Negative impact of noise on the principal component analysis of NMR data

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 signifi cant and large changes in low-concentration components that may lead to a skewed or irrelevant clustering of the NMR data. We have identifi ed an additional concern, very small and random fl uctuations 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

Utilities for Quantifying Separation in PCA/PLS-DA Scores Plots

Metabolic fingerprinting studies rely on interpretations drawn from low-dimensional representations of spectral data generated by methods of multivariate analysis such as PCA and PLS-DA. The growth of metabolic fingerprinting and chemometric analyses involving these lowdimensional 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

Predicting the \u3ci\u3ein vivo\u3c/i\u3e Mechanism of Action for Drug Leads using NMR Metabolomics

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. Towards this end, we describe an unbiased approach to predict in vivo mechanisms of action from NMR metabolomics data. Mycobacterium smegmatis, a nonpathogenic 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

Sample Preparation of \u3ci\u3eMycobacterium tuberculosis\u3c/i\u3e Extracts for Nuclear Magnetic Resonance Metabolomic Studies

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,1 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) 1H NMR and two-dimensional (2D) 1H-13C 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

Analysis of Metabolomic PCA Data using Tree Diagrams

Large amounts of data from high throughput metabolomic experiments are commonly visualized using a principal component analysis (PCA) 2D 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 PC scores space. A qualitative visual inspection of the clustering pattern in PCA score plots is a common protocol. This report 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 dataset. 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

Analysis of Metabolomic PCA Data using Tree Diagrams

Large amounts of data from high throughput metabolomic experiments are commonly visualized using a principal component analysis (PCA) 2D 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 PC scores space. A qualitative visual inspection of the clustering pattern in PCA score plots is a common protocol. This report 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 dataset. 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

NMR Analysis of a Stress Response Metabolic Signaling Network

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 sub-lethal 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 1H-13C HSQC and 1H-1H 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

Metabolomics Analysis Identifies D-Alanine-D-alanine Ligase as the Primary Lethal Target of D-cycloserine in Mycobacteria

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-Dalanine 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

Metabolomics Analysis Identifies D-Alanine-D-alanine Ligase as the Primary Lethal Target of D-cycloserine in Mycobacteria

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-Dalanine 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

CcpA regulates arginine biosynthesis in Staphylococcus aureus through repression of proline catabolism.

Staphylococcus aureus is a leading cause of community-associated and nosocomial infections. Imperative to the success of S. aureus is the ability to adapt and utilize nutrients that are readily available. Genomic sequencing suggests that S. aureus has the genes required for synthesis of all twenty amino acids. However, in vitro experimentation demonstrates that staphylococci have multiple amino acid auxotrophies, including arginine. Although S. aureus possesses the highly conserved anabolic pathway that synthesizes arginine via glutamate, we demonstrate here that inactivation of ccpA facilitates the synthesis of arginine via the urea cycle utilizing proline as a substrate. Mutations within putA, rocD, arcB1, argG and argH abolished the ability of S. aureus JE2 ccpA::tetL to grow in the absence of arginine, whereas an interruption in argJBCF, arcB2, or proC had no effect. Furthermore, nuclear magnetic resonance demonstrated that JE2 ccpA::ermB produced (13)C(5) labeled arginine when grown with (13)C(5) proline. Taken together, these data support the conclusion that S. aureus synthesizes arginine from proline during growth on secondary carbon sources. Furthermore, although highly conserved in all sequenced S. aureus genomes, the arginine anabolic pathway (ArgJBCDFGH) is not functional under in vitro growth conditions. Finally, a mutation in argH attenuated virulence in a mouse kidney abscess model in comparison to wild type JE2 demonstrating the importance of arginine biosynthesis in vivo via the urea cycle. However, mutations in argB, argF, and putA did not attenuate virulence suggesting both the glutamate and proline pathways are active and they, or their pathway intermediates, can complement each other in vivo