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Many methods of microbial proteome characterizations require large quantifies of cellular biomass (> 1-2 g) for sample preparation and protein identification. Our experimental approach differs from traditional techniques by providing the ability to identify the proteomic state of a microbe from a few milligrams of starting cellular material. The small-scale, guanidine lysis method minimizes sample loss by achieving cellular lysis and protein digestion in a single-tube experiment. For this experimental approach, the freshwater microbe Shewanella oneidensis MR-1 and the purple non-sulfur bacterium Rhodopseudomonas palustris CGA0010 were used as model organisms for technology development and evaluation. A 2-D LC-MS/MS comparison between a standard sonication lysis method and the small-scale guanidine lysis techniques demonstrates that the guanidine lysis method is more efficient with smaller sample amounts of cell pellet (i.e., down to 1 mg). The described methodology enables deeper proteome measurements from a few milliliters of confluent bacterial cultures. We also report a new protocol for efficient lysis from small amounts of natural biofilm samples for deep proteome measurements, which should greatly enhance the emerging field of environmental microbial community proteomics. This straightforward sample boiling protocol is complementary to the small-scale guanidine lysis technique, is amenable for small sample quantifies, and requires no special reagents that might complicate the MS measurements.

A profile likelihood algorithm is proposed for quantitative shotgun proteomics to infer the abundance ratios of proteins from the abundance ratios of isotopically labeled peptides derived from proteolysis. Previously, we have shown that the estimation variability and bias of peptide abundance ratios can be predicted from their profile signal-to-noise ratios. Given multiple quantified peptides for a protein, the profile likelihood algorithm probabilistically weighs the peptide abundance ratios by their inferred estimation variability, accounts for their expected estimation bias, and suppresses contribution from outliers. This algorithm yields maximum likelihood point estimation and profile likelihood confidence interval estimation of protein abundance ratios. This point estimator is more accurate than an estimator based on the average of peptide abundance ratios. The confidence interval estimation provides an 'error bar' for each protein abundance ratio that reflects its estimation precision and statistical uncertainty. The accuracy of the point estimation and the precision and confidence level of the interval estimation were benchmarked with standard mixtures of isotopically labeled proteomes. The profile likelihood algorithm was integrated into a quantitative proteomics program, called ProRata, freely available at www.MSProRata.org.

Algorithmic search engines bridge the gap between large tandem mass spectrometry data sets and the identification of proteins associated with biological samples. Improvements in these tools can greatly enhance biological discovery. We present a new scoring scheme for comparing tandem mass spectra with a protein sequence database. The MASPIC (Multinomial Algorithm for Spectral Profile-based Intensity Comparison) scorer converts an experimental tandem mass spectrum into a m/z profile of probability and then scores peak lists from potential candidate peptides using a multinomial distribution model. The MASPIC scoring scheme incorporates intensity, spectral peak density variations, and m/z error distribution associated with peak matches into a multinomial distribution. The scoring scheme was validated on two standard protein mixtures and an additional set of spectra collected on a complex ribosomal protein mixture from Rhodopseudomonas palustris. The results indicate a 5-15% improvement over Sequest for high-confidence identifications. The performance gap grows as sequence database size increases. Additional tests on spectra from proteinase-K digest data showed similar performance improvements demonstrating the advantages in using MASPIC for studying proteins digested with less specific proteases. All these investigations show MASPIC to be a versatile and reliable system for peptide tandem mass spectral identification.

There is currently a great need for rapid detection and positive identification of biological threat agents, as well as microbial species in general, directly from complex environmental samples. This need is most urgent in the area of homeland security, but also extends into medical, environmental, and agricultural sciences. Mass-spectrometry-based analysis is one of the leading technologies in the field with a diversity of different methodologies for biothreat detection. Over the past few years, 'shotgun' proteomics has become one method of choice for the rapid analysis of complex protein mixtures by mass spectrometry. Recently, it was demonstrated that this methodology is capable of distinguishing a target species against a large database of background species from a single-component sample or dual-component mixtures with relatively the same concentration (Dworzanski, J. P.; Snyder, A. P.; Chen, R.; Zhang, H.; Wishart, D.; Li, L. Anal. Chem. 2004, 76, 2355-2366). Here, we examine the potential of shotgun proteomics to analyze a target species in a background of four contaminant species. We tested the capability of a common commercial mass-spectrometry-based shotgun proteomics platform for the detection of the target species (Escherichia coli) at four different concentrations and four different time points of analysis. We also tested the effect of database size on positive identification of the four microbes used in this study by testing a small (13-species) database and a large (261-species) database. The results clearly indicated that this technology could easily identify the target species at 20% in the background mixture at a 60, 120, 180, or 240 min analysis time with the small database. The results also indicated that the target species could easily be identified at 20% or 6% but could not be identified at 0.6% or 0.06% in either a 240 min analysis or a 30 h analysis with the small database. The effects of the large database were severe on the target species where detection above the background at any concentration used in this study was impossible, though the three other microbes used in this study were clearly identified above the background when analyzed with the large database. This study points to the potential application of this technology for biological threat agent detection but highlights many areas of needed research before the technology will be useful in real world samples.

Site-directed mutagenesis of R67 dihydrofolate reductase reveals that the enzyme possesses a 'hot spot' binding surface, which binds both folate and NADPH ligands, during catalysis. Data indicate that the enzyme exhibits an endo conformer for cooperative binding of ligands, which suggests primitivity of the enzyme.

Shotgun proteomics experiments require the collection of thousands of tandem mass spectra; these sets of data will continue to grow as new instruments become available that can scan at even higher rates. Such data contain substantial amounts of redundancy with spectra from a particular peptide being acquired many times during a single LC-MS/MS experiment. In this article, we present MS2Grouper, an algorithm that detects spectral duplication, assesses groups of related spectra, and replaces these groups with synthetic representative spectra. Errors in detecting spectral similarity are corrected using a paraclique criterion—spectra are only assessed as groups if they are part of a clique of at least three completely interrelated spectra or are subsequently added to such cliques by being similar to all but one of the clique members. A greedy algorithm constructs a representative spectrum for each group by iteratively removing the tallest peaks from the spectral collection and matching to peaks in the other spectra. This strategy is shown to be effective in reducing spectral counts by up to 20% in LC-MS/MS datasets from protein standard mixtures and proteomes, reducing database search times without a concomitant reduction in identified peptides. [Copyright &y& Elsevier]

The expanding use of surfactants for proteome sample preparations has prompted the need to systematically optimize the application and removal of these MS-deleterious agents prior to proteome measurements. Here we compare four detergent cleanup methods (trichloroacetic acid (TCA) precipitation, chloroform/methanol/water (CMW) extraction, a commercial detergent removal spin column method (DRS) and filter-aided sample preparation (FASP)) to provide efficiency benchmarks with respect to protein, peptide, and spectral identifications in each case. Our results show that for protein-limited samples, FASP outperforms the other three cleanup methods, while at high protein amounts, all the methods are comparable. This information was used to investigate and contrast molecular weight-based fractionated with unfractionated lysates from three increasingly complex samples ( Escherichia coli K-12, a five microbial isolate mixture, and a natural microbial community groundwater sample), all of which were prepared with an SDS-FASP approach. The additional fractionation step enhanced the number of protein identifications by 8% to 25% over the unfractionated approach across the three samples.

Many key proteins, such as those involved in cellular signaling or transcription, are difficult to measure in microbial proteomic experiments due to the interfering presence of more abundant, dominant proteins. In an effort to enhance the identification of previously undetected proteins, as well as provide a methodology for selective enrichment, we evaluated and optimized immobilized metal affinity chromatography (IMAC) coupled with mass spectrometric characterization of extracellular proteins from an extremophilic microbial community. Seven different metals were tested for IMAC enrichment. The combined results added ∼20% greater proteomic depth to the extracellular proteome. Although this IMAC enrichment could not be conducted at the physiological pH of the environmental system, this approach did yield a reproducible and specific enrichment of groups of proteins with functions potentially vital to the community, thereby providing a more extensive biochemical characterization. Notably, 40 unknown proteins previously annotated as "hypothetical" were enriched and identified for the first time. Examples of identified proteins includes a predicted TonB signal sensing protein homologous to other known TonB proteins and a protein with a COXG domain previously identified in many chemolithoautotrophic microbes as having a function in the oxidation of CO.

An integrated computational/experimental approach was used to predict and identify signal peptide cleavages among microbial proteins of environmental biofilm communities growing in acid mine drainage (AMD). SignalP-3.0 was employed to computationally query the AMD protein database of >16,000 proteins, which resulted in 1,480 predicted signal peptide cleaved proteins. LC-MS/MS analyses of extracellular (secretome) microbial preparations from different locations and developmental states empirically confirmed 531 of these signal peptide cleaved proteins. The majority of signal-cleavage proteins (58.4%) are annotated to have unknown functions; however, Pfam domain analysis revealed that many may be involved in extracellular functions expected within the AMD system. Examination of the abundances of signal-cleaved proteins across 28 proteomes from biofilms collected over a 4-year period demonstrated a strong correlation with the developmental state of the biofilm. For example, class I cytochromes are abundant in early growth states, whereas cytochrome oxidases from the same organism increase in abundance later in development. These results likely reflect shifts in metabolism that occur as biofilms thicken and communities diversify. In total, these results provide experimental confirmation of proteins that are designed to function in the extreme acidic extracellular environment and will serve as targets for future biochemical analysis.

We compared the Arthrobacter chlorophenolicus proteome during growth on 4-chlorophenol, 4-nitrophenol, or phenol at 5 and 28 degrees C, both for the wild-type and a mutant strain with mass spectrometry based proteomics. A label-free workflow employing spectral counting identified 3749 proteins across all growth conditions, representing over 70% of the predicted genome and 739 of these proteins form the core proteome. Statistically significant differences were found in the proteomes of cells grown under different conditions including differentiation of hundreds of unknown proteins. The 4-chlorophenol-degradation pathway was confirmed, but not that for phenol.

Predicted orphan response regulators encoded in the Shewanella oneidensis MR-1 genome are poorly understood from a cellular function perspective. Our previous transcriptomic and proteomic analyses demonstrated that an annotated DNA-binding response regulator, SO2426, was significantly up-regulated in wild-type S. oneidensis cells at both the mRNA and protein levels in response to acute chromate [Cr(VI)] challenge, suggesting a potential regulatory role for this protein in metal stress pathways. To investigate the impact of SO2426 activity on chromate stress response at a genome-wide scale, we describe here comparative and temporal proteome characterizations using multidimensional HPLC-MS/MS and statistical analysis to identify differentially expressed proteins in biological replicates of wild-type S. oneidensis MR-1 and a so2426 deletion (Deltaso2426) strain, which exhibited an impaired Cr(VI) transformation rate compared to that of the parental strain. Global protein profiles were examined at different time intervals (0, 1, 3, 4 h) following exogenous chromate challenge. Results indicated that deletion of the so2426 gene negatively affected expression of a small protein subset (27 proteins) including those with annotated functions in siderophore biosynthesis (SO3032), Fe uptake (SO4743), intracellular Fe storage (Bfr1), and other transport processes. Cr(VI) exposure and subsequent transformation dramatically increased the number of differentially expressed proteins detected, with up-regulated abundance patterns observed largely for proteins involved in general stress protection and detoxification strategies, cell motility, and protein fate. In addition, the proteome data sets were mined for amino acids with potential post-translational modifications (PTMs) indicative of a level of gene expression regulation extending beyond the transcriptional control imposed by SO2426.

The recent surge in microbial genomic sequencing, combined with the development of high-throughput liquid chromatography-mass-spectrometry-based (LC/LC-MS/MS) proteomics, has raised the question of the extent to which genomic information of one strain or environmental sample can be used to profile proteomes of related strains or samples. Even with decreasing sequencing costs, it remains impractical to obtain genomic sequence for every strain or sample analyzed. Here, we evaluate how shotgun proteomics is affected by amino acid divergence between the sample and the genomic database using a probability-based model and a random mutation simulation model constrained by experimental data. To assess the effects of nonrandom distribution of mutations, we also evaluated identification levels using in silico peptide data from sequenced isolates with average amino acid identities (AAI) varying between 76 and 98%. We compared the predictions to experimental protein identification levels for a sample that was evaluated using a database that included genomic information for the dominant organism and for a closely related variant (95% AAI). The range of models set the boundaries at which half of the proteins in a proteomic experiment can be identified to be 77-92% AAI between orthologs in the sample and database. Consistent with this prediction, experimental data indicated loss of half the identifiable proteins at 90% AAI. Additional analysis indicated a 6.4% reduction of the initial protein coverage per 1% amino acid divergence and total identification loss at 86% AAI. Consequently, shotgun proteomics is capable of cross-strain identifications but avoids most cross-species false positives.

Proteome alterations in the metal-reducing bacterium Shewanella oneidensis MR-1 in response to different acute dose challenges (0.3, 0.5, or 1 mM) of the toxic metal chromate [Cr(VI)] were characterized with multidimensional HPLC-MS/MS. Proteome measurements were performed and compared on both quadrupole ion traps as well as linear trapping quadrupole mass spectrometers. We have found that the implementation of multidimensional liquid chromatography on-line with the rapid scanning, high throughput linear trapping quadrupole platform resulted in a dramatic increase in the number of measured peptides and, thus, the number of identified proteins. A total of 2406 functionally diverse, nonredundant proteins were identified in this study, representing a relatively deep proteome coverage for this organism. The core molecular response to chromate challenge under all three concentrations consisted predominantly of proteins with annotated functions in transport and binding (e.g., components of the TonB1 iron transport system, TonB-dependent receptors, and sulfate transporters) as well as a functionally undefined DNA-binding response regulator (SO2426) that might play a role in mediating metal stress responses. In addition, proteins annotated as a cytochrome c, a putative azoreductase, and various proteins involved in general stress protection were up-regulated at the higher Cr(VI) doses (0.5 and 1 mM) only. Proteins down-regulated in response to metal treatment were distributed across diverse functional categories, with energy metabolism proteins dominating. The results presented in this work demonstrate the dynamic dosage response of S. oneidensis to sub-toxic levels of chromate.

Recent studies have revealed a relationship between protein abundance and sampling statistics, such as sequence coverage, peptide count, and spectral count, in label-free liquid chromatography-tandem mass spectrometry (LC-MS/MS) shotgun proteomics. The use of sampling statistics offers a promising method of measuring relative protein abundance and detecting differentially expressed or coexpressed proteins. We performed a systematic analysis of various approaches to quantifying differential protein expression in eukaryotic Saccharomyces cerevisiae and prokaryotic Rhodopseudomonas palustris label-free LC-MS/MS data. First, we showed that, among three sampling statistics, the spectral count has the highest technical reproducibility, followed by the less-reproducible peptide count and relatively nonreproducible sequence coverage. Second, we used spectral count statistics to measure differential protein expression in pairwise experiments using five statistical tests: Fisher's exact test, G-test, AC test, t-test, and LPE test. Given the S. cerevisiae data set with spiked proteins as a benchmark and the false positive rate as a metric, our evaluation suggested that the Fisher's exact test, G-test, and AC test can be used when the number of replications is limited (one or two), whereas the t-test is useful with three or more replicates available. Third, we generalized the G-test to increase the sensitivity of detecting differential protein expression under multiple experimental conditions. Out of 1622 identified R. palustris proteins in the LC-MS/MS experiment, the generalized G-test detected 1119 differentially expressed proteins under six growth conditions. Finally, we studied correlated expression of these 1119 proteins by analyzing pairwise expression correlations and by delineating protein clusters according to expression patterns. Through pairwise expression correlation analysis, we demonstrated that proteins co-located in the same operon were much more strongly coexpressed than those from different operons. Combining cluster analysis with existing protein functional annotations, we identified six protein clusters with known biological significance. In summary, the proposed generalized G-test using spectral count sampling statistics is a viable methodology for robust quantification of relative protein abundance and for sensitive detection of biologically significant differential protein expression under multiple experimental conditions in label-free shotgun proteomics.

While genome sequencing is becoming ever more routine, genome annotation remains a challenging process. Identification of the coding sequences within the genomic milieu presents a tremendous challenge, especially for eukaryotes with their complex gene architectures. Here, we present a method to assist the annotation process through the use of proteomic data and bioinformatics. Mass spectra of digested protein preparations of the organism of interest were acquired and searched against a protein database created by a six-frame translation of the genome. The identified peptides were mapped back to the genome, compared to the current annotation, and then categorized as supporting or extending the current genome annotation. We named the classified peptides Expressed Peptide Tags (EPTs). The well-annotated bacterium Rhodopseudomonas palustris was used as a control for the method and showed a high degree of correlation between EPT mapping and the current annotation, with 86% of the EPTs confirming existing gene calls and less than 1% of the EPTs expanding on the current annotation. The eukaryotic plant pathogens Phytophthora ramorum and Phytophthora sojae, whose genomes have been recently sequenced and are much less well-annotated, were also subjected to this method. A series of algorithmic steps were taken to increase the confidence of EPT identification for these organisms, including generation of smaller subdatabases to be searched against, and definition of EPT criteria that accommodates the more complex eukaryotic gene architecture. As expected, the analysis of the Phytophthora species showed less correlation between EPT mapping and their current annotation. While approximately 76% of Phytophthora EPTs supported the current annotation, a portion of them (7.7% and 12.9% for P. ramorum and P. sojae, respectively) suggested modification to current gene calls or identified novel genes that were missed by the current genome annotation of these organisms.

Rhodopseudomonas palustris is a purple nonsulfur anoxygenic phototrophic bacterium that is ubiquitous in soil and water. R. palustris is metabolically versatile with respect to energy generation and carbon and nitrogen metabolism. We have characterized and compared the baseline proteome of a R. palustris wild-type strain grown under six metabolic conditions. The methodology for proteome analysis involved protein fractionation by centrifugation, subsequent digestion with trypsin, and analysis of peptides by liquid chromatography coupled with tandem mass spectrometry. Using these methods, we identified 1664 proteins out of 4836 predicted proteins with conservative filtering constraints. A total of 107 novel hypothetical proteins and 218 conserved hypothetical proteins were detected. Qualitative analyses revealed over 311 proteins exhibiting marked differences between conditions, many of these being hypothetical or conserved hypothetical proteins showing strong correlations with different metabolic modes. For example, five proteins encoded by genes from a novel operon appeared only after anaerobic growth with no evidence of these proteins in extracts of aerobically grown cells. Proteins known to be associated with specialized growth states such as nitrogen fixation, photoautotrophic, or growth on benzoate, were observed to be up-regulated under those states.

We present a comprehensive mass spectrometric approach that integrates intact protein molecular mass measurement ("top-down") and proteolytic fragment identification ("bottom-up") to characterize the 70S ribosome from Rhodopseudomonas palustris. Forty-two intact protein identifications were obtained by the top-down approach and 53 out of the 54 orthologs to Escherichia coli ribosomal proteins were identified from bottom-up analysis. This integrated approach simplified the assignment of post-translational modifications by increasing the confidence of identifications, distinguishing between isoforms, and identifying the amino acid positions at which particular post-translational modifications occurred. Our combined mass spectrometry data also allowed us to check and validate the gene annotations for three ribosomal proteins predicted to possess extended C-termini. In particular, we identified a highly repetitive C-terminal "alanine tail" on L25. This type of low complexity sequence, common to eukaryotic proteins, has previously not been reported in prokaryotic proteins. To our knowledge, this is the most comprehensive protein complex analysis to date that integrates two MS techniques.

Here we present a comprehensive method for proteome analysis that integrates both intact protein measurement ("top-down") and proteolytic fragment characterization ("bottom-up") mass spectrometric approaches, capitalizing on the unique capabilities of each method. This integrated approach was applied in a preliminary proteomic analysis of Shewanella oneidensis, a metal-reducing microbe of potential importance to the field of bioremediation. Cellular lysates were examined directly by the "bottom-up" approach as well as fractionated via anion-exchange liquid chromatography for integrated studies. A portion of each fraction was proteolytically digested, with the resulting peptides characterized by on-line liquid chromatography/tandem mass spectrometry. The remaining portion of each fraction containing the intact proteins was examined by high-resolution Fourier transform mass spectrometry. This "top-down" technique provided direct measurement of the molecular masses for the intact proteins and thereby enabled confirmation of post-translational modifications, signal peptides, and gene start sites of proteins detected in the "bottom-up" experiments. A total of 868 proteins from virtually every functional class, including hypotheticals, were identified from this organism.