Integrated Modeling of Gene Regulatory and Metabolic Networks in Mycobacterium tuberculosis.

TitleIntegrated Modeling of Gene Regulatory and Metabolic Networks in Mycobacterium tuberculosis.
Publication TypeJournal Article
Year of Publication2015
AuthorsMa S, Minch KJ, Rustad TR, Hobbs S, Zhou S-L, Sherman DR, Price ND
JournalPLoS Comput Biol
Date Published2015 Nov
KeywordsGene Regulatory Networks, Genome, Bacterial, Metabolic Networks and Pathways, Models, Biological, Mycobacterium tuberculosis, Systems Biology

Mycobacterium tuberculosis (MTB) is the causative bacterium of tuberculosis, a disease responsible for over a million deaths worldwide annually with a growing number of strains resistant to antibiotics. The development of better therapeutics would greatly benefit from improved understanding of the mechanisms associated with MTB responses to different genetic and environmental perturbations. Therefore, we expanded a genome-scale regulatory-metabolic model for MTB using the Probabilistic Regulation of Metabolism (PROM) framework. Our model, MTBPROM2.0, represents a substantial knowledge base update and extension of simulation capability. We incorporated a recent ChIP-seq based binding network of 2555 interactions linking to 104 transcription factors (TFs) (representing a 3.5-fold expansion of TF coverage). We integrated this expanded regulatory network with a refined genome-scale metabolic model that can correctly predict growth viability over 69 source metabolite conditions and predict metabolic gene essentiality more accurately than the original model. We used MTBPROM2.0 to simulate the metabolic consequences of knocking out and overexpressing each of the 104 TFs in the model. MTBPROM2.0 improves performance of knockout growth defect predictions compared to the original PROM MTB model, and it can successfully predict growth defects associated with TF overexpression. Moreover, condition-specific models of MTBPROM2.0 successfully predicted synergistic growth consequences of overexpressing the TF whiB4 in the presence of two standard anti-TB drugs. MTBPROM2.0 can screen in silico condition-specific transcription factor perturbations to generate putative targets of interest that can help prioritize future experiments for therapeutic development efforts.

Alternate JournalPLoS Comput. Biol.
PubMed ID26618656
PubMed Central IDPMC4664399
Grant ListU19 AI106761 / AI / NIAID NIH HHS / United States
1U19AI106761 / AI / NIAID NIH HHS / United States
2P50GM076547 / GM / NIGMS NIH HHS / United States
N01-AI-15447 / AI / NIAID NIH HHS / United States