Mapping and manipulating the Mycobacterium tuberculosis transcriptome using a transcription factor overexpression-derived regulatory network
| Autor: | Tige R. Rustad, Mark J. Hickey, David R. Sherman, Nitin S. Baliga, William Brabant, Kyle J. Minch, Nathan D. Price, Shuyi Ma, Serdar Turkarslan, Samuel J. Hobbs, Jessica K. Winkler |
|---|---|
| Rok vydání: | 2014 |
| Předmět: |
Transcription
Genetic Gene regulatory network Biology Regulon Transcriptome Mycobacterium tuberculosis 03 medical and health sciences Transcription (biology) Isoniazid Humans Tuberculosis Gene Regulatory Networks Cloning Molecular Promoter Regions Genetic Gene Transcription factor 030304 developmental biology Regulation of gene expression Genetics 0303 health sciences General transcription factor 030306 microbiology Research Gene Expression Regulation Bacterial biology.organism_classification 3. Good health Transcription Factors |
| Zdroj: | Genome Biology |
| ISSN: | 1474-760X |
| Popis: | Background 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. Results 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. Conclusion 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. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0502-3) contains supplementary material, which is available to authorized users. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full text from SpringerLink