Mapping and manipulating the Mycobacterium tuberculosis transcriptome using a transcription factor overexpression-derived regulatory network

• Published in: Genome biology 2014-01, Vol.15 (11), p.502-502, Article 502
• Main Authors: Rustad, Tige R, Minch, Kyle J, Ma, Shuyi, Winkler, Jessica K, Hobbs, Samuel, Hickey, Mark, Brabant, William, Turkarslan, Serdar, Price, Nathan D, Baliga, Nitin S, Sherman, David R
• Format: Article
• Language: English
• Subjects: Cloning, Molecular; Gene Expression Regulation, Bacterial - drug effects; Gene Regulatory Networks; Humans; Isoniazid - administration & dosage; Mycobacterium tuberculosis; Mycobacterium tuberculosis - drug effects; Mycobacterium tuberculosis - genetics; Mycobacterium tuberculosis - pathogenicity; Promoter Regions, Genetic; Regulon - drug effects; Transcription Factors - biosynthesis; Transcription Factors - genetics; Transcription, Genetic - drug effects; Transcriptome - genetics; Tuberculosis - genetics; Tuberculosis - microbiology
• ISSN: 1474-760X; 1465-6906; 1474-760X; 1465-6914
• DOI: 10.1186/s13059-014-0502-3

Mycobacterium tuberculosis senses and responds to the shifting and hostile landscape of the host. To characterize the underlying intertwined gene regulatory network governed by approximately 200 transcription factors of M. tuberculosis, we have assayed the global transcriptional consequences of overexpressing each transcription factor from an inducible promoter. We cloned and overexpressed 206 transcription factors in M. tuberculosis to identify the regulatory signature of each. We identified 9,335 regulatory consequences of overexpressing each of 183 transcription factors, providing evidence of regulation for 70% of the M. tuberculosis genome. These transcriptional signatures agree well with previously described M. tuberculosis regulons. The number of genes differentially regulated by transcription factor overexpression varied from hundreds of genes to none, with the majority of expression changes repressing basal transcription. Exploring the global transcriptional maps of transcription factor overexpressing (TFOE) strains, we predicted and validated the phenotype of a regulator that reduces susceptibility to a first line anti-tubercular drug, isoniazid. We also combined the TFOE data with an existing model of M. tuberculosis metabolism to predict the growth rates of individual TFOE strains with high fidelity. This work has led to a systems-level framework describing the transcriptome of a devastating bacterial pathogen, characterized the transcriptional influence of nearly all individual transcription factors in M. tuberculosis, and demonstrated the utility of this resource. These results will stimulate additional systems-level and hypothesis-driven efforts to understand M. tuberculosis adaptations that promote disease.