Machine Learning of All Mycobacterium tuberculosis H37Rv RNA-seq Data Reveals a Structured Interplay between Metabolism, Stress Response, and Infection

Yoo, Reo; Rychel, Kevin; Poudel, Saugat; Al-bulushi, Tahani; Yuan, Yuan; Chauhan, Siddharth; Lamoureux, Cameron; Palsson, Bernhard O.; Sastry, Anand

doi:10.1128/msphere.00033-22

Machine Learning of All Mycobacterium tuberculosis H37Rv RNA-seq Data Reveals a Structured Interplay between Metabolism, Stress Response, and Infection

Journal Article · Mon Mar 21 00:00:00 EDT 2022 · mSphere

DOI:https://doi.org/10.1128/msphere.00033-22· OSTI ID:1982986

^[1]; Rychel, Kevin ^[1]; Poudel, Saugat ^[1]; Al-bulushi, Tahani ^[1]; Yuan, Yuan ^[1]; Chauhan, Siddharth ^[1]; Lamoureux, Cameron ^[1]; Palsson, Bernhard O. ^[2]; Sastry, Anand ^[1]

Univ. of California, San Diego, La Jolla, CA (United States)
Univ. of California, San Diego, La Jolla, CA (United States); Technical Univ. of Denmark, Lyngby (Denmark)

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.

View Accepted Manuscript (DOE)

Research Organization:: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States). National Energy Research Scientific Computing Center (NERSC)

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES). Scientific User Facilities (SUF)

Grant/Contract Number:: AC02-05CH11231

OSTI ID:: 1982986

Journal Information:: mSphere, Journal Name: mSphere Journal Issue: 2 Vol. 7; ISSN 2379-5042

Publisher:: American Society for MicrobiologyCopyright Statement

Country of Publication:: United States

Language:: English

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59 BASIC BIOLOGICAL SCIENCES
Gene regulation
Independent component analysis
Machine learning
Microbiology
Mycobacterium tuberculosis
Transcriptomics

Machine Learning of All Mycobacterium tuberculosis H37Rv RNA-seq Data Reveals a Structured Interplay between Metabolism, Stress Response, and Infection

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