Omics4TB Disease Progression



Program directors: David Sherman and Alan Aderem, Center for Infectious Disease Research

Investigators: Nitin S. Baliga, Kevin Urdahl, Daniel Zak, and Robert Moritz

Funding source: NIH NIAID U19 AI106761

 Project home page:

Project objectives: Mycobacterium tuberculosis causes ~9 million new cases of active disease and 1.4 million deaths each year, and our tools to combat tuberculosis (TB) disease are universally outdated and overmatched. This project combines separate advances in systems biology and network modeling to produce an experimentally grounded and verifiable systems-level model of the MTB regulatory networks that affect disease progression. Our consortium of two projects and four Cores aim to reveal key features of TB disease progression in an iterative cycle: perturb carefully chosen subnetworks within both MTB and host; collect matched omics data sets; model, predict, and validate with new experiments.

Network analysis identifies Rv0324 and Rv0880 as regulators of bedaquiline tolerance in Mycobacterium tuberculosis

Publication: Nature Microbiology 2016 doi:10.1038/nmicrobiol.2016.78

The resilience of Mycobacterium tuberculosis (MTB) emerges from its ability to effectively counteract immunological, environmental and antitubercular challenges. Here, we demonstrate that MTB can tolerate drug treatment by adopting a tolerant state that can be deciphered through systems analysis of its transcriptional responses. Specifically, we demonstrate how treatment with the antitubercular drug bedaquiline activates a regulatory network that coordinates multiple resistance mechanisms to push MTB into a tolerant state. Disruption of this network, by knocking out its predicted transcription factors, Rv0324 and Rv0880, significantly increased bedaquiline killing and enabled the discovery of a second drug, pretomanid, that potentiated killing by bedaquiline. We demonstrate that the synergistic effect of this combination emerges, in part, through disruption of the tolerance network. We discuss how this network strategy also predicts drug combinations with antagonistic interactions, potentially accelerating the discovery of new effective combination drug regimens for tuberculosis.

Gene expression data reported in this study can be accessed at GEO with accession number GSE72459

The DNA-binding network of Mycobacterium tuberculosis

Publication: PMID: 25581030 Nat Commun, Volume 6, p.5829 (2015)

Mycobacterium tuberculosis (MTB) infects 30% of all humans and kills someone every 20-30 s. Here we report genome-wide binding for ~80% of all predicted MTB transcription factors (TFs), and assayed global expression following induction of each TF. The MTB DNA-binding network consists of ~16,000 binding events from 154 TFs. We identify >50 TF-DNA consensus motifs and >1,150 promoter-binding events directly associated with proximal gene regulation. An additional ~4,200 binding events are in promoter windows and represent strong candidates for direct transcriptional regulation under appropriate environmental conditions. However, we also identify >10,000 ‘dormant’ DNA-binding events that cannot be linked directly with proximal transcriptional control, suggesting that widespread DNA binding may be a common feature that should be considered when developing global models of coordinated gene expression.

Data sets and additional information can be found here.

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

Publication: PMID: 25380655 Genome Biol. 2014;15(11):502.

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.

Data sets and additional information can be found here.

A high-resolution network model for global gene regulation in Mycobacterium tuberculosis.

Publication:   PMID: 25232098  Nucleic Acids Res. 2014 Oct;42(18):11291-303

The resilience of Mycobacterium tuberculosis (MTB) is largely due to its ability to effectively counteract and even take advantage of the hostile environments of a host. In order to accelerate the discovery and characterization of these adaptive mechanisms, we have mined a compendium of 2325 publicly available transcriptome profiles of MTB to decipher a predictive, systems-scale gene regulatory network model. The resulting modular organization of 98% of all MTB genes within this regulatory network was rigorously tested using two independently generated datasets: a genome-wide map of 7248 DNA-binding locations for 143 transcription factors (TFs) and global transcriptional consequences of overexpressing 206 TFs. This analysis has discovered specific TFs that mediate conditional co-regulation of genes within 240 modules across 14 distinct environmental contexts. In addition to recapitulating previously characterized regulons, we discovered 454 novel mechanisms for gene regulation during stress, cholesterol utilization and dormancy. Significantly, 183 of these mechanisms act uniquely under conditions experienced during the infection cycle to regulate diverse functions including 23 genes that are essential to host-pathogen interactions. These and other insights underscore the power of a rational, model-driven approach to unearth novel MTB biology that operates under some but not all phases of infection.

Data sets and additional information can be found here.

A comprehensive map of genome-wide gene regulation in Mycobacterium tuberculosis.

Publication: PMID: 25977815 Sci Data. 2015 Mar 31;2:150010.

Mycobacterium tuberculosis (MTB) is a pathogenic bacterium responsible for 12 million active cases of tuberculosis (TB) worldwide. The complexity and critical regulatory components of MTB pathogenicity are still poorly understood despite extensive research efforts. In this study, we constructed the first systems-scale map of transcription factor (TF) binding sites and their regulatory target proteins in MTB. We constructed FLAG-tagged overexpression constructs for 206 TFs in MTB, used ChIP-seq to identify genome-wide binding events and surveyed global transcriptomic changes for each overexpressed TF. Here we present data for the most comprehensive map of MTB gene regulation to date. We also define elaborate quality control measures, extensive filtering steps, and the gene-level overlap between ChIP-seq and microarray datasets. Further, we describe the use of TF overexpression datasets to validate a global gene regulatory network model of MTB and describe an online source to explore the datasets.

Data sets and additional information can be found on these pages: ChIP-Seq, Expression Data, Network Model.