Uncovering Hidden Members and Functions of the Soil Microbiome Using De Novo Metaproteomics.

Metaproteomics has been increasingly utilized for high-throughput characterization of proteins in complex environments and has been demonstrated to provide insights into microbial composition and functional roles. However, significant challenges remain in metaproteomic data analysis, including creation of a sample-specific protein sequence database. A well-matched database is a requirement for successful metaproteomics analysis, and the accuracy and sensitivity of PSM identification algorithms suffer when the database is incomplete or contains extraneous sequences. When matched DNA sequencing data of the sample is unavailable or incomplete, creating the proteome database that accurately represents the organisms in the sample is a challenge. Here, we leverage a de novo peptide sequencing approach to identify the sample composition directly from metaproteomic data. First, we created a deep learning model, Kaiko, to predict the peptide sequences from mass spectrometry data and trained it on 5 million peptide-spectrum matches from 55 phylogenetically diverse bacteria. After training, Kaiko successfully identified organisms from soil isolates and synthetic communities directly from proteomics data. Finally, we created a pipeline for metaproteome database generation using Kaiko. We tested the pipeline on native soils collected in Kansas, showing that the de novo sequencing model can be employed as an alternative and complementary method to construct the sample-specific protein database instead of relying on (un)matched metagenomes. Our pipeline identified all highly abundant taxa from 16S rRNA sequencing of the soil samples and uncovered several additional species which were strongly represented only in proteomic data.

De novo sequencing and native mass spectrometry revealed hetero-association of dirigent protein homologs and potential interacting proteins in Forsythia × intermedia.

The discovery of dirigent proteins (DPs) and their functions in plant phenol biochemistry was made over two decades ago with Forsythia × intermedia. Stereo-selective, DP-guided, monolignol-derived radical coupling in vitro was then reported to afford the optically active lignan, (+)-pinoresinol from coniferyl alcohol, provided one-electron oxidase/oxidant capacity was present. It later became evident that DPs have several distinct sub-families, presumably with different functions. Some known DPs require other essential enzymes/proteins (e.g. oxidases) for their functions. However, the lack of a fully sequenced genome for Forsythia × intermedia made it difficult to profile other components co-purified with the (+)-pinoresinol forming DP. Herein, we used an integrated bottom-up, top-down, and native mass spectrometry (MS) approach to de novo sequence the extracted proteins via adaptation of our initial report of DP solubilization and purification. Using publicly available transcriptome and genomic data from closely related species, we identified 14 proteins that were putatively associated with either DP function or the cell wall. Although their co-occurrence after extraction and chromatographic separation is suggestive for potential protein-protein interactions, none were found to form stable protein complexes with DPs in native MS under the specific experimental conditions we have explored. Interestingly, two new DP homologs were found and they formed hetero-trimers. Molecular dynamics simulations suggested that similar hetero-trimers were possible between Arabidopsis DP homologs with comparable sequence similarities. Nevertheless, our integrated mass spectrometry method development helped prepare for future investigations directed to the discovery of novel proteins and protein-protein interactions. These advantages can be highly beneficial for plant and microbial research where fully sequenced genomes may not be readily available.

Constructing a Tandem Mass Spectral Library for Forensic Ricin Identification.

Ricin, a protein found in castor seeds, is a lethal toxin that is designated as a category 2 select agent, and cases of attempted ricin poisoning are relatively common. Many methods to detect protein toxins such as ricin use targeted liquid chromatography-tandem mass spectrometry (LC-MS/MS) to identify toxin peptides, usually tryptic peptides. The successful use of untargeted methods has also been reported. However, the use of untargeted proteomics methods, including database search, for peptide and protein identification is less common in forensic practice and may be unfamiliar to forensic science practitioners. Here, we propose a method to create spectral libraries of tryptic ricin peptides and use these libraries for ricin identification by spectral library search, which may be more familiar to forensic scientists because of the use of spectral libraries in small molecule identification. Peptide spectral libraries offer a direct comparison to an authentic standard, a key element of forensic analysis, but have not previously been used in a forensic context. To construct these spectral libraries, two pure ricin samples (one from a proposed standard reference material) were digested with trypsin and analyzed using a standard shotgun LC-MS/MS protocol. Spectral libraries were created from resulting tryptic peptides identified from filtered search results from four database search tools. The library was then used in a search using SpectraST on forensically realistic castor seed extracts. These castor seed samples were made using the crude methods commonly encountered in real-world ricin cases. Analysis showed that the spectral library search resulted in more peptides identified from crude castor seed samples compared to MS-GF+ and Sequest plus Percolator database searches. These results, the first published use of spectral library search to detect protein toxins in forensically relevant samples, suggest that computational comparison of putative ricin peptide spectra to library spectra can be an effective method to detect ricin in an unknown sample. Data are available via ProteomeXchange with identifier PXD013711.

Uniformly 15N-Labeled Recombinant Ricin A-Chain as an Internal Retention Time Standard for Increased Confidence in Forensic Identification of Ricin by Untargeted Nanoflow Liquid Chromatography-Tandem Mass Spectrometry.

Ricin, a toxic protein from the castor plant, is of forensic and biosecurity interest because of its high toxicity and common occurrence in crimes and attempted crimes. Qualitative methods to detect ricin are therefore needed. Untargeted liquid chromatography-tandem mass spectrometry (LC-MS/MS) proteomics methods are well suited because of their high specificity. Specificity in LC-MS/MS comes from both the LC and MS components. However, modern untargeted proteomics methods often use nanoflow LC, which has less reproducible retention times than standard-flow LC, making it challenging to use retention time as a point of identification in a forensic assay. We address this challenge by using retention times relative to a standard, namely, the uniformly 15N-labeled ricin A-chain produced recombinantly in a bacterial expression system. This material, added as an internal standard prior to trypsin digestion, produces a stable-isotope-labeled standard for every ricin tryptic peptide in the sample. We show that the MS signals for 15N and natural isotopic abundance ricin peptides are distinct, with mass shifts that correspond to the numbers of nitrogen atoms in each peptide or fragment. We also show that, as expected, labeled and unlabeled peptides coelute, with relative retention time differences of less than 0.2%.

Probabilistic Limit of Detection for Ricin Identification Using a Shotgun Proteomics Assay.

Robust and highly specific methods for the detection of the protein toxin ricin are of interest to the law enforcement community. In previous studies, methods based on liquid chromatography-tandem mass spectrometry shotgun proteomics have been proposed. The successful implementation of this approach relies on specific data evaluation criteria addressing (1) the quality of the mass spectrometric data, (2) the confidence of peptide identifications (peptide-spectrum matches), and (3) the number and sequence specificity of peptides detected. We present such data evaluation criteria and use a novel approach to establish the limit of detection for this ricin assay. Specifically, we use logistic regression to determine the probability of detection for individual ricin peptides at different concentrations. We then apply basic rules from probability theory, combining these individual peptide probabilities into an overall assay limit of detection. This procedure yields an assay limit of detection for ricin at 42.5 ng on column or 21.25 ng/µL for a 2-µL injection. We also show that, despite the conventional wisdom that detergents are deleterious to mass spectrometric analyses, the presence of Tween-20 did not prevent detection of ricin peptides, and indeed assays performed in buffers that included Tween-20 gave better results than assays performed using other buffer formulations with or without detergent removal.

Proteomics Goes to Court: A Statistical Foundation for Forensic Toxin/Organism Identification Using Bottom-Up Proteomics.

Bottom-up proteomics is increasingly being used to characterize unknown environmental, clinical, and forensic samples. Proteomics-based bacterial identification typically proceeds by tabulating peptide "hits" (i.e., confidently identified peptides) associated with the organisms in a database; those organisms with enough hits are declared present in the sample. This approach has proven to be successful in laboratory studies; however, important research gaps remain. First, the common-practice reliance on unique peptides for identification is susceptible to a phenomenon known as signal erosion. Second, no general guidelines are available for determining how many hits are needed to make a confident identification. These gaps inhibit the transition of this approach to real-world forensic samples where conditions vary and large databases may be needed. In this work, we propose statistical criteria that overcome the problem of signal erosion and can be applied regardless of the sample quality or data analysis pipeline. These criteria are straightforward, producing a p-value on the result of an organism or toxin identification. We test the proposed criteria on 919 LC-MS/MS data sets originating from 2 toxins and 32 bacterial strains acquired using multiple data collection platforms. Results reveal a > 95% correct species-level identification rate, demonstrating the effectiveness and robustness of proteomics-based organism/toxin identification.

Mutations in global regulators lead to metabolic selection during adaptation to complex environments.

Adaptation to ecologically complex environments can provide insights into the evolutionary dynamics and functional constraints encountered by organisms during natural selection. Adaptation to a new environment with abundant and varied resources can be difficult to achieve by small incremental changes if many mutations are required to achieve even modest gains in fitness. Since changing complex environments are quite common in nature, we investigated how such an epistatic bottleneck can be avoided to allow rapid adaptation. We show that adaptive mutations arise repeatedly in independently evolved populations in the context of greatly increased genetic and phenotypic diversity. We go on to show that weak selection requiring substantial metabolic reprogramming can be readily achieved by mutations in the global response regulator arcA and the stress response regulator rpoS. We identified 46 unique single-nucleotide variants of arcA and 18 mutations in rpoS, nine of which resulted in stop codons or large deletions, suggesting that subtle modulations of ArcA function and knockouts of rpoS are largely responsible for the metabolic shifts leading to adaptation. These mutations allow a higher order metabolic selection that eliminates epistatic bottlenecks, which could occur when many changes would be required. Proteomic and carbohydrate analysis of adapting E. coli populations revealed an up-regulation of enzymes associated with the TCA cycle and amino acid metabolism, and an increase in the secretion of putrescine. The overall effect of adaptation across populations is to redirect and efficiently utilize uptake and catabolism of abundant amino acids. Concomitantly, there is a pronounced spread of more ecologically limited strains that results from specialization through metabolic erosion. Remarkably, the global regulators arcA and rpoS can provide a "one-step" mechanism of adaptation to a novel environment, which highlights the importance of global resource management as a powerful strategy to adaptation.

Changes in protein expression across laboratory and field experiments in Geobacter bemidjiensis.

Bacterial extracellular metal respiration, as carried out by members of the genus Geobacter, is of interest for applications including microbial fuel cells and bioremediation. Geobacter bemidjiensis is the major species whose growth is stimulated during groundwater amendment with acetate. We have carried out label-free proteomics studies of G. bemidjiensis 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 c-type cytochromes, many of which are known to be involved in extracellular metal reduction in other, better-characterized Geobacter species. Some proteins with multiple homologues in G. bemidjiensis (OmcS, OmcB) had different expression patterns than observed for their G. sulfurreducens 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 G. bemidjiensis. 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.

The succinated proteome.

The post-translational modifications (PTMs) of cysteine residues include oxidation, S-glutathionylation, S-nitrosylation, and succination, all of which modify protein function or turnover in response to a changing intracellular redox environment. Succination is a chemical modification of cysteine in proteins by the Krebs cycle intermediate, fumarate, yielding S-(2-succino)cysteine (2SC). Intracellular fumarate concentration and succination of proteins are increased by hyperpolarization of the inner mitochondrial membrane, in concert with mitochondrial, endoplasmic reticulum (ER) and oxidative stress in 3T3 adipocytes grown in high glucose medium and in adipose tissue in obesity and diabetes in mice. Increased succination of proteins is also detected in the kidney of a fumarase deficient conditional knock-out mouse which develops renal cysts. A wide range of proteins are subject to succination, including enzymes, adipokines, cytoskeletal proteins, and ER chaperones with functional cysteine residues. There is also some overlap between succinated and glutathionylated proteins, suggesting that the same low pKa thiols are targeted by both. Succination of adipocyte proteins in diabetes increases as a result of nutrient excess derived mitochondrial stress and this is inhibited by uncouplers, which discharge the mitochondrial membrane potential (ΔΨm) and relieve the electron transport chain. 2SC therefore serves as a biomarker of mitochondrial stress or dysfunction in chronic diseases, such as obesity, diabetes, and cancer, and recent studies suggest that succination is a mechanistic link between mitochondrial dysfunction, oxidative and ER stress, and cellular progression toward apoptosis. In this article, we review the history of the succinated proteome and the challenges associated with measuring this non-enzymatic PTM of proteins by proteomics approaches.

Cross-linking and mass spectrometry methodologies to facilitate structural biology: finding a path through the maze.

Multiprotein complexes, rather than individual proteins, make up a large part of the biological macromolecular machinery of a cell. Understanding the structure and organization of these complexes is critical to understanding cellular function. Chemical cross-linking coupled with mass spectrometry is emerging as a complementary technique to traditional structural biology methods and can provide low-resolution structural information for a multitude of purposes, such as distance constraints in computational modeling of protein complexes. In this review, we discuss the experimental considerations for successful application of chemical cross-linking-mass spectrometry in biological studies and highlight three examples of such studies from the recent literature. These examples (as well as many others) illustrate the utility of a chemical cross-linking-mass spectrometry approach in facilitating structural analysis of large and challenging complexes.

Detection and identification of heme c-modified peptides by histidine affinity chromatography, high-performance liquid chromatography-mass spectrometry, and database searching.

Multiheme c-type cytochromes (proteins with covalently attached heme c 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 c modified peptides are typically not observed or, if observed, not identified. Using a recently reported histidine affinity chromatography (HAC) procedure, we enriched heme c tryptic peptides from purified bovine heart cytochrome c, two bacterial decaheme cytochromes, and subjected these samples to LC-MS/MS analysis. Enriched bovine cytochrome c samples yielded 3- to 6-fold more confident peptide-spectrum matches to heme c containing peptides than unenriched digests. In unenriched digests of the decaheme cytochrome MtoA from Sideroxydans lithotrophicus ES-1, heme c 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 c peptide spiked into E. coli 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 c peptides. The results suggest that accounting for the double thioether link between heme c 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 c.

A temperature-dependent conformational change of NADH oxidase from Thermus thermophilus HB8.

Using molecular dynamics simulations and steady-state fluorescence spectroscopy, we have identified a conformational change in the active site of a thermophilic flavoenzyme, NADH oxidase from Thermus thermophilus HB8 (NOX). The enzyme's far-UV circular dichroism spectrum, intrinsic tryptophan fluorescence, and apparent molecular weight measured by dynamic light scattering varied little between 25 and 75°C. However, the fluorescence of the tightly bound FAD cofactor increased approximately fourfold over this temperature range. This effect appears not to be due to aggregation, unfolding, cofactor dissociation, or changes in quaternary structure. We therefore attribute the change in flavin fluorescence to a temperature-dependent conformational change involving the NOX active site. Molecular dynamics simulations and the effects of mutating aromatic residues near the flavin suggest that the change in fluorescence results from a decrease in quenching by electron transfer from tyrosine 137 to the flavin.

Identification of c-type heme-containing peptides using nonactivated immobilized metal affinity chromatography resin enrichment and higher-energy collisional dissociation.

The c-type cytochromes play essential roles in many biological activities of both prokaryotic and eukaryotic cells, including electron transfer, enzyme catalysis, and induction of apoptosis. We report a novel enrichment strategy for identifying c-type heme-containing peptides that uses nonactivated IMAC resin. The strategy demonstrated at least 7-fold enrichment for heme-containing peptides digested from a cytochrome c protein standard, and quantitative linear performance was also assessed for heme-containing peptide enrichment. Heme-containing peptides extracted from the periplasmic fraction of Shewanella oneidensis MR-1 were further identified using higher-energy collisional dissociation tandem mass spectrometry. The results demonstrated the applicability of this enrichment strategy to identify c-type heme-containing peptides from a highly complex biological sample and, at the same time, confirmed the periplasmic localization of heme-containing proteins during suboxic respiration activities of S. oneidensis MR-1.

Bayesian Inference for Integrating Yarrowia lipolytica Multiomics Datasets with Metabolic Modeling.

Optimizing the metabolism of microbial cell factories for yields and titers is a critical step for economically viable production of bioproducts and biofuels. In this process, tuning the expression of individual enzymes to obtain the desired pathway flux is a challenging step, in which data from separate multiomics techniques must be integrated with existing biological knowledge to determine where changes should be made. Following a design-build-test-learn strategy, building on recent advances in Bayesian metabolic control analysis, we identify key enzymes in the oleaginous yeast Yarrowia lipolytica that correlate with the production of itaconate by integrating a metabolic model with multiomics measurements. To this extent, we quantify the uncertainty for a variety of key parameters, known as flux control coefficients (FCCs), needed to improve the bioproduction of target metabolites and statistically obtain key correlations between the measured enzymes and boundary flux. Based on the top five significant FCCs and five correlated enzymes, our results show phosphoglycerate mutase, acetyl-CoA synthetase (ACSm), carbonic anhydrase (HCO3E), pyrophosphatase (PPAm), and homoserine dehydrogenase (HSDxi) enzymes in rate-limiting reactions that can lead to increased itaconic acid production.

Flying blind, or just flying under the radar? The underappreciated power of de novo methods of mass spectrometric peptide identification.

Mass spectrometry-based proteomics is a popular and powerful method for precise and highly multiplexed protein identification. The most common method of analyzing untargeted proteomics data is called database searching, where the database is simply a collection of protein sequences from the target organism, derived from genome sequencing. Experimental peptide tandem mass spectra are compared to simplified models of theoretical spectra calculated from the translated genomic sequences. However, in several interesting application areas, such as forensics, archaeology, venomics, and others, a genome sequence may not be available, or the correct genome sequence to use is not known. In these cases, de novo peptide identification can play an important role. De novo methods infer peptide sequence directly from the tandem mass spectrum without reference to a sequence database, usually using graph-based or machine learning algorithms. In this review, we provide a basic overview of de novo peptide identification methods and applications, briefly covering de novo algorithms and tools, and focusing in more depth on recent applications from venomics, metaproteomics, forensics, and characterization of antibody drugs.

Applications and challenges of forensic proteomics.

Mass spectrometry-based proteomics has been a useful tool for addressing numerous questions in basic biology research for many years. This success, combined with the maturity of mass spectrometric instrumentation, the ever-increasing availability of protein sequence databases derived from genome sequencing, and the growing sophistication of data analysis methods, places proteomics in a position to have an important role in biological forensics. Because proteins contain information about genotype (sequence) and phenotype (expression levels), proteomics methods can both identify biological samples and characterize the conditions that produced them. In addition to serving as a valuable orthogonal method to genomic analyses, proteomics can be used in cases where nucleic acids are absent, degraded, or uninformative. Mass spectrometry provides both broad applicability and exquisite specificity, often without customized detection reagents like primers or antibodies. This review briefly introduces proteomics methods, and surveys a variety of forensic applications (including criminal justice, historical, archaeological, and national security areas). Finally, challenges and crucial areas for further research are addressed.

Perspective on Improving Environmental Monitoring of Biothreats.

For more than a decade, the United States has performed environmental monitoring by collecting and analyzing air samples for a handful of biological threat agents (BTAs) in order to detect a possible biological attack. This effort has faced numerous technical challenges including timeliness, sampling efficiency, sensitivity, specificity, and robustness. The cost of city-wide environmental monitoring using conventional technology has also been a challenge. A large group of scientists with expertise in bioterrorism defense met to assess the objectives and current efficacy of environmental monitoring and to identify operational and technological changes that could enhance its efficacy and cost-effectiveness, thus enhancing its value. The highest priority operational change that was identified was to abandon the current concept of city-wide environmental monitoring because the operational costs were too high and its value was compromised by low detection sensitivity and other environmental factors. Instead, it was suggested that the focus should primarily be on indoor monitoring and secondarily on special-event monitoring because objectives are tractable and these operational settings are aligned with likelihood and risk assessments. The highest priority technological change identified was the development of a reagent-less, real-time sensor that can identify a potential airborne release and trigger secondary tests of greater sensitivity and specificity for occasional samples of interest. This technological change could be transformative with the potential to greatly reduce operational costs and thereby create the opportunity to expand the scope and effectiveness of environmental monitoring.

Ricin-like proteins from the castor plant do not influence liquid chromatography-mass spectrometry detection of ricin in forensically relevant samples.

The toxic protein ricin (also known as RCA60), found in the seed of the castor plant (Ricinus communis) is frequently encountered in law enforcement investigations. The ability to detect ricin by analyzing its proteolytic (tryptic) peptides by liquid chromatography-tandem mass spectrometry (LC-MS/MS) is well established. However, ricin is just one member of a family of proteins in R. communis with closely related amino acid sequences, including R. communis agglutinin I (RCA120) and other ricin-like proteins (RLPs). Inferring the presence of ricin from its constituent peptides requires an understanding of the specificity, or uniqueness to ricin, of each peptide. Here we describe the set of ricin-derived tryptic peptides that can serve to uniquely identify ricin in distinction to closely-related RLPs and to proteins from other species. Other ricin-derived peptide sequences occur only in the castor plant, and still others are shared with unrelated species. We also characterized the occurrence and relative abundance of ricin and related proteins in an assortment of forensically relevant crude castor seed preparations. We find that whereas ricin and RCA120 are abundant in castor seed extracts, other RLPs are not represented by abundant unique peptides. Therefore, the detection of peptides shared between ricin and RLPs (other than RCA120) in crude castor seed extracts most likely reflects the presence of ricin in the sample.

Protein abundances can distinguish between naturally-occurring and laboratory strains of Yersinia pestis, the causative agent of plague.

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 Yersinia pestis 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 Y. pestis. 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 Y. pestis 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.

Effects of bacterial inactivation methods on downstream proteomic analysis.

Inactivation of pathogenic microbial samples is often necessary for the protection of researchers and to comply with local and federal regulations. By its nature, biological inactivation causes changes to microbial samples, potentially affecting observed experimental results. While inactivation-induced damage to materials such as DNA has been evaluated, the effect of various inactivation strategies on proteomic data, to our knowledge, has not been discussed. To this end, we inactivated samples of Yersinia pestis and Escherichia coli by autoclave, ethanol, or irradiation treatment to determine how inactivation changes liquid chromatography-tandem mass spectrometry data quality as well as apparent protein content of cells. Proteomic datasets obtained from aliquots of samples inactivated by different methods were highly similar, with Pearson correlation coefficients ranging from 0.822 to 0.985 and 0.816 to 0.985 for E. coli and Y. pestis, respectively, suggesting that inactivation had only slight impacts on the set of proteins identified. In addition, spectral quality metrics such as distributions of various database search algorithm scores remained constant across inactivation methods, indicating that inactivation does not appreciably degrade spectral quality. Though overall changes resulting from inactivation were small, there were detectable trends. For example, one-sided Fischer exact tests determined that periplasmic proteins decrease in observed abundance after sample inactivation by autoclaving (α=1.71×10(-2) for E. coli, α=4.97×10(-4) for Y. pestis) and irradiation (α=9.43×10(-7) for E. coli, α=1.21×10(-5) for Y. pestis) when compared to controls that were not inactivated. Based on our data, if sample inactivation is necessary, we recommend inactivation with ethanol treatment with secondary preference given to irradiation.