UNIVERSITY of CALIFORNIA Los Angeles Dissecting the Regulatory

UNIVERSITY OF CALIFORNIA

Los Angeles

Dissecting the Regulatory Strategies of NFkappaB RelA Target Genes

in the Inflammatory Response

A dissertation submitted in partial satisfaction of the

requirements for the degree Doctor of Philosophy

in Microbiology, Immunology, and Molecular Genetics

Kim Anh Ngo

2019

Kim Anh Ngo

2019 ABSTRACT OF THE DISSERTATION

Dissecting the Regulatory Strategies of NFkappaB RelA Target Genes

in the Inflammatory Response

Kim Anh Ngo

Doctor of Philosophy in Microbiology, Immunology, and Molecular Genetics

University of California, Los Angeles, 2019

Professor Alexander Hoffmann, Chair

The NFkB family member RelA is a ubiquitously expressed potent transcriptional activator that is induced by exposure to pathogens and inflammatory cytokines to activate the expression of a large number of inflammatory and immune-response genes. Its nuclear activity is induced from a latent cytoplasmic pool by stimulus-responsive degradation of IkB proteins, and the complex signaling mechanisms that regulate its activity are well understood. Less well characterized are the mechanisms that allow nuclear NFkB RelA activity to select its target genes and produce gene-specific expression. While many genes have been identified to be potentially NFkB regulated, there is no database that lists the NFkB target genes in a particular physiological condition, defined cell types and stimulus.

Chapter 1 presents a general overview of IKK-IkB-NFkB signaling system. Chapter 2 reports the primary study in this dissertation in which includes approaches such as biochemistry, molecular biology, mouse genetics, Next-Generation Sequencing, and mathematical modeling to dissect the regulatory strategies of NFkB RelA endogenous target genes in the inflammatory response. Chapter 3 summarizes our findings and provide a future direction to this study.

In Chapter 2, to dissect gene-specific regulatory strategies resulting from NFkB activation in response to inflammation, we stringently defined a list of direct RelA target genes by integrating physical/DNA binding (ChIP-seq) and functional/transcriptional data (RNA-seq) datasets. We then dissected each gene’s regulatory strategy by testing RelA variants in a novel primary-cell genetic complementation assay. All endogenous target genes required that RelA makes DNA-base-specific contacts, and none could be activated by the DNA binding domain alone. However, endogenous target genes differed widely in how they employ the two transactivation domains (TAD). Through model-aided analysis of the dynamic timecourse data we reveal gene-specific synergy and redundancy of TA1 and TA2. Given that post-translational modifications control TA1 activity and affinity for coactivators determines TA2 activity, the differential TA logics suggests context-dependent vs. context-independent control of endogenous

RelA-target genes. While some inflammatory initiators appear to require co-stimulatory TA1 activation, inflammatory resolvers are a part of the NFkB RelA core response where TA2 activity mediates activation even when TA1 is inactive.

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COMMITTEE PAGE

The dissertation of Kim Anh Ngo is approved.

Arnold J. Berk

Lili Yang

Siavash K. Kurdistani

Alexander Hoffmann, Committee Chair

University of California, Los Angeles

2019

DEDICATION

I dedicate this work to my family, especially my parents for giving me life. To my mom Hoa Ho, and to my siblings Caroline Ngo, Tri Ngo, Hoang Minh Ngo, and Le Ngo for their constant support and encouragement.

To my principal investigator and advisor, Professor Alexander Hoffmann at UCLA. His passion and enthusiasm for doing science have inspired me and are the reasons why I went to graduate school.

TABLE OF CONTENTS

ABSTRACT OF THE DISSERTATION ii

COMMITTEE PAGE iv

DEDICATION v

TABLE OF CONTENTS vi

LIST OF FIGURES viii

LIST OF SUPPLEMENTARY TABLES x

ACKNOWLEDGEMENTS xi

VITA xv

PUBLICATIONS xvi

CHAPTER 1: General Introduction 1

NFkB, IkB, and IKK PROTEIN FAMILIES 2

NFkB SIGNALING SYSTEM 9

DNA RECOGNITION BY NFkB DIMERS 14

NFkB TRANSACTIVATION 16

FOCUS OF STUDY 17

REFERENCES 20

CHAPTER 2: Dissecting the Regulatory Strategies of NFkB RelA Target Genes in the

Inflammatory Response 29

ABSTRACT 30

INTRODUCTION 31

RESULTS 35

Stringent identification of TNF-responsive NFkB RelA target genes 35

An experimental system to dissect regulatory strategies of NFkB RelA target genes 40

All TNF-induced NFkB target genes require that RelA makes base-specific contacts

within the kB element 48

All TNF-induced NFkB target genes are regulated by two C-terminal TA domains 55

A model-aided analysis reveals gene-specific logic gates formed by RelA TA1 and TA2

A knock-in mouse reveals that regulatory strategies of RelA target genes are generally

conserved in different cell types but are often stimulus-specific 66

MATERIALS AND METHODS 78

ACKNOWLEDGEMENTS 88

REFERENCES 90

CHAPTER 3: Conclusions and Future Directions 95

REFERENCES 103

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LIST OF FIGURES

CHAPTER 1: General Introduction

Figure 1.1 NFkB protein family 4

Figure 1.2 IkB and IKK protein families 8

Figure 1.3 Two NFkB signaling pathways 12

Figure 1.4 Cross-talk in NFkB pathways 13

Figure 1.5 RelA modular domain structures 19

CHAPTER 2: Dissecting the regulatory strategies of NFkB RelA target genes in the inflammatory response

Figure 2.1 Identifying NFkB RelA target genes in the TNF response of murine embryonic fibroblasts 38

Figure 2.2 p53 deficiency does not affect TNF-induced gene expression 41

Figure 2.3 A RelA genetic complementation system for studying the control of endogenous NFkB RelA target genes 44

Figure 2.4 Retroviral transduction of RelA in p53-/-crel-/-rela-/- MEF provide a suitable complementation system for studying NFkB RelA target genes 46

Figure 2.5 High affinity binding by RelA is required for the TNF-induced expression of all NFkB target genes 50

Figure 2.6 High affinity DNA binding by RelA is required for all TNF-induction of NFkB target genes 52

Figure 2.7 The RelA C-terminal portion is required for TNF induction of all NFkB target genes 57

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Figure 2.8 RelA transactivation domains TA1 and TA2 both contribute to the TNF response of NF-kB target genes 59

Figure 2.9 A math model-aided analysis reveals gene-specific requirements for TA1 and TA2 64

Figure 2.10 Generation of a RelA Transactivation Domain mutant knock-in mouse strain

Figure 2.11 A knock-in mouse reveals that the dual TAD-requirement pertains to other cell-types and stimuli 68

Figure 2.12 Lack of RelA-TAD-dependence in the transcriptional response of MEFs to TNF or LPS is not due to cRel compensation 72

CHAPTER 3: Conclusions and Future Directions

Figure 3.1 The RelA TA2 domain functions primarily via intrinsic affinity for CBP/p300 100

LIST OF SUPPLEMENTARY TABLES

The following items are available as external Excel files.

Supplementary Table 2.1 List of RelA binding locations and TNF-induced gene expression for

104 NFkB target genes

Supplementary Table 2.2: Gene names and relative expression data plotted in Figure 2.11B for the 37 TNF-responsive genes in wild-type and RelATA/TA MEFs and BMDMs

Supplementary Table 2.3: Gene names and relative expression data plotted in Figure 2.11B for the 481 LPS-responsive genes in wild-type and RelATA/TA MEFs and BMDMs

Supplementary Table 2.4: Gene names and percentage of expression data plotted in Figure 2.11C for 37 TNF-responsive genes in RelATA/TA MEFs and BMDMs

Supplementary Table 2.5: Gene names and percentage of expression data plotted in Figure 2.11C for 481 LPS-responsive genes in RelATA/TA MEFs and BMDMs

Supplementary Table 2.6: Gene names and relative expression data plotted in Figure 2.11E for the 146 TNF and LPS-responsive genes in wild-type and RelATA/TA MEFs

Supplementary Table 2.7: Gene names and relative expression data plotted in Figure 2.11E for the 185 TNF and LPS-responsive genes in wild-type and RelATA/TA BMDMs

Supplementary Table 2.8: Gene names and percentage of expression data plotted in Figure 2.11F for 146 TNF and LPS-responsive genes in RelATA/TA and crel-/-RelATA/TA MEFs

Supplementary Table 2.9: Gene names and percentage of expression data plotted in Figure 2.11F for 185 TNF and LPS-responsive genes in RelATA/TA BMDMs

Supplementary Table 2.10: Gene names and relative expression data plotted in Figure 2.12A for the 146 TNF and LPS-responsive genes in crel-/- and crel-/-RelATA/TA MEFs

ACKNOWLEDGEMENTS

Accomplishing this dissertation and enjoying doing scientific research would not have been possible without the contribution and help from many people. I would first like to thank my principal investigator and advisor, Professor Alexander Hoffmann, for his enthusiasm, mentorship, and support throughout these 8 years of graduate study. His great success has had a profound impact on my scientific career. When I started my scientific research career as a soon- to-be college graduate, Alex’s enthusiasm and passion for science inspired me to pursue a career in research. Alex has provided an excellent lab environment and resources for all lab members.

He has taught me how to think more objectively and write more critically. I really appreciate

Alex for his constant encouragement and for giving me the opportunity to mentor undergraduate students and lab assistants. I am very grateful to have had the pleasure of working in his lab in various roles: starting out as an undergraduate volunteer, then becoming a lab assistant and manager for 3 years, and coming back as a graduate student. Thank you for everything.

Special thanks to Professor Gourisankar Ghosh, who is a wonderful scientist and mentor, as a neighboring principal investigator at UCSD. He has so much enthusiasm for science and was a kind, helpful, and encouraging collaborator. His lab members kindly provided help and guidance before and during my first 3 years of graduate study. Jessica Ho and Vivien Ya-Fan

Wang were helpful in giving technical advice as well as general advice about graduate school.

Dr. Smarajit Polley and Dr. Yidan Li are wonderful friends and were very helpful in providing technical and scientific advice. I am grateful to have worked next to Professor Gourisankar

Ghosh’s lab.

I want to sincerely thank the other members of my doctoral committee, Professor Arnold

J. Berk, Professor Lili Yang, and Professor Siavash K. Kurdistani, for their insightful advices on my project and their expert input on genome-wide sequencing data analysis. Additionally, I want to acknowledge and thank the UCLA Warsaw Fellowship and National Institute of Health (NIH) grants GM117134 and AI12784 for funding support of this dissertation work.

I would especially like to thank all the former and current lab mates in the Signaling

Systems Laboratory with whom I’ve crossed paths, for making this lab a fun place to work. I have so many great memories with this group and have enjoyed being a part of it. Thank you to

Dr. Soumen Basak, for teaching me biochemical techniques and how to make effective presentations even before my graduate study. As a key senior lab mate, he shared his extensive knowledge of NFkB signaling, his experience in doing scientific research, and many scientific ideas. Dr. Masataka Asigiri, a visiting scholar from Japan, whom is a knowledgeable immunologist had provided me with so much help in our scientific discussions. Jonathan

Almaden, also known as my “big brother” in lab, for supporting and guiding me in my project and life as a graduate student. Eason (Yu-Sheng) Lin, for being in the same program year as me and for allowing me to vent on stressful days. Dr. Jesse Vargas for helpful guidance and feedback on my research proposals. Dr. Roberto Spreafico, for being another wonderful “big brother” in lab. He is a phenomenal scientist, immunologist, and bioinformatician and has taught me how to analyze sequencing data in the R program and how to use UNIX-based terminal commands. A big thank-you to Marie Grossett, Dr. Simon Mitchell, Dr. Marie Metzig, Dr.

Catera Wilder, Dr. Stefanie Luecke, Diane Lefaudeux, Dr. Yi Liu, Emily Chen, and Brian Kim for their friendship, encouragement, and my outlet for frustration. I especially want to thank my

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two undergraduate assistants for their help with sequencing analysis, Kensei Kishimoto and

Aditya Pimplaskar. They also contributed to the work in Chapter 2 of this dissertation. Diane

Lefaudeux, Katherine Sheu, Ning Wang, and Adewunmi Adelaja for technical help in R programming. I also want to thank everyone for their great feedback during sub-group and lab meetings.

I want to thank my friends from neighboring labs on the third floor of Natural science building (NSB) at UCSD from Professor Patricia Jennings’ and Professor Partho Ghosh’s labs for making the third floor of NSB fun and interactive. DJ Burban, Kendra Hailey, and Kaitlin

Fisher from the Jennings lab for their friendship, support, and desserts. Brent Hamaoka from the

Partho lab for his technical advice in protein chemistry and for being a great beach volleyball coach.

My utmost gratitude to my family, relatives, and friends for their endless support and understanding. My mom and siblings, for always being supportive and encouraging me to push forward to completion. My dad Thong Viet Ngo and grandpa Morris Wilson who are no longer here but who always believed that I have the talents to succeed. A special thanks to my second family at Tinh Xa Ngoc Dang Buddhist Temple in San Diego, for their support and care. My wonderful friends since grade school, college, and graduate school, for all their unwavering support and friendship. My best friends, Amy Hoang and Loan Tran, for always being great friends and listeners when I need to vent on stressful days.

This dissertation would not have been possible without the people who have contributed directly to the work presented in Chapter 2. Dr. Roberto Spreafico and Dr. Frank (Zhang) Cheng processed sequencing data and helped with some downstream analysis. Jeremy Davis-Turak

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helped with the control gene expression analysis comparing WT and p53-/- mouse fibroblast cells.

Kensei Kishimoto and Aditya Pimplaskar helped with downstream ChIP- and RNA-sequencing analyses and contributed to sequencing results shown in Chapter 2. Emily Chen helped with generating RNA libraries for chromatin-associated RNA-seq experiment; Ning Wang helped with HOMER motif enrichment analysis; Dr. Simon Mitchell and Amy Tam contributed to the mathematical modeling results shown in Chapter 2. Dr. Yi Liu helped with BMDM isolation.

Eason Lin helped with documenting the knock-in mutant mice phenotypes. Lastly, Diane

Lefaudeux for her teaching and providing her amazing technical advice in R programming.

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VITA

Education

2008 Bachelor of Science, University of California, San Diego

2013 Master of Science, University of California, San Diego

Academic Research Experience

2007-2008 Undergraduate Assistant, Signaling Systems Lab, UCSD

2008-2010 Lab Assistant III and Manager, Signaling Systems Lab, UCSD

2011-2019 Graduate Research, Signaling Systems Lab, UCLA and UCSD

Industrial Research Experience

2010-2011 Research Associate Scientist, Atrium Staffing

Awards and Fellowships

2015 Sidney Rittenberg Award, UCLA, Department of MIMG

2017-2018 Warsaw Fellowship, UCLA, Department of MIMG

PUBLICATIONS

Ngo KA, Kishimoto K, Davis-Turak J, Pimplaskar A, Cheng Z, Spreafico R, Chen EYH, Tam A, Mitchell S, Hoffmann A. (2019). Dissecting the Regulatory Strategies of NFkappaB RelA Target Genes in the Inflammatory Response Reveals Differential Logics of Two Transactivation Domains. Submitted.

Fortmann KT, Lewis RD, Ngo KA, Fagerlund R, Hoffmann A. (2015). A Regulated, Ubiquitin- Independent Degron in IkBα. J. Mol. Biol. 427, 2748-2756.

Tsui R, Kearns JD, Lynch C, Vu D, Ngo KA, Basak S, Ghosh G, Hoffmann A. (2015). IkBβ enhances the generation of the low-affinity NFkB/RelA homodimer. Nat. Commun. 6: 7068.

Almaden JV, Tsui R, Liu YC, Birnbaum H, Shokhirev MN, Ngo KA, Davis-Turak JC, Otero D, Basak S, Rickert RC, Hoffmann A. (2014). A pathway switch directs BAFF signaling to distinct NFkB transcription factors in maturing and proliferating B cells. Cell Rep. 9: 2098-2111.

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CHAPTER 1:

General Introduction

NFkB, IkB, and IKK PROTEIN FAMILIES

Nuclear Factor kappaB (NFkB) was first discovered as a DNA-binding complex in activated B-cells (Sen and Baltimore, 1986). It was then characterized as a ubiquitously expressed potent transcriptional activator that is activated by a wide range of intracellular and extracellular signals, such as inflammatory cytokines, bacterial pathogens, viruses, environmental stress, genotoxic stress, and developmental inter-cellular signals, to induce the expression of many genes (Baeuerle and Henkel, 1994; Hoffmann et al., 2006; Liu et al., 2017;

Stanic et al., 2004; Zhang et al., 2017). NFkB is widely recognized as a master regulator of inflammatory response, but at the same time it is a major transcription factor for both innate and adaptive immune system that controls the transcription of genes involved in many cellular processes such as inflammation, cell survival, cell proliferation, cell differentiation, viral response, and apoptosis. In addition, NFkB signaling is activated by two distinct pathways known as canonical (“classical”) and non-canonical (“alternative”) pathways, which are important for inflammatory gene expression and genes involved in developmental processes, respectively. Improper regulation of NFkB activity has been linked to inflammatory and autoimmune diseases, such as rheumatoid arthritis and Crohn’s disease, septic shock, non-

Hodgkin B cell lymphoma, viral infection, and cancer (Hoesel and Schmid, 2013; Liu and Malik,

2006; Monaco and Paleolog, 2004; Nakshatri et al., 1997; Tak and Firestein, 2001; Xia et al.,

2014). NFkB transcriptional activity can have important physiological functions and however, pathophysiological functions if not appropriately controlled. Because of its important role in health and disease, NFkB signaling has been a potential therapeutic target in the prevention and

treatment of cancer and human diseases (Greten and Karin, 2004; Gupta et al., 2010; Luo et al.,

2005; Monaco and Paleolog, 2004; Roman-Blas and Jimenez, 2006; Sarkar and Li, 2008).

The NFkB family of transcription factors consists of 5 proteins: RelA (also called p65),

RelB, cRel, p50, and p52 encoded by RELA, RELB, REL, NFKB1, and NFKB2 genes, respectively. These proteins all share an N-terminal Rel homology region (RHR) that is 300 amino acids long and has three functions: sequence-specific DNA binding, dimerization and inhibitory protein binding (Figure 1.1) (Ghosh et al., 2012, 1998; Siebenlist et al., 1994). These five NFkB subunits can form 15 possible combinations of NFkB dimers through homo- and hetero-dimerization. Following the RHR is a nuclear localization sequence (NLS), and a C- terminal transcription activation domain (TAD) that is unique to RelA, RelB, and cRel proteins. p50 and p52 result from precursor proteins: p105 (NFKB1) and p100 (NFKB2), respectively, through post-translational cleavage of a C-terminal region containing ankyrin repeat domains

(ARDs) (Baeuerle and Henkel, 1994; Fan and Maniatis, 1991). Hence, p50 and p52 can act as transcriptional activators when forming heterodimers with Rel-containing members with a C- terminal TAD. On the other hand, p50 and p52 homodimers function as transcriptional repressors due to the lack of TAD (Cramer et al., 1997; Ghosh et al., 1995; Müller et al., 1995).

Of all the NFkB dimers, RelA:p50 heterodimer is predominant, and was the first NFkB dimer to be described (Kopp and Ghosh, 1995; Verma et al., 1995). Further, RelA/p65 is ubiquitously expressed and was characterized to contain two transactivation sub-domains that are within its C- terminal TAD; however, cRel-containing dimers are found to be more highly expressed in mature lymphoid cells (Boffa et al., 2003; Köntgen et al., 1995; Tumang et al., 1998).

Figure 1.1 NFkB protein family. (A) Members of NFkB family. Left column: gene symbol, middle column: protein name, right column: the number of amino acids in the human protein. RHR, Rel homology region; TAD, transactivation domain. (B) Five NFkB subunits can form 15 possible different combinations of NFkB dimers through homo- and hetero-dimerization. Adapted from Hoffmann and Baltimore, 2006; O’Dea and Hoffmann, 2009.

Inhibitors of kB (IkB) are a family of inhibitory proteins that regulate NFkB activity by sequestering NFkB dimers in the cytoplasm of resting cells and releasing them upon signaling input. IkB blocks the nuclear localization of NFkB dimers by blocking their nuclear localization signal (NLS), and regulates DNA-binding of NFkB dimers through the formation of stable

IkB:NFkB complexes that inhibit NFkB binding to NFkB-binding sites, called kB DNA

(Baeuerle and Baltimore, 1988; Huxford et al., 1998; Jacobs and Harrison, 1998; Kearns et al.,

2006; Nolan et al., 1991; Thanos and Maniatis, 1995; Truhlar et al., 2006; Zabel and Baeuerle,

1990). The IkB family consists of IkBa, IkBb, IkBe, IkBd, IkBg, IkBx, and Bcl3 that are encoded by NFKBIA, NFKBIB, NFKBIE, NFKB2, NFKB1, NFKBIZ, and BCL3, respectively

(Figure 1.2A). Of note, NFKB2 and NFKB1 also encode for NFkB proteins, p52 and p50, respectively. However, the C-terminal containing ARDs in their precursor proteins, p100 and p105, respectively, make them as IkB-like proteins. Hence, p100 and p105 homodimers, IkBd and IkBg, respectively, can bind to NFkB dimers and inhibit their translocation into the nucleus.

The C-terminal region of IkB proteins contain ARDs, which is the hallmark of the IkB proteins.

The ARDs of IkBs associate with the RHR of NFkB dimers to form the IkB:NFkB complex

(Huxford et al., 1998; Jacobs and Harrison, 1998; Malek et al., 2003). IkB proteins also have other functional domains, such as the signal response domain (SRD) in the N-terminus or C- terminus. IkBa and IkBb additionally contain an acidic carboxy-terminal region that is rich in the amino acids proline (P), glutamic acid (E), serine (S), and threonine (T), known as the PEST- like region, in which is required for inhibiting NFkB binding to DNA (Ernst et al., 1995).

However, IkBb has an additional essential and positive function, which is to stabilize the low

affinity RelA:RelA homodimer. Also, it may be considered a bona fide chaperone for the

RelA:RelA homodimer formation (Tsui et al., 2015). Furthermore, classical IkBs (IkBa, IkBb,

IkBe) and IkBd, and IkBg are primarily found in the cytoplasm, which inhibit nuclear NFkB activity by masking their nuclear localization signals (NLS) and by interfering with the capacity of NFkB to bind to DNA (Baeuerle and Baltimore, 1988; Huxford et al., 1998; Jacobs and

Harrison, 1998; Nolan et al., 1991; Zabel and Baeuerle, 1990). Importantly, these IkB promoter regions contain NFkB binding sites that positively regulate the expression of the IkBs; hence,

IkBs are target genes of NFkB and their expression is important for the negative feedback control of NFkB activity (Hoffmann et al., 2002, 2006). The atypical IkB-like proteins (Bcl3, and IkBx) are found primarily in the nucleus where they are strongly induced after NFkB activation and modulate NFkB activity both positively and negatively depending on the target genes (Muta, 2006; Muta et al., 2003; Trinh et al., 2008; Zhang et al., 1994).

An essential component in the activation of NFkB signaling pathway is the IkB kinase

(IKK) complex that is the gatekeeper for NFkB activation because IKK protein complex mediates the signaling-dependent degradation of IkB proteins in which frees NFkB to translocate into the nucleus and activate gene transcription (Israël, 2010; Rushe et al., 2008; Yamaoka et al.,

1998; DiDonato et al., 1997; Mercurio et al., 1997; Zandi et al., 1997; Malinin et al., 1997).

Structural studies have provided protein structural information on the IKK complexes which were shown to form a multimeric complex with NFkB and IkB. In the canonical or “classical”

NFkB pathway, IKK complex contains two kinase subunits, IKKa (or IKK1) and IKKb (or

IKK2), and a non-catalytic subunit, NFkB essential modulator (NEMO or IKKg). IKKa and

IKKb are structurally similar in that both proteins contain an amino-terminal kinase domain and two protein-protein interaction motifs in their carboxyl terminus, called the leucine zipper (LZ) and helix-loop-helix domain (HLH) (Figure 1.2B). Another important domain within C-terminal portion of IKKa and IKKb proteins is a NEMO binding domain (NBD) that allows the IKK complex formation. NEMO protein does not harbor any kinase domain or enzymatic activity, but instead it acts as an adapter protein. NEMO contains two coiled-coil domains (CC1/2), a leucine zipper domain, and a zinc finger domain. The N-terminal CC1 region of NEMO interacts with the IKK C-terminal kinase tails and the CC2-LZ domains mediate oligomerization and polyubiquitin binding, key features that govern NEMO function (Grubisha et al., 2010). NEMO is essential for the activation of the IKK complex in the canonical NFkB signaling pathway.

Deficiency in these IKK proteins have been shown to cause developmental defects in neurulation and liver degeneration in mice (Li et al., 2000; Rudolph et al., 2000). When activated, IKKa and

IKKb directly phosphorylate IkBs at their signal response domain (SRD), triggering ubiquitination and rapid degradation of the inhibitors by the 26S proteasome. This process releases the NFkB dimer, which then stably translocates to the nucleus and activates expression of many genes. On the other hand, NFkB-inducing kinase (NIK) is required for the activation of the “alternative” or non-canonical NFkB pathway where NIK has been shown to only phosphorylate IKKa, and not IKKb in vitro (Ling et al., 1998; Malinin et al., 1997; Régnier et al., 1997; Xiao et al., 2001). Of note, MAP3K14 is the gene encoding NIK because it was first identified as a mitogen-activated protein (MAP) kinase kinase kinase (MAP3K) related kinase due to its homology with other MAP3Ks in its enzymatic domain (Malinin et al., 1997). In response to a developmental stimulus, activated NIK phosphorylates and activates IKKa through

its C-terminal kinase domain. Also, a unique molecular component of NIK is in its carboxyl region called the TRAF2-binding domain (TBD), in which it allows its association to the TRAF2 adaptor protein for its stabilization in NFkB activation (Song et al., 1997).

Figure 1.2 IkB and IKK protein families. Members of the (A) IkB and (B) IKK families. Left column: gene symbol, middle column: protein name, right column: number of amino acids in each human protein. RHR, Rel homology region; SRD, signal response domain; ARDs, ankyrin repeat domains; DD, death domain; PEST, proline, glutamic acid, serine, and threonine rich-like region; KD, kinase domain; LZ, leucine zipper domain; HLH, helix-loop-helix domain; CC1/2, coiled-coil domains; Z; zinc finger domain; NBD, NEMO-binding domain; BR, basic region; PRR, proline rich region; TBD: TRAF binding domain. These schematics are adapted from Hayden and Ghosh, 2008; Malinin et al., 1997; O’Dea and Hoffmann, 2009; Régnier et al., 1997; Thu and Richmond, 2010.

NFkB SIGNALING SYSTEM

NFkB dimers can be activated through two different pathways referred to as the canonical and non-canonical NFkB signaling pathways (Hayden and Ghosh, 2008; O’Dea and

Hoffmann, 2009; Pomerantz and Baltimore, 2002). These pathways differ in respect of inducers, activating kinases, and specific IkB and NFkB molecules involved (Figure 1.3). Genes activated by the canonical pathway are activated rapidly and are generally involved in inflammation, whereas the genes induced by the non-canonical pathway are generally important for developmental programs (Pomerantz and Baltimore, 2002; Sun, 2011).

The canonical NFkB pathway

Activation of the canonical or “classical” NFkB signaling pathway is triggered by a variety of inflammatory and pathogen-derived signals, and through receptors like members of the

TNF receptor (TNFR) super-family, Toll-like receptors (TLRs), Interleukin receptors, antigen of

B and T cells. Signaling through these results in the innate immune and inflammatory response

(Figure 1.3A). The downstream signaling complex that is activated is a trimeric IKK complex of the catalytic subunits IKKa, IKKb, and the regulatory subunit NEMO. Although NFkB activation is regulated/prevented by the three prototypical inhibitors, IkBa, IkBb, and IkBe

(Baeuerle, 1998; Huxford et al., 1998; Jacobs and Harrison, 1998; Kearns et al., 2006; Malek et al., 2003); IkBa is the key regulator of the dynamics of NFkB activity in the canonical pathway because deficiency in IkBa in mice was shown to exhibit neonatal lethality due to elevated

NFkB activity (Beg et al., 1995). The primary NFkB dimers that are activated are RelA and cRel-containing homo- and hetero-dimers. In response to a stimulus, IkBs are phosphorylated by

the NEMO/IKK complex, particularly by IKKa and IKKb, leading to subsequent ubiquitination and proteasomal degradation (Ghosh et al., 1998). IkB degradation frees NFkB to translocate to the nucleus and bind DNA which then activates transcription of numerous of target genes. While the activation of the canonical NFkB pathway is essential for eliciting the inflammatory response, prolonged NFkB activity is detrimental, for example, causing chronic inflammation

(Tak and Firestein, 2001). Hence, a second important molecular event in the canonical NFkB signaling is the inducible synthesis and activation of the IkBs which results in a post-induction repression of the NFkB response, referred to as the negative feedback control of NFkB activity

(Hoffmann et al., 2002).

The non-canonical NFkB pathway

In contrast to the canonical NFkB signaling pathway, activation of the non-canonical or

“alternative” NFkB signaling pathway occurs in response to developmental processes that are induced through Lymphotoxin b receptor (LTbR), RANK, BAFFR, CD40, and CD27 signaling

(Sun, 2011); (Figure 1.3B). Mice deficient in molecular components of the non-canonical NFkB signaling pathway - RELB-/-, NFKB2-/-, NIK-/- - showed defective LTbR-induced NFkB transcriptional activity, abnormalities in lymphoid tissue development and antibody responses, and defective Peyer’s Patch tissue development (Weih and Caamaño, 2003; Yilmaz, 2003; Yin et al., 2001). The activation mechanism does not occur through NEMO activity but requires the activity of NFkB-inducing kinase (NIK). Activation of NFkB via the non-canonical pathway involves NIK- and IKKa-dependent phosphorylation of p100 associated with RelB. This induces

the proteolytic processing of p100 to generate the NFkB family member p52 (Coope et al., 2002;

Senftleben et al., 2001; Xiao et al., 2001). This then allows the formation of the p52: RelB heterodimer (Sun, 2011), which activates gene expression. Activated non-canonical NFkB signaling results in more sustained signaling where the kinetics involve a slow build-up and long-lasting activity, as opposed to the more transient canonical NFkB signaling pathway.

Crosstalk between canonical and non-canonical pathways

Because activation of the canonical pathway is generally associated with inflammation while activation of non-canonical pathway is mostly associated with developmental signals, these pathways are presumed to transduce signals independently and elicit distinct physiological functions. However, NFkB RelA:p50 dimers can be activated in response to both inflammatory and developmental stimuli via crosstalk signaling between NFkB signaling pathways (Basak and

Hoffmann, 2008; Basak et al., 2007; Shih et al., 2011, 2012). This allows the diverse biological functions of NFkB to be adapted to the cell type and context (Figure 1.4). An example of the cross-talk signaling between canonical and non-canonical NFkB signaling pathways is LTbR signaling, in which it is critical for lymphoid organogenesis. LTbR signaling was shown to regulate primary lymphoid development and maturation (B and T lymphocytes), secondary lymphoid development (spleen, lymph nodes, and Peyer’s patch), and their microenvironment for antigen recognition (Fütterer et al., 1998; Rennert et al., 1998; Weih and Caamaño, 2003).

However, LTbR signaling is not sufficient for lymphoid architecture, but TNFR signaling is also required as inhibition of TNFR signaling during LTbR signaling resulted in a lack of lymph node formation (Rennert et al., 1998).

Figure 1.3 Two NFkB signaling pathways. (A) Canonical or “classical” NFkB signaling pathway. Canonical NFkB signaling involves NEMO-dependent IKK complex, prototypical IkBs (IkBa, IkBb, and IkBe), and NFkB RelA and cRel-containing homo- and heterodimers that are activated in response to inflammatory cytokines and pathogens. (B) Non-canonical or “alternative” NFkB signaling pathway. Non-canonical NFkB signaling is induced in response to developmental stimuli and involves the activation of NIK/IKKa complex and p100 proteasomal processing to p52, resulting in NFkB p52:RelB dimers.

Furthermore, Basak et al. reported that activation of LTbR signaling in murine embryonic fibroblast (MEF) cells resulted in NFkB DNA binding activity by both pre-existing RelA:p50 and RelB:p50 dimers and a late-induced RelB:p52 dimer. Surprisingly, RelA:p50 was not associated with classical NFkB-IkB complex, but bound to the p100 homodimer/IkBd. LTbR signaling induces IkBd degradation through the non-canonical signaling via NIK and IKKa

(Basak et al., 2007). In TNF-primed MEF cells, Basak et al. found elevated IkBd-NFkB complexes, suggesting a cross-talk mechanism that controls canonical and non-canonical IkB proteins and NFkB signaling.

Figure 1.4 Cross-talk in NFkB pathways. Cross-talk mechanism between canonical and non- canonical NFkB signaling pathways for NFkB activation. Adapted from Basak and Hoffmann, 2008; Basak et al., 2007; Shih et al., 2011.

Regulation of NFkB activity for immune homeostasis

Tight regulation of NFkB activity is important for immune homeostasis due to the broad range of genes that NFkB can activate. Dysregulation or misregulation of NFkB transcriptional activity has been implicated in many human malignancies and cancers (Baldwin, 2001). Use of genetic knockouts in NFkB:IkB:IKK signaling components have shown the importance of the

NFkB signaling pathways in regulating many biological functions, such as inflammatory responses, lymphoid organogenesis, B-cell survival and maturation, dendritic cell activation, and bone metabolism (Baeuerle, 1998; Xiao et al., 2001). However, aberrant NFkB activity is a hallmark feature of several inflammatory diseases, such as arthritis, Crohn’s disease, and atherosclerosis (Bourcier et al., 1997; Brand et al., 1997; Tak and Firestein, 2001) which makes it a therapeutic target for treatments of inflammation. Hence, in healthy condition, NFkB activity is usually transient to mediate a cellular response and is then inactivated by newly synthesized

IkB proteins that bind to NFkB dimers and export them back out to the cytoplasm. This negative feedback control of NFkB is important to ensure that NFkB signaling is turned on only when necessary for biological functions and is then properly regulated for immune homeostasis.

DNA RECOGNITION BY NFkB DIMERS

Upon translocation into the nucleus, the NFkB complex binds to sequence-specific DNA known as the “kB sites” on target genes. The contact of both NFkB subunits with the DNA is required for DNA binding and transcriptional activation (Chen et al., 1998b; Kunsch et al.,

1992). Structural and functional studies of NFkB-DNA complexes have demonstrated that

NFkB dimers bind to a variety of kB sites where NFkB recognizes 9 to 11 base pairs kB DNA elements of the sequence: 5’-GGGRNNNNYCC-3’, where R = A or G; N = any nucleotide (A,

C, G, or T); Y = C or T (Chen and Ghosh, 1999; Chen et al., 1998b, 2000; Fujita et al., 1992;

Ghosh et al., 1995; Kunsch et al., 1992; Müller et al., 1995; Udalova et al., 2000; Zabel et al.,

1991). Additionally, crystal structures of NFkB dimers in active DNA-bound state have revealed how each subunit of NFkB contacts each half-site of the full kB DNA sequence (Chen et al.,

1998a, 2000; Huxford et al., 1998; Jacobs and Harrison, 1998). These studies highlight the plasticity of NFkB dimers in recognizing kB site sequences with a single half-site in kB DNA sequences. Each NFkB dimer has distinct binding specificities to kB site sequences that influence the selective regulation of NFkB target genes (Siggers et al., 2012). Siggers et al. have performed a comprehensive unbiased characterization of DNA binding by eight different NFkB dimers using protein-binding microarrays (PBMs)-determined z scores. Their work further separates NFkB dimers into three distinct DNA-binding classes: p50 and p52 homodimer preferentially bind to 11 or 12 bp sites; NFkB heterodimers preferentially bind to 10 bp sites; and cRel or RelA homodimers preferentially bind to 9 bp sites.

Structural studies of NFkB have shown that native NFkB from nuclear extracts occurs in a protein complex of more than 200 kDa, significantly higher than the total combined mass of the reconstituted purified p50 and RelA proteins (115 kDa) (Urban et al., 1991). This suggests that gene regulation by NFkB is more complex as it may act together with a variety of other chromatin-associated factors (Kim et al., 2012; Mukherjee et al., 2013; Ohmori and Hamilton,

1993; Wan and Lenardo, 2009; Zhong et al., 1998). A prominent study indicated the potential for

the DNA-binding domain (DBD) within the N-terminal RHR of NFkB RelA to recruit either histone deacetylase 1 (HDAC1) or histone acetyl transferase CBP/p300 co-activator depending on the phosphorylation status of S276 in RelA (Zhong et al., 2002). Further, recent studies indicated that recruitment to endogenous NFkB targets may be mediated by CBP interacting with the NFkB RelA C-terminal TAD (Lecoq et al., 2017; Mukherjee et al., 2013), suggesting the involvement of cofactors in NFkB-dependent transcription of target genes. Other NFkB cofactors that have been reported to interact with NFkB dimers at gene regulatory regions to mediate gene expression are E2F1 (Lim et al., 2007), FOXM1 (Zhao et al., 2014), and RPS3

(Mulero et al., 2018; Wan et al., 2007).

NFkB TRANSACTIVATION

Transcriptional activation by NFkB occurs in the C-terminal TAD found in NFkB Rel- containing subunits: RelA, RelB, and cRel (Figure 1.1A). As mentioned above, NFkB p50 and p52 members can only act as transcriptional activators when forming heterodimers with Rel- containing members due to their lack in C-terminal TAD. NFkB transactivation can influence the chromatin state through recruitment of chromatin modifying co-activators and repressors (i.e

CBP/p300 and HDAC1, respectively). One of the mechanisms that could regulate this selective interaction with NFkB co-factors is the specific post-translational modification (PTM), such as phosphorylation, of NFkB proteins (Zhong et al., 2002). A substantial literature has addressed the identification and potential role of post-translational modifications of NFkB subunits, reviewed in Huang et al., 2010; Perkins, 2006. A prominent study demonstrated the importance

of RelA S276 phosphorylation in mediating CBP/p300 co-activator for NFkB-dependent transcriptional activity as introduction of S276A or S276E mutations showed complex mouse phenotypes and transcriptional defects (Dong et al., 2008). Further, S276 phosphorylation of

RelA can regulate RelA acetylation that involves CBP/p300. Phosphorylation of RelA at S276 was shown to promote its acetylation at K310 which enhances transcriptional activity (Chen et al., 2005). Further, acetylation of RelA at K314 was reported to be important for late NFkB- dependent gene expression (Rothgiesser et al., 2010). Such post-translational modifications of

NFkB RelA were reported to alter interactions with transcriptional cofactors and regulate NFkB transcriptional activity either by enhancing or repressing NFkB activity, some examples in the regulation of NFkB activity.

FOCUS OF STUDY

Whereas many studies of inflammatory signaling have led to a good understanding of how nuclear NFkB activity is regulated, many questions about how nuclear NFkB selects its target genes and produces gene-specific expression remain to be investigated. Prior structure- function studies of the NFkB RelA subunit identified the domain structure and key amino acid residues in biochemical and transient transfection assays (Figure 1.5). However, numerous reports show that in the native chromatin context gene regulation by NFkB is more complex, as it may collaborate with a variety of other chromatin-associated factors.

The NFkB RelA (or p65) subunit has been extensively studied because it is known for the strong transcription activating potential of NFkB (Schmitz and Baeuerle, 1991). Indeed,

NFkB RelA is considered the central regulator of inflammation, capable of fast, protein- synthesis-independent activation of gene expression within minutes (Hayden and Ghosh, 2008).

The signaling mechanisms involved in regulation of NFkB RelA activity have been elucidated in detail, allowing for the remarkably precise recapitulation of experimental data with mathematical models of the network (Mitchell et al., 2016; Basak et al., 2012). As such, we have focused on

RelA to dissect the regulatory strategies of inflammatory response genes that are targets of NFkB

RelA.

Aside from containing a conserved amino-terminal RHR, previous reports have identified

RelA to have at least two TADs (Moore et al., 1993; Schmitz and Baeuerle, 1991).

Transactivation sub-domain 1 (TA1) consist of the most C-terminal 30 amino acids, while

Transactivation sub-domain 2 (TA2) is directly adjacent to TA1. It was initially thought that

TA1 is the principal transactivation domain for RelA transcriptional activity because structural features of the TA1 domain are highly conserved across species, and because TA1 was shown to be sufficient for inducing transcriptional activity (Schmitz and Baeuerle, 1991). However, when deleting TA1, Schmitz and Baeuerle showed that the unique C-terminal third of RelA contained at least one other distinct activation domain that is directly adjacent to TA1 (Schmitz and

Baeuerle, 1991).

In Chapter 2 of this dissertation, I addressed the questions of how NFkB selects target gene binding and by which mechanisms it activates transcription of endogenous target genes. I have established a genetic complementation system for RelA variants to probe their regulatory strategies in the inflammatory response. In this model system, I examined the NFkB transcriptional response to an inflammatory stimulus called tumor necrosis factor (TNF).

Figure 1.5 RelA modular domain structures. NFkB RelA functional domain structures were identified from biochemical and exogenous reporter gene assays. RelA N-terminus was identified to contain a DNA-binding domain that binds directly to target gene DNA at kB sites. RelA C-terminal region was identified to possess transactivation function from its two transactivation sub-domains, TA2 and TA1. The structural domain features were identified through in vitro and transient gene reporter studies (Chen et al., 1998; Mukherjee et al., 2013; Schmitz and Baeuerle, 1991) that led to the identification of RelA protein functional domains shown in the schematics for human (hRelA) and mouse (mRelA) RelA protein in amino acid sequences.

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CHAPTER 2:

Dissecting the Regulatory Strategies of NFkB RelA

Target Genes in the Inflammatory Response

ABSTRACT

NFkB RelA is the potent transcriptional activator of inflammatory response genes. We stringently defined a list of direct RelA target genes by integrating physical (ChIPseq) and functional (RNAseq in knockouts) datasets. We then dissected each gene’s regulatory strategy by testing RelA variants in a novel primary-cell genetic complementation assay. All endogenous target genes required that RelA makes DNA-base-specific contacts, and none could be activated by the DNA binding domain alone. However, endogenous target genes differed widely in how they employ the two transactivation domains. Through model-aided analysis of the dynamic timecourse data we reveal gene-specific synergy and redundancy of TA1 and TA2. Given that post-translational modifications control TA1 activity and intrinsic affinity for coactivators determines TA2 activity, the differential TA logics suggests context-dependent vs. context- independent control of endogenous RelA-target genes. While some inflammatory initiators appear to require co-stimulatory TA1 activation, inflammatory resolvers a part of the NFkB

RelA core response where TA2 activity mediates activation even when TA1 is inactive.

INTRODUCTION

An impactful concept of molecular biology is the modular domain organization of transcription factors (Keegan et al., 1986), typically distinguishing between a DNA-binding domain (DBD) and a separable transcriptional activation domain (TAD) that could be fused to a heterologous DBD. Prominent mammalian TFs, including NFkB RelA (Schmitz and Baeuerle,

1991; Schmitz et al., 1994), conform to the modular domain model. Such studies utilized exogenous reporter genes that provided a convenient assay for TF activity. However, they lacked the physiological context of endogenous regulatory regions, which may involve complex protein- protein interactions, and are often considerable distances from the transcription start site. Indeed, subsequent studies provided numerous examples where the functional distinction between DNA binding and transcriptional activation did not neatly segregate into distinct structural domains, with the DBD providing transcriptional activity (Corton et al., 1998) or, conversely, not being required for TF recruitment to the target gene (Kovesdi et al., 1986).

Given that the regulatory context of endogenous target genes determines a TF’s mode of function, next generation TF structure-function studies may be considered probes of the regulatory diversity of its endogenous target genes. However, only with the advent of quantitative transcriptomic and epigenomic measurement capabilities enabled by Next

Generation Sequencing has it become feasible to undertake such studies. The present study is leveraging such technological development to dissect the regulatory strategies of inflammatory response genes that are regulatory targets of NFkB RelA.

The NFkB family member RelA is a ubiquitously expressed potent transcriptional activator that is induced by exposure to pathogens and inflammatory cytokines to activate the

expression of a large number inflammatory and immune-response genes (Hayden and Ghosh,

2008; Hoffmann and Baltimore, 2006). The signaling mechanisms involved in regulating NFkB

RelA activity have been elucidated in detail (Basak et al., 2012; Mitchell et al., 2016), but there is much more uncertainty about how it controls endogenous target genes. Indeed, while many genes have been identified to be potentially NFkB regulated (http://www.bu.edu/nf-kb/gene- resources/target-genes/), there is no database that lists the NFkB target genes in a particular physiological condition, defined cell type and stimulus.

RelA’s domain organization is characterized by the Rel Homology Region (RHR), which mediates dimerization and DNA binding functions (Baeuerle and Baltimore, 1989) and was structurally characterized by X-ray crystallography (Chen et al., 1998; Chen et al., 2000).

However, it is possible that for some endogenous target genes, promoter recruitment of RelA is mediated primarily by protein-protein interactions, for example via pre-bound CBP (Mukherjee et al., 2013).

RelA’s C-terminus contains two transactivation domains, TA1 and TA2 (Moore et al.,

1993; Schmitz and Baeuerle, 1991). TA1 consists of the last 30 amino acids in sequence in the very C-terminus, while TA2 is directly adjacent to TA1. The initial thought of TA1 being the principal transactivation domain for RelA transcriptional activity is due to the fact that structural features of TA1 domain are highly conserved across species and that it has been shown to be sufficient and potent in inducing transcriptional activity (Schmitz and Baeuerle, 1991); however, when deleting these residues, the authors showed that the unique C-terminal third of RelA contained at least another distinct transcriptional activation domain that is directly adjacent to

TA1. TA1 consists of an amphipathic helix characteristic of an acidic activation domain. TA2

interacts with CBP/p300, and its structure with the CBP-derived TAZ1 peptide was characterized by nuclear magnetic resonance (Mukherjee et al., 2013; Nyqvist et al., 2019).

However, CBP/p300 was also reported to interact with the RHR via phosphorylated S276

(Zhong et al., 2002), and knock-in mutations of the S276A or S276E mutation showed complex mouse phenotypes and transcriptional defects (Dong et al., 2008). To add to this complexity, it also remains possible that trans-activation of some target genes is mediated by other sequences within RelA, for example between RHR and TA2/TA1.

Interestingly, a key difference between TA1 and TA2 is the role of post-translational modification. Unlike TA2, TA1’s conformation is thought to be regulated (Savaryn et al., 2016) by phosphorylation of any of 7 potential sites (S529, S535, S536, S543, S547, S550, and S551 in human RelA) altering cofactor interactions (Milanovic et al., 2014), and hence transcriptional activation potential (Hochrainer et al., 2013). In particular, S536 is a prominent phospho- acceptor site (Christian et al., 2016), that is a reliable biomarker for RelA activity. This suggests a model in which TA1’s transactivation activity is dependent on a phosphorylation event that integrates the activity of other signaling pathways, such as CKII, CaMKIV, RSK1, TAK1/TAB1,

PI3K/Akt, ATM, and IKKe (Bae et al., 2003; Bao et al., 2010; Bird et al., 1997; Bohuslav et al.,

2004; Buss et al., 2004; Haller et al., 2002; Moreno et al., 2010; Sabatel et al., 2012; Sakurai et al., 2003; Wang et al., 2000; Yoboua et al., 2010), whereas TA2’s transactivation potential is intrinsic to its biochemical structure. Remarkably, however, it remains unknown whether NFkB response genes show differential regulation by TA1 vs. TA2; this is an important question as

TA2-regulated genes would be expected to provide for a core NFkB-mediated inflammatory response, whereas TA1-regulated genes are dependent on other signaling pathways as well.

Here, we report the stringent identification of endogenous NFkB target genes induced by

TNF in primary fibroblasts, and a genetic complementation system for RelA variants to probe their regulatory strategies. We found that all RelA target gene binding was dependent on base- specific contacts within the kB sequence, and all transactivation was dependent on the two C- terminal activation domains; neither protein-protein interactions proved sufficient for target gene selection, nor was the RHR sufficient to drive transcriptional activation. However, we discovered remarkable gene-specific regulatory strategies involving TA1 and TA2. Though most genes were regulated by both TA1 and TA2, the two transactivation functioned either redundantly or synergistically, forming logical OR or AND gates in a gene-specific manner. While OR gate genes were robustly activated by the constitutive TA2 activity, on AND gate genes TA2’s role is to boost the PTM-regulated TA1 activity. Thus, our study distinguishes NFkB RelA target genes as being largely signaling context independent vs. largely context-dependent.

RESULTS

Stringent identification of TNF-responsive NFkB RelA target genes

To identify high-confidence NFkB RelA target genes, we applied two rigorous criteria, namely: (i) TNF-induced NFkB/RelA binding to the target gene’s regulatory regions, and (ii)

TNF-induced transcriptional activation that is genetically NFkB-dependent. To examine the genome-wide binding pattern of NFkB, we performed chromatin immunoprecipitation followed by sequencing (ChIP-seq) with RelA-specific antibody during a short time-course of TNF stimulation of 0, 0.5hr and 3hr in wild-type (WT) primary murine embryonic fibroblasts (MEFs).

Bioinformatic analysis of the replicate datasets (FDR<0.01) identified in 9,829 RelA peaks that were TNF-inducible at 0.5hr or 3hr (Figure 2.1A). Overall, these ChIP-enriched RelA binding events were notably enhanced at 0.5hr and largely remained at 3hr (Figure 2.1B, left panel). We identified the locations of these RelA peaks to be 44% intergenic, 21% within promoter regions, and 36% within intronic regions of genes (Figure 2.1B, right panel). Genome browser tracks of two known NFkB target genes, Nfkbia and Cxcl10 confirmed easily discernible peaks above a low background that are highly inducible in response to TNF and highly reproducible between the two datasets (Figure 2.1C).

To assess the NFkB-dependent transcriptional response to TNF, we performed gene expression analysis of nascent transcripts via chromatin-associated RNA-seq (caRNA-seq) in

WT and crel-/-rela-/- MEF cells (Figure 2.1D). We chose caRNA-seq analysis rather than polyA+

RNA-seq to ensure that we identified transcriptional induction rather than changes in mRNA levels mediated by post-transcriptional mechanisms, for example, changes in mRNA half-life.

Previous work had shown that the absence of RelA can be partially compensated by the NFkB

family member cRel (Hoffmann et al., 2003), necessitating the use of the combination knockout to assess RelA-dependent gene expression. Our 1-hour time-course analysis revealed 419 TNF- inducible genes in WT MEFs whose caRNA levels were more than two-fold induced in WT

MEFs at any time point after TNF stimulation relative to the unstimulated (0hr) time point

(Figure 2.1D). Setting the maximum RPKM for each gene to 1 and the basal RPKM in unstimulated WT cells to be 0, we plotted data from crel-/-rela-/- MEF cells next to WT data. We found that 259 genes had peak expression values that were less than half in the mutant relative to

WT, and 160 genes showed expression that was more than half, leading us to label the former as largely “NFkB-dependent” and the latter largely “NFkB-independent”. Motif enrichment analysis of the regulatory DNA regions associated with transcription start sites using HOMER

(Heinz et al., 2010) revealed a preponderance of kB sites in the NFkB-dependent genes but no highly significant motif for NFkB-independent genes (Figure 2.1E). Focusing on those that are protein-coding (229 and 120, respectively), we undertook gene ontology (GO) analysis with

Enrichr Ontologies (Kuleshov et al., 2016) for over-represented functional pathways in each category. We found that the top five most highly enriched GO terms (by p-value) for the NFkB- dependent genes described the inflammatory response and NFkB activation (Figure 2.1E), whereas the NFkB-independent genes did not yield highly significant terms. Line graphs of two

NFkB-dependent genes, Nfkbia and Cxcl10 (Figure 2.1F) exemplify the high degree of NFkB- dependence in their TNF-induced expression.

To develop the list of stringent NFkB RelA-regulated target genes, we intersected the above-described physical/binding data (within 10kb of each gene’s transcription start site) and the genetic/functional data and further ensured that the nascent transcripts are indeed processed

into mature mRNA by performing polyA+ RNA-seq from total RNA isolated from WT MEF cells in response to TNF, extending our time course analysis to 3 hours (0.5, 1, and 3 hr time points). Selecting for peak fold-change in response to TNF of ≥ 2, an FDR of <0.01, and ≥50%

NFkB dependence of the maximum value in the time course, we generated a list of 113 stringently selected NFkB/RelA-regulated target genes (Figure 2.1G). The replicate datasets showed a high level of concordance. The corresponding caRNA-seq data (Figure 2.1H) indicates that aside from the very early gene group, the timing of peak mRNA abundance may not be driven by transcriptional mechanisms, but rather post-transcriptional, such as mRNA processing

(Pandya-Jones et al., 2013) or mRNA half-life (Hao and Baltimore, 2009). Mapping the RelA

ChIP-seq data associated with each gene (Figure 2.1I) identified probable RelA binding events responsible for gene induction, but did not reveal a correlation between RelA binding location and transcriptional induction dynamics.

Figure 2.1 Identifying NFkB RelA target genes in the TNF response of murine embryonic fibroblasts

WT Input-R1 Input-R2 0-R1 0-R2 0.5h-R1 0.5h-R2 3h-R1 3h-R2 A G rep #1 rep #2 H WT crel-/-rela-/- I 40 -10kb -5kb TSS 5kb 10kb 0 .5 1 3 0 .5 1 3 hr 0 15 45 60 0 15 45 60 min −10kb −5kb TSS +5kb +10kb 20 Tnf Tnf 1 Tnf 1 0 Nfkbiz Nfkbiz Tnf Tnf -0.5 5’3’ .5kb Nfkbiz Cxcl1 Cxcl1 Cxcl1 Nfkbiz Nfkbiz Cxcl2 Cxcl2 Cxcl2 Cxcl1 Cxcl1 Cxcl2 Cxcl2 Rasgef1b Rasgef1b Rasgef1b Rasgef1b Rasgef1b 70 Gem Gem Gem Gem Gem Pim1 Pim1 Pim1 Pim1 60 Pim1 0.8 0.8 Slc25a25 Slc25a25 Slc25a25 Slc25a25 Slc25a25 Gadd45b 50 Gadd45b Gadd45b Gadd45b Gadd45b Itpkc Itpkc Itpkc Itpkc Itpkc 40 Tnfaip3 Tnfaip3 Tnfaip3 Tnfaip3 Tnfaip3 Lif Lif Lif Lif Lif peaks 30 Nfkbid Nfkbid 0.6 Nfkbid Nfkbid Nfkbid Rgs16 Rgs16 Rgs16 0.6 Rgs16 Rgs16 20 Map3k8 Map3k8 Map3k8 Map3k8 Map3k8 RelA Nfkbia Nfkbia Nfkbia Nfkbia Nfkbia 10 Irf1 Irf1 Irf1 Irf1 Irf1 Zc3h12a Zc3h12a Zc3h12a Zc3h12a Zc3h12a 0 Rnd1 Rnd1 Rnd1 Rnd1 Rnd1 Cd83 Cd83 0.4 Cd83 Cd83 Cd83 Noct Noct Noct 0.4 Noct Noct Rel Rel Rel Rel Rel Tnfaip6 Tnfaip6 Tnfaip6 Tnfaip6 Tnfaip6 Rnf19b Rnf19b Rnf19b Rnf19b Rnf19b Nfatc1 Nfatc1 Nfatc1 Nfatc1 Nfatc1 Zswim4 Zswim4 Zswim4 Zswim4 Zswim4 Rsl1 Rsl1 -0.5 5’ 3’ .5kb Promoter 0.2 Rsl1 Rsl1 Intronic Rsl1 Tmem200b Tmem200b Tmem200b 0.2 Intergenic Tmem200b Tmem200b Bcl3 Bcl3 B Bcl3 Bcl3 Bcl3 Rab20 Rab20 50 0-R1 35.6 % Rab20 Rab20 0-R2 20.6 % Rab20 Ccl20 Ccl20 Ccl20 Ccl20 Ccl20 Birc3 .5H-R1 Intronic Promoter Birc3 40 .5H-R2 Birc3 Birc3 Birc3 Cxcl5 Cxcl5 Cxcl5 3H-R1 35.6 % 20.6 % Cxcl5 0 Cxcl5 Foxs1 Foxs1 30 3H-R2 Foxs1 Foxs1 Foxs1 Klhl25 Klhl25 Klhl25 Klhl25 Klhl25 0 Rrad Rrad 20 Rrad Rrad Rrad Ell2 Ell2 Intergenic Ell2 Ell2 Ell2 Slfn2 Slfn2 10 43.7 % Slfn2 Slfn2 Slfn2 Ccl2 Ccl2 Ccl2 Ccl2 Ccl2 Ccl7

Normalized coverage Normalized Ccl7 0 Ccl7 Ccl7 Ccl7 Zfp429 Zfp429 Zfp429 -0.5 5’ 3’ .5kb Zfp429 Zfp429 C9orf72 C9orf72 Genomic Region (5’ à 3’) 43.7 % C9orf72 C9orf72 C9orf72 Mt2 Mt2 Mt2 Mt2 Mt2 Stx11 Stx11 Stx11 Stx11 Stx11 Sqstm1 Sqstm1 C Sqstm1 Sqstm1 Sqstm1 Nfkbib Nfkbib Nfkbib Nfkbib Nfkbib Ninj1 Ninj1 Input - R1 Nfkbia Cxcl10 Ninj1 Ninj1 Ninj1 Uap1 Uap1 Input - R2 Uap1 Uap1 Uap1 Ptx3 Ptx3 0 - R1 Ptx3 Ptx3 Ptx3 Csf1 Csf1 0 - R2 Csf1 Csf1 Csf1 Vcam1 Vcam1 Vcam1 0.5H - R1 Vcam1 Vcam1 Tnfaip2 Tnfaip2 Tnfaip2 Tnfaip2 0.5H - R2 Tnfaip2 Il1rl1 Il1rl1 Il1rl1 Il1rl1 Il1rl1 Cxcl10 Cxcl10 3H - R1 Cxcl10 Cxcl10 Cxcl10 Relb Relb 3H - R2 Relb Relb Relb Birc2 Birc2 Birc2 Birc2 Birc2 Trim47 Trim47 Trim47 Trim47 Trim47 Cd44 Cd44 Cd44 Cd44 Cd44 Litaf -/- -/- Litaf Litaf WT crel rela Litaf Litaf Tlr2 Tlr2 Tlr2 Tlr2 D Tlr2 Nfkbie Nfkbie 0 15 45 60 0 15 45 60 min Nfkbie Nfkbie dependent dependent Nfkbie dependent 1 dependent dependent dependent Clic4 dependent Clic4 dependent dependent dependent dependent dependent dependent Clic4 dependent Clic4 dependent dependent dependent dependent Clic4 dependent dependent 0.8 dependent dependent Mfhas1 dependent Mfhas1 dependent dependent dependent dependent dependent dependent dependent Mfhas1 Mfhas1 dependent dependent dependent 1 Mfhas1 dependent dependent dependent dependent 0.6 dependent Icam1 dependent dependent dependent dependent Icam1 dependent dependent dependent dependent Icam1 dependent Icam1 dependent dependent Icam1 dependent dependent dependent dependent dependent 0.4 dependent Rab8b dependent dependent Rab8b dependent dependent dependent dependent dependent Rab8b dependent Rab8b dependent dependent dependent dependent dependent Rab8b dependent 0.8 dependent dependent 0.2 Sod2 dependent dependent Sod2 dependent dependent dependent dependent dependent Sod2 dependent Sod2 dependent dependent dependent dependent Sod2 dependent dependent dependent dependent Abtb2 dependent 0 dependent dependent dependent Abtb2 dependent dependent dependent Abtb2 dependent Abtb2 dependent dependent dependent dependent Abtb2 dependent dependent dependent dependent Ripk2 dependent dependent dependent dependent 0.6 Ripk2 dependent dependent dependent Ripk2 dependent Ripk2 dependent dependent dependent dependent dependent dependent Ripk2 dependent dependent Pdgfb dependent dependent dependent Pdgfb dependent dependent dependent dependent Pdgfb dependent Pdgfb dependent dependent dependent dependent dependent Pdgfb dependent dependent

dependent dependent dependent Rhbdf2 dependent Rhbdf2 dependent dependent - dependent dependent dependent Rhbdf2 dependent Rhbdf2 dependent dependent 0.4 dependent dependent dependent Rhbdf2 dependent dependent B dependent dependent Nfkb2 dependent Nfkb2 dependent dependent dependent dependent dependent Nfkb2 dependent Nfkb2

! dependent dependent dependent dependent dependent dependent dependent Nfkb2 dependent dependent Slc2a6 dependent Slc2a6 dependent dependent dependent dependent dependent Slc2a6 dependent Slc2a6 dependent dependent dependent dependent dependent

NF dependent dependent 0.2 Slc2a6 dependent dependent Bahcc1 Bahcc1 dependent dependent dependent dependent dependent dependent Bahcc1 dependent Bahcc1 dependent dependent dependent dependent dependent dependent Bahcc1 dependent dependent dependent Angptl4 dependent Angptl4 dependent dependent dependent dependent dependent dependent Angptl4 Angptl4 dependent dependent dependent dependent (RU) Expression Inducible dependent dependent Angptl4 dependent dependent dependent 0 dependent Hivep1 dependent Hivep1 dependent dependent dependent dependent dependent Hivep1 Hivep1 dependent dependent dependent dependent dependent dependent Hivep1 dependent dependent dependent dependent Gfpt2 Gfpt2 dependent dependent dependent dependent dependent dependent Gfpt2 Gfpt2 dependent dependent dependent dependent Gfpt2 dependent dependent dependent dependent dependent dependent Cxcl16 Cxcl16 dependent dependent dependent dependent dependent dependent Cxcl16 dependent Cxcl16 dependent dependent dependent Cxcl16

independent independent independent independent Cilp2 independent independent Cilp2 independent independent independent independent Cilp2 Cilp2 independent independent independent independent independent independent Cilp2 independent independent independent independent independent Traf3 independent Traf3 independent independent independent independent Traf3 Traf3 independent independent independent independent independent independent Traf3 independent independent independent independent independent Tnip1 Tnip1 independent independent independent independent independent Tnip1 Tnip1 independent independent independent independent independent independent Tnip1 independent independent independent independent independent Eno2 Eno2 independent independent independent independent independent Eno2 independent Eno2 independent independent independent independent Eno2 independent independent independent independent independent Il15ra independent Il15ra independent independent independent independent independent Il15ra independent Il15ra independent independent independent independent Il15ra independent independent independent independent independent independent independent Serpina3g independent Serpina3g independent independent independent Serpina3g independent Serpina3g independent independent independent Serpina3g independent independent independent independent independent independent independent independent Micall2 Micall2 independent independent independent independent independent Micall2 independent Micall2 independent independent

- Micall2 independent independent independent independent independent independent independent independent Bid independent Bid independent independent

B independent independent Bid independent Bid independent independent independent independent independent Bid independent independent ! independent independent independent Gch1 independent Gch1 independent independent independent independent Gch1 independent Gch1 independent independent independent independent Gch1 independent independent independent independent independent independent independent NF Cx3cl1 independent independent Cx3cl1 independent independent independent Cx3cl1 Cx3cl1 independent independent independent independent Cx3cl1 independent wt.0 wt.15 wt.45 wt.60 acko.0 acko.15 acko.45 acko.60 Rcl1 Rcl1 Rcl1 Rcl1 Rcl1 Fas Fas -ln(p-value) Fas Fas Fas Nfkb1 Nfkb1 Nfkb1 Nfkb1 Nfkb1 Jak2 Jak2 E NF!B 62 Jak2 Jak2 Jak2 Tjp2 Tjp2 Tjp2 Tjp2 Tjp2 Phldb1 Phldb1 Phldb1 Phldb1 Phldb1 GO:0002544 chronic inflammatory response 33 H2-K1 H2−K1 H2−K1 H2−K1 H2-K1 Camkk2 Camkk2 GO:0090594 inflammatory response to wounding 33 Camkk2 Camkk2 Camkk2 H2-Q4 H2−Q4 H2−Q4 H2−Q4 H2-Q4 GO:0006954 inflammatory response 33 Atp2b4 Atp2b4 Atp2b4 Atp2b4 Atp2b4 Cdk6 Cdk6 Cdk6 Cdk6 Cdk6 GO:0002526 acute inflammatory response 33 Ccl5 Ccl5 Ccl5 Ccl5 Ccl5 GO:0007252 I-kappaB phosphorylation 31 Parp8 Parp8 Parp8 Parp8 Parp8 Slc7a2 Slc7a2 Slc7a2 Slc7a2 Slc7a2 Gbp2 Gbp2 Gbp2 Gbp2 Gbp2 Stk10 Stk10 Stk10 Stk10 Stk10 F 60 15 Pdlim4 Pdlim4 WT Nfkbia Pdlim4 Pdlim4 Pdlim4 Cxcl10 Col27a1 Col27a1 Col27a1 Col27a1 Col27a1 crel-/-rela-/- Gsdmd Gsdmd Gsdmd Gsdmd Gsdmd 40 10 Psmb10 Psmb10 Psmb10 Psmb10 Psmb10 Stat5a Stat5a Stat5a Stat5a Stat5a Tapbp Tapbp Tapbp Tapbp Tapbp 20 5 Gsap Gsap Gsap Gsap Gsap Alpk1 Alpk1 Alpk1 Alpk1 Alpk1 mRNA (RPKM) mRNA Mcc Mcc Mcc Mcc Mcc Radil 0 0 Radil Radil Radil Radil 0 15 45 60 0 15 45 60 min WT.1.0.bam WT.1.0.5.bam WT.1.1.bam WT.1.3.bam WT.2.0.bam WT.2.0.5.bam WT.2.1.bam WT.2.3.bam wt.0.filtered.bam wt.15.filtered.bam wt.45.filtered.bam wt.60.filtered.bam acko.0.filtered.bam acko.15.filtered.bam acko.45.filtered.bam acko.60.filtered.bam

Figure 2.1 Identifying NFkB RelA target genes in the TNF response of murine embryonic fibroblasts. (A) Genome-wide binding of NFkB RelA in wild-type (WT) primary murine embryonic fibroblasts (MEF) stimulated with TNF (10ng/mL) at 0.5hr and 3hr by chromatin immunoprecipitation followed by sequencing (ChIP-seq) with RelA specific antibody. 9,829 RelA binding events were identified in two independent replicate experiments. Heatmap shows read density along ± 0.5kb of DNA centered around the peak read density for each binding event and ordered by high to low read density. (B) (Left panel) The normalized RelA ChIP-seq density over identified peaks within the genome. (Right panel) Pie chart shows where RelA peaks are found to be located. (C) Genome browser tracks of two replicates (R1, R2) of RelA binding events in WT MEFs for two known NFkB target genes, Nfkbia and Cxcl10. (D) Heatmap of the relative induced expression of 419 nascent transcripts measured by via chromatin-associated RNA sequencing (caRNA-seq), whose maximal induced expression was ≥ 1 RPKM and ≥ 2 fold over basal. Of these 419 genes, 229 genes were protein-coding and NFkB-dependent, as their maximal expression was less than 50% in crel-/-rela-/- compared to WT MEF. The heatmap of gene expression is ordered by high to low NFkB dependence. (E) De novo motif and gene ontology analyses for NFkB-dependent genes identified the most highly enriched motif (within -1.0 to 0.3kb of TSS) and GO terms (using Enrichr Ontologies tool (Kuleshov et al., 2016). For NFkB- independent genes top motifs and GO terms showed substantially less significance. (F) Line plots of chromatin-associated RNA (caRNA) abundance (RPKM) for known NFkB target genes Nfkbia and Cxcl10 genes. (G) Heatmap of mature mRNA (polyA+RNA) relative induced expression (as in D) for 113 NFkB target genes, which were defined by the presence of RelA ChIP-seq peak and TNF-inducible NFkB-dependent caRNA expression. Two biological replicates of WT MEFs are shown, ordered by their peak time of expression. (H) Heatmap of relative induced expression of nascent transcripts determined by caRNA-seq for these 113 NFkB-dependent genes shown in the same order as in G. (I) A map of 297 RelA-binding peaks identified by ChIP-seq for each of the 113 NFkB target genes within ±10kb of TSS, shown in the same order as in G and H.

An experimental system to dissect regulatory strategies of NFkB RelA target genes

To characterize the regulatory strategies of NFkB RelA target genes by probing them for their requirements of RelA functional domains, we developed an experimental workflow for retroviral complementation of RelA-deficient MEF cells with specifically engineered variants of

RelA, and subsequent transcriptomic analysis in response to TNF. In order to prolong the lifespan of primary MEFs (Todaro and Green, 1963), which would otherwise not be amenable for such genetic manipulation, we investigated whether we could use MEFs deficient in p53.

Transcripts induced by TNF, IL-1b, or LPS, revealed similar gene expression patterns between two biological replicates of p53-deficient MEFs and their littermate WT control (Figure 2.2A).

Pairwise comparisons revealed high reproducibility in inducible gene expression between p53-/- and WT MEFs at each time point (Figure 2.2B-E ), justifying the use of p53-deficient MEFs for studies of inflammatory response genes.

To develop the primary MEF RelA complementation system, we bred knockout mouse strains to produce a strain that is not only deficient in NFkB subunits but also p53, which allowed production of primary p53-/-crel-/-rela-/- MEF cells from E12.5 embryos (prior to embryonic lethality of this genotype at E13). Following expansion of these cells for about a week, we retrovirally transduced them with RelA-WT (Figure 2.3A), selected transduced cells with puromycin, and examined complementation by biochemical assays. Immunoblotting revealed RelA expression in reconstituted cells at about the same level as WT control MEFs

(Figure 2.4A). Electrophoretic mobility shift assay (EMSA) with a kB site probe showed strong

DNA binding activity following TNF stimulation (Figure 2.4B).

Figure 2.2 p53 deficiency does not affect TNF-induced gene expression

Figure 2.2 p53 deficiency does not affect TNF-induced gene expression. (A) Heatmap visualization of upregulated genes in response to IL-1b (10ng/mL), TNF (10ng/mL), or LPS (100ng/mL) stimulations at indicated times in WT or p53-/- MEFs. One biological replicate of WT and two biological replicates of p53-/- MEFs are shown. Genes with ³100 normalized counts in at least one condition and an induction ³4 were sorted into five groups via K-means clustering. (B) Scatterplots between significantly up-regulated genes in all genotypes at all time points of IL-1β stimulation are plotted, with number of genes and R2 value indicated. Rows are 0.5, 1, 3 and 8hr respectively. Columns are p53-/--2 vs. p53-/--1, p53-/--1 vs. WT, and p53-/--2 vs. WT, respectively. We restricted our analysis to genes that were up-regulated by at least 2-fold in one of the two conditions compared. (C) Scatterplots between significantly up-regulated genes in all genotypes at all time points of LPS stimulations are plotted, with number of genes and R2 value indicated. (D) Scatterplots between significantly up-regulated genes in all genotypes at all time points of TNF stimulations are plotted, with number of genes and R2 value indicated. (E) Summary of correlations between different stimuli conditions where bar plots of the R2 values from panels B-D are shown.

To further test the genetic complementation system, we examined NFkB RelA binding via RelA ChIP-seq using RelA-WT reconstituted MEFs with and without 0.5hr of TNF stimulation, on previously identified endogenous NFkB target genes (Figure 2.1G), such as

Nfkbia and Cxcl10 (Figure 2.3B). Importantly, we found that RelA binding peaks identified in

WT MEF control were indeed also present in the reconstituted RelA-WT cells (Figure 2.3C).

Next, we examined gene expression responses to TNF in reconstituted MEFs stimulated with TNF using polyA+ RNA-seq. We found high reproducibility in replicate reconstituted

MEFs on Nfkbia and Cxcl10 (Figure 2.3D). Extending our analysis to the 113 NFkB target genes, a scatter plot analysis demonstrated good correlations in polyA+RNA expression patterns between WT MEF control and the reconstituted RelA-WT MEFs at each time point analysis

(Figure 2.4C). Specifically, we found that 104 genes satisfied the same criteria applied to WT and crel-/-rela-/- MEFs, namely TNF fold change over time of ≥2, an FDR of <0.01 and ≥50%

NFkB dependence of the maximum expression value in the time-course (Figure 2.3E), thereby validating the primary MEF complementation system. In sum, our analysis revealed 104 NFkB target genes (Supplementary Table 2.1) that showed robust RelA DNA binding and RelA- dependent TNF-inducible expression at both nascent and mature mRNA transcript level.

Figure 2.3 A RelA genetic complementation system for studying the control of endogenous NFkB RelA target genes

p53-/-crel-/-rela-/- A Rel Homology Transactivation E F Region (RHR) Domain (TAD) RelA-WT EV RelA-WT EV -10kb−10kb -5kb−5kb TSSTSS 5kb+5kb 10kb+10kb 0 .5 1 3 0 .5 1 3 0 .5 1 3 0 .5 1 3 hr 1 RelA-WT NTD DD TA2 TA1 Nfkbiz Nfkbiz Nfkbiz Nfkbiz NLS Cxcl1 Cxcl1 Cxcl1 Cxcl1 1 19 306 429 520 549 Cxcl2 Cxcl2 Cxcl2 Cxcl2 Tnf Tnf Tnf Tnf DNA binding dimerization transactivation Slc25a25 Slc25a25 Slc25a25 Tnfaip3 Slc25a25 0.8 Tnfaip3 Tnfaip3 Tnfaip3 Lif Lif Lif Lif Gadd45b TNF Gadd45b Gadd45b Gadd45b RelA ChIP-seq Map3k8 Map3k8 Map3k8 Map3k8 Gem Gem Gem Gem RNA-seq Nfkbia Nfkbia -/- -/- -/- Nfkbia Nfkbia 0.6 p53 crel rela Itpkc Itpkc Itpkc Itpkc MEF Ccl20 Ccl20 Ccl20 Ccl20 Pim1 Pim1 Pim1 Pim1 Rgs16 Rgs16 Rgs16 Rgs16 Rnd1 Rnd1 B Rnd1 Rnd1 0.4 Irf1 Input - R1 Nfkbia Cxcl10 Irf1 Irf1 Irf1 Cxcl5 Cxcl5 Input - R2 Cxcl5 Cxcl5 0 - R1 Nfkbid Nfkbid Nfkbid Nfkbid Zc3h12a WT 0 - R2 Zc3h12a Zc3h12a Zc3h12a 0.5h - R1 Tmem200b Tmem200b Tmem200b Tmem200b Rasgef1b 0.2 0.5h - R2 Rasgef1b Rasgef1b Rasgef1b Input - R1 Zfp429 Zfp429 Zfp429 Zfp429 Input - R2 Noct Noct WT Noct Noct - 0 - R1 Rsl1 Rsl1 Rsl1 0 - R2 Rsl1 Klhl25 Klhl25 Klhl25 Klhl25 0.5h - R1 Zswim4

RelA Zswim4 0.5h - R2 Zswim4 Zswim4 0 Csf1 Csf1 Csf1 Csf1 Rab20 Rab20 Rab20 Rab20 Nfatc1 Nfatc1 Nfatc1 Nfatc1 Bcl3 Bcl3 Bcl3 Ccl2 Bcl3 Ccl2 C Ccl2 Ccl7 Ccl2 Ccl7 Input-R1 Input-R2 0-R1 0-R2 0.5h-R1 0.5h-R2 Ccl7 Birc3 Ccl7 Birc3 Foxs1 Birc3 40 Birc3 Foxs1 Foxs1 C9orf72 Foxs1 30 C9orf72 20 C9orf72 Rnf19b C9orf72 Rnf19b 10 Rnf19b Tnfaip6 Rnf19b Tnfaip6 0 Tnfaip6 Rrad Tnfaip6 Rrad -0.5 5’ 3’ .5kb Rrad Tnfaip2 Rrad Tnfaip2 Tnfaip2 Trim47 Tnfaip2 Trim47 Trim47 Nfkbib Trim47 Nfkbib Nfkbib Stx11 Nfkbib Stx11 Stx11 Bahcc1 Stx11 Bahcc1 Bahcc1 Ptx3 Bahcc1 Ptx3 Ptx3 Vcam1 Ptx3 Vcam1 Vcam1 Vcam1 Rhbdf2 Rhbdf2 Rhbdf2 Uap1 Rhbdf2 Uap1 Uap1 Uap1 Clic4 kb_siteClic4 Clic4 Clic4 Slfn2 Slfn2 Slfn2 Slfn2 Tlr2 Tlr2 Tlr2 complete kb site Tlr2 Rab8b Rab8b 60 Rab8b Rab8b Ripk2 Ripk2 Ripk2 Mt2 Ripk2 half kb site 50 Mt2 Mt2 Mt2 Birc2 Birc2 Birc2 Nfkbie Birc2 40 Nfkbiepeak Nfkbie Relb Nfkbie Relb Relb Relb Cd83 Cd83 30 Cd83 Cd83 Micall2 Micall2 Micall2 Micall2 Icam1 Icam1 20 Icam1 Icam1 Sod2 Sod2 Sod2 Sod2 Ninj1 Ninj1 peaks Ninj1 Ninj1 10 Mfhas1 Mfhas1 Mfhas1 Mfhas1 Il1rl1 Il1rl1 Il1rl1 0 Il1rl1 Cd44 RelA Cd44 Cd44 Cd44 Nfkb2 Nfkb2 Nfkb2 Nfkb2 Cilp2 Cilp2 Cilp2 Cilp2 Gch1 Gch1 Gch1 Gch1 Pdgfb Pdgfb Pdgfb Pdgfb Cxcl10 Cxcl10 Cxcl10 Cxcl10 Hivep1 Hivep1 Hivep1 Hivep1 Nfkb1 Nfkb1 Nfkb1 Nfkb1 Fas Fas Fas Fas Cx3cl1 Cx3cl1 Cx3cl1 Cx3cl1 Tnip1 Tnip1 Tnip1 Tnip1 Angptl4 Angptl4 Angptl4 Angptl4 Bid Bid Bid Bid Pdlim4 Pdlim4 Pdlim4 Pdlim4 Alpk1 Alpk1 Alpk1 Alpk1 Traf3 Traf3 Traf3 Traf3 Gfpt2 Gfpt2 Gfpt2 Gfpt2 Slc7a2 Slc7a2 Slc7a2 Slc7a2 Camkk2 Camkk2 Camkk2 Camkk2 Cxcl16 Cxcl16 Cxcl16 Cxcl16 Slc2a6 Slc2a6 Slc2a6 Slc2a6 Tjp2 Tjp2 Tjp2 Tjp2 Gsap Gsap Gsap Gsap Gsdmd Gsdmd Gsdmd Gsdmd Jak2 Jak2 Jak2 Jak2 Serpina3g Serpina3g Serpina3g Serpina3g Phldb1 Phldb1 Phldb1 Phldb1 -0.5 5’ 3’ .5kb Il15ra Il15ra Il15ra Il15ra H2-K1 H2−K1 H2−K1 H2-K1 Ccl5 Ccl5 Ccl5 Ccl5 Stk10 Stk10 Stk10 Stk10 D H2-Q4 H2−Q4 H2−Q4 H2-Q4 800 Cdk6 Cdk6 Cdk6 Cdk6 Nfkbia WT.R1 Cxcl10 Parp8 Parp8 Parp8 Parp8 WT.R2 Tapbp Tapbp Tapbp Tapbp 600 RelA-WT.R1 Stat5a Stat5a Stat5a Stat5a RelA-WT.R2 Atp2b4 Atp2b4 Atp2b4 Atp2b4 400 Gbp2 Gbp2 Gbp2 Gbp2 Col27a1 Col27a1 Col27a1 Col27a1 Psmb10 Psmb10 Psmb10 Psmb10 200

mRNA (RPKM) mRNA Replicate 1 Replicate 2 Presence of !B elements 0 0 0.5 1 3 0 0.5 1 3 hr Inducible Expression (RU) ≥ 1 full-length !B element ≥ 1 half !B element 0 0.2 0.4 0.6 0.8 1

Figure 2.3 A RelA genetic complementation system for studying the control of endogenous NFkB RelA target genes. (A) Schematic of the domain structure of the mouse RelA protein (with relevant amino acid numbers) and the primary cells to be complemented, and the experimental assays to be performed. (B) Genome browser tracks of RelA binding events on the Nfkbia and Cxcl10 genes in WT MEFs and p53-/-crel-/-rela-/- MEFs reconstituted with RelA-WT, data from two independent experiments (R1, R2) are shown. (C) Heatmap of the read density along ±0.5kb of RelA ChIP-seq peaks following 0.5hr TNF treatment for 104 genes identified as NFkB-dependent genes in WT MEFs and p53-/-crel-/-rela-/- MEFs reconstituted with RelA-WT. (D) Line plots of polyA+RNA expression (RPKM) of Nfkbia and Cxcl10 genes. (E) Heatmap of relative induced expression in reconstituted RelA-WT and Empty Vector (EV) in p53-/-crel-/-rela-/- MEFs as determined by polyA+RNA. Two independent experiments are shown. (F) A map of the NFkB binding sites (kB DNA sequences) within each of the 223 RelA ChIP-seq peaks identified with an FDR <0.01 and associated with the 104 NFkB target genes. Full kB elements refer to 9, 10, and 11 bp sequences conforming to the consensus: 5’-GGR(N3-5)YCC-3’, where R = A or G; Y = C or T; N = any nucleotide (A, C, G, or T). Half kB elements refer to 5’-GGR-3’ and 5’-YCC-3’.

Figure 2.4 Retroviral transduction of RelA in p53-/-crel-/-rela-/- MEF provide a suitable complementation system for studying NFkB RelA target genes

Figure 2.4 Retroviral transduction of RelA in p53-/-crel-/-rela-/- MEF provide a suitable complementation system for studying NFkB RelA target genes. (A) Western blotting of RelA levels and a-Tubulin as loading control in unstimulated WT MEFs and p53-/-crel-/-rela-/- MEFs reconstituted RelA-WT and empty pBABE-puro (EV). (B) Analysis of nuclear NFkB DNA- binding activity by Electrophoretic Mobility Shift Assay (EMSA) for kB DNA and NF-Y (loading control) probes of nuclear extracts prepared at indicated times after TNF stimulation from same cells as (B). (C) Scatter plots for all 113 identified genes with polyA+ RNA-seq showing their RPKM in log2 for each time point (0, 0.5hr, 1hr, and 3hr) in response to TNF between WT MEF cells and p53-/-crel-/-rela-/- MEF reconstituted with RelA-WT. R2 values of 0.69 to 0.83 are found across all time points and outliers were removed to produce the list of 104 target genes in subsequent analyses. (D) A map of the NFkB binding sites (kB DNA sequences) within each of the 234 RelA ChIP-seq peaks identified with an FDR <0.05 and associated with the 104 NFkB target genes. Full kB elements refer to 9, 10, and 11 bp sequences conforming to the consensus: 5’-GGR(N3-5)YCC-3’, where R = A or G; Y = C or T; N = any nucleotide (A, C, G, or T). Half kB elements refer to 5’-GGR-3’ and 5’-YCC-3’. (E) Correlation between RelA peak size and relative affinity of kB site sequence. Normalized counts of RelA ChIP-seq peaks retrieved from RelA ChIP-seq data with RelA-WT expressing MEFs are plotted against PBMA- determined z-scores of strongest kB binding site sequences for RelA:RelA, RelA:p50, and p50:p50 dimer retrieved from (Siggers et al., 2012).

All TNF-induced NFkB target genes require that RelA makes base-specific contacts within the kB element

Prior structural and functional studies of NFkB-DNA complexes demonstrated that

NFkB binds to a variety of kB sites where NFkB recognizes 9 to 11 base pair palindromic kB

DNA elements to the sequence: 5’-GGR(N3-5YCC)-3’ (Hoffmann et al., 2006). Using this information, we identified all kB DNA elements that are associated with all identifiable RelA

ChIP-seq peaks (Figure 2.1I) for each of the 104 genes (Supplementary Table 2.1). Mapping the

RelA ChIP-seq data and kB DNA elements associated with each gene within 10kb of their TSS

(Figure 2.3F), we found kB DNA elements for 223 RelA binding peaks on 104 genes. These 223

RelA binding events harbor 617 full kB and 12,768 half kB DNA elements with an FDR of

<0.01. When the FDR is relaxed to <0.05, 234 RelA binding events are identified harboring 663 full kB and 14,072 half kB DNA elements, and the conclusions are confirmed (Figure 2.4D).

Overall, we did not observe a correlation between the relative affinity of the kB site sequence determined by in vitro protein binding-DNA microarrays (Siggers et al., 2012) and the number of reads within a peak (Figure 2.4E).

To determine whether DNA-base-specific contacts of the RelA protein are in fact required for recruiting NFkB to target sites and for activating target genes upon inflammatory stimulation, we generated a triple amino acid mutation (R35A, Y36A, and E39A), depicted in

Figure 2.5A, termed the RelA DNA-binding mutant (RelADB). The mutations abrogate base- specific contacts within the kB sequence (Figure 2.5B) (Chen et al., 1998a). We retrovirally transduced this DNA-binding mutant into primary p53-/-crel-/-rela-/- MEFs and examined NFkB by

biochemical assays. Immunoblotting confirmed expression of reconstituted RelADB protein, albeit at slightly lower levels (about 75%) than RelA-WT (Figure 2.5C). Co-immunoprecipitation confirmed that the RelADB mutant is able to associate with p50 to form dimeric NFkB and to interact with p105, IkBa and IkBb to form higher order IkB-NFkB complexes, characteristic of latent NFkB in unstimulated cells (Figure 2.6A). We next assessed the NFkB DNA-binding activity via EMSA and found that RelADB showed no kB site-binding activity in response to TNF stimulation unlike the RelA-WT control (Figure 2.5D), though immunoblotting of nuclear extracts confirmed nuclear presence (Figure 2.6B). In sum, the RelADB mutant does not support high affinity NFkB DNA-binding in vitro.

We next examined NFkB RelA binding in vivo by RelA ChIP-seq. On previously identified 9,829 endogenous chromatin sites (Figure 2.1A), RelADB mutant showed dramatically diminished TNF-induced RelA binding (Figure 2.5E). For example, on Nfkbia and Cxcl10 genes, TNF-induced RelA binding was dramatically reduced (Figure 2.6C). Focusing on the 104

NFkB target genes (Figure 2.3E), we evaluated every previously identified RelA binding peak and found that the vast majority showed a substantial > 2-fold reduction in the RelADB mut

(Figure 2.5F). Detailed differential RelA ChIP peak analysis (Supplementary Table 2.1) revealed that those few RelA peaks that were less affected by the RelADB mutant were generally less prominent in wild-type cells (Figure 2.5G), which we interpreted to be indicative of the strong contribution of high affinity DNA binding ability. Furthermore, they generally showed poorer kB site motifs, as quantified by in vitro protein-DNA microarray studies (Figure 2.5H and Figure

2.6D). Overall, our analysis reveals that NFkB RelA selects the majority of inflammatory response target genes via direct RelA-DNA interactions via base-specific contacts.

Figure 2.5 High affinity binding by RelA is required for the TNF-induced expression of all NFkB target genes

Rel Homology Transactivation A Region (RHR) Domain (TAD) F I EV RelA-WT RelADB -10kb -5kb TSS 5kb 10kb 0 .5 1 3 0 .5 1 3 0 .5 1 3 hr RelA-WT 1 1 1 NTD DD TA2 TA1 Nfkbiz Nfkbiz Nfkbiz Nfkbiz NLS Nfkbiz Cxcl1 Cxcl1 Cxcl1 Cxcl1 1 19 306 429 520 549 Cxcl1 Cxcl2 Cxcl2 Cxcl2 Cxcl2 R35A Cxcl2 Y36A Tnf Tnf Tnf Tnf Tnf E39A Slc25a25 Slc25a25 Slc25a25 0.8 Slc25a25 0.8 Slc25a25 0.8 Tnfaip3 Tnfaip3 Tnfaip3 Tnfaip3 Tnfaip3 Lif Lif Lif Lif Lif DB Gadd45b Gadd45b Gadd45b Gadd45b RelA NTD DD TA2 TA1 Gadd45b NLS Map3k8 Map3k8 Map3k8 Map3k8 Map3k8 Gem Gem Gem Gem 1 19 306 429 520 549 0.6 0.6 0.6 Gem Nfkbia Nfkbia Nfkbia Nfkbia Nfkbia Itpkc Itpkc Itpkc Itpkc Itpkc Ccl20 Ccl20 Ccl20 Ccl20 Pim1 Pim1 Pim1 Ccl20 Pim1 B Rgs16 Rgs16 Rgs16 Rgs16 Pim1 Rnd1 Rnd1 0.4 Rnd1 0.4 Rnd1 0.4 Rgs16 Irf1 Irf1 Irf1 Irf1 Rnd1 Cxcl5 Cxcl5 Cxcl5 Cxcl5 Irf1 Nfkbid Nfkbid Nfkbid Nfkbid Cxcl5 Zc3h12a Zc3h12a Zc3h12a Zc3h12a Tmem200b Tmem200b Tmem200b Nfkbid Tmem200b 0.2 0.2 0.2 p50 Rasgef1b Rasgef1b Rasgef1b Rasgef1b Zc3h12a Zfp429 Zfp429 Zfp429 Zfp429 Tmem200b Noct Noct Noct Noct Rasgef1b Rsl1 Rsl1 Rsl1 Rsl1 Zfp429 Klhl25 Klhl25 Klhl25 Klhl25 Zswim4 Zswim4 0 Zswim4 0 Zswim4 0 Noct Csf1 Csf1 Csf1 Csf1 Rsl1 Rab20 Rab20 Rab20 Rab20 Klhl25 Nfatc1 Nfatc1 Nfatc1 Nfatc1 Zswim4 Bcl3 Bcl3 Bcl3 Csf1 Bcl3 Ccl2 Ccl2 Ccl2 Ccl2 Rab20 Ccl7 Ccl7 Ccl7 Ccl7 Nfatc1 Birc3 Birc3 Birc3 Birc3 Bcl3 Foxs1 Foxs1 Foxs1 Foxs1 Ccl2 C9orf72 C9orf72 C9orf72 C9orf72 Ccl7 Rnf19b Rnf19b Rnf19b Rnf19b Birc3 Tnfaip6 Tnfaip6 Tnfaip6 Tnfaip6 Foxs1 Rrad Rrad Rrad Rrad C9orf72 Tnfaip2 Tnfaip2 Tnfaip2 Tnfaip2 Rnf19b Trim47 Trim47 Trim47 Trim47 Tnfaip6 Nfkbib Nfkbib Nfkbib Nfkbib Rrad Stx11 Stx11 Stx11 Stx11 Tnfaip2 Bahcc1 Bahcc1 Bahcc1 Bahcc1 Trim47 Ptx3 Ptx3 Ptx3 Ptx3 Nfkbib Vcam1 Vcam1 Vcam1 Vcam1 Stx11 Rhbdf2 Bahcc1 Rhbdf2 Rhbdf2 Rhbdf2 Uap1 Uap1 Uap1 Uap1 Ptx3 Clic4 Vcam1 Clic4 Clic4 Clic4 Rhbdf2 Slfn2 Slfn2 Slfn2 Slfn2 RelA Uap1 Tlr2 Tlr2 Tlr2 Tlr2 Clic4 Rab8b Rab8b Rab8b Rab8b Slfn2 Ripk2 Ripk2 Ripk2 Ripk2 Tlr2 Mt2 Mt2 Mt2 Mt2 Rab8b Birc2 Birc2 Birc2 Birc2 Ripk2 Nfkbie Nfkbie Nfkbie Nfkbie Mt2 Relb Relb Relb Relb Birc2 Cd83 Cd83 Cd83 Cd83 Nfkbie Micall2 Micall2 Micall2 Micall2 Relb Icam1 Icam1 Icam1 Icam1 RelA- DB Cd83 Sod2 Sod2 Sod2 Sod2 C RelA .) 1.2 Micall2 Ninj1 Ninj1 Ninj1 Ninj1 EV WT Icam1 Mfhas1 Mfhas1 Mfhas1 Mfhas1 Sod2 a.u Il1rl1 Il1rl1 Il1rl1 Il1rl1 Ninj1 Cd44 Mfhas1 Cd44 Cd44 Cd44 - RelA 0.8 Il1rl1 Nfkb2 Nfkb2 Nfkb2 Nfkb2 Cd44 Cilp2 Cilp2 Cilp2 Cilp2 Nfkb2 Gch1 Gch1 Gch1 Gch1 - I!Bβ Cilp2 Pdgfb Pdgfb Pdgfb Pdgfb Gch1 Cxcl10 Cxcl10 Cxcl10 Cxcl10 0.4 Pdgfb Hivep1 Hivep1 Hivep1 Hivep1 WCE Cxcl10 Nfkb1 Nfkb1 Nfkb1 Nfkb1 - I!B⍺ Hivep1 Fas Fas Fas Fas Nfkb1 Cx3cl1 Cx3cl1 Cx3cl1 Cx3cl1 Fas Tnip1 Tnip1 Tnip1 Tnip1 RelA Protein. ( 0 Cx3cl1 Angptl4 RelA- DB Angptl4 Angptl4 Angptl4 - Histone 3 EV RelA Tnip1 Bid Bid Bid Bid WT Angptl4 Pdlim4 Pdlim4 Pdlim4 Pdlim4 Bid Alpk1 Alpk1 Alpk1 Alpk1 Pdlim4 Traf3 Traf3 Traf3 Traf3 DB Alpk1 Gfpt2 Gfpt2 Gfpt2 Gfpt2 EV RelA-WT RelA Traf3 Slc7a2 Slc7a2 Slc7a2 Slc7a2 D Gfpt2 Camkk2 Camkk2 Camkk2 Camkk2 Slc7a2 0.25 .5 1 1.5 2 3 0.25 .5 1 1.5 2 3 0 .25 .5 1 1.5 2 3 hr Camkk2 Cxcl16 Cxcl16 Cxcl16 Cxcl16 Cxcl16 Slc2a6 Slc2a6 Slc2a6 Slc2a6 Slc2a6 Tjp2 Tjp2 Tjp2 Tjp2 Tjp2 Gsap Gsap Gsap Gsap B Gsap Gsdmd Gsdmd Gsdmd Gsdmd

! Gsdmd Jak2 Jak2 Jak2 Jak2 Jak2 Serpina3g Serpina3g Serpina3g Serpina3g Serpina3g Phldb1 Phldb1 Phldb1 Phldb1 NF * NS Phldb1 Il15ra Il15ra Il15ra Il15ra Il15ra H2-K1 H2−K1 H2−K1 H2−K1 H2-K1 Ccl5 Ccl5 Ccl5 Ccl5

Y Ccl5 Stk10 Stk10 Stk10 Stk10 - Stk10 H2-Q4 H2−Q4 H2−Q4 H2−Q4 H2-Q4 Cdk6 Cdk6 Cdk6 Cdk6 Cdk6

NF Parp8 Parp8 Parp8 Parp8 Parp8 Tapbp Tapbp Tapbp Tapbp Tapbp Stat5a Stat5a Stat5a Stat5a Stat5a Atp2b4 Atp2b4 Atp2b4 Atp2b4 Atp2b4 Gbp2 Gbp2 Gbp2 Gbp2 Gbp2 Col27a1 Col27a1 Col27a1 Col27a1 Col27a1 E RelA-WT RelADB Psmb10 Psmb10 Psmb10 Psmb10 Psmb10 DB 0-R1 0-R2 0.5h-R1 0.5h-R2 0-R1 0-R2 0.5h-R1 0.5h-R2 Reduction in RelA binding Inducible Expression (RU) 40 30 < 2-fold ≥ 2-fold 0 0.2 0.4 0.6 0.8 1 20 10 0 -0.5 5’ 3’ .5kb G p-value = 1.614E-4 H -16 0.164288006226494RelA:p50 0.073135506788583RelA:RelA p-value = < 2.2E 12.5 AVG normalized counts 60

) 1010.0 2 50 6 1010 reduction_cat reduction_cat score peaks category7.5 40 - not_reduced not_reduced 4 not_reduced_DBmut p50 zscores p65 zscores reduced reduced − not_reduced_Rel−WT−

RelA 30 5.0 5reduced_DBmut 55 p65 p65 reduced_Rel−WT 20 2 PDBMAz

log2(Normalized counts +1) 2.5 RelA-WT 10 DB Normalized counts (Log counts Normalized 0 RelA not_reduced reduced not_reduced reduced 0 not- not- reduced_Rel−WT reduced_DBmut not_reduced_Rel−WTnot_reduced_DBmut reduction_catreduced reduction_catreduced reduced peakscategorynot-reduced peaks reduced reduced

Figure 2.5 High affinity binding by RelA is required for the TNF-induced expression of all NFkB target genes. (A) Schematic representation of the targeted mutations in the DNA binding mutant of murine RelA (R35A, Y36A, E39A). (B) Schematic of the kB DNA contacts made by RelA:p50 NFkB heterodimer, adapted from (Chen et al., 1998a). The heterodimer structure reveals base-specific contacts made by specific amino acids in the RelA protein, indicating the critical roles of R35, Y36 and E39. Arrows denote hydrogen bonds; brown ovals indicate van der Waals contacts. (C) Protein expression of RelA, IkBb, IkBa, and Histone 3 as loading control, by immunoblotting in unstimulated cells genetically complemented with indicated EV, RelA-WT and RelADB. Quantified protein expression is shown on right. (D) Analysis of nuclear NFkB activity by EMSA with kB and NF-Y (loading control) probes of nuclear extracts prepared at indicated times after TNF stimulation of genetically complemented cells shown in (C). *NS is non-specific. The data is representative of two independent experiments. (E) Heatmap of RelA peaks at 9,829 locations defined in Figure 2.1A, in genetically complemented (RelA WT and RelADB) p53-/-crel-/-rela-/- MEFs in response to 0.5hr of TNF stimulation. (F) A map of RelA binding events associated with 104 NFkB target genes indicating whether RelA-binding is reduced ≥ 2 fold in the RelADB mutant (red bar). (G) Quantification and comparison of RelA peaks shown in (F). Averaged normalized counts from two replicates of RelA ChIP-seq experiments in RelA-WT or RelADB expressing MEFs are plotted to compare RelA peaks that highly dependent on high affinity binding by RelA vs. those that are less dependent. For each category, Mann-Whitney U-test indicates that peaks less dependent on high affinity binding by RelA show a lower read count in RelA-WT control cells. (H) Correlation between RelA ChIP- seq peaks and PBMA-determined z-scores of strongest kB binding site sequences for RelA:p50 and RelA:RelA dimers retrieved from Siggers et al. Of the 7 10-bp kB site sequences within not- reduced RelA peaks, 5 with the strongest z-scores were plotted. (I) Heatmap for the transcriptional TNF response of the 104 target genes in genetically complemented RelA-WT and RelADB cells, analyzed by polyA+RNA-seq.

Figure 2.6 High affinity DNA binding by RelA is required for all TNF-induction of NFkB target genes

Figure 2.6 High affinity DNA binding by RelA is required for all TNF-induction of NFkB target genes. (A) RelA DNA binding mutant shows normal interactions with p50, IkB⍺ and IkBβ as detected by RelA immunoprecipitation using whole-cell extracts (WCE) from unstimulated cells followed by immunoblotting. (B) Nuclear extracts (NE) and cytoplasmic extracts (CE) from TNF-stimulated MEFs were analyzed by immunoblotting for antibodies against RelA, IkBb, IkBa, p105/p50, p84 (nuclear loading control), and ⍺-Tubulin (cytoplasmic loading control). (C) Genome browser tracks of RelA binding events in reconstituted RelA-WT and RelADB cells for known NFkB target genes, Nfkbia and Cxcl10, in response to 0.5 hr of TNF stimulation. Data is representative of two independent experiments. (D) Correlation between RelA peak dependence on RelA’s high DNA affinity and PBMA-determined z-scores of strongest kB binding site sequences for p50:p50 dimer retrieved from Siggers et al. Of the seven 10-bp kB site sequences within not-reduced RelA peaks, the five with the strongest z-scores were plotted. (E) Line plots of polyA+RNA-seq data for Nfkbia and Cxcl10 genes shown in RPKM in response to TNF at indicated times in WT and RelADB-reconstituted in p53-/-crel-/-rela-/- MEFs. (F) Principal Component Analysis (PCA) plot of the 104 NFkB-dependent genes in response to TNF at indicated times using log2 of RPKM. (G) Genome browser tracks of RelA ChIP-seq for Rrad gene in reconstituted RelA-WT and RelADB mutant cells.

Next, we investigated whether the reduced kB recruitment of the DNA binding mutant results in a diminished transcriptional response of NFkB target genes. We performed polyA+

RNA-seq in reconstituted MEFs stimulated with TNF over a 3-hour time-course analysis (0.5, 1, and 3 hr). Examining the model target genes, Nfkbia and Cxcl10, we found that RelADB mutant did not support their gene expression (Figure 2.6E). Extending the analysis to the 104 NFkB target genes, we found that the TNF-induced gene expression response was almost entirely abolished in the RelADB mutant cells (Figure 2.5I). This is also evident in a principal component analysis of the 104 genes (Figure 2.6F) which shows that the transcriptional response of the mutant largely overlaps with the empty vector control. Only one gene retained 50% transcriptional activation, Rrad (Ras related glycolysis inhibitor and calcium channel regulator).

Close examination revealed some residual RelA binding within the promoter region (Figure

2.6G), including a kB binding site at -123 bp (5’-GGGAATCCCC-3’) whose 4G-stretch could provide sufficient affinity to heterodimeric NFkB via the p50 binding partner (Cheng et al.,

2011).

In sum, our analysis showed that for the vast majority of genes the triple amino acid mutation abrogates TNF-induced in vivo DNA binding (RelA ChIP data) and target gene expression. Our results suggest that RelA target gene selection depends on base-specific interactions between RelA and the kB site; our data do not rule out that protein-protein interactions with chromatin-bound factors contribute to binding site selectivity, but indicates that such interactions do not suffice for the activation of inflammatory response NFkB target genes.

All TNF-induced NFkB target genes are regulated by two C-terminal TA domains

To investigate the role of RelA’s C-terminal region in the transcriptional activation of endogenous NFkB target genes, we first generated two truncated variant forms of murine RelA; a full truncation of its C-terminal region (deleting amino acids 326-549; D326-549), referred to as C-termD, and a deletion specifically of its two TA domains (deleting residues 429-549; D429-

549), referred to as TADD (Figure 2.7A).

Immunoblotting of reconstituted MEFs showed close to WT expression of the TADD mutant but slightly lower levels of RelA, IkBa and IkBb in C-termD mutant (Figure 2.7B).

Nevertheless NFkB DNA-binding activity was detected by EMSA at comparable levels to that of reconstituted RelA-WT controls (Figure 2.7C). Upon stimulation, both mutants showed a normal onset of TNF-inducible binding activity of NFkB; however, post-induction repression characteristic of TNF-induced NFkB activity was abrogated, indicative of defective negative feedback control. Indeed, no TNF-induced Nfkbia mRNA expression was observed in either mutant (Figure 2.7D).

Extending the analysis to RNA-seq data of the 104 NFkB target genes, the PCA plot showed that both mutations result in severe deficiency in TNF-induced gene expression response

(Figure 2.7E), largely equivalent to the EV control. Single gene analysis by heatmap visualization confirmed this global picture (Figure 2.7F): none of the 104 genes showed normal

TNF induction, with only one gene, Slfn2, being induced to about 50% in the TADD mutant. In sum, despite the elevated TNF-induced NFkB activity in these mutant cells (due to deficient

negative feedback control), the data indicates that all transcriptional activation functions of

NFkB require the C-terminal activation domains TA1 and TA2.

To determine whether NFkB target genes have differential requirements for either TA1 or TA2, we constructed variants of RelA (Figure 2.8A), a deletion mutant lacking residues 520 to

549 that define the TA1 (hereafter is referred to as TA1D), a double amino acid mutant L449A and F473A, which abrogates the RelA TA2 function of interacting with the TAZ1 domain of transcriptional coactivator, CBP/p300 (Mukherjee et al., 2013), referred to as (TA2CI), and a

D double mutant (TA2CITA1 ). (Internal deletion of TA2 appeared to disrupt the function of TA1, which prompted us to design point mutations within the domain.) Following reconstitution of p53-/-crel-/-rela-/- MEFs, we confirmed that expression levels of RelA variants and the canonical

IkBs were close to those in RelA-WT control (Figure 2.8B). Further, gel mobility shift analysis confirmed that these targeted mutations did not interfere with NFkB dimerization, activation, and

DNA binding to kB sites upon TNF stimulation (Figure 2.8C). However, similar to C-termD and

TADD mutants examined previously we found that NFkB DNA-binding activity was persistent over the 3-hour time course indicative of a loss of proper negative feedback control. Indeed,

TNF-induced Nfkbia mRNA expression was substantially diminished in single mutants and abrogated in the double mutant (Figure 2.8D).

Figure 2.7 The RelA C-terminal portion is required for TNF induction of all NFkB target genes

Rel Homology Transactivation Δ Δ A Region (RHR) Domain (TAD) F EV RelA-WT C-term TAD 0 .5 1 3 0 .5 1 3 0 .5 1 3 0 .5 1 3 hr 1 1 1 RelA-WT NTD DD TA2 TA1 Nfkbiz Nfkbiz Nfkbiz Nfkbiz

NLS Cxcl1 Cxcl1 Cxcl1 Cxcl1 1 19 306 429 520 549 Cxcl2 Cxcl2 Cxcl2 Cxcl2 Tnf Δ Tnf Tnf Tnf RelA C-term NTD DD Slc25a25 Slc25a25 Slc25a25 Slc25a25 NLS Tnfaip3 Tnfaip3 0.8 Tnfaip3 0.8 Tnfaip3 0.8 1 19 306 Lif Lif Lif Lif Gadd45b Gadd45b Gadd45b Gadd45b Map3k8 RelA TADΔ NTD DD Map3k8 Map3k8 Map3k8

NLS Gem Gem Gem Gem Nfkbia 1 19 306 428 Nfkbia 0.6 Nfkbia 0.6 Nfkbia 0.6 Itpkc Itpkc Itpkc Itpkc Ccl20 Ccl20 Ccl20 Ccl20 Pim1 Pim1 Pim1 Pim1 RelA- C- Δ Rgs16 Rgs16 Rgs16 Rgs16 TAD Rnd1 B EV WT termΔ Rnd1 Rnd1 Rnd1 Irf1 Irf1 0.4 Irf1 0.4 Irf1 0.4 1 Cxcl5 Cxcl5 Cxcl5 Cxcl5 Nfkbid Nfkbid Nfkbid Nfkbid 0.8 Zc3h12a Zc3h12a Zc3h12a Zc3h12a RelA Tmem200b Tmem200b Tmem200b Tmem200b Rasgef1b Rasgef1b Rasgef1b Rasgef1b 0.6 Zfp429 Zfp429 0.2 Zfp429 0.2 Zfp429 0.2 Noct Noct Noct Noct 0.4 Rsl1 Rsl1 Rsl1 Rsl1 Klhl25 Klhl25 Klhl25 Klhl25 I!Bβ Zswim4 Zswim4 Zswim4 Zswim4 WCE 0.2 Csf1 Csf1 Csf1 Csf1 Rab20 Rab20 0 Rab20 0 Rab20 0 I!B⍺ (RU) Expression Inducible 0 Nfatc1 Nfatc1 Nfatc1 Nfatc1 Bcl3 Bcl3 Bcl3 Bcl3 Actin Ccl2 Ccl2 Ccl2 Ccl2 Ccl7 Ccl7 Ccl7 Ccl7 Birc3 Birc3 Birc3 Birc3 Foxs1 Foxs1 Foxs1 Foxs1 Δ Δ C9orf72 C9orf72 C9orf72 C9orf72 C EV RelA-WT C-term TAD Rnf19b Rnf19b Rnf19b Rnf19b Tnfaip6 Tnfaip6 Tnfaip6 Tnfaip6 0.25.5 11.5 2 3 0.25.5 11.5 2 3 0.25.5 11.5 2 3 0.25.5 11.5 2 3 hr Rrad Rrad Rrad Rrad Tnfaip2 Tnfaip2 Tnfaip2 Tnfaip2 Trim47 Trim47 Trim47 Trim47 Nfkbib Nfkbib Nfkbib Nfkbib Stx11 Stx11 Stx11 Stx11 B Bahcc1 Bahcc1 Bahcc1 Bahcc1 ! Ptx3 Ptx3 Ptx3 Ptx3 Vcam1 Vcam1 Vcam1 Vcam1 NF Rhbdf2 Rhbdf2 Rhbdf2 Rhbdf2 Uap1 Uap1 Uap1 Uap1 Clic4 Clic4 Clic4 Clic4 Slfn2 Slfn2 Slfn2 Slfn2

Y Tlr2 Tlr2 Tlr2 Tlr2 - Rab8b Rab8b Rab8b Rab8b Ripk2 Ripk2 Ripk2 Ripk2 NF Mt2 Mt2 Mt2 Mt2 Birc2 Birc2 Birc2 Birc2 Nfkbie Nfkbie Nfkbie Nfkbie Relb Relb Relb Relb Cd83 Cd83 Cd83 Cd83 Micall2 Micall2 Micall2 Micall2 D Icam1 Icam1 Icam1 Icam1 Sod2 Sod2 Sod2 Sod2 800 Ninj1 Ninj1 Ninj1 Ninj1 EV Mfhas1 Mfhas1 Mfhas1 Mfhas1 RelA-WT Nfkbia Cxcl10 Il1rl1 Il1rl1 Il1rl1 Il1rl1 600 Δ Cd44 Cd44 Cd44 Cd44 C-term Nfkb2 Δ Nfkb2 Nfkb2 Nfkb2 TAD Cilp2 Cilp2 Cilp2 Cilp2 400 Gch1 Gch1 Gch1 Gch1 Pdgfb Pdgfb Pdgfb Pdgfb Cxcl10 200 Cxcl10 Cxcl10 Cxcl10 mRNA (RPKM) mRNA Hivep1 Hivep1 Hivep1 Hivep1 Nfkb1 Nfkb1 Nfkb1 Nfkb1 0 Fas Fas Fas Fas Cx3cl1 Cx3cl1 Cx3cl1 Cx3cl1 0 0.5 1 3 0 0.5 1 3 hr Tnip1 Tnip1 Tnip1 Tnip1 Angptl4 Angptl4 Angptl4 Angptl4 Bid Bid Bid Bid log2 RPKM Pdlim4 Pdlim4 Pdlim4 Pdlim4 E Alpk1 log2Log2 RPKMRPKM Alpk1 Alpk1 Alpk1 30 Type Traf3 Traf3 Traf3 Traf3 30 Gfpt2 Gfpt2 Gfpt2 Gfpt2 30 TypeEV Slc7a2 Slc7a2 Slc7a2 Slc7a2 EV Camkk2 Camkk2 Camkk2 Camkk2 RelAEV -WT Cxcl16 Cxcl16 Cxcl16 Cxcl16 20 RelA−WT Slc2a6 20 Δ Slc2a6 Slc2a6 Slc2a6 20 CRelA-term−−WTDC'term Tjp2 Tjp2 Tjp2 Tjp2 RelAΔ −DC'term Gsap Gsap Gsap Gsap TADRelA−DTAD Gsdmd Gsdmd Gsdmd Gsdmd 10 10 RelA−DTAD Jak2 Jak2 Jak2 Jak2 10 Serpina3g Serpina3g Serpina3g Serpina3g Time Phldb1 Phldb1 Phldb1 Phldb1 Time Il15ra Il15ra Il15ra Il15ra 0 00 H2-K1 H2−K1 H2−K1 H2−K1 0hr Ccl5 Ccl5 Ccl5 Ccl5 PC2 (9.3%) 0.0hr0hr 0.5hr Stk10 Stk10 Stk10 Stk10 H2-Q4

PC2 (9.3%) H2−Q4 H2−Q4 H2−Q4 PC2 (9.3%) -10 0.5hr0.5hr −10−10 1.0hr Cdk6 Cdk6 Cdk6 Cdk6 1.0hr1.0hr Parp8 Parp8 Parp8 Parp8 3.0hr Tapbp Tapbp Tapbp Tapbp -20 3.0hr Stat5a Stat5a Stat5a Stat5a −20 3.0hr Atp2b4 Atp2b4 Atp2b4 Atp2b4 −20 Gbp2 -20 -10 0 10 20 30 Gbp2 Gbp2 Gbp2 −20−10 0 10 20 30 Col27a1 Col27a1 Col27a1 Col27a1 −20−10PC10PC1 (79%)10 (79%)20 30 Psmb10 Psmb10 Psmb10 Psmb10 RELA_WT_0hr RELA_WT_0.5hr RELA_WT_1.0hr RELA_WT_3.0hr EV_0hr RELA_WT_0hr EV_0.5hr RELA_WT_0.5hr EV_1.0hr RELA_WT_1.0hr EV_3.0hr RELA_WT_3.0hr Cterm_0hr EV_0hr Cterm_0.5hr EV_0.5hr Cterm_1.0hr EV_1.0hr Cterm_3.0hr EV_3.0hr TAD_0hr Cterm_0hr TAD_0.5hr Cterm_0.5hr TAD_1.0hr Cterm_1.0hr TAD_3.0hr Cterm_3.0hr TAD_0hr TAD_0.5hr TAD_1.0hr TAD_3.0hr PC1 (79%) RELA_WT_0hr RELA_WT_0.5hr RELA_WT_1.0hr RELA_WT_3.0hr EV_0hr EV_0.5hr EV_1.0hr EV_3.0hr Cterm_0hr Cterm_0.5hr Cterm_1.0hr Cterm_3.0hr TAD_0hr TAD_0.5hr TAD_1.0hr TAD_3.0hr

Figure 2.7 The RelA C-terminal portion is required for TNF induction of all NFkB target genes. (A) Schematic illustrates the RelA variants to be tested: RelA C-termD (aa 1-325) and RelA TADD (aa 1-428). (B) Protein expression of RelA, IkBb, IkBa, and Actin as loading control, in indicated unstimulated cells assayed by immunoblotting. (C) Analysis of nuclear NFkB activity by EMSA with kB and NF-Y (loading control) probes of nuclear extracts prepared at indicated times after TNF stimulation of genetically complemented cells shown in (B). (D) Line plots of mRNA expression for NFkB target genes Nfkbia and Cxcl10 genes in response to TNF in same cells. (E) PCA plot of all 104 NFkB target genes in response to TNF in same cells. (F) Heatmap of the transcriptional response to TNF of the 104 target genes in same cells. Relative induced expression is shown.

Figure 2.8 RelA transactivation domains TA1 and TA2 both contribute to the TNF response of NF-kB target genes A E Type Log2log2RPKM RPKM Rel Homology Transactivation EV Region (RHR) Domain (TAD) 20 EV 20 RelARelA-WT−WT RelA-WT NTD DD TA2 TA1 RelAΔ −DTAD

NLS TAD CI 1 19 306 429 520 549 10 TA1RelAΔ mt 10 CI RelA TADΔ NTD DD TA2RelA−DTA1 NLS CI Δ 1 19 306 428 TA2 TA1(CI DeltaTA1) 0 RelA mt RelA TA1Δ NTD DD TA2 0 Time NLS 1 19 306 429 519 0hr

PC2 (11.4%) Time L449 F473A −10-10 0.5hr 1.0hr CI RelA TA2 NTD DD TA2 TA1 PC2 (11.4%) 0hr NLS 3.0hr0.5hr 1 19 306 429 520 549 -20 L449 F473A −20 1.0hr −-20 −10-10 00 1010 2020 RelA TA2CITA1Δ NTD DD TA2 NLS PC1 (78.9%) 3.0hr 1 19 306 429 519 PC1 (78.9%)

Δ Δ CI CI Δ B Δ F EV RelA-WT TAD TA1 TA2 TA2 TA1 WT - Δ Δ CI TA1 0 .5 1 3 0 .5 1 3 0 .5 1 3 0 .5 1 3 0 .5 1 3 0 .5 1 3 hr CI 1 1 1 Nfkbiz Nfkbiz Nfkbiz Nfkbiz Cxcl1 Cxcl1 Cxcl1 Cxcl1 EV RelA TAD TA1 TA2 TA2 Cxcl2 Cxcl2 Cxcl2 Cxcl2 Tnf Tnf Tnf Tnf Slc25a25 Slc25a25 Slc25a25 Slc25a25 * NS Tnfaip3 Tnfaip3 0.8 Tnfaip3 0.8 Tnfaip3 0.8 1 Lif Lif Lif Lif Gadd45b Gadd45b Gadd45b Gadd45b Map3k8 Map3k8 Map3k8 Map3k8 RelA Gem Gem Gem Gem 0.8 Nfkbia Nfkbia Nfkbia Nfkbia Itpkc Itpkc 0.6 Itpkc 0.6 Itpkc 0.6 Ccl20 Ccl20 Ccl20 Ccl20 0.6 Pim1 Pim1 Pim1 Pim1 Rgs16 Rgs16 Rgs16 Rgs16 I!Bβ Rnd1 Rnd1 Rnd1

WCE Rnd1 0.4 Irf1 Irf1 Irf1 Irf1 Cxcl5 Cxcl5 0.4 Cxcl5 0.4 Cxcl5 0.4 Nfkbid Nfkbid Nfkbid Nfkbid I!B⍺ 0.2 Zc3h12a Zc3h12a Zc3h12a Zc3h12a Tmem200b Tmem200b Tmem200b Tmem200b Rasgef1b Rasgef1b Rasgef1b Rasgef1b

Inducible Expression (RU) Expression Inducible 0 Zfp429 Zfp429 Zfp429 Zfp429 Actin Noct Noct 0.2 Noct 0.2 Noct 0.2 Rsl1 Rsl1 Rsl1 Rsl1 Klhl25 Klhl25 Klhl25 Klhl25 Zswim4 Zswim4 Zswim4 Zswim4 Csf1 Csf1 Csf1 Csf1 Rab20 Rab20 Rab20 Rab20 Nfatc1 Nfatc1 0 Nfatc1 0 Nfatc1 0 C Bcl3 Bcl3 Bcl3 Bcl3 Ccl2 Ccl2 Ccl2 Ccl2 Δ Δ Ccl7 Ccl7 Ccl7 Ccl7 EV RelA-WT TAD TA1 Birc3 Birc3 Birc3 Birc3 Foxs1 Foxs1 Foxs1 Foxs1 0.25.5 11.5 2 3 0.25.5 11.5 2 3 0.25.5 1 1.5 2 3 0.25.5 1 1.5 2 3 hr C9orf72 C9orf72 C9orf72 C9orf72 Rnf19b Rnf19b Rnf19b Rnf19b Tnfaip6 Tnfaip6 Tnfaip6 Tnfaip6 Rrad Rrad Rrad Rrad Tnfaip2 Tnfaip2 Tnfaip2 Tnfaip2 B Trim47 Trim47 Trim47 Trim47

! Nfkbib Nfkbib Nfkbib Nfkbib Stx11 Stx11 Stx11 Stx11 Bahcc1 Bahcc1 Bahcc1 Bahcc1 NF Ptx3 Ptx3 Ptx3 Ptx3 Vcam1 Vcam1 Vcam1 Vcam1 Rhbdf2 Rhbdf2 Rhbdf2 Rhbdf2 Uap1 Uap1 Uap1 Uap1 Clic4 Clic4 Clic4 Clic4 Y Slfn2 Slfn2 Slfn2 - Slfn2 Tlr2 Tlr2 Tlr2 Tlr2 Rab8b Rab8b Rab8b Rab8b

NF Ripk2 Ripk2 Ripk2 Ripk2 Mt2 Mt2 Mt2 Mt2 Birc2 Birc2 Birc2 Birc2 Nfkbie Nfkbie Nfkbie Nfkbie Relb Relb Relb Relb RelA-WT TA2CI TA2CITA1Δ Cd83 Cd83 Cd83 Cd83 Micall2 Micall2 Micall2 Micall2 Icam1 Icam1 Icam1 Icam1 0.25.5 11.5 2 0.25.5 11.5 2 0.25.5 11.5 2 hr Sod2 Sod2 Sod2 Sod2 Ninj1 Ninj1 Ninj1 Ninj1 Mfhas1 Mfhas1 Mfhas1 Mfhas1 Il1rl1 Il1rl1 Il1rl1 Il1rl1 Cd44 Cd44 Cd44 B Cd44 Nfkb2 Nfkb2 Nfkb2 Nfkb2 ! Cilp2 Cilp2 Cilp2 Cilp2 Gch1 Gch1 Gch1 Gch1 Pdgfb Pdgfb Pdgfb Pdgfb NF Cxcl10 Cxcl10 Cxcl10 Cxcl10 Hivep1 Hivep1 Hivep1 Hivep1 Nfkb1 Nfkb1 Nfkb1 Nfkb1 Fas Fas Fas Fas Cx3cl1 Cx3cl1 Cx3cl1

Y Cx3cl1

- Tnip1 Tnip1 Tnip1 Tnip1 Angptl4 Angptl4 Angptl4 Angptl4 Bid Bid Bid Bid

NF Pdlim4 Pdlim4 Pdlim4 Pdlim4 Alpk1 Alpk1 Alpk1 Alpk1 Traf3 Traf3 Traf3 Traf3 Gfpt2 Gfpt2 Gfpt2 Gfpt2 Slc7a2 Slc7a2 Slc7a2 Slc7a2 Camkk2 Camkk2 Camkk2 Camkk2 Cxcl16 Cxcl16 Cxcl16 Cxcl16 Slc2a6 Slc2a6 Slc2a6 Slc2a6 D Tjp2 Tjp2 Tjp2 Tjp2 800 Gsap Gsap Gsap Gsap EV Cxcl10 Gsdmd Gsdmd Gsdmd Gsdmd Nfkbia Jak2 Jak2 Jak2 Jak2 RelA-WT Serpina3g Serpina3g Serpina3g Serpina3g 600 TADΔ Phldb1 Phldb1 Phldb1 Phldb1 Δ Il15ra Il15ra Il15ra Il15ra TA1 H2-K1 H2−K1 H2−K1 H2−K1 TA2CI Ccl5 Ccl5 Ccl5 Ccl5 400 Stk10 Stk10 Stk10 Stk10 TA2CITA1Δ H2-Q4 H2−Q4 H2−Q4 H2−Q4 Cdk6 Cdk6 Cdk6 Cdk6 200 Parp8 Parp8 Parp8 Parp8 Tapbp Tapbp Tapbp Tapbp mRNA (RPKM) mRNA Stat5a Stat5a Stat5a Stat5a Atp2b4 Atp2b4 Atp2b4 Atp2b4 0 Gbp2 Gbp2 Gbp2 Gbp2 Col27a1 Col27a1 Col27a1 Col27a1 0 0.5 1 3 0 0.5 1 3 hr Psmb10 Psmb10 Psmb10 Psmb10 RELA_WT_0hr RELA_WT_0.5hr RELA_WT_1.0hr RELA_WT_3.0hr EV_0hr EV_0.5hr EV_1.0hr EV_3.0hr TAD_0hr TAD_0.5hr TAD_1.0hr TAD_3.0hr TA1_0hr TA1_0.5hr TA1_1.0hr TA1_3.0hr CI_mut_0hr CI_mut_0.5hr CI_mut_1.0hr CI_mut_3.0hr CI_delTA1_mut_0hr CI_delTA1_mut_0.5hr CI_delTA1_mut_1.0hr CI_delTA1_mut_3.0hr RELA_WT_0hr RELA_WT_0.5hr RELA_WT_1.0hr RELA_WT_3.0hr RELA_WT_0hr EV_0hr RELA_WT_0.5hr EV_0.5hr RELA_WT_1.0hr EV_1.0hr RELA_WT_3.0hr EV_3.0hr EV_0hr TAD_0hr EV_0.5hr TAD_0.5hr EV_1.0hr TAD_1.0hr EV_3.0hr TAD_3.0hr TAD_0hrTA1_0hr TAD_0.5hrTA1_0.5hr TAD_1.0hrTA1_1.0hr TAD_3.0hrTA1_3.0hr TA1_0hrCI_mut_0hr TA1_0.5hrCI_mut_0.5hr TA1_1.0hrCI_mut_1.0hr TA1_3.0hrCI_mut_3.0hr CI_mut_0hr CI_delTA1_mut_0hr CI_mut_0.5hr CI_delTA1_mut_0.5hr CI_mut_1.0hr CI_delTA1_mut_1.0hr CI_mut_3.0hr CI_delTA1_mut_3.0hr CI_delTA1_mut_0hr CI_delTA1_mut_0.5hr CI_delTA1_mut_1.0hr CI_delTA1_mut_3.0hr

Figure 2.8 RelA transactivation domains TA1 and TA2 both contribute to the TNF response of NF-kB target genes. (A) The schematic illustrates the RelA variants to be tested: a deletion mutant of TA1, a CBP-interaction mutant of TA2 (TA2CI) and a combination mutant. (B) Protein expression of RelA, IkBb, IkBa, and Actin as loading control, in indicated unstimulated cells assayed by immunoblotting with antibodies specific to RelA, IkBb, IkBa, and b-Actin. (C) Analysis of nuclear NFkB activity by EMSA with kB and NF-Y (loading control) probes of nuclear extracts prepared at indicated times after TNF stimulation of genetically complemented cells shown in (B). (D) Line plots of mRNA expression for Nfkbia and Cxcl10 genes in response to TNF stimulation in same cells. (E) PCA plot of all 104 NFkB target genes in response to TNF in same cells. (F) Heatmap of the transcriptional response of the 104 target genes in same cells. Relative induced expression is shown.

To extend the gene expression analysis to the 104 NFkB target genes, we analyzed RNA- seq data for all described mutants. PCA revealed that both single mutants showed global deficiencies in the TNF-induced gene expression response, while the double mutant appears almost entirely deficient in transcriptional activation, akin to the complete TADD mutant (Figure

2.8E). Interestingly, visualizing the data at single gene resolution in the heatmap format (Figure

2.8F) revealed subtle differences in the functionality of the mutants that warranted a quantitative analysis.

A model-aided analysis reveals gene-specific logic gates formed by RelA TA1 and TA2

For each RelA target gene to assess the functional requirements of TA1 and TA2 quantitatively, we had to address the potentially confounding effect of alterations in the NFkB activation time course (Figure 2.8C). We developed a mathematical formalism that considers both the measured time courses of mRNA and NFkB DNA-binding activity that drives transcription; hence, an ordinary differential equation (ODE) model of RelA-dependent gene expression was constructed (Figure 2.9A). The model consists of a single ODE representing the rate of change of mRNA that results from NFkB-driven synthesis and mRNA decay (see

Methods). NFkB-driven mRNA synthesis is a function of the amount of NFkB DNA binding activity and the specific activation activity (#$%&) of the particular RelA variant.

Given the DNA binding activity measured for each RelA TA mutant, we could identify the activation rate (#$%&) for each gene that optimally accounts for the measured mRNA profile

(Figure 2.9B). Other parameters ()*, ,-.//, #012) were also fit but assumed to be the same for all

RelA variants, as they represent intrinsic properties of the gene promoter of mRNA. Particle

swarm optimization was used to minimize the distance between simulated RNA time courses and the measured RNA-seq data for each of the 104 genes and the model was able to recapitulate the experimental findings indicating that altered activation strengths associated with each TA mutant could explain the altered RNA-seq results (Figure 2.9C). A stringent criterion (ln(dist) < −2.5, see Methods) was applied to exclude 28 genes with unsatisfactory fits. For the remaining genes a

D CI wide range of activation strengths (#$%&) values were identified for the TA1 and TA2 mutants, from ~1 (where TA mutation did not affect gene induction strength relative to RelA-WT) to 0

(where TA mutation of a single TA domain entirely abrogated gene expression) (Figure 2.9C).

Our hope for this analysis was to identify differences in relative dependence on TA1 vs.

TA2 among the 104 RelA target genes, and we found modest specificity: for example inflammatory genes Cxcl2, Cd83 and ICAM1 were more TA1-dependent, whereas Pdlim4 (Src inhibitor), Gsap (protease) showed a higher degree of TA2-dependence. However, for many

D CI genes the optimally parameterized #$%& values for TA1 and TA2 mutants were correlated

(Figure 2.9C) with marked differences in the degree of the decrease associated with both mutants

(Figure 2.9D). For example, Cxcl1, Cxcl2, and Tnf, showed a substantial decrease in transcriptional activity with mutating either TA domain (Figure 2.9D and E bottom left), whereas

Il15ra and Il1rl1 when either TA domain was removed (Figure 2.9D and E top right). On the former class of genes, the inability of either TA domain to function effectively on its own suggests that TA1 and TA2 function synergistically, whereas on the latter class of genes, they seem to function redundantly or independently. Thus, the unexpected conclusion from this analysis is that RelA target genes differ whether they impose an AND gate or OR gate logic on the two TA domains within RelA. Given that the activity of TA1 is dependent on the

phosphorylation status of serines in its amphipathic helix, an AND vs OR gate logic determines distinct regulatory role of TA2: in the latter case, TA2 may be primary driver of transcriptional activation, but in the former case, TA2 merely assists or boosts TA1-driven gene activation.

Figure 2.9 A math model-aided analysis reveals gene-specific requirements for TA1 and TA2

A EMSA Simulated B Nfkbiz 1 RNA Cxcl1 Cxcl2 Tnf Slc25a25 Tnfaip3 0.8 Lif Gadd45b Map3k8 Gem Nfkbia Itpkc 0.6 RelA Ccl20 Pim1 Rgs16 Rnd1 Irf1 !"#$ &' ( Cxcl5 0.4 Nfkbid Zc3h12a RNA Tmem200b Rasgef1b ! Zfp429 567 Noct 0.2 Rsl1 Klhl25 2 Zswim4 )RNA ! / RelA Csf1 "#$ $ Rab20 = 2 2 − !567 / RNA Nfatc1 0 Bcl3 )- &' + RelA$ Ccl2 Ccl7 Birc3 Foxs1 Model C9orf72 Rnf19b Tnfaip6 Rrad Tnfaip2 Trim47 Nfkbib Stx11 Bahcc1 Ptx3 RNA-seq Simulation K Vcam1 C act D Rhbdf2 Uap1 Clic4 Slfn2

Δ Δ Tlr2 Rab8b Ripk2 Mt2 1.2 Birc2 TA1 TA1 WT WT Nfkbie

- - Relb CI CI CI Δ CI Δ CI Δ Cd83 Micall2 Icam1 Sod2 Ninj1TA2 RelA TA2 TA1 TA2 RelA TA2 TA1 TA2 TA2 TA1 Mfhas1 Redundant 1 Il1rl1 CI Nfkbiz Cd44 - Nfkb2 specific Cxcl1 Cilp2 Cxcl2 Gch1 Pdgfb Tnf Cxcl10 Slc25a25 Hivep1 Tnfaip3 Nfkb1 0.8 Fas Lif Cx3cl1 Gadd45b Tnip1 Map3k8 Δ Angptl4 Bid Gem Pdlim4 Nfkbia Alpk1 Traf3 Itpkc TA1 Gfpt2 Ccl20 0.6 Slc7a2 Camkk2 Pim1 act Cxcl16 Rgs16 Slc2a6 K Tjp2 Rnd1 Gsap Irf1 Gsdmd Cxcl5 Jak2 Serpina3g Nfkbid 0.4 Phldb1 Zc3h12a Il15ra Tmem200b H2−K1 Ccl5 Rasgef1b Stk10 Zfp429 H2−Q4 TA1 Cdk6 Noct Parp8 Rsl1 Tapbp - 1 0.2 Stat5a specific Klhl25 Atp2b4 Zswim4 Gbp2 Col27a1 Csf1 Psmb10 Rab20 Synergistic RELA_WT_0hr RELA_WT_0.5hr RELA_WT_1.0hr RELA_WT_3.0hr EV_0hr EV_0.5hr EV_1.0hr EV_3.0hr S467A_0hr S467A_0.5hr S467A_1.0hr S467A_3.0hr CI_S467A_0hr CI_S467A_0.5hr Nfatc1CI_S467A_1.0hr CI_S467A_3.0hr CI_S467A_dTA1_0hr CI_S467A_dTA1_0.5hr CI_S467A_dTA1_1.0hr CI_S467A_dTA1_3.0hr Bcl3 Ccl2 0 Ccl7 0.5 0 0.2 0.4 0.6 0.8 1 1.2 Birc3 Foxs1 CI C9orf72 Kact TA2 Rnf19b Tnfaip6 Rrad

Tnfaip2 (RU) Expression Inducible Synergy Specificity Trim47 0 Nfkbib

CI Δ Redundant Stx11 TA1 Bahcc1 Ptx3 Vcam1 TA2 TA1 Additive 0 Rhbdf2 1 Uap1 CI Clic4 Synergistic TA2 Slfn2 Tlr2 Kact Rab8b Ripk2 Mt2 Birc2 Nfkbie 0 Relb Cd83 E Micall2 Icam1 Sod2 1 Ninj1 Pdlim4 Il1rl1 Mfhas1 Il1rl1 0.8 Cd44 Nfkb2 Cilp2 0.6 Gch1 Pdgfb Cxcl10 0.4 Hivep1 Nfkb1 Fas 0.2 Cx3cl1 Tnip1 0 Angptl4 Bid Pdlim4 Alpk1 act

Traf3 K 1 Gfpt2 Slc7a2 Tnf Birc2 Camkk2 Cxcl16 0.8 Slc2a6 Tjp2 0.6 Gsap Gsdmd Jak2 0.4 Serpina3g Phldb1 Il15ra 0.2 H2-K1 Ccl5 Stk10 0 H2-Q4 Δ Δ Δ Δ Cdk6 CI CI

Parp8 WT WT Tapbp - - TA1 TA1 TA1 TA1 Stat5a TA2 TA2

Atp2b4 CI CI

Gbp2 RelA RelA Col27a1 Psmb10 TA2 TA2

Figure 2.9 A math model-aided analysis reveals gene-specific requirements for TA1 and TA2. (A) Schematic of the ordinary differential equation (ODE) used to obtain simulated mRNA dynamics from quantified NFkB activity timecourses. (B) Schematic of the Particle Swarm Optimization (PSO) pipeline used to quantify activation strengths (#$%&) for TAD mutants. First )*, ,-.//, #012 are fit to RelA-WT data with #$%& = 1. The identified parameters are then maintained with new #$%& fit for each TAD mutant to obtain remaining activation strength (see Methods). (C) experimental (left, RNA-seq) and simulated best-fit mRNA time-course (middle)

CI Δ heatmaps, along with identified #$%& for TA2 (yellow) and TA1 (pink). #$%& is shown relative to RelA-WT with brighter colors indicating closer activity to RelA-WT (no effect of mutation) and white indicating complete loss of activity in the mutant (#$%& = 0). Gray indicated a fit with CI Δ ln(dist) < −2.5 (see methods) was not found. (D) Scatter plot of #$%& for TA2 and TA1 for 76 genes with good fit in panel C. Center color represents effect of either single mutation (from green #$%& = 1 with no change from RelA-WT and redundancy with respect to a single mutation, to blue #$%& = 0 where either mutation abrogates gene induction and both domains work synergistically to achieve full gene activation). Border color represents off-diagonal effect where a gene shows specific loss of induction in TA2CI (pink) or TA1Δ (yellow). Gray lines indicate standard deviation of #$%& from 3 repeated runs of optimization pipeline. (E) Bar graphs of #$%& CI Δ CI Δ (relative to mean RelA-WT #$%&) for RelA-WT, TA2 , TA1 and TA2 TA1 mutants, shown for 4 example genes arranged to indicate their relative positions in panel D. Error bars indicate standard deviation of #$%& from 3 repeated runs of optimization pipeline.

A knock-in mouse reveals that regulatory strategies of RelA target genes are generally conserved in different cell types but are often stimulus-specific

The complementation of RelA-deficient primary MEF cells with mutated variants of

RelA allowed us to study the effects of numerous specific mutations on the TNF-response. To dissect regulatory strategies of endogenous target genes in other cell types and in response to other stimuli, we engineered a mouse strain that harbors the previously described RelA

D TA2CITA1 variant as a knock-in mutation, termed RelATA (Figure 2.10A). Homozygous RelATA/TA mice are not embryonic lethal, unlike RelA-/- mice, but are sickly and smaller than their heterozygous littermates (Figure 2.10B), whose genotype was verified by PCR (Figure 2.10C).

To examine whether RelA target genes showed similar or differential regulatory strategies in other cell types and other inflammatory stimuli, we produced an extensive RNA-seq data by stimulating wild-type and RelATA/TA primary MEFs and BMDMs with TNF or Toll-Like 4 Receptor ligand lipopolysaccharide (LPS).

We first identified genes that were activated in both MEFs and BMDMs by a stringent

log2 fold change >2 threshold (Figure 2.11A). We found 37 such genes in response to TNF stimulation and 481 in response to LPS (Figure 2.11A). After peak-normalizing the data for each gene, we plotted the relative expression data from WT and RelATA/TA MEFs and BMDMs in order of induction peak time in WT MEFs and subsequent hierarchical clustering (Figure 2.11B). We found that the majority of gene activation events were diminished in both mutant MEFs and

BMDMs. We evaluated the phenotype by plotting the percentage of expression in RelATA/TA cells relative to the WT peak expression at the same time point for each gene (Figure 2.11C). In MEFs

Figure 2.10 Generation of a RelA Transactivation Domain mutant knock-in mouse strain

Figure 2.10 Generation of a RelA Transactivation Domain mutant knock-in mouse strain. (A) Schematic of the knock-in strategy, first targeting the RelA gene with a mutant allele containing the CI mutation in TA2 and deletion of TA1, as well as a Neo gene that is flanked by FRT sites, then deleting Neo with FRT. (B) Confirmation of genotype. PCR product using primer set NDEL1B and NDEL2 with genomic DNA from tail DNA isolated from indicated pups was used to screen mice. PCR product for wild-type band showed at 273 bp, mutant band at 371 bp, visualized by 2% agarose gel. Genotyping results for Mouse IDs corresponds to the mice in (C). (C) Photo of 12 day-old littermate mice of indicated genotypes (WT, RelATA/-, and RelATA/TA) prior to isolation of bone-marrow.

Figure 2.11 A knock-in mouse reveals that the dual TAD-requirement pertains to other cell-types and stimuli

MEF TNF BMDM TNF MEF LPS BMDM LPS TNF LPS A B WT RelATA/TA RelATA/TA TA/TA TA/TA C BMDM_TNF WT WT RelA WT RelA MEF BMDM MEF BMDM

Marco 0 .5 1 3 0 .5 1 3 0 .5 1 3 0 .5 1 3 0 .5 1 3 0 .5 1 3 0 .5 1 3 0 .5 1 3 hr 100 Saa3Mab21l3 100 Gm28580 Rsad2 Mir7013Mir7682 MEF_TNF Dusp1 Scarna9Snord14a Fosb 1 Mir6955Gm22574 (310) Nr4a1 Mir8094 Osm 1 Scarna9Snora30 Rsad2 Mir186Mir221 Fosb Mir7682Snord14a Shisa3Gm11874 Egr2Ppp1r15a Tnfaip3 Mir7059Mir7026 Egr1 80 Mir7063 Fosb Mir6905Fos Gm23442Gm23645 Gm28580Gm17567 Mir222Gm28347 Mir677Gm22574 Tnfaip3 Gm15148 80 Junb Gm42793Mir6955 Hivep3Cxcl3 Mir7013Mir221 Pim1 CpCsf2 (159) Mir8094 Il12bCcl17 Junb Dusp4 AregGdf15 0.8 Lad1Gpr84 Dusp5Csrnp1 Pim1 Il4i1Sele Skil Rab11fip1 Ier2 Ier2Dusp8 60 Nos2Arl5c NfkbidGm17024 Tnfsf9 Adora2aC3 Ier2 Zfp36Btg2 Tlr2Cxcl1 Gm6377Egr3 Il1bVash1 MaffJunb Tnfsf9 Fam49aBatf 60 Dusp1 Cd83Prdm1 Dram1Rnd1 Rasgef1b Acod1 Cd83 NfkbiaIer3 Sod2Mycl Dusp1 Nfkbiz Fpr2 Gm23344Mir6963 0.6 40 Il1aTnfsf9 0.8 Tnfsf9 Icam1 Snord19Mir186 Acod1 Atp2b4 Egr1 Gm15148Ifnb1 Ebi3Tfec Gm23442Gm23645 Tlr2 H2Csf3−M2 Egr1 CpMir222 Clec4ePtges BatfFam49a NfkbieFas Cst7Exoc3l4 Tlr2 Igsf6Ptx3 40 Cxcl2 Pla2g16Slc7a11 Gm16685E230013L22Rik Slco3a1 Stx11 20 Nr4a3Tnfaip3 Stat5aIl18bp Nfkbia Cxcl2 Dram1Cdyl2 Gm13889Prdm1 Pstpip2Rassf4 0.4 Cx3cl1Ccl20 Birc3 Stx11 Arg2 Vash1 Tnip3 Zfp36 Malt1 RelbCcl22 Hcar2Traf1 Mmp10 Bcl2a1a Nfkb2Slc7a2 NfkbidSlc7a11 Zfp36 Mycl Icosl Heatr9Nfkb1 Rassf4 Ptx3 0 Fpr1Slco3a1 20 Gpr18Gch1 Mmp10 Edn1 Nfkbiz FasEnpp4 Birc3Vcam1 Zc3h12aCxcl1 Gm20186 Plagl2Nfkbiz Nfkbiz Tnip3 Nfkb2Ripk2 Gpr84Arg2 Lcn2 0.6 Il1b 0.2 Traf1St3gal1 Clec4e Gm20186 Nfkb1 Ebi3Sod2 Hcar2 Gm17024 Csf1Rab11fip1 Zfp36 Cd40Bcl3 122 37 273 Bcl2a1aRelb Gm42793Ciita Gm17024 Tlr2Nfkbie Nlrp3Mapkapk2 SeleIcosl H2Tnfsf15−Q6 0 Rnd1 Zfp36 Ppp1r15a Nod2 Cxcl2 Atf3 Tnfaip2 Steap4Ikbke AW112010Ccl5 Noct Slc2a6 Acod1Cd40 Rasgef1bPstpip2 Atf3 Tnfsf15Tnfsf8 Csf1 Lad1H2−Q6 0 Pnrc1Irak2 Ptges Noct Zc3h12aExoc3l4 Ccl17 Tnfsf9 Gm11874Gm28347 NfkbibPla2g16 Cx3cl1Csf3 Casp4 JunbTnfaip2 Tnfsf9 Fpr1Adora2a Marcksl1Maff Ripk2Fpr2 RelaKpna3 Ccl22 Casp4 Ehd1Acod1 Vcam1Il12b Tnf Gadd45b H2Slc2a6−M2 Cxcl9 Cdyl2Enpp4 Mir7063Mir7059 Nod2Gpr18 Gadd45b Shisa3 Slc7a2 AW112010 0.4 Il4i1Mir1943 Cst7 genes 154 = N Ccl20 Ccl5 Il1a Cxcl9 Malt1 Gm16685Icam1 Stat5aSdc4 Pim1 Steap4 Gm11874 Il27 Lcn2Nos2 Ier3Slc11a2

Saa3 genes 28 = N Gch1 Pim1 Marco Gbp6 Mir7026 C3Cxcl3 Gm17024Heatr9 Csf2 Gm11874 Zfp36Egr3 Tfec Irf1 Nlrp3 Btg2Mir677 Tnfaip3 E230013L22Rik Ier2 Cxcl2Tnf Mir1943 Irf1 Igsf6 Gm23344Il18bp E230013L22RikArl5c Tnfsf8 Hivep3Ikbke E230013L22Rik Edn1Gm13889 Snora30 Tnfaip3 SrgnSdc4 H2−M2 Gm17567Mir6905 NfkbibMarcksl1 Adamts4Gm25323 Tnfaip3 St3gal1 Ptgs2Gem Plagl2Rela Pde4b Mapkapk2Slc11a2 H2-M2 Gm38357Zc3h12c Irak2 Il6 Noct Ehd1 Plscr1Serpine1 Rapgef2 C3 Gm17334 Ccl9Tnfrsf1b Dusp2 Noct Jdp2Rhbdf2 Il10Lif 0.2 Parp8 Nr4a3Cybb Skil Mab21l3Atp2b4 C3 Bcl2a1dCd200 Gem Tnfrsf1b TNF, log2FC ≥ 2 Ankrd33b Irf1 Clic5 Socs3 Pdgfb Klf8 Cd14Il10 Cxcl9Pik3r5 Ankrd33b Zc3h12cGadd45b Jdp2 Pnrc1Rel Rapgef2Tagap Cxcl16 Pdgfb SrgnSerpina3g Cpeb4 Nlrp3 Sp100H2−K2 Gm6377Tet2 Ube2l6Crlf3 Ccl5 Ccrl2Phf11a Nlrp3 H2Oas2−T22 NoctHtr2a Trim26Atp10a Gpr132Bcl2a1b G530011O06RikTiparp Ccl5 Snord19Whamm Tmcc3 Serpina3f Ccl7 Il10raDgkh Irf1 Cd69Mir155hg Nfil3Cited2 1110038F14RikKatna1 Ccl7 Stk40Serpine1 Cxcl16Med13 Dusp16Ifi207 A630072M18RikBcl2a1c Gm25323 Irf1 Car13Gm43814 Gm17334Mmp10 Itga4 E230013L22Rik Med13 Fam208b Dnaja2 Gbp6 Olr1 Tet2Hivep2 Usp12 E230013L22Rik 0 A630072M18Rik Jak2Cemip2 LckSpryd7 Hdc PtprjTlk2 Gbp6 Zbtb5Dusp16 Slfn3 Psmb10 Tnip1 Resf1Etnk1 Gm43197Misp Sap30Dse Csf1 Gnb4Gadd45b Tnip1 Abtb2Epsti1 Ms4a4cGm13822 N4bp1Apobec3 Casp4H2−Q4 Ccdc25Snx10 Csf1 CybbCpeb4 MEF_LPS BMDM_LPS Xaf1 Lacc1 Gm20186 Plekha4Dhx58 Ifi47 Rmdn3Ptprj Tor1aip2Trim34a Abtb2Rhbdf2 Gm20186 GcaUsb1 Gm20559Gbp7 March5Socs7 Fam241aEgr2 Tlr3 Ifi47 Trim21 Gbp7AA467197 Arhgef3Cish C3 Gm4951 H2−K2 Oas1b Ankrd33b Hivep1 Il15raIfi208 Pcgf5 C3 Nlrc5 Socs7Stx11 Batf2Irf7 Gdap10 Mx1Gbp2 Ankrd33b Helz2Clec2d (939) (805) Ifit3b Cd14 Cxcl9 Ifit3 Myd88Dse Igtp Tnip1 Mtmr14 Irgm2Usp18 Gbp11 Cxcl9 Ifi211Isg15 Phlda1Zbp1 Slfn5Helz2 Lcp2Parp14 Setdb2 Tnip1 Dcp2Tmcc3 DaxxIsg20 Gbp3 H2−M2 Gbp9Serpinb2 Nfkbiz Ikzf1Notch1 Gm5970Gbp11 CflarTap2 H2-M2 Mx2Ifit2 Snx10Phf11c Cxcl9Iigp1 Sap30Zup1 Trim30c Nfkbiz Calhm6 Asap3Slfn8 Nt5c3Parp9 Acod1 Tmem140 Rel Tor3aPsmb9 Gadd45b Gbp2 Acod1 Nod1Oasl2 Arel1Ccl3 Peli1Tap1 Gbp5Dtx3l Arid5a Gadd45b Asap3Phf11d Ikzf1Olr1 Dnaja2 Ccl5 Zup1Gm8995 Cxcl2 Oas1bRnf135 Ppm1kParp9 Il15Xaf1 Ccl5 Ifi204Rnf213 TapbpApobec3 BambiPhf11c−ps1 Ube2l6Tap1 Notch1 Cxcl2 Parp10Slfn3 AdarPml Map3k8 Gbp6 Oas1aTent4a Tnfsf10Herc6 Gm20559Stat2 Fosb Tor3aGca Gbp6 Slfn1 Ddx58 Eif2ak2Nampt B3gnt5Pim1 Ifi35Trim30a Stat2Phf11b Spsb1 Fosb H2−K1 Misp Daxx Rsad2 Trim30d Ccl9 CishIrf2 Trim30d Rsad2 Gm13822Oas1g Dusp1 Trim30aRnf19b Upp1Slfn4 NmiCxcl10 Mxd1Trim25 Irf1Parp12 Samhd1 Dusp1 Rnf213Ppm1k Ifi47 Sp140Ddx60 Daam1 Gbp4Gm49342 Egr1 Resf1March5 Ifi47 Ifit1 Arid5aTmem140 Slfn9Ifi209 Ccdc25 Oasl1Ifi205 Hivep2Ccl4 BC023105Gm12185 Egr1 Setdb2Slfn2 Gm11874 Socs1Irgm1 Rnf31N4bp1 Dll1Ifi206 Ier2 Clic4Nampt Gm11874 Ifih1 Sp100 Ccl12Lipg Atp10aBambi−ps1 Cmpk2Ccl7 Ifi213Ifi35 Rsad2 Ier2 Trafd1 Slamf7 Tlr3 Pdgfb Ifi203−ps Ptpn2 Mndal Atf3 Mx1Olfr56 Pdgfb Il27Gbp6 Etnk1 458 481 324 Clic5Calhm6 Stk40Gm12764 Mmp13Cd69 Slfn5Tlk2 Tnfsf10 Atf3 Ifi204Nlrc5 Csf1 Zbp1Gbp5 Samd9l Gm5431Pcgf5 Junb Spryd7Eif2ak2 Csf1 Hdc Socs3Trim34a Herc6Jak2 Znfx1 Ifi44Ms4a4c Gm5431Tor1aip2 Phf11a Junb Peli1Ifi209 Stx11 Gnb4Znfx1 Areg Samd9l Irf7 Katna1Whamm Nlrp3 Slamf7Irgm2 Stx11 Ptgs2 Tent4a Cxcl10Gm12764 Csrnp1Adar Cxcl11Tgtp2 Dll1Stat1 genes 327 = N Il6Gm12250 Nlrp3 Dgkh Tlr2 Olfr56Tgtp1 Cd274Gm8995 Dtx3l Ccl7 Ccl2Samhd1 Tlr2 Parp14Trim21 Trim30c Ifi47Il15 Ddx60Ifi47 Serpina3fSerpina3g Slfn19930111J21Rik1 Gbp3 Ccl7 Trim26Ifi211 Casp4 Phf11b Gdf15 Ch25hCcl2 Nod1 Tagap Gm17024 Dusp8Oas3 Casp4 Pde4b genes 9 = N Dusp2Pim1 Ifi203Epsti1−ps Ccrl2 IgtpErrfi1

Mir155hg MEF_TNF_WT_dep MEF_TNF_MUT_dep BMDM_Mut_dep Map3k8 Gm17024 Il15ra Mmp10 Rmdn3Cd200 Rtp4Dusp4 Car13 Slfn9 Gm43814Ggct FosbCxcl11 Mmp10 Ccl3 Mir6381

WT_0H_AVG WT_0.5H_AVG WT_1H_AVG WT_3H_AVG MUT_0H_AVG MUT_0.5H_AVG MUT_1H_AVG MUT_3H_AVG BMDM_WT.0.NT BMDM_WT.0.5.TNF BMDM_WT.1.0.TNF BMDM_WT.3.0.TNF BMDM_RelA BMDM_RelA BMDM_RelA BMDM_RelA Phf11d Pml Casp4H2−Q4 Usp18Usb1 Bcl2a1dStx11 Gm49342 Bcl3Lif Oas2Gm12250 Bcl2a1bPik3r5 MafkIfi206 Bcl2a1cGpr132 Irgm1Dhx58 Fam241aLcp2 Tent5aIl10ra Lacc1Arhgef3 Ifi208Psmb9 1110038F14RikCiita Ifi44Isg20 Kpna3Hivep1 Ifi205Sp140 Nt5c3Parp8 GgctMx2 Slc23a2Arel1 Crlf3Ccl7 H2Tapbp−K1 Oasl2Gbp9 Itga4Ptpn2 Serpinb2Irf2 Klf8Mtmr14 Oas1aIfi207 Tap2Psmb10 Slfn8Ccl12 Plscr1Adamts4 Upp1AA467197 Cemip2Clec2d Plekha4Rsad2 Rtp4Parp12 Ifnb1 Clic4Myd88 Ifit2Spsb1 Gm43197Dcp2 Nr4a1Ifi203 Tent5a % of inducible expression in Isg15Cmpk2 Expression relative to WT Nmi 9930111J21Rik1Oas3 Ifih1Gm12185 LPS, log2FC ≥ 2 Stat1Rnf135 Tgtp2Rgs1 Zbtb5Ddx58 Trim25Ch25h Gdap10Gypc TiparpSocs1 Fam208bRnf31 Tnfsf4Themis2 Parp10 TA/TA H2−T22 Ifi213Trafd1 Oas1gCcl8 Daam1Usp12 RelA relative to max (WT) Mmp10Ifit1 peak expression Ccl4 Gbp4 Slfn2Cflar MndalAtf3 Atf3Rgs1 Tgtp1Dusp5 Fgl2Gm38357 Slc23a2Cited2 − − − − Ccl8 Dusp1 Tnfsf4Ifit1bl1 Egr1 Otud1Mir6381 GypcZcchc2 Noct Nfil3Batf2 TADmut.0.NT TADmut.0.5.TNF TADmut.1.0.TNF TADmut.3.0.TNF B3gnt5 Sectm1aIl33 BC023105Lck Cd274Pou3f1 Mxd1Ifit3 Phlda1Errfi1 AW011738Mir6963 Rnf19bAW011738 Ifit3bLipg Themis2Tmem171 Pou3f1Otud1 Htr2aZcchc2 G530011O06RikMmp13 RgccMafk Slfn4Osm WT.UT.0.bam WT.LPS.0.5.bam WT.LPS.1.bam WT.LPS.3.bam TADmut.UT.0.bam TADmut.LPS.0.5.bam TADmut.LPS.1.bam TADmut.LPS.3.bam BMDM_WT.0.NT BMDM_WT.0.5.LPS BMDM_WT.1.0.LPS BMDM_WT.3.0.LPS BMDM_RelA.TADmut.0.NT BMDM_RelA.TADmut.0.5.LPS BMDM_RelA.TADmut.1.0.LPS BMDM_RelA.TADmut.3.0.LPS FosOasl1 Tmem171Rgcc 0 20 40 60 80 100 Gm5970Fgl2 0 0.2 0.4 0.6 0.8 1 Ifit1bl1 Gm4951Iigp1 Il33Sectm1a MEF_LPS_WT_dep MEF_LPS_MUT_dep BMDM_Mut_dep

D E MEF TNF MEF LPS BMDM TNF BMDM LPS F RelATA/TA RelATA/TA WT RelATA/TA WT RelATA/TA WT RelATA/TA WT RelATA/TA MEF BMDM MEF_LPS TNF LPS TNF LPS 0 .5 1 3 0 .5 1 3 0 .5 1 3 0 .5 1 3 0 .5 1 3 0 .5 1 3 0 .5 1 3 0 .5 13 hr Marco 100 Il1b 1 Scarna9 1 Cilp2 100 Snord71 Ifit1 Nr3c2 Mir7682 Cxcl1 Gm17024 Saa3 Snord87 Cxcl2 Swap70 Egln3 Gm24187 Tnf Tarm1 Gm11874 (939) Zfp36 Snord71 Madcam1 Snord37 Mir24−1 Micall2 Gm25128 Gm17024 Mfsd7a Chil1 80 Gm26143 80 MEF_TNF Ier3 0.8 Mllt6 0.8 Cd74 Mir6988 Nfkbiz Sod2 Id3 Fosb H2−K1 Cxcl3 Il4i1 Gm22355 C3 Acp5 Il6 Gm15694 Tnip1 Cacnb3 Junb Mir7063 C3 Ptgs2 Gm8221 Neurl3 Gm15720 Arrdc4 Spib 60 Tlr2 60 (159) Fos 0.6 Mapkapk2 0.6 Selp Dram1 Ier2 Nlrp3 Gpr84 Sod2 Dusp1 Mir1938 Fpr2 Egr1 Il12b Nos2 Tnfsf9 Btg2 Sqstm1 Trim47 H2−M2 Zhx2 Mmp3 Pde4b Egr2 Atf3 Steap4 Zc3h12c Nr4a1 0.4 Cxcl16 40 Gpr85 40 Atf3 Jak2 0.4 Zbtb7c Slco4a1 H2−K2 Gpr85 Ccl20 Arrdc4 Rtp4 Tslp E230013L22Rik Ankrd33b Tnfrsf1b Nfkbia Tnfaip3 Rnd1 Pde4b Arg2 Nfkbia Gm24187 Cxcl2 Mfsd7a Calhm6 Cxcl5 Gm5416 Irf1 0.2 Gng4 Cx3cl1 20 Bcl2a1a 20 Tnfaip3 Acod1 0.2 Gm22303 Has1 Id3 Icam1 Fpr1 Csf2 Cdkn1a Gm16685 Mmp14 Sele Zfp36 Ptges Gng4 Marcks Tlr2 Tarm1 Sik1 Snord65 Snn Maff 0 Rnd3 Csf2 Parp8 0 Gadd45b Slc44a1 0 Itga2 0 St3gal1 Gem Eva1b Il1b Bcl2a1d Tagap Gm20186 Tnfrsf1b Lif Gm43814 Mllt6 Rgs16 Gm42793 Ier3 Stx11 Relb Gm42793 Ccrl2 Cdc42ep4 Cxcl9 13 146 793 Fosl1 Mmp14 Csf1 Zhx2 Tnfsf15 Zc3h12a Zbp1 Icosl Jdp2 Cd83 Egr1 Tnfsf9 Fosb Nfkbie Tagap Mir155hg Ccl9 Csf1 Arid5a Fpr2 Tmem132e Pnrc1 Nr4a3 Batf2 Slc2a6 Efna2 Psme2 Gm11874 Noct Pim1 St3gal1 Gm27676 Noct Ier2 H2−M2 Acod1 Rel Cst7 Cxcl1 Cxcl16 Egr3 Gm22303 Ripk2 Cdc42ep4 Tlr2 Rnd1 Gm44364 Csrnp1 Dusp1 Dirc2 Ereg Gm15720 E230013L22Rik Swap70 Phlda1 Rnf135 Sele Cst7 Errfi1 Ifit1bl1 Nfkbiz Ccl5 E230013L22Rik Cd83 Gm28727 Slc25a25 Spryd7 Jak2 Nfkbid Snn Nfkb2 Zbtb5 Tnfsf9 Zc3h12c Slc25a25 Psmb10 Slfn2 Gdf15 Vcam1 Cdkn1a Jdp2 Casp4 Nlrp3 Il4i1 Zc3h12a Psme2 Gm8773 Gprc5c Tnfaip2 Fam241a Pik3r5 Gm11874 H2−Q6 Trim21 Mtmr14 Nfkb1 Stx11 F2rl1 Mir27b Eva1b Il15ra Gm37486 Ankrd33b Marcks Fas Mir7682 Birc3 Zbp1 Serpina3g Tnfsf9 Mycl Rnd3 Gm43197 Ccl5 Calhm6 Gch1 Pstpip2 Gbp6 MEF, log2FC ≥ 2 Casp4 Ube2l6 Angptl4 Rnf135 Mmp10 Parp10 Ptx3 Il15 Tnfaip2 Cacnb3 Gch1 Apobec3 Znfx1 Zfp36 Tapbp Tlr2 Acp5 Frmd6 Bdkrb1 Snord87 Tnfsf11 Gca Ifi205 Acod1 Pim1 Gm44364 genes 120 N= H2−K1 genes 130 N= Gpr84 Gm27676 Egr3 Ccl9 Gm11874 Dram1 Foxs1 Gm37486 Icam1 Trim21 Nfkbid Irf1 Pnrc1 Zfp36 Ripk2 Casp4 Fas Ccl4 Nfkbie Bcl2a1d Gbp6 Slc44a1 Cxcl9 Apobec3 Tnf Phldb1 Lipg Tnfaip3 Olfr56 Cxcl3 Mx2 Mir27b Cxcl10 Tapbp Ereg Trip10 Serpina3f Mitf Bcl3 Spryd7 Tgtp1 Il18bp Gbp5 Mitf BMDM_LPS Ifi203−ps Gm29367 Ccl8 C3 Cd69 Znfx1 Tmem132e Tnfsf4 Mmp10 Ifi209 Itga2 Trip10 Gadd45b Ifi47 H2−Q4 Mir6963 H2−Q4 Ier2 Cxcl9 Gdf15 Parp14 Snord37 Csrnp1 Ifi203−ps (805) Ifi47 Il15 Stx11 Igtp Parp8 Nr3c2 Ifit3 Rel Rtp4 Gbp3 Gm5416 Sik1 Fosb Gbp7 Ccl4 Casp4 Gm12250 BMDM_TNF Saa3 Frmd6 Lif Mx2 Rsad2 Steap4 Tnfsf15 Serpina3g Ifi203 H2−Q6 Osm Serpina3f Isg15 Mmp10 Tnfsf4 Cilp2 Gca Ifi47 Mmp10 Cd74 Gm28727 Ch25h Mir24−1 Zbtb7c Phldb1 Gbp3 Atf3 (310) Gbp2 H2−M2 Gprc5c Gm25128 Il1rl1 Gbp6 Dirc2 Ccl2 Dusp1 Noct Il18bp Chil1 Olfr56 Ccrl2 Egr1 Mmp3 Ccl5 Bdkrb1 Batf2 C3 Mir6988 Irf1 Ifit3 Madcam1 Csf1 Cxcl10 Mir6963 Ifi209 Lipg Nos2 Gbp6 Mir155hg Ifit3b Gm16685 Ifi203 Pim1 Osm Spib Gm26143 Rgs16 Snord65 Ccl5 Arg2 Ifit1bl1 Igtp Parp14 Gbp5 Psmb10 Tgtp1 Gm14870 H2−M2 Irf1 Il15ra Mir7063 Acod1 Efna2 Zfp296 Snord73a Icosl Ifi47 Mir6921 Gm14870 Gbp7 Gm22355 Cx3cl1 Isg15 Gbp2 Mir3057 Ccl12 Zbtb5 Cxcl9 Mir6955 Rsad2 Gm12250 Gem Gm22574 Ccl11 Bcl2a1a Gm23645 Rsad2 H2−K2 Scarna9 Ch25h Marco Ifi205 Sgk1 Egln3 Gm29367 Ccl12 Il12b 125 185 620 Gm20186 Slco4a1 Rsad2 Tma16 Fpr1 Gm8221 Ptx3 Gm3788 Phlda1 Fam20c Csf1 Sgk1 Fosl1 Plscr1 Vcam1 Gm23645 Has1 Nfkbiz Trim47 Gadd45b Foxp4 Ccl7 Noct Lcn2 Il1rl1 Tma16 Ccl8 Mefv Slc2a6 Nfkbiz Ccl11 Nck1 Nfkb2 Sp140 Mapkapk2 Micall2 Mir3057 Nlrp3 Fam20c Ier2 Cxcl2 Angptl4 Foxp4 Clec4e Gm3788 Nfkb1 Resf1 Btg2 Slc31a2 Zfp296 Gm22574 Tnip3 Srgn Neurl3 Cxcl2 Pstpip2 Bambi−ps1 Nr4a3 Junb Mycl Klf6 Nlrp3 Gm27326 Ptges Mir23b Gm16098 Sqstm1 Tnip1 Hivep2 Gm17024 Gm43814 Cd69 Slc31a2 Cers6 Gm22748 Maff Gm43197 Ccl7 Mx1 Junb Gadd45b Ccl2 Stfa2 Pik3r5 Sdc4 Relb Plscr1 Stfa2 Nck1 Egr2 Gm22748 Stx11 Gm27326 Ptgs2 Mtmr14 Clec4e Rnf19b Slfn2 Ube2l6 Gm16098 Mefv Il6 Parp10 Tnip3 B3gnt5 B3gnt5 Lcn2 Egr1 Gm17024 Ccl20 Snord73a Arid5a Rnf19b Selp Cers6 Gm8773 Resf1 E230013L22Rik Junb Dusp1 Hivep2 BMDM, log2FC ≥ 2 Birc3 Mir6921 Nr4a1 Mir23b Bcl3 Sdc4 Mir1938 Mir6955 Ccl7 Bambi−ps1 genes 55 N= Tslp Srgn Atf3 Mx1 Tnfsf11 Fam241a F2rl1 Klf6 Cxcl5 Pim1 Sp140 Tnfaip3 Fos Ccl7 Foxs1 Fosb Gm15694 WT_0H_AVG WT_0.5H_AVG WT_1H_AVG WT_3H_AVG MUT_0H_AVG MUT_0.5H_AVG MUT_1H_AVG MUT_3H_AVG WT_0.UT WT_0.5.LPS WT_1.0.LPS WT_3.0.LPS MUT_0.UT MUT_0.5.LPS MUT_1.0.LPS MUT_3.0.LPS Ifit3b WT.0.NT WT.0.5.TNF WT.1.0.TNF WT.3.0.TNF RelA RelA RelA RelA WT_0.NT WT_0.5.LPS WT_1.0.LPS WT_3.0.LPS RelA RelA RelA RelA Errfi1 Ifit1 WT_dep Mut_dep Mut_LPS_dep WT_dep Mut_dep LPS_Mut_dep N= 26 genes 26 N= − − − − − − − − TADmut.0.NT TADmut.0.5.TNF TADmut.1.0.TNF TADmut.3.0.TNF TADmut_0.NT TADmut_0.5.LPS TADmut_1.0.LPS TADmut_3.0.LPS

Figure 2.11 A knock-in mouse reveals that the dual TAD-requirement pertains to other cell-types and stimuli. (A) Venn diagrams indicating of overlap in induced transcriptional response in MEFs and BMDMs. Induced genes satisfy two criteria: log2 fold-change ≥ 2 and max RPKM > 1, by polyA+RNA-seq over a 3 hr timecourse analysis. (Left panel) In response to TNF (10ng/mL) 159 genes are induced in WT MEFs and 310 are induced in WT BMDMs. (Right panel) In response to LPS (100ng/mL), 939 genes are induced in WT MEFs and 805 induced genes in WT BMDMs. (B) Heatmaps of relative expression of induced genes in WT and RelATA/TA mutant MEFs or BMDMs in response to (Left panel) TNF or (Right panel) LPS. Gene names and data plotted in heatmaps are listed in Supplementary Table 2.2 and Table 2.3 for TNF and LPS, respectively. (C) NFkB dependence of induced genes: percentage of peak expression in RelATA/TA mutant vs. WT. (Left panel) Genes induced by TNF in MEFs and BMDMs. Gene names and data plotted in heatmap are listed in Supplementary Table 2.4. (Right panel) Genes induced by LPS in MEFs and BMDMs. Gene names and data plotted in heatmap are listed in Supplementary Table 2.5. (D) Venn diagrams indicating of overlap in induced transcriptional response to TNF and LPS (Left panel) In WT MEFs, 159 genes were induced by TNF and 939 by LPS. (Right panel) In WT BMDMs, 310 genes were induced by TNF and 805 were induced by LPS. (E) Heatmaps of relative expression of TNF- vs. LPS-induced genes in WT and RelATA/TA mutant (Left panel) MEFs or (Right panel) BMDMs. Gene names and data plotted in heatmap are listed in Supplementary Table 2.6 and Table 2.7 for MEFs and BMDMs, respectively. (F) NFkB dependence of induced genes: percentage of peak expression in RelATA/TA mutant vs. WT. (Left panel) Genes induced by MEFs by TNF and LPS. Gene names and data plotted in heatmap are listed in Supplementary Table 2.8. (Right panel) Genes induced by BMDMs by TNF and LPS. Gene names and data plotted in heatmap are listed in Supplementary Table 2.9.

the majority of genes were TA-dependent (28/37), and most of these (25/28) were also TA- dependent in BMDMs. In response to LPS, the majority of genes were TA-independent in both

MEFs and BMDMs, given the strong activation of NFkB-independent signaling pathways of

IRF3/ISGF3 and MAPK p38. However, of the 154 genes that were identified as TA-dependent in

MEFs, about 90% showed an equivalent degree of TA-dependence in BMDMs. These data suggest that regulatory strategies of RelA target genes are generally conserved in different cell types when induced by the same stimulus.

We then asked whether regulatory strategies of NFkB target genes may be stimulus- specific. To this end we identified 146 genes that were induced by both TNF and LPS in MEFs and 185 that were induced by both stimuli in BMDMs (Figure 2.11D). Heatmap visualization of the gene expression timecourses revealed that most genes induced rapidly by TNF peaked later in response to LPS (Figure 2.11E left and right panels). Examining their RelA TA-dependence by collapsing the data to the peak timepoint, we found that of the 120 MEF genes that were TA- dependent in response to TNF, only 60% showed an equivalent degree of TA-dependence in response to LPS, and 15% were entirely TA-independent (Figure 2.11F left and right panels).

Similarly, of the 26 genes whose TNF induction was TA-independent half showed TA- dependence in response to LPS. In BMDMs the lack of equivalence was even more pronounced: only 25% showed an equivalent degree of TA-dependence (or independence) in response to TNF and LPS, and more than 40% showed entirely opposite regulatory requirements for RelA’s TA domains.

We wondered whether compensation by cRel, which was documented in RelA-/- MEFs

(Hoffmann et al., 2003), was affecting our results. We thus bred the RelATA knock-in strain to a

crel-/- background and produced RNA-seq data from crel-/-RelATA/TA and crel-/- MEFs in response to

TNF and LPS stimulations. Focusing on the previously mentioned list of 146 genes that were found induced by both TNF and LPS in WT MEFs, we found 145 also induced in crel-/- MEF

controls (Figure 2.12A, only Mmp10 gene did not satisfy the stringent criteria of log2FC >2 and max RPKM >1; thus, it was grayed out). Our results with crel-/-RelATA/TA and RelATA/TA MEFs demonstrate that the lack of RelA TA-dependence is generally not due to cRel compensation

(Figure 2.12B).

In sum, our transcriptomic analysis using newly developed RelATA/TA mice revealed that regulatory strategies of RelA target genes were generally conserved between MEFs and BMDMs when the same stimulus was used, but that in either cell type, RelA TA dependence observed in response to TNF was not well correlated with dependence in response to LPS. These data indicate that many NFkB target genes engage differential regulatory strategies to different stimuli, consistent with the notion that RelA transactivation activity may integrate signaling pathways that are triggered by some stimuli but not others.

Figure 12.12 Lack of RelA-TAD-dependence in the transcriptional response of MEFs to TNF or LPS is not due to cRel compensation

Figure 2.12 Lack of RelA-TAD-dependence in the transcriptional response of MEFs to TNF or LPS is not due to cRel compensation. (A) Heatmaps of the transcriptional response 146 genes that are induced in cRel-/- MEFs in response to TNF and LPS. Shown is the relative expression in cRel-/- and cRel-/-RelATA/TA mutant MEFs in response to (Left panel) TNF or (Right panel) LPS. Gene names and data plotted in heatmap are listed in Supplementary Table 2.10. (B) NFkB dependence of TNF- and LPS-induced genes in MEFs: percentage of peak expression in (Left panel) RelATA/TA mutant vs. WT, and (Right panel) cRel-/-RelATA/TA mutant vs. WT. Gene names and data plotted in heatmap are listed in Supplementary Table 2.8.

SUPPLEMENTARY TABLES

Supplementary Table 2.1 List of RelA binding locations and TNF-induced gene expression for 104 NFkB target genes. The table shows all identifiable RelA-binding peaks and their associated kB DNA elements for each of the 104 NFkB target genes, from -10kb to gene TSS and to +10kb downstream of gene TSS.

Column A: Genomic location of each RelA ChIP-seq peak. Column B: Distance from gene TSS for start of RelA peak. Column C: Distance from gene TSS for end of RelA peak. Column D: Gene coding strand direction (-1 refers to antisense or negative strand, 1 refers to sense or positive strand). Column E: each kB sequence respective to its RelA ChIP-seq peak. Column F: Genomic location of each kB sequence relative to gene TSS Column G: ChIP-seq score for RelA peak in reconstituted RelA-WT (Figure 2.3 and Figure 2.5). Column H: ChIP-seq score for RelA peak in reconstituted RelADB mutant (Figure 2.5). Column I: Ratio of RelA ChIP-seq score (RelADB mutant over RelA-WT). Column J: RelA ChIP-seq peak classification based on results in Column I (not_reduced is defined by ratio of RelADB mutant over RelA-WT ≥0.5 and reduced is if ratio of RelADB mutant over RelA- WT <0.5). Column K: Percentage of dependence on NFkB based on nascent transcript expression, analyzed by caRNA-seq in WT vs crel-/-rela-/- MEFs stimulated with TNF.

Column L: Fold-induction (Log2) of nascent transcript expression (caRNA-seq) in response to TNF in WT MEFs. Peak expression in WT divide by its unstimulated condition.

Column M: Mean of fold-induction (Log2) of mRNA, analyzed by polyA+RNA-seq in WT MEFs stimulated with TNF. Column N: Ensemble Gene ID for each gene annotated with biomaRt (R). Column O: Gene symbol. Column P: Description of Gene.

Supplementary Table 2.2

The table contains the relative expression data plotted in heatmap visualized in Figure 2.11B. 37 genes were found upregulated in both WT MEFs and BMDMs in response to TNF stimulation

by a stringent log2 fold change >2 threshold. For each gene, relative expression data in WT or

RelATA/TA MEFs and BMDMs were plotted by peak-normalizing the data in order of the time point of peak induction in their respective WT control (see Materials and Methods) and subsequent hierarchical clustering was performed within each peak timing of induction. Of note, averaged

RPKMs from WT and RelATA/TA MEFs data (from two replicate) were used to calculate relative expression for each gene. Same ordering of genes were plotted in WT and RelATA/TA BMDMs data.

Scale of 0 to 1; 0 corresponds to no inducible expression and 1 corresponds to similar expression in WT control, respectively.

Supplementary Table 2.3:

The table contains the relative expression data plotted in heatmap visualized in Figure 2.11B. 481 genes were found upregulated in both WT MEFs and BMDMs in response to LPS stimulation by

TA/TA a stringent Log2 fold change >2 threshold. For each gene, relative expression data in WT or RelA

MEFs and BMDMs were plotted by peak-normalizing the data in order of the time point of peak induction in their respective WT control and subsequent hierarchical clustering was performed within each peak timing of induction. Same ordering of genes were plotted in WT and RelATA/TA

BMDMs data. Scale of 0 to 1; 0 corresponds to no inducible expression and 1 corresponds to similar expression in WT control, respectively.

Supplementary Table 2.4:

The table contains the percentage of expression data for the 37 TNF-responsive genes in RelATA/TA

MEFs and BMDMs that were plotted in Figure 2.11C heatmap. For each gene in response to TNF stimulation, averaged percentage of expression data (from two replicate) in RelATA/TA MEFs and

BMDMs (1 replicate) relative to their respective WT peak expression at the same time point were

plotted. Same ordering of genes were plotted for percentage of expression data in WT and RelATA/TA

BMDMs. For heatmap visualization, these genes were first identified by their TA-dependence relative to WT MEFs (<50% referred to as TA-dependent and ³50% referred to as TA- independent). Within each category, genes were furthered ordered by their TA-dependence found in BMDM data. Scale of 0 to 100 corresponds to no expression and 100 is similar expression to

WT control, respectively.

Supplementary Table 2.5:

This table contains the percentage of expression data for the 481 LPS-responsive genes in RelATA/TA

MEFs and BMDMs that were plotted in Figure 2.11C heatmap. For each gene, percentage of expression in RelATA/TA MEFs and BMDMs relative to their respective WT peak expression at the same time point were calculated. For heatmap visualization, these genes were first identified by their TA-dependence relative to WT MEFs (<50% referred to as TA-dependent and ³50% referred to as TA-independent). Within each category, genes were furthered ordered by their TA- dependence found in BMDM data. Scale of 0 to 100 corresponds to no expression and 100 is similar expression to WT control, respectively.

Supplementary Table 2.6:

The table contains the relative expression data for 146 genes induced in WT and RelATA/TA MEFs by

TNF and LPS stimulation found by a stringent log2 fold change >2 threshold that were plotted in

Figure 2.11E (left panel) heatmap. For each gene, relative expression data in WT and RelATA/TA MEFs in response to either TNF or LPS stimulation were calculated. Of note, averaged RPKMs from WT and RelATA/TA MEFs data (from two replicate) in response to TNF were used to calculate relative

expression for each gene. Ordering of gene list is based on TNF-stimulated data in WT and same ordering of genes (from top to bottom of heatmap and table) were shown for LPS-stimulated data.

Scale of 0 to 1; 0 corresponds to no inducible expression and 1 corresponds to similar expression in WT control, respectively.

Supplementary Table 2.7:

The table shows the relative expression data for 185 genes induced in BMDMs by TNF and LPS stimulation identified in WT BMDMs data. Heatmap visualization of this data in WT and RelATA/TA

BMDMs is shown in Figure 2.11E (right panel) heatmap. For each gene, relative expression data in WT and RelATA/TA BMDMs in response to either TNF or LPS stimulation were calculated.

Ordering of gene list is based on TNF-stimulated data in WT and same ordering of genes (from top to bottom of heatmap) were shown for LPS-stimulated data. Scale of 0 to 1; 0 corresponds to no inducible expression and 1 corresponds to similar expression in WT control, respectively.

Supplementary Table 2.8:

The table shows the percentage of expression data for the 146 upregulated genes in RelATA/TA and

146 genes in crel-/-RelATA/TA MEFs in response to TNF and LPS stimulation, visualized in Figure

2.11F and Figure 2.12B, respectively. These heatmaps represent the degree of RelA TAD dependence by evaluating the phenotype of RelATA/TA and crel-/-RelATA/TA mice. For each gene, percentage of expression data in RelATA/TA or crel-/-RelATA/TA relative to their respective WT or crel-/- peak expression at the same time point. Scale of 0 to 100 corresponds to no expression and 100 is similar expression to WT or crel-/- control, respectively.

Supplementary Table 2.9:

The table shows the percentage of expression data for the 185 upregulated genes in RelATA/TA

BMDMs in response to TNF and LPS stimulation, visualized in Figure 2.11F (right panel). These heatmaps represent the degree of RelA TAD dependence by evaluating the phenotype of RelATA/TA mice. For each gene, percentage of expression data in RelATA/TA BMDMs relative to their respective

WT peak expression at the same time point. Scale of 0 to 100 corresponds to no expression and

100 is similar expression to WT control, respectively.

Supplementary Table 2.10:

The table shows the relative expression data for 146 genes induced in crel-/- and crel-/-RelATA/TA MEFs by TNF and LPS stimulation. Heatmap visualization of these genes in crel-/- and crel-/-RelATA/TA MEFs are visualized in Figure 2.12A. Of note, Mmp10 gene is included in this gene list to preserve the ordering of genes as in WT MEFs control; however, the gene did not satisfy the stringent criteria

-/- -/- TA/TA of log2FC >2 and max RPKM >1 in crel and crel RelA MEFs and thus, “NA” was filled in for the data. Only 145 genes were also induced in crel-/- MEF controls and was plotted in Figure 2.12A

(left panels).

MATERIALS AND METHODS

Reagents

For cell stimulations we used 10ng/mL mouse TNF (R&D Systems # 410-MT),

100ng/mL LPS (Sigma, B5:055), and 10ng/mL mouse IL-1b (R&D Systems, catalog #401-ML-

CF) to stimulate cells. pBABE-puro plasmid vector is commercially available through Addgene cat #1764. Antibodies used for western blotting were specific for RelA (Santa Cruz

Biotechnology, sc-372, sc-109), IkBa (Santa Cruz Biotechnology, sc-371), IkBb (Santa Cruz

Biotechnology, sc-945), p50 (BioBharati LifeScience #BB-AB0080), a-Tubulin (Santa Cruz

Biotechnology, sc-5286), b-actin (Santa Cruz Biotechnology, sc-1615), Histone 3 (Abcam,

#ab1791), and p84 (Abcam, #ab131268).

Construction of Mutant RelA Plasmids

The pBABE-puro plasmid vector containing RelA was constructed by ligating the polymerase chain reaction (PCR) amplified coding region of murine RelA (amino acids 1 to 549) restricted with EcoRI and SalI. Agilent QuikChange II Site-Directed Mutagenesis Kit (Agilent

Technologies) was used for all RelA point mutation and deletion variants. DNA binding mutant

(RelADB) within amino-terminal were mutated by a triple substitution at residues Arg35, Tyr36, and Glu39 to Ala (R35A, Y36A, R39A). Mutagenesis of the carboxyl-terminal region for C- termD, TADD, and TA1D were performed by displacing residues Pro326, Ala429, and Ser520, respectively, with a STOP codon sequence. All site-specific mutations were verified by Sanger

sequencing analysis and sequencing analyses of the resulting RelA mutants showed that the targeted sites were the only changes in the DNA sequence.

Cell Culture

Wild-type or mutant primary MEFs were generated from E11.5-13.5 embryos and cultured in Dulbecco’s Modified Eagle’s Medium (DMEM) supplemented with 10% bovine calf serum (BCS), 1% penicillin-streptomycin and 1% L-Glutamine. BMDMs were made through the isolation of 5 x 106 bone marrow cells from mouse femurs of WT or RelATA/TA C57BL/6 mice following red blood cell lysis and seven-day culture with 30% L929-conditioned medium.

Genetic Complementation of MEFs

For transfection, Platinum-E (Plat-E) retroviral packaging cell line (Morita et al., 2000), a modified cell line derived from HEK-293T, were plated on 10-cm plates 16-hr the day before transfection at 50% confluent in DMEM supplemented with 10% fetal calf serum, 1% penicillin- streptomycin and 1% L-Glutamine. Cells were transfected using 300µL of Opti-MEM medium

(ThermoFisher Scientific) and polyethylenimine (PEI; 1µg/µL in 1xPBS pH4.5, Polysciences

#23966-2) with 7µg of retroviral construct DNA, pBABE-puro EV control or RelA expressing constructs (4:1 ratio of PEI (µL):plasmid DNA (µg)). Transfection complex (Opti-MEM medium, PEI reagent, plasmid DNA) were incubated for 20-min at room temperature then added drop-wise to Plat-E cells for 6 hours. Transfected media was replaced with fresh DMEM media containing 10% fetal calf serum, 1% penicillin-streptomycin and 1% L-Glutamine. Cells were further incubated for a total of 48-hr post-transfection and prior to collecting viruses. Viruses-

containing supernatant were filtered through a 0.45 µm filter and used to infect p53-/-crel-/-rela-/-

MEFs with the addition of 4µg/mL Polybrene. 48-hr post-infection of cells with viruses the stably transduced cells were selected with 2µg/mL puromycin for a total of 72 hours. Post selection with puromycin, puromycin-containing medium is removed and cells are passaged twice for recovery prior to being expanded in culture for experiments. pBABE-puro empty vector without the RelA gene fragment is used as a negative control, to maintain a stable retrovirally transduced RelA knockout cell line, meanwhile pBABE-puro containing full-length

RelA (RelA-WT) is used as a positive RelA cell line control.

Western Blotting

For whole cell extracts, 3 x 106 cells were lysed with RIPA buffer containing 1mM PMSF and 1mM DTT followed by lysing with 1x SDS-PAGE sample buffer containing 5% b- mercaptoethanol. Cell lysates were separated by SDS-PAGE and subjected to western blotting.

Nuclear and cytoplasmic extracts were performed with standard methods as described previously

(Hoffmann et al, 2002; Werner et al, 2005). Western blots were probed with antibodies against

RelA (Santa Cruz Biotechnology, sc-372), RelA (Santa Cruz Biotechnology, sc-109), IkBa

(Santa Cruz Biotechnology, sc-371), IkBb (Santa Cruz Biotechnology, sc-945), p50 (BioBharati

LifeScience #BB-AB0080), a-Tubulin (Santa Cruz Biotechnology, sc-5286), b-actin (Santa

Cruz Biotechnology, sc-1615), Histone 3 (Abcam, #ab1791), p84 (Abcam, #ab131268).

Electrophoretic Mobility Shift Assay (EMSA)

EMSA was carried out with standard methods as described previously (Hoffmann et al,

2002; Werner et al, 2005). In brief, 2.5µL total normalized nuclear extracts were incubated for

15-min with 0.01pmol of P32-labeled 38bp double-stranded oligonucleotide containing two consensus kB sites (5’-GCTACAAGGGACTTTCCGCTGGGGACTTTCCAGGGAGG-3’; or with NF-Y loading control (5′-GATTTTTTCCTGATTGGTTAAA-3′ ; 5’-

ACTTTTAACCAATCAGGAAAAA-3’) in binding buffer [10mM Tris-Cl (pH 7.5), 50mM

NaCl, 10% glycerol, 1% NP-40, 1mM EDTA, 0.1mg/mL Poly(deoxyinosinic-deoxycytidylic)], in a final reaction of 6µL. The reaction mixtures were run on a non-denaturing 5% acrylamide

(30:0.8) gel containing 5% glycerol and 1X TGE buffer [24.8mM Tris, 190 mM glycine, 1mM

EDTA] at 200 volts for 2 hours. The gel was visualized by Typhoon Scanner (GE Healthcare

Life Sciences), in which the NF-Y loading control gel was used to normalize for loading variability.

Immunoprecipitation

Whole cell extracts (WCE) were lysed with RIPA buffer containing 1mM PMSF and

1mM DTT. Anti-RelA antibody (Santa Cruz Biotechnology, sc-372G) was conjugated to prewashed magnetic protein-G beads (Life Technologies Dynabeads) for 30-min at room temperature followed by addition unstimulated WCE in RIPA buffer containing 1mM PMSF and

1mM DTT and incubation overnight (~ 16-hr). The beads were then washed thoroughly with 1X

TBS-T buffer and IP samples were eluted with 1X SDS-sample buffer containing 5% b-

mercaptoethanol. Samples were resolved on SDS-PAGE and western blotting with antibodies specific for anti-RelA, anti-IkBa, anti-IkBb, and anti-p50.

RNA Isolation and Sequencing (RNA-seq)

MEF cells at ~80% confluence were serum-starved by overnight incubation in

Dulbecco’s Modified Eagle’s Medium supplemented with 0.5% bovine calf serum (BCS), 1% penicillin-streptomycin and 1% L-Glutamine prior to stimulation. For chromatin-associated

RNA-seq (caRNA-seq), nascent RNA transcripts were prepared from the chromatin fraction

(Tong et al., 2016) using TRIzol according to the manufacturer’s instruction (Life Technologies) and purified using Direct-zol RNA Microprep Kit (Zymo Research cat#R2060). RNA libraries were prepared from ribo-depleted using KAPA Stranded RNA-Seq Kit with RiboErase (KAPA

Biosystems, cat#KK8483) with 400ng of starting total RNA material. For polyA+RNA-seq analysis, total RNA was isolated with the Qiagen RNeasy Mini Kit according to the manufacturer’s instructions, quantified using Epoch Spectrophotometer System (Biotek), and purified from 1µg of starting total RNA material using oligo (dT) magnetic beads before fragmenting at high temperature with divalent cations. Complementary DNA libraries were generated using KAPA Stranded mRNA-Seq Kit (KAPA Biosystems, cat#KK8421), and quantitation was performed using Qubit 2.0 fluorometer using dsDNA BR assay kit #Q32853.

Sequencing was performed on Illumina HiSeq 2000 and HiSeq 4000 with single-end 50-bp sequencing, according to manufacturer’s recommendations and prepared by Broad Stem Cell

Research Center core facility at the University of California, Los Angeles.

RNA-seq Data Analysis

Reads were aligned with STAR to Gencode mouse mm10 genome and RefSeq genes and featureCounts were used to obtain aligned raw counts. Only uniquely mapped reads with a mapping quality score of ³20 were kept for further analysis, using samtools. Read counts were normalized for library size and transcript length by conversion to RPKM and gene below the maximum RPKM <1 were excluded from downstream analysis. DESeq2 was used identify induced genes for subsequent analysis using the following criteria: ³2-fold increase in expression at any one time point over basal with FDR threshold of 0.01. Principal components were calculated with prcomp package and plotted with ggplot. caRNA-seq and polyA+RNA-seq heatmaps were plotted using pheatmap package. HOMER (Heinz et al., 2010) was used to assess for enrichment of de novo and known transcription factor binding motifs for NFkB dependent genes with promoter sequences 1 kb upstream and 0.3 kb downstream of the transcription start site. An in-depth description of this software can be found at homer.ucsd/edu/homer. Gene ontology analysis in biological processes for NFkB target genes were performed using Enrichr

(Chen et al., 2013; Kuleshov et al., 2016). The 5 most enriched GO terms were selected for

NFkB-dependent genes.

Chromatin Immunoprecipitation and Sequencing (ChIP-seq)

MEF cells (8 x 106 cells per plate) were serum-starved overnight with 0.5% BCS medium, then stimulated with 10ng/mL TNF at indicated time points. For ChIP, cells were double cross- linked with 100mM disuccinimidyl glutarate/PBS solution (DSG, ThermoFisher Scientific

#20593) for 30-min followed by 1% methanol-free Formaldehye/PBS solution (ThermoFisher

Scientific #28908) for 15-min. Cross-linked cells were quenched with 125mM Glycine for 5 min and washed twicewith cold PBS followed by snap freezing with dry ice. The cell pellets were thawed and lysed in Lysis Buffer 1, on ice. Cells were briefly sonicated with Diagenode

Bioruptor 300 sonication system (medium power, 15 sec ON/15 sec OFF, 2 cycles) in Diagenode

1.5mL TPX microtubes (100µl-max 300 µl/tube). After centrifugation, cell pellets were lysed in

Lysis Buffer 2 and incubated for 10 min at room temperature. Following centrifugation, cell nuclear pellets were lysed in Lysis Buffer 3. Nuclear lysates were sonicated with Diagenode

Bioruptor 300 sonication system (low power, 30 sec ON/30 sec OFF, 12 cycles) in Diagenode

1.5mL TPX microtubes. Sonication was stopped every 4 cycles for incubation on ice for 1 min, gentle inversion and pulse-spin. Sonicated nuclear supernatant containing DNA fragments were consolidated with the same sample into a single 1.5 mL polypropelene tube. The lysates were centrifuged at max speed for 10 min at 4ºC. The supernatants were transferred to 2 mL no-stick microtubes (Phenix Research cat# MH-820S) with 3 volumes of Dilution buffer and 5µg anti-

RelA antibody (Santa Cruz Biotechnology, sc-372), incubated overnight at 4ºC. Next day, antibody-chromatin complexes were incubated with 30µL magnetic protein G beads (Life

Technologies Dynabeads protein G #1004D) for 5 hours at 4ºC. Chromatin-immunoprecipitates were washed 3 times (4 min wash at 4ºC) with each of the following buffer in this order: low-salt wash buffer, high-salt wash buffer, LiCl buffer, and TE buffer. ChIP DNA was eluted in elution buffer overnight at 65ºC. 1% inputs and ChIP DNA fragments were subjected to reverse cross- linking, RNase A digestion (50µg), Proteinase K digestion (50µg), and purification with

AMPure XP beads (Beckman Coulter). Input DNA samples were quantified with dsDNA BR assay kit #Q32853, and ChIP DNA samples were quantified with dsDNA HS assay kit

(Q32854). Quantitation was performed using Qubit 2.0 fluorometer. ChIP-Seq DNA libraries were prepared from 5ng of Inputs or ChIP DNA using NEBNext Ultra DNA Library Prep Kit for

Illumina (New England BioLabs, # E7370L).

ChIP Buffer composition

Lysis Buffer 1: 50mM HEPES-KOH (pH 7.6), 140mM NaCl, 1mM EDTA, 10% Glyercol, 0.5%

NP-40, 0.25% Triton X-100 [with freshly added 1X EDTA-free protease inhibitors].

Lysis Buffer 2: 10mM Tris-Cl (pH 8.0), 200mM NaCl, 1mM EDTA, 0.5mM EGTA [with freshly added 1X EDTA-free protease inhibitors].

Lysis Buffer 3: 10mM Tris-Hcl (pH 8.0), 100mM NaCl, 1mM EDTA, 0.5mM EGTA, 0.1% Na

Deoxycholate, 0.5% N-lauroylsarcosine sodium salt, 0.2% SDS [with freshly added 1X EDTA- free protease inhibitors].

Dilution Buffer: 10mM Tris-Cl (pH 8.0), 160mM NaCl, 1mM EDTA, 0.01% SDS, 1.2% Triton

X-100 [with freshly added 1X EDTA-free protease inhibitors].

Low Salt Wash Buffer: 50mM HEPES-KOH (pH 7.6), 140mM NaCl, 1mM EDTA, 1% Triton

X-100, 0.1% Na Deoxycholate, 0.1% SDS [with freshly added 0.5X EDTA-free protease inhibitors].

High Salt Wash Buffer: 50mM HEPES-KOH (pH 7.6), 500mM NaCl, 1mM EDTA, 1% Triton

X-100, 0.1% Na Deoxycholate, 0.1% SDS [with freshly added 0.5X EDTA-free protease inhibitors].

LiCl Buffer: 20mM Tris-HCl (pH 8.0), 250mM LiCl, 1mM EDTA, 0.5% Na Deoxycholate,

0.5% NP-40

TE Buffer: 10mM Tris-HCl (pH 8.0), 1mM EDTA

Elution Buffer: 10mM Tris-HCl (pH 8.0), 1mM EDTA, 1% SDS

ChIP-seq Data Analysis

Sequencing reads were aligned to the mouse mm10 genome and RefSeq genes using

Bowtie 2 (Langmead and Salzberg, 2012) and filtered using Samtools. Uniquely mapped reads were used to identify peaks for each sample individually with MACS2 version 2.1.0 using default settings except for FDR of 0.01. Differential RelA binding events were plotted as heatmap using plotHeatmap from deepTools (Ramírez et al., 2016). ChIP-seq peak annotation for genomic locations were plotted as pie chart using ChIPseeker (Yu et al., 2015). For mapping of kB elements to NFkB target genes, any TNF-inducible RelA ChIP-seq peaks within ±10kb from gene TSS were annotated to kB elements (unique half and full kB DNA sequences were annotated within the mm10 genome) and were mapped to each RelA ChIP-seq peak centered at

500bp window (±250bp from each peak of kB sequence) on every gene using Bedtools intersect. Full-length kB elements were defined by the consensus sequence

5’GGRNNNNNYCC-3’ for unique 9, 10, or 11bp DNA sequence; where R is a A or G, Y is a T or C, and N is any nucleotide. kB half sites were defined by the consensus sequence 5’GGR or

YCC-3’ DNA sequence. kB motifs were extracted from UCSC mm10 genome using Biostrings and BSgenome packages. Mapping of kB motifs and RelA ChIP-seq peaks were visualized by ggplot2 geom_segment. Scatter plots and box plots of RelA ChIP-seq data were visualized by ggplot. Wilcox.test function in R was used to find the statistical significance of reduced vs non- reduced RelA peaks (Mann-Whitney U-test). Integrative Genomics Viewer (IGV) was used to acquire RelA ChIP-seq tracks for example genes (Robinson et al., 2011).

Computational Modeling

An ODE model was constructed representing the rate of change of mRNA for each gene.

A >RNA #$%& ∙ RelA& = A A − #012 ∙ RelA >? )* + RelA&

RelA is a time-dependent variable obtained by quantifying RelA DNA-binding activity from

EMSA and linearly interpolating between discrete timepoints. Basal mRNA for a given parameter set was obtained by analytically solving the ODE with RelA fixed at the basal value:

A #$%& ∙ RelAC RNAC = A A #012 ∙ ()* + RelAC )

Induced time-course response was obtained using ode23s in MATLAB (The MathWorks, Inc.).

The distance between experimental and modeled mRNA time courses was quantified using a combination of Pearson correlation and MSE to take into account both dynamics and absolute values (dist = (1 − Pearson) + MSE). As )*, ,-.// , #012 are assumed to be gene-specific properties, unaffected by TAD mutations these were first fit to WT data using particle swarm optimization (PSO)_ to minimize the distance between modelled mRNA time-course and

measured RNA-seq (50 particles, terminating at either 100 epochs or distance below 0.001). As the RNA-seq time-course did not capture a decrease in mRNA levels for late-induced genes,

#012 could not be fit but was assumed to be equivalent to an 8 hour half-life. Following the fit to the WT data )*, ,-.//, #012 were fixed for data from mutant cells and a new #$%& was obtained for each TAD mutation using the same optimization approach.

Animal Use

Wild-type and gene-deficient C57BL/6 mice were maintained in specific pathogen-free condition at the University of California, Los Angeles. The animal protocol for this study were approved by the Institutional Animal Care and Use Committee and by the University of

California, Los Angeles Division of Laboratory Animal Medicine. Targeted Neo deleted RelA transactivation domain heterozygous knock-in mutant mice were generated by ingenious

Technology Lab (iTL, www.genetargeting.com).

Data Availability

All sequencing data presented in this publication have been deposited to the NCBI Gene

Expression Omnibus (GEO) and accessible under SuperSeries accession number GSE132540, https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE132540.

ACKNOWLEDGEMENTS

The work presented in this chapter was a collaborative effort and is currently unpublished. The CBP-interacting mutant plasmid, pBABE-puro RelA-TA2CI, and the anti-p50 antibody were generous gifts from Dr. Gourisankar Ghosh (UCSD). Roberto Spreafico and

Zhang Cheng helped process ChIP-seq and RNA-seq data. Jeremy Davis-Turak helped with analysis in the control gene expression (Figure 2.2). Kensei Kishimoto and Aditya Pimplaskar helped analyze ChIP-seq and RNA-seq data. Emily Yu Hsin Chen helped with the chromatin- associated RNA-seq experiment. Mathematical modeling was performed by Simon Mitchell with

assistance from Amy Tam. Ning Wang helped with HOMER motif enrichment analysis. Yi Liu helped with BMDM isolation. Eason Lin helped with documenting knock-in mutant mice phenotype. Diane Lefaudeux provided technical advice in R programming. The dissertation author is the primary researcher and author of this study. Kensei Kishimoto, Jeremy Davis-

Turak, Aditya Pimplaskar, Zhang Cheng, Roberto Spreafico, Emily Yu Hsin Chen, Amy Tam, and Simon Mitchell are co-authors. Alexander Hoffmann is the corresponding author.

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CHAPTER 3:

Conclusions and Future Directions

In Chapter 2 of this dissertation, we focused on the RelA subunit of the transcription factor

NFkB. We revealed gene-specific regulatory strategies of endogenous RelA target genes in primary cells by probing their stimulus-induced expression with engineered RelA variants. We stringently identified target genes by both functional and physical binding criteria. We developed an experimental system to test mutational variants in primary cells, and systematically analyzed both chromatin DNA recruitment and transcriptional trans-activation at single gene resolution. A key transcriptional activation domain mutation was introduced by a knock-in mouse line to examine other cell types and stimuli and a mathematical modeling approach enabled a quantitative analysis to reveal gene-specific logic gates formed by the two TA domains.

Although we focused only on inducible genes that are expressed when NFkB is functionally active, the genes that NFkB can down-regulate via gene repression are also worth exploring in future work. Identifying endogenous genes that are expressed or repressed by NFkB activity can explain the differential outcomes of NFkB activation in healthy versus unhealthy cells that may be important for developing novel therapeutic interventions designed to target NFkB responses.

In this study, we first showed that in primary fibroblasts, RelA recruitment and transcriptional activation of all endogenous NFkB target genes induced by TNF are dependent on direct high-affinity DNA binding of RelA. A previously report had shown in X-ray structures of NFkB dimer-DNA complex that binding to only one half-site of kB DNA targets is sufficient for DNA recognition (Chen et al., 2000). Here in our study, we observed that the DNA base- specific contacts made by both NFkB subunits to kB DNA targets are indispensable for the downstream gene activation of target genes. In our RelADB mutant analysis, the dimerization partner p50 may still contribute to DNA binding, but it appears insufficient to mediate

downstream gene expression. Hence, the transcriptional activation potential of NFkB was inhibited as loss of NFkB transcriptional activity was observed (Figure 2.5H). Nevertheless, to confirm our findings in Chapter 2 and our observations that p50 binding in our RelADB mutant analysis is insufficient for TNF-induced activation of NFkB target genes, ChIP -seq experiments with a p50-specific antibody could confirm whether the genomic locations where RelA binding was inhibited in the RelADB mutant cell line show unaltered p50 binding. Alternatively, for a deeper understanding of the mechanisms of DNA binding, we could use biophysical approaches to assess DNA binding in the RelADB mutant. In this regard, performing structural characterization of the RelADB mutant in complex with DNA through X-ray crystallography or nuclear magnetic resonance (NMR) may address whether base-specific contacts with kB DNA are fully prevented in RelADB. Solving the structures of RelADB mutant as a RelA homodimer or as a heterodimer with p50 could allow a more accurate assessment of the physical properties of

RelA-containing dimers binding to DNA and possibly confirm that p50 is required for DNA binding, but insufficient for NFkB-mediated transcription of genes in a RelADB mutant context.

While the gene expression response to TNF is rapid, future studies may address whether the dependence on DNA base-specific contacts also pertains to long-term expression dynamics mediated by NFkB. An example of inducers that mediate longer NFkB activity and long-term expression dynamics is bacterial lipopolysaccharide (LPS) that activates toll-like receptor 4

(TLR4) signaling. NFkB activation in response to LPS stimulation has been shown to result in more delayed and persistent NFkB activity (Werner et al., 2005). Additionally, LPS-induced activation of the TLR4 signaling pathways is more complex as it results in the activation of other transcription factor activity in addition to NFkB, such as interferon regulatory factors (IRFs).

Although IRFs are known to co-regulate TLR4-induced genes with NFkB (Lu et al., 2008), e.g. the expression of Ccl5/RANTES gene (Tong et al., 2016), we predict that the dependence on

DNA base-specific contacts in NFkB RelA may also pertains to TLR4-response genes.

Our studies further indicate that all endogenous target genes induced by TNF require the C- terminal region of RelA that harbors two transactivation domains, TA1 and TA2. That is remarkable given that phosphorylation of RelA at S276 was reported to play critical roles in recruiting CBP/p300 (Zhong et al., 1998), and knock-in mutant mice harboring the S276A mutation showed striking animal phenotypes and alterations in the expression of many genes, including some known NFkB target genes (Dong et al., 2008). Our analysis suggests that the intact

N-terminal RHR containing phosphorylated S276 is insufficient for TNF-induced NFkB target gene activation and may support the notion that the phenotype of S276 mutation may be due to alterations at larger time and epigenetic scales (Cheng et al., 2008).

In this dissertation, mathematical modeling was also used to assess the functional requirements of each RelA transactivation domain quantitatively, which may be confounded in the experimental system (i.e. alterations in the NFkB activation time course, Figure 2.8C).

Hence, by probing target genes for the roles of the two C-terminal activation domains, TA1 and

TA2, we found remarkable gene-specificity in our mathematical model-aided analysis. Target genes did not differ very much in whether they were more highly regulated by TA1 vs. TA2, but they differed in whether they required only either TA or required both TAs for gene activation.

In other words, some NFkB target genes allow TA1 and TA2 to function independently, forming a logical OR gate while other NFkB target genes require the synergistic function of TA1 and

TA2, with the TAs forming a logical AND gate. These distinct regulatory strategies may be

mediated by distinct mechanistic requirements for the recruitment of molecular co-activators. For example, genes that exhibit an OR gate logic, might be activated sufficiently by recruiting

CBP/p300 via TA2, but genes exhibiting an AND gate logic might need to recruit not only

CBP/p300 but also additional co-activators via TA1.

The functionality of TA1 was characterized as being regulated by PTMs, particularly phosphorylation, while TA2 has intrinsic transactivation activity via its interaction with

CBP/p300. Yet S467 within TA2 was reported as a potential phosphorylation site that may dampen its activity (Buss et al., 2004a; Geng et al., 2009; Mao et al., 2009; Schwabe and Sakurai, 2005).

To investigate whether S467 plays a role in the TNF-induced expression of NFkB target genes in fibroblasts, we constructed the S467A variant within full length RelA, TA1 deleted RelA, or the

CBP-interaction mutant TA2CI (Figure 3.1A). Gel mobility shift analysis showed TA2S467A did not alter the DNA-binding activity of NFkB compared to each respective parent construct (Figure

3.1B, cf. Figure 2.8C), and protein expression levels of these RelA variants and the canonical IkBa and IkBb were normal (Figure 3.1C). We then undertook polyA+ RNA-seq analysis and PCA revealed only minimal alteration in the TNF-induced gene expression response with S467A in the

WT or TA2CI context (Figure 3.1D). Furthermore, in a heatmap with single-gene resolution (Figure

3.1E), we observed very little effect by the S467A mutation. Only chemokines Cxcl1, Cxcl2 and

Ccl20, which are rapidly and highly induced, showed a slight reduction but no enhancement. Our data thus confirms that an intrinsic CBP/p300 interaction affinity (probed with the TA2CI mutation) is the primary mode of function of the TA2 domain. This contrasts with how TA1’s functionality is enhanced by phosphorylation events mediated by coordinated kinases such as CKII and IKKe

(O’shea and Perkins, 2008; Buss et al., 2004b; Wang et al., 2000).

Figure 3.1 The RelA TA2 domain functions primarily via intrinsic affinity for CBP/p300. (A) Schematic of the RelA variants involving mutation of the phosphorylation site S467. (B) Analysis of nuclear NFkB activity by EMSA of nuclear extracts prepared at indicated times after TNF stimulation of genetically complemented fibroblasts of indicated genotype. (C) Protein expression of RelA, IkBb, IkBa, and Actin in unstimulated cells as assayed by immunoblotting. (D) PCA plot of the transcriptional TNF response of all 104 NFkB target genes at indicated times and cell variants. (E) Heatmap of relative induced expression in response to TNF stimulation of the 104 target genes in same cell variants, as analyzed by polyA+RNA-seq.

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What are the implications of this regulatory distinction of TA1 vs. TA2 for the transcriptional control of NFkB target genes, which differ in how they employ TA1 and TA2? Considering NFkB target genes that are regulated redundantly by TA1 OR TA2, we would expect these genes to respond to all inflammatory stimuli that activate the IKK-NFkB axis as TA2 is effective for their induction, and regulated TA1 activity may merely provide some modulation. On the other hand,

NFkB target genes that are regulated synergistically by TA1 AND TA2 will be strongly activated only by those inflammatory stimuli that also induce the phosphorylation of TA1 serines that potentiate its acidic activation function. Alternatively, TA1 phosphorylation may be a function of the micro-environmental context, thus rendering the activation of these genes tissue context- dependent. We note, for such genes, controlled by an AND gate of TA1 and TA2, the role of TA2 is to amplify the effect of phosphorylation-mediated regulation of TA1 in activity, rendering them more responsive to these PTMs then if they were regulated only by TA1.

Examining the types of genes that are regulated redundantly vs. synergistically by TA1 and

TA2, we noted that many genes that provide for negative regulation of inflammation, such as

Il15ra, IL1rl1, Trim47, Slfn2 or RelB fall into the former category. For these genes, TA redundancy means that they constitute an inflammatory core response that includes provisions for inflammatory resolution. In contrast, the latter category includes genes that are key initiators of an inflammatory response such as TNF, Ccl20, Ccl2, Cxcl1. For these genes, TA synergy means that their expression is activated only in response to a subset of inflammatory stimuli, or when tissue- environmental context provides for the co-stimulatory signal that TA1 requires.

The here-described paradigm of NFkB RelA-responsive gene expression being either redundantly or synergistically mediated by TA1 and TA2 should prompt a range of further studies

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that extend past work on the signals and pathways that control TA1 phosphorylation and the molecular mechanisms that determine whether a target gene requires the dual TA1/TA2 functionality. We may imagine that the involvement of differential co-activators, mediator complexes, the nucleosome movements or other complex chromatin interactions, or recruitment or release of RNA polymerase may play a role in determining diverse regulatory logics.

More broadly, the study in this dissertation suggests that the structure-function relationship of key transcription factors provides an informative probe to dissect the diverse gene regulatory strategies that govern their many target genes. Dissecting the regulatory mechanisms of how endogenous gene expression is regulated is important to understand the TFs’ physiological function and can therefore help in the development of therapeutic interventions.

Quantitative datasets produced by Next Generation Sequencing approaches allow a focus on endogenous genes that may be effectively quantitatively evaluated with gene-specific resolution using mathematical models of the gene expression process to reveal regulatory diversity instead of the more traditional bioinformatics methods focusing on average behavior, assuming certain commonalities. Thus, we hope that our study provides an analysis blueprint for future studies; indeed, advances of CRISPR/Cas9 technology that enable the engineering of variants into the endogenous gene locus is powering studies of endogenous gene regulatory circuits at single gene resolution.

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