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

Ma S, Minch KJ, Rustad TR, Hobbs S, Zhou SL, Sherman DR, Price ND - PLoS Comput. Biol. (2015)

Bottom Line: The development of better therapeutics would greatly benefit from improved understanding of the mechanisms associated with MTB responses to different genetic and environmental perturbations.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Chemical and Biomolecular Engineering, University of Illinois, Urbana-Champaign, Illinois, United States of America.

ABSTRACT
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.

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Experimental overexpressed vs. not overexpressed doubling time ratios of TFs with high confidence MTBPROM2.0 predictions.(A) Doubling time ratios of all TFs predicted by MTBPROM 2.0. (B) Doubling time ratios of the high confidence TFs identified by the logistic regression model as likely to be correctly predicted by MTBPROM2.0. Doubling time ratios greater than 10 are shown truncated. The bars are color-coded red if MTBPROM2.0 simulation predicted a growth defect upon the overexpression of each TF, and blue if no defect was predicted. The dashed line indicates the growth defect cutoff threshold at the 85th percentile of doubling times.
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pcbi.1004543.g002: Experimental overexpressed vs. not overexpressed doubling time ratios of TFs with high confidence MTBPROM2.0 predictions.(A) Doubling time ratios of all TFs predicted by MTBPROM 2.0. (B) Doubling time ratios of the high confidence TFs identified by the logistic regression model as likely to be correctly predicted by MTBPROM2.0. Doubling time ratios greater than 10 are shown truncated. The bars are color-coded red if MTBPROM2.0 simulation predicted a growth defect upon the overexpression of each TF, and blue if no defect was predicted. The dashed line indicates the growth defect cutoff threshold at the 85th percentile of doubling times.

Mentions: To extend the predictive scope of the PROM framework, we modified the simulation to enable prediction of transcription factor overexpression growth phenotypes, using the same input gene expression dataset to train conditional probabilities (see Methods for details). As validation, we compared the MTBPROM2.0 predicted overexpression growth ratios to experimentally measured doubling time ratios of the TF overexpression strains with and without the induction of overexpression [26]. Fig 2, Panel A shows experimentally measured overexpressed vs. not overexpressed doubling time ratios of the TFs predicted by MTBPROM2.0, where a higher doubling time ratio indicates a greater growth defect upon TF overexpression. The bars are color-coded based on whether the MTBPROM2.0 simulation predicted a growth defect upon the overexpression of each TF. Using the 85th percentile (corresponding to a doubling time ratio of 3.3) as an experimental cutoff threshold to delineate growth defect vs. no defect, we evaluated the ability of MTBPROM2.0 to correctly distinguish between these groups. The overall MCC was 0.2, with precision = 0.23, and recall = 0.69.


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

Ma S, Minch KJ, Rustad TR, Hobbs S, Zhou SL, Sherman DR, Price ND - PLoS Comput. Biol. (2015)

Experimental overexpressed vs. not overexpressed doubling time ratios of TFs with high confidence MTBPROM2.0 predictions.(A) Doubling time ratios of all TFs predicted by MTBPROM 2.0. (B) Doubling time ratios of the high confidence TFs identified by the logistic regression model as likely to be correctly predicted by MTBPROM2.0. Doubling time ratios greater than 10 are shown truncated. The bars are color-coded red if MTBPROM2.0 simulation predicted a growth defect upon the overexpression of each TF, and blue if no defect was predicted. The dashed line indicates the growth defect cutoff threshold at the 85th percentile of doubling times.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4664399&req=5

pcbi.1004543.g002: Experimental overexpressed vs. not overexpressed doubling time ratios of TFs with high confidence MTBPROM2.0 predictions.(A) Doubling time ratios of all TFs predicted by MTBPROM 2.0. (B) Doubling time ratios of the high confidence TFs identified by the logistic regression model as likely to be correctly predicted by MTBPROM2.0. Doubling time ratios greater than 10 are shown truncated. The bars are color-coded red if MTBPROM2.0 simulation predicted a growth defect upon the overexpression of each TF, and blue if no defect was predicted. The dashed line indicates the growth defect cutoff threshold at the 85th percentile of doubling times.
Mentions: To extend the predictive scope of the PROM framework, we modified the simulation to enable prediction of transcription factor overexpression growth phenotypes, using the same input gene expression dataset to train conditional probabilities (see Methods for details). As validation, we compared the MTBPROM2.0 predicted overexpression growth ratios to experimentally measured doubling time ratios of the TF overexpression strains with and without the induction of overexpression [26]. Fig 2, Panel A shows experimentally measured overexpressed vs. not overexpressed doubling time ratios of the TFs predicted by MTBPROM2.0, where a higher doubling time ratio indicates a greater growth defect upon TF overexpression. The bars are color-coded based on whether the MTBPROM2.0 simulation predicted a growth defect upon the overexpression of each TF. Using the 85th percentile (corresponding to a doubling time ratio of 3.3) as an experimental cutoff threshold to delineate growth defect vs. no defect, we evaluated the ability of MTBPROM2.0 to correctly distinguish between these groups. The overall MCC was 0.2, with precision = 0.23, and recall = 0.69.

Bottom Line: The development of better therapeutics would greatly benefit from improved understanding of the mechanisms associated with MTB responses to different genetic and environmental perturbations.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Chemical and Biomolecular Engineering, University of Illinois, Urbana-Champaign, Illinois, United States of America.

ABSTRACT
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.

Show MeSH
Related in: MedlinePlus