The Functional Consequences of Variation in Transcription Factor Binding

Abstract

One goal of human genetics is to understand how the information for precise and dynamic gene expression programs is encoded in the genome. The interactions of transcription factors (TFs) with DNA regulatory elements clearly play an important role in determining gene expression outputs, yet the regulatory logic underlying functional transcription factor binding is poorly understood. Many studies have focused on characterizing the genomic locations of TF binding, yet it is unclear to what extent TF binding at any specific locus has functional consequences with respect to gene expression output. To evaluate the context of functional TF binding we knocked down 59 TFs and chromatin modifiers in one HapMap lymphoblastoid cell line. We then identified genes whose expression was affected by the knockdowns. We intersected the gene expression data with transcription factor binding data (based on ChIP-seq and DNase-seq) within 10 kb of the transcription start sites of expressed genes. This combination of data allowed us to infer functional TF binding. Using this approach, we found that only a small subset of genes bound by a factor were differentially expressed following the knockdown of that factor, suggesting that most interactions between TF and chromatin do not result in measurable changes in gene expression levels of putative target genes. We found that functional TF binding is enriched in regulatory elements that harbor a large number of TF binding sites, at sites with predicted higher binding affinity, and at sites that are enriched in genomic regions annotated as “active enhancers.”

Chromatin profiling of Drosophila CNS subpopulations identifies active transcriptional enhancers

“…Considering all 102 fragments with midline primordium activity, 55% of the fragments do not reside near a known midline-expressed gene. Other studies have concluded that gene expression is frequently unaffected by transcription factor binding (Cusanovich et al, 2014) and fragments tested for enhancer activity occasionally show patterns that do not accurately reflect expression of the nearby gene (Kvon et al, 2014). However, in this study we provide evidence that enhancers with specific patterns of activity (midline) frequently reside in inactive chromatin regions and are unlikely to be employed in vivo despite their enhancer potential.…”

The genetic and regulatory architecture of ERBB3-type 1 diabetes susceptibility locus

“…Some of these variations could be of functional significance particularly those that lie within the binding sites of known TFs. Generally, functional TF binding is enriched at sites that are enriched in active enhancers, regulatory elements harboring multiple TF binding sites, and at sites with predicted higher binding affinity (Andersson et al, 2014;Cusanovich et al, 2014). Our analysis points towards the functional significance of SNPs rs2292239, rs3741499 and rs4759229 based on the overlap with an active enhancer element, CLIP-Seq peaks and DNase I footprints coverage, and the existence of multiple TF binding sites.…”

Characterization of regulatory sequences in alternative promoters of hypermethylated genes associated with tumor resistance to cisplatin

“…Considering that the transcription machinery utilizes alternative promoters for regulating differential transcription (10,16) and the aberrant use of one alternative promoter over another may result in disease, including cancer (11), we hypothesized that cisplatin resistance may be mediated by a differential usage of alternative promoters with variable regulatory sequences, TFBS and CGIs. Transcription factors and their binding sites in a given promoter are key elements in controlling the rate and extent of mRNA synthesis (19,27). However, the interaction between transcription factors and cis-regulatory modules, which contain the TFBS in promoter sequences, has not been clearly determined (27)(28)(29)(30)(31)(32)(33).…”

“…Furthermore, the first main class (A) may be divided into two subclasses, namely C and D; subclass C contained 30 alternative promoters, of which 26 contained CGIs. Groups E and F were identified in subclass C, which were represented by 19 The evolutionary distances between the two major classes A and B and subclasses C and D were in the range of 0.0125 and 0.03125, respectively, exhibiting weak evolutionary ties between them; however, stronger ties were observed among the alternative promoters in group E and F within subclass C, which were composed mainly from alternative promoters with CGIs (93%). The alternative promoters that lacked CGIs were distributed in two unrelated groups, B and D.…”

“…The TFBS and the CGIs targeted for DNA methylation, significantly affect the functional activities of alternative promoters (6)(7)(8)(9)18,19). To date, the role and molecular characteristics of alternative promoters have not been investigated in platinum drug resistance.…”