Dataset Information

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.

SUBMITTER: Vijayabaskar MS

PROVIDER: S-EPMC6855567 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

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

Vijayabaskar M S MS Goode Debbie K DK Obier Nadine N Lichtinger Monika M Emmett Amber M L AML Abidin Fatin N Zainul FNZ Shar Nisar N Hannah Rebecca R Assi Salam A SA Lie-A-Ling Michael M Gottgens Berthold B Lacaud Georges G Kouskoff Valerie V Bonifer Constanze C Westhead David R DR

PLoS computational biology 20191104 11

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, les ...[more]

PMID: 31682597

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Project description:BackgroundPost-transcriptional gene regulation controls the amount of protein produced from an individual mRNA by altering rates of decay and translation. Many sequence elements that direct post-transcriptional regulation have been found; in mammals, most such elements are located within the 3' untranslated regions (3'UTRs). Comparative genomic studies demonstrate that mammalian 3'UTRs contain extensive conserved sequence tracts, yet only a small fraction corresponds to recognized elements, implying that many additional novel elements exist. Despite a variety of computational, molecular, and biochemical approaches, identifying functional 3'UTRs elements remains difficult.ResultsWe created a high-throughput cell-based screen that enables identification of functional post-transcriptional 3'UTR regulatory elements. Our system exploits integrated single-copy reporters, which are expressed and processed as endogenous genes. We screened many thousands of short random sequences for their regulatory potential. Control sequences with known effects were captured effectively using our approach, establishing that our methodology was robust. We found hundreds of functional sequences, which we validated in traditional reporter assays, including verifying their regulatory impact in native sequence contexts. Although 3'UTRs are typically considered repressive, most of the functional elements were activating, including ones that were preferentially conserved. Additionally, we adapted our screening approach to examine the effect of elements on RNA abundance, revealing that most elements act by altering mRNA stability.ConclusionsWe developed and used a high-throughput approach to discover hundreds of post-transcriptional cis-regulatory elements. These results imply that most human 3'UTRs contain many previously unrecognized cis-regulatory elements, many of which are activating, and that the post-transcriptional fate of an mRNA is largely due to the actions of many individual cis-regulatory elements within its 3'UTR.

Project description:BackgroundIn eukaryotes, transcriptional regulation is usually mediated by interactions of multiple transcription factors (TFs) with their respective specific cis-regulatory elements (CREs) in the so-called cis-regulatory modules (CRMs) in DNA. Although the knowledge of CREs and CRMs in a genome is crucial to elucidate gene regulatory networks and understand many important biological phenomena, little is known about the CREs and CRMs in most eukaryotic genomes due to the difficulty to characterize them by either computational or traditional experimental methods. However, the exponentially increasing number of TF binding location data produced by the recent wide adaptation of chromatin immunoprecipitation coupled with microarray hybridization (ChIP-chip) or high-throughput sequencing (ChIP-seq) technologies has provided an unprecedented opportunity to identify CRMs and CREs in genomes. Nonetheless, how to effectively mine these large volumes of ChIP data to identify CREs and CRMs at nucleotide resolution is a highly challenging task.ResultsWe have developed a novel graph-theoretic based algorithm DePCRM for genome-wide de novo predictions of CREs and CRMs using a large number of ChIP datasets. DePCRM predicts CREs and CRMs by identifying overrepresented combinatorial CRE motif patterns in multiple ChIP datasets in an effective way. When applied to 168 ChIP datasets of 56 TFs from D. melanogaster, DePCRM identified 184 and 746 overrepresented CRE motifs and their combinatorial patterns, respectively, and predicted a total of 115,932 CRMs in the genome. The predictions recover 77.9% of known CRMs in the datasets and 89.3% of known CRMs containing at least one predicted CRE. We found that the putative CRMs as well as CREs as a whole in a CRM are more conserved than randomly selected sequences.ConclusionOur results suggest that the CRMs predicted by DePCRM are highly likely to be functional. Our algorithm is the first of its kind for de novo genome-wide prediction of CREs and CRMs using larger number of transcription factor ChIP datasets. The algorithm and predictions will hopefully facilitate the elucidation of gene regulatory networks in eukaryotes. All the predicted CREs, CRMs, and their target genes are available at http://bioinfo.uncc.edu/mniu/pcrms/www/.

Dataset Information

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

Publications

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

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