Dataset Information

Transcription factor paralogs orchestrate alternative gene regulatory networks by context-dependent cooperation with multiple cofactors.

ABSTRACT: In eukaryotes, members of transcription factor families often exhibit similar DNA binding properties in vitro, yet orchestrate paralog-specific gene regulatory networks in vivo. The serially homologous first (T1) and third (T3) thoracic legs of Drosophila, which are specified by the Hox proteins Scr and Ubx, respectively, offer a unique opportunity to address this paradox in vivo. Genome-wide analyses using epitope-tagged alleles of both Hox loci in the T1 and T3 leg imaginal discs, the precursors to the adult legs and ventral body regions, show that ~8% of Hox binding is paralog-specific. Binding specificity is mediated by interactions with distinct cofactors in different domains: the Hox cofactor Exd acts in the proximal domain and is necessary for Scr to bind many of its paralog-specific targets, while in the distal leg domain, the homeodomain protein Distal-less (Dll) enhances Scr binding to a different subset of loci. These findings reveal how Hox paralogs, and perhaps paralogs of other transcription factor families, orchestrate alternative downstream gene regulatory networks with the help of multiple, context-specific cofactors.

SUBMITTER: Feng S

PROVIDER: S-EPMC9249852 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Transcription factor paralogs orchestrate alternative gene regulatory networks by context-dependent cooperation with multiple cofactors.

Feng Siqian S Rastogi Chaitanya C Loker Ryan R Glassford William J WJ Tomas Rube H H Bussemaker Harmen J HJ Mann Richard S RS

Nature communications 20220701 1

In eukaryotes, members of transcription factor families often exhibit similar DNA binding properties in vitro, yet orchestrate paralog-specific gene regulatory networks in vivo. The serially homologous first (T1) and third (T3) thoracic legs of Drosophila, which are specified by the Hox proteins Scr and Ubx, respectively, offer a unique opportunity to address this paradox in vivo. Genome-wide analyses using epitope-tagged alleles of both Hox loci in the T1 and T3 leg imaginal discs, the precurso ...[more]

PMID: 35778382

Similar Datasets

Project description:ObjectiveWhile oesophageal squamous cell carcinoma remains infrequent in Western populations, the incidence of oesophageal adenocarcinoma (EAC) has increased sixfold to eightfold over the past four decades. We aimed to characterise oesophageal cancer-specific and subtypes-specific gene regulation patterns and their upstream transcription factors (TFs). DESIGN: To identify regulatory elements, we profiled fresh-frozen oesophageal normal samples, tumours and cell lines with chromatin immunoprecipitation sequencing (ChIP-Seq). Mathematical modelling was performed to establish (super)-enhancers landscapes and interconnected transcriptional circuitry formed by master TFs. Coregulation and cooperation between master TFs were investigated by ChIP-Seq, circularised chromosome conformation capture sequencing and luciferase assay. Biological functions of candidate factors were evaluated both in vitro and in vivo.ResultsWe found widespread and pervasive alterations of the (super)-enhancer reservoir in both subtypes of oesophageal cancer, leading to transcriptional activation of a myriad of novel oncogenes and signalling pathways, some of which may be exploited pharmacologically (eg, leukemia inhibitory factor (LIF) pathway). Focusing on EAC, we bioinformatically reconstructed and functionally validated an interconnected circuitry formed by four master TFs-ELF3, KLF5, GATA6 and EHF-which promoted each other's expression by interacting with each super-enhancer. Downstream, these master TFs occupied almost all EAC super-enhancers and cooperatively orchestrated EAC transcriptome. Each TF within the transcriptional circuitry was highly and specifically expressed in EAC and functionally promoted EAC cell proliferation and survival.ConclusionsBy establishing cancer-specific and subtype-specific features of the EAC epigenome, our findings promise to transform understanding of the transcriptional dysregulation and addiction of EAC, while providing molecular clues to develop novel therapeutic modalities against this malignancy.

Project description:Small noncoding RNAs (sRNAs) are key regulators of bacterial gene expression. Through complementary base pairing, sRNAs affect mRNA stability and translation efficiency. Here, we describe a network inference approach designed to identify sRNA-mediated regulation of transcript levels. We use existing transcriptional data sets and prior knowledge to infer sRNA regulons using our network inference tool, the Inferelator This approach produces genome-wide gene regulatory networks that include contributions by both transcription factors and sRNAs. We show the benefits of estimating and incorporating sRNA activities into network inference pipelines using available experimental data. We also demonstrate how these estimated sRNA regulatory activities can be mined to identify the experimental conditions where sRNAs are most active. We uncover 45 novel experimentally supported sRNA-mRNA interactions in Escherichia coli, outperforming previous network-based efforts. Additionally, our pipeline complements sequence-based sRNA-mRNA interaction prediction methods by adding a data-driven filtering step. Finally, we show the general applicability of our approach by identifying 24 novel, experimentally supported, sRNA-mRNA interactions in Pseudomonas aeruginosa, Staphylococcus aureus, and Bacillus subtilis Overall, our strategy generates novel insights into the functional context of sRNA regulation in multiple bacterial species.IMPORTANCE Individual bacterial genomes can have dozens of small noncoding RNAs with largely unexplored regulatory functions. Although bacterial sRNAs influence a wide range of biological processes, including antibiotic resistance and pathogenicity, our current understanding of sRNA-mediated regulation is far from complete. Most of the available information is restricted to a few well-studied bacterial species; and even in those species, only partial sets of sRNA targets have been characterized in detail. To close this information gap, we developed a computational strategy that takes advantage of available transcriptional data and knowledge about validated and putative sRNA-mRNA interactions for inferring expanded sRNA regulons. Our approach facilitates the identification of experimentally supported novel interactions while filtering out false-positive results. Due to its data-driven nature, our method prioritizes biologically relevant interactions among lists of candidate sRNA-target pairs predicted in silico from sequence analysis or derived from sRNA-mRNA binding experiments.

Dataset Information

Transcription factor paralogs orchestrate alternative gene regulatory networks by context-dependent cooperation with multiple cofactors.

Publications

Transcription factor paralogs orchestrate alternative gene regulatory networks by context-dependent cooperation with multiple cofactors.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets