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In mycobacteria, d-alanine is an essential precursor for peptidoglycan biosynthesis. The only confirmed enzymatic pathway to form d-alanine is through the racemization of l-alanine by alanine racemase (Alr, EC 5.1.1.1). Nevertheless, the essentiality of Alr in Mycobacterium tuberculosis and Mycobacterium smegmatis for cell survivability in the absence of d-alanine has been a point of controversy with contradictory results reported in the literature. To address this issue, we examined the effects of alr inactivation on the cellular metabolism of M. smegmatis. The M. smegmatis alr insertion mutant TAM23 exhibited essentially identical growth to wild-type mc 2 155 in the absence of d-alanine. NMR metabolomics revealed drastically distinct phenotypes between mc 2 155 and TAM23. A metabolic switch was observed for TAM23 as a function of supplemented d-alanine. In the absence of d-alanine, the metabolic response directed carbon through an unidentified transaminase to provide the essential d-alanine required for survival. The process is reversed when d-alanine is available, in which the d-alanine is directed to peptidoglycan biosynthesis. Our results provide further support for the hypothesis that Alr is not an essential function of M. smegmatis and that specific Alr inhibitors will have no bactericidal action.

Various missense mutations in the cytoprotective protein DJ-1 cause rare forms of inherited parkinsonism. One mutation, M26I, diminishes DJ-1 protein levels in the cell but does not result in large changes in the three-dimensional structure or thermal stability of the protein. Therefore, the molecular defect that results in loss of M26I DJ-1 protective function is unclear. Using NMR spectroscopy near physiological temperature, we found that the picosecond-nanosecond dynamics of wild-type and M26I DJ-1 are similar. In contrast, elevated amide hydrogen/deuterium exchange rates indicate that M26I DJ-1 is more flexible than the wild-type protein on longer timescales and that hydrophobic regions of M26I DJ-1 are transiently exposed to solvent. Tryptophan fluorescence spectroscopy and thiol crosslinking analyzed by mass spectrometry also demonstrate that M26I DJ-1 samples conformations that differ from the wild-type protein at 37°C. These transiently sampled conformations are unstable and cause M26I DJ-1 to aggregate in vitro at physiological temperature but not at lower temperatures. M26I DJ-1 aggregation is correlated with pathogenicity, as the structurally similar but non-pathogenic M26L mutation does not aggregate at 37°C. The onset of dynamically driven M26I DJ-1 instability at physiological temperature resolves conflicting literature reports about the behavior of this disease-associated mutant and illustrates the pitfalls of characterizing proteins exclusively at room temperature or below, as key aspects of their behavior may not be apparent., (© 2015 The Protein Society.)

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.

Over the past decade, metabolomics has emerged as an important technique for systems biology. Measuring all the metabolites in a biological system provides an invaluable source of information to explore various cellular processes, and to investigate the impact of environmental factors and genetic modifications. Nuclear magnetic resonance (NMR) spectroscopy is an important method routinely employed in metabolomics. NMR provides comprehensive structural and quantitative information useful for metabolomics fingerprinting, chemometric analysis, metabolite identification and metabolic pathway construction. A successful metabolomics study relies on proper experimental protocols for the collection, handling, processing and analysis of metabolomics data. Critically, these protocols should eliminate or avoid biologically-irrelevant changes to the metabolome. We provide a comprehensive description of our NMR-based metabolomics procedures optimized for the analysis of bacterial metabolomes. The technical details described within this manuscript should provide a useful guide to reliably apply our NMR-based metabolomics methodology to systems biology studies.

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.

We previously hypothesized that Staphylococcus epidermidis senses a diverse set of environmental and nutritional factors associated with biofilm formation through a modulation in the activity of the tricarboxylic acid (TCA) cycle. Herein, we report our further investigation of the impact of additional environmental stress factors on TCA cycle activity and provide a detailed description of our NMR methodology. S. epidermidis wild-type strain 1457 was treated with stressors that are associated with biofilm formation, a sublethal dose of tetracycline, 5% NaCl, 2% glucose, and autoinducer-2 (AI-2). As controls and to integrate our current data with our previous study, 4% ethanol stress and iron-limitation were also used. Consistent with our prior observations, the effect of many environmental stress factors on the S. epidermidis metabolome was essentially identical to the effect of TCA cycle inactivation in the aconitase mutant strain 1457-acnA::tetM. A detailed quantitative analysis of metabolite concentration changes using 2D (1)H-(13)C HSQC and (1)H-(1)H TOCSY spectra identified a network of 37 metabolites uniformly affected by the stressors and TCA cycle inactivation. We postulate that the TCA cycle acts as the central pathway in a metabolic signaling network.

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.)

Proline utilization A (PutA) is a membrane-associated multifunctional enzyme that catalyzes the oxidation of proline to glutamate in a two-step process. In certain, gram-negative bacteria such as Pseudomonas putida, PutA also acts as an auto repressor in the cytoplasm, when an insufficient concentration of proline is available. Here, the N-terminal residues 1-45 of PutA from P. putida (PpPutA45) are shown to be responsible for DNA binding and dimerization. The solution structure of PpPutA45 was determined using NMR methods, where the protein is shown to be a symmetrical homodimer (12 kDa) consisting of two ribbon-helix-helix (RHH) structures. DNA sequence recognition by PpPutA45 was determined using DNA gel mobility shift assays and NMR chemical shift perturbations (CSPs). PpPutA45 was shown to bind a 14 base-pair DNA oligomer (5'-GCGGTTGCACCTTT-3'). A model of the PpPutA45-DNA oligomer complex was generated using Haddock 2.1. The antiparallel beta-sheet that results from PpPutA45 dimerization serves as the DNA recognition binding site by inserting into the DNA major groove. The dimeric core of four alpha-helices provides a structural scaffold for the beta-sheet from which residues Thr5, Gly7, and Lys9 make sequence-specific contacts with the DNA. The structural model implies flexibility of Lys9 which can make hydrogen bond contacts with either guanine or thymine. The high sequence and structure conservation of the PutA RHH domain suggest interdomain interactions play an important role in the evolution of the protein., ((c) 2008 Wiley-Liss, Inc.)

Staphylococcus epidermidis is a major nosocomial pathogen primarily infecting immunocompromised individuals or those with implanted biomaterials (e.g., catheters). Biomaterial-associated infections often involve the formation of a biofilm on the surface of the medical device. In S. epidermidis, polysaccharide intercellular adhesin (PIA) is an important mediator of biofilm formation and pathogenesis. Synthesis of PIA is regulated by at least three DNA binding proteins (IcaR, SarA, and sigma(B)) and several environmental and nutritional conditions. Previously, we observed the environmental conditions that increased PIA synthesis decreased tricarboxylic acid (TCA) cycle activity. In this study, S. epidermidis TCA cycle mutants were constructed, and the function of central metabolism in PIA biosynthesis was examined. TCA cycle inactivation altered the metabolic status of S. epidermidis, resulting in a massive derepression of PIA biosynthetic genes and a redirection of carbon from growth into PIA biosynthesis. These data demonstrate that the bacterial metabolic status is a critical regulatory determinant of PIA synthesis. In addition, these data lead us to propose that the TCA cycle acts as a signal transduction pathway to translate external environmental cues into intracellular metabolic signals that modulate the activity of transcriptional regulators.

D-Cycloserine (DCS) is only used with multidrug-resistant strains of tuberculosis because of serious side effects. DCS is known to inhibit cell wall biosynthesis, but the in vivo lethal target is still unknown. We have applied NMR-based metabolomics combined with principal component analysis to monitor the in vivo effect of DCS on Mycobacterium smegmatis. Our analysis suggests DCS functions by inhibiting multiple protein targets.

We describe a general protocol for using comparative NMR metabolomics data to infer in vivo efficacy, specificity and toxicity of chemical leads within a drug discovery program. The methodology is demonstrated using Aspergillus nidulans to monitor the activity of urate oxidase and orotidine-5'-phosphate decarboxylase and the impact of 8-azaxanthine, an inhibitor of urate oxidase. 8-azaxanthine is shown to inhibit A. nidulans hyphal growth by in vivo inactivation of urate oxidase.

Principal component analysis (PCA) is routinely applied to the study of NMR based metabolomic data. PCA is used to simplify the examination of complex metabolite mixtures obtained from biological samples that may be composed of hundreds or thousands of chemical components. PCA is primarily used to identify relative changes in the concentration of metabolites to identify trends or characteristics within the NMR data that permits discrimination between various samples that differ in their source or treatment. A common concern with PCA of NMR data is the potential over emphasis of small changes in high concentration metabolites that would over-shadow significant and large changes in low-concentration components that may lead to a skewed or irrelevant clustering of the NMR data. We have identified an additional concern, very small and random fluctuations within the noise of the NMR spectrum can also result in large and irrelevant variations in the PCA clustering. Alleviation of this problem is obtained by simply excluding the noise region from the PCA by a judicious choice of a threshold above the spectral noise.