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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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TF overexpression growth defect prediction performance.Performance of MTBPROM2.0 at predicting TF overexpression growth defects compared to two alternative methods: (1) iMAT and (2) whether the overexpressing TF repressed any essential metabolic genes. Performance is quantified by the MCC.
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pcbi.1004543.g003: TF overexpression growth defect prediction performance.Performance of MTBPROM2.0 at predicting TF overexpression growth defects compared to two alternative methods: (1) iMAT and (2) whether the overexpressing TF repressed any essential metabolic genes. Performance is quantified by the MCC.

Mentions: Fig 2, Panel B shows the MTBPROM2.0 predictions of overexpression growth defect for the 46 TFs with high confidence scores based on the logistic regression model. Using the same 85th percentile cutoff threshold for growth defect as for evaluating all the TFs, MTBPROM2.0 predicted growth defect with a MCC of 0.47 (precision = 0.45, recall = 0.71, p < 0.01 Fisher’s exact test) (see Fig 3). MTBPROM2.0 achieved an improved performance compared to predicting growth defect based on the whether the TF significantly repressed essential metabolic genes (MCC = 0.31, precision = 0.26, recall = 0.88) or based on condition-specific metabolic models generated by overlaying overexpression microarray data with iMAT [9, 10, 28] (MCC = 0.24, precision = 0.31, recall = 0.50) (see Methods for details on these alternative prediction strategies).


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)

TF overexpression growth defect prediction performance.Performance of MTBPROM2.0 at predicting TF overexpression growth defects compared to two alternative methods: (1) iMAT and (2) whether the overexpressing TF repressed any essential metabolic genes. Performance is quantified by the MCC.
© Copyright Policy
Related In: Results  -  Collection

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

pcbi.1004543.g003: TF overexpression growth defect prediction performance.Performance of MTBPROM2.0 at predicting TF overexpression growth defects compared to two alternative methods: (1) iMAT and (2) whether the overexpressing TF repressed any essential metabolic genes. Performance is quantified by the MCC.
Mentions: Fig 2, Panel B shows the MTBPROM2.0 predictions of overexpression growth defect for the 46 TFs with high confidence scores based on the logistic regression model. Using the same 85th percentile cutoff threshold for growth defect as for evaluating all the TFs, MTBPROM2.0 predicted growth defect with a MCC of 0.47 (precision = 0.45, recall = 0.71, p < 0.01 Fisher’s exact test) (see Fig 3). MTBPROM2.0 achieved an improved performance compared to predicting growth defect based on the whether the TF significantly repressed essential metabolic genes (MCC = 0.31, precision = 0.26, recall = 0.88) or based on condition-specific metabolic models generated by overlaying overexpression microarray data with iMAT [9, 10, 28] (MCC = 0.24, precision = 0.31, recall = 0.50) (see Methods for details on these alternative prediction strategies).

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