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d-Cycloserine is an effective second line antibiotic used as a last resort to treat multi (MDR)- and extensively (XDR) drug resistant strains of Mycobacterium tuberculosis . d-Cycloserine interferes with the formation of peptidoglycan biosynthesis by competitive inhibition of alanine racemase (Alr) and d-alanine-d-alanine ligase (Ddl). Although the two enzymes are known to be inhibited, the in vivo lethal target is still unknown. Our NMR metabolomics work has revealed that Ddl is the primary target of DCS, as cell growth is inhibited when the production of d-alanyl-d-alanine is halted. It is shown that inhibition of Alr may contribute indirectly by lowering the levels of d-alanine, thus allowing DCS to outcompete d-alanine for Ddl binding. The NMR data also supports the possibility of a transamination reaction to produce d-alanine from pyruvate and glutamate, thereby bypassing Alr inhibition. Furthermore, the inhibition of peptidoglycan synthesis results in a cascading effect on cellular metabolism as there is a shift toward the catabolic routes to compensate for accumulation of peptidoglycan precursors.

Metabolic fingerprinting studies rely on interpretations drawn from low-dimensional representations of spectral data generated by methods of multivariate analysis such as principal components analysis and projection to latent structures discriminant analysis. The growth of metabolic fingerprinting and chemometric analyses involving these low-dimensional scores plots necessitates the use of quantitative statistical measures to describe significant differences between experimental groups. Our updated version of the PCAtoTree software provides methods to reliably visualize and quantify separations in scores plots through dendrograms employing both nonparametric and parametric hypothesis testing to assess node significance, as well as scores plots identifying 95% confidence ellipsoids for all experimental groups., (Copyright © 2012 Elsevier Inc. All rights reserved.)

Mycobacterium tuberculosis is a major cause of mortality in human beings on a global scale. The emergence of both multi- (MDR) and extensively-(XDR) drug-resistant strains threatens to derail current disease control efforts. Thus, there is an urgent need to develop drugs and vaccines that are more effective than those currently available. The genome of M. tuberculosis has been known for more than 10 years, yet there are important gaps in our knowledge of gene function and essentiality. Many studies have since used gene expression analysis at both the transcriptomic and proteomic levels to determine the effects of drugs, oxidants, and growth conditions on the global patterns of gene expression. Ultimately, the final response of these changes is reflected in the metabolic composition of the bacterium including a few thousand small molecular weight chemicals. Comparing the metabolic profiles of wild type and mutant strains, either untreated or treated with a particular drug, can effectively allow target identification and may lead to the development of novel inhibitors with anti-tubercular activity. Likewise, the effects of two or more conditions on the metabolome can also be assessed. Nuclear magnetic resonance (NMR) is a powerful technology that is used to identify and quantify metabolic intermediates. In this protocol, procedures for the preparation of M. tuberculosis cell extracts for NMR metabolomic analysis are described. Cell cultures are grown under appropriate conditions and required Biosafety Level 3 containment, harvested, and subjected to mechanical lysis while maintaining cold temperatures to maximize preservation of metabolites. Cell lysates are recovered, filtered sterilized, and stored at ultra-low temperatures. Aliquots from these cell extracts are plated on Middlebrook 7H9 agar for colony-forming units to verify absence of viable cells. Upon two months of incubation at 37 °C, if no viable colonies are observed, samples are removed from the containment facility for downstream processing. Extracts are lyophilized, resuspended in deuterated buffer and injected in the NMR instrument, capturing spectroscopic data that is then subjected to statistical analysis. The procedures described can be applied for both one-dimensional (1D) H NMR and two-dimensional (2D) H-(13)C NMR analyses. This methodology provides more reliable small molecular weight metabolite identification and more reliable and sensitive quantitative analyses of cell extract metabolic compositions than chromatographic methods. Variations of the procedure described following the cell lysis step can also be adapted for parallel proteomic analysis.

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.

Staphylococcus epidermidis is a skin-resident bacterium and a major cause of biomaterial-associated infections. The transition from residing on the skin to residing on an implanted biomaterial is accompanied by regulatory changes that facilitate bacterial survival in the new environment. These regulatory changes are dependent upon the ability of bacteria to "sense" environmental changes. In S. epidermidis, disparate environmental signals can affect synthesis of the biofilm matrix polysaccharide intercellular adhesin (PIA). Previously, we demonstrated that PIA biosynthesis is regulated by tricarboxylic acid (TCA) cycle activity. The observations that very different environmental signals result in a common phenotype (i.e. increased PIA synthesis) and that TCA cycle activity regulates PIA biosynthesis led us to hypothesize that S. epidermidis is "sensing" disparate environmental signals through the modulation of TCA cycle activity. In this study, we used NMR metabolomics to demonstrate that divergent environmental signals are transduced into common metabolomic changes that are "sensed" by metabolite-responsive regulators, such as CcpA, to affect PIA biosynthesis. These data clarify one mechanism by which very different environmental signals cause common phenotypic changes. In addition, due to the frequency of the TCA cycle in diverse genera of bacteria and the intrinsic properties of TCA cycle enzymes, it is likely the TCA cycle acts as a signal transduction pathway in many bacteria.

Large amounts of data from high-throughput metabolomic experiments are commonly visualized using a principal component analysis (PCA) two-dimensional scores plot. The question of the similarity or difference between multiple metabolic states then becomes a question of the degree of overlap between their respective data point clusters in principal component (PC) scores space. A qualitative visual inspection of the clustering pattern in PCA scores plots is a common protocol. This article describes the application of tree diagrams and bootstrapping techniques for an improved quantitative analysis of metabolic PCA data clustering. Our PCAtoTree program creates a distance matrix with 100 bootstrap steps that describes the separation of all clusters in a metabolic data set. Using accepted phylogenetic software, the distance matrix resulting from the various metabolic states is organized into a phylogenetic-like tree format, where bootstrap values 50 indicate a statistically relevant branch separation. PCAtoTree analysis of two previously published data sets demonstrates the improved resolution of metabolic state differences using tree diagrams. In addition, for metabolomic studies of large numbers of different metabolic states, the tree format provides a better description of similarities and differences between each metabolic state. The approach is also tolerant of sample size variations between different metabolic states., (Copyright 2009 Elsevier Inc. All rights reserved.)