The Transcription Factor PU. 1 Is Enriched at Inflammatory Bowel Disease Risk Loci in CD56+ Cells

The Transcription Factor PU.1 Is Enriched At

Inflammatory Bowel Disease Risk Loci in CD56+ Cells

A thesis submitted to the

Graduate School of the University of Cincinnati

In partial fulfillment of the requirements for the degree of

Master of Science

In the division of Immunology of the College of Medicine

By:

Fazeela Yaqoob

M.Phil. Government College University Lahore, Pakistan, 2011

August 2017

Committee Chair: Stephen Waggoner, Ph.D.

Jonathan Katz, Ph.D.

Leah Kottyan, Ph.D.

Abstract

Inflammatory bowel disease (IBD) affects the well-being of 1.6 million people in the United

States. The etiology of IBD is strongly linked to genetic risk loci and to a dysregulated immune response against the intestinal microbiome. Genome-wide association studies identified more than 200 discrete genetic loci associated with risk for IBD, but mechanistic understanding of how these sites collectively promote disease is lacking. We hypothesize that altered binding of transcription factors (TFs) at IBD risk loci is a mechanism to globally promote gene expression changes associated with IBD. We used an innovative algorithm to assess intersection between known IBD genetic risk loci and transcription factor binding (ChIP-Seq) in a variety of cell types to reveal that PU.1 binding in human CD56+ cells (highest scoring dataset) overlaps variants at more than half of the IBD risk loci assessed (62 of 112, 3.7-fold enrichment, p<10-30). The majority (but not all) of human CD56+ cells are natural killer (NK) cells, which are implicated in mouse models of IBD pathogenesis and observed to accumulate in the inflamed intestines of

IBD patients. In this thesis, we optimized conditions for PU.1 ChIP in the human NK cell line,

KHYG1, and observed successful PU.1 enrichment of sites known to be bound by PU.1. These results position our lab to assess allele-dependent PU.1 binding to IBD risk loci in primary human NK cells, which we hypothesize is a mechanism contributing to genetic risk for IBD.

Acknowledgements

I first and foremost would like to thank my mentor Stephen Waggoner for his sincere efforts and thoughts in developing me a scientist. I always learned something new under his guidance and I will keep idealizing him as a scientist. I am also thankful to all members of the Waggoner lab, a group of talented researchers.

I would say a big thanks to my co-mentor Stacey Cranert. I found myself lucky that I got a chance to meet her and work with her. Undoubtedly, I found her amazing in every scientific discipline and technique.

I am highly thankful to David Ochayon who was around whenever I needed him. He trained me to think like a researcher and act accordingly.

iii

Table of Contents

1. Introduction 1

1.1. Inflammatory Bowel Disease: An autoimmune disorder with a genetic component 1 1.2. Translating genetics of disease risk to functional genomics 2 1.3. Regulatory Element Locus intersection (RELI) 3 1.4. ILCs regulation of intestinal homeostasis and inflammation 8 1.5. PU.1 regulates CD56+ cells differentiation and functioning 10 1.6. ChIP-Seq: A method for identification of gene regulatory elements 11

2. Materials and Methods 14

3. Results

3.1. PU.1 is expressed in Human Natural Killer cell lines KHYG1 and NK-92 20 3.2. KHYG1PU.1 ChIP-PCR reveals that PU.1is enriched at CD11b Promoter in NK cells 22 3.3. PU.1 binds to LIMD1 promoter 27

4. Discussion 31

5. Conclusion 37

6. References 39

Appendix 50

List of Tables and Figures

Figure 1 RELI (Regulatory Element Locus Intersection) working scheme: A new computational approach to discover gene regulatory mechanisms 5 Figure 2 Global view of RELI results – all diseases versus all TFs 6 Figure 3 Steps involved in Chromatin Immuno-precipitation (ChIP) 17 Figure 4 Western blots for endogenous expression of PU.1 in KHYG1 and NK-92 cell lines 21 Figure 5 Sonication Optimization in KHYG1 and NK-92 24 Figure 6 PU.1 is enriched in KHYG1 at CD11b promoter 25

Figure 7 PU.1 is enriched in KHYG1 at CD11b promoter immuno- precipitated with monoclonal antibody 26

Figure 8 PU.1 is enriched in KHYG1 at LIMD1 promoter through standard PCR 28 Figure 9 PU.1 is enriched in KHYG1 at LIMD1 promoter as evident by qPCR 29 Figure 10 TRC1, non-targeting shRNA control vector 38 Table1 PU.1 binds to IBD risk loci more often than expected by chance 7 Table2 List of positive (+) and negative (-) primer sets used to amplify IPs and input DNA 18 Table3 Percent Input Method to calculate PU.1 enrichment 19 Table4 Fold enrichment Method to calculate PU.1 enrichment 19 Table5 Calculation of PU.1 fold enrichment at LIMD1 and GAPDH promoter regions through ΔΔCt method 30

List of Abbreviations

Name Description

IBD Inflammatory Bowel Disease

TF/TFs Transcription Factor/Transcription Factors

NK Cells Natural Killer Cells ILCs Innate Lymphoid Cells

DSS Dextran Sodium Sulphate

ChIP Chromatin Immunoprecipitation

U6 U6 Promoter

cppt Central polypurine tract

hPGK Human Phosphoglycerate tract

puroR Puromycin resistance gene for mammalian selection SIN/3” LTR 3’ self-inactivating long terminal repeat

f1 ori F1 origin of replication

ampR Ampicillin resistance gene for bacterial selection

pUCori pUC origin of replication

5’ LTR 5’ long terminal repeat

Psi RNA packaging signals

RRE Rev response element

vii

viii

1. Introduction:

1.1. Inflammatory Bowel Disease: An autoimmune disorder with a genetic component

Inflammatory bowel disease (IBD) encompasses chronic inflammation of the gut as categorized into two anatomically similar but physiologically distinct debilitating conditions, Ulcerative Colitis

(UC) and Crohn’s disease (CD)[1]–[4]. Although IBD is not fatal in nature, its relapsing and remitting nature, along with a range of symptoms, makes this disorder a significant burden on human health[5]–[8]. Regardless of many shared clinical features, UC and CD exhibit distinct histopathology, localization, and incidence [1], [9]. There is currently no known cure for IBD and treatment is limited to life-long symptomatic care through anti-inflammatory and immunosuppressive drugs [10]. The prevalence of IBD is highest in Europe and North America, and it is rapidly increasing in other parts of the world. Currently, 1.6 million Americans are suffering from IBD, while worldwide prevalence approaches 4 million individuals[11]. IBD is more common among Ashkenazi Jews, who are five to eight times more likely to develop IBD compared to non-Jews[12]. Moreover, populations earlier thought to be at “low risk”, such as

Indians and Japanese, are experiencing an increased prevalence in IBD[2], [13].

The intestinal immune compartment is the largest in the body with a continuous exposure to antigens from both diet and the symbiotic bacteria [14]. It also serves as an opening to large numbers of pathogenic microbes. The integrity of the gut immune system is maintained by a balance between eradication of pathogenic microbes and a tolerance to symbiotic ones, and this is crucial to maintain homeostatic conditions [15]. In IBD, tolerance to intestinal and environmentally acquired antigens is disrupted due to an excessive and abnormal immune response in a genetically susceptible host. This results in a breakdown of the intestinal epithelial

barrier and gut malfunction [1], [16]. Due to the complex nature and multifactorial etiology ofIBD, it is thought that many factors are involved in disease course. Among these, three main factors are genetics, the environment (including microbes, diet, and drugs), and the host immune system[4], [17].

1.2. Translating genetics of disease risk to functional genomics:

The present theories for the etiology of IBD highlight the significance of genetics in the context of defective gut immune responses to microbes. Twin studies, although few in number, are important sources to determine the role of a genetic component in both UC and CD etiology[18].In one large European study, CD concordance rate in monozygotic twins was found to be high (20-50%) [19], [20]. While they shared the same environment, the concordance for

CD was low (less than 10 %) among dizygotic twins[21]. The risk of IBD among first- degree relatives of patients with CD and UC is also 10 times higher than the control population[18].These data combined with many other genetic epidemiological studies provide strong rationale for the role of genetics in IBD development.

Genome-wide association studies (GWAS) have provided new insight into IBD etiology by the identification of 215 IBD risk loci and 99 confirmed association studies [22]–[24]. A third of these risk loci are shared between UC and CD [1]. Over 90% IBD risk loci are located in non-coding regions of the genome and are enriched in gene regulatory elements[25]. Many of these regions also serve as expression quantitative risk loci (eQTLs) for nearby genes by being tightly linked to them [26]; however, GWAS do not convey much information about casual variants underlying these associations [24]. Therefore, robust molecular work is required to understand how IBD variants collectively promote disease in a genotype-dependent manner [27], [26].

Single nucleotide polymorphisms (SNPs) are the most common form of variations in the human genome[28]. Trait-associated non-coding variants are enriched in regulatory elements and are categorized into rare, common, coding, non-coding, and functional variants. Although it is hard to identify the rare variants associated with common diseases, a possible approach for annotating non-coding variants is to ascertain which of them are linked with transcription factor binding. This concept is quite significant as TF binding is largely determined by local sequences, thereby decreasing the search areas for eQTLs. Rare functional variants with allele specific transcription factors binding could be responsible for genetic effects that make the host susceptible to certain diseases, including autoimmune diseases[29]. This is based on an emerging hypothesis that TFs bind to phenotype-associated SNPs with different strength [30],

[31].

1.3. Regulatory Element Locus Intersection (RELI):

How a functional SNP affects gene regulation through allele-specific transcription factor binding is an area of active research[32]. Our center has recently developed an algorithm called “RELI”

(Regulatory Element Locus Intersection) to find relationships between genetic diseases and transcription factors binding across different independent nucleotide sequences.

RELI accepts variants as inputs and expands them into LD blocks. The observed overlap is recorded between each LD block and each TF ChIP-Seq dataset. If a variant in a given LD block overlaps a peak in a TF ChIP-Seq dataset, then that pair (LD block and dataset) is marked as a hit. In the next step, the expected overlap is estimated between each LD block and each dataset. This is done by randomly selecting a reference variant from the LD block. A distance vector measures the distance of each variant in the block from its reference variant. An artificial LD block is generated and members of this artificial LD block are overlapped with each dataset like observed overlaps. In the last step, significance of the overlap between input variant 3

dataset and each dataset is estimated through comparison of observed counts and expected counts. This scheme is illustrated in figure 1.

Using the RELI algorithm, we characterized the overlapping binding of many distinct TFs in many disparate cells in types with the genomic position of IBD risk loci. We collected ChIP-seq data for various transcription factors from different sources including ENCODE, Cistrome ChIP,

Redmap ChIP, and Pazar ChIP(Fig 1 and 2). These datasets, which included a variety of leukocyte cell types and other non-hematopoietic cells, were cross-referenced with IBD risk alleles (Table 1). The unbiased analysis of different cell types is an important aspect of our exploratory approach to discover novel gene regulatory mechanisms of IBD genetic risk. This is because there is evidence implicating a large number of different cell types in IBD, including dendritic cells, monocytes, macrophages, effector T cells, antibodies secreting B cells, innate lymphoid cells (ILCs), intestinal epithelial cells and stromal cells [33].Interestingly, RELI revealed a robust overlap of 62 of 112 IBD risk alleles with a PU.1ChIP-seq dataset from peripheral blood CD56+ cells (mostly human ILCs, the largest proportion of which in blood are natural killer (NK) cells). These associations are highly significant (p<10-30), indicating that PU.1 binds to these risk loci in CD56+ cells more than would be expected by chance. The next highest scoring datasets were PU.1 binding at IBD risk loci in myeloid cells (THP-1, monocytes, macrophages) and CD3+ T cells, which along with NK cells, suggests that RELI implicates PU.1 in IBD genetic risk in a series of cell likely to be involved in IBD. Further these results are specific, as the vast majority of the 1,630 ChIP-Seq datasets assessed do not align to IBD risk loci. In addition, not all PU.1 ChIP-Seq datasets are significant. For example, a PU.1 ChIP-seq datasets from the embryonic stem cell line, WIBR3,does not align with IBD risk loci.

Interestingly, PU.1 bound regions in CD56+ cells are known to affect nearby gene expression, including ETS-1 [34], IL10 [35], IL-15, and IL-12 [36]. These genes play critical roles in NK cell

development and function. In total, these observations provide rationale for our hypothesis that

PU.1 binding at IBD risk loci in NK cells may contribute to IBD development and pathogenesis.

Source of Cells used in Intersection Enrichment Corrected ChIP Data PU.1 ChIP with 112 IBD ** P-value experiment risk loci*

CistromeChIP CD56+ cells 62/112 3.71 1.64E-31 from peripheral blood

PazarChIP THP1 (detail) 64/112 3.02 6.03E-25

ReMapChIP Monocyte 49/112 3.32 3.91E-21

CistromeChIP CD3+ cells 37/112 4.09 3.23E-20 from peripheral blood

ReMapChIP Macrophages 42/112 2.24 2.60E-07 in peripheral blood

Table 1. PU.1 binds to IBD risk loci more often than expected by chance. Far left column showing sourced from where ChIP datasets were retrieved. Onward from left, next column is showing cells types used in ChIP experiment. *This column is depicting PU.1 binding at a location only if it bound at the location of more than one risk variant in a single locus.**Enrichment was calculated through a permutation analysis based on random variants chosen to match allele frequencies and LD block structure.

1.4. ILCs regulation of intestinal homeostasis and inflammation:

There are two main arms of the immune system; innate and adaptive. The innate immune system provides the first line of defense against foreign antigens prior to adaptive immune system activation. Adaptive immunity is acquired during life time of an individual upon antigenic exposure and based on the activities of B and T lymphocytes. Innate immune system not only removes cancerous and virally infected cells but also regulates the activities of the adaptive immune system. A stable interaction between the innate and the adaptive intestinal immune cells and the microbiota are primarily responsible for maintaining the physiological non- inflammatory state of the gut. Current studies highlight the altered function of innate immune cells, especially the ILCs, in the disruption of this balanced state. A better understanding of the operative mechanisms controlling ILC function and contribution to pathogenesis of IBD are needed[37].

ILCs originate from common lymphoid progenitors along with other lymphocytes but, unlike B and T cells, they do not undergo receptor rearrangement on antigenic exposure [37]–[39]. They are divided into 3 groups, ILC1s, ILC2s, and ILC3s, based on certain transcription factors requirement and lineage specific cytokines production[38], [40]. Thus, each type of ILC is implicated in specific processes and diseases. The remainder of this thesis will focus on NK cells and type 1 ILCs, which represent that overwhelming majority of CD56+ cells in blood and therefore are the likely source of PU.1 binding registers in the ChIP-Seq data set that forms the basis of our hypothesis.

Group 1 ILCs or types 1 ILCs include conventional NK cells (cNK), CD127+ ILC1s, and

CD103+ILC1s (intra-epithelial ILC1s) [39]. CD56 is commonly considered a lineage marker for human NK cells [41]–[43]. NK cells possess cytotoxic activities and play a key role in the

removal of transformed and virally infected cells [38], [44]. NK cells possess several activating and inhibitory receptors and recognize transformed and unhealthy cells by the absence of surface expression of class I MHC (MHC-I).Unlike NK cells, ILC1s express low levels ofthe cytolytic proteins, perforin and granzyme B, and are poorly cytotoxic. Moreover, ILC1 do not express the IL-15α receptor part of IL-15R, which is critical for NK cells, and ILC1s are also devoid of killer immunoglobulin like receptors (KIRs) that prototypically define NK cell functionality [45]. In contrast, both NK cells and ILC1 express the transcription factor T-bet

(Tbx21) and express IFN- in response to IL-12, IL-15, and IL-18[46]–[51] . Whereas NK cells are prominent in lymphoid tissues and the blood, ILC1s cells are overrepresented in the intestinal mucosa [52].

Our group and others have implicated NK cells in suppression of T cell responses during infection and inflammation. Frequently, this suppression of T cells by NK cells is dependent on perforin-mediated cytotoxicity [53]. In fact, perforin-dependent lytic activities of NK cells can suppress T cell-mediated colitis in a mouse model of IBD involving transfer of donor T cells into immune-deficient mice [54]. In a separate dextran sulphate sodium (DSS) induced mouse model of colitis, NK cells blocked neutrophil pro-inflammatory response in the gut to lessen disease [55]. Furthermore, NK cells are implicated in promoting intestinal health and healing through secretion of IL-22 [56], and IL-10[57].

In contrast to these protective functions, there are also reports regarding the role of type 1 ILCs in IBD pathology upon dysregulation. Both ILC1s and NK cells expand in inflamed intestines of patients suffering from IBD [58]. ILCs also promote immune-mediated intestinal pathology in mouse model of IBD. They secrete critical cytokines known to have roles in IBD pathogenesis like TNF, IL-17, and IFN-γ. ILC1s expand in the ileum of people suffering from IBD as compared to control subjects. Many ILC1s genes andILC1 controlling genes are part of IBD susceptibility

loci [38]. In this context, we hypothesize that dysregulated and pathogenic subsets of ILC1s originate due to changes in their gene regulatory networks, and this could be due to differential binding of TF, PU.1, at IBD relevant risk loci in a genetic susceptible host. This in turn leads to a change in the expression of their critical immune-mediated genes involved in the maintenance of gut homeostasis.

1.5. PU.1 regulates CD56+ cells differentiation and functioning

The transcription factor PU.1, also called spi1, is a member of E26-transformation-specific (Ets) transcription factors family. The name PU.1 was assigned due to binding of this TF to a purine- rich (PU box) DNA sequence (5-GAGGAA-3’) to its motif [44]. It controls self-renewal of hematopoietic and progenitor stem cells as well as maturation of myeloid and lymphoid cells by regulating certain growth factors and cytokine receptor’s genes [59].

PU.1 has important functions to perform in NK cells; for example, PU.1 is required in differentiation of hematopoietic stem cells (HSCs) to NK cell precursors. Mice with knocked down PU.1 had reduced numbers of NK cells. Additionally, NK cells were phenotypically abnormal in terms of reduced surface receptor (IL-7 receptor alpha, IL- expression and in response to growth promoting cytokines like IL-2 and IL-12 [44], [60]. PU.1 binds directly at IL-

7Rα promoter in NK cells and could possibly affect the functional responsiveness of NK cells by means of affecting their IFN-γ production. One such TF is ETS-1; whose promoter is occupied by PU.1 [34]. ETS, in turn, controls the production of perforin, a cytolytic protein present in NK cells granules. In the absence of PU.1 binding, transcriptional activity is enhanced at ETS-1 promoter, leading to increased perforin production [61]. This in turn enhances the cytotoxic activities of ILC1s in the gut and could possibly contribute to IBD.

PU.1 regulates the expression of genes likely to have key roles in the immune response and cytokines production. One of such cytokine is IL-10. IL-10 is an immunosuppressive cytokine and helps in maintaining self-integrity of tissues. In monocytes, PU.1 acts as a suppressor of IL-

10 gene transcription by binding to IL-10 gene promoter. In addition, PU.1 was found to have detrimental effects in the form of an overall decreased transcriptional activity and resultant IL-

10 production [62].IL-10 is important in NK cells functioning, too. NK cells are known to modulate the behavior of other immune cells, specifically B and T lymphocytes, through IL-10 production [63] .We hypothesize that enhanced PU.1 binding at IL-10 promoter in NK cells can decrease IL-10 production, thus diminishing its immunosuppressive activities in the gut environment and further promoting inflammation. Additionally IL-10 locus serves as an important expression quantitative loci (eQTL) for nearby genes and is believed to be strongly linked to IBD risk locus [64]. It is also our top candidate for expression studies in the presence or absence of

PU.1 in the context of IBD risk.

The critical roles of PU.1 in NK cell biology and the statistically significant overlap of IBD risk loci with PU.1 binding sites in CD56+, collectively provide a strong rationale for looking for the target binding sites of this factor in NK cells. The techniques used for this purpose are chromatin immunoprecipitation and parallel next-generation sequencing (ChIP-Seq).

1.6. ChIP-Seq: A method for identification of gene regulatory elements

The interaction of transcription factors and other chromatin proteins with DNA can impact gene expression and consequently cell differentiation and function [65]. Identification of binding sites of transcription factors is key to exploring gene regulatory networks [66]. ChIP-Seq (chromatin immunoprecipitation along with next-generation sequencing) has revolutionized the mapping of

DNA-protein interactions [67]. ChIP was developed more than a decade ago for mapping

transcription factor (TF) occupancy across the genome [68]–[70]. ChIP-Seq assists to determine whether a TF binds at a SNP or a TF binds differentially to one allele than the other. ChIP experiments are designed and executed in a variety of ways and this depends heavily on the biology of factor under study.[71]. In a typical ChIP assay, protein complexes that are in contact with DNA are crosslinked to their binding sites, the chromatin is digested into short fragments, and then DNA regions that interact with the TF of our interest are immune-precipitated (IP). A genome-wide readout of the protein binding sites is produced by paired end-sequencing of millions of different DNA fragments (ChIPSeq). This helps us to discover those genomic sites that are enriched in a pool of precipitated DNA fragments. The reads obtained from sequencing are compared to reference genome to analyze the TFs binding sites in our IP samples through computational software[72]–[76].

Before the development of ChIP-Seq, genomic DNA segments enriched in this manner were identified by DNA hybridization to microarray (ChIP-chip) but ChIP-Seq has replaced ChIP-chip due to its high-resolution coverage. Now ChIP-Seq has been used successfully for genomic level studies Involving TFs, histones modification, DNA modifying enzymes and other chromatin associated proteins [69].

The role of innate and adaptive immune system has been much appreciated in the complex multigene pathology involved in IBD. This is supported by genetics and epigenetics analysis along with several experimental studies conducted in human and mouse model of mucosal inflammation [77], [58], [78]. Dysregulated ILC1s may contribute to intestinal inflammation through cytokine production, lymphocyte recruitment, and reorganization of the inflammatory tissue. These activities are controlled at genomic level due to altered binding of TF, PU.1. With all this background information, advancement in bioinformatics in the form of new statistical tools and pipeline analysis and progress in the development of new techniques to study genes

and TF behavior at micro level encourage us to explore the behavior of PU.1 at genomic level.

This forms the basis of the findings presented in this thesis.

Summary of Introduction:

 IBD, a chronic inflammation of the gut, arises due to aberrant immune responses against

the gut microbiome in genetically susceptible humans.

 GWAS have provided compelling evidence for the contribution of genetic factors in the

onset and progression of IBD.

 IBD relevant genetic variants are enriched in non-coding regions of genome which are

regulatory in nature.

 Our novel algorithm RELI implicates PU.1 in IBD genetic risk in a series of cell likely to

be involved in IBD, including ILCs.

 One possible mechanism through which non-coding variants can affect gene activities is

differential TFs binding to SNPs.

 Dysregulation of innate lymphocytes (ILCs), including NK cells, in inflamed gut tissues of

patients is implicated in the onset and progression of IBD.

 PU.1 is critical for differentiation of ILCs in the human gut and modifies the gene

regulatory networks impacting the overall balance between pathogenic and protective

subsets of ILCs.

 An improved understanding of how pathogenic ILCs emerge and contribute to IBD

pathogenesis would yield novel insights into therapeutic strategies for IBD patients.

2. Materials and Methods:

2.1. Cell culture

The NK-92 (natural killer cell lymphoma cell line) and KHYG1 (natural killer cell leukemia cell line) cell lines used in this study were obtained from ATCC. KHYG1 were cultured in RPMI-1640 supplemented with 10% FBS, L-glutamine, penicillin/streptomycin, sodium pyruvate and non- essential amino acids (NEAA). IL-2 was added separately at the rate of 40 U/mL. NK-92 cell line were grown in Alpha Minimum Essential medium, with 2 mM L-glutamine and 1.5 g/L sodium bicarbonate provided in addition to 10% FBS and other essential nutrients (0.2 mM inositol;

0.1 mM 2-mercaptoethanol; 0.02 mM folic acid; 100-200 U/mL recombinant IL-2; adjust to a final concentration of 12.5% horse serum and 12.5% fetal bovine serum). IL-2 which was added separately at the rate of 100 U/ml. Cells were allowed to reach full confluency (80-90%) before using them in assays. Confluency rates for KHYG1 and NK-92 are 1 x 106 cells/mL and 0.5 x

106 cells/mL respectively.

2.2. Western Blot

We performed western blot to confirm the endogenous expression of PU.1 in both KYG1 and

NK-92 cell lines. In short, 10 x 107 NK-92 and KHYG1 were collected, spun down at 325 g for 3 minutes. Cell pellets was washed with ice-cold PBS in between 2 times. These cells were lysed in 1X RIPA buffer (NuPAGE cat # 89900) at the rate of 400 uL buffer/4 x106 cells. To prevent maximum degradation of proteins, sometimes, I also used an SDS-free buffer (Cell lysis buffer,

Cell Signaling # 9803)to break open the cells and release nuclear proteins. Cells were kept on

ice for 30 minutes at room temperature and then spun down to collect the supernatant. Pellet was discarded and supernatant was used for further analysis. About 20ul of sample was used at a time and rest of sample was aliquoted and stored at -800C for future use. Sample was diluted with 1 part of Laemmeli sample buffer (4X) at the rate of 1:1. Alternatively, samples were diluted with 2 parts of sample buffer (1:2) without consequence for cell lysate preparation. Dithiothritol

(DTT) (1 M) was added to a final concentration of 85 mM and sample was boiled at 95-100 0C for 5 minutes in water. Samples were immediately cooled down on ice and loaded on SDS-

PAGE (12% gel). Both NuPAGE pre-caste gel (10% Bis-Tris gel, 1.0 mm) as well as Invitrogen

SureCaste system (cat# HC1000SR) handmade gels was used at different time points to run the samples. Samples were run for 120 minutes at 90 volts and then transferred on nitrocellulose pre-cut blotting membrane (novex, cat#LC2000) through wet transfer.

Occasionally, I tried iBlot dry blotting gel system with iBlot dry transfer nitrocellulose (cat#

IB301002) for quick transfer. After transfer, the blot was blocked with 5% milk to avoid non- specific binding of antibodies. Blots were washed 3 times with 1X TBS-T (Tris-buffered saline with twenn-20) and incubated with primary antibody, anti-PU.1 polyclonal antibody (Cell signaling # 2266) diluted in 5% milk overnight. Next day after three TBS-T washes, blot was incubated with HRP donkey anti-rabbit IgG (biolegend # 406401) again diluted in 5% milk and rotated for 1 hour at room temperature. After five TBS-T washes, blot was developed with ECL reagent and detected with chemiluminescent tray.

2.3. Chromatin Immunoprecipitation (ChIP)

10-20 million KHYG1 cells were collected and fixed in 16% methanol-free formaldehyde (Pierce

#28906) to final concentration of 1% in culture media flask. Flask was rotated for 10 minutes at room temperature and cross-linking reaction was stopped by adding 1.25M glycine by

1/10thvolume of cells (e.g. for cells in 30 mLof media add 3 mL of 1.25 M glycine). Cells were

lysed in home-made 1ml ChIP cell lysis buffer (20mM Tris-HCl pH: 8.0, 85mM KCl,0.5% NP-40

+ protease inhibitors) for 10 minutes. After 5 minutes centrifugation at 5000 rpm, supernatant was discarded and nuclear pellet was put on ice. This pellet was lysed in nuclear lysis buffer

(50mM Tris-HCl pH:8.0, 10mM EDTA, 0.1% SDS + protease inhibitors) on ice for 10 minutes cells prior to sonication with manual sonicator (Cole-Parmer 130-Watt Ultrasonic Processor). I used Covaris S220 sonication system to get uniformly sized fragment and found both methods useful after optimization. After sonication, chromatin was divided into different tubes with respect to number of IPs while some chromatin was saved as input and to check sonication efficiency. After pre-clearing with ChIP grade agarose beads, chromatin was immune- precipitated with anti-PU.1 (Purified anti-spi1 antibody biolegend # 7C6B05) and a mock reaction was performed with an irrelevant antibody (Purified mouse IgG1 cat# 555746). After overnight incubation at 40C Ag-Ab complex were pulled down with salmon sperm blocked ChIP grade protein A+G agarose beads (Thermo Fisher# 26159). Beads were washed five times with these buffers in the order given (low salt wash buffer (0.1 % SDS, 1 % Triton-X 100, 2 mM

EDTA, 150 mM NaCl, 20 mMTris-HCl and 150 mMNaCl), high salt wash buffer (0.1 % SDS, 1 %

Triton-X 100, 2 mM EDTA and 500 mMNaCl, 20 mMTris-HCl and 00 mMNaCl)LiCl wash buffer

(0.25 mMLiCl , 1 % IGEPAL-CA630/ 1% NP-40, 1% deoxycholic acid, 1 mM EDTA, 10 mMTris-

HCL pH 8.1) and final two washed with 1 X TE buffer). All buffers were cold and 5-10 minutes washing was done in between in each wash and tubes were changes during last wash to prevent non-specific binding of beads. DNA was eluted from beads and DNA-proteins were reverse cross-linked at overnight incubation with 5M NaCl at 65˚C. Onward from this step, input

DNA was treated the same way like all other IPs. After Proteinase K and RNAase treatment

DNA was eluted either by phenol-chloroform or mini-elute PCR purification kit. Both regular and qPCR were run to check the pull down of DNA associated with TF of our interest.

2.4. Quantitative Real- time PCR

Roche 480 RT-PCT (qPCR) was used to amplify DNA eluted from our IPs and control samples.

Two sets of positive primer pairs and two sets of negative primer pairs were used to run PCR.

Primer sequences are shown in table2. Positive primers used were covering he promoter region of CD11b and LIMD1 while negative primer pairs were CNAP and GAPDH. For regular

PCR, DNA was run on ethidium bromide gel and fold enrichment was calculated by densitometry or regular while relative method of fold enrichment (-ΔΔCt) was employed for qPCR data calculation. This method is shown in Table 3 and 4) below with imaginary values.

PCR condition were as follows: initial denaturation at 95˚C for 5 minutes, denaturation at 95 ˚C for 30 sec, annealing at 56-72˚C for 30 sec, extension at 72˚C for 1 minute (35 X) and final extension 72 ˚C for 5 minutes.

Primers Sequence Tm

Fw:5′-GTTTGGGTCAGGAGCTGGGGA-3′ CD11b 63.5 ˚C Rev:5′-AACCACAAGGAAGCCACCAAAG-3′

Fw:5’-AACCACAAGGAAGCCACCAAAG 3’ CD11b 65 ˚C Rev:5’- ACACGAATTCCAGGAGGCAG 3’

Fw:5’-ATGGGGAAGGTGAAGGTCG -3’ GAPDH 56 ˚C Rev:5- GGGGTCATTGATGGCAACAATA -3’

Fw:5´-ATGGTTGCCACTGGGGATCT-3´ CNAP 56.8 ˚C Rev: 5´-TGCCAAAGCCTAGGGGAAGA-3´

Fw: 5′ -GCAGCAGGGACTGCGCCTGGCG 3’ LIMD1 72 ˚C Rev: 5′ -GGGGCTGGCGGCCCATTGTCCG 3’

Fw: 5’ -GCCACTCAGT TTAGGGGCAA GC 3’ USLIMD1 60 ˚C Rev:5-’AAATCCTCAGTGCTTACCCCAG 3’

Table 2. : List of positive (+) and negative (-) primer sets used to amplify IPs and input DNA

3. Results:

a. PU.is expressed in Human Natural Killer cell lines KHYG1 and NK-92

Alterations in the gene regulatory networks of ILC1s have been shown to affect their ability to maintain homeostasis in the digestive tract. We hypothesize that this shift is due to differential binding of the transcription factor PU.1 over IBD relative loci in ILC1s. Previous studies have shown that mature NK cells maintain PU.1 expression throughout their development[44]. Due to this suspected role of ILCs and PU.1 in IBD and involvement in inflammation, we tried to check the endogenous expression of PU.1 protein in NK cell lines

NK-92 and KHYG1. We used two different set of numbers for NK-92 i.e. 4 million and 10 million cells. Although PU.1 expressed in both sets, its expression was much higher in 10 million cells that is a recommended range for doing western for NK cells anyways. PU.1 expression was also good in 10 million KHYG1 cells. We detected PU.1 protein band close to 42KDa (Fig.1) on membrane, which corresponds to the expected size for PU.1 in literature.

3.2. KHYG1 PU.1 ChIP-PCR reveals that PU.1 is enriched atCD11b promoter in NK cells.

3.2.1. Sonication:

Sonication Optimization: Chromatin immunoprecipitation (ChIP) was performed as described in section 2.3. of materials and methods. A successful ChIP assay depends on many factors; one of these is a high-quality sonication. For ChIP-Seq, ideal fragments range is

100-300bp. To achieve this size range sonication, optimization was done by a variety of methods and under different operating conditions. First, a manual ultrasonic processor was used to shear chromatin under different cycles (14x, 16x, 18x, 20x, 22x and 24x) with15s

ON and 30s OFF conditions Fig.5A. To improve DNA yield, special Diagenode TPX tubes were used for sonication as these tubes bind little DNA with their walls (Fig.5B). Lastly, covaris S220 system was used to sonicate KHYG1 and NK-92 cells and compared side by side with manual sonicator shearing. Chromatin sonicated with covaris resulted in the correct size and in good density as compared to chromatin broken down by manual sonicator (Fig. 5C).While density was comparable in KHYG1 samples, DNA fragments were bigger in the manual ultrasonic processor (Fig. 5C).

ChIP was performed as described in section 2.3. of previous section and DNA was eluted in

1X TE buffer. Reaction volume was 20 uL for each sample. EconoTaq polymerase enzyme was used to amplify DNA.IPs and input DNAs were amplified with positive primer set

CD11b. (Fig. 6A). PU.1 is believed to regulate expression of CD11b and binds to its promoter in the acute promyelocytic leukemia (APL) derived cell line NB4. With this information and by using UCSC genome browser and NCBI we explored the predicting binding sites of PU.1 and designed 2- primer sets based on the annotated promoter

sequence for CD11b (i.e. Integrin alpha M (ITGAM)). These primers amplified a region of

177 bp (not shown) encompassing the -22/-12 PU.1 binding site on the CD11b promoter[79]. A second set of primers was used to amplify promoter region of CD11b of about 293 bp directly upstream of transcription start site (TSS) (Fig. 6A & 7A).

We identified two regions with no predicted PU.1binding site to use as negative control for our experiment. The first region corresponds to GAPDH gene (not shown for CD11b), the gene known to have no predicted PU.1 binding sites and there is no literature onPU.1 regulation of GAPDH gene. The second negative control primer set used was CNAP1

(chromosome condensation-related SMC-associated protein gene) (Fig. 6B). It binds a region of genomic DNA between the gene GAPDH and the chromosome condensation- related SMC-associated protein CNAP1. The region between these two sites normally does not bind by TFs so logically this locus is not enriched by ChIP. The amplified region with this primer set is 174 bp. PU.1 was found enriched at CD11b promoter compared to the negative control region CNAP. This corresponded to a pulldown of 0.5% of the input while comparatively, the control IgG antibody could pulled down 0.03% of the input signal (Fig.

6C). PU.1 was 10-fold enriched at CD11b promoter.

To improve ChIP signals at CD11b promoter, we immuno-precipitated PU.1 with two sets of antibodies, i.e. Monoclonal and polyclonal (Fig. 7A). While there was background noise with polyclonal antibody, reaction was much more efficient with monoclonal antibody.

Densitometry was used to compare the percent input pull down by PU.1 at CD11b promoter and at control CNAP region. PU.1 binding DNA was enriched at CD11b promoter (Fig. 7B).

3.3. PU.1 binds to LIMD1 promoter

After observing PU.1 enrichment at CD11b promoter, we chose another positive binding region for PU.1 corresponds to a tumor suppressor gene LIMD1 promoter. LIMD1 (Lim domains containing protein 1) is a renowned tumor suppressor gene (TSG) present on chromosome region 3q21.3. This region is believed to undergo deletion, epigenetic modification and loss of heterozygosity in many cancers. LIMD1 has been shown in the literature to be bound by PU.1 due to presence of PU.1 consensus binding sequence 5’–GGAA- in LIMD1 promoter[80]. Both

ChIP and electro mobility shift assay (EMSA) in U937 cell line has demonstrated PU.1 binding to LIMD1 promote in literature[80]. Based on this observation, we hypothesize that PU.1 should bind to LIMD1 promoter in NK cells as well. To test this, we performed ChIP exactly the way done for CD11b except at the last step, eluted DNA was amplified with LIMD1 primers. Later,

PCR products were run on 2% agarose gel.

Densitometry was used to calculate fraction of input pulled down by PU.1 at LIMD1 promoter against GAPDH (Fig. 8A). PU.1 pulled down at LIMD1 promoter was roughly equal to 1.75% of input DNA as compare to IgG which pulled down just 0.5% input DNA.(Fig. 8B).Fold enrichment was approximately equal to 3. We performed qPCR with same primers for IPs and input DNA and enrichment was calculated by ΔΔCt method (Fig.9). Amplification curves are shown in Fig.

9A).IgG1 was found equally enriched over LIMD1 and GAPDH promoters comparatively to PU.1 that was more enriched at LIMD1 promoter than at negative control region GAPDH (Fig. 9B).

Calculations are given in (Table 5).

4. Discussion:

Natural killer (NK) cells are well known for their ability to eliminate transformed (tumor) cells and virus-infected cells [81]. In addition, NK cells are important regulators of the adaptive immune system [39]. Each of these roles of NK cells putatively contribute to maintenance of homeostasis and pathogenesis of disease within the intestinal compartment. The gut is a dynamic environment where the innate and adaptive immune systems are constantly by microbial and dietary antigens. A balance must be achieved in the gut between tolerance to the microbiota or dietary antigens and elimination of harmful pathogens. NK cells and other type1

ILCs contribute to immune defense in the intestines by targeting pathogenic microbes or by producing inflammatory cytokines. Yet, exaggerated inflammatory responses by these cells can be harmful to the intestines and contribute to pathological conditions. In this regard, it is notable that NK cells and ILC1s accumulate in the inflamed intestinal tissues of IBD patients.

Alternatively, NK cells inhibition of T cells responses may promote mucosal homeostasis and alleviate disease. This hypothesis is supported by exacerbated colitis in mouse models of disease in the absence of NK cells. The duality of ILC and NK cell function in the intestines is putatively controlled by changes within gene regulatory networks. There is a pressing need to understand the genetic and epigenetic mechanisms governing the functional activity of type 1

ILCs in order to appreciate how these cells dysfunction and how they might be manipulated in therapy of disease.

Our overall hypothesis holds that PU.1 binds to IBD genetic risk loci in CD56+ NK cells in an allele-dependent manner, resulting in altered gene expression contributing to pathologic functionality of these cells. The rationale for this hypothesis came from novel RELI-mediated determination of statistically significant overlap between genetic locations associated with IBD risk and PU.1 ChIP-Seq binding registers in human CD56+ cells. This analysis suggests that 31

PU.1 may bind more than half of IBD risk loci in NK cells, leaving open the possibility that the

DNA sequence at these loci may alter PU.1 binding and expression of associated genes. The goal of this proposal was to bridge these bioinformatics analyses to eventual assessment of allele-specific binding of PU.1 to IBD risk loci in human NK cells from patients who are heterozygous at these loci.

Genetic variants can alter the ability of TFs to bind to genome and this can affect the expression of neighboring, or even distant, genes [82]. PU.1 is a vital transcription factor in the differentiation of NK cell. The expression of specific receptors for growth factors and cytokines, including c-kit, Il-7 receptor and IL-12 receptor, are controlled by PU.1 in NK cells [44]. In this manner, PU.1 plays a fundamental role in the maturation of and function of NK cells [60]. To study the interaction of PU.1 protein with the genome of human NK cells, we optimized ChIP in these cells and established a capacity for epigenetic profiling of NK cells in our laboratory. We first confirmed, using immunoblot, that PU.1 is expressed in human NK cell lines, KHYG1 and

NK92. Using ChIP-PCR and these NK cell lines, we measured binding of PU.1 at the promoters of CD11b and LIMD1, two genes bearing PU.1 consensus binding in their promoter regions and demonstrating PU.1 dependent expression in other cell types. We did not observe enrichment of

PU.1 at control loci (CNAP and GAPDH) in these experiments, confirming the quality of our

ChIP procedure. From a technical point of view, this Master’s thesis successfully established and optimized the techniques necessary to further explore our hypothesis concerning allele- specific PU.1 binding of IBD risk loci in primary NK cells.

In a study done by Bernink et al., (2013), increased numbers ofILC1s were present in the gut during inflammation. They found a high frequency of IFN- γ producing ILC1s in the inflamed intestinal lamina propria of Crohn’s Disease patients when compared to non-inflamed Crohns’

Disease patients and people with non-inflamed unaffected intestine. Being a pro-inflammatory

cytokines, IFN-γ promotes inflammation in the intestines by enhancing neutrophils infiltration and activation of macrophages, endothelial cells and lymphocytes in the gut [83]. Several studies conducted in mice without a functional adaptive immune system, have highlight the role of ILCs in the gut inflammation. Additionally, several drugs used to treat IBD related inflammation target ILCs, in order to reduce their numbers and frequency, as they are major producers of IFN-γ in the intestinal environment [84] . In DSS-induced colitis model of mouse, a robust production of IFN-γ was observed. In contrast, IFN-γ deficient mice could not develop

DSS-induced colitis [83]. IFN-γ gene promoter is a direct target of PU.1 as shown by previous studies where PU.1 binding at IFN-γ promoter led to aberrant IFN-γ production. In NK cells, IFN-

γ production is controlled by cytokine IL-12 which in turn is a direct target of PU.1. Thus, PU.1 directly and indirectly affects the production of IFN- γ in NK cells and promotes gut inflammation

[85]. A second indirect relationship is through PU.1 dependent monocyte-macrophages IL-12 production. PU.1 expressed abundantly in macrophages and control IL-12 production there [86],

[87] . This IL-12 is further required for IFN-γ production in NK cells. PU.1 ChIP-Seq datasets from monocytes and macrophages are 2nd, 3rd and 5th order of most significant datasets for IBD risk loci in our study.

NK cells perform important immuno-regulatory functions in the gut environment possibly through the killing of activated CD4+ T cells via perforin and granzyme-mediated ways [54], [53]. NK cells require ETS-1 for perforin expression to maintain their cytolytic activities and PU.1 is known to inhibit ETS-1 expression. This suppression impacts the proliferation and phenotype of

ILCs [61].

Both NK and ILC1s occupy strategic locations within intestinal tissues [88], [39] that facilitate their contribution to immune defense and pathological processes. For their cytolytic activities against variety of pathogens, NK cell require IL-10. IL-10 is an immunosuppressive cytokine that

is tightly linked with IBD and our top IBD risk loci candidate as well. IL-10 has a central role in

IBD, as it regulates T- cells responses against gut microflora. In IL-10 deficient rodent model of

IBD, inflammation and T-cells related immune responses were reported [89]. It is known that

IBD risk variants reduced enhancer activities at IL-10 promoters. On the other hand, PU.1 suppresses the expression of IL-10 by binding to its promoter. This could possibly the binding effects of PU.1 at IL-10 promoter in NK cells with IBD-related genotype that affect the immunosuppressive activities of IL-10 in the gut [62], [35].

NK cells not merely killed virus-infected cells but they are also producers of vital cytokines like

IL-22. IL-22 producing RORγt+ NKp46+IL-22 are involved in maintenance of intestinal mucosal immune system [90]. IL-22 has a protective role as evident by mouse model of IBD where IL-22 deficient mice were prone to T- cells mediated colitis [91]. It would be interesting to note that type 3 ILCs are a major producers of IL-22 in the healthy gut under homeostatic conditions. It restores epithelial lining and helps in healing [92]. ILC2s and ILC3s are documented in many

IBD studies and implicated in disease progression [48]. In steady state, ILC3s represent the most abundant class of ILCs in the gut. Yet, during inflammation, ILC3 become outnumbered by

ILC1s [93]. In fact, ILC3s demonstrate plasticity and can acquire an ILC1-like phenotype. [46],

[94].

In order to facilitate use of ChIP-Seq to measure allelic binding of PU.1 at specific sites (e.g.

IBD risk loci) in NK cells, we optimized PU.1ChIP and analyzed enrichment of the CD11b promoter. In acute promyelocytic leukemia (APL) cells, PU.1 was enriched at this promoter and regulated CD11b expression. PU.1also regulated neutrophils differentiation by being recruited to the CD11b promoter [79]. In mouse NK cells, CD11b serves as a maturation marker and an adhesion molecule. In order to further validate our ChIP results, we examined PU.1 binding in the promoter of a tumor suppressor gene, LIMD1. The promoter region of the LIMD1 gene has a

binding sequence for the transcription factor, PU.1, which was confirmed in U937 cell line.

Artificial reduction of PU.1 expression resulted in a 90 % reduction of transcriptional activity at the LIMD1 promoter [80]. We observed 3 fold enrichment of PU.1 at LIMD1 promoter.

PU.1 enrichment has been reported in other autoimmune diseases risk loci as well. A study of

148 transcription factors in GM12878 cells, an EBV-transformed B lymphoblastoid cell line, found that PU.1 was highly enriched at both Systemic Lupus Erythmatosus (SLE) and

Rhumatoid Arthritis (RA) risk loci. Based on this study, we speculate that gene regulatory elements can control autoimmune-related gene expression through common pathways and functions. Once fully elucidated, findings from our studies will not only help in understanding epigenetic basis of IBD but also other related autoimmune disorders as well. The ChIP-Seq datasets that we used in our studies were obtained from CD56+ cells; however we could not identify the various groups of ILCs. By optimizing PU.1 ChIP in human NK cell lines, the work in this thesis will facilitate examination of allele-dependent binding of PU.1 in peripheral blood human NK cells from IBD patients who are heterozygous at IBD risk alleles.

One technical limitation of doing ChIP in primary NK cells is the need to perform this technique on tens of millions of cells. However, we can perform primary assays with NK cells from healthy individuals that are Heterozygous at these disease risk loci, or use well-established in Vitro expansion protocols to increase our yield of NK cells to overcome these limitations. We are also equipped to perform ChIP in mouse NK cells and intestine -derived ILCs subsets to check PU.1 enrichment in the promoters of iBD relevant genes.

We have several expectations from the datasets of PU.1ChIP-Seq analyses we recently performed with our NK cell lines. At heterozygous IBD risk loci in these genotyped cells, we will compare the number of ChIP-Seq reads that map to the chromosome carrying the IBD risk variant versus the chromosome with the non-risk variant. We will be able to strength these data 35

and further support our hypothesis by using RNA-Seq to demonstrate that the same genes alleleically bound by PU.1 are expressed in allelic fashion. These NK cell lines are also amenable to shRNA-mediated gene knockdown approached. We will use lentiviral shRNA- conferring vectors to knockdown PU.1 in NK cells lines (Fig.10). We will transduce cells with five shRNA having sequence complementary to 5 different regions of PU.1 mRNA to target it and ultimately silence it. We will use puromycin as a selection marker to distinguish cells who have taken up shRNA successfully against those who have not. As a control we have lentiviral backbone with a shRNA that does not target any mammalian gene.

After confirming PU.1 knocking down, we will perform RNA-Seq to altered gene expression.

PU.1 knock down was previous described in retinal cells of the eyes in the context of an

Experimental Autoimmune Uveoretinitis (EAU) model. The level of inflammation was high in retinal cells of the eye in the presence of PU.1 and this was dependent on IL-2 and IFN-γ production. These genes are directly regulated by PU.1. Upon knock down of PU.1, mRNA levels of IFN-γ and IL-2 were drastically reduced. IL-2 is interesting because it can induce ILC3 to acquire features of ILC1s including, including enhanced production of IFN-γ. IFN-γ, in turn, has a definite role in promoting gut inflammation. IL-12 is a stimulant for NK cells to secrete IFN-

γ so it governs a lot of functions in NK cells [95].

If our hypothesis that PU.1 binds differentially to IBD risk loci in CD56+ cells turns out to be true, we can assume that this differential binding of PU.1 could change the behavior and response of critical ILCs genes involved in maintaining gut homeostasis. This binding can further disturb important pathways involved in immunomodulatory roles of NK cells and could be responsible for the development, progression, and pathogenesis of IBD. Once clinicians know this information, they can develop strategies to target one of these mechanisms to modulate the behavior of ILCs. One or more pathways can also be targeted for treatment purposes.

5. Conclusion:

IBD is an incurable, life-long disease that reduces the quality of life of patients and requires sustained treatment for symptoms. To better cope up with this disease, we need to understand possible mechanisms contributing to severity of this disease. Genetics is a strong component that can predispose an individual to risk of IBD especially during early life. To better understand

IBD etiology, our group is studying the underlying genetic mechanisms that contribute to disease risk. We hypothesize that ILC1s and NK cells contribute to pathology as a result of differential binding of the transcription factor, PU.1, to IBD risk loci. Further, we hypothesize that this differential binding dysregulates the expression of immune genes and results in pathological behavior of type 1ILCs in the intestines. Work present in this manuscript is an effort to build a foundation for future endeavors that would likely end with an approach to treat autoimmune diseases like IBD.

7. References:

[1] B. Kohr, A. Gardet, and R. J. Xavier, “Genetics and pathogenesis of inflammatory bowel

disease,” Nature, vol. 474, no. 7351, pp. 307–317, 2011.

[2] A. N. Ananthakrishnan, “Epidemiology and risk factors for IBD,” Nat. Rev. Gastroenterol.

Hepatol., vol. 12, no. 4, pp. 205–217, 2015.

[3] S. Pathogens, “HHS Public Access,” vol. 1848, no. 3, pp. 3047–3054, 2016.

[4] J. Z. Liu et al., “Association analyses identify 38 susceptibility loci for inflammatory bowel

disease and highlight shared genetic risk across populations,” Nat. Genet., vol. 47, no. 9,

pp. 979–986, 2015.

[5] J. Cosnes, C. Gowerrousseau, P. Seksik, and A. Cortot, “Epidemiology and natural

history of inflammatory bowel diseases,” Gastroenterology, vol. 140, no. 6, pp. 1785–

1794, 2011.

[6] E. V. Loftus, “Clinical epidemiology of inflammatory bowel disease: Incidence,

prevalence, and environmental influences,” Gastroenterology, vol. 126, no. 6, pp. 1504–

1517, 2004.

[7] C. Abraham and J. H. Cho, “Inflammatory Bowel Disease,” New Engl. J. Med., vol. 361,

no. 21, pp. 2066–2078, 2009.

[8] E. Van Der Gracht, S. Zahner, and M. Kronenberg, “When Insult Is Added to Injury: Cross

Talk between ILCs and Intestinal Epithelium in IBD,” Mediators Inflamm., vol. 2016, 2016.

[9] D. C. Baumgart and W. J. Sandborn, “Inflammatory bowel disease: clinical aspects and

established and evolving therapies,” Lancet, vol. 369, no. 9573, pp. 1641–1657, 2007.

[10] Crohn’s & Colitis Foundation of America, “The Facts About Inflammatory Bowel

Diseases,” Inflamm. Bowel Dis., vol. 2, no. 2, p. 1, 2014.

[11] N. A. Molodecky et al., “Increasing incidence and prevalence of the inflammatory bowel

diseases with time, based on systematic review,” Gastroenterology, vol. 142, no. 1, p.

46–54.e42, 2012.

[12] L. A. Criswell et al., “Analysis of Families in the Multiple Autoimmune Disease Genetics

Consortium (MADGC) Collection: the PTPN22 620W Allele Associates with Multiple

Autoimmune Phenotypes,” Am. J. Hum. Genet., vol. 76, no. 4, pp. 561–571, 2005.

[13] S. C. Ng et al., “Incidence and phenotype of inflammatory bowel disease based on results

from the Asia-Pacific Crohn’s and colitis epidemiology study,” Gastroenterology, vol. 145,

no. 1, pp. 158–165, 2013.

[14] T. H. M. P. Consortium, “Structure, Function and Diversity of the Healthy Human

Microbiome,” Nature, vol. 486, no. 7402, pp. 207–214, 2013.

[15] K. J. Maloy and F. Powrie, “Intestinal homeostasis and its breakdown in inflammatory

bowel disease,” Nature, vol. 474, no. 7351, pp. 298–306, 2011.

[16] W. Strober, I. J. Fuss, and R. S. Blumberg, “T HE I MMUNOLOGY

OF M UCOSAL M ODELS OF I

NFLAMMATION,” Annu. Rev. Immunol., vol. 20, no. 1, pp. 495–549, 2002.

[17] A. Frolkis et al., “Environment and the inflammatory bowel diseases.,” Can. J.

Gastroenterol., vol. 27, no. 3, pp. e18-24, 2013.

[18] L. Halme, P. Paavola-Sakki, U. Turunen, M. Lappalainen, M. Färkkilä, and K. Kontula,

“Family and twin studies in inflammatory bowel disease,” World J. Gastroenterol., vol. 12,

no. 23, pp. 3668–3672, 2006.

[19] C. Tysk, E. Lindberg, and G. Jarnerot, “Ulcerative colitis and Crohn â€TM s disease in an

unselected population of monozygotic and dizygotic twins . A study of heritability and the

influence of smoking,” pp. 990–996, 1988.

[20] J. Halfvarson, L. Bodin, C. Tysk, E. Lindberg, and G. Järnerot, “Inflammatory bowel

disease in a Swedish twin cohort: A long- term follow-up of concordance and clinical

characteristics,” Gastroenterology, vol. 124, no. 7, pp. 1767–1773, 2003.

[21] N. P. Thompson, R. Driscoll, R. E. Pounder, and A. J. Wakefield, “Genetics versus

environment in inflammatory bowel disease: results of a British twin study,” Bmj, vol. 312,

no. 7023, pp. 95–96, 1996.

[22] C. Cotsapas et al., “Pervasive sharing of genetic effects in autoimmune disease,” PLoS

Genet., vol. 7, no. 8, 2011.

[23] a Franke et al., “Meta-Analysis Increases to 71 the Tally of confirmed {C}rohn’s Disease

Susceptibility Loci,” Nat. Genet., vol. 42, no. 12, pp. 1118–1125, 2010.

[24] Y. Luo et al., “Exploring the genetic architecture of inflammatory bowel disease by whole-

genome sequencing identifies association at ADCY7,” Nat. Genet., vol. 49, no. 2, pp.

186–192, 2017.

[25] I. Hulur et al., “Enrichment of inflammatory bowel disease and colorectal cancer risk

variants in colon expression quantitative trait loci.,” BMC Genomics, vol. 16, p. 138, 2015.

[26] J. M. Peloquin et al., “Characterization of candidate genes in inflammatory bowel

disease–associated risk loci,” JCI Insight, vol. 1, no. 13, pp. 1–18, 2016.

[27] M. Gutierrez-Arcelus, S. S. Rich, and S. Raychaudhuri, “Autoimmune diseases —

connecting risk alleles with molecular traits of the immune system,” Nat. Rev. Genet., vol.

17, no. 3, pp. 160–174, 2016.

[28] M. Fareed and M. Afzal, “Single nucleotide polymorphism in genome-wide association of

human population: A tool for broad spectrum service,” Egypt. J. Med. Hum. Genet., vol.

14, no. 2, pp. 123–134, 2013.

[29] E. E. Schadt et al., “Mapping the genetic architecture of gene expression in human liver,”

PLoS Biol., vol. 6, no. 5, pp. 1020–1032, 2008.

[30] T. Lappalainen et al., “Transcriptome and genome sequencing uncovers functional

variation in humans,” Nature, vol. 501, no. 7468, pp. 506–511, 2013.

[31] A. Ameur, A. Rada-Iglesias, J. Komorowski, and C. Wadelius, “Identification of candidate

regulatory SNPs by combination of transcription-factor-binding site prediction, SNP

genotyping and haploChIP,” Nucleic Acids Res., vol. 37, no. 12, 2009.

[32] H.-J. Jin, S. Jung, A. R. DebRoy, and R. V Davuluri, “Identification and validation of

regulatory SNPs that modulate transcription factor chromatin binding and gene

expression in prostate cancer,” Oncotarget, vol. 7, no. 34, 2016.

[33] M. Z. Cader and A. Kaser, “Recent advances in inflammatory bowel disease: mucosal

immune cells in intestinal inflammation,” Gut, vol. 62, no. 11, pp. 1653–64, 2013.

[34] H. Koizumi et al., “Identification of a killer cell-specific regulatory element of the mouse

perforin gene: an Ets-binding site-homologous motif that interacts with Ets-related

proteins.,” Mol. Cell. Biol., vol. 13, no. 11, pp. 6690–701, 1993.

[35] H. Wang et al., “Histone Deacetylase Inhibitor LAQ824 Augments Inflammatory

Responses in Macrophages through Transcriptional Regulation of IL-10,” J. Immunol.,

vol. 186, no. 7, pp. 3986–3996, 2011.

[36] T. Musikacharoen, A. Oguma, Y. Yoshikai, N. Chiba, A. Masuda, and T. Matsuguchi,

“Interleukin-15 induces IL-12 receptor β1 gene expression through PU.1 and IRF 3 by

targeting chromatin remodeling,” Blood, vol. 105, no. 2, pp. 711–720, 2005.

[37] C. S. N. Klose and D. Artis, “Innate lymphoid cells as regulators of immunity,

inflammation and tissue homeostasis,” Nat. Immunol., vol. 17, no. 7, pp. 765–774, 2016.

[38] R. Goldberg, N. Prescott, G. M. Lord, T. T. MacDonald, and N. Powell, “The unusual

suspects—innate lymphoid cells as novel therapeutic targets in IBD,” Nat. Rev.

Gastroenterol. Hepatol., vol. 12, no. 5, pp. 271–283, 2015.

[39] M. M. Shikhagaie, K. Germar, S. M. Bal, X. R. Ros, and H. Spits, “diseases,” Nat. Publ.

Gr., no. February, 2017.

[40] H. Spits et al., “Innate lymphoid cells — a proposal for uniform nomenclature,” Nat. Rev.

Immunol., vol. 13, no. 2, pp. 145–149, 2013.

[41] L. L. Lanier, J. H. Phillips, J. Hackett, M. Tutt, and V. Kumar, “Natural killer cells:

definition of a cell type rather than a function.,” J. Immunol., vol. 137, no. 9, pp. 2735–9,

1986.

[42] A. Nagler, L. L. Lanier, S. Cwirla, J. H. Phillips, and J. H. Phillips, “Comparative studies of

human FcRIII-positive and negative natural killer cells Information about subscribing to

The Journal of Immunology is COMPARATIVE STUDIES OF HUMAN FcRIII-POSITIVE

AND NEGATIVE NATURAL KILLER CELLS,” 2009.

[43] O. Cohavy and S. R. Targan, “CD56 marks an effector T cell subset in the human

intestine.,” J. Immunol., vol. 178, no. 9, pp. 5524–32, 2007.

[44] F. Colucci, S. I. Samson, R. P. Dekoter, O. Lantz, H. Singh, and J. P. Di Santo,

“Differential requirement for the transcription factor PU . 1 in the generation of natural

killer cells versus B and T cells,” vol. 97, no. 9, pp. 2625–2633, 2017.

[45] J. H. Bernink et al., “Human type 1 innate lymphoid cells accumulate in inflamed mucosal

tissues,” vol. 14, no. 3, 2013.

[46] S. Li, D. Yang, T. Peng, Y. Wu, Z. Tian, and B. Ni, “Innate lymphoid cell-derived cytokines

in autoimmune diseases,” J. Autoimmun., 2017.

[47] S. J. Szabo, S. T. Kim, G. L. Costa, X. Zhang, C. G. Fathman, and L. H. Glimcher, “A

Novel Transcription Factor, T-bet, Directs Th1 Lineage Commitment,” Cell, vol. 100, no.

6, pp. 655–669, 2000.

[48] J. H. Bernink et al., “Human type 1 innate lymphoid cells accumulate in inflamed mucosal

tissues,” Nat. Immunol., vol. 14, no. 3, pp. 221–229, 2013.

[49] D. Artis and H. Spits, “The biology of innate lymphoid cells,” Nature, vol. 517, no. 7534,

pp. 293–301, 2015.

[50] M. M. Shikhagaie, K. Germar, S. M. Bal, X. R. Ros, and H. Spits, “Innate lymphoid cells in

autoimmunity: emerging regulators in rheumatic diseases,” Nat. Rev. Rheumatol., vol. 13,

no. 3, pp. 164–173, 2017.

[51] A. N. J. McKenzie, H. Spits, and G. Eberl, “Innate Lymphoid Cells in Inflammation and

Immunity,” Immunity, vol. 41, no. 3, pp. 366–374, 2014.

[52] D. Ivanova, R. Krempels, J. Ryfe, K. Weitzman, D. Stephenson, and J. P. Gigley, “NK

cells in mucosal defense against infection,” Biomed Res. Int., vol. 2014, 2014.

[53] C. Rydyznski et al., “Generation of cellular immune memory and B-cell immunity is

impaired by natural killer cells,” Nat. Commun., vol. 6, p. 6375, 2015.

[54] S. N. Waggoner, M. Cornberg, L. K. Selin, and R. M. Welsh, “Natural killer cells act as

rheostats modulating antiviral T cells,” Nature, vol. 481, no. 7381, pp. 394–398, 2011.

[55] L. J. Hall et al., “Natural killer cells protect mice from DSS-induced colitis by regulating

neutrophil function via the NKG2A receptor,” Mucosal Immunol., vol. 6, no. 5, pp. 1016–

1026, 2013.

[56] M. Colonna, “Interleukin-22-Producing Natural Killer Cells and Lymphoid Tissue Inducer-

like Cells in Mucosal Immunity,” Immunity, vol. 31, no. 1, pp. 15–23, Jul. 2009.

[57] J. Jiang, C. E. Parker, J. R. Fuller, T. H. Kawula, and C. H, “NIH Public Access,” Anal

chim Acta, vol. 605, no. 1, pp. 70–79, 2008.

[58] A. Geremia et al., “IL-23–responsive innate lymphoid cells are increased in inflammatory

bowel disease,” J. Exp. Med., vol. 208, no. 6, pp. 1127–1133, 2011.

[59] S. R. Mckercher et al., “Targeted disruption of the PU . 1 gene results in multiple

hematopoietic abnormalities,” vol. 15, no. 20, pp. 5647–5658, 1996.

[60] F. Colucci, S. I. Samson, R. P. Dekoter, O. Lantz, H. Singh, and J. P. Di Santo,

“Differential requirement for the transcription factor PU . 1 in the generation of natural

killer cells versus B and T cells,” vol. 97, no. 9, pp. 2625–2633, 2016.

[61] H. D. Lacorazza et al., “The ETS protein MEF plays a critical role in perforin gene

expression and the development of natural killer and NK-T cells,” Immunity, vol. 17, no. 4,

pp. 437–449, 2002.

[62] E. Reuss, R. Fimmers, A. Kruger, C. Becker, C. Rittner, and T. Höhler, “Differential

regulation of interleukin-10 production by genetic and environmental factors – a twin

study,” Genes Immun., vol. 3, no. 7, pp. 407–413, 2002.

[63] S. M. Blois et al., “NK cell-derived IL-10 is critical for DC-NK cell dialogue at the maternal-

fetal interface.,” Sci. Rep., vol. 7, no. 1, p. 2189, 2017.

[64] J. Z. Liu and C. A. Anderson, “Genetic studies of Crohn’s disease: Past, present and

future,” Best Pract. Res. Clin. Gastroenterol., vol. 28, no. 3, pp. 373–386, 2014.

[65] K. Struhl, “Chromatin and transcription factors : who ’ s on first ? Recent results suggest

that nucleosomes and,” vol. 3, no. 4, pp. 4–6, 1993.

[66] X. Wang and X. Zhang, “Pinpointing transcription factor binding sites from ChIP-seq data

with SeqSite,” BMC Syst. Biol., vol. 5, no. Suppl 2, p. S3, 2011.

[67] A. Manuscript, “NIH Public Access,” Growth (Lakeland), vol. 23, no. 1, pp. 1–7, 2008.

[68] A. S. Weinmann, P. S. Yan, M. J. Oberley, T. H. M. Huang, and P. J. Farnham, “Isolating

human transcription factor targets by coupling chromatin immunoprecipitation and CpG

island microarray analysis,” Genes Dev., vol. 16, no. 2, pp. 235–244, 2002.

[69] E. Helman et al., “Quantifying ChIP-seq data : a spiking method providing an internal

reference for A cloud-compatible bioinformatics pipeline for ultrarapid pathogen

identification,” vol. 24, no. 7, pp. 1157–1168, 2014.

[70] R. Jothi, S. Cuddapah, A. Barski, K. Cui, and K. Zhao, “Genome-wide identification of in

vivo protein-DNA binding sites from ChIP-Seq data,” Nucleic Acids Res., vol. 36, no. 16,

pp. 5221–5231, 2008.

[71] S. Landt and G. Marinov, “ChIP-seq guidelines and practices of the ENCODE and

modENCODE consortia,” Genome …, no. Park 2009, pp. 1813–1831, 2012.

[72] A. Valouev et al., “Genome-wide analysis of transcription factor binding sites based on

ChIP-Seq data,” Nat. Methods, vol. 5, no. 9, pp. 829–834, 2008.

[73] G. Robertson et al., “Genome-wide profiles of STAT1 DNA association using chromatin

immunoprecipitation and massively parallel sequencing,” Nat. Methods, vol. 4, no. 8, pp.

651–657, 2007.

[74] E. R. Mardis, “ChIP-seq: welcome to the new frontier,” Nat Methods, vol. 4, no. 8, pp.

613–614, 2007.

[75] A. Barski et al., “High-Resolution Profiling of Histone Methylations in the Human

Genome,” Cell, vol. 129, no. 4, pp. 823–837, 2007.

[76] B. Wold and R. M. Myers, “Sequence census methods for functional genomics,” Nat.

Methods, vol. 5, no. 1, pp. 19–21, 2007.

[77] P. Kiesler, I. J. Fuss, and W. Strober, “Experimental Models of Inflammatory Bowel

Diseases,” C. Cell. Mol. Gastroenterol. Hepatol., vol. 1, no. 2, pp. 154–170, 2015.

[78] T. Takayama et al., “Imbalance of NKp44+NKp46− and NKp44−NKp46+ Natural Killer

Cells in the Intestinal Mucosa of Patients With Crohn’s Disease,” Gastroenterology, vol.

139, no. 3, p. 882–892.e3, 2010.

[79] F. Brugnoli et al., “Vav1 and PU.1 are recruited to the CD11b promoter in APL-derived

promyelocytes: Role of Vav1 in modulating PU.1-containing complexes during ATRA-

induced differentiation,” Exp. Cell Res., vol. 316, no. 1, pp. 38–47, 2010.

[80] D. E. Foxler, V. James, S. J. Shelton, T. Q. D. A. Vallim, P. E. Shaw, and T. V. Sharp,

“PU.1 is a major transcriptional activator of the tumour suppressor gene LIMD1,” FEBS

Lett., vol. 585, no. 7, pp. 1089–1096, 2011.

[81] S. Yusung, D. McGovern, L. Lin, D. Hommes, V. Lagishetty, and J. Braun, “NK cells are

biologic and biochemical targets of 6-mercaptopurine in Crohn’s disease patients,” Clin.

Immunol., vol. 175, pp. 82–90, 2017.

[82] A. A. Pai, J. K. Pritchard, and Y. Gilad, “The Genetic and Mechanistic Basis for Variation

in Gene Regulation,” PLoS Genet., vol. 11, no. 1, 2015.

[83] R. Ito et al., “Interferon-gamma is causatively involved in experimental inflammatory

bowel disease in mice,” Clin. Exp. Immunol., vol. 146, no. 2, pp. 330–338, 2006.

[84] H. H. Uhlig et al., “Differential Activity of IL-12 and IL-23 in Mucosal and Systemic Innate

Immune Pathology,” Immunity, vol. 25, no. 2, pp. 309–318, 2006.

[85] G. Trinchieri, “Interleukin-12 and Interferon-y,” vol. 147, no. 6, pp. 1534–1538, 1995.

[86] R. A. Demarco, M. P. Fink, and M. T. Lotze, “Monocytes promote natural killer cell

interferon gamma production in response to the endogenous danger signal HMGB1,”

Mol. Immunol., vol. 42, no. 4 SPEC. ISS., pp. 433–444, 2005.

[87] G. Coma et al., “Treatment of monocytes with interleukin (IL)-12 plus IL-18 stimulates

survival, differentiation and the production of CXC chemokine ligands (CXCL)8, CXCL9

and CXCL10,” Clin. Exp. Immunol., vol. 145, no. 3, pp. 535–544, 2006.

[88] G. F. Sonnenberg and D. Artis, “Innate lymphoid cells in the initiation, regulation and

resolution of inflammation,” Nat. Med., vol. 21, no. 7, pp. 698–708, 2015.

[89] M. W. Leach, N. J. Davidson, M. M. Fort, F. Powrie, and D. M. Rennick, “The Role of IL-

10 in Inflammatory Bowel Disease: ‘Of Mice and Men,’” Toxicol. Pathol., vol. 27, no. 60,

pp. 123–133, 1999.

[90] X. Xu et al., “Conventional NK cells can produce IL-22 and promote host defense in

Klebsiella pneumoniae pneumonia.,” J. Immunol., vol. 192, no. 4, pp. 1778–86, 2014.

[91] N. Satoh-Takayama et al., “Microbial Flora Drives Interleukin 22 Production in Intestinal

NKp46+ Cells that Provide Innate Mucosal Immune Defense,” Immunity, vol. 29, no. 6,

pp. 958–970, 2008.

[92] E. A. Kiss and A. Diefenbach, “Role of the aryl hydrocarbon receptor in controlling

maintenance and functional programs of RORγt+ innate lymphoid cells and intraepithelial

lymphocytes,” Front. Immunol., vol. 3, no. MAY, pp. 1–11, 2012.

[93] A. Eken, A. K. Singh, and M. Oukka, “Interleukin 23 in Crohn’s disease.,” Inflamm. Bowel

Dis., vol. 20, no. 3, pp. 587–95, 2014.

[94] S. Sedda, I. Marafini, M. M. Figliuzzi, F. Pallone, and G. Monteleone, “An overview of the

role of innate lymphoid cells in gut infections and inflammation,” Mediators Inflamm., vol.

2014, 2014.

[95] G. Ferlazzo et al., “Distinct roles of IL-12 and IL-15 in human natural killer cell activation

by dendritic cells from secondary lymphoid organs,” Proc. Natl. Acad. Sci. U. S. A., vol.

101, pp. 16606–16611, 2004.

Appendix

ChIP-Reagents

1. 1 M LiCl (Mol.Wt. LiCl= 42.39 g):- Dissolve 4.2 g NaHCO3 in 100 ml of H2O to make 0.5 M NaHCO3 Add 42.39 g of LiCl in 1 litre of H2O to make 1 M LiCl. To make 0.1 M LiCl you need to add 4.239 g in 1 litre of H2O or 0.423 g of LiCl in 100 ml of water/OR make a 1:10 7. 10 X TE buffer stock (I liter):- dilution of 1 M sample. Use 100 ml of 1 M Tris-Hcl pH 7.5 and 20 ml 0.5 M EDTA pH 8.0, upto 880 ml ddH2O to make 1 litre 10 X TE 2. 10 % sodium Deoxycholate:- 8. 1 X TE buffer solution (1 litre):- 10 g Sodium deoxycholate+ 100 ml ddH2O Use 10 ml of 1 M Tris-Hcl pH 7.5 and 2 ml 0.5 M EDTA pH 8.0, upto 980 ml ddH2O to make 1 litre 1 X TE 3. 10 % SDS (100 ml):- 9. I M Tris-HCl pH 7.5:- To prepare 100 ml of 10 % SDS solution, weigh 10 grams SDS in 250 ml conical flask Dissolve 121.14 g Tris (American / beaker. Add 80 ml deionized / Milli-Q water Bioanalytical #AB14042) in 800 ml and mix it. Heat to 68 °C if necessary. dH2O.Adjust pH to 7.5 with the appropriate Adjust the volume to 100 ml with deionized / volume of concentrated HCl. Bring final Milli-Q water.Optional : One can filter the volume to 1 liter with deionized solution to remove any undissolved water.Autoclave and store at room material. temperature.

10. 0.5 M EDTA pH 8.0 :- 4. 5 M NaCl (1 litre, mol. Wt 58.4 g):- To prepare EDTA at 0.5 M (pH 8.0): Add To make a 5 M solution, weigh (5 moles X 186.1 g of disodium EDTA. to 800 mL of 58.4 g / mole =) 292 g NaCl and dissolve in H2O. Stir vigorously on a magnetic stirrer. water to a final volume of 1 liter. Adjust the pH to 8.0 with NaOH (~20 g of NaOH pellets). Dispense into aliquots and 5. 1% Triton-X:- sterilize by autoclaving.

I ml Triton-X 11. 1.25 M Glycine (Mol. Wt. 75 g) (500 ml):- 6. 0.5 M NaHCO3 (84.007 g):- 50

Dissolve 46.87 g glycine in ddH2O to make 15. Elution buffer (100 ml) :- 500 ml 1.25 M glycine 1 % SDS (use 10 ml of 10 % SDS), 0.1 M 12. Dilution Buffer (100 ml):- NaHCO3 (use 20 ml of 0.5 M NaHCO3)+ 70 ml dd H2O to make 100 ml elution buffer. 20 mMTris-HCl (use 2 ml of I M Tris-HCl pH: 7.5), 1 % Triton-X 100 (Use 1 ml), 2 mM EDTA (use 400ul of 0.5 M EDTA), 150 mMNaCl (use 3 ml of 5 M NaCl)= Total 6.4 16. High salt wash buffer: ml + upto 93.6 ml H2O to make 100 ml 0.1 % SDS (use 1 ml of 10 % SDS), 1 % dilution buffer Triton-X 100 (Use 1 ml), 2 mM EDTA (use 13. Lysis Buffer (100 ml):- 400ul of 0.5 M EDTA), 500mMNaCl (use 10 ml of 5 M NaCl), 20mMTris-HCl (use 2 ml of 50 mMTris-HCl (use 5 ml of 1 M Tris-HCl, I M Tris-HCl pH: 7.5) = 7.4 ml + 92.6 ml pH 8.0), 1 % SDS (10 ml of 10 % SDS), 10 H2O to make 100 ml Low salt wash buffer. mM EDTA (use 2 ml of 0.5 M EDTA)=17 ml+ 83 m ddH2O to make 100 ml lysis 17. LiCl (Lithium Chloride wash buffer): buffer. 0.25 mMLiCl (use 25 ul of 1 M LiCl), 1 % 14. Low salt wash buffer: IGEPAL-CA630/ 1% NP-40 (use 1 ml IGEPAL/NP-40), 1% deoxycholic acid (use 0.1 % SDS (use 1 ml of 10 % SDS), 1 % 10 ml of 10 % deoxycholic acid), 1 mM Triton-X 100 (Use 1 ml), 2 mM EDTA (use EDTA (use 200 ul of 0.5 M EDTA), 10 400ul of 0.5 M EDTA), 150 mMNaCl (use 3 mMTris-HCL pH 8.1 (use 1 ml of 1M Tris- ml of 5 M NaCl), 20mMTris-HCl (use 2 ml of HCl pH 7.5)= 12.25 ml+ 87.75 ml H2O to I M Tris-HCl pH: 7.5) = 7.4 ml + 92.6 ml make 100 ml LiCl wash buffer. H2O to make 100 ml Low salt wash buffer.