ARESTY RUTGERS UNDERGRADUATE RESEARCH JOURNAL, VOLUME I, ISSUE III This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. VITAMIN D RECEPTOR BINDING WITH DNA IN DUODENAL CRYPT, DUODENAL VILLI, & COLONIC EPITHELIAL CELLS OF MICE DENNIS A. ALDEA, ROHIT AITA, SOHAIB HASSAN, EVAN S. COHEN, JOSEPH HUR, OSCAR PELLÓN-CÁRDENAS, LEI CHEN, MICHAEL P. VERZI (FACULTY ADVISOR) ✵ ABSTRACT Vitamin D receptor (VDR) is a transcription factor that mediates calcium absorption by intestinal epithelial cells. Although calcium absorption is ca- nonically thought to occur only in the small intestine, recent studies have shown that VDR activity in the co- lon alone is sufficient to prevent calcium deficiency in mice. Here, we further investigate VDR activity in the colon. We assess VDR-DNA binding in mouse duodenal crypt, duodenal villi, and colonic epithelial cells using Chromatin Immunoprecipitation se- quencing (ChIP-seq). We find that most VDR-respon- sive elements are common to all intestinal epithelial cells, though some VDR-responsive elements are re- gionally-enriched and exhibit greater VDR-binding affinity in either duodenal epithelial cells or colonic epithelial cells. We also assess chromatin accessibil- ity in the same three cell types using Assay for Trans- posase-Accessible Chromatin sequencing (ATAC- seq). By integrating the VDR ChIP-seq and ATAC- seq data, we find that regionally-enriched VDR-re- sponsive elements exhibit greater chromatin acces- sibility in the region of their enrichment. Finally, we assess the transcription factor motifs present in VDR- responsive elements. We find that duodenum- and colon-enriched VDR-responsive elements exhibit different sets of transcription factor motifs other than VDR, suggesting that VDR may act together with dif- ferent partner transcription factors in the two re- gions. Our work is the first investigation of VDR-DNA binding in the colon and provides a basis for further investigations of VDR activity in the colon. 1 INTRODUCTION INTESTINAL ANATOMY The small and large intestines are a pair of digestive organs that form the lower half of the vertebrate gas- trointestinal tract following the stomach. The small intestine is divided into the duodenum, the jejunum, and the ileum (FIGURE 1). The large intestine follows the small intestine and is divided into the cecum, the colon, and the rectum. Both the small and large in- testines are lined by the intestinal epithelium—a layer of cells separating the contents of the intestines from the rest of the body. In the small intestine, the intes- tinal epithelium is a tightly folded structure consist- ing of villi—finger-like protrusions, and crypts—thin in- vaginations between villi.[6] The folded structure of the intestinal epithelium increases the surface area available for nutrient absorption in the small intes- tine. Villi are composed of differentiated epithelial cells, and crypts are composed of undifferentiated intestinal stem cells. Intestinal stem cells in the crypts differentiate and migrate to the villi, maintaining the intestinal epithelium even as epithelial cells are con- stantly shed from the villi.[5] Villi are not present in the colon; instead, the colonic epithelium is a heteroge- neous mixture of differentiated epithelial cells and undifferentiated stem cells.[6] VITAMIN D RECEPTOR Vitamin D receptor (VDR) is a transcription factor present in intestinal epithelial cells. Transcription fac- tors are proteins that regulate the transcription of genes from DNA to RNA. Transcription factors bind to particular genomic regions near their target genes, inducing conformational changes in the DNA ARESTY RUTGERS UNDERGRADUATE RESEARCH JOURNAL, VOLUME I, ISSUE III that either promote or inhibit transcription of the tar- get genes. Most transcription factors can only bind to genomic regions located in open chromatin— DNA that is not condensed by histone proteins. A transcription factor’s responsive elements are the open genomic regions to which the transcription factor binds. A transcription factor’s binding specific- ity results from the presence of particular nucleotide sequences, called transcription factor motifs, in the transcription factor’s responsive elements. VDR binds calcitriol (1,25-dihydroxyvitamin D3), which is the activated form of vitamin D. Vitamin D, whether synthesized by skin cells as vitamin D3 or obtained from the diet as vitamin D2, is inactive and must be metabolized in the kidneys to calcitriol be- fore binding with VDR. From the kidneys, calcitriol enters the bloodstream and ultimately ligates with and activates VDR in intestinal epithelial cells. Once activated by calcitriol, VDR induces the transcription of genes required for calcium absorption by the in- testinal epithelial cells. VDR dysfunction is implicated in inflamma- tory bowel disease (IBD) and colorectal cancer (CRC). Certain atypical variants of the VDR gene are associated with increased prevalence of IBD.[8] IBD patients also exhibit reduced levels of VDR in the in- testinal epithelium. These findings suggest a link be- tween VDR dysfunction and IBD. Clinical studies of CRC patients have shown that different variants of VDR correlate with differences in patient survival.[21] Metastatic CRC tumor cells exhibit reduced VDR ex- pression compared to typical intestinal cells.[16] Re- duced levels of VDR expression enhance the abnor- mal Wnt/β-catenin pathway that drives the growth of most CRC tumors.[11] Although the precise role of VDR deficiency in CRC tumor formation is still un- known, these findings suggest that VDR inhibition drives the metastasis of CRC tumors. One of the best appreciated roles of VDR is its mediation of calcium absorption in the small in- testine. Knockout studies investigate the function of a protein by inactivating the gene encoding the pro- tein and observing the effect. When Vdr is knocked out in both the small and large intestines, the Vdr- knockout mice develop calcium deficiencies and ul- timately rickets, a disease characterized by severe re- duction in bone density.[20] However, when Vdr is knocked out in the small intestine but not in the co- lon, the mice are still able to absorb enough calcium to avoid developing rickets.[7] Though these studies show that VDR is active in the colon, the differences in VDR-DNA binding between the duodenum and colon have not yet been investigated. RESEARCH OBJECTIVES Here, we investigate VDR-DNA binding in the duo- denum and colon using laboratory mice (Mus mus- culus) as model organisms. Mice and humans both express VDR, and Vdr-knockout mice exhibit a phe- notype analogous to severe vitamin D deficiency in humans.[20] These findings suggest that the regula- tory role of VDR is similar in mice and humans. We assess VDR-DNA binding in mouse duo- denal villi, duodenal crypt, and colonic epithelial cells using Chromatin Immunoprecipitation se- quencing (ChIP-seq). By comparing VDR-DNA bind- FIGURE 1: THE GASTROINTESTINAL TRACT [2] The small and large intestines are the two organs of the lower gastrointestinal tract. The small intestine follows the stomach and is divided into the duodenum, the jejunum, and the ileum. The large intestine follows the small intestine and is divided into the cecum (not shown), the colon, and the rectum (not shown). ARESTY RUTGERS UNDERGRADUATE RESEARCH JOURNAL, VOLUME I, ISSUE III ing across cell types, we identify VDR-responsive el- ements that are regionally-enriched—exhibiting greater VDR-binding affinity in either the duodenum or in the colon. These regionally-enriched VDR-re- sponsive elements cannot result from differences in VDR motif presence, since genomic sequences do not differ between cell types. We hypothesize that differences in VDR-DNA binding result from differ- ences in open chromatin regions between cell types. We examine this hypothesis by assessing chromatin accessibility in each cell type using Assay for Transposase-Accessible Chromatin sequencing (ATAC-seq). By integrating the VDR ChIP-seq and the ATAC-seq data, we find that regionally-enriched VDR-responsive elements exhibit greater chromatin accessibility in the region of their enrichment. Thus, we conclude that differential chromatin accessibility causes differential VDR-DNA binding between intes- tinal regions. Finally, we assess the transcription factor binding site motifs present in VDR-responsive ele- ments. We find that duodenum- and colon-enriched VDR-responsive elements exhibit different sets of binding site motifs for transcription factors other than VDR, suggesting that VDR may act in conjunc- tion with different partner transcription factors in the two regions. Our work is the first survey of VDR-respon- sive elements in the colon. We find that tissue-en- riched VDR-responsive elements in duodenal and colonic epithelium cells differ in chromatin accessi- bility and secondary transcription factor binding mo- tifs. These findings provide a basis for further inves- tigations of differences in VDR-mediated gene ex- pression between the small and large intestines. Un- derstanding the action of VDR specific to the colon may explain the role of VDR in inflammatory bowel disease and colorectal cancer. 2 METHODOLOGY EXPERIMENTAL MICE Four wild-type mice (C57BL/6J strain) were used as experimental animals. All animal protocols were ap- proved by the Rutgers Institutional Animal Care and Use Committee. The mice were given food and wa- ter ad libitum and exposed to a 12 h light, 12 h dark cycle. The mice were euthanized when they were 4– 6 weeks old. One hour prior to euthanasia, the mice were treated with 10 ng/g body mass 1,25-dihy- droxyvitamin D3 (Caymon Chemical: #11792), in- jected intraperitoneally. SAMPLE PREPARATION Tissue samples were harvested from duodenal villi, duodenal crypts, and colonic epithelium of the mice immediately following euthanasia. Duodenal villi, duodenal crypts, and colonic epithelial cells were ex- tracted from the tissue samples and pelleted by cen- trifugation.[4] CHIP-SEQ PROTOCOL & ANALYSIS VDR-targeted ChIP-seq was conducted on the duo- denal villi, duodenal crypts, and colonic epithelial cells using mouse monoclonal IgG2a VDR antibody D-6 (Santa Cruz Biotechnology: #sc-13133) and rab- bit polyclonal IgG VDR antibody C-20 (Santa Cruz Bi- otechnology: #sc-1008). The VDR binding site library was purified and amplified using QIAquick PCR pu- rification kit #50 (Qiagen: #28104) before being se- quenced. Sequencing adapters were removed from the VDR ChIP-seq read FASTQ files using NGmerge.[9] Each pair of forward and reverse adapter-trimmed read FASTQ files was aligned to mouse genome assembly mm9 using Bowtie2.[10,15] Each alignment SAM file was converted to an align- ment BAM file using the SAMtools suite.[13] A composite VDR ChIP-seq alignment BAM file was generated for each cell type by combining each set of replicate VDR ChIP-seq alignment BAM files using the merge utility in the SAMtools suite. An alignment track BigWig file was generated from each composite and replicate alignment BAM file us- ing the bamCoverage utility in the deepTools suite.[18] VDR-binding peaks were identified from each VDR ChIP-seq alignment BAM file using the callpeak utility in MACS.[22] Peaks overlapping EN- CODE mm9 blacklisted regions were removed from each peak set BED file using the subtract utility in the BEDtools suite.[17] The ENCODE blacklists list ge- nomic regions known to yield false ChIP-seq signals ARESTY RUTGERS UNDERGRADUATE RESEARCH JOURNAL, VOLUME I, ISSUE III due to the inaccuracies of a particular genome as- sembly.[1] Each peak set BED file was then shifted so that each peak would be centered on its summit–the nucleotide with the greatest ChIP-seq signal within the peak region, as determined by MACS. The sum- mit of each peak represents the most probable tran- scription factor binding site; therefore, summit-cen- tering ensures that each peak is centered around the binding site. For each cell type composite, the VDR ChIP- seq signal was plotted versus distance from the near- est VDR ChIP-seq peak using the SiteproBW pro- gram included in the Cistrome suite.[14] ATAC-SEQ PROTOCOL & ANALYSIS ATAC-seq was conducted on the duodenal villi, du- odenal crypts, and colonic epithelial cells using Nex- tera Tn5 transposase (Illumina: #FC-121-1030). The transposed chromatin was purified and amplified us- ing QIAquick PCR purification kit #50 (Qiagen: #28104) before being sequenced. Sequencing adapters were removed from the ATAC-seq read FASTQ files using NGmerge.[9] Each pair of forward and reverse adapter-trimmed read FASTQ files was aligned to mouse genome as- sembly mm9 using Bowtie2.[10,15] Each alignment SAM file was converted to an alignment BAM file us- ing the SAMtools suite.[13] A composite ATAC-seq alignment BAM file was generated for each cell type by combining each set of replicate ATAC-seq alignment BAM files using the merge utility in the SAMtools suite. An alignment track BigWig file was generated from each compo- site and replicate alignment BAM file using the bam- Coverage utility in the deepTools suite.[18] The median alignment size of each ATAC- seq alignment BAM file was determined using the CollectInsertSizeMetrics utility included in the Picard suite.[3] Each alignment BAM file was converted to a BED file using the bamtobed utility included in the BEDtools suite.[17] Peak region BED files were called from each alignment BED file using MACS.[22] It was necessary to convert the alignment BAM files to BED files so that MACS would properly interpret the non- overlapping forward and reverse peaks typical of ATAC-seq but not of ChIP-seq. MACS was run with a shift distance of negative one-half the median align- ment size and an extsize distance equal to the me- dian alignment size for each alignment BED file. For each cell type composite, the ATAC-seq signal was plotted versus distance from the nearest ATAC-seq peak using the SiteproBW program in- cluded in the Cistrome suite.[14] DIFFERENTIAL BINDING ANALYSIS Peaks exhibiting differential VDR-binding affinities between cell types were identified using DiffBind.[19] The following contrasts were examined: colonic epi- thelium versus duodenal crypts, colonic epithelium versus duodenal villi, and duodenal crypts versus du- odenal villi. Each set of differentially bound peaks was filtered to only include peaks assigned a signifi- cance value less than 0.001 by DiffBind. Each filtered peak set was exported to a BED file. For each contrast, the VDR ChIP-seq signals of each contrasted cell type were plotted versus the enriched VDR ChIP-seq peak sets for each con- trasted cell type using the SiteproBW program in- cluded in the Cistrome suite.[14] MOTIF ANALYSIS Transcription factor motifs were identified from the composite VDR ChIP-seq peak set BED files of each cell type using HOMER. Due to limited computa- tional resources, the size of each composite peak set was reduced by random sampling. Sample peak set BED files were generated by randomly selecting one-fifth of the peaks in each composite peak set BED file. HOMER was used to identify motifs en- riched in each sample peak set BED file. Transcription factor motifs were also identi- fied from each differential VDR ChIP-seq peak set BED file using HOMER. However, the smaller size of the differential peak sets meant that sampling was not necessary; the entirety of each differential peak set BED file was analyzed using HOMER. 3 RESULTS GENOME ALIGNMENT All of the VDR ChIP-seq read FASTQ files were suc- cessfully aligned to mouse genome assembly ARESTY RUTGERS UNDERGRADUATE RESEARCH JOURNAL, VOLUME I, ISSUE III mm9.[15] The successful genomic alignments are in- dicated by the high alignment rates; all samples ex- hibited alignment rates greater than 85%, and all but one sample exhibited alignment rates greater than 90% (TABLE 1). PEAK CALLING The accuracy of VDR ChIP-seq peak calling was as- sessed by comparing each cell type’s composite peak set BED file to its composite track BigWig file. The resulting signal plots indicate that all of the com- posite peak sets exhibit a majority of reads near the centers of peak regions (FIGURE 2). Thus, we confirm that the VDR-responsive elements identified by MACS exhibit elevated VDR binding, as expected of responsive elements. DIFFERENTIAL BINDING ANALYSIS The correlation between VDR ChIP-seq peak sets was assessed using DiffBind.[19] The resulting corre- lation matrix indicates that the colonic epithelium peak sets are all more closely correlated to each TABLE 1: VDR CHIP-SEQ GENOME ALIGNMENT METRICS [A] Composite alignment BAM files were constructed using SAMtools merge.[14] [B] Genome alignment was conducted using mouse genome assembly mm9 and Bowtie2.[10,15] [C] Duplicate alignments were removed using MACS filterdup.[22] ARESTY RUTGERS UNDERGRADUATE RESEARCH JOURNAL, VOLUME I, ISSUE III FIGURE 2: VDR CHIP-SEQ SIGNAL VERSUS DISTANCE FROM VDR CHIP-SEQ PEAKS Most VDR ChIP-seq reads are located near VDR ChIP-seq peaks. VDR ChIP-seq signal versus distance from nearest VDR ChIP- seq peak. Figure generated using SiteproBW.[14] FIGURE 3: VDR CHIP-SEQ DIFFERENTIAL BINDING HEATMAP The difference between colonic and duodenal VDR ChIP- seq peak sets is greater than the difference between du- odenal crypt and duodenal villi VDR ChIP-seq peak sets. Darker shading in the heatmap represents greater simi- larity between peak sets; greater vertical separation in the dendrogram represents greater difference between peak sets. Figure generated by DiffBind.[19] FIGURE 4: DIFFERENTIAL VDR CHIP-SEQ PEAK SET SIZES The majority of VDR ChIP-seq peaks are common to duo- denal villi, duodenal crypt, and colonic epithelial cells. A small minority of VDR ChIP-seq peaks differ between duo- denal epithelial and colonic epithelial cells. ARESTY RUTGERS UNDERGRADUATE RESEARCH JOURNAL, VOLUME I, ISSUE III other than to any of the duodenal crypt or duodenal villi peak sets (FIGURE 3). The correlation matrix also in- dicates that the duodenal crypt and duodenal villi peak sets do not differ significantly. The majority of VDR ChIP-seq peaks are common to all three cell types. Out of the 23,381 VDR-binding sites exhibited in either colonic epithe- lial or duodenal villi cells, only 1,741 sites differ be- FIGURE 5: VDR CHIP-SEQ SIGNAL VERSUS DISTANCE FROM DIFFERENTIAL VDR CHIP-SEQ PEAKS Differential VDR ChIP-seq peaks exhibit greater VDR binding in the tissue of their enrichment. VDR ChIP-seq signal versus distance from nearest differential VDR ChIP-seq peak. Figure generated by SiteproBW.[14] FIGURE 6: ATAC-SEQ SIGNAL VERSUS DISTANCE FROM DIFFERENTIAL VDR CHIP-SEQ PEAKS Differential VDR ChIP-seq peaks exhibit greater chromatin accessibility in the tissue of their enrichment. ATAC-seq signal versus distance from nearest differential VDR ChIP-seq peak. Figure generated by SiteproBW.[14] ARESTY RUTGERS UNDERGRADUATE RESEARCH JOURNAL, VOLUME I, ISSUE III TABLE 2: VDR CHIP-SEQ PEAK CALLING METRICS [A] Composite alignment BAM files were constructed using SAMtools merge.[14] [B] Peak calling was conducted using MACS.[22] [C] Peaks included in the ENCODE mm9 blacklist were removed.[1] TABLE 3: DIFFERENTIAL VDR CHIP-SEQ PEAK SET METRICS [A] Enriched peaks were determined using DiffBind. [19] [B] 𝑝𝑝 < 0.001 ARESTY RUTGERS UNDERGRADUATE RESEARCH JOURNAL, VOLUME I, ISSUE III tween the two cell types (TABLE 3). There is a compa- rable difference between colonic epithelial and du- odenal crypt cells, which differ in only 1,102 VDR- binding sites out of 20,751 sites total. Nonetheless, these small differences are far greater than the mi- nuscule difference between duodenal crypt and du- odenal villi cells, which differ by only 85 VDR-binding sites out of 20,479 sites total (FIGURE 4). The accuracy of the differential binding anal- ysis performed by DiffBind was assessed by compar- ing each cell type’s enriched peak set BED file against all of the composite track BigWig files. The resulting signal plots indicate that all of the tissue- enriched peak sets exhibit greater overlap with VDR- responsive elements in the tissue of their enrichment than with VDR-binding sites in other tissues (FIGURE 5). Thus, we confirm that the tissue-enriched VDR-re- sponsive elements identified by DiffBind exhibit ele- vated VDR binding in the tissue of their enrichment, as expected of tissue-enriched responsive elements. COMPARISON OF VDR CHIP-SEQ & ATAC-SEQ To investigate our hypothesis that regionally-en- riched VDR binding results from differential chroma- tin accessibility between tissues, we compared duo- denum- and colon-enriched VDR-binding peaks BED files against all of the composite ATAC-seq Big- Wig files. The resulting signal plots indicate that all of the regionally-enriched VDR ChIP-seq peak sets exhibit greater overlap with open chromatin sites in the region of their enrichment than with open chro- matin sites in the other region (FIGURE 6). Thus, we conclude that differences in VDR binding result from differences in chromatin accessibility between re- gions. MOTIF FINDING Due to limited computational resources, motif find- ing was conducted on samples generated by ran- domly selecting one-fifth of the peaks in each com- posite VDR ChIP-seq peak set (TABLE 2). VDR is the most significant motif present in any of the composite VDR ChIP-seq peak set sam- ples (FIGURE 7), indicating that the VDR ChIP-seq was performed correctly. Besides VDR motifs, the colon- enriched peak set samples also exhibit HOXB13, FIGURE 7: VDR MOTIF PRESENCE IN VDR CHIP-SEQ PEAK SET SAMPLES VDR motif was the top transcription factor motif identified in all of the composite VDR ChIP-seq peak sets. Motifs were identified using HOMER on a random sample of one- fifth of each composite peak set FIGURE 8: VDR AND SECONDARY MOTIF PRESENCES IN DIFFER- ENTIAL VDR CHIP-SEQ PEAK SETS Colon- and duodenum-enriched VDR ChIP-seq peaks ex- hibit different secondary transcription factor motifs other than VDR. Motifs were identified using HOMER on each differential peak set. ARESTY RUTGERS UNDERGRADUATE RESEARCH JOURNAL, VOLUME I, ISSUE III CDX2, and FOXA2 motifs, whereas the crypt- and villi-enriched peak set samples exhibit HNF4α, ERRA, and GATA4 (FIGURE 8). These results suggest that dif- ferences in VDR binding between the duodenal epi- thelium and colonic epithelium may result from dif- ferences in the helper transcription factors that facil- itate VDR-DNA binding. 4 DISCUSSION We found that the VDR-binding profile of colonic epithelial cells is largely similar to that of du- odenal villi and duodenal crypt cells. Out of about twenty thousand VDR binding sites total, only a mi- nority of several hundred binding sites differ be- tween colonic epithelial cells and duodenal epithe- lial cells. Nonetheless, this difference is greater than that between duodenal villi and duodenal crypt cells, which differ in less than one hundred binding sites. By comparing VDR ChIP-seq and ATAC-seq data, we found that colon- and duodenum-enriched VDR-binding sites exhibited greater chromatin ac- cessibility in the tissue of their enrichment. We also determined that colon- and duodenum-enriched VDR-binding sites exhibit distinct sets of secondary (i.e. non-VDR) transcription factor motifs. Colon-en- riched VDR-binding sites exhibit HOXB13, CDX2, and FOXA2 motifs; duodenum-enriched VDR-bind- ing sites exhibit HNF4α, GATA4, and ERRA motifs. These findings concur with those of a previous inves- tigation which found that VDR-binding sites in the duodenum exhibited HNF4α and GATA4 motifs in addition to VDR motifs.[12] Tissue-specific secondary transcription fac- tors may cause differential VDR binding, either by causing the differences in open chromatin observed at differential binding sites, or by directly binding to VDR and affecting VDR-DNA binding. Possible bind- ing interactions between VDR and secondary tran- scription factors could be examined using protein immunoprecipitation assays. Additional investiga- tions can be conducted using intestinal organoid models. By knocking out secondary transcription factors in intestinal organoids, the role of these fac- tors in VDR-mediated regulation of gene expression could be determined. Such investigations would ad- vance our understanding of VDR’s role in intestinal health and diseases, including colorectal cancers, and possibly offer new treatments for those affected by these conditions. 5 DATA & SOURCE CODE ACCESS The complete data collected in this investi- gation are available upon request. This investigation did not involve human subjects, and these data do not include HIPAA-protected health information. The source code for the analysis pipeline used in this investigation is available upon request. The following programs were used in the analysis pipeline: NGmerge v0.3,[9] Bowtie2 v2.2.6,[10] SAMtools v0.1.19,[13] deepTools v3.3.0,[18] MACS v2.1.0,[22] BEDtools v2.17.0,[17] Cistrome v0.6.7,[14] Pi- card v2.18.27,[3] DiffBind v1.16.3,[19] HOMER v4.8.3∎ 6 REFERENCES [1] Amemiya, H. M., Kundaje, A., & Boyle, A. P. (2019). The EN- CODE blacklist: Identification of problematic regions of the genome. Scientific Reports, 9(1), 9354. [2] Blausen Medical Communications. (2014). Blausen 0432 GastroIntestinalSystem.png. HTTPS://COMMONS.WIKIMEDIA.ORG/WIKI/FILE:BLAUSEN_0432_GASTROIN- TESTINALSYSTEM.PNG [3] Broad Institute. Picard. HTTPS://BROADINSTITUTE.GITHUB.IO/PICARD [4] Chen, L., Toke, N. H., Luo, S., Vasoya, R. P., Fullem, R. L., Par- thasarathy, A., Perekatt, A. O., & Verzi, M. P. (2019). A rein- forcing HNF4-SMAD4 feed-forward module stabilizes enter- ocyte identity. Nature Genetics, 51, 777–785. [5] Crosnier, C., Stamataki, S., & Lewis, J. (2006). Organizing cell renewal in the intestine: Stem cells, signals and combinato- rial control. Nature Reviews Genetics, 7(5), 349–359. [6] de Santa Barbara, P., van den Brink, G. R., & Roberts, D. J. (2003). Development and differentiation of the intestinal ep- ithelium. Cellular and Molecular Life Sciences, 60(7), 1322– 1332. [7] Dhawan, P., Veldurthy, V., Yehia, G., Hsaio, C., Porta, A., Kim, K., Patel, N., Lieben, L., Verlinden, L., Carmeliet, G., & Christakos, S. (2017). Transgenic expression of the vitamin D receptor restricted to the ileum, cecum, and colon of vitamin D receptor knockout mice rescues vitamin D receptor–de- pendent rickets. Endocrinology, 158(11), 3792–3804. [8] Eloranta, J. J., Wenger, C., Mwinyi, J., Hiller, C., Gubler, C., Vavricka, S. R., Fried, M., Kullak-Ublick, G. A., & Swiss IBD Cohort Study Group. (2011). Association of a common vita- min D-binding protein polymorphism with inflammatory bowel disease. Pharmacogenetics and Genomics, 21(9), 559–564. [9] Gaspar, J. M. (2018). NGmerge: Merging paired-end reads via novel empirically-derived models of sequencing errors. BMC Bioinformatics, 19, 536. https://commons.wikimedia.org/wiki/File:Blausen_0432_GastroIntestinalSystem.png https://commons.wikimedia.org/wiki/File:Blausen_0432_GastroIntestinalSystem.png https://broadinstitute.github.io/picard ARESTY RUTGERS UNDERGRADUATE RESEARCH JOURNAL, VOLUME I, ISSUE III [10] Langmead, B., & Salzberg, S. L. (2012). Fast gapped-read alignment with Bowtie 2. Nature Methods, 9(4), 357–359. [11] Larriba, M. J., Ordóñez-Morán, P., Chicote, I., Martín-Fernán- dez, G., Puig, I., Muñoz, A., & Pálmer, H. G. (2011). Vitamin D receptor deficiency enhances Wnt/β-catenin signaling and tumor burden in colon cancer. PLOS One, 6(8), e23524. [12] Lee, S. M., Riley, E. M., Meyer, M. B., Benkusky, N. A., Plum, L. A., DeLuca, H. F., & Pike, J. W. (2015). 1,25-Dihy- droxyvitamin D3 controls a cohort of vitamin D receptor tar- get genes in the proximal intestine that is enriched for cal- cium-regulating components. Journal of Biological Chemis- try, 290(29), 18199–18215. [13] Li, H., Handsaker, B., Wysoker, A., Fennell, T., Ruan, J., Homer, N., Marth, G., Abecasis, G., Durbin, R., & 1000 Ge- nome Project Data Processing Subgroup. (2009). The se- quence alignment/map format and SAMtools. Bioinformat- ics, 25(16), 2078–2079. [14] Liu, T., Ortiz, J. A., Taing, L., Meyer, C. A., Lee, B., Zhang, Y., Shin, H., Wong, S. S., Ma, J., Lei, Y., Pape, U. J., Poldinger, M., Chen, Y., Yeung, K., Brown, M., Turpaz, Y., & Liu, X. S. (2011). Cistrome: An integrative platform for tran- scriptional regulation studies. Genome Biology, 12(8), R83. [15] Mouse Genome Sequencing Consortium. (2002). Initial se- quencing and comparative analysis of the mouse genome. Nature, 420(6915), 520–562. [16] Pálmer, H. G., Larriba, M. J., García, J. M., Ordóñez-Mo- rán, P., Peña, C., Peiró, S., Puig, I., Rodríguez, R., de la Fuente, R., Bernad, A., Pollán, M., Bonilla, F., Gamallo, C., de Herreros, A. G., & Muñoz, A. (2004). The transcription factor SNAIL represses vitamin D receptor ex- pression and responsiveness in human colon cancer. Nature Medicine, 10(9), 917–919. [17] Quinlan, A. R., & Hall, I. M. (2010). BEDtools: A flexible suite of utilities for comparing genomic features. Bioinformatics, 26(6), 841–842. [18] Ramírez, F., Ryan, D. P., Grüning, B., Bhardwaj, V., Kilpert, F., Richter, A. S., Heyne, S., Dündar, F., & Manke, T. (2014). DeepTools2: A next generation web server for deep-se- quencing data analysis. Nucleic Acids Research, 44(W1), W160–W165. [19] Stark, R., & Brown, D. (2011). DiffBind: Differential binding analysis of ChIP-seq peak data. HTTPS://BIOCONDUCTOR.ORG/PACKAGES/RELEASE/BIOC/VI- GNETTES/DIFFBIND/INST/DOC/DIFFBIND.PDF [20] Suda, T., Masuyama, R., Bouillon, R., & Carmeliet, G. (2015). Physiological functions of vitamin D: What we have learned from global and conditional VDR knockout mouse studies. Current Opinion in Pharmacology, 22, 87–99. [21] Vaughan-Shaw, P. G., O’Sullivan, F., Farrington, S. M., Theo- doratou, E., Campbell, H., Duntop, M. G., & Zgaga, L. (2017). The impact of vitamin D pathway genetic variation and circulating 25-hydroxyvitamin D on cancer outcome: Systematic review and meta-analysis. British Journal of Can- cer, 116(8), 1092–1110. [22] Zhang, Y., Liu, T., Meyer, C. A., Eeckhoute, J., Johnson, D. S., Bernstein, B. E., Nusbaum, C., Myers, R. M., Brown, M., Li, W., & Liu, X. S. (2008). Model-based analysis of ChIP-seq (MACS). Genome Biology, 9(9), R137. Dennis Aldea is a senior undergraduate student in the genetics program at Rutgers New Brunswick. His interest in scientific research was sparked by his experience in the Rutgers Waksman Student Scholars Program, a biotechnology outreach program for high school students. Dennis currently works with Dr. Michael Verzi at the Human Genetics Institute of New Jersey, where he conducts bioinformatics analyses to investigate the genetic basis of intestinal diseases and cancers. https://bioconductor.org/packages/release/bioc/vignettes/DiffBind/inst/doc/DiffBind.pdf https://bioconductor.org/packages/release/bioc/vignettes/DiffBind/inst/doc/DiffBind.pdf