Binding sites for metabolic disease related transcription factors inferred at base pair resolution by chromatin immunoprecipitation and genomic microarrays

Abstract

We present a detailed in vivo characterization of hepatocyte transcriptional regulation in HepG2 cells, using chromatin immunoprecipitation and detection on PCR fragment-based genomic tiling path arrays covering the encyclopedia of DNA element (ENCODE) regions. Our data suggest that HNF-4alpha and HNF-3beta, which were commonly bound to distal regulatory elements, may cooperate in the regulation of a large fraction of the liver transcriptome and that both HNF-4alpha and USF1 may promote H3 acetylation to many of their targets. Importantly, bioinformatic analysis of the sequences bound by each transcription factor (TF) shows an over-representation of motifs highly similar to the in vitro established consensus sequences. On the basis of these data, we have inferred tentative binding sites at base pair resolution. Some of these sites have been previously found by in vitro analysis and some were verified in vitro in this study. Our data suggests that a similar approach could be used for the in vivo characterization of all predicted/uncharacterized TF and that the analysis could be scaled to the whole genome.

In vitro analysis of DNA–protein interactions by proximity ligation

“…These data indicate that the binding site predictions as previously calculated (29) are very accurate and of higher quality than ones previously reported (46)(47)(48)(49)(50)(51)(52)(53)(54)(55)(56)(57)(58)(59)(60), where in vitro methods such as EMSA and DNA footprinting were used to map the binding sites. The 50 sequences analyzed in this study contain sequences that were commonly identified as tentative HNF-4␣ binding sites by RadaIglesias et al (29) and in other previous studies (46)(47)(48)(49)(50) and that generally performed well in the PLA (red sequences in SI Table 1, e.g., APOA4/C3 intergenic and F10 promoter).…”

“…The present study also includes sequences for which there is a discrepancy between previous reports (47,50) and the predictions by Rada-Iglesias et al (29) but for which the PLA results support the Rada-Iglesias et al prediction calculations (green and blue sequences, SI Table 1). The APOA1 promoter includes three previously identified binding sequences (50) not identified by Rada-Iglesias et al (29). A putative binding site in the promoter was identified by Rada-Iglesias et al and performed better in PLA than did the three previously reported sequences (50).…”

“…The 50 sequences analyzed in this study contain sequences that were commonly identified as tentative HNF-4␣ binding sites by RadaIglesias et al (29) and in other previous studies (46)(47)(48)(49)(50) and that generally performed well in the PLA (red sequences in SI Table 1, e.g., APOA4/C3 intergenic and F10 promoter). The present study also includes sequences for which there is a discrepancy between previous reports (47,50) and the predictions by Rada-Iglesias et al (29) but for which the PLA results support the Rada-Iglesias et al prediction calculations (green and blue sequences, SI Table 1). The APOA1 promoter includes three previously identified binding sequences (50) not identified by Rada-Iglesias et al (29).…”

“…Three sequences identified by ChIP-chip analysis in HepG2 cells (29) and containing tentative HNF-4␣ binding sites predicted by the BioProspector software (30) were analyzed by PLA in HepG2 nuclear extracts, along with a probe containing the consensus sequence for p53-binding, serving as a negative control. The nuclear extract was diluted to find the amount that resulted in the highest signal-to-noise ratio (S/N) and to identify the highest detectable dilution (Fig.…”

Common Hepatic Nuclear Factor-4α Variants Are Associated With High Serum Lipid Levels and the Metabolic Syndrome

“…Similar to HNF-4␣, USF1 is a transcription factor, and many of the genes regulated by USF1 are also involved in lipid and glucose metabolism. A recent study demonstrates that sequences from sites bound by HNF-4␣ and USF1 showed significant overlap in HepG2 cells (45). Interactions between DNA sequence variants are expected to play a crucial role in complex traits, such as FCHL.…”