Understanding transcriptional regulation by integrative analysis of transcription factor binding data

Abstract

Statistical models have been used to quantify the relationship between gene expression and transcription factor (TF) binding signals. Here we apply the models to the large-scale data generated by the ENCODE project to study transcriptional regulation by TFs. Our results reveal a notable difference in the prediction accuracy of expression levels of transcription start sites (TSSs) captured by different technologies and RNA extraction protocols. In general, the expression levels of TSSs with high CpG content are more predictable than those with low CpG content. For genes with alternative TSSs, the expression levels of downstream TSSs are more predictable than those of the upstream ones. Different TF categories and specific TFs vary substantially in their contributions to predicting expression. Between two cell lines, the differential expression of TSS can be precisely reflected by the difference of TF-binding signals in a quantitative manner, arguing against the conventional on-and-off model of TF binding. Finally, we explore the relationships between TFbinding signals and other chromatin features such as histone modifications and DNase hypersensitivity for determining expression. The models imply that these features regulate transcription in a highly coordinated manner.[Supplemental material is available for this article.]Transcription factors (TFs) are critical for the transcriptional regulation of gene expression (Takahashi and Yamanaka 2006;Vaquerizas et al. 2009). In humans, they represent the largest family of proteins, accounting for around 10% of genes (Babu et al. 2004). There are two types of TFs: general and sequence-specific. The former TFs act cooperatively with RNA polymerase II and are ubiquitously involved in the transcription of a large fraction of genes (Lee and Young 2000). The latter TFs bind specific subsets of target genes, leading to distinct spatiotemporal patterns of gene expression (Kadonaga 2004). Although systematic gene expression quantification has been available for a decade from microarray experiments (Schena et al. 1995), only recently has the genome-wide identification of TF-binding sites become possible, owing to the development of chromatin immunoprecipitation followed by microarray (ChIP-chip) and sequencing (ChIP-seq) technologies (Ren et al. 2000;Johnson et al. 2007).In several previous studies, statistical models were constructed to study the regulatory functions of TF on gene expression based on the gene expression and TF-binding data (Ouyang et al. 2009;Cheng and Gerstein 2011). These studies showed that TFbinding signals around the transcription start sites (TSSs) of genes are predictive of gene expression levels with fairly high accuracy. But these studies have the following limitations: First, estimates of gene expression have relied on probes (microarray) or sequence reads (RNA-seq) spread across a gene, possibly across multiple unknown isoforms of that gene. It is often difficult to accurately determine the expression level of each transcript based on such data, which...

Transcription factor binding predicts histone modifications in human cell lines

“…Although these predictive analyses are valuable, such models do not provide mechanistic explanations for the specificity of gene expression, in the way that TF-based regulation does (1). Intriguingly, one analysis of the ENCODE data suggested that the addition of histone modification data resulted in only minor improvements to the prediction of gene expression levels based on TF-binding data (25). Furthermore, a recent study demonstrated that depletion of the canonical activating mark H3K4me3 has only a modest effect on transcription, weakening the case for the causative role of this mark in transcription (26).…”

Uniform, optimal signal processing of mapped deep-sequencing data

“…Recently, additional methods have been developed to predict expression from histone ChIP-seq [22][23][24][25] . These methods were used to predict RNA-seq-based expression levels, using a large number of histone ChIP-seq data sets as input.…”

Whole-genome bisulfite sequencing of multiple individuals reveals complementary roles of promoter and gene body methylation in transcriptional regulation

“…We have previously shown that histone modifications are strong indicators of expression levels [46,47]. Therefore, we next explored the relationship between DNA methylation and histone modifications in terms of indicating gene expression, and tested whether information on gene expression conveyed by DNA methylation is totally subsumed by that of histone modifications.…”