Decker, K. | bioRxiv Science

bioRxiv2020

iModulonDB: a knowledgebase of microbial transcriptional regulation derived from machine learning

Independent component analysis (ICA) of bacterial transcriptomes has emerged as a powerful tool for obtaining co-regulated, independently-modulated gene sets (iModulons), inferring their activities across a range of conditions, and enabling their association to known genetic regulators. By grouping and analyzing genes based on observations from big data alone, iModulons can provide a novel perspective into how the composition of the transcriptome adapts to environmental conditions. Here, we present iModulonDB (imodulondb.org), a knowledgebase of prokaryotic transcriptional regulation computed from high-quality transcriptomic datasets using ICA. Users select an organism from the home page and then search or browse the curated iModulons that make up its transcriptome. Each iModulon and gene has its own interactive dashboard, featuring plots and tables with clickable, hoverable, and downloadable features. This site enhances research by presenting scientists of all backgrounds with co-expressed gene sets and their activity levels, which lead to improved understanding of regulator-gene relationships, discovery of transcription factors, and the elucidation of unexpected relationships between conditions and genetic regulatory activity. The current release of iModulonDB covers three organisms (E. coli, S. aureus, and B. subtilis) with 204 iModulons, and can be expanded to cover many additional organisms.

systems biology↗

bioRxiv2020

Elucidation of regulatory modes for five two-component systems in Escherichia coli reveals novel relationships

Escherichia coli uses two-component systems (TCSs) to respond to environmental signals. TCSs affect gene expression and are parts of E. coli’s global transcriptional regulatory network (TRN). Here, we identified the regulons of five TCSs in E. coli MG1655: BaeSR and CpxAR, which were stimulated by ethanol stress; KdpDE and PhoRB, induced by limiting potassium and phosphate, respectively; and ZraSR, stimulated by zinc. We analyzed RNA-seq data using independent component analysis (ICA). ChIP-exo data was used to validate condition-specific target gene binding sites. Based on this data we (1) identify the target genes for each TCS; (2) show how the target genes are transcribed in response to stimulus; and (3) reveal novel relationships between TCSs, which indicate non-cognate inducers for various response regulators, such as BaeR to iron starvation, CpxR to phosphate limitation, and PhoB and ZraR to cell envelope stress. Our understanding of the TRN in E. coli is thus notably expanded.Importance E. coli is a common commensal microbe found in human gut microenvironment; however, some strains cause diseases like diarrhea, urinary tract infections and meningitis. E. coli’s two-component system (TCS) modulates target gene expression, specially related to virulence, pathogenesis and anti-microbial peptides, in response to environmental stimuli. Thus, it is of utmost importance to understand the transcriptional regulation of the TCSs to infer its environmental adaptation and disease pathogenicity. Utilizing a combinatorial approach integrating RNAseq, independent component analysis, ChIP-exo and data mining, we show that TCSs have five different modes of transcriptional regulation. Our data further highlights non-cognate inducers of TCSs emphasizing cross-regulatory nature of TCSs in E. coli and suggests that TCSs may have a role beyond their cognate functionalities. In summary, these results when further incorporated with genome scale metabolic models can lead to understanding of metabolic capabilities of bacteria and correctly predict complex phenotype under diverse conditions.View Full Text

systems biology↗