The Mycobacterium tuberculosis regulatory network and hypoxia.

We have taken the first steps towards a complete reconstruction of the Mycobacterium tuberculosis regulatory network based on ChIP-Seq and combined this reconstruction with system-wide profiling of messenger RNAs, proteins, metabolites and lipids during hypoxia and re-aeration. Adaptations to hypoxia are thought to have a prominent role in M. tuberculosis pathogenesis. Using ChIP-Seq combined with expression data from the induction of the same factors, we have reconstructed a draft regulatory network based on 50 transcription factors. This network model revealed a direct interconnection between the hypoxic response, lipid catabolism, lipid anabolism and the production of cell wall lipids. As a validation of this model, in response to oxygen availability we observe substantial alterations in lipid content and changes in gene expression and metabolites in corresponding metabolic pathways. The regulatory network reveals transcription factors underlying these changes, allows us to computationally predict expression changes, and indicates that Rv0081 is a regulatory hub.

Global analysis of mRNA stability in Mycobacterium tuberculosis.

Mycobacterium tuberculosis (MTB) is a highly successful pathogen that infects over a billion people. As with most organisms, MTB adapts to stress by modifying its transcriptional profile. Remodeling of the transcriptome requires both altering the transcription rate and clearing away the existing mRNA through degradation, a process that can be directly regulated in response to stress. To understand better how MTB adapts to the harsh environs of the human host, we performed a global survey of the decay rates of MTB mRNA transcripts. Decay rates were measured for 2139 of the ~4000 MTB genes, which displayed an average half-life of 9.5 min. This is nearly twice the average mRNA half-life of other prokaryotic organisms where these measurements have been made. The transcriptome was further stabilized in response to lowered temperature and hypoxic stress. The generally stable transcriptome described here, and the additional stabilization in response to physiologically relevant stresses, has far-ranging implications for how this pathogen is able to adapt in its human host.

Alveolar Macrophages Provide an Early Mycobacterium tuberculosis Niche and Initiate Dissemination.

Mycobacterium tuberculosis (Mtb) infection is initiated in the distal airways, but the bacteria ultimately disseminate to the lung interstitium. Although various cell types, including alveolar macrophages (AM), neutrophils, and permissive monocytes, are known to be infected with Mtb, the initially infected cells as well as those that mediate dissemination from the alveoli to the lung interstitium are unknown. In this study, using a murine infection model, we reveal that early, productive Mtb infection occurs almost exclusively within airway-resident AM. Thereafter Mtb-infected, but not uninfected, AM localize to the lung interstitium through mechanisms requiring an intact Mtb ESX-1 secretion system. Relocalization of infected AM precedes Mtb uptake by recruited monocyte-derived macrophages and neutrophils. This dissemination process is driven by non-hematopoietic host MyD88/interleukin-1 receptor inflammasome signaling. Thus, interleukin-1-mediated crosstalk between Mtb-infected AM and non-hematopoietic cells promotes pulmonary Mtb infection by enabling infected cells to disseminate from the alveoli to the lung interstitium.

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

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.