Predicting the in Vivo Mechanism of Action for Drug Leads Using NMR Metabolomics
ACS Chemical Biology (Impact Factor: 5.33). 01/2012; 7(1):166-71. DOI: 10.1021/cb200348m
New strategies are needed to circumvent increasing outbreaks of resistant strains of pathogens and to expand the dwindling supply of effective antimicrobials. A common impediment to drug development is the lack of an easy approach to determine the in vivo mechanism of action and efficacy of novel drug leads. Toward this end, we describe an unbiased approach to predict in vivo mechanisms of action from NMR metabolomics data. Mycobacterium smegmatis, a non-pathogenic model organism for Mycobacterium tuberculosis, was treated with 12 known drugs and 3 chemical leads identified from a cell-based assay. NMR analysis of drug-induced changes to the M. smegmatis metabolome resulted in distinct clustering patterns correlating with in vivo drug activity. The clustering of novel chemical leads relative to known drugs provides a mean to identify a protein target or predict in vivo activity.
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- " mation from the treated mycobacterial cell ( de Carvalho et al . 2011 ; Chakraborty et al . 2013 ) . For this reason , these techniques have been es - pecially useful in determining the systems - level impact of drug treatment on M . tuberculosis physiology , providing key insights into the mechanisms of action of new ( de Carvalho et al . 2011 ; Halouska et al . 2012 ) and even estab - lished ( Halouska et al . 2013 ; Prosser and de Carvalho 2013 ) anti - TB drugs . For example , it was generally assumed that the second - line anti - TB drug , para - aminosalicylic acid ( PAS ) , com - petitively inhibits the essential dihydropteroate synthase in folate metabolism by acting as a mimetic of the natur"
ABSTRACT: Metabolism underpins the physiology and pathogenesis of Mycobacterium tuberculosis. However, although experimental mycobacteriology has provided key insights into the metabolic pathways that are essential for survival and pathogenesis, determining the metabolic status of bacilli during different stages of infection and in different cellular compartments remains challenging. Recent advances-in particular, the development of systems biology tools such as metabolomics-have enabled key insights into the biochemical state of M. tuberculosis in experimental models of infection. In addition, their use to elucidate mechanisms of action of new and existing antituberculosis drugs is critical for the development of improved interventions to counter tuberculosis. This review provides a broad summary of mycobacterial metabolism, highlighting the adaptation of M. tuberculosis as specialist human pathogen, and discusses recent insights into the strategies used by the host and infecting bacillus to influence the outcomes of the host-pathogen interaction through modulation of metabolic functions. Copyright © 2014 Cold Spring Harbor Laboratory Press; all rights reserved.
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- "Metabolic Tree diagrams (PCA-to-Tree programme) allowed a statistical evaluation of the degree of sample grouping displayed by both PCA and HCA (Werth et al. 2010). During the generation of these trees, bootstrapping numbers, which determine the statistically significant differences between the different clusters, are also obtained (Halouska et al. 2012). Here, two independent tree diagrams corresponding to the tobacco and sorghum samples were generated using the data from the two dimensional PCA score plots. "
ABSTRACT: Isonitrosoacetophenone (INAP, 2-keto-2-phenyl-acetaldoxime) is a novel inducer of plant defense. Oxime functional groups are rare in natural products, but can serve as substrates depending on existing secondary pathways. Changes in the metabolomes of sorghum and tobacco cells treated with INAP were investigated and chemometric tools and multivariate statistical analysis were used to investigate the changes in metabolite distribution patterns resulting from INAP elicitation. Liquid chromatography combined with mass spectrometry (UHPLC-MS) supplied unique chemical fingerprints that were generated in response to specific metabolomic events. Principal component analysis (PCA) together with hierarchical cluster analysis (HCA) and Metabolic Trees were used for data visualization. Orthogonal projections to latent structures discriminant analysis (OPLS-DA) and shared and unique structure (SUS) plots were exploited in parallel to reveal the changes in the metabolomes. PCA indicated that the cells responded differentially to INAP through changes in the metabolite profiles. Furthermore, HCA and Metabolic Trees showed that INAP induced metabolic perturbations in both cell lines and that homeostasis was re-established over time. OPLS-DA-based shared and unique structure (SUS) plots confirmed the results and revealed differences in the metabolites distribution patterns between tobacco and sorghum cells. Chemometric analyses of metabolomic data offers insight into changes in metabolism in response to chemical elicitation. Although similar, the response in sorghum cells was found to be more consistent and well-coordinated when compared to tobacco cells, indicative of the differences in secondary metabolism between cyanogenic and non-cyanogenic plants for oxime metabolism. Electronic supplementary material The online version of this article (doi: 10.1186/2193-1801-3-254) contains supplementary material, which is available to authorized users.
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- "The untargeted nature of metabolomics has allowed rapid and unbiased classification of numerous antimicrobial compounds according to their modes of action (Gao et al. 2007; Liu et al. 2009; Halouska et al. 2012). Investigation of the metabolic response of Staphylococcus aureus to triphenylbismuthdichloride revealed pyruvate dehydrogenase as a target for this novel antibiotic (Birkenstock et al. 2012). "
ABSTRACT: SUMMARY The discovery, development and optimal utilization of pharmaceuticals can be greatly enhanced by knowledge of their modes of action. However, many drugs currently on the market act by unknown mechanisms. Untargeted metabolomics offers the potential to discover modes of action for drugs that perturb cellular metabolism. Development of high resolution LC-MS methods and improved data analysis software now allows rapid detection of drug-induced changes to cellular metabolism in an untargeted manner. Several studies have demonstrated the ability of untargeted metabolomics to provide unbiased target discovery for antimicrobial drugs, in particular for antiprotozoal agents. Furthermore, the utilization of targeted metabolomics techniques has enabled validation of existing hypotheses regarding antiprotozoal drug mechanisms. Metabolomics approaches are likely to assist with optimization of new drug candidates by identification of drug targets, and by allowing detailed characterization of modes of action and resistance of existing and novel antiprotozoal drugs.