Omics4TB

https://www.patricbrc.org/public/patric/images/omics4tb.png

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: http://www.omics4tb.org

Project Data: PATRIC FTP Site and PATRIC Public Workspace

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of active disease and 1.4 million deaths each year, and our tools to combat tuberculosis (TB) disease are universally outdated and overmatched.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.

Summary of Omics4TB Datasets

Experiment

PMID

Data Set(s)

The DNA-binding network of Mycobacterium tuberculosis

25581030

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

25380655

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

25232098

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

25977815

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

27573104

MiR-155-regulated molecular network orchestrates cell fate in the innate and adaptive immune response to Mycobacterium tuberculosis

Absolute Proteome Composition and Dynamics during dormancy and resuscitation of Mycobacterium tuberculosis

26094805

MTB Transcriptional Regulator Induction Phenotypic (TRIP) Screen

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.

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

Publication: PMID: 27573104 Nat Microbiol. 2016 Jun 6;1(8):16078.

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.

Data sets and additional information can be found here.

MiR-155-regulated molecular network orchestrates cell fate in the innate and adaptive immune response to Mycobacterium tuberculosis.

Publication: PMID: 27681624 Proc Natl Acad Sci U S A. 2016 Oct 11;113(41):E6172-E6181. Epub 2016 Sep 28.

The regulation of host-pathogen interactions during Mycobacterium tuberculosis (Mtb) infection remains unresolved. MicroRNAs (miRNAs) are important regulators of the immune system, and so we used a systems biology approach to construct an miRNA regulatory network activated in macrophages during Mtb infection. Our network comprises 77 putative miRNAs that are associated with temporal gene expression signatures in macrophages early after Mtb infection. In this study, we demonstrate a dual role for one of these regulators, miR-155. On the one hand, miR-155 maintains the survival of Mtb-infected macrophages, thereby providing a niche favoring bacterial replication; on the other hand, miR-155 promotes the survival and function of Mtb-specific T cells, enabling an effective adaptive immune response. MiR-155-induced cell survival is mediated through the SH2 domain-containing inositol 5-phosphatase 1 (SHIP1)/protein kinase B (Akt) pathway. Thus, dual regulation of the same cell survival pathway in innate and adaptive immune cells leads to vastly different outcomes with respect to bacterial containment.

Data sets and additional information can be found here.

Absolute Proteome Composition and Dynamics during dormancy and resuscitation of Mycobacterium tuberculosis

Publication: PMID: 26094805 Cell Host Microbe. 2015 Jul 8;18(1):96-108

Mycobacterium tuberculosis remains a health concern due to its ability to enter a non-replicative dormant state linked to drug resistance. Understanding transitions into and out of dormancy will inform therapeutic strategies. We implemented a universally applicable, label-free approach to estimate absolute cellular protein concentrations on a proteome-wide scale based on SWATH mass spectrometry. We applied this approach to examine proteomic reorganization of M. tuberculosis during exponential growth, hypoxia-induced dormancy, and resuscitation. The resulting data set covering >2,000 proteins reveals how protein biomass is distributed among cellular functions during these states. The stress-induced DosR regulon contributes 20% to cellular protein content during dormancy, whereas ribosomal proteins remain largely unchanged at 5%-7%. Absolute protein concentrations furthermore allow protein alterations to be translated into changes in maximal enzymatic reaction velocities, enhancing understanding of metabolic adaptations. Thus, global absolute protein measurements provide a quantitative description of microbial states, which can support the development of therapeutic interventions.

Raw proteomics data is available from PeptideAtlas located here

Integrated relative quantification dataset is available from PATRIC workspace located here

MTB Transcriptional Regulator Induction Phenotypic (TRIP) Screen

MTB Transcriptional Regulator Induction Phenotypic (TRIP) Screen that uses amplicon-based screening for probing phenotype of each TF induced strain in a variety of stress conditions, in this case Isoniazid treatment. This dataset reports the log2 abundance fold change of each TFI strain, relative to no induction, in absence or presence of drug, averaged across experimental replicates. Also reported are the accompanying z-scores for each TFI strain under each condition. Data for the individual replicates are provided in the second tab.

Integrated dataset is available from the PATRIC workspace located here