ArticlemSphere2022
Machine Learning of All Mycobacterium tuberculosis H37Rv RNA-seq Data Reveals a Structured Interplay between Metabolism, Stress Response, and Infection.
Article in mSphere, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 1 of them a synthesis that pooled it.
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Who cites it
30 citing papers in PubMed, 1 synthesis or guideline pooled it, 48 citations in OpenAlex.
- Meta-analysis Driven Strain Design for Mitigating Oxidative Stresses Important in Biomanufacturing.ACS synthetic biology · 2024Pooled it
- Revealing transcriptomic responses inmSystems · 2026Article
- Transcript-Capture sequencing enriches mRNA ofNAR molecular medicine · 2026Article
- Transcript-Capture sequencing enriches mRNA ofbioRxiv : the preprint server for biology · 2025Article
- Systems-level characterization of EGFR kinase inhibitors reveals heterogeneous effects on Mtb-macrophage interactions.bioRxiv : the preprint server for biology · 2025Article
- A transcriptional plasticity-aware framework for RNA-seq differential expression analysis.Briefings in bioinformatics · 2025Article
- iModulonDB 2.0: dynamic tools to facilitate knowledge-mining and user-enabled analyses of curated transcriptomic datasets.Nucleic acids research · 2025Article
- Deciphering the proteome ofComputational and structural biotechnology journal · 2025Article
- Predicting fitness inFrontiers in tuberculosis · 2025Article
- Catalase activity deficiency sensitizes multidrug-resistant Mycobacterium tuberculosis to the ATP synthase inhibitor bedaquiline.Nature communications · 2024Article
- iModulonMiner and PyModulon: Software for unsupervised mining of gene expression compendia.PLoS computational biology · 2024Article
- Machine learning reveals the transcriptional regulatory network and circadian dynamics ofProceedings of the National Academy of Sciences of the United States of America · 2024Article
- Mycobacteria that cause tuberculosis have retained ancestrally acquired genes for the biosynthesis of chemically diverse terpene nucleosides.PLoS biology · 2024Article
- Proteome allocation is linked to transcriptional regulation through a modularized transcriptome.Nature communications · 2024Article
- sRNA expression profile of KPC-2-producing carbapenem-resistant Klebsiella pneumoniae: Functional role of sRNA51.PLoS pathogens · 2024Article
- Genetically encoded transcriptional plasticity underlies stress adaptation in Mycobacterium tuberculosis.Nature communications · 2024Article
- Improving gene set enrichment analysis (GSEA) by using regulation directionality.Microbiology spectrum · 2024Article
- Article
- High-resolution temporal profiling of E. coli transcriptional response.Nature communications · 2023Article
- A multi-scale expression and regulation knowledge base for Escherichia coli.Nucleic acids research · 2023Article
Corrections and comments
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Authors and funding
9 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Mycobacterium tuberculosis is one of the most consequential human bacterial pathogens, posing a serious challenge to 21st century medicine. A key feature of its pathogenicity is its ability to adapt its transcriptional response to environmental stresses through its transcriptional regulatory network (TRN). While many studies have sought to characterize specific portions of the M. tuberculosis TRN, and some studies have performed system-level analysis, few have been able to provide a network-based model of the TRN that also provides the relative shifts in transcriptional regulator activity triggered by changing environments. Here, we compiled a compendium of nearly 650 publicly available, high quality M. tuberculosis RNA-sequencing data sets and applied an unsupervised machine learning method to obtain a quantitative, top-down TRN. It consists of 80 independently modulated gene sets known as "iModulons," 41 of which correspond to known regulons. These iModulons explain 61% of the variance in the organism's transcriptional response. We show that iModulons (i) reveal the function of poorly characterized regulons, (ii) describe the transcriptional shifts that occur during environmental changes such as shifting carbon sources, oxidative stress, and infection events, and (iii) identify intrinsic clusters of regulons that link several important metabolic systems, including lipid, cholesterol, and sulfur metabolism. This transcriptome-wide analysis of the M. tuberculosis TRN informs future research on effective ways to study and manipulate its transcriptional regulation and presents a knowledge-enhanced database of all published high-quality RNA-seq data for this organism to date.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.