Detection and Identification of Heme <i>c</i>‑Modified Peptides by Histidine Affinity Chromatography, High-Performance Liquid Chromatography–Mass Spectrometry, and Database Searching

Multiheme c-type cytochromes (proteins with covalently attached heme <i>c</i> moieties) play important roles in extracellular metal respiration in dissimilatory metal-reducing bacteria. Liquid chromatography–tandem mass spectrometry (LC–MS/MS) characterization of c-type cytochromes is hindered by the presence of multiple heme groups, since the heme <i>c</i> modified peptides are typically not observed or, if observed, not identified. Using a recently reported histidine affinity chromatography (HAC) procedure, we enriched heme <i>c</i> tryptic peptides from purified bovine heart cytochrome <i>c</i>, two bacterial decaheme cytochromes, and subjected these samples to LC–MS/MS analysis. Enriched bovine cytochrome <i>c</i> samples yielded 3- to 6-fold more confident peptide–spectrum matches to heme <i>c</i> containing peptides than unenriched digests. In unenriched digests of the decaheme cytochrome MtoA from <i>Sideroxydans lithotrophicus</i> ES-1, heme <i>c</i> peptides for 4 of the 10 expected sites were observed by LC–MS/MS; following HAC fractionation, peptides covering 9 out of 10 sites were obtained. Heme <i>c</i> peptide spiked into <i>E. coli</i> lysates at mass ratios as low as 1 × 10^–4 was detected with good signal-to-noise after HAC and LC–MS/MS analysis. In addition to HAC, we have developed a proteomics database search strategy that takes into account the unique physicochemical properties of heme <i>c</i> peptides. The results suggest that accounting for the double thioether link between heme <i>c</i> and peptide, and the use of the labile heme fragment as a reporter ion, can improve database searching results. The combination of affinity chromatography and heme-specific informatics yielded increases in the number of peptide–spectrum matches of 20–100-fold for bovine cytochrome <i>c</i>

Detection and Identification of Heme <i>c</i>‑Modified Peptides by Histidine Affinity Chromatography, High-Performance Liquid Chromatography–Mass Spectrometry, and Database Searching

Multiheme c-type cytochromes (proteins with covalently attached heme <i>c</i> moieties) play important roles in extracellular metal respiration in dissimilatory metal-reducing bacteria. Liquid chromatography–tandem mass spectrometry (LC–MS/MS) characterization of c-type cytochromes is hindered by the presence of multiple heme groups, since the heme <i>c</i> modified peptides are typically not observed or, if observed, not identified. Using a recently reported histidine affinity chromatography (HAC) procedure, we enriched heme <i>c</i> tryptic peptides from purified bovine heart cytochrome <i>c</i>, two bacterial decaheme cytochromes, and subjected these samples to LC–MS/MS analysis. Enriched bovine cytochrome <i>c</i> samples yielded 3- to 6-fold more confident peptide–spectrum matches to heme <i>c</i> containing peptides than unenriched digests. In unenriched digests of the decaheme cytochrome MtoA from <i>Sideroxydans lithotrophicus</i> ES-1, heme <i>c</i> peptides for 4 of the 10 expected sites were observed by LC–MS/MS; following HAC fractionation, peptides covering 9 out of 10 sites were obtained. Heme <i>c</i> peptide spiked into <i>E. coli</i> lysates at mass ratios as low as 1 × 10^–4 was detected with good signal-to-noise after HAC and LC–MS/MS analysis. In addition to HAC, we have developed a proteomics database search strategy that takes into account the unique physicochemical properties of heme <i>c</i> peptides. The results suggest that accounting for the double thioether link between heme <i>c</i> and peptide, and the use of the labile heme fragment as a reporter ion, can improve database searching results. The combination of affinity chromatography and heme-specific informatics yielded increases in the number of peptide–spectrum matches of 20–100-fold for bovine cytochrome <i>c</i>

Protein abundances can distinguish between naturally-occurring and laboratory strains of <i>Yersinia pestis</i>, the causative agent of plague

<div><p>The rapid pace of bacterial evolution enables organisms to adapt to the laboratory environment with repeated passage and thus diverge from naturally-occurring environmental (“wild”) strains. Distinguishing wild and laboratory strains is clearly important for biodefense and bioforensics; however, DNA sequence data alone has thus far not provided a clear signature, perhaps due to lack of understanding of how diverse genome changes lead to convergent phenotypes, difficulty in detecting certain types of mutations, or perhaps because some adaptive modifications are epigenetic. Monitoring protein abundance, a molecular measure of phenotype, can overcome some of these difficulties. We have assembled a collection of <i>Yersinia pestis</i> proteomics datasets from our own published and unpublished work, and from a proteomics data archive, and demonstrated that protein abundance data can clearly distinguish laboratory-adapted from wild. We developed a lasso logistic regression classifier that uses binary (presence/absence) or quantitative protein abundance measures to predict whether a sample is laboratory-adapted or wild that proved to be ~98% accurate, as judged by replicated 10-fold cross-validation. Protein features selected by the classifier accord well with our previous study of laboratory adaptation in <i>Y</i>. <i>pestis</i>. The input data was derived from a variety of unrelated experiments and contained significant confounding variables. We show that the classifier is robust with respect to these variables. The methodology is able to discover signatures for laboratory facility and culture medium that are largely independent of the signature of laboratory adaptation. Going beyond our previous laboratory evolution study, this work suggests that proteomic differences between laboratory-adapted and wild <i>Y</i>. <i>pestis</i> are general, potentially pointing to a process that could apply to other species as well. Additionally, we show that proteomics datasets (even archived data collected for different purposes) contain the information necessary to distinguish wild and laboratory samples. This work has clear applications in biomarker detection as well as biodefense.</p></div

Overview of the samples represented in the assembled data sets.

<p>Overview of the samples represented in the assembled data sets.</p

More protein features than those reported in Table 2 can accurately classify laboratory vs. wild samples.

<p>The Lasso logistic regression classifier (LRC) was constructed in iterations, with the input data for each iteration consisting of all protein features not selected by the LRC in any previous iteration. The plots show the classifier accuracy on the vertical axis plotted against the number of iterations on the horizontal axis. The number of features selected in each iteration is the plotted symbol. <b>A</b>, LRCs using quantitative protein abundance data; <b>B</b>, LRCs using presence/absence data. Note that the accuracy value in the limit of large numbers of iterations is equal to the proportion of laboratory samples in the data, and represents the limit where the features used contain no information useful for classification.</p

Output of the LRC to distinguish wild from laboratory-adapted strains using relative protein abundance data.

<p>Each symbol represents the prediction of the LRC for an independent culture. Triangles represent cultures of wild strains. Circles represent laboratory-adapted strains. The horizontal axis value is the predicted probability that a culture is laboratory adapted and is non-linear; points are separated vertically in a random fashion to improve the visualization. See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0183478#sec003" target="_blank">Methods</a> for an explanation of τ. A. Colors represent wild versus laboratory-adapted. B. Colors represent the facility of preparation and analysis. C. Colors represent the laboratory medium in which the cultures were grown prior to analysis.</p

Illustration of the permutation test of the final LRC generated using 10-fold cross-validation and relative abundance features.

<p>The red histogram represents the distribution of the accuracy generated from 10,000 permutations of the laboratory-adapted/wild labels. This histogram represents the null, distribution, i.e., the distribution expected if no information relevant to distinguishing laboratory and wild samples were present in the data. The cross-validation estimate of the accuracy of the final LRC, 99.5% is illustrated by the dashed blue line. The distance of the blue line from the null distribution clearly indicates that the observed accuracy of the LRC did not occur by chance, supporting the conclusion that the data for laboratory-adapted and wild samples is truly different. Results for the other three LRC’s (2-fold with relative abundance, 10-fold with presence/absence, and 2-fold with presence/absence) were identical to this one.</p

Protein features selected to distinguish wild and laboratory-adapted <i>Y</i>. <i>pestis</i> in the logistic regression classifier using relative protein abundance data.

<p>Protein features selected to distinguish wild and laboratory-adapted <i>Y</i>. <i>pestis</i> in the logistic regression classifier using relative protein abundance data.</p

Changes in Protein Expression Across Laboratory and Field Experiments in <i>Geobacter bemidjiensis</i>

Bacterial extracellular metal respiration, as carried out by members of the genus <i>Geobacter</i>, is of interest for applications including microbial fuel cells and bioremediation. <i>Geobacter bemidjiensis</i> is the major species whose growth is stimulated during groundwater amendment with acetate. We have carried out label-free proteomics studies of <i>G. bemidjiensis</i> grown with acetate as the electron donor and either fumarate, ferric citrate, or one of two hydrous ferric oxide mineral types as electron acceptor. The major class of proteins whose expression changes across these conditions is <i>c-</i>type cytochromes, many of which are known to be involved in extracellular metal reduction in other, better-characterized <i>Geobacter</i> species. Some proteins with multiple homologues in <i>G. bemidjiensis</i> (OmcS, OmcB) had different expression patterns than observed for their <i>G. sulfurreducens</i> homologues under similar growth conditions. We also compared the proteome from our study to a prior proteomics study of biomass recovered from an aquifer in Colorado, where the microbial community was dominated by strains closely related to <i>G. bemidjiensis</i>. We detected an increased number of proteins with functions related to motility and chemotaxis in the Colorado field samples compared to the laboratory samples, suggesting the importance of motility for in situ extracellular metal respiration

Live Cell Chemical Profiling of Temporal Redox Dynamics in a Photoautotrophic Cyanobacterium

Protein reduction–oxidation (redox) modification is an important mechanism that allows microorganisms to sense environmental changes and initiate cellular responses. We have developed a quantitative chemical probe approach for live cell labeling and imaging of proteins that are sensitive to redox modifications. We utilize this <i>in vivo</i> strategy to identify 176 proteins undergoing ∼5–10-fold dynamic redox change in response to nutrient limitation and subsequent replenishment in the photoautotrophic cyanobacterium <i>Synechococcus</i> sp. PCC 7002. We detect redox changes in as little as 30 s after nutrient perturbation and oscillations in reduction and oxidation for 60 min following the perturbation. Many of the proteins undergoing dynamic redox transformations participate in the major components for the production (photosystems and electron transport chains) or consumption (Calvin–Benson cycle and protein synthesis) of reductant and/or energy in photosynthetic organisms. Thus, our <i>in vivo</i> approach reveals new redox-susceptible proteins and validates those previously identified <i>in vitro</i>