Hidden heterogeneity of transcription factor binding sites: A case study of SF-1. (October 2016)

• Record Type: Journal Article
• Authors: Levitsky, V.G.; Oshchepkov, D.Yu.; Klimova, N.V.; Ignatieva, E.V; Vasiliev, G.V.; Merkulov, V.M.; Merkulova, T.I.
• Is Part Of: Computational biology and chemistry. Volume 64(2016)
• Page Start: 19
• Page End: 32
• Publication Date: 2016-10
• Subjects: Transcription factor binding sites -- SF-1 -- Recognition models -- SiteGA -- oPWM -- Sitecon -- ChIP-seq -- Structural variants; Chemistry -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; Biochemistry -- Data processing; Biology -- Data processing; Molecular biology -- Data processing; Periodicals; Electronic journals; 542.85
• DOI: 10.1016/j.compbiolchem.2016.04.008
• Languages: English
• ISSNs: 1476-9271

Abstract

Graphical abstract: Highlights: At least two types of SF-1 binding sites differing in their primary structure are observed. The first type is more abundant, has more conserved structure, and in most cases is associated with gene activation. The second type is less conserved, less abundant, and more frequently involved in negative regulation; characteristic of these binding sites are interdependencies of particular positions. Abstract: Steroidogenic factor 1 (SF-1) belongs to a small group of the transcription factors that bind DNA only as a monomer. Three different approaches—Sitecon, SiteGA, and oPWM—constructed using the same training sample of experimentally confirmed SF-1 binding sites have been used to recognize these sites. The appropriate prediction thresholds for recognition models have been selected. Namely, the thresholds concordant by false positive or negative rates for various methods were used to optimize the discrimination of steroidogenic gene promoters from the datasets of non-specific promoters. After experimental verification, the models were used to analyze the ChIP-seq data for SF-1. It has been shown that the sets of sites recognized by different models overlap only partially and that an integration of these models allows for identification of SF-1 sites in up to 80% of the ChIP-seq loci. The structures of the sites detected using the three recognition models in the ChIP-seq peaks falling within the [–5000, +5000] region relative to the transcription start sites (TSS) extracted from the FANTOM5 project have been analyzed. The MATLIGN classified the frequency matrices for the sites predicted by oPWM, Sitecon, and SiteGA into two groups. The first group is described by oPWM/Sitecon and the second, by SiteGA. Gene ontology (GO) analysis has been used to clarify the differences between the sets of genes carrying different variants of SF-1 binding sites. Although this analysis in general revealed a considerable overlap in GO terms for the genes carrying the binding sites predicted by oPWM, Sitecon, or SiteGA, only the last method elicited notable trend to terms related to negative regulation and apoptosis. The results suggest that the SF-1 binding sites are different in both their structure and the functional annotation of the set of target genes correspond to the predictions by oPWM + Sitecon and SiteGA. Further application of Homer software for de novo identification of enriched motifs in ChIP-Seq data for SF-1ChIP-seq dataset gave the data similar to oPWM + Sitecon.