Genome-wide binding of the orphan nuclear receptor TR4 suggests its general role in fundamental biological processes
- Henriette O'Geen†1,
- Yu-Hsuan Lin†2, 3,
- Xiaoqin Xu1,
- Lorigail Echipare1,
- Vitalina M Komashko1,
- Daniel He1,
- Seth Frietze1,
- Osamu Tanabe2,
- Lihong Shi2,
- Maureen A Sartor3,
- James D Engel2 and
- Peggy J Farnham1Email author
© O'Geen et al; licensee BioMed Central Ltd. 2010
Received: 12 July 2010
Accepted: 2 December 2010
Published: 2 December 2010
The orphan nuclear receptor TR4 (human testicular receptor 4 or NR2C2) plays a pivotal role in a variety of biological and metabolic processes. With no known ligand and few known target genes, the mode of TR4 function was unclear.
We report the first genome-wide identification and characterization of TR4 in vivo binding. Using chromatin immunoprecipitation followed by high throughput sequencing (ChIP-seq), we identified TR4 binding sites in 4 different human cell types and found that the majority of target genes were shared among different cells. TR4 target genes are involved in fundamental biological processes such as RNA metabolism and protein translation. In addition, we found that a subset of TR4 target genes exerts cell-type specific functions. Analysis of the TR4 binding sites revealed that less than 30% of the peaks from any of the cell types contained the DR1 motif previously derived from in vitro studies, suggesting that TR4 may be recruited to the genome via interaction with other proteins. A bioinformatics analysis of the TR4 binding sites predicted a cis regulatory module involving TR4 and ETS transcription factors. To test this prediction, we performed ChIP-seq for the ETS factor ELK4 and found that 30% of TR4 binding sites were also bound by ELK4. Motif analysis of the sites bound by both factors revealed a lack of the DR1 element, suggesting that TR4 binding at a subset of sites is facilitated through the ETS transcription factor ELK4. Further studies will be required to investigate the functional interdependence of these two factors.
Our data suggest that TR4 plays a pivotal role in fundamental biological processes across different cell types. In addition, the identification of cell type specific TR4 binding sites enables future studies of the pathways underlying TR4 action and its possible role in metabolic diseases.
There are an estimated 1400 site-specific DNA binding factors encoded in the human genome . Although these factors can influence transcription when their binding sites are cloned in front of core promoters, they usually do not function alone. Most often, individual transcription factors collaborate to orchestrate gene expression through combinatorial binding to regulatory regions in chromatin . These regions, termed cis modules, thereby activate, repress or otherwise epigenetically modify the transcriptional responses of individual genes. Elucidating the position and activities of individual cis modules using reporter genes is time consuming and expensive. With recent advances in DNA sequencing technology, it is now feasible to generate global protein-DNA interaction profiles by chromatin immunoprecipitation (ChIP) followed by ultra-high-throughput sequencing . Cis modules can then often be identified by applying bioinformatics searches for one or more cis motifs recognized by unrelated alternative factors near the binding sites of the factor analyzed by ChIP-seq or by the co-localization of bound sites for two or more unrelated different site-specific factors.
Nuclear receptors (NRs) represent a special class of transcription factors that direct target gene transcription in a ligand-dependent fashion. NRs contain a DNA-binding domain that recognizes a specific DNA sequence, as well as a ligand binding domain that renders these factors environmentally-dependent regulators via interaction with distinct cognate ligands . The great majority of NRs homodimerize or heterodimerize with another NR, and then bind to two copies of a repeated hexanucleotide sequence (called a half-site) separated by variable spacing . The half-site consensus, AGGTCA, can occur in either orientation and variation from the consensus allows numerous alternative binding sites of (probably) variable affinity . Based on the number of spacer nucleotides separating the two half-sites and the orientation of the two half-sites relative to each other, NR binding sites have been categorized as direct repeats (DR0 - DR8), everted repeats (ER0 - ER8) or inverted repeats (IR0-IR8) .
NR2C2 (human testicular receptor 4, TR4, in the older nomenclature) belongs to the nuclear receptor superfamily and is termed an orphan receptor due to the fact that no ligand has been discovered [6–8]. TR4 was initially identified in hypothalamus, prostate, and testis cDNA libraries, but has since been demonstrated to be broadly expressed in many physiological systems [9, 10]. For example, TR4 has been shown to activate target gene expression in liver carcinoma HepG2 cells . In contrast, in erythroid cells, TR4 can heterodimerize with another closely related family member (TR2, or NR2C1) and binds to a DR1 (direct repeats with one nucleotide spacer) element to repress target gene transcription [12–15]. The binding affinity of the TR4 homodimer for the DR1 element in vitro is equivalent to that of the TR2:TR4 heterodimer , and TR4 mRNA is more abundant than TR2 in human erythroid cells (Tanabe, unpublished observations). However, the broader physiological functions for, and the in vivo genome-wide binding patterns of, this broadly expressed nuclear receptor are obscure. We therefore chose to initially investigate genome wide TR4 binding anticipating that these studies might reveal some common, but also perhaps some tissue-specific, metabolic processes to which this factor contributes.
In this study we investigated the first genome-wide identification of cellular targets of TR4 and preliminary characterization of TR4 in vivo binding in multiple cell types, including those in which TR4 has been suggested to be an activator (liver) and cells in which TR4 has been suggested to be a repressor (blood). Using ChIP-seq, we determined TR4 in vivo binding in four human ENCODE cell lines: K562 erythroleukemia cells, HepG2 liver carcinoma, HeLa cervical carcinoma, and GM12878 immortalized lymphoblast cells. TR4 binding patterns identified in the four diverse cell lines suggest that this factor controls cell metabolism by binding to the proximal promoter regions that are common to several hundred genes. Motif analysis shows that TR4 strongly prefers a DR1 sequence to all other categories of repeat elements in vivo. By integration of TR4 binding data with histone modification patterns and other genomic structures, we predict, and then experimentally test, putative cis modules.
Results and Discussion
Identification of genome-wide TR4 binding sites
TR4 target genes are involved in fundamental biological processes
Motif analysis suggests the importance of ETS family members in TR4 action
Transcription factors often regulate expression of nearby genes in combination with other transcription factors through complex cis regulatory modules . Our initial motif analysis revealed the significant recurrence of an ETS motif in addition to the DR1 element. Members of the ETS transcription factor family such as ELK4, E74A, and GABPA recognize the ETS core motif GGAA. Using 13,010 human promoter sequences, the ETS motif has been identified as one of those motifs exhibiting statistically significant clustering near the transcription start site . The ETS motif was predominantly found in the promoters of genes with essential cellular functions, such as ribosomal genes, mitochondrial ribosomal genes, basal transcription factor genes and proteosomal genes. The ETS motif is not only found at genes regulating similar processes as TR4 target genes, but also preferentially occurs 100 base pairs upstream of a transcription start site. The ETS motif occurs in a significant portion of TR4 binding sites (35% in K562, 57% in HepG2, 53% in HeLa, and 24% in GM12878 cells). Only about 10% of target genes contain both the DR1 and the ETS motif (Figure 7A). Combining both motifs can account for 67-78% of TR4 peaks (70% in K562, 78% in HepG2, 74% in HeLa, and 67% in GM12878 cells) suggesting a combinatorial role for ETS family members in TR4 function. Similar results were obtained using other de novo motif discovery programs such as NHR-Scan  and W-ChIPMotifs .
It has been postulated that the true binding site for transcription factors should be located under the center of the peak . We analyzed the distribution of both motifs relative to the center of the TR4 binding sites and found that the DR1 as well as the ETS motif are located under the peak center (Figure 7B). The close proximity of these binding sites suggests a cis regulatory network involving TR4 and ETS family members.
ETS transcription factor ELK4 co-occupies TR4 target sites
We wanted to test the hypothesis that TR4 and a member of the ETS family co-localize with TR4 in vivo using ChIP-seq. Motif analysis implicates the ETS family, but does not provide information as to which family member might bind to TR4 target sites. There is a high degree of functional redundancy between different members of the ETS transcription factors. Comparison of ELK1 and GABPA binding regions revealed redundant as well as unique targets between the two ETS family members [37, 38]. It has also been shown that ETS transcription factors interact with other transcription factors to regulate gene expression. For example, ELK1 is thought to function through cooperation with the serum response factor SRF [37, 39]. ChIP-chip analysis showed that 22% of all ELK1 binding regions were also bound by SRF, while the majority of ELK1 targets is SRF-independent.
While it had been established that TR4 plays a critical role in embryonic development, differentiation and lipid metabolism, the modes by which it functions were previously unclear. To obtain a better understanding of the TR4 modes of action, we used ChIP-seq technology to identify TR4 target genes in vivo in multiple cell lines. This allowed us to confirm TR4 binding in vivo to the direct repeat of AGGTCA separated by one nucleotide (also known as a DR1 element) at endogenous target sites in all four cell types examined. Using de novo motif discovery, we found that the ETS motif CCGGAA was significantly overrepresented in TR4 binding sites, suggesting a role for ETS family members in TR4 action. To confirm the co-occurrence of these two factors in vivo, we performed ChIP-seq for the ETS transcription factor ELK4 and we found that about one third of TR4 target sites were indeed bound by ELK4. Sites that are bound by both factors contain an ETS motif, but lack the DR1 element typically thought to recruit TR4. These data suggest that TR4 may regulate specific subsets of target genes through ETS dependent as well as ETS independent pathways. Future studies will focus on the interdependence of these two transcription factors. Thus our approach of defining genome-wide binding patterns for a factor, followed by motif analysis to suggest possible cis modules, and then genome-wide analysis of the putative co-localizing factor has worked well to identify a TR4-ELK4 cis module.
Interestingly, we identified TR4 target genes that are common to quite diverse cell types (representatives of blood, liver, and epidermal cells). These genes were involved in fundamental biological processes such as RNA metabolism and protein translation. In addition, TR4 also binds near genes that are highly cell type-specific. For example, in HepG2 cells TR4 binds near genes that are involved in organic acid, lipid and carbohydrate metabolism. TR4 knockout mice show insulin hypersensitivity  and TR4 can be induced by certain essential fatty acids resulting in TR4 activation followed by the up-regulation of the apolipoprotein E precursor (ApoE) and cytosolic phosphoenolpyruvate carboxykinase 1 PEPCK gene , which is thought to contribute to diabetics-induced hyperglycemia [40, 41]. Knowing the direct TR4 binding sites, it will be an interesting focus of future studies to evaluate the pathways underlying TR4 action and its possible role in metabolic diseases.
Cell culture and crosslinking
K562, HeLa, HepG2, and GM12878 cells for ChIP-seq were grown and crosslinked by the National Cell Culture Center (NCCC) as part of the ENCODE project. K562 and GM12878 cells were grown in RPMI supplemented with 10% fetal bovine serum (FBS), 2 mM L-Glutamine, 100 U/mL penicillin-streptomycin. HeLa and HepG2 cells were grown in DMEM medium supplemented with 10% FBS, 2 mM L-Glutamine, 100 U/mL penicillin-streptomycin. Cells were either processed for RNA isolation or crosslinked 10 minutes at a concentration of 1% formaldehyde, snap frozen and stored at -80C.
Chromatin immunoprecipitation (ChIP) assay and library preparation
ChIP assays and the libraries for Illumina sequencing were prepared as described in detail in O'Geen et al. 2010 . Briefly, chromatin from 108 cells was diluted with 5 volumes IP dilution buffer (50 mM Tris pH7.4, 150 mM NaCl, 1% (v/v) igepal, 0.25% (w/v) deoxycholic acid, 1 mM EDTA pH8) and incubated at 4C over night with either 50 μl of rabbit anti-TR4 antibody . 300 μl protein A agarose beads were added for 2 hours to capture the immune complexes. Beads were washed three times with IP dilution buffer and once with phosphate-buffered saline. ChIP assays using 20 μl rabbit anti-ELK4 (Santa Cruz Biotechnology sc-13030X) or 20 μl of monoclonal rabbit anti-ELK1 (Epitomics #1277-1) were performed using StaphA cells as described on the Farnham lab web site (http://www.genomecenter.ucdavis.edu/farnham/pdf/FarnhamLabChIP%20Protocol.pdf). For sequencing experiments, StaphA cells were only blocked with BSA and the preclearing step was omitted. After reversal of crosslinks and RNase treatment, ChIP DNA was purified and used directly for library preparation.
Sequencing and data analysis
Libraries were sequenced using the Illumina GA2 platform by the DNA Technologies Core Facility at the University of California-Davis (http://genomecenter.ucdavis.edu/dna_technologies/). The ChIP-seq data has been deposited in the NCBI Gene Expression Omnibus (accession number GSE24685). In addition, all TR4 ChIP-seq data can be visualized and downloaded from the UCSC browser at http://www.genome.ucsc.edu/cgi-bin/hgTrackUi?hgsid=169984430&c=chr9&g=wgEncodeYaleChIPseq. Peaks were called using the Sole-search software with default parameters (FDR0.0001, alpha value 0.001) using sequenced libraries of matched Input DNA for each cell type . Peak overlap analysis based on chromosomal coordinates as well as location analysis were also performed using the Sole-search software. Gene Ontology analysis was performed using ConceptGen to identify the functional categories enriched in the overlapping targets in 4 cell types. (p-value < 0.05, modified Fisher's exact test). In addition to GO terms, other concepts were tested for significant enrichment in the gene set. All Entrez Genes were used as background to determine the significance of over-representation.
In vivo binding sequences from TR4 peak files were retrieved from UCSC Genome Database (hg18, March 2006). Unbiased motif analysis was performed using MEME to identify statistically overrepresented motifs in the TR4 peak sequences present in 4 cell types. The following parameters were used "-dna -nmotifs 5 -mod zoops -minw 12 -maxw 20 -maxsize 2000000 -revcomp", which specify the number of motifs to search for, the zoops assumption (zero or one occurrence per peak sequence), the minimum motif length of 12 (length of a repeat element with no spacing between two half sites), the maximum motif length of 20 (length of a repeat element with 8 spacing nucleotides between two half sites), the maximum dataset size of 2,000,000 characters. Sequences were searched in forward and reverse orientation.
RNA preparation and Illumina expression arrays
RNA was prepared from three independent cultures of 106 HeLa or HepG2 cells using Invitrogen Trizol according to the manufacture's recommendations. The Illumina TotalPrep RNA amplification kit from Ambion (AMIL1791) was used to generate biotinylated, amplified RNA for hybridization with the Illumina Sentrix Expression Beadchips, HumanHt-12. The Sentrix gene expression beadchips used for this study consisted of a 12-array, 2 stripe format comprising approximately 48 k probes/array. In this collection 24,000 probes were from RefSeq sequences and 24,000 from other Genbank sequences (see http://www.illumina.com/pages.ilmn?ID=197 for more details). Arrays were processed as per manufacturer's instructions, scanned at medium PMT settings as recommended by the manufacturer, and analyzed using Bead Studio Software v. 2.3.41. Data was normalized using the "average" method, which simply adjusts the intensities of two populations of gene expression values such that the means of the populations become equal. Relative expression values were calculated using an algorithm provided by Bead Studio. The expression array data has been deposited in the NCBI Gene Expression Omnibus (accession numbers GSE24419 for HepG2 and GSE19146 for HeLa data).
ChIP assay and quantitative PCR (qPCR)
To confirm targets identified by ChIP-seq, all ChIP assays were performed using StaphA cells. 107 cells were used per ChIP experiment and adjusted amounts of the same antibodies and pre-immune serum (rabbit IgG) as described above. Immunoprecipitated DNA was purified and eluted in 50 μl water. 1 μl of ChIP DNA or 3 ng of Input DNA were used for qPCR analysis. Quantitative PCR experiments were performed at least in duplicates, from at least two independent ChIP assays on a Bio-Rad DNA Engine Opticon Real-Time PCR System using SYBR® Green Master PCR Mix (SIGMA) according to the manufacturer's instructions. Results were analyzed relative to input. Each target site was calculated as 2 to the power of the cycle threshold (cT) difference between input DNA and ChIP samples. Enrichments at target sites are compared to negative/unbound control regions CDH1 and CDH10 (see Additional file 2 for primer sequences).
We thank members of the Farnham lab for helpful discussion and Charles Nicolet of the DNA Technologies and Expression Analysis Core Facilities of the UC Davis Genome Center for assistance with sequencing the ChIP samples. We thank K. Bradnam for help with motif analysis, C. Sershen, G. Euskirchen, H. Monahan, M. Shi and P. Lacroute for help with DNA sequencing and P. Cayting and M. Wilson for help with database submission. This work was funded in part by Public Health Service grant 1U54HG004558 and NIH grants HL24415 and DK86956.
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