The authors have declared that no competing interests exist.
Conceived and designed the experiments: QZ YYW. Performed the experiments: QZ YH HL. Analyzed the data: QZ YF YH JF. Contributed reagents/materials/analysis tools: QZ YF YH. Wrote the paper: QZ YYW.
Retinoid x receptor α (RXRα) is abundantly expressed in the liver and is essential for the function of other nuclear receptors. Using chromatin immunoprecipitation sequencing and mRNA profiling data generated from wild type and RXRα-null mouse livers, the current study identifies the bona-fide hepatic RXRα targets and biological pathways. In addition, based on binding and motif analysis, the molecular mechanism by which RXRα regulates hepatic genes is elucidated in a high-throughput manner.
Close to 80% of hepatic expressed genes were bound by RXRα, while 16% were expressed in an RXRα-dependent manner. Motif analysis predicted direct repeat with a spacer of one nucleotide as the most prevalent RXRα binding site. Many of the 500 strongest binding motifs overlapped with the binding motif of specific protein 1. Biological functional analysis of RXRα-dependent genes revealed that hepatic RXRα deficiency mainly resulted in up-regulation of steroid and cholesterol biosynthesis-related genes and down-regulation of translation- as well as anti-apoptosis-related genes. Furthermore, RXRα bound to many genes that encode nuclear receptors and their cofactors suggesting the central role of RXRα in regulating nuclear receptor-mediated pathways.
This study establishes the relationship between RXRα DNA binding and hepatic gene expression. RXRα binds extensively to the mouse genome. However, DNA binding does not necessarily affect the basal mRNA level. In addition to metabolism, RXRα dictates the expression of genes that regulate RNA processing, translation, and protein folding illustrating the novel roles of hepatic RXRα in post-transcriptional regulation.
Retinoid x receptor (RXR) plays a critical role in metabolism, development, differentiation, proliferation, and cell death by regulating gene expression
The current study determines genome-wide RXRα binding in normal mouse livers by chromatin immunoprecipitation using specific anti-RXRα antibody followed by next generation sequencing (ChIP-seq). In addition, microarray was performed to identify genes that are differentially expressed in wild type and RXRα-null mouse livers. Combining the two datasets, we established the relationship between hepatic RXRα-DNA binding and RXRα-dependent gene expression. This global profiling of RXRα binding along with gene expression not only allows us to capture all RXRα downstream targets and pathways, but also helps us to understand the molecular mechanism by which hepatic RXRα regulates gene expression.
Nuclear Receptor | Target Gene | Our data location (motif) | Reported data location (motif) | Reference |
RARs | Prcka | −19 (NK) | −93∼−65 (NR) |
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Cyp26a1 | −1862 (DR5) | −2 kb (DR5) |
|
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RARβ | −341 (DR5) | −59 (DR5) |
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|
FXR | Nr0b2 | −271 (IR1, DR3) | −320∼−220 (NR) |
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Abcb11 | −220 ∼ 250 (IR1) | −240∼−140 (NR) |
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LXR | Abca1 | −183 (DR3, 4) | −70 (DR4) |
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Fasn | −658 (DR4) | −660 (DR4) |
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Pltp | −2.2 (ER3) | −2.6 kb (DR4) |
|
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PPARs | ACOX1 | −286 (NK) | −550 (DR1) |
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ALDH3A2 | −4.69 kb (DR1) | −4.63 kb (DR1) |
|
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Nfkbia | −111 (NK) | ≥−1.9 kb (DR1) |
|
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PXR | Cyp3a11 | −1.5 kb (ER1) | −1.5 kb (NR) |
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Slc01a4 | −10 kb (DR3, ER3) | −10 kb (NR) |
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|
Abcc3 | 3.8 kb (IR3, DR4) | 3.8 kb (NR) |
|
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CAR | Cyp2b10 | −2.3 kb (DR4, ER1) | −2.3 kb (NR) |
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Abcc2 | −97 (NK) | −400 (ER8) |
|
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VDR | Spp1 | −701 (DR3) | −761 (DR3) |
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Cyp24a1 | −322 (NK) | −265 (DR3) |
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Pckra | 114 kb (ER3, DR1) | 127 kb (NR) |
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|
TR | Cyp7a1 | −3 kb (DR0) | −3 kb (DR0, DR4) |
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Thrsp | −1375 (DR0, IR4) | −1385 (NR) | ||
Egr | −90 (IR5) | −112∼−77 (IR) |
|
DR, direct repeat; ER, everted repeat; Hu, human; IR, inverted repeat; kb, kilo-base pair; Ms, mouse; NK, not known NR, not reported.
The generation of hepatocyte RXRα-deficient mice was described in previous publications
(A) Venn diagrams of RXRα-bound genes (III+IV+V), hepatic genes (I+II+III+IV), and RXRα-dependent genes (II+III)
Total RNA was extracted using TRIzol Reagent (Invitrogen Co., CA) and purified with the RNeasy Mini Kit (Qiagen Inc., CA). The quantity and quality of the total RNA were assessed by Bioanalyzer 2100 (Agilent Technologies, CA). Complementary DNA was made using High Capacity RNA-to-cDNA Kit (Applied Biosystems, CA). Affymetrix chips (MOE 430A 2.0) that covered about 14,000 mouse genes were used. Microarray (n = 3 per group) and data processing as well as the methods used for data validation were described in our previous publication
Each bar represents an RXRα binding site on the mouse genome. UIK (green): up-regulated in RXRα KO liver; DIK (red): down-regulated in RXRα KO liver.
Frozen livers were fixed in 1% formaldehyde (pH = 7) for 15 minutes before being quenched with 0.125 M glycine. Following cell lysis, the nuclear fraction was extracted and sonicated to produce 300–500 base-pair (bp) DNA fragments. Genomic DNA (Input) was prepared by treating aliquots of chromatin with RNase, proteinase K and heated for de-crosslinking, followed by ethanol precipitation. Chromatin (30 µg) was precleared by Dynase beads (Invitrogen Co., CA) before incubation with a ChIP-quality anti-RXRα antibody (Santa Cruz, CA). An antibody to IgG (Santa Cruz, CA) and RNA Pol II (Millipore, MA) was used as negative and positive control, respectively. Samples were incubated with prepared Dynase beads at 4°C overnight, followed by de-crosslinking and purification. DNA fragment library was size-selected (175–225 bp) on an agarose gel. Amplified DNAs (DNA library) were sequenced on the Illumina Genome Analyzer II. For ChIP-seq data validation, DNA fragments generated based on above mentioned method (n = 3) were quantified by real-time PCR with Power SYBR® Green PCR Master Mix (Life Technologies Co., CA).
(A) Global profiling of RXRα binding motifs in mouse liver genome predicted by Hidden Markov Model. DR: direct repeat; ER: everted repeat; IR: inverted repeat. (B) Out of the top 500 strongest bindings, the most common motif contains three half nuclear receptor binding sites, which may form two overlapped DR1s sharing the middle half site. (C) The other common motif contains a GC box that matches to the Sp1 binding site.
Gene Numbers | |||||
Biological Processes |
RXRα-Bound (%) | UIK# | DIK# | Bonferroni | |
oxidation reduction (120) | 109 (90.8) | 65 | 55 | 1.50E-18 | 4.40E-15 |
translation (50) | 24 (48.0) | 10 | 40 | 1.30E-05 | 3.70E-02 |
lipid biosynthetic process (48) | 42 (87.5) | 32 | 16 | 5.40E-07 | 1.60E-03 |
generation of precursor metabolites and energy (47) | 40 (85.1) | 27 | 20 | 9.70E-08 | 2.80E-04 |
fatty acid metabolic process (41) | 39 (95.1) | 19 | 22 | 1.50E-09 | 4.40E-06 |
steroid metabolic process (36) | 31 (86.1) | 26 | 10 | 1.60E-08 | 4.70E-05 |
cofactor metabolic process (36) | 33 (91.7) | 20 | 16 | 3.90E-07 | 1.10E-03 |
carboxylic acid biosynthetic process (29) | 28 (96.6) | 18 | 11 | 2.90E-06 | 8.50E-03 |
organic acid biosynthetic process (29) | 28 (96.6) | 18 | 11 | 2.90E-06 | 8.50E-03 |
coenzyme metabolic process (29) | 29 (100) | 17 | 12 | 3.90E-06 | 1.10E-02 |
protein folding (26) | 15 (57.7) | 10 | 16 | 1.20E-05 | 3.30E-02 |
electron transport chain (24) | 21 (87.5) | 14 | 10 | 1.20E-05 | 3.50E-02 |
steroid biosynthetic process (22) | 17 (77.3) | 17 | 5 | 4.80E-08 | 1.40E-04 |
sterol metabolic process (22) | 18 (81.8) | 18 | 4 | 2.20E-07 | 6.50E-04 |
anti-apoptosis (20) | 18 (90.0) | 6 | 14 | 3.30E-05 | 9.30E-02 |
cholesterol metabolic process (19) | 15 (78.9) | 15 | 4 | 4.10E-06 | 1.20E-02 |
sterol biosynthetic process (14) | 10 (71.4) | 14 | 0 | 1.10E-07 | 3.30E-04 |
cholesterol biosynthetic process (11) | 7 (63.6) | 11 | 0 | 3.30E-06 | 9.50E-03 |
Biological processes were obtained from DAVID functional annotation.
# UIK: up-regulated gene in RXRα KO mouse liver; DIK: down-regulated gene in RXRα KO mouse liver.
Primary image analysis and base calling were performed using Genome Analyzer Pipeline Software (Illumina Inc., CA). All sequenced reads were aligned to mm9 mouse reference genome using bowtie version 0.12.7
Genes up-regulated (A) or down-regulated (B) due to hepatic RXRα deficiency were subjected to DAVID functional annotation. The gene-term association relationship was generated using the functional annotation clustering tool in the DAVID website. Gray areas indicate the gene-term associations have been established by the literatures. Black areas show the gene-term relationships can exist, but requires experimental validation. Explanation for some of the listed terms shown in A: Domain: HIN-200 is a domain of HIN-200 protein, PIRSF018550 is a protein of PIR super family with serial number of 018550, IPR004021 is a domain of protein HIN-200/IF120x. Domain: DAPIN is a domain for apoptosis and interferon response. IPR004020: Pyrin is a subclass of DAPIN domain that interacts with proteins that have pyrin domain.
David Biological Functional Annotation | Number of gene (%) | Bonferroni | |
regulation of transcription | 1605 (11.8) | 3.20E-14 | 1.90E-10 |
Transcription | 1329 (9.8) | 7.70E-22 | 4.70E-18 |
intracellular signaling cascade | 729 (5.4) | 2.80E-23 | 1.70E-19 |
phosphate metabolic process | 696 (5.1) | 3.90E-24 | 2.30E-20 |
phosphorus metabolic process | 696 (5.1) | 3.90E-24 | 2.30E-20 |
protein localization | 613 (4.5) | 6.40E-24 | 3.90E-20 |
phosphorylation | 575 (4.2) | 2.20E-19 | 1.30E-15 |
oxidation reduction | 559 (4.1) | 3.00E-26 | 1.80E-22 |
establishment of protein localization | 531 (3.9) | 8.90E-20 | 5.40E-16 |
protein transport | 527 (3.9) | 1.20E-19 | 7.20E-16 |
protein amino acid phosphorylation | 514 (3.8) | 7.10E-18 | 4.30E-14 |
macromolecule catabolic process | 504 (3.7) | 1.80E-11 | 1.10E-07 |
positive regulation of macromolecule metabolic process | 497 (3.7) | 1.00E-13 | 6.10E-10 |
regulation of transcription from RNA polymerase II promoter | 478 (3.5) | 9.70E-12 | 5.80E-08 |
positive regulation of biosynthetic process | 439 (3.2) | 1.10E-12 | 6.60E-09 |
positive regulation of cellular biosynthetic process | 436 (3.2) | 7.20E-13 | 4.30E-09 |
protein catabolic process | 436 (3.2) | 5.40E-12 | 3.30E-08 |
positive regulation of macromolecule biosynthetic process | 423 (3.1) | 8.30E-14 | 5.00E-10 |
positive regulation of nitrogen compound metabolic process | 418 (3.1) | 4.10E-13 | 2.50E-09 |
positive regulation of nucleobase, nucleoside, nucleotide and nucleic acid metabolic process | 407 (3.0) | 2.70E-13 | 1.60E-09 |
negative regulation of macromolecule metabolic process | 401 (2.9) | 2.70E-12 | 1.60E-08 |
cell death | 398 (2.9) | 3.80E-11 | 2.30E-07 |
positive regulation of gene expression | 390 (2.9) | 5.90E-13 | 3.60E-09 |
positive regulation of transcription | 384 (2.8) | 3.70E-14 | 2.20E-10 |
intracellular transport | 354 (2.6) | 5.00E-15 | 3.00E-11 |
cellular response to stress | 325 (2.4) | 1.30E-11 | 7.90E-08 |
nitrogen compound biosynthetic process | 253 (1.9) | 7.40E-13 | 4.50E-09 |
cellular macromolecule localization | 250 (1.8) | 6.90E-12 | 4.20E-08 |
cellular protein localization | 248 (1.8) | 1.10E-11 | 6.80E-08 |
cofactor metabolic process | 161 (1.2) | 1.90E-12 | 1.10E-08 |
David Biological Functional Annotation | Number of gene (%) | Bonferroni | |
regulation of transcription | 703 (10.8) | 2.10E-07 | 9.00E-04 |
transcription | 554 (8.5) | 2.00E-05 | 0.084 |
regulation of RNA metabolic process | 494 (7.6) | 2.80E-08 | 1.20E-04 |
regulation of transcription, DNA-dependent | 493 (7.6) | 3.60E-09 | 1.60E-05 |
ion transport | 253 (3.9) | 3.50E-07 | 0.002 |
cell adhesion | 198 (3.0) | 1.20E-05 | 0.052 |
biological adhesion | 198 (3.0) | 1.40E-05 | 0.059 |
metal ion transport | 166 (2.5) | 1.10E-06 | 0.005 |
neuron differentiation | 163 (2.5) | 2.10E-09 | 9.00E-06 |
cell motion | 138 (2.1) | 9.00E-06 | 0.039 |
cell-cell signaling | 120 (1.8) | 1.40E-07 | 6.30E-04 |
neuron development | 117 (1.8) | 1.50E-06 | 0.006 |
regulation of small GTPase mediated signal transduction | 101 (1.5) | 3.00E-08 | 1.30E-04 |
cell part morphogenesis | 87 (1.3) | 1.30E-05 | 0.054 |
cell projection morphogenesis | 86 (1.3) | 2.60E-06 | 0.011 |
regulation of Ras protein signal transduction | 77 (1.2) | 9.30E-06 | 0.04 |
cell morphogenesis involved in neuron differentiation | 77 (1.2) | 1.20E-05 | 0.051 |
synaptic transmission | 76 (1.2) | 9.20E-06 | 0.04 |
neuron projection morphogenesis | 74 (1.1) | 2.30E-05 | 0.096 |
axonogenesis | 70 (1.1) | 1.70E-05 | 0.072 |
axon guidance | 50 (0.8) | 1.10E-06 | 0.005 |
spinal cord development | 27 (0.4) | 2.40E-05 | 0.099 |
The sequences that were 100 bp up and downstream from the summits of the top 500 peaks, which had the highest peak scores, were subjected to motif analysis using by MEME-ChIP (Multiple EM for Motif Elicitation)
RNA extracted from wild type and RXRα-null livers (n = 3–4) were subjected to real time PCR to determine the expression level of the studied genes. Data were normalized to glyceraldehyde 3-phosphate dehydrogenase (GAPDH) mRNA level.**:
Endocrine receptors | Orphan nuclear receptors | Nuclear | ||
Receptor cofactors | ||||
Ar | Hnf1a | Rara | Nr1d1 (Rev-erbα) |
Ppargc1a |
Nr3c1 (Gr) | Hnf1b | Rarb |
Nr1d2(Rev-erbβ) | Ppargc1b |
Nr3c2 (Mr) | Hnf4a | Rarg | Nr2c1 (Tr2) | Ncor2 |
Esr1 | Nr1h3 (Lxr) | Rxra | Nr2c2 (Tr4) | Thrap3 |
Esr2 | Nr1h4 (Fxrα) | Rxrb | Nr2e3 (Pnr) | |
Pgr | Nr1h5 (Fxrβ) | Rxrg | Nr2f1 (COUP-TF1) | |
Nr1i2 (Pxr) | Thra | Nr2f2 (COUP-TF2) | ||
Nr1i3 (Car) | Thrb | Nr2f6 (COUP-TF3) | ||
Ppara | Rora | Nr4a1 (Nur77) | ||
Ppard | Rorc | Nr4a3 (Nor1) | ||
Pparg | Ror1 | Nr6a1 (Gcnf) | ||
Esrra | Ror2 | Nr0b2 (Shp1) | ||
Esrrb | Nr5a1 | |||
Esrrg | Nr5a2 |
: RXRα-dependent genes confirmed by real-time PCR.
For ChIP-qPCR data and microarray data, the difference between two groups was analyzed by Student's
Genes with known RXRα heterodimer response elements were found to be bound by RXRα at similar locations in our ChIP-seq data to what has been previously reported (
Microarray data showed that there were 9068 genes with detectable signals (mRNA levels), which are referred to as “hepatic genes”. Among these, 768 were significantly up-regulated (UIK: up-regulated in KO) and 696 genes were down-regulated (DIK: down-regulated in KO) in the hepatocyte RXRα KO mouse livers. Thus, 16.14% of hepatic genes (1464 out of 9068) were expressed in an RXRα-dependent manner.
There were 109971 confident RXRα peaks detected in 14816 genes in the mouse liver. The average is 7.4 peaks per gene. Remarkably, 79.1% (7174) of the hepatic genes were bound by RXRα (
The frequencies of promoter peaks are similar for RXRα-dependent (12.2%) and -independent (12.6%) genes (
In terms of chromosomal distribution, RXRα binding occurred more frequently on chromosome 5 and 11 (956 and 1115 RXRα binding sites, respectively) than others (
Since RXRα dimerizes with multiple nuclear receptors, its binding sites are composed of diverse binding motifs. The result predicted by the Hidden Markov Model identified DR1 (12.5%) as the most common motif for RXRα binding in mouse liver followed by DR4, inverted repeat (IR) 1, DR3, and IR3. Furthermore, spacers 1, 3, and 4 were relatively more prevalent than other spacers (
Biological functional analysis of both UIK and DIK genes demonstrated that RXRα-dependent genes participated predominantly in oxidation/reduction, lipid metabolism, generation of precursor metabolites and energy, cofactor metabolism, carboxylic acid biosynthesis, organic acid biosynthesis, coenzyme metabolism, protein folding, electron transport chain, translation and apoptosis. Among these, an average of 84% of the genes was bound by RXRα (
There were four UIK genes, including spleen tyrosine kinase (Sykb), 3-hydroxy-3-methylglutaryl-Coenzyme A synthase 1 (Hmgcs1), isopentenyl-diphosphate delta isomerase (Idi1), and NADP dependent steroid dehydrogenase-like (Nsdhl), which were not bound by RXRα and are also involved in steroid and cholesterol metabolism, suggesting RXRα could directly and indirectly regulate steroid and cholesterol homeostasis (
Surprisingly, many steroid and orphan nuclear receptor genes were bound by RXRα in mouse livers (
It has been more than two decades since the cloning of RXRα. Accumulated literature clearly indicates the importance of this nuclear receptor in regulating liver disease processes such as metabolic syndrome, alcoholic liver disease, chronic hepatitis C, and liver cancer, which comprise some of the most serious worldwide health issues today
Promoter and enhancer regions are regarded as the most important regulatory regions in the control of transcription
As a master nuclear receptor, RXRα plays an essential role in effecting the biological actions of other adopted orphan nuclear receptors. Based on the “1–2–3–4–5 rule”, RXR and its partners preferentially bind to specific motifs composed of hexamers (A/GGGTCA) separated by a various number of nucleotides
Recent studies have reported the mouse liver genome-wide binding profile of FXR
It is important to note that our results did not show any significant correlation between peak score and the fold change in mRNA level caused by RXRα deficiency. In addition, the binding characteristic (location of the peak) is similar between UIK and DIK genes. This finding suggests that physical interaction of RXRα is essential, but not sufficient for predicting the subsequent transcriptional effect.
The role of retinoids in other organs such as the eye and skin is well known. However, retinoids are stored, processed, and metabolized in the liver. In addition, the liver is also a retinoid target organ and yet the action of retinoids in the liver has been overlooked. This study, for the first time, demonstrates the potential biological effect of endogenous ligands of RXRα in the liver. Taken together, as an active partner of many nuclear receptors, our reported data showed that RXRα and its endogenous ligands control liver metabolism and function in general.
The authors thank Dr. Stan Svojanovsky for his assistance in microarray data processing. We also thank Zoe Raglow and Julia Wu for editing the manuscript, and Drs. Grace Guo, Ann Thomas, and Yue Cui for sharing with us the technique of ChIP assay and the methodology of data analysis.