Steven M Halouska

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 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.

Predicting the in Vivo 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. 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.

Tricarboxylic Acid Cycle-Dependent Regulation of Staphylococcus epidermidis Polysaccharide Intercellular Adhesin Synthesis

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.

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) 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.

Using NMR Metabolomics to Investigate Tricarboxylic Acid Cycle-dependent Signal Transduction in Staphylococcus epidermidis

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.

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 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.

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-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.

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 13C5 labeled arginine when grown with 13C5 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.

Solution structure of the Pseudomonas putida protein PpPutA45 and its DNA complex.

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 β‐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 α‐helices provides a structural scaffold for the β‐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. Proteins 2009.

Transient sampling of aggregation-prone conformations causes pathogenic instability of a parkinsonian mutant of DJ-1 at physiological temperature

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.