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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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Representative time-course growth and metabolic activity of wild-type and whiB4-overexpression strains of MTB after treatment with drugs ethionamide (ETH) and isoniazid (INH).(A, C) The growth time-courses measured by OD600 of wild-type (blue) and whiB4-overexpressing MTB strains (red) without drug (pale, dashed lines) and post treatment with 3μM ETH (Panel A) and 2μM INH (Panel C). (B, D) Time-courses of metabolic activity measured by Alamar Blue reduction. Data represent mean ± standard deviation of three biological replicates.
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pcbi.1004543.g004: Representative time-course growth and metabolic activity of wild-type and whiB4-overexpression strains of MTB after treatment with drugs ethionamide (ETH) and isoniazid (INH).(A, C) The growth time-courses measured by OD600 of wild-type (blue) and whiB4-overexpressing MTB strains (red) without drug (pale, dashed lines) and post treatment with 3μM ETH (Panel A) and 2μM INH (Panel C). (B, D) Time-courses of metabolic activity measured by Alamar Blue reduction. Data represent mean ± standard deviation of three biological replicates.

Mentions: To experimentally validate, we tested the overexpression of the TF whiB4 (Rv3681c), which MTBPROM2.0 predicted to have a growth defect when exposed to each of four agents: ethionamide (ETH), isoniazid (INH), a coumarin analog (IMTB009), and a guanosine analog (IMTB0044). We compared the growth (by OD600) and metabolic activity (by Alamar Blue Assay [31]) of wild-type H37Rv with a strain overexpressing whiB4 in the presence of each drug (see Methods for details). TF overexpression did not alter sensitivity to IMTB009 or IMTB044 (data not shown), but did synergize with the inhibitory activity of ETH and INH. Fig 4 shows representative growth and metabolic activity time-course profiles of the wild-type strain and the strain overexpressing whiB4 from one of three experiments (each performed with three biological replicates). While no appreciable growth difference was detected between the two strains in the absence of ETH, dosing the strains with 3μM of ETH (approximately 0.5x the MIC) resulted in significantly more growth inhibition (3-fold lower OD600 at 14 days post drug, Fig 4, Panel A) and less metabolic activity (Fig 4, Panel B) in the strain overexpressing whiB4 compared to wild-type. Similarly, dosing the strains with 2μM of INH (approximately 0.6x the MIC) resulted in significantly more growth inhibition (2-fold lower OD600 at 14 days post drug, Fig 4, Panel C) and less metabolic activity (Fig 4, Panel D) in the strain overexpressing whiB4 compared to wild-type. In addition, we tested four drugs predicted by the model not to synergize with whiB4 (IMTB001, IMTB031, IMTB036, and IMTB041), and observed no differential growth upon exposure to these compounds (data not shown).


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)

Representative time-course growth and metabolic activity of wild-type and whiB4-overexpression strains of MTB after treatment with drugs ethionamide (ETH) and isoniazid (INH).(A, C) The growth time-courses measured by OD600 of wild-type (blue) and whiB4-overexpressing MTB strains (red) without drug (pale, dashed lines) and post treatment with 3μM ETH (Panel A) and 2μM INH (Panel C). (B, D) Time-courses of metabolic activity measured by Alamar Blue reduction. Data represent mean ± standard deviation of three biological replicates.
© Copyright Policy
Related In: Results  -  Collection

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

pcbi.1004543.g004: Representative time-course growth and metabolic activity of wild-type and whiB4-overexpression strains of MTB after treatment with drugs ethionamide (ETH) and isoniazid (INH).(A, C) The growth time-courses measured by OD600 of wild-type (blue) and whiB4-overexpressing MTB strains (red) without drug (pale, dashed lines) and post treatment with 3μM ETH (Panel A) and 2μM INH (Panel C). (B, D) Time-courses of metabolic activity measured by Alamar Blue reduction. Data represent mean ± standard deviation of three biological replicates.
Mentions: To experimentally validate, we tested the overexpression of the TF whiB4 (Rv3681c), which MTBPROM2.0 predicted to have a growth defect when exposed to each of four agents: ethionamide (ETH), isoniazid (INH), a coumarin analog (IMTB009), and a guanosine analog (IMTB0044). We compared the growth (by OD600) and metabolic activity (by Alamar Blue Assay [31]) of wild-type H37Rv with a strain overexpressing whiB4 in the presence of each drug (see Methods for details). TF overexpression did not alter sensitivity to IMTB009 or IMTB044 (data not shown), but did synergize with the inhibitory activity of ETH and INH. Fig 4 shows representative growth and metabolic activity time-course profiles of the wild-type strain and the strain overexpressing whiB4 from one of three experiments (each performed with three biological replicates). While no appreciable growth difference was detected between the two strains in the absence of ETH, dosing the strains with 3μM of ETH (approximately 0.5x the MIC) resulted in significantly more growth inhibition (3-fold lower OD600 at 14 days post drug, Fig 4, Panel A) and less metabolic activity (Fig 4, Panel B) in the strain overexpressing whiB4 compared to wild-type. Similarly, dosing the strains with 2μM of INH (approximately 0.6x the MIC) resulted in significantly more growth inhibition (2-fold lower OD600 at 14 days post drug, Fig 4, Panel C) and less metabolic activity (Fig 4, Panel D) in the strain overexpressing whiB4 compared to wild-type. In addition, we tested four drugs predicted by the model not to synergize with whiB4 (IMTB001, IMTB031, IMTB036, and IMTB041), and observed no differential growth upon exposure to these compounds (data not shown).

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