Dataset Information

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

ABSTRACT: 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.

SUBMITTER: Peterson EJ

PROVIDER: S-EPMC4191388 | biostudies-literature | 2014 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

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

Peterson Eliza J R EJ Reiss David J DJ Turkarslan Serdar S Minch Kyle J KJ Rustad Tige T Plaisier Christopher L CL Longabaugh William J R WJ Sherman David R DR Baliga Nitin S NS

Nucleic acids research 20140917 18

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 netwo ...[more]

PMID: 25232098

Similar Datasets

Project description:Recently, the diversity of the Mycobacterium tuberculosis complex (MTBC) population structure has been described in detail. Based on geographical separation and specific host pathogen co-evolution shaping MTBC virulence traits, at least 20 major lineages/genotypes have evolved finally leading to a clear influence of strain genetic background on transmissibility, clinical presentation/outcome, and resistance development. Therefore, high resolution genotyping for characterization of strains in larger studies is mandatory for understanding mechanisms of host-pathogen-interaction and to improve tuberculosis (TB) control. Single nucleotide polymorphisms (SNPs) represent the most reliable markers for lineage classification of clinical isolates due to the low levels of homoplasy, however their use is hampered either by low discriminatory power or by the need to analyze a large number of genes to achieve higher resolution. Therefore, we carried out de novo sequencing of 26 genes (approx. 20000 bp per strain) in a reference collection of MTBC strains including all major genotypes to define a highly discriminatory gene set. Overall, 161 polymorphisms were detected of which 59 are genotype-specific, while 13 define deeper branches such as the Euro-American lineage. Unbiased investigation of the most variable set of 11 genes in a population based strain collection (one year, city of Hamburg, Germany) confirmed the validity of SNP analysis as all strains were classified with high accuracy. Taken together, we defined a diagnostic algorithm which allows the identification of 17 MTBC phylogenetic lineages with high confidence for the first time by sequencing analysis of just five genes. In conclusion, the diagnostic algorithm developed in our study is likely to open the door for a low cost high resolution sequence/SNP based differentiation of the MTBC with a very high specificity. High throughput assays can be established which will be needed for large association studies that are mandatory for detailed investigation of host-pathogen-interaction during TB infection.

Dataset Information

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

Publications

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

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets