Identification of gene specific cis-regulatory elements during differentiation of mouse embryonic stem cells: An integrative approach using high-throughput datasets

Abstract

Gene expression governs cell fate, and is regulated via a complex interplay of transcription factors and molecules that change chromatin structure. Advances in sequencing-based assays have enabled investigation of these processes genome-wide, leading to large datasets that combine information on the dynamics of gene expression, transcription factor binding and chromatin structure as cells differentiate. While numerous studies focus on the effects of these features on broader gene regulation, less work has been done on the mechanisms of gene-specific transcriptional control. In this study, we have focussed on the latter by integrating gene expression data for the in vitro differentiation of murine ES cells to macrophages and cardiomyocytes, with dynamic data on chromatin structure, epigenetics and transcription factor binding. Combining a novel strategy to identify communities of related control elements with a penalized regression approach, we developed individual models to identify the potential control elements predictive of the expression of each gene. Our models were compared to an existing method and evaluated using the existing literature and new experimental data from embryonic stem cell differentiation reporter assays. Our method is able to identify transcriptional control elements in a gene specific manner that reflect known regulatory relationships and to generate useful hypotheses for further testing.

Interactions between lineage‐associated transcription factors govern haematopoietic progenitor states

“…Genome‐wide binding profiles are often used for cross‐validation. These, however, also face limitations, because singular TF binding events constitute a poor predictor of gene regulation (ENCODE Project Consortium, 2012; Calero‐Nieto et al , 2014; Kellis et al , 2014; Vijayabaskar et al , 2019). Arguably, the main limitation is the lack of gold standards—sets of verified, functional connections which can be used to objectively evaluate and refine inference methods.…”

ZNF146/OZF and ZNF507 target LINE-1 sequences

“…6c , Table 1 ). Two differentially expressed genes had ZNF507 ChIP-seq peaks within 10 kb of their transcription start sites and twelve genes had peaks located within 100 kb, a common distance cutoff for cis regulatory elements ( Vijayabaskar et al 2019 ), suggesting these genes could potentially be directly regulated by ZNF507.…”

Data integration for inferring context-specific gene regulatory networks

“…Gene-centric methods leverage gene expression and a small number of regulatory signals from multiple conditions to explain the expression level of a gene as a function of the regulatory activity of CREs. Current methods have used regularized regression [40,44] or mutual information [41] to link elements to genes (Figure 2B, [38]). These methods also vary in the size of the neighborhood around a gene to search for regulatory elements, the specific regulatory signal used to correlate or predict expression, and the number of cell lines examined.…”

“…These methods also vary in the size of the neighborhood around a gene to search for regulatory elements, the specific regulatory signal used to correlate or predict expression, and the number of cell lines examined. In particular, FOCS and Vijayabaskar et al [40,44] used regression to select CREs for a gene within the 100 Kb and 500 Kb region, respectively, around the transcription start site (TSS). Instead, MICMIC [41] used conditional mutual information to predict which regulatory elements, defined by CpG methylation levels, within the 300 Kb window of a gene's TSS explain the expression of the gene.…”

“…A number of approaches exist (refer the study by Xu et al [38] for a comprehensive review); here, we review recent methods that integrate multiple types of regulatory genomic data to predict such interactions. These methods can be grouped into (Figure 1C) (a) gene-centric approaches, which predict the regulatory elements by learning a model for each gene [39][40][41], and (b) global approaches, which learn a single model for all gene-element pairs [42,43].…”