Literature DB >> 22007661

Predicting the in vivo mechanism of action for drug leads using NMR metabolomics.

Steven Halouska, Robert J Fenton, Raúl G Barletta, Robert Powers.   

Abstract

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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Year:  2011        PMID: 22007661      PMCID: PMC3262886          DOI: 10.1021/cb200348m

Source DB:  PubMed          Journal:  ACS Chem Biol        ISSN: 1554-8929            Impact factor:   5.100


  34 in total

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3.  Mycobacterium smegmatis D-Alanine Racemase Mutants Are Not Dependent on D-Alanine for Growth.

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5.  Amiodarone interactions with membrane lipids and with growth of Bacillus stearothermophilus used as a model.

Authors:  S M Rosa; M C Antunes-Madeira; A S Jurado; V V Madeira
Journal:  Appl Biochem Biotechnol       Date:  2000-06       Impact factor: 2.926

6.  Multidrug resistance of a porin deletion mutant of Mycobacterium smegmatis.

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7.  Comparison of assays for detection of agents causing membrane damage in Staphylococcus aureus.

Authors:  Alexander John O'Neill; Keith Miller; Brunello Oliva; Ian Chopra
Journal:  J Antimicrob Chemother       Date:  2004-11-05       Impact factor: 5.790

8.  Anti-staphylococcal activity and mode of action of clofazimine.

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Journal:  J Antimicrob Chemother       Date:  2004-02-04       Impact factor: 5.790

9.  Distribution and characterization of beta-lactamases of mycobacteria and related organisms.

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  37 in total

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2.  NMR Metabolomics Protocols for Drug Discovery.

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Journal:  Methods Mol Biol       Date:  2019

Review 3.  Analysis of bacterial biofilms using NMR-based metabolomics.

Authors:  Bo Zhang; Robert Powers
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Review 4.  Comparative mass spectrometry-based metabolomics strategies for the investigation of microbial secondary metabolites.

Authors:  Brett C Covington; John A McLean; Brian O Bachmann
Journal:  Nat Prod Rep       Date:  2017-01-04       Impact factor: 13.423

5.  Macrophage-Mediated Clofazimine Sequestration Is Accompanied by a Shift in Host Energy Metabolism.

Authors:  Julie Trexel; Gi S Yoon; Rahul K Keswani; Cora McHugh; Larisa Yeomans; Victor Vitvitsky; Ruma Banerjee; Sudha Sud; Yihan Sun; Gus R Rosania; Kathleen A Stringer
Journal:  J Pharm Sci       Date:  2016-12-20       Impact factor: 3.534

6.  High-throughput metabolomic analysis predicts mode of action of uncharacterized antimicrobial compounds.

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Journal:  Sci Transl Med       Date:  2018-02-21       Impact factor: 17.956

7.  Revisiting Protocols for the NMR Analysis of Bacterial Metabolomes.

Authors:  Steven Halouska; Bo Zhang; Rosmarie Gaupp; Shulei Lei; Emily Snell; Robert J Fenton; Raul G Barletta; Greg A Somerville; Robert Powers
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8.  NMR Metabolomics Analysis of Parkinson's Disease.

Authors:  Shulei Lei; Robert Powers
Journal:  Curr Metabolomics       Date:  2013

9.  Extending the Scope of 1H NMR Spectroscopy for the Analysis of Cellular Coenzyme A and Acetyl Coenzyme A.

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Journal:  Anal Chem       Date:  2019-01-17       Impact factor: 6.986

10.  Utilities for quantifying separation in PCA/PLS-DA scores plots.

Authors:  Bradley Worley; Steven Halouska; Robert Powers
Journal:  Anal Biochem       Date:  2012-10-15       Impact factor: 3.365

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