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

• DOI: 10.1128/msphere.00033-22
• Publication Date: 2022-03-21T08:30:19Z
• Authors: Reo Yoo; Kevin Rychel; Saugat Poudel; Tahani Al-bulushi; Yuan Yuan; Siddharth Chauhan; Cameron Lamoureux; Bernhard O. Palsson; Anand Sastry
• Keywords: Regulon

Mycobacterium tuberculosis is one of the most consequential human bacterial pathogens, posing a serious challenge to 21st century medicine. A key feature its pathogenicity ability adapt transcriptional response environmental stresses through regulatory network (TRN). While many studies have sought characterize specific portions M. TRN, and some performed system-level analysis, few been able provide network-based model TRN that also provides relative shifts in regulator activity triggered by changing environments. Here, we compiled compendium nearly 650 publicly available, high quality RNA-sequencing data sets applied an unsupervised machine learning method obtain quantitative, top-down TRN. It consists 80 independently modulated gene known as "iModulons," 41 which correspond regulons. These iModulons explain 61% variance organism's response. We show (i) reveal function poorly characterized regulons, (ii) describe occur during changes such shifting carbon sources, oxidative stress, infection events, (iii) identify intrinsic clusters regulons link several important metabolic systems, including lipid, cholesterol, sulfur metabolism. This transcriptome-wide analysis informs future research on effective ways study manipulate regulation presents knowledge-enhanced database all published high-quality RNA-seq for this organism date. IMPORTANCE H37Rv world's impactful large part success relies differential expression genes environment. The driven primarily network, focuses identifying quantifying coregulated previous relied molecular measurements, manuscript utilized alternative technique performs set transcriptomic data. approach less reliant hypotheses about role systems allows discovery new biological findings already collected better understanding structure will implications design improved therapeutic approaches.