Flux balance analysis of mycolic acid pathway: targets for anti-tubercular drugs

Exploring the metabolic profile of A. baumannii for antimicrobial development using genome-scale modeling

With the emergence of multidrug-resistant bacteria, the World Health Organization published a catalog of microorganisms urgently needing new antibiotics, with the carbapenem-resistant Acinetobacter baumannii designated as "critical". Such isolates, frequently detected in healthcare settings, pose a global pandemic threat. One way to facilitate a systemic view of bacterial metabolism and allow the development of new therapeutics is to apply constraint-based modeling. Here, we developed a versatile workflow to build high-quality and simulation-ready genome-scale metabolic models. We applied our workflow to create a metabolic model for A. baumannii and validated its predictive capabilities using experimental nutrient utilization and gene essentiality data. Our analysis showed that our model iACB23LX could recapitulate cellular metabolic phenotypes observed during in vitro experiments, while positive biomass production rates were observed and experimentally validated in various growth media. We further defined a minimal set of compounds that increase A. baumannii's cellular biomass and identified putative essential genes with no human counterparts, offering new candidates for future antimicrobial development. Finally, we assembled and curated the first collection of metabolic reconstructions for distinct A. baumannii strains and analyzed their growth characteristics. The presented models are in a standardized and well-curated format, enhancing their usability for multi-strain network reconstruction.

Micro-nanoemulsion and nanoparticle-assisted drug delivery against drug-resistant tuberculosis: recent developments

Tuberculosis (TB) is a major global health problem and the second most prevalent infectious killer after COVID-19. It is caused by Mycobacterium tuberculosis (Mtb) and has become increasingly challenging to treat due to drug resistance. The World Health Organization declared TB a global health emergency in 1993. Drug resistance in TB is driven by mutations in the bacterial genome that can be influenced by prolonged drug exposure and poor patient adherence. The development of drug-resistant forms of TB, such as multidrug resistant, extensively drug resistant, and totally drug resistant, poses significant therapeutic challenges. Researchers are exploring new drugs and novel drug delivery systems, such as nanotechnology-based therapies, to combat drug resistance. Nanodrug delivery offers targeted and precise drug delivery, improves treatment efficacy, and reduces adverse effects. Along with nanoscale drug delivery, a new generation of antibiotics with potent therapeutic efficacy, drug repurposing, and new treatment regimens (combinations) that can tackle the problem of drug resistance in a shorter duration could be promising therapies in clinical settings. However, the clinical translation of nanomedicines faces challenges such as safety, large-scale production, regulatory frameworks, and intellectual property issues. In this review, we present the current status, most recent findings, challenges, and limiting barriers to the use of emulsions and nanoparticles against drug-resistant TB.

A systems level approach to study metabolic networks in prokaryotes with the aromatic amino acid biosynthesis pathway

Metabolism of an organism underlies its phenotype, which depends on many factors, such as the genetic makeup, habitat, and stresses to which it is exposed. This is particularly important for the prokaryotes, which undergo significant vertical and horizontal gene transfers. In this study we have used the energy-intensive Aromatic Amino Acid (Tryptophan, Tyrosine and Phenylalanine, TTP) biosynthesis pathway, in a large number of prokaryotes, as a model system to query the different levels of organization of metabolism in the whole intracellular biochemical network, and to understand how perturbations, such as mutations, affects the metabolic flux through the pathway - in isolation and in the context of other pathways connected to it. Using an agglomerative approach involving complex network analysis and Flux Balance Analyses (FBA), of the Tryptophan, Tyrosine and Phenylalanine and other pathways connected to it, we identify several novel results. Using the reaction network analysis and Flux Balance Analyses of the Tryptophan, Tyrosine and Phenylalanine and the genome-scale reconstructed metabolic pathways, many common hubs between the connected networks and the whole genome network are identified. The results show that the connected pathway network can act as a proxy for the whole genome network in Prokaryotes. This systems level analysis also points towards designing functional smaller synthetic pathways based on the reaction network and Flux Balance Analyses analysis.

Elucidating the Antimycobacterial Mechanism of Action of Ciprofloxacin Using Metabolomics

In the interest of developing more effective and safer anti-tuberculosis drugs, we used a GCxGC-TOF-MS metabolomics research approach to investigate and compare the metabolic profiles of Mtb in the presence and absence of ciprofloxacin. The metabolites that best describe the differences between the compared groups were identified as markers characterizing the changes induced by ciprofloxacin. Malic acid was ranked as the most significantly altered metabolite marker induced by ciprofloxacin, indicative of an inhibition of the tricarboxylic acid (TCA) and glyoxylate cycle of Mtb. The altered fatty acid, myo-inositol, and triacylglycerol metabolism seen in this group supports previous observations of ciprofloxacin action on the Mtb cell wall. Furthermore, the altered pentose phosphate intermediates, glycerol metabolism markers, glucose accumulation, as well as the reduction in the glucogenic amino acids specifically, indicate a flux toward DNA (as well as cell wall) repair, also supporting previous findings of DNA damage caused by ciprofloxacin. This study further provides insights useful for designing network whole-system strategies for the identification of possible modes of action of various drugs and possibly adaptations by Mtb resulting in resistance.

An insight to flux-balance analysis for biochemical networks

Systems biology is one of the integrated ways to study biological systems and is more favourable than the earlier used approaches. It includes metabolic pathway analysis, modelling, and regulatory as well as signal transduction for getting insights into cellular behaviour. Among the various techniques of modelling, simulation, analysis of networks and pathways, flux-based analysis (FBA) has been recognised because of its extensibility as well as simplicity. It is widely accepted because it is not like a mechanistic simulation which depends on accurate kinetic data. The study of fluxes through the network is informative and can give insights even in the absence of kinetic data. FBA is one of the widely used tools to study biochemical networks and needs information of reaction stoichiometry, growth requirements, specific measurement parameters of the biological system, in particular the reconstruction of the metabolic network for the genome-scale, many of which have already been built previously. It defines the boundaries of flux distributions which are possible and achievable with a defined set of genes. This review article gives an insight into FBA, from the extension of flux balancing to mathematical representation followed by a discussion about the formulation of flux-balance analysis problems, defining constraints for the stoichiometry of the pathways and the tools that can be used in FBA such as FASIMA, COBRA toolbox, and OptFlux. It also includes broader areas in terms of applications which can be covered by FBA as well as the queries which can be addressed through FBA.

Genetic Diversity in Mycobacterium tuberculosis Clinical Isolates and Resulting Outcomes of Tuberculosis Infection and Disease

Tuberculosis claims more human lives than any other bacterial infectious disease and represents a clear and present danger to global health as new tools for vaccination, treatment, and interruption of transmission have been slow to emerge. Additionally, tuberculosis presents with notable clinical heterogeneity, which complicates diagnosis, treatment, and the establishment of nonrelapsing cure. How this heterogeneity is driven by the diversity ofclinical isolates of the causative agent, Mycobacterium tuberculosis, has recently garnered attention. Herein, we review advances in the understanding of how naturally occurring variation in clinical isolates affects transmissibility, pathogenesis, immune modulation, and drug resistance. We also summarize how specific changes in transcriptional responses can modulate infection or disease outcome, together with strain-specific effects on gene essentiality. Further understanding of how this diversity of M. tuberculosis isolates affects disease and treatment outcomes will enable the development of more effective therapeutic options and vaccines for this dreaded disease.

Strategic Moves of "Superbugs" Against Available Chemical Scaffolds: Signaling, Regulation, and Challenges

Superbugs' resistivity against available natural products has become an alarming global threat, causing a rapid deterioration in public health and claiming tens of thousands of lives yearly. Although the rapid discovery of small molecules from plant and microbial origin with enhanced bioactivity has provided us with some hope, a rapid hike in the resistivity of superbugs has proven to be the biggest therapeutic hurdle of all times. Moreover, several distinct mechanisms endowed by these notorious superbugs make them immune to these antibiotics subsequently causing our antibiotic wardrobe to be obsolete. In this unfortunate situation, though the time frame for discovering novel "hit molecules" down the line remains largely unknown, our small hope and untiring efforts injected in hunting novel chemical scaffolds with unique molecular targets using high-throughput technologies may safeguard us against these life-threatening challenges to some extent. Amid this crisis, the current comprehensive review highlights the present status of knowledge, our search for bacteria Achilles' heel, distinct molecular signaling that an opportunistic pathogen bestows to trespass the toxicity of antibiotics, and facile strategies and appealing therapeutic targets of novel drugs. Herein, we also discuss multidimensional strategies to combat antimicrobial resistance.

A systematic evaluation of Mycobacterium tuberculosis Genome-Scale Metabolic Networks

Metabolism underpins the pathogenic strategy of the causative agent of TB, Mycobacterium tuberculosis (Mtb), and therefore metabolic pathways have recently re-emerged as attractive drug targets. A powerful approach to study Mtb metabolism as a whole, rather than just individual enzymatic components, is to use a systems biology framework, such as a Genome-Scale Metabolic Network (GSMN) that allows the dynamic interactions of all the components of metabolism to be interrogated together. Several GSMNs networks have been constructed for Mtb and used to study the complex relationship between the Mtb genotype and its phenotype. However, the utility of this approach is hampered by the existence of multiple models, each with varying properties and performances. Here we systematically evaluate eight recently published metabolic models of Mtb-H37Rv to facilitate model choice. The best performing models, sMtb2018 and iEK1011, were refined and improved for use in future studies by the TB research community.

MtrP, a putative methyltransferase in Corynebacteria, is required for optimal membrane transport of trehalose mycolates

Pathogenic bacteria of the genera Mycobacterium and Corynebacterium cause severe human diseases such as tuberculosis (Mycobacterium tuberculosis) and diphtheria (Corynebacterium diphtheriae). The cells of these species are surrounded by protective cell walls rich in long-chain mycolic acids. These fatty acids are conjugated to the disaccharide trehalose on the cytoplasmic side of the bacterial cell membrane. They are then transported across the membrane to the periplasm where they act as donors for other reactions. We have previously shown that transient acetylation of the glycolipid trehalose monohydroxycorynomycolate (hTMCM) enables its efficient transport to the periplasm in Corynebacterium glutamicum and that acetylation is mediated by the membrane protein TmaT. Here, we show that a putative methyltransferase, encoded at the same genetic locus as TmaT, is also required for optimal hTMCM transport. Deletion of the C. glutamicum gene NCgl2764 (Rv0224c in M. tuberculosis) abolished acetyltrehalose monocorynomycolate (AcTMCM) synthesis, leading to accumulation of hTMCM in the inner membrane and delaying its conversion to trehalose dihydroxycorynomycolate (h2TDCM). Complementation with NCgl2764 normalized turnover of hTMCM to h2TDCM. In contrast, complementation with NCgl2764 derivatives mutated at residues essential for methyltransferase activity failed to rectify the defect, suggesting that NCgl2764/Rv0224c encodes a methyltransferase, designated here as MtrP. Comprehensive analyses of the individual mtrP and tmaT mutants and of a double mutant revealed strikingly similar changes across several lipid classes compared with WT bacteria. These findings indicate that both MtrP and TmaT have nonredundant roles in regulating AcTMCM synthesis, revealing additional complexity in the regulation of trehalose mycolate transport in the Corynebacterineae.

Kinase Targets for Mycolic Acid Biosynthesis in Mycobacterium tuberculosis

BACKGROUND

Mycolic acids (MAs) are the characteristic, integral building blocks for the mycomembrane belonging to the insidious bacterial pathogen Mycobacterium tuberculosis (M.tb). These C60-C90 long α-alkyl-β-hydroxylated fatty acids provide protection to the tubercle bacilli against the outside threats, thus allowing its survival, virulence and resistance to the current antibacterial agents. In the post-genomic era, progress has been made towards understanding the crucial enzymatic machineries involved in the biosynthesis of MAs in M.tb. However, gaps still remain in the exact role of the phosphorylation and dephosphorylation of regulatory mechanisms within these systems. To date, a total of 11 serine-threonine protein kinases (STPKs) are found in M.tb. Most enzymes implicated in the MAs synthesis were found to be phosphorylated in vitro and/or in vivo. For instance, phosphorylation of KasA, KasB, mtFabH, InhA, MabA, and FadD32 downregulated their enzymatic activity, while phosphorylation of VirS increased its enzymatic activity. These observations suggest that the kinases and phosphatases system could play a role in M.tb adaptive responses and survival mechanisms in the human host. As the mycobacterial STPKs do not share a high sequence homology to the human's, there have been some early drug discovery efforts towards developing potent and selective inhibitors.

OBJECTIVE

Recent updates to the kinases and phosphatases involved in the regulation of MAs biosynthesis will be presented in this mini-review, including their known small molecule inhibitors.

CONCLUSION

Mycobacterial kinases and phosphatases involved in the MAs regulation may serve as a useful avenue for antitubercular therapy.

Meta-Omics- and Metabolic Modeling-Assisted Deciphering of Human Microbiota Metabolism

The human microbiota is a complex community of commensal, symbiotic, and pathogenic microbes that play a crucial role in maintaining the homeostasis of human health. Such a homeostasis is maintained through the collective functioning of enzymatic genes responsible for the production of metabolites, enabling the interaction and signaling within microbiota as well as between microbes and the human host. Understanding microbial genes, their associated chemistries and functions would be valuable for engineering systemic metabolic pathways within the microbiota to manage human health and diseases. Given that there are many unknown gene metabolic functions and interactions, increasing efforts have been made to gain insights into the underlying functions of microbiota metabolism. This can be achieved through culture-independent metagenomic approaches and metabolic modeling to simulate the microenvironment of human microbiota. In this article, the recent advances in metagenome mining and functional profiling for the discovery of the genetic and biochemical links in human microbiota metabolism as well as metabolic modeling for simulation and prediction of metabolic fluxes in the human microbiota are reviewed. This review provides useful insights into the understanding, reconstruction, and modulation of the human microbiota guided by the knowledge acquired from the basic understanding of the human microbiota metabolism.

Microenvironment of Mycobacterium smegmatis Culture to Induce Cholesterol Consumption Does Cell Wall Remodeling and Enables the Formation of Granuloma-Like Structures

Pathogenic species of mycobacteria are known to use the host cholesterol during lung infection as an alternative source of carbon and energy. Mycobacteria culture in minimal medium (MM) has been used as an in vitro experimental model to study the consumption of exogenous cholesterol. Once in MM, different species of mycobacteria start to consume the cholesterol and initiate transcriptional and metabolic adaptations, upregulating the enzymes of the methylcitrate cycle (MCC) and accumulating a variety of primary metabolites that are known to be important substrates for cell wall biosynthesis. We hypothesized that stressful pressure of cultures in MM is able to induce critical adaptation for the bacteria which win the infection. To identify important modifications in the biosynthesis of the cell wall, we cultured the fast-growing and nonpathogenic Mycobacterium smegmatis in MM supplemented with or without glycerol and/or cholesterol. Different from the culture in complete medium Middlebrook 7H9 broth, the bacteria when cultured in MM decreased growth and changed in the accumulation of cell wall molecules. However, the supplementation of MM with glycerol and/or cholesterol recovered the accumulation of phosphatidylinositol mannosides (PIMs) and other phospholipids but maintained growth deceleration. The biosynthesis of lipomannan (LM) and of lipoarabinomannan (LAM) was significantly modulated after culture in MM, independently of glycerol and/or cholesterol supplementation, where LM size was decreased (LM_13-25KDa) and LAM increased (LAM_37-100KDa), when compared these molecules after bacteria culture in complete medium (LM_17-25KDa and LAM_37-50KDa). These changes modified the cell surface hydrophobicity and susceptibility against H₂O₂. The infection of J774 macrophages with M. smegmatis, after culture in MM, induced the formation of granuloma-like structures, while supplementation with cholesterol induced the highest rate of formation of these structures. Taken together, our results identify critical changes in mycobacterial cell wall molecules after culture in MM that induces cholesterol accumulation, helping the mycobacteria to increase their capacity to form granuloma-like structures.

System pharmacogenomics application in infectious diseases

The new era in systems pharmacology has revolutionized the human biology. Its applicability, precise treatment, adequate response and safety measures fit into all the paradigm of medical/clinical practice. The importance of mathematical models in understanding the disease pathology and epideomology is now being realized. The advent of high-throughput technologies and the emergence of systems biology have resulted in the creation of systems pharmacogenomics and the focus is now on personalized medicine. However, there are some regulatory issues that need to be addresssed; are we ready for this universal adoption? This article details some of the infectious disease pharmacogenomics to the developments in this area.

Computational Prediction of Synthetic Lethals in Genome-Scale Metabolic Models Using Fast-SL

In this chapter, we describe Fast-SL, an in silico approach to predict synthetic lethals in genome-scale metabolic models. Synthetic lethals are sets of genes or reactions where only the simultaneous removal of all genes or reactions in the set abolishes growth of an organism. In silico approaches to predict synthetic lethals are based on Flux Balance Analysis (FBA), a popular constraint-based analysis method based on linear programming. FBA has been shown to accurately predict the viability of various genome-scale metabolic models. Fast-SL builds on the framework of FBA and enables the prediction of synthetic lethal reactions or genes in different organisms, under various environmental conditions. Predicting synthetic lethals in metabolic network models allows us to generate hypotheses on possible novel genetic interactions and potential candidates for combinatorial therapy, in case of pathogenic organisms. We here summarize the Fast-SL approach for analyzing metabolic networks and detail the procedure to predict synthetic lethals in any given metabolic model. We illustrate the approach by predicting synthetic lethals in Escherichia coli. The Fast-SL implementation for MATLAB is available from https://github.com/RamanLab/FastSL/ .

In Silico Analysis of the Small Molecule Content of Outer Membrane Vesicles Produced by Bacteroides thetaiotaomicron Indicates an Extensive Metabolic Link between Microbe and Host

The interactions between the gut microbiota and its host are of central importance to the health of the host. Outer membrane vesicles (OMVs) are produced ubiquitously by Gram-negative bacteria including the gut commensal Bacteroides thetaiotaomicron. These vesicles can interact with the host in various ways but until now their complement of small molecules has not been investigated in this context. Using an untargeted high-coverage metabolomic approach we have measured the small molecule content of these vesicles in contrasting in vitro conditions to establish what role these metabolites could perform when packed into these vesicles. B. thetaiotaomicron packs OMVs with a highly conserved core set of small molecules which are strikingly enriched with mouse-digestible metabolites and with metabolites previously shown to be associated with colonization of the murine GIT. By use of an expanded genome-scale metabolic model of B. thetaiotaomicron and a potential host (the mouse) we have established many possible metabolic pathways between the two organisms that were previously unknown, and have found several putative novel metabolic functions for mouse that are supported by gene annotations, but that do not currently appear in existing mouse metabolic networks. The lipidome of these OMVs bears no relation to the mouse lipidome, so the purpose of this particular composition of lipids remains unclear. We conclude from this analysis that through intimate symbiotic evolution OMVs produced by B. thetaiotaomicron are likely to have been adopted as a conduit for small molecules bound for the mammalian host in vivo.

Constraint-based modeling identifies new putative targets to fight colistin-resistant A. baumannii infections

Acinetobacter baumannii is a clinical threat to human health, causing major infection outbreaks worldwide. As new drugs against Gram-negative bacteria do not seem to be forthcoming, and due to the microbial capability of acquiring multi-resistance, there is an urgent need for novel therapeutic targets. Here we have derived a list of new potential targets by means of metabolic reconstruction and modelling of A. baumannii ATCC 19606. By integrating constraint-based modelling with gene expression data, we simulated microbial growth in normal and stressful conditions (i.e. following antibiotic exposure). This allowed us to describe the metabolic reprogramming that occurs in this bacterium when treated with colistin (the currently adopted last-line treatment) and identify a set of genes that are primary targets for developing new drugs against A. baumannii, including colistin-resistant strains. It can be anticipated that the metabolic model presented herein will represent a solid and reliable resource for the future treatment of A. baumannii infections.

Stoichiometric Representation of Gene-Protein-Reaction Associations Leverages Constraint-Based Analysis from Reaction to Gene-Level Phenotype Prediction

Genome-scale metabolic reconstructions are currently available for hundreds of organisms. Constraint-based modeling enables the analysis of the phenotypic landscape of these organisms, predicting the response to genetic and environmental perturbations. However, since constraint-based models can only describe the metabolic phenotype at the reaction level, understanding the mechanistic link between genotype and phenotype is still hampered by the complexity of gene-protein-reaction associations. We implement a model transformation that enables constraint-based methods to be applied at the gene level by explicitly accounting for the individual fluxes of enzymes (and subunits) encoded by each gene. We show how this can be applied to different kinds of constraint-based analysis: flux distribution prediction, gene essentiality analysis, random flux sampling, elementary mode analysis, transcriptomics data integration, and rational strain design. In each case we demonstrate how this approach can lead to improved phenotype predictions and a deeper understanding of the genotype-to-phenotype link. In particular, we show that a large fraction of reaction-based designs obtained by current strain design methods are not actually feasible, and show how our approach allows using the same methods to obtain feasible gene-based designs. We also show, by extensive comparison with experimental 13C-flux data, how simple reformulations of different simulation methods with gene-wise objective functions result in improved prediction accuracy. The model transformation proposed in this work enables existing constraint-based methods to be used at the gene level without modification. This automatically leverages phenotype analysis from reaction to gene level, improving the biological insight that can be obtained from genome-scale models.

Improved Metabolic Models for E. coli and Mycoplasma genitalium from GlobalFit, an Algorithm That Simultaneously Matches Growth and Non-Growth Data Sets

Constraint-based metabolic modeling methods such as Flux Balance Analysis (FBA) are routinely used to predict the effects of genetic changes and to design strains with desired metabolic properties. The major bottleneck in modeling genome-scale metabolic systems is the establishment and manual curation of reliable stoichiometric models. Initial reconstructions are typically refined through comparisons to experimental growth data from gene knockouts or nutrient environments. Existing methods iteratively correct one erroneous model prediction at a time, resulting in accumulating network changes that are often not globally optimal. We present GlobalFit, a bi-level optimization method that finds a globally optimal network, by identifying the minimal set of network changes needed to correctly predict all experimentally observed growth and non-growth cases simultaneously. When applied to the genome-scale metabolic model of Mycoplasma genitalium, GlobalFit decreases unexplained gene knockout phenotypes by 79%, increasing accuracy from 87.3% (according to the current state-of-the-art) to 97.3%. While currently available computers do not allow a global optimization of the much larger metabolic network of E. coli, the main strengths of GlobalFit are already played out when considering only one growth and one non-growth case simultaneously. Application of a corresponding strategy halves the number of unexplained cases for the already highly curated E. coli model, increasing accuracy from 90.8% to 95.4%.

Mathematical models that aim to describe the complete metabolism of a cell help us understand cellular metabolic capabilities and evolution, and aid the biotechnological design of microbial strains with desired properties. Draft models are frequently improved through adjustments that increase the agreement of growth/non-growth predictions with observations from gene knockout experiments. Automated methods for this task typically correct one erroneous prediction after the other. We present GlobalFit, a novel method that can consider all experiments and all possible changes simultaneously to identify model modifications that are globally optimal (i.e., that correct the largest possible number of wrong predictions while introducing sets of changes that are most compatible with existing knowledge). This becomes computationally very hard when considering large metabolic models; however, a reduced application of GlobalFit that only looks at small subsets of experiments simultaneously works very well in practice. Allowing only changes that are conservative (e.g., introducing new reactions only if supported by significant genomic evidence), GlobalFit halves the number of wrong growth/non-growth predictions for the state-of-the-art metabolic models of E. coli and Mycoplasma genitalium, increasing prediction accuracy to 95.4% and 93.0%, respectively. By additionally allowing less conservative changes, we are able to improve accuracy further to 97.3% for the M. genitalium model.

A Mutation in the 16S rRNA Decoding Region Attenuates the Virulence of Mycobacterium tuberculosis

Mycobacterium tuberculosis contains a single rRNA operon that encodes targets for antituberculosis agents, including kanamycin. To date, only four mutations in the kanamycin binding sites of 16S rRNA have been reported in kanamycin-resistant clinical isolates. We hypothesized that another mutation(s) in the region may dramatically decrease M. tuberculosis viability and virulence. Here, we describe an rRNA mutation, U1406A, which was generated in vitro and confers resistance to kanamycin while highly attenuating M. tuberculosis virulence. The mutant showed decreased expression of 20% (n = 361) of mycobacterial proteins, including central metabolic enzymes, mycolic acid biosynthesis enzymes, and virulence factors such as antigen 85 complexes and ESAT-6. The mutation also induced three proteins, including KsgA (Rv1010; 16S rRNA adenine dimethyltransferase), which closely bind to the U1406A mutation site on the ribosome; these proteins were associated with ribosome maturation and translation initiation processes. The mutant showed an increase in 17S rRNA (precursor 16S rRNA) and a decrease in the ratio of 30S subunits to the 70S ribosomes, suggesting that the U1406A mutation in 16S rRNA attenuated M. tuberculosis virulence by affecting these processes.

Viability, morphology, and proteome of Mycobacterium smegmatis MSMEG_0319 knockout strain

Mycobacterium tuberculosis Rv0228, a membrane protein, is predicted as a drug target through computational methods. MSMEG_0319 (MS0319) in Mycobacterium smegmatis mc(2) 155 is the ortholog of Rv0228. To study the effect of MS0319 protein on M. smegmatis, an MS0319 gene knockout strain (ΔMS0319) was generated via a homologous recombination technique in this study. The results showed that the lack of MS0319 protein in mc(2) 155 cells led to the loss of viability at nonpermissive temperature. Scanning electron microscopy and transmission electron microscopy observations showed drastic changes in cellular shape especially cell wall disruption in ΔMS0319 cells. Proteomic analysis of ΔMS0319 cells through LC-MS/MS revealed that 462 proteins had changes in their expressions by lacking MS0319 protein. The M. tuberculosis orthologs of these 462 proteins were found through BLASTp search and functional clustering and metabolic pathway enrichment were performed on the orthologs. The results revealed that most of them were enzymes involved in metabolism of carbohydrates and amino acids, indicating that Rv0228 played an important role in cellular metabolism. All these results suggested Rv0228 as a potential target for development of antituberculosis drugs.

A whole genome bioinformatic approach to determine potential latent phase specific targets in Mycobacterium tuberculosis

Current Tuberculosis treatment is long and expensive, faces the increasing burden of MDR/XDR strains and lack of effective treatment against latent form, resulting in an urgent need of new anti-TB drugs. Key to TB biology is its capacity to fight the host's RNOS mediated attack. RNOS are known to display a concentration dependent mycobactericidal activity, which leads to the following hypothesis "if we know which proteins are targeted by RNOS and kill TB, we we might be able to inhibit them with drugs resulting in a synergistic bactericidal effect". Based on this idea, we performed an Mtb metabolic network whole proteome analysis of potential RNOS sensitive and relevant targets which includes target druggability and essentiality criteria. Our results, available at http://tuberq.proteinq.com.ar yield new potential TB targets, like I3PS, while also providing and updated view of previous proposals becoming an important tool for researchers looking for new ways of killing TB.

Flux Balance Analysis with Objective Function Defined by Proteomics Data-Metabolism of Mycobacterium tuberculosis Exposed to Mefloquine

We present a study of the metabolism of the Mycobacterium tuberculosis after exposure to antibiotics using proteomics data and flux balance analysis (FBA). The use of FBA to study prokaryotic organisms is well-established and allows insights into the metabolic pathways chosen by the organisms under different environmental conditions. To apply FBA a specific objective function must be selected that represents the metabolic goal of the organism. FBA estimates the metabolism of the cell by linear programming constrained by the stoichiometry of the reactions in an in silico metabolic model of the organism. It is assumed that the metabolism of the organism works towards the specified objective function. A common objective is the maximization of biomass. However, this goal is not suitable for situations when the bacterium is exposed to antibiotics, as the goal of organisms in these cases is survival and not necessarily optimal growth. In this paper we propose a new approach for defining the FBA objective function in studies when the bacterium is under stress. The function is defined based on protein expression data. The proposed methodology is applied to the case when the bacterium is exposed to the drug mefloquine, but can be easily extended to other organisms, conditions or drugs. We compare our method with an alternative method that uses experimental data for adjusting flux constraints. We perform comparisons in terms of essential enzymes and agreement using enzyme abundances. Results indicate that using proteomics data to define FBA objective functions yields less essential reactions with zero flux and lower error rates in prediction accuracy. With flux variability analysis we observe that overall variability due to alternate optima is reduced with the incorporation of proteomics data. We believe that incorporating proteomics data in the objective function used in FBA may help obtain metabolic flux representations that better support experimentally observed features.

Salmonella typhimurium and Escherichia coli dissimilarity: Closely related bacteria with distinct metabolic profiles

Live attenuated strains of Salmonella typhimurium have been extensively investigated as vaccines for a number of infectious diseases. However, there is still little information available concerning aspects of their metabolism. S. typhimurium and Escherichia coli show a high degree of similarity in terms of their genome contents and metabolic networks. However, this work presents experimental evidence showing that significant differences exist in their abilities to direct carbon fluxes to biomass and energy production. It is important to study the metabolism of Salmonella to elucidate the formation of acetate and other metabolites involved in optimizing the production of biomass, essential for the development of recombinant vaccines. The metabolism of Salmonella under aerobic conditions was assessed using continuous cultures performed at dilution rates ranging from 0.1 to 0.67 h(-1), with glucose as main substrate. Acetate assimilation and glucose metabolism under anaerobic conditions were also investigated using batch cultures. Chemostat cultivations showed deviation of carbon towards acetate formation, starting at dilution rates above 0.1 h(-1). This differed from previous findings for E. coli, where acetate accumulation was only detected at dilution rates exceeding 0.4 h(-1), and was due to the lower rate of acetate assimilation by S. typhimurium under aerobic conditions. Under anaerobic conditions, both microorganisms mainly produced ethanol, acetate, and formate. A genome-scale metabolic model, reconstructed for Salmonella based on an E. coli model, provided a poor description of the mixed fermentation pattern observed during Salmonella cultures, reinforcing the different patterns of carbon utilization exhibited by these closely related bacteria.

Interplay between constraints, objectives, and optimality for genome-scale stoichiometric models

High-throughput data generation and genome-scale stoichiometric models have greatly facilitated the comprehensive study of metabolic networks. The computation of all feasible metabolic routes with these models, given stoichiometric, thermodynamic, and steady-state constraints, provides important insights into the metabolic capacities of a cell. How the feasible metabolic routes emerge from the interplay between flux constraints, optimality objectives, and the entire metabolic network of a cell is, however, only partially understood. We show how optimal metabolic routes, resulting from flux balance analysis computations, arise out of elementary flux modes, constraints, and optimization objectives. We illustrate our findings with a genome-scale stoichiometric model of Escherichia coli metabolism. In the case of one flux constraint, all feasible optimal flux routes can be derived from elementary flux modes alone. We found up to 120 million of such optimal elementary flux modes. We introduce a new computational method to compute the corner points of the optimal solution space fast and efficiently. Optimal flux routes no longer depend exclusively on elementary flux modes when we impose additional constraints; new optimal metabolic routes arise out of combinations of elementary flux modes. The solution space of feasible metabolic routes shrinks enormously when additional objectives---e.g. those related to pathway expression costs or pathway length---are introduced. In many cases, only a single metabolic route remains that is both feasible and optimal. This paper contributes to reaching a complete topological understanding of the metabolic capacity of organisms in terms of metabolic flux routes, one that is most natural to biochemists and biotechnologists studying and engineering metabolism.

Using a genome-scale metabolic model of Enterococcus faecalis V583 to assess amino acid uptake and its impact on central metabolism

Increasing antibiotic resistance in pathogenic bacteria necessitates the development of new medication strategies. Interfering with the metabolic network of the pathogen can provide novel drug targets but simultaneously requires a deeper and more detailed organism-specific understanding of the metabolism, which is often surprisingly sparse. In light of this, we reconstructed a genome-scale metabolic model of the pathogen Enterococcus faecalis V583. The manually curated metabolic network comprises 642 metabolites and 706 reactions. We experimentally determined metabolic profiles of E. faecalis grown in chemically defined medium in an anaerobic chemostat setup at different dilution rates and calculated the net uptake and product fluxes to constrain the model. We computed growth-associated energy and maintenance parameters and studied flux distributions through the metabolic network. Amino acid auxotrophies were identified experimentally for model validation and revealed seven essential amino acids. In addition, the important metabolic hub of glutamine/glutamate was altered by constructing a glutamine synthetase knockout mutant. The metabolic profile showed a slight shift in the fermentation pattern toward ethanol production and increased uptake rates of multiple amino acids, especially l-glutamine and l-glutamate. The model was used to understand the altered flux distributions in the mutant and provided an explanation for the experimentally observed redirection of the metabolic flux. We further highlighted the importance of gene-regulatory effects on the redirection of the metabolic fluxes upon perturbation. The genome-scale metabolic model presented here includes gene-protein-reaction associations, allowing a further use for biotechnological applications, for studying essential genes, proteins, or reactions, and the search for novel drug targets.

CONSTRICTOR: constraint modification provides insight into design of biochemical networks

Advances in computational methods that allow for exploration of the combinatorial mutation space are needed to realize the potential of synthetic biology based strain engineering efforts. Here, we present Constrictor, a computational framework that uses flux balance analysis (FBA) to analyze inhibitory effects of genetic mutations on the performance of biochemical networks. Constrictor identifies engineering interventions by classifying the reactions in the metabolic model depending on the extent to which their flux must be decreased to achieve the overproduction target. The optimal inhibition of various reaction pathways is determined by restricting the flux through targeted reactions below the steady state levels of a baseline strain. Constrictor generates unique in silico strains, each representing an “expression state”, or a combination of gene expression levels required to achieve the overproduction target. The Constrictor framework is demonstrated by studying overproduction of ethylene in Escherichia coli network models iAF1260 and iJO1366 through the addition of the heterologous ethylene-forming enzyme from Pseudomonas syringae. Targeting individual reactions as well as combinations of reactions reveals in silico mutants that are predicted to have as high as 25% greater theoretical ethylene yields than the baseline strain during simulated exponential growth. Altering the degree of restriction reveals a large distribution of ethylene yields, while analysis of the expression states that return lower yields provides insight into system bottlenecks. Finally, we demonstrate the ability of Constrictor to scan networks and provide targets for a range of possible products. Constrictor is an adaptable technique that can be used to generate and analyze disparate populations of in silico mutants, select gene expression levels and provide non-intuitive strategies for metabolic engineering.

Systems-level modeling of mycobacterial metabolism for the identification of new (multi-)drug targets

Systems-level metabolic network reconstructions and the derived constraint-based (CB) mathematical models are efficient tools to explore bacterial metabolism. Approximately one-fourth of the Mycobacterium tuberculosis (Mtb) genome contains genes that encode proteins directly involved in its metabolism. These represent potential drug targets that can be systematically probed with CB models through the prediction of genes essential (or the combination thereof) for the pathogen to grow. However, gene essentiality depends on the growth conditions and, so far, no in vitro model precisely mimics the host at the different stages of mycobacterial infection, limiting model predictions. These limitations can be circumvented by combining expression data from in vivo samples with a validated CB model, creating an accurate description of pathogen metabolism in the host. To this end, we present here a thoroughly curated and extended genome-scale CB metabolic model of Mtb quantitatively validated using 13C measurements. We describe some of the efforts made in integrating CB models and high-throughput data to generate condition specific models, and we will discuss challenges ahead. This knowledge and the framework herein presented will enable to identify potential new drug targets, and will foster the development of optimal therapeutic strategies.

TuberQ: a Mycobacterium tuberculosis protein druggability database

In 2012 an estimated 8.6 million people developed tuberculosis (TB) and 1.3 million died from the disease [including 320 000 deaths among human immunodeficiency virus (HIV)-positive people]. There is an urgent need for new anti-TB drugs owing to the following: the fact that current treatments have severe side effects, the increasing emergence of multidrug-resistant strains of Mycobacterium tuberculosis (Mtb), the negative drug-drug interactions with certain HIV (or other disease) treatments and the ineffectiveness against dormant Mtb. In this context we present here the TuberQ database, a novel resource for all researchers working in the field of drug development in TB. The main feature of TuberQ is to provide a druggability analysis of Mtb proteins in a consistent and effective manner, contributing to a better selection of potential drug targets for screening campaigns and the analysis of targets for structure-based drug design projects. The structural druggability analysis is combined with features related to the characteristics of putative inhibitor binding pockets and with functional and biological data of proteins. The structural analysis is performed on all available unique Mtb structures and high-quality structural homology-based models. This information is shown in an interactive manner, depicting the protein structure, the pockets and the associated characteristics for each protein. TuberQ also provides information about gene essentiality information, as determined from whole cell-based knockout experiments, and expression information obtained from microarray experiments done in different stress-related conditions. We hope that TuberQ will be a powerful tool for researchers working in TB and eventually will lead to the identification of novel putative targets and progresses in therapeutic activities. Database URL: http://tuberq.proteinq.com.ar/

Structural Annotation of the Mycobacterium tuberculosis Proteome

Efforts from the TB Structural Genomics Consortium together with those of tuberculosis structural biologists worldwide have led to the determination of about 350 structures, making up nearly a tenth of the pathogen's proteome. Given that knowledge of protein structures is essential to obtaining a high-resolution understanding of the underlying biology, it is desirable to have a structural view of the entire proteome. Indeed, structure prediction methods have advanced sufficiently to allow structural models of many more proteins to be built based on homology modeling and fold recognition strategies. By means of these approaches, structural models for about 2,877 proteins, making up nearly 70% of the Mycobacterium tuberculosis proteome, are available. Knowledge from bioinformatics has made significant inroads into an improved annotation of the M. tuberculosis genome and in the prediction of key protein players that interact in vital pathways, some of which are unique to the organism. Functional inferences have been made for a large number of proteins based on fold-function associations. More importantly, ligand-binding pockets of the proteins are identified and scanned against a large database, leading to binding site-based ligand associations and hence structure-based function annotation. Near proteome-wide structural models provide a global perspective of the fold distribution in the genome. New insights about the folds that predominate in the genome, as well as the fold combinations that make up multidomain proteins, are also obtained. This chapter describes the structural proteome, functional inferences drawn from it, and its applications in drug discovery.

Drug resistance to Mycobacterium tuberculosis: from the traditional Chinese view to modern systems biology

The pathogen, Mycobacterium tuberculosis (M. tuberculosis) is a well-evolved, organized pathogen that has developed drug resistance, specifically multidrug resistance (MDR) and extensive drug resistance (XDR). This review primarily summarizes the mechanisms of drug resistance by M. tuberculosis according to the traditional Chinese view. The traditional Chinese view of drug resistance includes: the physical barrier of the cell wall; mutations relating to current anti-TB agents; drug efflux pumps; and drug stress, including the SOS response systems, the mismatch repair systems and the toxin-antitoxin systems. In addition, this review addresses the integrated systems biology of genomics, transcriptomics, proteomics, metabolomics and interactomics. Development of the various levels of systems biology has enabled determination of the anatomy of bacteria. Finally, the current review proposes that further investigation regarding the population of individuals with a high drug metabolic speed is vital to further understand drug resistance in M. tuberculosis.

Proteomic analysis of Mycobacterium tuberculosis isolates resistant to kanamycin and amikacin

Kanamycin (KM) and amikacin (AK) are the key aminoglycoside drugs against tuberculosis (TB) and resistance to them severely affects the options for treatment. Many explanations have been proposed for drug resistance to these drugs but still some mechanisms are unknown. Proteins are the functional moiety of the cell and manifest in most of the biological processes; so, these are potential foci for the development of new therapeutics, diagnostics and vaccine. We examined the KM and AK resistant isolates of Mycobacterium tuberculosis using proteomic analysis comprising of two dimensional gel electrophoresis (2DGE), matrix assisted laser desorption ionization time-of-flight/time-of flight (MALDI-TOF/TOF) and bioinformatic tools like BLASTP, InterProScan, KEGG motif scan and molecular docking. Proteins intensities of twelve spots were found to be consistently increased in KM and AK resistant isolates and these were identified as Rv3867, Rv1932, Rv3418c, Rv1876, Rv2031c, Rv0155, Rv0643c, Rv3224, Rv0952, and Rv0440. Among these, Rv3867 and Rv3224 were identified as proteins with unknown function. All the proteins identified were cellular proteins. Molecular docking shows the proper interaction of both drugs with these molecules. Also, Rv1876 and Rv3224 were found to be probably involved in iron regulation/metabolism indicating the role of iron in imparting resistance to second line drugs.

BIOLOGICAL SIGNIFICANCE

The study that was carried out shows that two dimensional electrophoresis along with mass spectrometry is still the best approach for proteomic analysis. To the best of our knowledge it is the first ever report on proteomic analysis of M. tuberculosis isolates resistant to second line drugs (kanamycin and amikacin). The major finding implicates that the genes/proteins involved in iron metabolism and the two hypothetical proteins (Rv3867 and Rv3224) might be playing some crucial role in contributing resistance to second line drugs. Further exploitation in this direction may lead to the development of newer therapeutics against tuberculosis.

Characterization of the metabolic requirements in yeast meiosis

The diploid yeast Saccharomyces cerevisiae undergoes mitosis in glucose-rich medium but enters meiosis in acetate sporulation medium. The transition from mitosis to meiosis involves a remarkable adaptation of the metabolic machinery to the changing environment to meet new energy and biosynthesis requirements. Biochemical studies indicate that five metabolic pathways are active at different stages of sporulation: glutamate formation, tricarboxylic acid cycle, glyoxylate cycle, gluconeogenesis, and glycogenolysis. A dynamic synthesis of macromolecules, including nucleotides, amino acids, and lipids, is also observed. However, the metabolic requirements of sporulating cells are poorly understood. In this study, we apply flux balance analyses to uncover optimal principles driving the operation of metabolic networks over the entire period of sporulation. A meiosis-specific metabolic network is constructed, and flux distribution is simulated using ten objective functions combined with time-course expression-based reaction constraints. By systematically evaluating the correlation between computational and experimental fluxes on pathways and macromolecule syntheses, the metabolic requirements of cells are determined: sporulation requires maximization of ATP production and macromolecule syntheses in the early phase followed by maximization of carbohydrate breakdown and minimization of ATP production in the middle and late stages. Our computational models are validated by in silico deletion of enzymes known to be essential for sporulation. Finally, the models are used to predict novel metabolic genes required for sporulation. This study indicates that yeast cells have distinct metabolic requirements at different phases of meiosis, which may reflect regulation that realizes the optimal outcome of sporulation. Our meiosis-specific network models provide a framework for an in-depth understanding of the roles of enzymes and reactions, and may open new avenues for engineering metabolic pathways to improve sporulation efficiency.

Network-based assessment of the selectivity of metabolic drug targets in Plasmodium falciparum with respect to human liver metabolism

Background

The search for new drug targets for antibiotics against Plasmodium falciparum, a major cause of human deaths, is a pressing scientific issue, as multiple resistance strains spread rapidly. Metabolic network-based analyses may help to identify those parasite’s essential enzymes whose homologous counterparts in the human host cells are either absent, non-essential or relatively less essential.

Results

Using the well-curated metabolic networks PlasmoNet of the parasite Plasmodium falciparum and HepatoNet1 of the human hepatocyte, the selectivity of 48 experimental antimalarial drug targets was analyzed. Applying in silico gene deletions, 24 of these drug targets were found to be perfectly selective, in that they were essential for the parasite but non-essential for the human cell. The selectivity of a subset of enzymes, that were essential in both models, was evaluated with the reduced fitness concept. It was, then, possible to quantify the reduction in functional fitness of the two networks under the progressive inhibition of the same enzymatic activity. Overall, this in silico analysis provided a selectivity ranking that was in line with numerous in vivo and in vitro observations.

Conclusions

Genome-scale models can be useful to depict and quantify the effects of enzymatic inhibitions on the impaired production of biomass components. From the perspective of a host-pathogen metabolic interaction, an estimation of the drug targets-induced consequences can be beneficial for the development of a selective anti-parasitic drug.

Applications of system-level models of metabolism for analysis of bacterial physiology and identification of new drug targets

For nearly all of the 20th century, biologists gained considerable insights into the fundamental principles of cellular dynamics by examining select modules of biochemical processes. This form of analysis provides detailed information about the workings of the examined pathways. However, any attempt to alter the normal function of bacteria (perhaps for industrial or medicinal goals) requires a detailed global understanding of cellular mechanisms. The reductionist mode of analysis cannot provide the required information for developing the needed perspective on the complex interactions of biochemical pathways. Thankfully, the increasing availability of microbial genomic, transcriptomic, proteomic and other high-throughput data permits system-level analyses of microbiology. During the past two decades, systems biologists have developed constraint-based genome-scale models (GSM) of metabolism for a variety of pathogens. These models are important tools for assessing the metabolic capabilities of various genotypes. Simultaneously, new computational methods have been developed that use these network reconstructions to answer an array of important immunological questions. The objective of this article is to briefly review some of the uses of GSMs for studying bacterial metabolism under different conditions and to discuss how the calculated solutions can be used for rational design of drugs.

Understanding communication signals during mycobacterial latency through predicted genome-wide protein interactions and boolean modeling

About 90% of the people infected with Mycobacterium tuberculosis carry latent bacteria that are believed to get activated upon immune suppression. One of the fundamental challenges in the control of tuberculosis is therefore to understand molecular mechanisms involved in the onset of latency and/or reactivation. We have attempted to address this problem at the systems level by a combination of predicted functional protein:protein interactions, integration of functional interactions with large scale gene expression studies, predicted transcription regulatory network and finally simulations with a boolean model of the network. Initially a prediction for genome-wide protein functional linkages was obtained based on genome-context methods using a Support Vector Machine. This set of protein functional linkages along with gene expression data of the available models of latency was employed to identify proteins involved in mediating switch signals during dormancy. We show that genes that are up and down regulated during dormancy are not only coordinately regulated under dormancy-like conditions but also under a variety of other experimental conditions. Their synchronized regulation indicates that they form a tightly regulated gene cluster and might form a latency-regulon. Conservation of these genes across bacterial species suggests a unique evolutionary history that might be associated with M. tuberculosis dormancy. Finally, simulations with a boolean model based on the regulatory network with logical relationships derived from gene expression data reveals a bistable switch suggesting alternating latent and actively growing states. Our analysis based on the interaction network therefore reveals a potential model of M. tuberculosis latency.

MetaMerge: scaling up genome-scale metabolic reconstructions with application to Mycobacterium tuberculosis

Reconstructed models of metabolic networks are widely used for studying metabolism in various organisms. Many different reconstructions of the same organism often exist concurrently, forcing researchers to choose one of them at the exclusion of the others. We describe MetaMerge, an algorithm for semi-automatically reconciling a pair of existing metabolic network reconstructions into a single metabolic network model. We use MetaMerge to combine two published metabolic networks for Mycobacterium tuberculosis into a single network, which allows many reactions that could not be active in the individual models to become active, and predicts essential genes with a higher positive predictive value.

Identifying co-targets to fight drug resistance based on a random walk model

BACKGROUND

Drug resistance has now posed more severe and emergent threats to human health and infectious disease treatment. However, wet-lab approaches alone to counter drug resistance have so far still achieved limited success due to less knowledge about the underlying mechanisms of drug resistance. Our approach apply a heuristic search algorithm in order to extract active network under drug treatment and use a random walk model to identify potential co-targets for effective antibacterial drugs.

RESULTS

We use interactome network of Mycobacterium tuberculosis and gene expression data which are treated with two kinds of antibiotic, Isoniazid and Ethionamide as our test data. Our analysis shows that the active drug-treated networks are associated with the trigger of fatty acid metabolism and synthesis and nicotinamide adenine dinucleotide (NADH)-related processes and those results are consistent with the recent experimental findings. Efflux pumps processes appear to be the major mechanisms of resistance but SOS response is significantly up-regulation under Isoniazid treatment. We also successfully identify the potential co-targets with literature confirmed evidences which are related to the glycine-rich membrane, adenosine triphosphate energy and cell wall processes.

CONCLUSIONS

With gene expression and interactome data supported, our study points out possible pathways leading to the emergence of drug resistance under drug treatment. We develop a computational workflow for giving new insights to bacterial drug resistance which can be gained by a systematic and global analysis of the bacterial regulation network. Our study also discovers the potential co-targets with good properties in biological and graph theory aspects to overcome the problem of drug resistance.

Pathogenesis in tuberculosis: transcriptomic approaches to unraveling virulence mechanisms and finding new drug targets

Tuberculosis (TB) remains a major health problem worldwide. Attempts to control this disease have proved difficult owing to our poor understanding of the pathobiology of Mycobacterium tuberculosis and the emergence of strains that are resistant to multiple drugs currently available for treatment. Genome-wide expression profiling has provided new insight into the transcriptome signatures of the bacterium during infection, notably of macrophages and dendritic cells. These data indicate that M. tuberculosis expresses numerous genes to evade the host immune responses, to suit its intracellular life style, and to respond to various antibiotic drugs. Among the intracellularly induced genes, several have functions in lipid metabolism, cell wall synthesis, iron uptake, oxidative stress resistance, protein secretion, or inhibition of apoptosis. Herein we review these findings and discuss possible ways to exploit the data to understand the complex etiology of TB and to find new effective drug targets.

Comparative and functional genomics of Rhodococcus opacus PD630 for biofuels development

The Actinomycetales bacteria Rhodococcus opacus PD630 and Rhodococcus jostii RHA1 bioconvert a diverse range of organic substrates through lipid biosynthesis into large quantities of energy-rich triacylglycerols (TAGs). To describe the genetic basis of the Rhodococcus oleaginous metabolism, we sequenced and performed comparative analysis of the 9.27 Mb R. opacus PD630 genome. Metabolic-reconstruction assigned 2017 enzymatic reactions to the 8632 R. opacus PD630 genes we identified. Of these, 261 genes were implicated in the R. opacus PD630 TAGs cycle by metabolic reconstruction and gene family analysis. Rhodococcus synthesizes uncommon straight-chain odd-carbon fatty acids in high abundance and stores them as TAGs. We have identified these to be pentadecanoic, heptadecanoic, and cis-heptadecenoic acids. To identify bioconversion pathways, we screened R. opacus PD630, R. jostii RHA1, Ralstonia eutropha H16, and C. glutamicum 13032 for growth on 190 compounds. The results of the catabolic screen, phylogenetic analysis of the TAGs cycle enzymes, and metabolic product characterizations were integrated into a working model of prokaryotic oleaginy.

Open source drug discovery--a new paradigm of collaborative research in tuberculosis drug development

It is being realized that the traditional closed-door and market driven approaches for drug discovery may not be the best suited model for the diseases of the developing world such as tuberculosis and malaria, because most patients suffering from these diseases have poor paying capacity. To ensure that new drugs are created for patients suffering from these diseases, it is necessary to formulate an alternate paradigm of drug discovery process. The current model constrained by limitations for collaboration and for sharing of resources with confidentiality hampers the opportunities for bringing expertise from diverse fields. These limitations hinder the possibilities of lowering the cost of drug discovery. The Open Source Drug Discovery project initiated by Council of Scientific and Industrial Research, India has adopted an open source model to power wide participation across geographical borders. Open Source Drug Discovery emphasizes integrative science through collaboration, open-sharing, taking up multi-faceted approaches and accruing benefits from advances on different fronts of new drug discovery. Because the open source model is based on community participation, it has the potential to self-sustain continuous development by generating a storehouse of alternatives towards continued pursuit for new drug discovery. Since the inventions are community generated, the new chemical entities developed by Open Source Drug Discovery will be taken up for clinical trial in a non-exclusive manner by participation of multiple companies with majority funding from Open Source Drug Discovery. This will ensure availability of drugs through a lower cost community driven drug discovery process for diseases afflicting people with poor paying capacity. Hopefully what LINUX the World Wide Web have done for the information technology, Open Source Drug Discovery will do for drug discovery.

Challenges, successes and hopes in the development of novel TB therapeutics

Despite efficacious drugs for treatment, TB continues to affect enormous numbers of patients throughout the world. Failure to control TB may be related to the biological characteristics of Mycobacterium tuberculosis, the nature of susceptible hosts often impoverished and poorly supported by healthcare infrastructure and the complex treatment regimens that must be used. Challenges to anti-TB drug development include the organism's slow replication, the ability of M. tuberculosis to survive in a dormant state and to persist despite therapy, its impregnable cell wall and its capacity to develop resistance to drugs. The need for extended therapy using combinations of drugs remains a practical obstacle to effective control in poor, malnourished and diseased communities most susceptible to TB. High-throughput screening of candidate agents and investigation of drugs already in use for other infections are yielding promising new candidates for TB treatment. New families of drugs entering clinical trials include 5-nitroimidazoles, diarylquinolines and ethylene diamines. Increasing funding initiatives, advances in the biology of TB and strategies for drug discovery have rejuvenated the pipeline of new drugs for TB, promising an expanding armamentarium of effective drugs with improved tolerability and potential to treat drug-resistant cases.