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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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Comparison of the regulatory-metabolic model attributes.The features are compared between the initial regulatory-metabolic model constructed for MTB described in [16] (MTBPROM1.0) and the updated model (MTBPROM2.0). MTBPROM2.0 contains additional coverage of regulation and metabolism, with improved prediction of essential metabolic genes and growth viability in carbon and nitrogen sources, as quantified by the Matthews Correlation Coefficient (MCC, see Methods). The PROM simulation framework has also been extended to predict TF overexpression in addition to knockout phenotypes.
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pcbi.1004543.g001: Comparison of the regulatory-metabolic model attributes.The features are compared between the initial regulatory-metabolic model constructed for MTB described in [16] (MTBPROM1.0) and the updated model (MTBPROM2.0). MTBPROM2.0 contains additional coverage of regulation and metabolism, with improved prediction of essential metabolic genes and growth viability in carbon and nitrogen sources, as quantified by the Matthews Correlation Coefficient (MCC, see Methods). The PROM simulation framework has also been extended to predict TF overexpression in addition to knockout phenotypes.

Mentions: We have reconstructed an updated regulatory-metabolic model for MTB using the PROM framework. The updated model, MTBPROM2.0, incorporates a knowledge expansion in both the metabolic and regulatory components. Fig 1 summarizes the updated information incorporated into the model (additional information is provided in Table 1, S1 Text, and S1 Table).


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)

Comparison of the regulatory-metabolic model attributes.The features are compared between the initial regulatory-metabolic model constructed for MTB described in [16] (MTBPROM1.0) and the updated model (MTBPROM2.0). MTBPROM2.0 contains additional coverage of regulation and metabolism, with improved prediction of essential metabolic genes and growth viability in carbon and nitrogen sources, as quantified by the Matthews Correlation Coefficient (MCC, see Methods). The PROM simulation framework has also been extended to predict TF overexpression in addition to knockout phenotypes.
© Copyright Policy
Related In: Results  -  Collection

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

pcbi.1004543.g001: Comparison of the regulatory-metabolic model attributes.The features are compared between the initial regulatory-metabolic model constructed for MTB described in [16] (MTBPROM1.0) and the updated model (MTBPROM2.0). MTBPROM2.0 contains additional coverage of regulation and metabolism, with improved prediction of essential metabolic genes and growth viability in carbon and nitrogen sources, as quantified by the Matthews Correlation Coefficient (MCC, see Methods). The PROM simulation framework has also been extended to predict TF overexpression in addition to knockout phenotypes.
Mentions: We have reconstructed an updated regulatory-metabolic model for MTB using the PROM framework. The updated model, MTBPROM2.0, incorporates a knowledge expansion in both the metabolic and regulatory components. Fig 1 summarizes the updated information incorporated into the model (additional information is provided in Table 1, S1 Text, and S1 Table).

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