberine. qPCR.: quantitative real-time PCR. Error bars represent standard error of mean.

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3.3 Dissection of candidate enhancers HSS6, HSS8 and HSS11

and identification of key transcription factor binding sites.

3.3.1 Dissection of HSS6, HSS8 and HSS11 candidate enhancers Based on the previous results, we decided to further investigate the precise sequences that conferred the transcriptional activity to the candidate enhancers HSS6, HSS8 and HSS11. Since the length of these candidate enhancers varies from 500 to 700 bp, we decided to subclone smaller DNA fragments of roughly 150 bp. This was done to define more precisely the sequence with strong reporter activity to perform finer analysis on those sequences. Specifically, we cloned three fragments both for HSS6 and HSS8, and four fragments for HSS11. Similar to previous experiments, enhancer activity was measured by reporter assays.

We observed that for the candidate enhancers HSS6 and HSS8 most of the reporter activity was concentrated within one of the three fragments. In the case of HSS6 the reporter activity was focused in fragment HSS6-3 (Figure 3.6A), while for HSS8 the reporter activity was mostly present in fragment HSS8-2 (Figure 3.6B). In the case of HSS11, the reporter activity was shared between two fragments, HSS11-1 and HSS11-3 (Figure 3.6C). Interestingly, these two smaller fragments had a stronger reporter activity than the full HSS11 (34-fold, 36-fold and 26-fold, respectively), suggesting that potential repressor sites could be present in the other fragments. These experiments allowed us to narrow down the reporter activity of the candidate enhancers into more defined sequences.

0

10

20

30

40

50

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pGL3 HSS1 HSS2(P) HSS3 HSS4 HSS5 HSS6 HSS7(P) HSS8 HSS9 HSS10 HSS11 (+)Control

C1 C3

RLA

Berberine6uM Lovastatine2uM Control

Figure 23: Reporter activity of candidate enhancers under the presence of Lovastatin and Berberine. Of all the regulatory elements present within the PCSK9 locus, it can be observed that the candidate enhancer HHS6, HSS8 & HSS11 and the promoter sequences, HHS2 and HSS7, are the elements most responsive having their activity either upregulated by lovastatin or downregulated by berberine. RLA.: Relative luciferase activity. Error bars represent standard error of mean.

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3.3.2 In-silicon mapping of transcription factor binding sites We decided to further investigate the fragments identified in the previous section by doing a transcription factor binding site analysis with TRANSFAC® (Matys et al., 2006). TRANSFAC® is a software that allows the mapping of transcription factor binding sites by using a curated database of eukaryotic transcription factors, their genomic binding sites and DNA binding profiles. We focused our analysis on binding sites for hepatic transcription factors or strong transcriptional activators with a sequence identity of 90% or higher. Based on this analysis we were able to identify several binding sites for each of the four enhancer fragments. Specifically, for HSS6-1 we were able to identify three conserved binding sites for HNF3alpha/beta. In the case of HSS8-2, we could identify binding sites for FOS/JUN and a CEBPA/B. For HSS11-1 we observed binding sites for CEBPA/B adjacent to a GATA4/5. And finally, for HSS11-3 we could identify binding sites for SP1 and HNF3alpha/beta. The binding of some of these transcription factors was validated by analyzing available ChIP-Seq tracks in cell lines of hepatic origin.

Figure 24: Dissection candidate enhancer HSS6, HHS8 and HSS11. (A) For HHS6, we identified the region HSS6-3 as the one responsible for the majority of the reporter activity. (B) For HHS8, we were able to identify fragment HSS8-2 as the one with the strongest activity. (C) In the case of HSS11, we were able to identify two sequences with strong activity. RLA.: Relative luciferase activity. Error bars represent standard error of mean.

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3.3.3 Identification of biologically important transcription factor binding sites within the HHS6-1, HSS8-2, HSS11-1 & HSS11-3 fragments

To further investigate the role of the identified transcription factor binding sites within enhancer fragments, we decided to deconstruct and evaluate the roles of each of these binding sites by following two strategies. First, we cloned individual binding sites flanked by a few adjacent nucleotides, and second, we precisely destroyed each of those binding sites within the smaller fragment. After generating these mutated fragments, we measured their reporter activity to assess the enhancer activity and understand the role of these transcription factor binding sites.

For HSS6-1, we first evaluated constructs harboring each of the individual HNF3alpha/beta binding sites. In these constructs we observed that each of these three binding sites provided significant reporter activity (5- to 14-fold over promoter alone construct), being the two outer sites significantly stronger than the middle one (Figure 3.7A). Suggesting that the three sites were of importance for HSS6-1 reporter activity. Then, we generated three HSS6-1 constructs, having each of the binding sites mutated independently. In these constructs we observed that the mutated fragments had a decreased reporter activity, somewhat proportional to the activity of the individual sites (Figure 3.7B). Taken together, these results clearly suggest that the enhancer activity of HSS6-1 is distributed between the three HNF3alpha/beta binding sites.

We followed a similar strategy for HSS8-2, in this case, we first evaluated the constructs containing only CEBPA/B and FOS/JUN binding sites. We observed that the CEBPA/B binding site provided minimal activity, while the FOS/JUN site provided most of the transcriptional activity (37-fold over the promoter construct alone) (Figure 3.8A). We then generate mutant constructs with either of the two binding sites mutated. In line with previous results, we observed that mutation of the FOS/JUN sites drastically decreased enhancer activity, while mutation of the CEBPA/B sites only had a small effect on the reporter activity (Figure 3.8B). Based on these results we concluded that the FOS/JUN binding site alone drives most of the transcription activity of HSS8-3.

Then, we tested how the activity of the HSS11-1 was distributed between the CEBPA/B and the GATA4/5 binding sites. When we tested the reporter activity of constructs harboring only CEBPA/B or GATA4/5 binding sites, we observed that both sequences provided significant reporter activity (12-fold for CEBPA/B and 16-fold for GATA4/5) (Figure 3.9A). Moreover, the mutation of either of these binding sites significantly within the full fragment decreased the overall activity of the construct (Figure 3.9B), demonstrating that both CEBPA/B and the GATA4/5 binding sites are important for the overall reporter activity of HSS11-1.

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Figure 25: Mapping of important binding sites within fragment HHS6-1 with strong transcriptional activity. (A) Each of the individual HNF3alpha/beta binding sites provides significant reporter activity within HSS6-1 fragment. (B) Mutation of each of these HNF3alpha/beta binding sites correspondingly decreases the total reporter activity of the fragment indicating its importance. RLA.: Relative luciferase activity. Error bars represent standard error of mean.

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Figure 26: Mapping of important binding sites within fragment HHS8-2 with strong transcriptional activity. (A) The FOS/JUN binding site provides most of the transcriptional activity within the HSS8-2 fragment. (B) Mutation of the FOS/JUN site within the HSS8-2 fragment also corroborates it importance of this site for reporter activity. RLA.: Relative luciferase activity. Error bars represent standard error of mean.

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Figure 27: Mapping of important binding sites within fragment HHS11-1 with strong transcriptional activity. (A) Both of the CEPA/B and GATA4/5 binding sites provide significant reporter activity within HSS11-1 fragment. (B) Mutation of any of those two sites within HSS11-1 decreases the total reporter activity of the fragment confirming their importance. RLA.: Relative luciferase activity. Error bars represent standard error of mean.

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Finally, we tested how the activity of HSS11-3 was also distributed between the SP1 and the HNF3alpha/beta binding sites. In this case we observed that the both, SP1 and HNF3alpha/beta binding sites, presented significant reporter activity (10- and 15-fold over promoter construct alone) (Figure 3.10A). In addition, the mutation of either of the binding sites within the HSS11-3 fragment significantly decreased the overall activity (Figure 3.10B), showing, that similar to HSS11-1, the activity of this constructs were also mediated by the two binding sites. Based, on these results we concluded that both binding sites, SP1 and the HNF3alpha/beta, are important for HSS11-3 enhancer activity.

Taken together these results point out that hepatic specific transcription factors are necessary for reporter activity; They mediate their positive effects through different modalities, either working independently (HHS8-2), together with other hepatic transcription factors (HSS6-3, HSS11-1), or with the help of more general activators (HSS11-3), overall suggesting that these DNA regulatory elements present within the PCSK9 locus are under complex regulation. Importantly, some of the transcription factor involved in the activity of these sequences have already been implicated in PCSK9 regulation(Dong et al., 2010, 2015, 2015; Li et al., 2009), supporting the likely biological importance of these candidate enhancers.

3.4 Attempted genome deletion of PCSK9 candidate enhancers

HSS6, HSS8 and HSS9

Based on the sum of the reporter assays, we decided to generate homozygous deletions of HSS6, HSS8 and HSS11 by CRISPR/Cas9 to evaluate their role on PCSK9 endogenous expression. We attempted to generate homozygous candidate enhancers deletions by using a px330 vector targeting sequences flanking each of these enhancers. The px330 vector codes for a Cas9 protein and a sgRNA which can be used to efficiently generate genome editing within a locus of interest (Cong et al., 2013). Unfortunately, after multiple attempts, we were not able to obtain these deletions. HEPG2 cells are known to be hard to transiently transfect, and we hypothesized that this in conjunction with inefficient editing could be a major limitation to produce deletions within this cells. To test the editing efficiency of CRISPR/Cas9, we transfected a px330 vector harboring a validated sgRNA targeting the EMX1 locus on HEK 293T, HCT-116, eHAP & HEPG2 cell lines and tested the editing efficiency by a T7E1 assay. We observed that genome editing in HEPG2 cells was highly inefficient when compared to the other cell lines (Figure 3.11). Our limited capacity to do genome editing on HEPG2 cells limited our capacity to further study the role of these potential enhancers on PCSK9 expression. Although we explored using other hepatic cell lines to generate the enhancer deletions, we ultimately decided not to pursue this project due to time constrains.

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Figure 28: Mapping of important binding sites within fragment HHS11-3 with strong transcriptional activity. (A) Both of the HNF3alpha/beta and SP1 binding sites provide significant reporter activity within HSS11-3 fragment. (B) Mutation of any of those two sites within HSS11-3 drastically decreases the total reporter activity of the fragment confirming their importance. RLA.: Relative luciferase activity. Error bars represent standard error of mean.

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3.5 Discussion and Future Directions Reporter assays demonstrated that within the PKCS9 locus the candidate enhancers HSS6, HSS8 & HSS11 present strong transcriptional activity in HEPG2 cells. These experiments also showed that the activity of these candidate enhancers is circumscribed to hepatic cells and that can be regulated, in a similar fashion to PCSK9 expression, by small compounds like lovastatin and berberine. Moreover, through bioinformatic analysis and fine mutagenic studies we subsequentially identify multiple transcription factor binding sites within each of these regulatory elements that are necessary for transcriptional activity. Importantly, many of the binding sites identified permit the binding of transcription factor specific to hepatic cells, many of which have been implicated in PCSK9 regulation (Dong et al., 2010, 2015; Dubuc et al., 2004; Li et al., 2009). Taken together, these results strongly suggest that these DNA regulatory elements act as transcriptional enhancers and regulated PCSK9 expression. Interestingly, analysis of previously generated Hi-C data from human hepatic primary cells shows that the region around the PCSK9 promoter forms long-range interactions with a distal region that harbors these three DNA regulatory elements (Figure 3.12) (Leung et al., 2015), further supporting that their role as transcriptional enhancers.

In order to corroborate the role of HSS6, HSS8 & HSS11 as transcriptional enhancers, it is imperative to corroborate the role of these sequences on PCSK9

Figure 29: CRISPR/Cas9 editing on HEPG2 cells is highly inefficient. To test the editing efficiency of CRISPR/Cas9 on HEPG2 and other cell lines we used a px330 vector targeting a validated target site within the EMX1 locus and assayed its editing efficiency by T7E1 assay. Successful by CRISPR/Cas9 genome editing is revealed by the T7E1 assay as the generation of two smaller bands than the control, the stronger the bands the more percentage of locus edited. Based on the T7E1 assay, it can be observed that the targeting of the EMX1 locus was really efficient in HEK 293T and eHAP cells, moderately successful in HC-116 cells and non-existent in HEPG2 cells.

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endogenous expression by genomic deletion experiments. Unfortunately, as previously shown by me and others, the cell line HEPG2 is not amenable to genome editing by CRISPR/Cas9; And consequently limits our ability to study the effects of enhancer deletions on PCSK9 gene expression. The identification of more amenable cell systems that allow genome editing therefore becomes necessary. Since this work was initially done, reports have shown that CRISPR/Cas9 genome editing has been successfully accomplished in certain hepatoma derived cell lines and primary hepatocytes (Sandy et al., 2017; Scharf et al., 2018). Making them potential systems that can be used to expand our understanding of the role of these regulatory elements on PCSK9 expression.

If these sequences are validated as enhancers, it would be interesting to further study their endogenous role on PCSK9 expression under basal conditions and under the presence of lovastatin and berberine. This could be of provide some key insights about PCSK9 regulation, especially since these small molecules are known to regulate many tissue-specific transcription factors (Dong et al., 2015; Li et al., 2009; Stormo et al., 2014); And therefore could regulate the formation and stability of enhancer-promoter interactions. Advancing our understanding of PCSK9 regulation would not only allow us to evaluate its potential as a locus to image enhancer-promoter interactions but could also provide important clues and insights that could be medical importance and use.

Figure 30: The PCSK9 promoter region forms significant long-range interactions with the upstream region that harbors candidate enhancers. Analysis of previously generated Hi-C data from human hepatic primary cells using the region around the PCSK9 promoter region as an anchor demonstrates that interaction between PCSK9 gene and the distal region that harbors these regulatory elements occur. Virtual 4c plot was generated from the online tool: http://promoter.bx.psu.edu/hi-c/virtual4c.php.

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3.6 Methods

3.6.1 Cell culture HEPG2, RPE-1 hTERT cells were cultured in accordance with ATCC guidelines. Briefly, DMEM and DMEM-F12 media was used to culture HEPG2 and RPE-1 hTERT cells, respectively. The media was supplemented with 10% fetal bovine serum, non-essential amino acid and penicillin-streptomycin. The cells were maintained in a humidified incubator at 5% CO2 and were passed every time 2 days or every time cell reached ~80% confluency. 3.6.2 Real-time qPCR Total RNA was isolated from HEPG2 cells using RNeasy Kit, complementary DNA (cDNA) was generated using Maxima First Strand cDNA Synthesis Kit and qPCRs reactions were prepared using SYBR FAST qPCR Mix and ran in a CFX96 real-time PCR Detection System. In all cases, manufacturer guidelines were followed to optimize the amplification conditions. Primer sequences used for real-time qPCRs were designed with Primer3 online tool (http://bioinfo.ut.ee/primer3-0.4.0/). Quantitation cycle (Cq) values were used to further determine the levels of the specific mRNA assayed.

3.6.3 Reporter luciferase assays Luciferase reporter constructs were generated using pGL3-SV40 luciferase reporter vector. Candidate enhancers were cloned upstream of the SV40 promoter between NheI and XhoI sites. PCR was used to generate the fragments within the candidate enhancers were generated by PCR, Primers were used to clone the single binding sites and the QuickChange II mutagenesis kit was used to generate the candida enhancer mutants. Reporter constructs were transiently transfected into cells along with a control luciferase plasmid, luciferase activity was measured using a Dual-Glo luciferase kit in a GloMax-Multi Detection System.

3.6.4 Genome editing To generate candidate enhancer deletion on HEPG2 cells guide RNAs (gRNAs) were designed using the online CRISPR design tool (http://crispr.mit.edu/) and cloned into a modified px330 vector harboring a Venus fluorescent protein cassette. For each enhancer deletion two vectors coding for gRNAs surrounding the enhancer locus were transfected into HCT-116 cells. A day after transfection, GFP positive cells were sorted by fluorescence-activated cell sorting (FACS) and single plated into 96 well plates. 10 days after plating, the colonies were genotyped by genomic PCR to find clones with the desired deletions.

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3.6.5 T7E1 Assay The efficiency of CRISPR/Cas9 editing was determined by T7 endonuclease I (T7E1) assay. T7E I recognizes and cleaves mismatched heteroduplex DNA which arises from hybridization of wild-type and mutant DNA strands. The hybridization reaction contained 200 ng of PCR DNA in KAPA high GC buffer and 50 mM potassium chloride, and was performed on a thermocycler by incubating the sample at 95°C for 5 min and then decreasing the temperature 5°C/min to 5°C. NEB Buffer 2 and T7EI were added to digest the re-annealed DNA. After 1 hour of incubation at 37°C, the reaction was quenched gel loading dye and the product was resolved on 2% agarose gel containing SYBR gold.

3.7 Appendix

3.7.1 Candidate enhancer sequences

§ HSS1: TGTCCTGGGGCTGGCCTTGCCGTAGCTGCTAAATAGTTGACGAAACCAGTCCAGAGAGGGGAGGTGACTGCCAGGGTCGCACAGCTCAAGCTGGGGAACTCGCTGGGAAAACTGTCAGCTCTGGGCAGCAGCTTGACTTCCACTGTAAGCCCCAGCCCCCAGGGTCAAACACTGGCTCTGGTGCTGGCAGAGGCAGCCCACTAGCCTGTTTCAAAGGCTGAGAAGGCCCAGGAGTCTGCCCTGTGCTCCACCAGTTCTGCCCTGAGACTTTCCTACAGAGTACAGGTTTTGATGTTCAGTTTTAAAGGCAAGAATCAATAACCTTCTGCCCCATCAGGTGACCCCTTGTGCCTGTCCCACCCCTTTATTGACTGACCTCGGCTCAGTCAGGTCAGTTCCTGAAGGTCAGTGTGTGGAGGGGAGGCTGTTCTTTCCCAGAAAGGCCTTCCCCAGGCCTGGTGCTCTGGCCTCTGGAGGACTTCCTGGAGAAGTCCCTTCTTTGGGGTCCCAGTCAGTGTATGGGAAGCCCTTATTGCATGACCTGGCACG

§ HSS2: TAAGGTCAGTGTGCAGGGTGCATAAAGGGCAGAGGCCGGAGGGGGTCCAGGCTAAGTTTAGAAGGCTGCCAGGTTAAGGCCAGTGGAAAGAATTCGGTGGGCAGCGAGGAGTCCACAGTAGGATTGATTCAGAAGTCTCACTGGTCAGCAGGAGACAAGGTGGACCCAGGAAACACTGAAAAGGTGGGCCCGGCAGAACTTGGAGTCTGGCATCCCACGCAGGGTGAGAGGCGGGAGAGGAGGAGCCCCTAGGGCGCCGGCCTGCCTTCCAGCCCAGTTAGGATTTGGGAGTTTTTTCTTCCCTCTGCGCGTAATCTGACGCTGTTTGGGGAGGGCGAGGCCGAAACCTGATCCTCCAGTCCGGGGGTTCCGTTAATGTTTAATCAGATAGGATCGTCCGATGGGGCTCTGGTGGCGTGATCTGCGCGCCCCAGGCGTCAAGCACCCACACCCTAGAAGGTTTCCGCAGCGACGTCGAGGCGCTCATGGTTGCAGGCGGGCGCCGCCGTTCAGTTCAGGGTCTGAGCCTGGAGGAGTGAGCCAGGCAGTGAGACTGGCTCGGGCGGGCCGGGACGCGTCGTTGCAGCAGCGGCTCCCAGCTCCCAGCCAGGATTCC

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§ HSS3: ACCAGCCTCCCATCTCAGTGTCTCCAGCTGGGCCATGCACTGCTTCCTACCACCATTATAACTGGACCACAGATCCCGAGCTGCCACCTCCAGGAAGCCGGCCCGGATTCAGTGCCCATCTGACTGAGTTCCCGCAGCACCTAGCCTGTCCCTTCTGTGAGACTTGGCTCCTCTTGCCTCCTATCACAGTCGCCTGTGCTCTCATCTGCCTCCCTGTGGACTCTGAGGATGAGGCGAGGGCTGATTCAGCTCAGGGGTGAGAGCAGGTGCCAGCAGCTTATGGGCTGGGCCGGGCCTGGGCAGTGGGCAGGCCGTGGGCTCAGATGACAGGTACCTGGCGCCCTCTGCTGGTCTAGCTGGGCAAGGCTGCTGCTCCAGAGTGGCCGGAAGCTTCCTGGGAGAGGCCTGGTTCTCTGGGCCTGTCCCTCTCTCCCTATTCCTTCCTTCCTCCCTTCCTGAAACTGCGTCCTCCAAATCCTGCTCCAGCACCAGCAACCCCCTGCTTTTCAACAGCCTCTGGCAGCGTTTC

§ HSS4: GCACAAGTTATTTAGGCGTGGTAATGGTGGGGCTGGGTGTAGGACGAGGAGGATCCCCGGGGGTGGAAGTGAATGTCATAGCTAGGCTCGTGGAACTGGGAGGAGGGAGCTGGAGCAGGGAGGGGGATGCGGCCCTCGCAGGAGCACAGGACATGGCTGCCCCTGGGATCAGCCTTGGCAGATGGCCAGACGGCCAGTGCTCAGCACAGAGCCTGGCATAGTGGCTGCGGAATGAGTGAAAGCTGAAGTTCTGGACCCTGAAAGAAGGAAGGAGAAGGAAGGGCATGGCAGGAACCACCAAGTAGGCAGCTTGACCACATCGTGCCTGAGAGTGCCTGACAGTGCCAGGTGCCTCGGCCCCGGGCCATGAACGATCGTTTGTGAGCCTTCTGAGCTGCAGGCGGGCGACTGAGAACAGCCTGGCGGGCTGAGCCCCTGGCCCTGAGGACGCAGCCGTGTATTTTTTCCCCTGTGGCTCACGGGGCCTGGGAAGCACAATGTCCTCCATTACCGCAACTCCTCAAT

§ HSS5: AGGTAGGCCAAGTAACAAACATACCCAGTATCACTTGGCTTGTGAGTAACTGAGCCAGGATCCCACACTCAAGTCTGTGGCACTCTGGAGCTCATGCTATTTCCATTTTCTCAGAATGCTCTGAAAGTTCCATATAAATATTGCTAATTTGGAGGAGGCTGAGCCAGGAAGAGGTGGCCACGAGGGCTGAGTAGGTAGTGTGGTGAGTATGATGGACTTTGGAGTCAAACAAATCTGGGTTGAGCTCTGTCTATGCAATTACTCATTATATGACCTTGTGTGTATTTCTACTCTCTCTAAGCCTCAATTTCCTCATCTTTACAATAGGTATAATAATAGCATCCACTTAAAAAGGCTGCTATTCGTAGAAAATAAATTAGTGCTTATAAAGCACTTAGCAAAGTGGCCAGTACAAGCAAGTACTCAATAAATATCTCTGTTTTTACTACTAAACATATGTGTTCAGCAATTATAAAATTGTAATATAGATGGGTTGCATCTAGACACATGTACATTATAGCAATGAACATCATTGTCAC

§ HSS6: GCTAGCAATCCAGCGCCATTTGCATTTCATATGGAATTTTAGGAAGTTTTCGAGATGTTTTAGTTTAGAAGCTAAACATGACATACCAGGTTCTTCTCTGTCTGGCCTTGCATACCTATCCAGTTCCCTCTCCATTCATCTCTGCCCTGACATGCACTCAGGAGGCTCCCTGGGCCTCAGCATTCCTCATTTGCGCGCCTCTGGTCAAGCTAGTACCTCTGAATGCCCTTTCCCATCCCCTTCTCAGAGTTCACCTCACACATTCCGAAGCCAGCTCAGGGCCACCTCTGAAACATTTAGAGCACTCCCCTGCCCATCCAGATGCTCATTAAATAGTGTGCATCTCTGTCACCGTTCTTTCATGTTATTTTATAATTGTCTCTTTATGTGTCTCTCACCCCCACAGACTGAGCCTTAGGGCAAACAATGTCTTGTTCACTTTTGTCCTTCCAGCTTCTAGCACGGTGCTTTGTGCACAGTAAACACTCCAGCGGCTGTTGACCAATGAATGAATGAAGATTGTAGGCTCAAATATTCAAGTTATCAAAGTGCAAAGAATGAGAGGTTGTTTGCACTGGACTTCAAAAATAAATAGCCTGGACCTGTACTGTCTTACACAACA

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§ HSS7: GCGCGGAGCGCTGCTGAGCTTGAACACCACCCAGGCGCGCACGTAGTAGTAGATATCGAAGATAAGCGAGAGCGGCAGGAGGAAGAGGCACACGAACACCCAGCGCTGGTGGATGAGCACGAACTCCAGCCCCTTCAGGCGCACCCACAGCAGGAAGAGCAGCGCGCACACGGCCAGCGACACGGCGGGCTCCATGGTGCGGCGCCGCGCGGTAAGCGCTGCGGGTTCGCGCCTCCTGTCACTGCCGCCAGCTCCGCGCCTGGCCCGCTCTGCGCCTGTAGCCCACAGCCCGGGGAGCCCGGGATCGCCCGCCGCCTCGCGATTGGCTCGGGTCGCCGAGGTTCGGTGGGCCGAGACCCGGGCGGCGACCAATGGCGAGCCGCGCCGGGGATCGGACCAGGAACCCGCAGCTTTCATTGGCTGCGGGAGTCGGACCGCTCCACCCTTTTCTCCCCGCCGCCGCGCGAGGCGGGGGTGCCAGGGACTGGGTGCCGGTGGGGTCCGCTGCGAATACTGGGGGTGGGCAGTTGGGACTCAGGGCCCCAGGCCCTCGGGGCTGCGGGGCCGCCTCTTCCAAACGCGCCTTAAGCGGTTTTGCAGTGGCTTTCCGCCGGAGGGCTGGGAGGCGCCCCGGCCCTGAGTCAGTCACCCTTTGGCCGGACAG

§ HSS8: GACAAATGTGAACCAAGTGATCACTGCTGAAGACAAACAAACTGCAAAATGCTGCAAAGAGAGGCTCACCCTCACAAGTGATCCAAGAACTGCAAAATAAAACGAGATACTATTTTTAACGCTATCAGATTGGCAAAAATGAAAGAGTGATCCTATTTAGTGTTGGCAAGAATGTAGGGAAAAAGGTGTTCTCACAGACCATTACTACAAGTAGGCATAAACACAGCTCTTCTCGAGGGCTGTTTGGTCATATCTATTAATATTTAATGTGCTCAAACCTTTGTGTAACAAAGTCCATTTTTCTGACTCAATCCTACACAAATATTTGTACAGAAGGACAGGATAAATGTGAGTCTTAAATCTTCAATGCAATCTTATTTGTAAGAGCAAAACAGTTTATGCAACATAAATGTCAGTAAGGGCTAAATTAGGCCAATTATTTACATGCAAAAATGGGATATTACAGAATGAAATGAAGACATGCACACATACATATACAC

§ HSS9:

GGGGGTGGTGACTCCCCTTGGTCCCATCCCTCCTCAGCTTTCCTACCCCTGAGAGCCTCAAGGGAACACCTCAGAGCAGCTGGAAAGCTCTGGTCTAGATGTCCTTGCTAGGTGACTTGGTGCTAGGTGACACACACTGTCTGGGCTTTGGCCTCAGGCAGATCCAGGGGCCCAGGAGGGCCAGAGCACTTCAGCTCAATGTCCGGTATTGCCACCGCTGTTCCAGGACCTCCAGCACTTACCGCAGTGTCCCACAGGGAGCAATGTTTGCATAGTGCTAAAGTCTGCAGGCTGATATGTCAGGGAACCGACACTGAACCAGGAGTCAGAAGCTGGAATTTAAATCTGTTCTGTGCTAGCTGTGTGTCTTCAGAGAAGTCTCTTTCCCTTTCTGGGCCTCATTTCCTTATTTGTAGAGAGGATTGTACCGGATGATTTTATAGCCCTTCACAACTTTAAAATGTTTTAAGTATGTATATAGGTATAGATTAAGAGTCTTAAG

§ HSS10: TTTGTCTTCAGATAGTTCTCCACATGCTTAAAGAACCACGGCTTGTAGTAATTGCCAATGCTATTCAGCTGAAATGACAGAGGGCATTGTGATCAGCCTGCAGACAAGCGCCCAGCACCATCGGGGTGTAACAGCGACACCCGGCTTCTGTTCAGCACTCCTGGCCTTTTCAAAGCTCTTTCATGTCTATTACTGCCTTTGAGACTCTTGAAAGCTGGTGAGGTTAGGCACAGCGGGTATCCTCTAGCAGAGAGAGCAGGAGCTCAAAGAGGTTAAGTGGTGTGTGCCCAGGGCCAAACAGTGAGTCCCCGACAGTCTAGAGAACTCAGCGACCCCGCCTAGCCTGGTGCAAGTGTTTAACAGAGTTAAACATTTAACTGCCCTGATGCAGTGCCCCATGCAGGGGAATTCCCTCCCTCCCTCCCTTCCCACCTTCCCCAAGGCCAGGGAAGCACGAACTCTGGGAATGGGTCAGCCCCTCAGTGCAACCCAGGGCTCATGGCACCAACTGGTCATGGCACTTTCCCTGCT

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GTCCCTGGACCAAGCTCCACAGTCCTCCGGGTACAGCACTCCCCAGGCAGCCACCATCGTCTGGAGGATGCTCACATGAATAAGGCTCATACCTCCCTGGCCCTGGGGGCTCCCACACCCAAAT

§ HSS11: CCACATGTCCTTGGCCACTGGCTCTCACAACATACCCTGGAGGAGGGGACAGAAATTACTACCCGTGGGGATCCAGTGTGGGAGCGGAAGTGAGCATGTGGCCTAGTGACTGTTTTTGTAAATTTGGTTTTACTGGGGCCAGTCATCCCTATTCATTTATGTACCATCCCTGGCTGTTTCCATGTGGCAGATCTGAGGAGCTGTGATAGATTCTGTAGCTTGGAATGCCTAAAATATTTGTTGTTTGGCCTTTTAGGAAAGTTTGCTGACCCCTAGGCTCTGTCACCCTGCCCTACATTCACAGTCTCCATAGCTTCCATTGCAAGTGTTTGCTCTCAGCCATCACCGCCTCTCTTCCTAGCTTACGCCCTCTAGTCCCCTGACTTCAATGCTCCACTCCACCCAGGGCATCCTGTCCACCATATCAGTCCCTTGTCAGTCCCTGGGGGCCTCTCACATCCGGCCTCCCCTCCATAGCTGGCTTAGATTTCATCATAACACACCCTTGGGTCCTCACTCTTGCCTACACATCACACTCCCTGC

§ C1: GCCTCTGTGCCTAGGAGGTTTCTCCTGCCCCGTCAGAACTCCAACTGCCTTCTCAAGTGTCCCTTCTGTGAATCTGTCCCTTCACCCCTCATCCCCATCTGTTCCTCTCTCTGCTTTGTGCACATTCGGGACCTCGCACGCATCGCAAAGCCATTACCGTATTGTGCCCCAGAAGGTGCCCCAGAAGTTAGGACTGAGCGCCATCCATCCCTGAAGCCCTGTGTGTACGAGTTCCTAACAGCTCAGTGTCCCCCACACAGTAGGGGCTCAACCAGAGACTGCCTGGGGACCTGGGTGGACTCCTAGCTCTACTGTCTGTTCCCAGGTTCTGCCACGTTGGGTCGCTCGCCTTGCCTGCGTGGACCTCGATGCTTACCTCTGTCAAATGGGCGTGCTCTTTCCCTCCCTGCCTCGGGGTGTCATGGGAGCCCGCTCGCAAGGATCCCGAAAGCTGGGGTTAGCGCGCCCTCTGCTGGACGATGAAAGACTCCGCGCGCTCTGCTCCCCAGTCCCCCAGGTGGCCCGGGGGAGGTGAAGTAAGGCGGGAGGGCGGGGGGACTACAGGAGAGGAGG C2: TCCAAAGAGGCATCTTCCCCCTAAATTTGAATATTTCTGATACCGAGGTTCATTGTTAAGGCCTAATTGGCAGCATCCTTTCTTTCTTGGTGGCACACGAACTAATGGTGTCATGCAATTGATGCCATCTTACTTTCAATAAAATATGGCAGTTCTTTGGAGTATCTACAATAAAGATCAGAAAACTTTTCTGTAGAAGTTCAGATAGTAAATATTTTAGGCTTTGTGGGCCGTATGGTCTCTGTTGTAATGACTCAAATTTGCTCTCGTAATGTTAAGGAAGCCATAGACAATACGGAAATCTTAAATGAGTGTGGCTGTGTTCAATAAAGCTATTTTTGGACACTGGCTTTTGAATTTCACATAATTTTTTTTTTTTTTTGAGATGAAGACTCACACTGTCGCCTGGGCTGATGTGCGGTGGGGCAATCTCGGCTCGCTGCAGCCTCCATCTCCTGGGTTCAAGTGATTCTTCTGCCTCAGCCTCCCGAGTGGCTGGGACTACAGGCGC

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3.7.2 Fragments of HSS6, HSS8 & HSS11

§ HSS6-1 GCTAGCAATCCAGCGCCATTTGCATTTCATATGGAATTTTAGGAAGTTTTCGAGATGTTTTAGTTTAGAAGCTAAACATGACATACCAGGTTCTTCTCTGTCTGGCCTTGCATACCTATCCAGTTCCCTCTCCATTCATCTCTGCCCTGACATGCACTCAGGAGGCTCCCTGGGCCTCAGCATTCCTCATTTGCGCGCCTCTGGTCAAGCTAG

§ HSS6-2 CGCCTCTGGTCAAGCTAGTACCTCTGAATGCCCTTTCCCATCCCCTTCTCAGAGTTCACCTCACACATTCCGAAGCCAGCTCAGGGCCACCTCTGAAACATTTAGAGCACTCCCCTGCCCATCCAGATGCTCATTAAATAGTGTGCATCTCTGTCACCGTTCTTTCATGTTATTTTATAATTGTCTCTTTATGTGTCTCTCACCCCCACAGACTGAGC

§ HSS6-3

CTCTCACCCCCACAGACTGAGCCTTAGGGCAAACAATGTCTTGTTCACTTTTGTCCTTCCAGCTTCTAGCACGGTGCTTTGTGCACAGTAAACACTCCAGCGGCTGTTGACCAATGAATGAATGAAGATTGTAGGCTCAAATATTCAAGTTATCAAAGTGCAAAGAATGAGAGGTTGTTTGCACTGGACTTCAAAAATAAATAGCCTGGACCTGTACTGTCTTACACAACA

§ HSS8-1 GACAAATGTGAACCAAGTGATCACTGCTGAAGACAAACAAACTGCAAAATGCTGCAAAGAGAGGCTCACCCTCACAAGTGATCCAAGAACTGCAAAATAAAACGAGATACTATTTTTAACGCTATCAGATTGGCAAAAATGAAAGAGTGATCCTATTTAGTGTTGGCAAGAATGTAGGGAAAAAGGTGTTCTCACAGACCATTACTACAAGTAGGCATAAACACAGCTCTTCTCGAGG

§ HSS8-2 AACACAGCTCTTCTCGAGGGCTGTTTGGTCATATCTATTAATATTTAATGTGCTCAAACCTTTGTGTAACAAAGTCCATTTTTCTGACTCAATCCTACACAAATATTTGTACAGAAGGACAGGATAAATGTGAGTCTTAAATCTTCAATGCAATCTTATTTGTAAGAGCAAAACAGTTTATGCAACATA

§ HSS8-3 GAGCAAAACAGTTTATGCAACATAAATGTCAGTAAGGGCTAAATTAGGCCAATTATTTACATGCAAAAATGGGATATTACAGAATGAAATGAAGACATGCACACATACATATACAC

§ HSS11-1 CCACATGTCCTTGGCCACTGGCTCTCACAACATACCCTGGAGGAGGGGACAGAAATTACTACCCGTGGGGATCCAGTGTGGGAGCGGAAGTGAGCATGTGGCCTAGTGACTGTTTTTGTAAATTTGGTTTTACTGGGGCCAGTCATCCCTATTCATTTATGTACCATCCCTGGCTGTTTCCATGTGGCAGATCTGAGGAGCTGTGATAGATTC

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§ HSS11-2 CAGATCTGAGGAGCTGTGATAGATTCTGTAGCTTGGAATGCCTAAAATATTTGTTGTTTGGCCTTTTAGGAAAGTTTGCTGACCCCTAGGCTCTGTCACCCTGCCCTACATTCACAGTCTC

§ HSS11-3 TAGGAAAGTTTGCTGACCCCTAGGCTCTGTCACCCTGCCCTACATTCACAGTCTCCATAGCTTCCATTGCAAGTGTTTGCTCTCAGCCATCACCGCCTCTCTTCCTAGCTTACGCCCTCTAGTCCC

§ HSS11-4 CCTAGCTTACGCCCTCTAGTCCCCTGACTTCAATGCTCCACTCCACCCAGGGCATCCTGTCCACCATATCAGTCCCTTGTCAGTCCCTGGGGGCCTCTCACATCCGGCCTCCCCTCCATAGCTGGCTTAGATTTCATCATAACACACCCTTGGGTCCTCACTCTTGCCTACACATCACACTCCCTG

* Underlined Sequences represent identified binding sites

3.7.3 Primers for real-time qPCR

Name Sequence qPCR PCSK9 Fw GGCAGGTTGGCAGCTGTTT qPCR PCSK9 Rv CGTGTAGGCCCCGAGTGT qPCR b-Actin Fw TCCCTGGAGAAGAGCTACGA qPCR b-Actin Rv AGCACTGTGTTGGCGTACAG

qPCR GAPDH Fw AATCCCATCACCATCTTCCA qPCR GAPDH Rv TGGACTCCACGACGTACTCA

qPCR DHCR24 Fw GCCGCTCTCGCTTATCTTCG

qPCR DHCR24 Rv GTCTTGCTACCCTGCTCCTT

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Chapter 4

Selective Activation of Alternative MYC Core Promoters by Wnt-Responsive Enhancers

4.1 Motivation The core promoter plays an essential function in integrating multiple transcriptional signals, including tissue-specific enhancer mediated activation to modulate the rate of transcription initiation (Danino et al., 2015; Kadonaga, 2012; Ngoc et al., 2017). As has been previously discussed, the core promoter can be defined as a short stretch of DNA surrounding the TSS that mediates directional transcription initiation. Importantly, core promoters are diverse in their composition of cis-control DNA and can contain multiple elements such as the transcription factor IIB (TFIIB) recognition sites (BREu and BREd), TATA box binding site, initiator sequence (INR) and downstream core promoter element (DPE) (Lenhard et al., 2012; Ngoc et al., 2017), between the most common. These diverse core promoter elements can serve two roles in which they can modulate the basal activity of core promoters (Müller and Tora, 2014; Nogales et al., 2017), and they can regulate the core promoter capacity to be activated by specific enhancers and other distal cis-regulatory sequences (Butler and Kadonaga, 2001; Davis and Schultz, 2000; Juven-Gershon et al., 2008; Zabidi and Stark, 2016).

Numerous reports have shown that in metazoans, most genes are under the control of multiple alternative promoters (Carninci et al., 2006; Davuluri et al., 2008; Singer et al., 2008; Sun et al., 2011). In fact, the most recent report from the FANTOM consortium (The FANTOM Consortium and the RIKEN PMI and Clst (dgt), 2014), which characterized more than 900 human cell lines and cell samples by using CAGE found that genes, on average, can utilize 4 robust TSSs (The FANTOM Consortium and the RIKEN PMI and Clst (dgt), 2014). Interestingly, alternative promoters have been found to be differentially utilized across different tissues (Carninci et al., 2006; Steinthorsdottir et al., 2004), during development and upon the presence of specific stimuli (Broome et al., 1987;

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Carninci et al., 2006; Haberle et al., 2014), suggesting that fine tuning of alternative promoters may be important for proper gene expression. Importantly, misregulation of alternative core promoter usage has been found to be associated with pathological states including cancer and neurodegenerative disorders (Agarwal et al., 1996; Pedersen et al., 2002; Tan et al., 2007). Although some evidence suggests that promoter usage is largely dependent on the epigenetic landscape of chromatin (Albert et al., 2001; Angeloni et al., 2007; Archey et al., 1999; Turner et al., 2008) and the presence of proximal transcription factor binding sites (Ngondo and Carbon, 2014; Pozner et al., 2007b; Vitezic et al., 2010), the specific mechanisms that could regulate alternative promoter usage remains poorly understood.

In the next sections I present our investigation on the role of the two alternative MYC promoters on precise activation by distal enhancers and on the mechanisms that allow such specific interactions. This work was recently published on Genes (Bardales et al., 2018). We first find, by analyzing CAGE data, that the two MYC promoters can be differentially regulated across different human cell samples. Importantly, using synthetic reporter assays, we found that the activity of the MYC promoters embedded in transgenic constructs does not recapitulate the differential promoter usage observed in the context of the endogenous MYC locus, suggesting that promoter usage is likely to be regulated by distal regulatory elements. This led us to test in colon carcinoma cells if distal MYC enhancers that are responsive to Wnt signaling could differentially regulate the endogenous activity of the two promoters. We observed that Wnt-responsive enhancers preferentially activate the P1 promoter upon Wnt induction. In addition, by using CRISPR/Cas9, we confirmed that enhancer deletion preferentially downregulates the activity of the P1 promoter. Finally, we demonstrated that preferential activation of the P1 promoter is mediated by distinct promoter elements associated with P1 that differ from P2. Taken together, these results suggest that alternative promoters can mediate cell-type selective transcription regulation by facilitating selective interactions with distal and proximal Wnt-responsive enhancers.

4.2 MYC Alternative promoters can be differentially regulated To gain a better understanding of how the two alternative MYC promoters might be differentially deployed, we surveyed P1 and P2 promoter usage across 869 human cell types and tissues from the FANTOM database (The FANTOM Consortium and the RIKEN PMI and Clst (dgt), 2014) (Figure 4.1A). We counted the number of CAGE reads coming from each promoter within a 11 bp window to calculate the P2 to P1 promoter usage ratio and its distribution across cell lines (Figure 4.11B). We observed that the P2 promoter preferentially drives transcription of the MYC gene across the different cell types in agreement with previous reports (DesJardins

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and Hay, 1993; Wierstra and Alves, 2008). Importantly, this analysis showed that the P2 to P1 promoter usage ratio distribution across the cell lines was very broad, spanning over two orders of magnitude (P2/P1CAGE = (0.37 to 34.24)). This suggested that multiple regulatory sequences, including cell specific enhancers and proximal promoter elements, may be important to differentially regulate the activity of the tandem MYC promoters.

Based on these preliminary observations, we investigated whether the in vivo promoter usage could be explained by differential promoter activation as a consequence of enhancer bias, derived from the transcriptional network. For this, we measured the independent activity of the P1 and P2 promoters by determining the activity of luciferase constructs harboring the core P2 or P1 promoter sequences across eight cell lines with different P2/P1CAGE ratios. This allowed us to calculate the synthetic P2 to P1 promoter activity ratio across multiple cell lines relative to endogenous promoter usage in the context of intact chromosomes (Figure 4.1C). These results showed that in all of the cell lines that were tested, the P2 promoter displayed a higher basal activity (P2/P1Luiferase> 1) and this ratio was maintained across different cell lines (P2/P1Luciferase = (1.8, 4.4)), especially when compared to the endogenous CAGE values. This result suggests that regulation of the two MYC promoters outside of their genomic context does not recapitulate the broad range of P2 to P1 promoter usage ratio observed for endogenous MYC in their native chromatin context. This finding suggests that distal cis-regulatory elements are likely to be, at least in part, responsible for differential activation of MYC promoter P2 versus P1.

Next, we explored if changes in the transcriptional regulatory network during differentiation could lead to changes in alternative promoter usage. We analyzed 21 time courses of human cells exposed to differentiation cues and stimuli from the FANTOM database (Arner et al., 2015). In 7 out of these 21 time courses, we observed a significant change in the P1/P2 promoter usage along the time course, suggesting that changes in the transcription regulatory network influenced differential promoter usage in a cell-type dependent manner. In addition, we observed that upregulation of either the P1 or P2 promoter usage was possible. For example, during Adipocyte differentiation, P2 promoter usage was stimulated (Figure 4.1D), while during neuronal differentiation the P1 promoter activity was increased (Figure 4.1E). Importantly, upregulation of either P1 and P2 promoter usage was independent of MYC expression, since the total levels of MYC decreased in both time courses over time (Figure 4.2). This analysis suggests that transcription regulatory networks and distal enhancers could be responsible for differentially modulating the activation of the two alternate MYC promoters.

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Figure 31: MYC transcription is driven by two alternative promoters which are differentially regulated in vivo. (A) Cap analysis of gene expression (CAGE) reads show that MYC gene is expressed by two alternative promoters, P1 and P2; (B) Histogram of promoter usage ratio across 869 samples obtained from the FANTOM5 database; (C) In vivo promoter usage ratio cannot be explained by promoter activity alone; (D) Adipocyte differentiation leads to stronger usage of the P2 promoter; (E) Neuronal differentiation leads to stronger usage of the P1 promoter. ORF: open reading frames. Error bars represent standard error of mean.

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Figure 32: Total MYC expression by CAGE on adipocyte and neuronal differentiation. (A) Adipocyte differentiation leads to minor downregulation of MYC expression. (B) Neuronal differentiation leads to downregulation of MYC expression. cpm: count per million reads. Error bars represent standard error of mean. Error bars represent standard error of mean.

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4.3 Wnt-responsive enhancers preferentially activate the P1 promoter

To investigate if distal regulatory elements could specifically regulate the differential usage of MYC promoters, we tested how enhancers could activate the transcription from the P1 and P2 promoters in the HCT-116 cells. We chose to work with the HCT-116 colon carcinoma cells where multiple Wnt-responsive enhancers have been reported to regulate MYC expression (Rennoll and Yochum, 2015). These Wnt-responsive enhancers are located throughout the MYC locus, as can be seen based on their TCF7L2, RNA Polymerase II, and H3K27Ac chromatin immunoprecipitation sequencing (ChIP-Seq) profiles (Figure 4.3A). To test how the Wnt-responsive enhancers regulate the activity of the two alternative MYC promoters, we induced the Wnt pathway by treating the cells with different concentrations of LiCl (Yochum et al., 2010), which promotes the relocalization of β-catenin and the subsequent activation of Wnt target genes (Figure 4.3B), such as MYC (Rennoll and Yochum, 2015; Shah et al., 2015; Yochum et al., 2010). Interestingly, we found that transcription from the P1 promoter is preferentially activated (Figure 4.3C), whereas transcription from P2 was not significantly increased (Figure 4.3D). Importantly, the described minor MYC promoter P0 didn’t drive MYC expression in this cell line, no endogenous transcription was detected and the promoter showed extremely low activity by luciferase assays (Figure 4.4). This result suggests that Wnt induction preferentially activates transcription from the P1 promoter in HCT-116 cells.

To further probe how Wnt-responsive enhancers may regulate the MYC promoters, we developed a system to independently quantify the fold activation of the P1 and P2 promoters by the presence or absence of the Wnt-responsive enhancer. We used the previously generated core P1 and P2 promoters to clone a Wnt-responsive enhancer or control sequence. The Wnt-responsive enhancer consists of 8 strong TCF7L2 binding sites, whereas the control sequence has the 8 TCF7L2 binding sites mutated making it unresponsive to Wnt (Veeman et al., 2003). We observed that the P1 promoter harboring the Wnt-responsive enhancer was specifically and strongly activated over 12 fold in comparison to the control sequence after activation of the Wnt pathway (Figure 4.5A). By contrast, the P2 promoter harboring the Wnt-responsive enhancer was mildly activated by the Wnt pathway, increasing the P2 activity by 6 fold (Figure 4.5B). We believe that the activation of the P2 promoter observed by reporter assays in contrast to the endogenous locus could be in part due to the transcriptional saturation of the P2 promoter on its endogenous context. Taken together, these results further support the finding that Wnt-responsive enhancers preferentially activate transcription from the P1 promoter.

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0

1

2

3

4

pGL3 P0 P1 P2

RLA

Figure 33: In HCT-116 cells, activation of Wnt-responsive enhancers preferentially upregulates the P1 promoter. (A) Multiple Wnt-responsive enhancers (shaded in red) regulate MYC transcription. (B) Wnt induction promotes relocalization of β-catenin; (C) Wnt induction strongly upregulates initiation from the P1 promoter; (D) Wnt induction does not upregulate initiation from the P2 promoter. qPCR: quantitative real-time PCR. Error bars represent standard error of mean.

Figure 34: Activity of MYC’s promoters by luciferase assay. The P0, P1 & P2 MYC promoters were cloned from the -100 to +50 with respect to the transcription start site into a pGL3 vector to assess promoter strength. As can be seen, transcription initiating from P0 is minimal in comparison to P1 or P2. RLA.: Relative luciferase activity. Error bars represent standard error of mean.

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Figure 35: Activation of Wnt-responsive enhancers preferentially upregulates the P1 promoter by reporter assays. (A) Induction of Wnt-responsive enhancer strongly activates transcription of the P1 promoter (B) Induction of Wnt-responsive enhancer mildly activates transcription of the P1 promoter. Error bars represent standard error of mean. Error bars represent standard error of mean.

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4.4 Wnt-enhancer deletions preferentially downregulate transcription from the P1 promoter

Next, we investigated the effect of deleting Wnt-responsive enhancers on the two alternative MYC promoters. Five enhancers were selected across the MYC topological domain which are located at approximately 1 kb, 7 kb, 335 kb, 405 kb and 550 kb from the MYC gene promoter based on their strong binding profiles for TCF7L2 and RNA Pol II. Some of these enhancers have been previously shown to form long-range interactions with the MYC gene by capturing Hi-C, to regulate promoter activity by luciferase assays (Jäger et al., 2015; Ling et al., 2013; Tuupanen et al., 2012; Yochum et al., 2008b, 2010) and to be associated with SNPs that predispose to MYC transcription upregulation (Tuupanen et al., 2009). These enhancers start from most 3’ distal to the MYC gene body (Figure 4.6A). Next, we used CRISPR/Cas9 to generate five different cell lines with homozygous enhancer deletions (Figure 4.6B). Here, sequence-specific sgRNAs were designed to flank each enhancer to generate homozygous enhancer deletions surrounding the TCF7L2 binding sites, generating genomic deletion between 775 bp and 2403 bp. When we tested the effect of these enhancer deletions on MYC mRNA, we found that in four cases, the deletions caused significant downregulation of total MYC mRNA levels (Figure 4.6C). suggesting that these enhancers are important for MYC regulation across the cell cycle. When the specific activity of the two alternative MYC promoters was measured, we observed that the enhancer deletions preferentially downregulated transcription initiation from the P1 promoter (Figure 4.6D), while only a minor reduction of transcription initiation was observed from the P2 promoter (Figure 4.6E). Interestingly, downregulation of MYC mRNA levels caused by the single enhancer deletions was accompanied by defects in cell growth (Figure 4.7). In sum, these genome editing experiments further support the preferential regulatory relationship between the select set of Wnt-responsive enhancers and the P1 promoter of MYC.

It is important to note that we then decided to perform double enhancer deletions of four selected pairs (E1 and E2, E2 and E3, E2 and E5 & E3 and E5) following the same CRISPR/Cas9 genome editing procedure. Although we were able to generate clonal cell lines with double enhancer deletions, these cell lines presented changes in their morphological characteristics and proliferation when compared to wild-type HCT-116 cells. We hypothesize, that a second enhancer deletion caused a further downregulation of MYC expression and other subsequent changes that drastically affected the healthiness of the engineered cell lines. Therefore, we decided not to further pursue the study of how these double deletions affected MYC expression.

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Figure 36: Wnt-responsive enhancer deletions preferentially downregulate transcription from the P1 promoter. (A) Five enhancers were selected across the MYC locus to be deleted by using CRISPR/Cas9; (B) Effect of enhancer deletion on MYC mRNA levels; (C) Enhancer deletions strongly downregulate transcription from the P1 promoter; (D) While in the P2 promoter the enhancer deletions only cause minor downregulation. Error bars represent standard error of mean.

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Figure 37: Growth curves of single enhancer deleted HCT-116 clonal lines. Growth curves were performed in wild type HCT-116 and in the five enhancer deleted clonal lines. As can be observed, the growth dynamics of some of these cells has been drastically affected when compared to the wily-type HCT-116. dE1-dE5: Cell lines with single enhancer deleted from E1 to E5. Error bars represent standard error of mean.

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4.5 Distinct Core Promoter Architecture Influences Specific Enhancer-Promoter Communication

Previous studies have shown that enhancer-promoter specificity can be mediated by the presence or absence of different core promoter elements (Butler and Kadonaga, 2001; Juven-Gershon et al., 2008; Zabidi and Stark, 2016). To determine whether core promoter architecture comprised of specific elements might also mediate the selectivity of Wnt-responsive enhancers, we analyzed the promoter architecture of the two MYC core promoters. Interestingly, we observed that these two promoter possess rather distinct core promoter element composition (Figure 4.8A). Importantly, the type of promoter elements that are present in the two distinct MYC promoters are known to be highly conserved in mammals (Broome et al., 1987), suggesting that the arrangement of the core promoter elements could be important for specific regulation.

We then investigated the role of the core promoter elements that are present in the two MYC promoters. We first tested the role of the BREu which is only present within the P1 promoter, on Wnt-dependent activation. We observed that mutation of the BREu motif in P1 had little effect on its basal promoter activity (Figure 4.8B). In contrast, the presence of the BREu motif on the P1 promoter was crucial for Wnt-responsive enhancer-mediated activation (Figure 4.8C). Furthermore, we observed that insertion of a consensus BREu motif into P2 had little effect on its basal activity, but increased its maximum fold activation (Supplementary Materials Figure S7). The function gain associated with the presence of the BREu motif suggests that this is a promoter element that potentiates the capacity of P1 to be selectively activated by Wnt-responsive enhancers.

Lastly, we tested the role of INR and DPE motif, only present in the P2 promoter, may have on the promoter capacity to be activated by the Wnt-responsive enhancers. We generated P1 promoter containing either a consensus INR or DPE motif. The basal activity of these promoters was increased 8-fold and 2-fold for the INR and DPE P1 promoter variants, respectively (Figure 4.8D). Intriguingly, the presence of these two consensus motifs decreased the capacity of the P1 promoter to be activated by Wnt-responsive enhancers (Figure 4.8E) but did not decrease the maximum promoter activity). Conversely, disrupting the strong INR or DPE sequences of the native P2 promoter resulted in a decrease in its basal promoter activity accompanied by an increased fold activation induced by the Wnt-enhancers (Figure 4.9). These results suggest that the presence of INR and DPE, at least for the P2 promoter, might dampen the fold activation of enhancer-induced transcription by elevating the basal promoter activity. Overall, these results indicate that selective activation of promoters by enhancers can be genetically encoded by the presence of different promoter elements.

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Figure 38: Promoter architecture mediates differential enhancer communication. (A) The two MYC alternative core promoters possesses dissimilar promoter architecture. While the P1 promoter has a TATA box and a BREUP motif, the P2 promoter possess a strong TATA box, an initiator sequence (INR) and a downstream core promoter elements (DPE) motif; (B) P1 promoter lacking the BREu motif decreased minimally the basal activity; (C) P1 promoter lacking the BREu motif had a drastic effect on the maximum fold activation; (D) P1 promoter with either a consensus INR or DPE motif have stronger basal activity; (E) The addition of a consensus INR or DPE motif to the P1 promoter diminishes fold activation. R.L.A.: Relative luciferase activity. Error bars represent standard error of mean.

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4.6 Discussion

The usage of alternative promoters as drivers of transcription initiation is a prevalent phenomenon that occurs throughout metazoans (Davuluri et al., 2008; Yochum et al., 2008b). Although numerous reports have shown that differential regulation of alternative promoter is important for proper development and cellular maintenance (Davuluri et al., 2008; Haberle et al., 2014; The FANTOM Consortium and the RIKEN PMI and Clst (dgt), 2014), the mechanisms that regulate promoter usage are limited and have focused on the role of promoter proximal regulation (DesJardins and Hay, 1993; Turner et al., 2008; Zheng et al., 2016). Here, we show that selective enhancers can differentially regulate the activity of the two MYC core

Figure 39: Effect of core promoter mutants on P2 basal activity and fold activation. (A) P2 promoter with a BREu motif generates a slight decrease of the basal promoter activity. (B) P2 promoter with a BREu motif has a decreased fold activation. (C) P2 promoters lacking DPE or INR motif shown downregulation in the promoter basal activity. (D) P2 promoter lacking DPE or INR motif have a decreased fold activation. R.L.A.: Relative luciferase activity. Error bars represent standard error of mean.

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promoters, further expanding upon the early reports which have focused on MYC proximal promoter elements that regulated transcription factor binding or mRNA stability (Albert et al., 2001; DesJardins and Hay, 1993; Hay et al., 1987; Meulia et al., 1992). Specifically, we found that the MYC promoters, P1 and P2, are differentially deployed across different tissues and cell types, and those distal regulatory elements are necessary for selective regulation of alternative core promoters, in expanding the repertoire of possible mechanisms that could regulate MYC transcription (Wierstra and Alves, 2008). In addition, by working with HCT-116 colon carcinoma cells, we demonstrated that Wnt-responsive enhancers preferentially activate the P1 promoter in the genomic context and by reporter assays. Finally, we showed that the preferential activation of the P1 promoter is dependent on specific promoter elements and its promoter architecture revealing and important aspect of the mechanism directing alternative core promoter specificity.

Previous studies have reported that certain promoter elements present within the core promoter architecture can be important to specify enhancer-promoter communication (Juven-Gershon et al., 2008; Merli et al., 1996; Zabidi and Stark, 2016). Here, we have extended these earlier studies by establishing that promoter elements present in the P1 promoter allow a more precise communication with distal enhancers. Moreover, this selective promoter-enhancer communication is mediated by the combined action of multiple core promoter elements via two mechanisms. First, the presence of a BREu motif potentiates activation of P1 independent of its basal activity. And second, the presence of INR or DPE motifs apparently limits the range of enhancer dependent promoter activation by increasing the basal activity of the core promoter. This means that in HCT-116 cells, the P2 promoter serves as a basal promoter, largely insensitive to Wnt-responsive enhancers, whereas the P1 promoter can be fine-tuned by Wnt-responsive enhancers. The presence of these two differentially regulated promoters could be important for precise regulation of transcription initiation, especially for the genes that are tightly regulated, such as MYC.

Multiple mechanisms are thought to regulate the specific formation of enhancer-promoter interactions that include chromosome topology, changes in the epigenetic chromatin landscape and biochemical compatibility (Arensbergen et al., 2014; Jin et al., 2013; Li and Noll, 1994; Merli et al., 1996; Shlyueva et al., 2014; Visel et al., 2009). The observation that small changes in alternative core promoter elements can differentially communicate with distal enhancers could further diversify the combinatorial specificity of these highly regulated interactions (Arensbergen et al., 2014). Interestingly, genome-wide analysis of mammalian promoters has shown that the presence of alternative promoters is overrepresented in highly regulated genes (Baek et al., 2007). The MYC gene is exquisitely regulated at the transcriptional level, with more than 270 enhancers annotated in the enhancer

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atlas to regulate MYC, of which, dozens have been functionally validated (Fulco et al., 2016; Sotelo et al., 2010; Uslu et al., 2014; Zhang et al., 2016a). The differential usage of two alternative MYC core promoters would significantly expand the network and specificity of possible long distance enhancer/promoter interactions that are necessary to mediate different temporal and spatial transcriptional outcomes.

4.7 Methods

4.7.1 CAGE data analysis The CAGE profile of the MYC locus was obtained from the FANTOM5 database (http://fantom.gsc.riken.jp). We accessed the Zenbu data viewer of the human promoterome (https://bit.ly/2JmEnVR) to obtain the total number of reads from the MYC P1 and P2 promoters in a 11 bp window around the TSS. A threshold of 0.1 cpm. was set for each promoter before we determined the frequency distribution of the log2 (P2/P1). Similarly, for the time courses, we accessed the Zenbu data viewer human time course (https://bit.ly/2HpU9Pd), to obtain the number of reads for all the differentiation time courses which were used to calculate the log2 (P2/P1) promoter usage ratio at the different time points. The protocols describing the time courses and specific cell lines used are described by Arner et al. (2015). 4.7.2 Cell culture All cell lines used in this study were cultured in accordance with ATCC and Riken Cell bank guidelines in their suggested media supplemented with 10% fetal bovine serum in a humidified incubator at 5% CO2. For HCT-116 cells, Wnt induction was achieved by treating the cells with LiCl solution at different concentrations for 6 h (for Real time quantitative PCRs) or 24 h (for luciferase reporter assays). 4.7.3 Cell growth curves Cells were plated into 96 well E-plates and grown in a xCELLigence-RCTA SP system. Cells were seeded in quadruplicates for all conditions. In addition, wells with only media were used as a control. The measurement was taken every 10 min for 120 h. We fitted the growth curve using a logistic function with three characteristic parameters: the length of lag phase, the generation time and the maximum cell index.

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4.7.4 Quantification of P1 and P2 promoter activities To quantify the P1 and P2 promoter activities, we developed a PCR assay with primers immediately downstream of the MYC P1 promoter (5′-CTTGGCGGGAAAAAGAACGG-3′ & 5′-AGTTAGATAAAGCCCCGAAAACC-3′) and the MYC P2 promoter (5′-AGCGAATAGGGGGCTTCGC-3ʹ) and (5′-TCGTGGATGCGGCAAGGGTT-3′. In addition, we used a 264 bp DNA sequence that could be amplified by both sets of primers to determine the amplification efficiency and the relative transcription from the P1 and the P1 + P2 promoter, and sub sequentially, the transcription from the P2 promoter. 4.7.5 Reporter luciferase assay Luciferase reporter constructs were generated using pGL3-Basic luciferase reporter vector. A 150 bp stretch of the MYC promoters P1 (Chr8,128748216:128748365) or P2 (Chr8,128748378:128748527) was cloned between XhoI and HindIII sites to generate the promoter alone constructs. Furthermore, to test the effect of the Wnt pathway of the fold activation, a Wnt responsive enhancer consisting of 8 TCF7L2 or a control sequence with mutated TCF7L2 DNA binding sites was cloned right upstream from both promoters. Between KpnI and NheI sites, the maps for the plasmids were shared in the annex S1. The QuickChange II mutagenesis kit was used to generate the promoter motif mutants. The primers used for the site direct mutagenesis are listed in Table S1. The final mutant promoter sequences were shared in the Annex S2. Reporter constructs were transiently transfected into cells along with a control luciferase plasmid (Renilla luciferase SV40), before luciferase activity was measured using a Dual-Glo luciferase kit in a GloMax-Multi Detection System. 4.7.6 Real-time qPCR Total RNA was isolated from HCT-116 cells and other cell lines using RNeasy Kit complementary DNA (cDNA) was generated using Maxima First Strand cDNA Synthesis Kit and qPCRs reactions were prepared using SYBR FAST qPCR mix and ran in a CFX96 Real-Time PCR Detection System. In all cases, manufacturer guidelines were followed to optimize the amplification conditions. Primer sequences used for Real time quantitative PCRs were designed with Primer3 online tool (http://bioinfo.ut.ee/primer3-0.4.0/) and are listed in Table S2. Quantitation cycle (Cq) values were used to further determine the levels of the specific messenger RNA (mRNA) assayed. 4.7.7 Genome Editing To generate enhancer deleted HCT-116 clones, guide RNAs (gRNAs) were designed using the online CRISPR design tool (http://crispr.mit.edu/) and cloned

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into a modified px330 harboring a Venus fluorescent protein cassette. For each enhancer deletion two vectors coding for gRNAs surrounding the enhancer locus were transfected into HCT-116 cells. A day after transfection, GFP positive cells were sorted by FACS and single plated into 96 well plates. 10 days after plating, the colonies were genotyped by genomic PCR to find clones with the desired deletions. The primers listed in Table S3 were used to generate the different px330 vectors used in this study.

4.8 Appendix

4.8.1 Luciferase reporter sequences All the reporter plasmids were build using the the Pgl3-Basic reporter plasmid. The reporter plasmids sequences presented in the article were inserted in the Pgl3-Basic between KpnI and HindIII sites. Promoter sequences are underline. enhancer sequences, when present, are double underline.

§ P1 Promoter: GGTACCGAGCTCTTACGCGTGCTAGCCCGGGCTCGACCGGGTTCCCAAAGCAGAGGGCGTGGGGGAAAAGAAAAAAGATCCTCTCTCGCTAATCTCCGCCCACCGGCCCTTTATAATGCGAGGGTCTGGACGGCTGAGGACCCCCGAGCTGTGCTGCTCGCGGCCGCCACCGCCGGGCCCCGGCCGTCCCTGGCTCCAAGCTT

§ P2 promoter: GGTACCGAGCTCTTACGCGTGCTAGCCCGGGCTCGACTGCCTCGAGAAGGGCAGGGCTTCTCAGAGGCTTGGCGGGAAAAAGAACGGAGGGAGGGATCGCGCTGAGTATAAAAGCCGGTTTTCGGGGCTTTATCTAACTCGCTGTAGTAATTCCAGCGAGAGGCAGAGGGAGCGAGCGGGCGGCCGGCTAGGGTGGAAGAGCAAGCTT

§ Wnt-responsive enhancer + P1 promoter: GGTACCGAGCTCTTACGCGAGATCAAAGGGGGTAAGATCAAAGGGGGTAAGATCAAAGGGGCGCGAGATCAAAGGGGGTAAGATCAAAGGGGGTAAGATCAAAGGGGGTAAGATCAAAGGGGCGCGCCCGCGTGCTAGCCCGGGCTCGACCGGGTTCCCAAAGCAGAGGGCGTGGGGGAAAAGAAAAAAGATCCTCTCTCGCTAATCTCCGCCCACCGGCCCTTTATAATGCGAGGGTCTGGACGGCTGAGGACCCCCGAGCTGTGCTGCTCGCGGCCGCCACCGCCGGGCCCCGGCCGTCCCTGGCTCCAAGCTT

§ Wnt-mutated enhancer + P1 promoter: GGTACCTTACGCGAGGCCAAAGGGGGTAAGGCCAAAGGGGGTAAGGCCAAAGGGGGTAAGGCCAAAGGCGCGAGGCCAAAGGGGGTAAGGCCAAAGGGGGTAAGGCCAAAGGGGGTAAGGCCAAAGGCCCGGGCTCGAGCTAGCCCGGGCTCGACCGGGTTCCCAAAGCAGAGGGCGTGGGGGAAAAGAAAAAAGATCCTCTCTCGCTAATCTCCGCCCACCGGCCCTTTATAATG

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CGAGGGTCTGGACGGCTGAGGACCCCCGAGCTGTGCTGCTCGCGGCCGCCACCGCCGGGCCCCGGCCGTCCCTGGCTCCAAGCT

§ Wnt-responsive enhancer + P2 promoter: GGTACCGAGCTCTTACGCGAGATCAAAGGGGGTAAGATCAAAGGGGGTAAGATCAAAGGGGCGCGAGATCAAAGGGGGTAAGATCAAAGGGGGTAAGATCAAAGGGGGTAAGATCAAAGGGGCGCGCCCGCGTGCTAGCCCGGGCTCGACTGCCTCGAGAAGGGCAGGGCTTCTCAGAGGCTTGGCGGGAAAAAGAACGGAGGGAGGGATCGCGCTGAGTATAAAAGCCGGTTTTCGGGGCTTTATCTAACTCGCTGTAGTAATTCCAGCGAGAGGCAGAGGGAGCGAGCGGGCGGCCGGCTAGGGTGGAAGAGCAAGCTT

§ Wnt-mutated enhancer + P2 promoter: GGTACCTTACGCGAGGCCAAAGGGGGTAAGGCCAAAGGGGGTAAGGCCAAAGGGGGTAAGGCCAAAGGCGCGAGGCCAAAGGGGGTAAGGCCAAAGGGGGTAAGGCCAAAGGGGGTAAGGCCAAAGGCCCGGGCTCGAGCTAGCCCGGGCTCGACTGCCTCGAGAAGGGCAGGGCTTCTCAGAGGCTTGGCGGGAAAAAGAACGGAGGGAGGGATCGCGCTGAGTATAAAAGCCGGTTTTCGGGGCTTTATCTAACTCGCTGTAGTAATTCCAGCGAGAGGCAGAGGGAGCGAGCGGGCGGCCGGCTAGGGTGGAAGAGCAAGCTT

4.8.2 Sequence of core promoter mutants § P1 WT

GGTTCCCAAAGCAGAGGGCGTGGGGGAAAAGAAAAAAGATCCTCTCTCGCTAATCTCCGCCCACCGGCCCTTTATAATGCGAGGGTCTGGACGGCTGAGGACCCCCGAGCTGTGCTGCTCGCGGCCGCCACCGCCGGGCCCCGGCCGTCC

§ P1 BREm

GGTTCCCAAAGCAGAGGGCGTGGGGGAAAAGAAAAAAGATCCTCTCTCGCTAATCTCCGCCCACaacacgTTTATAATGCGAGGGTCTGGACGGCTGAGGACCCCCGAGCTGTGCTGCTCGCGGCCGCCACCGCCGGGCCCCGGCCGTCC

§ P1 TATAm

GGTTCCCAAAGCAGAGGGCGTGGGGGAAAAGAAAAAAGATCCTCTCTCGCTAATCTCCGCCCACCGGCCCgcgccccTGCGAGGGTCTGGACGGCTGAGGACCCCCGAGCTGTGCTGCTCGCGGCCGCCACCGCCGGGCCCCGGCCGTCC

§ P1 INRm GGTTCCCAAAGCAGAGGGCGTGGGGGAAAAGAAAAAAGATCCTCTCTCGCTAATCTCCGCCCACCGGCCCTTTATAATGCGAGGGTCTGGACGGCTGAtcagttCCGAGCTGTGCTGCTCGCGGCCGCCACCGCCGGGCCCCGGCCGTCC

§ P1 DPE

GGTTCCCAAAGCAGAGGGCGTGGGGGAAAAGAAAAAAGATCCTCTCTCGCTAATCTCCGCCCACCGGCCCTTTATAATGCGAGGGTCTGGACGGCTGAGGACCCCCGAGCTGTGCTGCTCGCGGCCGCagacctCGGGCCCCGGCCGTCC

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§ P2 WT CTGCCTCGAGAAGGGCAGGGCTTCTCAGAGGCTTGGCGGGAAAAAGAACGGAGGGAGGGATCGCGCTGAGTATAAAAGCCGGTTTTCGGGGCTTTATCTAACTCGCTGTAGTAATTCCAGCGAGAGGCAGAGGGAGCGAGCGGGCGGCC

§ P2 BREm

CTGCCTCGAGAAGGGCAGGGCTTCTCAGAGGCTTGGCGGGAAAAAGAACGGAGGGAGGGATCGgggcgccTATAAAAGCCGGTTTTCGGGGCTTTATCTAACTCGCTGTAGTAATTCCAGCGAGAGGCAGAGGGAGCGAGCGGGCGGCCG

• P2 TATAm CTGCCTCGAGAAGGGCAGGGCTTCTCAGAGGCTTGGCGGGAAAAAGAACGGAGGGAGGGATCGCGCTGAGgcgccccGCCGGTTTTCGGGGCTTTATCTAACTCGCTGTAGTAATTCCAGCGAGAGGCAGAGGGAGCGAGCGGGCGGCCG

§ P2 INRm

CTGCCTCGAGAAGGGCAGGGCTTCTCAGAGGCTTGGCGGGAAAAAGAACGGAGGGAGGGATCGCGCTGAGTATAAAAGCCGGTTTTCGGGGCTTTATCggaccCGCTGTAGTAATTCCAGCGAGAGGCAGAGGGAGCGAGCGGGCGGCCG

§ P2 DPE

CTGCCTCGAGAAGGGCAGGGCTTCTCAGAGGCTTGGCGGGAAAAAGAACGGAGGGAGGGATCGCGCTGAGTATAAAAGCCGGTTTTCGGGGCTTTATCTAACTCGCTGTAGTAATTCCAGCGAGAGGCctcatGAGCGAGCGGGCGGCCG

* Sequences that have been bolded represent mutated nucleotides

4.8.3 Primers for real-time qPCR

Name Sequence qPCR b-Actin Fw TCCCTGGAGAAGAGCTACGA qPCR b-Actin Rv AGCACTGTGTTGGCGTACAG

qPCR GAPDH Fw AATCCCATCACCATCTTCCA qPCR GAPDH Rv TGGACTCCACGACGTACTCA

qPCR MYC Fw CAGCTGCTTAGACGCTGGATT qPCR MYC Rv GTAGAAATACGGCTGCACCGA qPCR P1+2 Fw AGCGAATAGGGGGCTTCGC qPCR P1+2 Rv TCGTGGATGCGGCAAGGGTT qPCR P1 Fw CTTGGCGGGAAAAAGAACGG qPCR P1 Rv AGTTAGATAAAGCCCCGAAAACC

89

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Specific communication between enhancers and alternative promoters … · 2019-04-03 · Specific communication between enhancers and alternative promoters mediates finer regulation