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

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.

Figure 1

Biochemical, genetic and genomic knowledgebase of Mtb - iOSDD890. A) Profile of metabolism in Mtb based on iOSDD890 reconstruction and its comparision with iNJ661; B) Number of new genes added to iNJ661; C) Number of new pathways and respective genes.

Figure 2

Metabolic visualization through Systems 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.

Figure 3

Essential genes in the metabolism of Mtb required for its growth and survival. A) Protein concentration of single gene knock-out lethal genes, (B) enzymatically low efficient genes and (C) metabolically low efficient genes with respect to mean abundance of complete Mtb proteome. Literature based qualitative assessment of (D) single gene knock-out essential genes, (E) enzymatically low efficient genes, (F) metabolically low efficient genes and (G) genes belonging to all the structural modules respectively (H) transcriptional control of genes mapped to various reaction topological modules on reaction-reaction graph of Mtb metabolism.

Figure 4

Metabolic Persister Genes (MPGs) in the metabolism of Mtb A) Bi-phasic killing and emergence of persisters upon drug exposure; B) Directional re-routing of metabolic fluxes resulting in the adaptation of the bacterium and the emergence of persiters; C) SBSM illustrating the metabolite and reaction connectivity to inhA, the target of Isoniazid; D) Loss of metabolic information in terms of metabolites, genes and reaction following inhA knock-out; E) Gain of function following inhA knock-out showing persister metabolites (PM), persister genes (MPGs) and persister reactions (PR); F-G) Gene expression status of 60 MPG’s on treatment with Isoniazid at 1 μg/ml for 2 hr and 6 hr in vivo H) Transcriptional control of 60 MPGs I) Expression status of nadA ~ E operon on treatment with Isoniazid at 1 μg/ml for 2 hr and 6 hr in vivo.

Figure 5

Metabolic mechanism of adaptation. A) Flux arrest in glycolysis, citric acid and mycolic acid pathway; B) Activation of NAD pathway de novo as computed based on directional re-routing of metabolic fluxes; C) Alternate mechanism of ATP generation based on proton motive force; Mechanism: The activation of nadA ~ E operon lead to de novo biosynthesis of NADH, pool which is then reduced to NAD following the activation of the nuoA ~ Eoperon coding for NDH-I. This maintains the electron flow with proton translocation, which increases the potential difference across cell membrane, and can potentiate ATP production, thereby providing the necessary energy when challenged with antibiotic stress.

Figure 6

Spectrum of metabolic persister genes in Mtb and pathway distribution of potential non-toxic drug targets. A) Spectrum of metabolic persister genes in Mtb. X-axis represents the total number of gene knockouts. Total number of MPG for respective knockout is shown above x-Axis and total number of affected genes is shown below x-axis (B) For both A and B the respective box-plots are shown with the region below median colored red representing genes of interest. C) Pathway level coverage of essential genes: All the essential genes and the metabolic persisters genes are plotted relative to total number of genes in a given pathway.