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Systems level mapping of metabolic complexity in Mycobacterium tuberculosis to identify high-value drug targets.

Vashisht R, Bhat AG, Kushwaha S, Bhardwaj A, OSDD ConsortiumBrahmachari SK - J Transl Med (2014)

Bottom Line: The reconstructed metabolism of Mtb encompasses 961 metabolites, involved in 1152 reactions catalyzed by 890 protein coding genes, organized into 50 pathways.Further, we formulate a novel concept of metabolic persister genes (MPGs) and compared our predictions with published in vitro and in vivo experimental evidence.Through such analyses, we report for the first time that de novo biosynthesis of NAD may give rise to bacterial persistence in Mtb under conditions of metabolic stress induced by conventional anti-tuberculosis therapy.

View Article: PubMed Central - PubMed

Affiliation: CSIR-Open Source Drug Discovery Unit, New Delhi, India. skb@igib.res.in.

ABSTRACT

Background: The effectiveness of current therapeutic regimens for Mycobacterium tuberculosis (Mtb) is diminished by the need for prolonged therapy and the rise of drug resistant/tolerant strains. This global health threat, despite decades of basic research and a wealth of legacy knowledge, is due to a lack of systems level understanding that can innovate the process of fast acting and high efficacy drug discovery.

Methods: The enhanced functional annotations of the Mtb genome, which were previously obtained through a crowd sourcing approach was used to reconstruct the metabolic network of Mtb in a bottom up manner. We represent this information by developing a novel Systems Biology Spindle Map of Metabolism (SBSM) and comprehend its static and dynamic structure using various computational approaches based on simulation and design.

Results: The reconstructed metabolism of Mtb encompasses 961 metabolites, involved in 1152 reactions catalyzed by 890 protein coding genes, organized into 50 pathways. By accounting for static and dynamic analysis of SBSM in Mtb we identified various critical proteins required for the growth and survival of bacteria. Further, we assessed the potential of these proteins as putative drug targets that are fast acting and less toxic. Further, we formulate a novel concept of metabolic persister genes (MPGs) and compared our predictions with published in vitro and in vivo experimental evidence. Through such analyses, we report for the first time that de novo biosynthesis of NAD may give rise to bacterial persistence in Mtb under conditions of metabolic stress induced by conventional anti-tuberculosis therapy. We propose such MPG's as potential combination of drug targets for existing antibiotics that can improve their efficacy and efficiency for drug tolerant bacteria.

Conclusion: The systems level framework formulated by us to identify potential non-toxic drug targets and strategies to circumvent the issue of bacterial persistence can substantially aid in the process of TB drug discovery and translational research.

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Related in: MedlinePlus

Metabolic visualization throughSystems Biology Spindle Map(SBSM).A) Conceptual formulation of SBSM representing the connection between exchange reactions, metabolites, genes and reactions; B) SBSM of Mtb representing its complete metabolic topology based upon the relationships between its biochemical, genomic and genetic information; C) Metabolite gene connectivity (metGene) distribution. Overall connectivity versus the connectivity of utilized metabolites in optimal metabolic physiology; D) Power law distribution of overall connectivity of metabolites to genes in SBSM; E) Power law distribution of active metabolite connectivity to genes in optimal SBSM F) Gene reaction (geneRxn) connectivity distribution. Overall connectivity versus the connectivity of utilized genes; G) Reaction gene (rxnGene) connectivity distribution. Overall connectivity versus the connectivity of active reactions H) optimal metabolic physiology obtained by optimizing for defined biomass function using Middlebrook media.
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Fig2: Metabolic visualization throughSystems Biology Spindle Map(SBSM).A) Conceptual formulation of SBSM representing the connection between exchange reactions, metabolites, genes and reactions; B) SBSM of Mtb representing its complete metabolic topology based upon the relationships between its biochemical, genomic and genetic information; C) Metabolite gene connectivity (metGene) distribution. Overall connectivity versus the connectivity of utilized metabolites in optimal metabolic physiology; D) Power law distribution of overall connectivity of metabolites to genes in SBSM; E) Power law distribution of active metabolite connectivity to genes in optimal SBSM F) Gene reaction (geneRxn) connectivity distribution. Overall connectivity versus the connectivity of utilized genes; G) Reaction gene (rxnGene) connectivity distribution. Overall connectivity versus the connectivity of active reactions H) optimal metabolic physiology obtained by optimizing for defined biomass function using Middlebrook media.

Mentions: To circumvent these challenges we developed a new method of metabolic visualization termed as Systems Biology Spindle Map (SBSM). As illustrated in (FigureĀ 2A), the components of SBSM include metabolites, genes and reactions that are arrayed with their respective pathways parallel to each other. The metabolites are classified into exchange and intracellular metabolites. The exchange metabolites are linked to intracellular metabolites by exchange reactions, representing all possible ways in which a given metabolite can be imported or exported, into or from the system.Figure 2


Systems level mapping of metabolic complexity in Mycobacterium tuberculosis to identify high-value drug targets.

Vashisht R, Bhat AG, Kushwaha S, Bhardwaj A, OSDD ConsortiumBrahmachari SK - J Transl Med (2014)

Metabolic visualization throughSystems Biology Spindle Map(SBSM).A) Conceptual formulation of SBSM representing the connection between exchange reactions, metabolites, genes and reactions; B) SBSM of Mtb representing its complete metabolic topology based upon the relationships between its biochemical, genomic and genetic information; C) Metabolite gene connectivity (metGene) distribution. Overall connectivity versus the connectivity of utilized metabolites in optimal metabolic physiology; D) Power law distribution of overall connectivity of metabolites to genes in SBSM; E) Power law distribution of active metabolite connectivity to genes in optimal SBSM F) Gene reaction (geneRxn) connectivity distribution. Overall connectivity versus the connectivity of utilized genes; G) Reaction gene (rxnGene) connectivity distribution. Overall connectivity versus the connectivity of active reactions H) optimal metabolic physiology obtained by optimizing for defined biomass function using Middlebrook media.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4201925&req=5

Fig2: Metabolic visualization throughSystems Biology Spindle Map(SBSM).A) Conceptual formulation of SBSM representing the connection between exchange reactions, metabolites, genes and reactions; B) SBSM of Mtb representing its complete metabolic topology based upon the relationships between its biochemical, genomic and genetic information; C) Metabolite gene connectivity (metGene) distribution. Overall connectivity versus the connectivity of utilized metabolites in optimal metabolic physiology; D) Power law distribution of overall connectivity of metabolites to genes in SBSM; E) Power law distribution of active metabolite connectivity to genes in optimal SBSM F) Gene reaction (geneRxn) connectivity distribution. Overall connectivity versus the connectivity of utilized genes; G) Reaction gene (rxnGene) connectivity distribution. Overall connectivity versus the connectivity of active reactions H) optimal metabolic physiology obtained by optimizing for defined biomass function using Middlebrook media.
Mentions: To circumvent these challenges we developed a new method of metabolic visualization termed as Systems Biology Spindle Map (SBSM). As illustrated in (FigureĀ 2A), the components of SBSM include metabolites, genes and reactions that are arrayed with their respective pathways parallel to each other. The metabolites are classified into exchange and intracellular metabolites. The exchange metabolites are linked to intracellular metabolites by exchange reactions, representing all possible ways in which a given metabolite can be imported or exported, into or from the system.Figure 2

Bottom Line: The reconstructed metabolism of Mtb encompasses 961 metabolites, involved in 1152 reactions catalyzed by 890 protein coding genes, organized into 50 pathways.Further, we formulate a novel concept of metabolic persister genes (MPGs) and compared our predictions with published in vitro and in vivo experimental evidence.Through such analyses, we report for the first time that de novo biosynthesis of NAD may give rise to bacterial persistence in Mtb under conditions of metabolic stress induced by conventional anti-tuberculosis therapy.

View Article: PubMed Central - PubMed

Affiliation: CSIR-Open Source Drug Discovery Unit, New Delhi, India. skb@igib.res.in.

ABSTRACT

Background: The effectiveness of current therapeutic regimens for Mycobacterium tuberculosis (Mtb) is diminished by the need for prolonged therapy and the rise of drug resistant/tolerant strains. This global health threat, despite decades of basic research and a wealth of legacy knowledge, is due to a lack of systems level understanding that can innovate the process of fast acting and high efficacy drug discovery.

Methods: The enhanced functional annotations of the Mtb genome, which were previously obtained through a crowd sourcing approach was used to reconstruct the metabolic network of Mtb in a bottom up manner. We represent this information by developing a novel Systems Biology Spindle Map of Metabolism (SBSM) and comprehend its static and dynamic structure using various computational approaches based on simulation and design.

Results: The reconstructed metabolism of Mtb encompasses 961 metabolites, involved in 1152 reactions catalyzed by 890 protein coding genes, organized into 50 pathways. By accounting for static and dynamic analysis of SBSM in Mtb we identified various critical proteins required for the growth and survival of bacteria. Further, we assessed the potential of these proteins as putative drug targets that are fast acting and less toxic. Further, we formulate a novel concept of metabolic persister genes (MPGs) and compared our predictions with published in vitro and in vivo experimental evidence. Through such analyses, we report for the first time that de novo biosynthesis of NAD may give rise to bacterial persistence in Mtb under conditions of metabolic stress induced by conventional anti-tuberculosis therapy. We propose such MPG's as potential combination of drug targets for existing antibiotics that can improve their efficacy and efficiency for drug tolerant bacteria.

Conclusion: The systems level framework formulated by us to identify potential non-toxic drug targets and strategies to circumvent the issue of bacterial persistence can substantially aid in the process of TB drug discovery and translational research.

Show MeSH
Related in: MedlinePlus