The Escherichia coli Transcriptome Mostly Consists of Independently Regulated Modules

Abstract

Underlying cellular responses is a transcriptional regulatory network (TRN) that modulates gene expression. A useful description of the TRN would decompose the transcriptome into targeted effects of individual transcriptional regulators. Here, we applied unsupervised learning to a compendium of high-quality Escherichia coli RNA-seq datasets to identify 70 statistically independent signals that modulate the expression of specific gene sets. We show that 50 of these transcriptomic signals represent the effects of currently characterized transcriptional regulators. Condition-specific activation of signals was validated by exposure of E. coli to new environmental conditions. The resulting decomposition of the transcriptome provided: (1) a mechanistic, systems-level, network-based explanation of responses to environmental and genetic perturbations, (2) a guide to gene and regulator function discovery, and (3) a basis for characterizing transcriptomic differences in multiple strains. Taken together, our results show that signal summation forms an underlying principle that describes the composition of a model prokaryotic transcriptome.

Revealing 29 sets of independently modulated genes inStaphylococcus aureus, their regulators and role in key physiological responses

“…However, beyond regulons, i-modulons can also describe other genomic features, such as strain differences and genetic alterations (e.g. gene knock-out) that can lead to change in gene co-expression 9,11 . The outcome of this approach is a biologically relevant, low dimensional mathematical representation of functional modules in the TRN that reconstruct most of the information content of the input RNAseq compendium ( Figure S1b ).…”

Laboratory evolution of multipleE. colistrains reveals unifying principles of adaptation but diversity in driving genotypes

“…To make sense of these expression changes, we applied an alternative RNA-seq analysis workflow that was shown to enable quantitative analysis of the E. coli transcriptome from the perspective of transcription factors (Sastry et al , 2019b) . The authors showed that independent component analysis (ICA) deconvolved a large compendium of E. coli MG1655 RNA-seq data into a linear combination of independent sources that reflect known regulons ("iModulons"), and source weightings ("iModulon activities"), which describe the global regulatory state (Sastry et al , 2019a) . Using the previous set of 92 iModulons, we transformed the flask-specific gene expression profiles into flask-specific iModulon activities (see Methods, Supplementary Figure 2 ).…”

“…Transcription factor and GO enrichments of differentially expressed gene sets Transcription factors (TFs) and the genes they are known to regulate were obtained from a study (Fang et al , 2017) . GO terms were obtained from (Sastry et al , 2019a) . This list was used to perform hypergeometric enrichment analysis of the differentially expressed genes determined from RNAseq analysis.…”

Adaptive laboratory evolution ofEscherichia coliunder acid stress

“…Here, however, we consider the mutations on rpoC to be associated with inducing faster growth rather than acid resistance. A comprehensive analysis on the 278 gene expression datasets of E. coli across diverse conditions has revealed that mutations on genes related to RNA polymerase typically lead to improved growth rate and reduced stress-related gene expression [41].…”

“…The study here provides a novel perspective on acid resistance mechanisms, as the commonly known acid resistance systems depend on rich medium or specific amino acids [42,43]. In addition to the analysis of genetic mutations and DEGs, further analysis can be performed to understand the change in regulatory actions using a recently developed approach [41]. Such analysis can be helpful in understanding the response to acid stress at the level of transcriptional regulation and revealing potential drivers behind the global adjustment of cellular response against acid stress.…”