ArticleNature communications2019
The Escherichia coli transcriptome mostly consists of independently regulated modules.
Article in Nature communications, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 156 papers, 1 of them a synthesis that pooled it.
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Who cites it
156 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Meta-analysis Driven Strain Design for Mitigating Oxidative Stresses Important in Biomanufacturing.ACS synthetic biology · 2024Pooled it
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- Determination of Gene-Level Fitness Contributions Across the Mucin-Utilization Network of Bacteroides thetaiotaomicron.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Review
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- A small molecule reduces both parkinsonism and l-dopa-induced dyskinesia in animal models of Parkinson's disease.Science translational medicine · 2026Article
- A systems-level atlas of carbon-response transcriptional states inProceedings of the National Academy of Sciences of the United States of America · 2026Article
- Simple biological controllers drive the evolution of soft modes.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Revealing transcriptomic responses inmSystems · 2026Article
- Genotype-fitness mapping of adaptive mutants reveals shifting low-dimensional structure across divergent environments.PLoS biology · 2026Article
- Aerobicity stimulon iniScience · 2026Article
- A slight mismatch between a gene's codon usage and the cellular tRNA supply is beneficial.Nature communications · 2026Article
- Approaches for accelerating microbial gene function discovery using artificial intelligence.Nature microbiology · 2026Review
- The transcriptional regulatory network of the Escherichia coli MG1655 reference strain.Nucleic acids research · 2026Article
- Transcriptional regulatory network analysis identifies conserved cis-antisense ncRNAs in the vancomycin and ceftriaxone stress response ofFrontiers in molecular biosciences · 2026Article
- Unraveling proteomic chaos by independent component analysis-ClpX proficiency promotes the iron and oxygen limitation responses ofmSystems · 2025Article
- Integrated genomic-transcriptomic analysis of clavulanic acid production in differentially productive Streptomyces clavuligerus strains.Scientific reports · 2025Article
- Transcriptional regulatory networks of the human gut symbiont Bacteroides thetaiotaomicron are uncovered using machine learning.Nucleic acids research · 2025Article
96 more citing papers are in PubMed but not listed here.
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10 authors.
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No grant is acknowledged in the PubMed record.
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 apply unsupervised machine learning to a diverse compendium of over 250 high-quality Escherichia coli RNA-seq datasets to identify 92 statistically independent signals that modulate the expression of specific gene sets. We show that 61 of these transcriptomic signals represent the effects of currently characterized transcriptional regulators. Condition-specific activation of signals is validated by exposure of E. coli to new environmental conditions. The resulting decomposition of the transcriptome provides: a mechanistic, systems-level, network-based explanation of responses to environmental and genetic perturbations; a guide to gene and regulator function discovery; and a basis for characterizing transcriptomic differences in multiple strains. Taken together, our results show that signal summation describes the composition of a model prokaryotic transcriptome.
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