










































  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

