Dataset integration identifies transcriptional regulation of microRNA genes by PPARc in differentiating mouse 3T3-L1 adipocytes

Peroxisome proliferator-activated receptor c (PPARc) is a key transcription factor in mammalian adipogenesis. Genome-wide approaches have identified thousands of PPARc binding sites in mouse adipocytes and PPARc upregulates hundreds of protein-coding genes during adipogenesis. However, no microRNA (miRNA) genes have been identified as primary PPARc-targets. By integration of four separate datasets of genomewide PPARc binding sites in 3T3-L1 adipocytes we identified 98 miRNA clusters with PPARc binding within 50 kb from miRNA transcription start sites. Nineteen mature miRNAs were upregulated 2-fold during adipogenesis and for six of these miRNA loci the PPARc binding sites were confirmed by at least three datasets. The upregulation of five miRNA genes miR-103-1 (host gene Pank3), miR-148b (Copz1), miR-182/96/183, miR-205 and miR-378 (Ppargc1b) followed that of Pparg. The PPARcdependence of four of these miRNA loci was demonstrated by PPARc knock-down and the loci of miR-103-1 (Pank3), miR-205 and miR-378 (Ppargc1b) were also responsive to the PPARc ligand rosiglitazone. Finally, chromatin immunoprecipitation analysis validated in silico predicted PPARc binding sites at all three loci and H3K27 acetylation was analyzed to confirm the activity of these enhancers. In conclusion, we identified 22 putative PPARc target miRNA genes, showed the PPARc dependence of four of these genes and demonstrated three as direct PPARc target genes in mouse adipogenesis. INTRODUCTION The need for understanding of the mechanisms controlling the differentiation of fibroblast-like pre-adipocytes to lipid-loaded adipocytes is due to the worldwide epidemic of obesity of high medical relevance (1). Adipogenesis is regulated by a network of transcription factors. The most prominent transcription factor in adipocytes is the nuclear receptor peroxisome proliferator-activated receptor g (PPARg) (2). During mouse adipogenesis the number of genomic binding sites for PPARg increases from a few to thousands (3–7) implicating that PPARg regulates hundreds of genes during adipogenesis. Therefore, the synthetic PPARg ligand rosiglitazone (RGZ) has been used in many countries in the therapy of type 2 diabetes mainly acting via its effects on gene regulation in adipocytes (8). The prerequisite for the direct transcriptional regulation of a given gene by PPARg is the presence of at least one specific PPARg binding site, referred to as PPAR response element (PPRE), in the regulatory regions flanking the gene’s transcription start site (TSS) [reviewed in (9)]. Direct DNA binding of PPARg takes place as a heterodimeric complex with another nuclear receptor, the retinoid X receptor (RXR), and PPREs are formed as a direct repeat of hexameric core binding motifs with one intervening nucleotide (DR1-type PPREs) (10,11). To promote the expression of its target genes, PPARg must overcome the transcriptionally repressive dense packaging of genomic DNA within chromatin. PPARg is also capable of repressing some of its target genes in a ligand-dependent manner either directly via recruitment of co-repressors upon agonist binding or via a mechanism called trans-repression (12,13). However, in adipocytes PPARg has been mainly linked to transcriptional activation of its target genes (3–5). *To whom correspondence should be addressed. Tel.: +352-4666446839; Fax: +352-4666446949; Email: [email protected] Nucleic Acids Research, 2012, 1–15 doi:10.1093/nar/gks025 The Author(s) 2012. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/ by-nc/3.0), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. Nucleic Acids Research Advance Access published February 7, 2012 by L asse Sinkonen on Feruary 8, 2012 http://narrdjournals.org/ D ow nladed from In addition to transcription factors and their co-factors, several new groups of small RNA molecules have been described in recent years as capable of controlling gene expression [reviewed in (14)]. One of the most important of these groups consists of small RNA molecules called microRNAs (miRNAs), which are endogenous noncoding transcripts transcribed mainly by RNA polymerase II (RNA Pol II) as their own primary transcripts (pri-miRNAs) or together with their host genes [reviewed in (15)]. The miRNA precursor (pre-miRNA) is cropped from the primary transcript by a complex known as Microprocessor that consists of two proteins, namely DROSHA and DGCR8. This pre-miRNA hairpin is then further processed into the mature miRNA duplex by an RNase III enzyme DICER. The mature miRNAs can identify their target mRNAs by base pairing to the partially complementary regions within the target mRNAs (16). miRNAs function by serving as guides to the proteins of the Argonaute family and other associated proteins, which together induce inhibition of translation as well as degradation of the targeted mRNAs (17,18). Currently, there are more than 700 known mature miRNAs in mouse (miRBase v18.0) and most of them can potentially target hundreds of mRNAs (19,20). In this way they show very comparable functions to transcription factors. Thus miRNAs can remarkably influence the transcriptomes of most eukaryotic cells. Still, fairly little is known about the transcriptional regulation of miRNA genes. Until recently, the progress was hampered by limited knowledge about the structure of miRNA genes, especially the location of their TSSs (21,22). Many miRNAs are transcribed together as clusters of several mature miRNAs. Considering this feature and the fact that each of these miRNAs can have a potential to regulate a vast number of target mRNAs, the transcriptional control of these miRNA genes needs to be both accurate and robust. And importantly, miRNAs have been shown to play key roles in the development and differentiation of most tissues and cell types (23). Also the formation of white adipose tissue in vivo and the differentiation of the mouse pre-adipocyte cell line 3T3-L1 depend on expression of miRNAs (24,25). Moreover, some miRNAs, such as miR-103, are regulators of adipogenesis in mouse (26). Several recent datasets of chromatin immunoprecipitation (ChIP)-based monitoring of genome-wide binding of PPARg during differentiation of 3T3-L1 cells identified thousands of genomic PPARg-bound sites suggesting hundreds of PPARg target genes (3–7). However, none of these are miRNA genes. Therefore, in this study we integrated four of the above mentioned datasets of genome-wide PPARg binding sites and mouse miRNA TSS annotations (3–6,21) and identified 98 miRNA clusters with PPARg binding within 50 kb from miRNA gene TSSs. Nineteen of these miRNAs (corresponding to 22 miRNA genes) are upregulated during adipogenesis and are putative PPARg targets. Further filtering resulted in the five miRNA genes miR-103-1 (host gene Pank3), miR-148b (Copz1), miR-182/96/183, miR-205 and miR-378 (Ppargc1b) that followed the upregulation of the Pparg gene during mouse adipogenesis. The transcription of all except miR-148b (Copz1) depends on PPARg in adipocytes and the loci of miR-103-1, miR-205 and miR-378 were also responsive to RGZ. Finally, ChIP assays validated three in silico predicted PPREs at the miR-103-1 locus, two at the miR-378 locus, and one at the miR-205 locus. In conclusion, we have identified a number of PPARg-regulated miRNA genes in mouse adipogenesis, which will serve the future integration of miRNAs to the core regulatory network of adipocyte differentiation and further characterize the extensive role of PPARg during this process. MATERIALS AND METHODS Integration of the genome-wide PPARc binding data and miRNA TSS annotation The different publically available datasets used in this study were unified on a common genome build on the basis of the coordinates from TSS annotation data for miRNA genes from Marson et al. (21), namely NCBI36/ mm8. Accordingly, the coordinates of the PPARg-bound sites from Nielsen et al. (4) and Lefterova et al. (6) were lifted over from mm9 to mm8 using the UCSC lift-over tool, while the coordinates from the datasets of Lefterova et al. (3) and Hamza et al. (5) were already based on mm8. For all datasets we used only published pre-analyzed data and the provided coordinates for PPARg-bound sites were used. First, miRNA gene TSSs within 50 kb of a PPARg binding site (Supplementary Table S1A–D) were identified. This was followed by checking whether an identified PPARg binding site overlaps with an in silico predicted PPRE (10) (Supplementary Table S2A–D). A third step detects, whether an identified PPARg binding site co-localizes with a CEBPa (CCAAT/ enhancer binding protein) binding site. As input data for the last step, PPARgand CEBPa-bound sites provided by Lefterova et al. (3) were used and a threshold of 1 kb was applied to define a co-localized region. Further details can be provided upon request.