A uniform proteomics MS/MS analysis platform utilizing open XML file formats

Mitochondrial protein interaction landscape of SS-31

Mitochondrial dysfunction underlies the etiology of a broad spectrum of diseases including heart disease, cancer, neurodegenerative diseases, and the general aging process. Therapeutics that restore healthy mitochondrial function hold promise for treatment of these conditions. The synthetic tetrapeptide, elamipretide (SS-31), improves mitochondrial function, but mechanistic details of its pharmacological effects are unknown. Reportedly, SS-31 primarily interacts with the phospholipid cardiolipin in the inner mitochondrial membrane. Here we utilize chemical cross-linking with mass spectrometry to identify protein interactors of SS-31 in mitochondria. The SS-31-interacting proteins, all known cardiolipin binders, fall into two groups, those involved in ATP production through the oxidative phosphorylation pathway and those involved in 2-oxoglutarate metabolic processes. Residues cross-linked with SS-31 reveal binding regions that in many cases, are proximal to cardiolipin-protein interacting regions. These results offer a glimpse of the protein interaction landscape of SS-31 and provide mechanistic insight relevant to SS-31 mitochondrial therapy.

Metabolomics Analysis Reveals Tissue-Specific Metabolite Compositions in Leaf Blade and Traps of Carnivorous Nepenthes Plants

Nepenthes is a genus of carnivorous plants that evolved a pitfall trap, the pitcher, to catch and digest insect prey to obtain additional nutrients. Each pitcher is part of the whole leaf, together with a leaf blade. These two completely different parts of the same organ were studied separately in a non-targeted metabolomics approach in Nepenthes x ventrata, a robust natural hybrid. The first aim was the analysis and profiling of small (50–1000 m/z) polar and non-polar molecules to find a characteristic metabolite pattern for the particular tissues. Second, the impact of insect feeding on the metabolome of the pitcher and leaf blade was studied. Using UPLC-ESI-qTOF and cheminformatics, about 2000 features (MS/MS events) were detected in the two tissues. They showed a huge chemical diversity, harboring classes of chemical substances that significantly discriminate these tissues. Among the common constituents of N. x ventrata are phenolics, flavonoids and naphthoquinones, namely plumbagin, a characteristic compound for carnivorous Nepenthales, and many yet-unknown compounds. Upon insect feeding, only in pitchers in the polar compounds fraction, small but significant differences could be detected. By further integrating information with cheminformatics approaches, we provide and discuss evidence that the metabolite composition of the tissues can point to their function.

Site-Specific N-Glycoproteomic Analysis Reveals Upregulated Sialylation and Core Fucosylation during Transient Regeneration Loss in Neonatal Mouse Hearts

Myocardial infarction (MI) is one of the leading causes of deaths worldwide. Because of the incapability of regeneration, the cardiomyocyte loss with MI is replaced by fibrotic scar tissue, which eventually leads to heart failure. Reconstructing regeneration of an adult human heart has been recognized as a promising strategy for cardiac therapeutics. A neonatal mouse heart, which possesses transient regenerative capacity at the first week after birth, represents an ideal model to investigate processes associated with cardiac regeneration. In this work, an integrated glycoproteomic and proteomic analysis was performed to investigate the differences in glycoprotein abundances and site-specific glycosylation between postneonatal day 1 (P1) and day 7 (P7) of mouse hearts. By large-scale profiling and quantifying more than 2900 intact N-glycopeptides in neonatal mouse hearts, we identified 227 altered N-glycopeptides between P1 and P7 hearts. By extracting protein changes from the global proteome data, the normalized glycosylation changes for site-specific glycans were obtained, which showed heterogeneity on glycosites and glycoproteins. Systematic analysis of the glycosylation changes demonstrated an overall upregulation of sialylation and core fucosylation in P7 mice. Notably, the upregulated sialylation was a comprehensive result of increased sialylated glycans with Neu5Gc, with both Neu5Gc and core fucose, and decreased sialylated glycans with Neu5Ac. The upregulated core fucosylation resulted from the increase of glycans containing both core fucose and Neu5Gc but not glycans containing sole core fucose. These data provide a valuable resource for future functional and mechanism studies on heart regeneration and discovery of novel therapeutic targets. All mass spectrometry proteomic data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the data set identifier PXD017139.

Full-Featured, Real-Time Database Searching Platform Enables Fast and Accurate Multiplexed Quantitative Proteomics

Multiplexed quantitative analyses of complex proteomes enable deep biological insight. While a multitude of workflows have been developed for multiplexed analyses, the most quantitatively accurate method (SPS-MS3) suffers from long acquisition duty cycles. We built a new, real-time database search (RTS) platform, Orbiter, to combat the SPS-MS3 method's longer duty cycles. RTS with Orbiter eliminates SPS-MS3 scans if no peptide matches to a given spectrum. With Orbiter's online proteomic analytical pipeline, which includes RTS and false discovery rate analysis, it was possible to process a single spectrum database search in less than 10 ms. The result is a fast, functional means to identify peptide spectral matches using Comet, filter these matches, and more efficiently quantify proteins of interest. Importantly, the use of Comet for peptide spectral matching allowed for a fully featured search, including analysis of post-translational modifications, with well-known and extensively validated scoring. These data could then be used to trigger subsequent scans in an adaptive and flexible manner. In this work we tested the utility of this adaptive data acquisition platform to improve the efficiency and accuracy of multiplexed quantitative experiments. We found that RTS enabled a 2-fold increase in mass spectrometric data acquisition efficiency. Orbiter's RTS quantified more than 8000 proteins across 10 proteomes in half the time of an SPS-MS3 analysis (18 h for RTS, 36 h for SPS-MS3).

Generation of a murine SWATH-MS spectral library to quantify more than 11,000 proteins

Targeted SWATH-MS data analysis is critically dependent on the spectral library. Comprehensive spectral libraries of human or several other organisms have been published, but the extensive spectral library for mouse, a widely used model organism is not available. Here, we present a large murine spectral library covering more than 11,000 proteins and 240,000 proteotypic peptides, which included proteins derived from 9 murine tissue samples and one murine L929 cell line. This resource supports the quantification of 67% of all murine proteins annotated by UniProtKB/Swiss-Prot. Furthermore, we applied the spectral library to SWATH-MS data from murine tissue samples. Data are available via SWATHAtlas (PASS01441).

Measurement(s)	Mouse Protein • mass spectrum • spectral library
Technology Type(s)	mass spectrometry • combined ms-ms + spectral library search
Sample Characteristic - Organism	Mus musculus

Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.11968230

Gut Microbial Protein Expression in Response to Dietary Patterns in a Controlled Feeding Study: A Metaproteomic Approach

Although the gut microbiome has been associated with dietary patterns linked to health, microbial metabolism is not well characterized. This ancillary study was a proof of principle analysis for a novel application of metaproteomics to study microbial protein expression in a controlled dietary intervention. We measured the response of the microbiome to diet in a randomized crossover dietary intervention of a whole-grain, low glycemic load diet (WG) and a refined-grain, high glycemic load diet (RG). Total proteins in stools from 9 participants at the end of each diet period (n = 18) were analyzed by LC MS/MS and proteins were identified using the Human Microbiome Project (HMP) human gut microbiome database and UniProt human protein databases. T-tests, controlling for false discovery rate (FDR) <10%, were used to compare the Gene Ontology (GO) biological processes and bacterial enzymes between the two interventions. Using shotgun proteomics, more than 53,000 unique peptides were identified including microbial (89%) and human peptides (11%). Forty-eight bacterial enzymes were statistically different between the diets, including those implicated in SCFA production and degradation of fatty acids. Enzymes associated with degradation of human mucin were significantly enriched in the RG diet. These results illustrate that the metaproteomic approach is a valuable tool to study the microbial metabolism of diets that may influence host health.

Identification of neurotoxic cross-linked amyloid-β dimers in the Alzheimer's brain

The primary structure of canonical amyloid-β-protein was elucidated more than 30 years ago, yet the forms of amyloid-β that play a role in Alzheimer's disease pathogenesis remain poorly defined. Studies of Alzheimer's disease brain extracts suggest that amyloid-β, which migrates on sodium dodecyl sulphate polyacrylamide gel electrophoresis with a molecular weight of ∼7 kDa (7kDa-Aβ), is particularly toxic; however, the nature of this species has been controversial. Using sophisticated mass spectrometry and sensitive assays of disease-relevant toxicity we show that brain-derived bioactive 7kDa-Aβ contains a heterogeneous mixture of covalently cross-linked dimers in the absence of any other detectable proteins. The identification of amyloid-β dimers may open a new phase of Alzheimer's research and allow a better understanding of Alzheimer's disease, and how to monitor and treat this devastating disorder. Future studies investigating the bioactivity of individual dimers cross-linked at known sites will be critical to this effort.

A Global Screen for Assembly State Changes of the Mitotic Proteome by SEC-SWATH-MS

Living systems integrate biochemical reactions that determine the functional state of each cell. Reactions are primarily mediated by proteins. In proteomic studies, these have been treated as independent entities, disregarding their higher-level organization into complexes that affects their activity and/or function and is thus of great interest for biological research. Here, we describe the implementation of an integrated technique to quantify cell-state-specific changes in the physical arrangement of protein complexes concurrently for thousands of proteins and hundreds of complexes. Applying this technique to a comparison of human cells in interphase and mitosis, we provide a systematic overview of mitotic proteome reorganization. The results recall key hallmarks of mitotic complex remodeling and suggest a model of nuclear pore complex disassembly, which we validate by orthogonal methods. To support the interpretation of quantitative SEC-SWATH-MS datasets, we extend the software CCprofiler and provide an interactive exploration tool, SECexplorer-cc.

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Global quantification of assembly state changes in the mitotic proteome

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Improved performance over thermostability measurement of proteome states

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Discovery of a mitotic disassembly intermediate of the nuclear pore complex

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Introduction of SECexplorer-cc, a publicly available online platform

Comparing Peptide Spectra Matches Across Search Engines

Mass spectrometry is extremely efficient for sequencing small peptides generated by, for example, a trypsin digestion of a complex mixture. Current instruments have the capacity to generate 50-100 K MSMS spectra from a single run. Of these ~30-50% is typically assigned to peptide matches on a 1% FDR threshold. The remaining spectra need more research to explain. We address here whether the 30-50% matched spectra provide consensus matches when using different database-dependent search pipelines. Although the majority of the spectra peptide assignments concur across search engines, our conclusion is that database-dependent search engines still require improvements.

Retention Time Prediction and Protein Identification

In bottom-up proteomics, proteins are typically identified by enzymatic digestion into peptides, tandem mass spectrometry and comparison of the tandem mass spectra with those predicted from a sequence database for peptides within measurement uncertainty from the experimentally obtained mass. Although now decreasingly common, isolated proteins or simple protein mixtures can also be identified by measuring only the masses of the peptides resulting from the enzymatic digest, without any further fragmentation. Separation methods such as liquid chromatography and electrophoresis are often used to fractionate complex protein or peptide mixtures prior to analysis by mass spectrometry. Although the primary reason for this is to avoid ion suppression and improve data quality, these separations are based on physical and chemical properties of the peptides or proteins and therefore also provide information about them. Depending on the separation method, this could be protein molecular weight (SDS-PAGE), isoelectric point (IEF), charge at a known pH (ion exchange chromatography), or hydrophobicity (reversed phase chromatography). These separations produce approximate measurements on properties that to some extent can be predicted from amino acid sequences. In the case of molecular weight of proteins without posttranslational modifications this is straightforward: simply add the molecular weights of the amino acid residues in the protein. For IEF, charge and hydrophobicity, the order of the amino acids, and folding state of the peptide or protein also matter, but it is nevertheless possible to predict the behavior of peptides and proteins in these separation methods to a degree which renders such predictions useful. This chapter reviews the topic of using data from separation methods for identification and validation in proteomics, with special emphasis on predicting retention times of tryptic peptides in reversed-phase chromatography under acidic conditions, as this is one of the most commonly used separation methods in bottom-up proteomics.

Using the Object-Oriented PowerShell for Simple Proteomics Data Analysis

Scripting languages such as Python and Bash are appreciated for solving simple, everyday tasks in bioinformatics. A more recent, object-oriented command shell and scripting language, PowerShell, has many attractive features: an object-oriented interactive command line, fluent navigation and manipulation of XML files, ability to explore and consume Web services from the command line, consistent syntax and grammar, rich regular expressions, and advanced output formatting. The key difference between classical command shells and scripting languages, such as bash, and object-oriented ones, such as PowerShell, is that in the latter the result of a command is a structured object with inherited properties and methods rather than a simple stream of characters. Conveniently, PowerShell is included in all new releases of Microsoft Windows and is available for Linux and macOS, making any data processing script portable. In this chapter we demonstrate how PowerShell in particular allows easy interaction with mass spectrometry data in XML formats, connection to Web services for tools such as BLAST, and presentation of results as formatted text or graphics. These features make PowerShell much more than "yet another scripting language."

Progress on Identifying and Characterizing the Human Proteome: 2019 Metrics from the HUPO Human Proteome Project

The Human Proteome Project (HPP) annually reports on progress made throughout the field in credibly identifying and characterizing the complete human protein parts list and making proteomics an integral part of multiomics studies in medicine and the life sciences. NeXtProt release 2019-01-11 contains 17 694 proteins with strong protein-level evidence (PE1), compliant with HPP Guidelines for Interpretation of MS Data v2.1; these represent 89% of all 19 823 neXtProt predicted coding genes (all PE1,2,3,4 proteins), up from 17 470 one year earlier. Conversely, the number of neXtProt PE2,3,4 proteins, termed the "missing proteins" (MPs), has been reduced from 2949 to 2129 since 2016 through efforts throughout the community, including the chromosome-centric HPP. PeptideAtlas is the source of uniformly reanalyzed raw mass spectrometry data for neXtProt; PeptideAtlas added 495 canonical proteins between 2018 and 2019, especially from studies designed to detect hard-to-identify proteins. Meanwhile, the Human Protein Atlas has released version 18.1 with immunohistochemical evidence of expression of 17 000 proteins and survival plots as part of the Pathology Atlas. Many investigators apply multiplexed SRM-targeted proteomics for quantitation of organ-specific popular proteins in studies of various human diseases. The 19 teams of the Biology and Disease-driven B/D-HPP published a total of 160 publications in 2018, bringing proteomics to a broad array of biomedical research.

PTMProphet: Fast and Accurate Mass Modification Localization for the Trans-Proteomic Pipeline

Spectral matching sequence database search engines commonly used on mass spectrometry-based proteomics experiments excel at identifying peptide sequence ions, and in addition, possible sequence ions carrying post-translational modifications (PTMs), but most do not provide confidence metrics for the exact localization of those PTMs when several possible sites are available. Localization is absolutely required for downstream molecular cell biology analysis of PTM function in vitro and in vivo. Therefore, we developed PTMProphet, a free and open-source software tool integrated into the Trans-Proteomic Pipeline, which reanalyzes identified spectra from any search engine for which pepXML output is available to provide localization confidence to enable appropriate further characterization of biologic events. Localization of any type of mass modification (e.g., phosphorylation) is supported. PTMProphet applies Bayesian mixture models to compute probabilities for each site/peptide spectrum match where a PTM has been identified. These probabilities can be combined to compute a global false localization rate at any threshold to guide downstream analysis. We describe the PTMProphet tool, its underlying algorithms, and demonstrate its performance on ground-truth synthetic peptide reference data sets, one previously published small data set, one new larger data set, and also on a previously published phosphoenriched data set where the correct sites of modification are unknown. Data have been deposited to ProteomeXchange with identifier PXD013210.

Optimization of TripleTOF spectral simulation and library searching for confident localization of phosphorylation sites

Tandem mass spectrometry (MS/MS) has been used in analysis of proteins and their post-translational modifications. A recently developed data analysis method, which simulates MS/MS spectra of phosphopeptides and performs spectral library searching using SpectraST, facilitates confident localization of phosphorylation sites. However, its performance has been evaluated only on MS/MS spectra acquired using Orbitrap HCD mass spectrometers so far. In this study, we have investigated whether this approach would be applicable to another type of mass spectrometers, and optimized the simulation and search conditions to achieve sensitive and confident site localization. Synthetic phosphopeptides and enriched K562 cell phosphopeptides were analyzed using a TripleTOF 6600 mass spectrometer before and after enzymatic dephosphorylation. Dephosphorylated peptides identified by X!Tandem database searching were subjected to spectral simulation of all possible single phosphorylations using SimPhospho software. Phosphopeptides were identified and localized by SpectraST searching against a library of the simulated spectra. Although no synthetic phosphopeptide was localized at 1% false localization rate under the previous conditions, optimization of the spectral simulation and search conditions for the TripleTOF datasets achieved the localization and improved the sensitivity. Furthermore, the optimized conditions enabled sensitive localization of K562 phosphopeptides at 1% false discovery and localization rates. These results suggest that accurate phosphopeptide simulation of TripleTOF MS/MS spectra is possible and the simulated spectral libraries can be used in SpectraST searching for confident localization of phosphorylation sites.

In Silico Tools and Phosphoproteomic Software Exclusives

Proteomics and phosphoproteomics have been emerging as new dimensions of omics. Phosphorylation has a profound impact on the biological functions and applications of proteins. It influences everything from intrinsic activity and extrinsic executions to cellular localization. This post-translational modification has been subjected to detailed study and has been an object of analytical curiosity with the advent of faster instrumentation. The major strength of phosphoproteomic research lies in the fact that it gives an overall picture of the workforce of the cell. Phosphoproteomics gives deeper insights into understanding the mechanism behind development and progression of a disease. This review for the first time consolidates the list of existing bioinformatics tools developed for phosphoproteomics. The gap between development of bioinformatics tools and their implementation in clinical research is highlighted. The challenge facing progress is ideally believed to be the interdisciplinary arena this field of research is associated with. For meaningful solutions and deliverables, these tools need to be implemented in clinical studies for obtaining answers to pharmacodynamic questions, saving time, costs and energy. This review hopes to invoke some thought in this direction.

Quality control of imbalanced mass spectra from isotopic labeling experiments

BACKGROUND

Mass spectra are usually acquired from the Liquid Chromatography-Mass Spectrometry (LC-MS) analysis for isotope labeled proteomics experiments. In such experiments, the mass profiles of labeled (heavy) and unlabeled (light) peptide pairs are represented by isotope clusters (2D or 3D) that provide valuable information about the studied biological samples in different conditions. The core task of quality control in quantitative LC-MS experiment is to filter out low-quality peptides with questionable profiles. The commonly used methods for this problem are the classification approaches. However, the data imbalance problems in previous control methods are often ignored or mishandled. In this study, we introduced a quality control framework based on the extreme gradient boosting machine (XGBoost), and carefully addressed the imbalanced data problem in this framework.

RESULTS

In the XGBoost based framework, we suggest the application of the Synthetic minority over-sampling technique (SMOTE) to re-balance data and use the balanced data to train the boosted trees as the classifier. Then the classifier is applied to other data for the peptide quality assessment. Experimental results show that our proposed framework increases the reliability of peptide heavy-light ratio estimation significantly.

CONCLUSIONS

Our results indicate that this framework is a powerful method for the peptide quality assessment. For the feature extraction part, the extracted ion chromatogram (XIC) based features contribute to the peptide quality assessment. To solve the imbalanced data problem, SMOTE brings a much better classification performance. Finally, the XGBoost is capable for the peptide quality control. Overall, our proposed framework provides reliable results for the further proteomics studies.

Human Proteome Project Mass Spectrometry Data Interpretation Guidelines 3.0

The Human Proteome Organization's (HUPO) Human Proteome Project (HPP) developed Mass Spectrometry (MS) Data Interpretation Guidelines that have been applied since 2016. These guidelines have helped ensure that the emerging draft of the complete human proteome is highly accurate and with low numbers of false-positive protein identifications. Here, we describe an update to these guidelines based on consensus-reaching discussions with the wider HPP community over the past year. The revised 3.0 guidelines address several major and minor identified gaps. We have added guidelines for emerging data independent acquisition (DIA) MS workflows and for use of the new Universal Spectrum Identifier (USI) system being developed by the HUPO Proteomics Standards Initiative (PSI). In addition, we discuss updates to the standard HPP pipeline for collecting MS evidence for all proteins in the HPP, including refinements to minimum evidence. We present a new plan for incorporating MassIVE-KB into the HPP pipeline for the next (HPP 2020) cycle in order to obtain more comprehensive coverage of public MS data sets. The main checklist has been reorganized under headings and subitems, and related guidelines have been grouped. In sum, Version 2.1 of the HPP MS Data Interpretation Guidelines has served well, and this timely update to version 3.0 will aid the HPP as it approaches its goal of collecting and curating MS evidence of translation and expression for all predicted ∼20 000 human proteins encoded by the human genome.

SimPhospho: a software tool enabling confident phosphosite assignment

Motivation

Mass spectrometry combined with enrichment strategies for phosphorylated peptides has been successfully employed for two decades to identify sites of phosphorylation. However, unambiguous phosphosite assignment is considered challenging. Given that site-specific phosphorylation events function as different molecular switches, validation of phosphorylation sites is of utmost importance. In our earlier study we developed a method based on simulated phosphopeptide spectral libraries, which enables highly sensitive and accurate phosphosite assignments. To promote more widespread use of this method, we here introduce a software implementation with improved usability and performance.

Results

We present SimPhospho, a fast and user-friendly tool for accurate simulation of phosphopeptide tandem mass spectra. Simulated phosphopeptide spectral libraries are used to validate and supplement database search results, with a goal to improve reliable phosphoproteome identification and reporting. The presented program can be easily used together with the Trans-Proteomic Pipeline and integrated in a phosphoproteomics data analysis workflow.

Availability and implementation

SimPhospho is open source and it is available for Windows, Linux and Mac operating systems. The software and its user’s manual with detailed description of data analysis as well as test data can be found at https://sourceforge.net/projects/simphospho/.

Supplementary information

Supplementary data are available at Bioinformatics online.

Deepening our understanding of HDL proteome

Introduction: High-density lipoprotein (HDL) particles are heterogeneous and their proteome is complex and distinct from HDL cholesterol. However, it is largely unknown whether HDL proteins are associated with cardiovascular protection. Areas covered: HDL isolation techniques and proteomic analyses are reviewed. A list of HDL proteins reported in 37 different studies was compiled and the effects of different isolation techniques on proteins attributed to HDL are discussed. Mass spectrometric techniques used for HDL analysis and the need for precise and robust methods for quantification of HDL proteins are discussed. Expert opinion: Proteins associated with HDL have the potential to be used as biomarkers and/or help to understand HDL functionality. To achieve this, large cohorts must be studied using precise quantification methods. Key factors in HDL proteome quantification are the isolation methodology and the mass spectrometry technique employed. Isolation methodology affects what proteins are identified in HDL and the specificity of association with HDL particles needs to be addressed. Shotgun proteomics yields imprecise quantification, but the majority of HDL studies relied on this approach. Few recent studies used targeted tandem mass spectrometry to quantify HDL proteins, and it is imperative that future studies focus on the application of these precise techniques.

Deep Profiling of the Cleavage Specificity and Human Substrates of Snake Venom Metalloprotease HF3 by Proteomic Identification of Cleavage Site Specificity (PICS) Using Proteome Derived Peptide Libraries and Terminal Amine Isotopic Labeling of Substrates (TAILS) N-Terminomics

Snakebite is a major medical concern in many parts of the world with metalloproteases playing important roles in the pathological effects of Viperidae venoms, including local tissue damage, hemorrhage, and coagulopathy. Hemorrhagic Factor 3 (HF3), a metalloprotease from Bothrops jararaca venom, induces local hemorrhage and targets extracellular matrix (ECM) components, including collagens and proteoglycans, and plasma proteins. However, the full substrate repertoire of this metalloprotease is unknown. We report positional proteomic studies identifying >2000 N-termini, including neo-N-termini of HF3 cleavage sites in mouse embryonic fibroblast secretome proteins. Terminal amine isotopic labeling of substrates (TAILS) analysis identified a preference for Leu at the P1' position among candidate HF3 substrates including proteins of the ECM and focal adhesions and the cysteine protease inhibitor cystatin-C. Interestingly, 190 unique peptides matched to annotated cleavage sites in the TopFIND N-termini database, suggesting that these cleavages occurred at a site prone to cleavage or might have been generated by other proteases activated upon incubation with HF3, including caspases-3 and -7, cathepsins D and E, granzyme B, and MMPs 2 and 9. Using Proteomic identification of cleavage site specificity (PICS), a tryptic library derived from THP-1 monocytic cells was used as HF3 substrates for identifying protease cleavage sites and sequence preferences in peptides. A total of 799 unique cleavage sites were detected and, in accordance with TAILS analysis using native secreted protein substrates of MEF cells, revealed a clear preference for Leu at P1'. Taken together, these results greatly expand the known substrate degradome of HF3 and reveal potential new targets, which may serve as a basis to better elucidate the complex pathophysiology of snake envenomation.

Proteomics analysis of asthenozoospermia and identification of glucose-6-phosphate isomerase as an important enzyme for sperm motility

Asthenozoospermia, in which sperm motility is affected, is one of the primary causes of male infertility. However, the exact mechanism responsible for the defective motility remains unknown. It is important to identify the precise proteins or pathways involved in sperm motility. The present study analyzed five asthenozoospermic sperm samples and five healthy controls using TMT-based quantitative method and identified 152 differentially expressed proteins, with 84 upregulated and 68 downregulated in asthenozoospermia. Four proteins (GPI, MDH1, PGAM1 and PGAM2) were found in several over-represented energy metabolism pathways using bioinformatics analysis. Glucose-6-phosphate isomerase (GPI), a rate-limiting enzyme converting glucose-6-phosphate to fructose-6-phosphate, was found to be significantly decreased in asthenozoospermia by Western blotting and ELISA on an extended sample size. Furthermore, substitution of glucose with fructose-6-phosphate significantly promoted asthenozoospermic sperm motility in vitro. Taken together, our results suggest that the poor motility of sperm in asthenozoospermia may partly result from defects in GPI-associated energy metabolism. SIGNIFICANCE: To identify the key proteins or pathways involved in sperm motility, the accurate TMT-based quantitative method was applied to characterize protein profiles of asthenozoospermic sperm. GPI, an enzyme involved in energy metabolism, was found to be differentially abundant, and validated by extended sample analysis. The supplement of the product of GPI, fructose-6-phosphate, could significantly improve sperm motility. Our study could provide new insights into the molecular basis of sperm motility and the improvement of motility in asthenozoospermia.

Remodeling Membrane Binding by Mono-Ubiquitylation

Ubiquitin (Ub) receptors respond to ubiquitylation signals. They bind ubiquitylated substrates and exert their activity in situ. Intriguingly, Ub receptors themselves undergo rapid ubiquitylation and deubiquitylation. Here we asked what is the function of ubiquitylation of Ub receptors? We focused on yeast epsin, a Ub receptor that decodes the ubiquitylation signal of plasma membrane proteins into an endocytosis response. Using mass spectrometry, we identified lysine-3 as the major ubiquitylation site in the epsin plasma membrane binding domain. By projecting this ubiquitylation site onto our crystal structure, we hypothesized that this modification would compete with phosphatidylinositol-4,5-bisphosphate (PIP₂) binding and dissociate epsin from the membrane. Using an E. coli-based expression of an authentic ubiquitylation apparatus, we purified ubiquitylated epsin. We demonstrated in vitro that in contrast to apo epsin, the ubiquitylated epsin does not bind to either immobilized PIPs or PIP₂-enriched liposomes. To test this hypothesis in vivo, we mimicked ubiquitylation by the fusion of Ub at the ubiquitylation site. Live cell imaging demonstrated that the mimicked ubiquitylated epsin dissociates from the membrane. Our findings suggest that ubiquitylation of the Ub receptors dissociates them from their products to allow binding to a new ubiquitylated substrates, consequently promoting cyclic activity of the Ub receptors.

In vivo proteomics identifies the competence regulon and AliB oligopeptide transporter as pathogenic factors in pneumococcal meningitis

Streptococcus pneumoniae (pneumococci) is a leading cause of severe bacterial meningitis in many countries worldwide. To characterize the repertoire of fitness and virulence factors predominantly expressed during meningitis we performed niche-specific analysis of the in vivo proteome in a mouse meningitis model, in which bacteria are directly inoculated into the cerebrospinal fluid (CSF) cisterna magna. We generated a comprehensive mass spectrometry (MS) spectra library enabling bacterial proteome analysis even in the presence of eukaryotic proteins. We recovered 200,000 pneumococci from CSF obtained from meningitis mice and by MS we identified 685 pneumococci proteins in samples from in vitro filter controls and 249 in CSF isolates. Strikingly, the regulatory two-component system ComDE and substrate-binding protein AliB of the oligopeptide transporter system were exclusively detected in pneumococci recovered from the CSF. In the mouse meningitis model, AliB-, ComDE-, or AliB-ComDE-deficiency resulted in attenuated meningeal inflammation and disease severity when compared to wild-type pneumococci indicating the crucial role of ComDE and AliB in pneumococcal meningitis. In conclusion, we show here mechanisms of pneumococcal adaptation to a defined host compartment by a proteome-based approach. Further, this study provides the basis of a promising strategy for the identification of protein antigens critical for invasive disease caused by pneumococci and other meningeal pathogens.

Pneumococci are one of the most common and aggressive meningitis pathogens associated with mortality rates between 10% and 30%. Due to severe complications during therapeutic intervention, prevention strategies to combat pneumococcal meningitis (PM) are preferred. The vaccines available are so far suboptimal and inefficient to prevent serious PM. Hence, deciphering the mechanisms employed by pneumococci to encounter and survive in the cerebrospinal fluid (CSF) will pave the way for the development of new antimicrobial strategies. This work used an in vivo proteome-based approach to identify pneumococcal proteins expressed in the CSF during acute meningitis. This strategy identified a nutrient uptake system and regulatory system to be highly expressed in the CSF and being crucial for PM. Knocking out two of the highly in vivo expressed proteins (AliA and ComDE) in S. pneumoniae yields to a significant increase in survival and decrease in pathogen burden of infected mice. These host compartment specific expressed pneumococcal antigens represent promising candidates for antimicrobials or protein-based vaccines.

A comprehensive evaluation of popular proteomics software workflows for label-free proteome quantification and imputation

Label-free mass spectrometry (MS) has developed into an important tool applied in various fields of biological and life sciences. Several software exist to process the raw MS data into quantified protein abundances, including open source and commercial solutions. Each software includes a set of unique algorithms for different tasks of the MS data processing workflow. While many of these algorithms have been compared separately, a thorough and systematic evaluation of their overall performance is missing. Moreover, systematic information is lacking about the amount of missing values produced by the different proteomics software and the capabilities of different data imputation methods to account for them.In this study, we evaluated the performance of five popular quantitative label-free proteomics software workflows using four different spike-in data sets. Our extensive testing included the number of proteins quantified and the number of missing values produced by each workflow, the accuracy of detecting differential expression and logarithmic fold change and the effect of different imputation and filtering methods on the differential expression results. We found that the Progenesis software performed consistently well in the differential expression analysis and produced few missing values. The missing values produced by the other software decreased their performance, but this difference could be mitigated using proper data filtering or imputation methods. Among the imputation methods, we found that the local least squares (lls) regression imputation consistently increased the performance of the software in the differential expression analysis, and a combination of both data filtering and local least squares imputation increased performance the most in the tested data sets.

Systems structural biology measurements by in vivo cross-linking with mass spectrometry

This protocol describes a workflow for utilizing large-scale cross-linking with mass spectrometry (XL-MS) to make systems-level structural biology measurements in complex biological samples, including cells, isolated organelles, and tissue samples. XL-MS is a structural biology technique that provides information on the molecular structure of proteins and protein complexes using chemical probes that report the proximity of probe-reactive amino acids within proteins, typically lysine residues. Information gained through XL-MS studies is often complementary to more traditional methods, such as X-ray crystallography, nuclear magnetic resonance, and cryo-electron microscopy. The use of MS-cleavable cross-linkers, including protein interaction reporter (PIR) technologies, enables XL-MS studies on protein structures and interactions in extremely complex biological samples, including intact living cells. PIR cross-linkers are designed to contain chemical bonds at specific locations within the cross-linker molecule that can be selectively cleaved by collision-induced dissociation or UV light. When broken, these bonds release the intact peptides that were cross-linked, as well as a reporter ion. Conservation of mass dictates that the sum of the two released peptide masses and the reporter mass equals the measured precursor mass. This relationship is used to identify cross-linked peptide pairs. Release of the individual peptides permits accurate measurement of their masses and independent amino acid sequence determination by tandem MS, allowing the use of standard proteomics search engines such as Comet for peptide sequence assignment, greatly simplifying data analysis of cross-linked peptide pairs. Search results are processed with XLinkProphet for validation and can be uploaded into XlinkDB for interaction network and structural analysis.

Novel interconnections of HOG signaling revealed by combined use of two proteomic software packages

Modern quantitative mass spectrometry (MS)-based proteomics enables researchers to unravel signaling networks by monitoring proteome-wide cellular responses to different stimuli. MS-based analysis of signaling systems usually requires an integration of multiple quantitative MS experiments, which remains challenging, given that the overlap between these datasets is not necessarily comprehensive. In a previous study we analyzed the impact of the yeast mitogen-activated protein kinase (MAPK) Hog1 on the hyperosmotic stress-affected phosphorylome. Using a combination of a series of hyperosmotic stress and kinase inhibition experiments, we identified a broad range of direct and indirect substrates of the MAPK. Here we re-evaluate this extensive MS dataset and demonstrate that a combined analysis based on two software packages, MaxQuant and Proteome Discoverer, increases the coverage of Hog1-target proteins by 30%. Using protein-protein proximity assays we show that the majority of new targets gained by this analysis are indeed Hog1-interactors. Additionally, kinetic profiles indicate differential trends of Hog1-dependent versus Hog1-independent phosphorylation sites. Our findings highlight a previously unrecognized interconnection between Hog1 signaling and the RAM signaling network, as well as sphingolipid homeostasis.

A first step towards building spectral libraries as complementary tools for snake venom proteome/peptidome studies

Snake venoms are complex mixtures of a large number of distinct proteins and peptides with biological activity. Peptide spectral libraries are compilations of previously identified MS/MS spectra obtained from proteomics experiments. Here we present the generation and use of a Venom Peptidome and a Venom Proteome spectral library for the analysis of venom proteomes and peptidomes from distinct snake species.

Proteomics Standards Initiative Extended FASTA Format

Mass-spectrometry-based proteomics enables the high-throughput identification and quantification of proteins, including sequence variants and post-translational modifications (PTMs) in biological samples. However, most workflows require that such variations be included in the search space used to analyze the data, and doing so remains challenging with most analysis tools. In order to facilitate the search for known sequence variants and PTMs, the Proteomics Standards Initiative (PSI) has designed and implemented the PSI extended FASTA format (PEFF). PEFF is based on the very popular FASTA format but adds a uniform mechanism for encoding substantially more metadata about the sequence collection as well as individual entries, including support for encoding known sequence variants, PTMs, and proteoforms. The format is very nearly backward compatible, and as such, existing FASTA parsers will require little or no changes to be able to read PEFF files as FASTA files, although without supporting any of the extra capabilities of PEFF. PEFF is defined by a full specification document, controlled vocabulary terms, a set of example files, software libraries, and a file validator. Popular software and resources are starting to support PEFF, including the sequence search engine Comet and the knowledge bases neXtProt and UniProtKB. Widespread implementation of PEFF is expected to further enable proteogenomics and top-down proteomics applications by providing a standardized mechanism for encoding protein sequences and their known variations. All the related documentation, including the detailed file format specification and example files, are available at http://www.psidev.info/peff .

The signaling adaptor BCAP inhibits NLRP3 and NLRC4 inflammasome activation in macrophages through interactions with Flightless-1

B cell adaptor for phosphoinositide 3-kinase (PI3K) (BCAP) is a signaling adaptor that activates the PI3K pathway downstream of B cell receptor signaling in B cells and Toll-like receptor (TLR) signaling in macrophages. BCAP binds to the regulatory p85 subunit of class I PI3K and is a large, multidomain protein. We used proteomic analysis to identify other BCAP-interacting proteins in macrophages and found that BCAP specifically associated with the caspase-1 pseudosubstrate inhibitor Flightless-1 and its binding partner leucine-rich repeat flightless-interacting protein 2. Because these proteins inhibit the NLRP3 inflammasome, we investigated the role of BCAP in inflammasome function. Independent of its effects on TLR priming, BCAP inhibited NLRP3- and NLRC4-induced caspase-1 activation, cell death, and IL-1β release from macrophages. Accordingly, caspase-1-dependent clearance of a Yersinia pseudotuberculosis mutant was enhanced in BCAP-deficient mice. Mechanistically, BCAP delayed the recruitment and activation of pro-caspase-1 within the NLRP3/ASC preinflammasome through its association with Flightless-1. Thus, BCAP is a multifunctional signaling adaptor that inhibits key pathogen-sensing pathways in macrophages.

The challenges of peptidomics in complementing proteomics in a clinical context

Naturally occurring peptides, including growth factors, hormones, and neurotransmitters, represent an important class of biomolecules and have crucial roles in human physiology. The study of these peptides in clinical samples is therefore as relevant as ever. Compared to more routine proteomics applications in clinical research, peptidomics research questions are more challenging and have special requirements with regard to sample handling, experimental design, and bioinformatics. In this review, we describe the issues that confront peptidomics in a clinical context. After these hurdles are (partially) overcome, peptidomics will be ready for a successful translation into medical practice.

Mapping Citrullinated Sites in Multiple Organs of Mice Using Hypercitrullinated Library

Protein citrullination (or deimination), an irreversible post-translational modification, has been implicated in several physiological and pathological processes, including gene expression regulation, apoptosis, rheumatoid arthritis, and Alzheimer's disease. Several research studies have been carried out on citrullination under many conditions. However, until now, challenges in sample preparation and data analysis have made it difficult to confidently identify a citrullinated protein and assign the citrullinated site. To overcome these limitations, we generated a mouse hyper-citrullinated spectral library and set up coordinates to confidently identify and validate citrullinated sites. Using this workflow, we detect a four-fold increase in citrullinated proteome coverage across six mouse organs compared with the current state-of-the art techniques. Our data reveal that the subcellular distribution of citrullinated proteins is tissue-type-dependent and that citrullinated targets are involved in fundamental physiological processes, including the metabolic process. These data represent the first report of a hyper-citrullinated library for the mouse and serve as a central resource for exploring the role of citrullination in this organism.

In Vitro and In Vivo Proteomic Comparison of Human Neural Progenitor Cell-Induced Photoreceptor Survival

Retinal degenerative diseases lead to blindness with few treatments. Various cell-based therapies are aimed to slow the progression of vision loss by preserving light-sensing photoreceptor cells. A subretinal injection of human neural progenitor cells (hNPCs) into the Royal College of Surgeons (RCS) rat model of retinal degeneration has aided in photoreceptor survival, though the mechanisms are mainly unknown. Identifying the retinal proteomic changes that occur following hNPC treatment leads to better understanding of neuroprotection. To mimic the retinal environment following hNPC injection, a co-culture system of retinas and hNPCs is developed. Less cell death occurs in RCS retinal tissue co-cultured with hNPCs than in retinas cultured alone, suggesting that hNPCs provide retinal protection in vitro. Comparison of ex vivo and in vivo retinas identifies nuclear factor (erythroid-derived 2)-like 2 (NRF2) mediated oxidative response signaling as an hNPC-induced pathway. This is the first study to compare proteomic changes following treatment with hNPCs in both an ex vivo and in vivo environment, further allowing the use of ex vivo modeling for mechanisms of retinal preservation. Elucidation of the protein changes in the retina following hNPC treatment may lead to the discovery of mechanisms of photoreceptor survival and its therapeutic for clinical applications.

Integration of Two In-depth Quantitative Proteomics Approaches Determines the Kallikrein-related Peptidase 7 (KLK7) Degradome in Ovarian Cancer Cell Secretome

Kallikrein-related peptidase 7 (KLK7) is a serine peptidase that is over expressed in ovarian cancer. In vitro functional analyses have suggested KLK7 to play a cancer progressive role, although monitoring of KLK7 expression has suggested a contradictory protective role for KLK7 in ovarian cancer patients. In order to help delineate its mechanism of action and thereby the functional roles, information on its substrate repertoire is crucial. Therefore, in this study a quantitative proteomics approach-PROtein TOpography and Migration Analysis Platform (PROTOMAP)-coupled with SILAC was used for in-depth analysis of putative KLK7 substrates from a representative ovarian cancer cell line, SKOV-3, secreted proteins. The Terminal Amine Isotopic Labeling of Substrates (TAILS) approach was used to determine the exact cleavage sites and to validate qPROTOMAP-identified putative substrates. By employing these two technically divergent approaches, exact cleavage sites on 16 novel putative substrates and two established substrates, matrix metalloprotease (MMP) 2 and insulin growth factor binding protein 3 (IGFBP3), were identified in the SKOV-3 secretome. Eight of these substrates were also identified on TAILS analysis of another ovarian cancer cell (OVMZ-6) secretome, with a further seven OVMZ-6 substrates common to the SKOV-3 qPROTOMAP profile. Identified substrates were significantly associated with the common processes of cell adhesion, extracellular matrix remodeling and cell migration according to the gene ontology (GO) biological process analysis. Biochemical validation supports a role for KLK7 in directly activating pro-MMP10, hydrolysis of IGFBP6 and cleavage of thrombospondin 1 with generation of a potentially bioactive N-terminal fragment. Overall, this study constitutes the most comprehensive analysis of the putative KLK7 degradome in any cancer to date, thereby opening new avenues for KLK7 research.

Expanding the Use of Spectral Libraries in Proteomics

The 2017 Dagstuhl Seminar on Computational Proteomics provided an opportunity for a broad discussion on the current state and future directions of the generation and use of peptide tandem mass spectrometry spectral libraries. Their use in proteomics is growing slowly, but there are multiple challenges in the field that must be addressed to further increase the adoption of spectral libraries and related techniques. The primary bottlenecks are the paucity of high quality and comprehensive libraries and the general difficulty of adopting spectral library searching into existing workflows. There are several existing spectral library formats, but none captures a satisfactory level of metadata; therefore, a logical next improvement is to design a more advanced, Proteomics Standards Initiative-approved spectral library format that can encode all of the desired metadata. The group discussed a series of metadata requirements organized into three designations of completeness or quality, tentatively dubbed bronze, silver, and gold. The metadata can be organized at four different levels of granularity: at the collection (library) level, at the individual entry (peptide ion) level, at the peak (fragment ion) level, and at the peak annotation level. Strategies for encoding mass modifications in a consistent manner and the requirement for encoding high-quality and commonly seen but as-yet-unidentified spectra were discussed. The group also discussed related topics, including strategies for comparing two spectra, techniques for generating representative spectra for a library, approaches for selection of optimal signature ions for targeted workflows, and issues surrounding the merging of two or more libraries into one. We present here a review of this field and the challenges that the community must address in order to accelerate the adoption of spectral libraries in routine analysis of proteomics datasets.

Robust determination of differential abundance in shotgun proteomics using nonparametric statistics

Label-free shotgun mass spectrometry enables the detection of significant changes in protein abundance between different conditions. Due to often limited cohort sizes or replication, large ratios of potential protein markers to number of samples, as well as multiple null measurements pose important technical challenges to conventional parametric models. From a statistical perspective, a scenario similar to that of unlabeled proteomics is encountered in genomics when looking for differentially expressed genes. Still, the difficulty of detecting a large fraction of the true positives without a high false discovery rate is arguably greater in proteomics due to even smaller sample sizes and peptide-to-peptide variability in detectability. These constraints argue for nonparametric (or distribution-free) tests on normalized peptide values, thus minimizing the number of free parameters, as well as for measuring significance with permutation testing. We propose such a procedure with a class-based statistic, no parametric assumptions, and no parameters to select other than a nominal false discovery rate. Our method was tested on a new dataset which is available via ProteomeXchange with identifier PXD006447. The dataset was prepared using a standard proteolytic digest of a human protein mixture at 1.5-fold to 3-fold protein concentration changes and diluted into a constant background of yeast proteins. We demonstrate its superiority relative to other approaches in terms of the realized sensitivity and realized false discovery rates determined by ground truth, and recommend it for detecting differentially abundant proteins from MS data.

Listeriolysin O-dependent host surfaceome remodeling modulates Listeria monocytogenes invasion

Listeria monocytogenes is a pathogenic bacterium that invades epithelial cells by activating host signaling cascades, which promote bacterial engulfment within a phagosome. The pore-forming toxin listeriolysin O (LLO), which is required for bacteria phagosomal escape, has also been associated with the activation of several signaling pathways when secreted by extracellular bacteria, including Ca2+ influx and promotion of L. monocytogenes entry. Quantitative host surfaceome analysis revealed significant quantitative remodeling of a defined set of cell surface glycoproteins upon LLO treatment, including a subset previously identified to play a role in the L. monocytogenes infection process. Our data further shows that the lysosomal-associated membrane proteins LAMP-1 and LAMP-2 are translocated to the cellular surface and those LLO-induced Ca2+ fluxes are required to trigger the surface relocalization of LAMP-1. Finally, we identify late endosomes/lysosomes as the major donor compartments of LAMP-1 upon LLO treatment and by perturbing their function, we suggest that these organelles participate in L. monocytogenes invasion.

Flexible and Fast Mapping of Peptides to a Proteome with ProteoMapper

Bottom-up proteomics relies on the proteolytic or chemical cleavage of proteins into peptides, the identification of those peptides via mass spectrometry, and the mapping of the identified peptides back to the reference proteome to infer which possible proteins are identified. Reliable mapping of peptides to proteins still poses substantial challenges when considering similar proteins, protein families, splice isoforms, sequence variation, and possible residue mass modifications, combined with an imperfect and incomplete understanding of the proteome. The ProteoMapper tool enables a comprehensive and rapid mapping of peptides to a reference proteome. The indexer component creates a segmented index for an input proteome from a FASTA or PEFF file. The ProMaST component provides ultrafast mapping of one or more input peptides against the index. ProteoMapper allows searches that take into account known sequence variation encoded in PEFF files. It also enables fuzzy searches to find highly similar peptides with residue order changes or other isobaric or near-isobaric substitutions within a specified mass tolerance. We demonstrate an example of a one-hit-wonder identification in PeptideAtlas that may be better explained by a combination of catalogued and uncatalogued sequence variation in another highly observed protein. ProteoMapper is a free and open source, available for local use after downloading, embedding in other applications, as an online web tool at http://www.peptideatlas.org/map , and as a web service.

In vivo Proteomics Approaches for the Analysis of Bacterial Adaptation Reactions in Host-Pathogen Settings

Proteome profiling of bacteria internalized by host cells is still a challenging task, due to low amounts of bacterial proteins in host-pathogen settings and the high amounts of contaminating host proteins. Here, we describe a workflow for the enrichment of intracellular bacteria by fluorescence activated cell sorting which in combination with highly sensitive LC-MS/MS allows monitoring of about 1200 proteins from 2 to 4 × 10⁶ internalized bacterial cells as starting material.

Visualization and application of amino acid retention coefficients obtained from modeling of peptide retention

We introduce a method for data inspection in liquid separations of peptides using amino acid retention coefficients and their relative change across experiments. Our method allows for the direct comparison between actual experimental conditions, regardless of sample content and without the use of internal standards. The modeling uses linear regression of peptide retention time as a function of amino acid composition. We demonstrate the pH dependency of the model in a control experiment where the pH of the mobile phase was changed in controlled way. We introduce a score to identify the false discovery rate on peptide spectrum match level that corresponds to the set of most robust models, i.e. to maximize the shared agreement between experiments. We demonstrate the method utility in reversed-phase liquid chromatography using 24 datasets with minimal peptide overlap. We apply our method on datasets obtained from a public repository representing various separation designs, including one-dimensional reversed-phase liquid chromatography followed by tandem mass spectrometry, and two-dimensional online strong cation exchange coupled to reversed-phase liquid chromatography followed by tandem mass spectrometry, and highlight new insights. Our method provides a simple yet powerful way to inspect data quality, in particular for multidimensional separations, improving comparability of data at no additional experimental cost.

Characterization of Athabasca lean oil sands and mixed surficial materials: Comparison of capillary electrophoresis/low-resolution mass spectrometry and high-resolution mass spectrometry

RATIONALE

Oil sands mining in Alberta, Canada, requires removal and stockpiling of considerable volumes of near-surface overburden material. This overburden includes lean oil sands (LOS) which cannot be processed economically but contain sparingly soluble petroleum hydrocarbons and naphthenic acids, which can leach into environmental waters. In order to measure and track the leaching of dissolved constituents and distinguish industrially derived organics from naturally occurring organics in local waters, practical methods were developed for characterizing multiple sources of contaminated water leakage.

METHODS

Capillary electrophoresis/positive-ion electrospray ionization low-resolution time-of-flight mass spectrometry (CE/LRMS), high-resolution negative-ion electrospray ionization Orbitrap mass spectrometry (HRMS) and conventional gas chromatography/flame ionization detection (GC/FID) were used to characterize porewater samples collected from within Athabasca LOS and mixed surficial materials. GC/FID was used to measure total petroleum hydrocarbon and HRMS was used to measure total naphthenic acid fraction components (NAFCs). HRMS and CE/LRMS were used to characterize samples according to source.

RESULTS

The amounts of total petroleum hydrocarbon in each sample as measured by GC/FID ranged from 0.1 to 15.1 mg/L while the amounts of NAFCs as measured by HRMS ranged from 5.3 to 82.3 mg/L. Factors analysis (FA) on HRMS data visually demonstrated clustering according to sample source and was correlated to molecular formula. LRMS coupled to capillary electrophoresis separation (CE/LRMS) provides important information on NAFC isomers by adding analyte migration time data to m/z and peak intensity.

CONCLUSIONS

Differences in measured amounts of total petroleum hydrocarbons by GC/FID and NAFCs by HRMS indicate that the two methods provide complementary information about the nature of dissolved organic species in a soil or water leachate samples. NAFC molecule class O_x S_y is a possible tracer for LOS seepage. CE/LRMS provides complementary information and is a feasible and practical option for source evaluation of NAFCs in water.

Site-Specific Profiling of Serum Glycoproteins Using N-Linked Glycan and Glycosite Analysis Revealing Atypical N-Glycosylation Sites on Albumin and α-1B-Glycoprotein

Most serum proteins are N-linked glycosylated, and therefore the glycoproteomic profiling of serum is essential for characterization of serum proteins. In this study, we profiled serum N-glycoproteome by our recently developed N-glycoproteomic method using solid-phase extraction of N-linked glycans and glycosite-containing peptides (NGAG) coupled with LC-MS/MS and site-specific glycosylation analysis using GPQuest software. Our data indicated that half of identified N-glycosites were modified by at least two glycans, with a majority of them being sialylated. Specifically, 3/4 of glycosites were modified by biantennary N-glycans and 1/3 of glycosites were modified by triantennary sialylated N-glycans. In addition, two novel atypical glycosites (with N-X-V motif) were identified and validated from albumin and α-1B-glycoprotein. The widespread presence of these two glycosites among individuals was further confirmed by individual serum analyses.

Visualizing and comparing results of different peptide identification methods

In mass spectrometry-based proteomics, peptides are typically identified from tandem mass spectra using spectrum comparison. A sequence search engine compares experimentally obtained spectra with those predicted from protein sequences, applying enzyme cleavage and fragmentation rules. To this, there are two main alternatives: spectral libraries and de novo sequencing. The former compares measured spectra with a collection of previously acquired and identified spectra in a library. De novo attempts to sequence peptides from the tandem mass spectra alone. We here present a theoretical framework and a data processing workflow for visualizing and comparing the results of these different types of algorithms. The method considers the three search strategies as different dimensions, identifies distinct agreement classes and visualizes the complementarity of the search strategies. We have included X! Tandem, SpectraST and PepNovo, as they are in common use and representative for algorithms of each type. Our method allows advanced investigation of how the three search methods perform relatively to each other and shows the impact of the currently used decoy sequences for evaluating the false discovery rates.

StPeter: Seamless Label-Free Quantification with the Trans-Proteomic Pipeline

Label-free quantification has grown in popularity as a means of obtaining relative abundance measures for proteomics experiments. However, easily accessible and integrated tools to perform label-free quantification have been lacking. We describe StPeter, an implementation of Normalized Spectral Index quantification for wide availability through integration into the widely used Trans-Proteomic Pipeline. This implementation has been specifically designed for reproducibility and ease of use. We demonstrate that StPeter outperforms other state-of-the art packages using a recently reported benchmark data set over the range of false discovery rates relevant to shotgun proteomics results. We also demonstrate that the software is computationally efficient and supports data from a variety of instrument platforms and experimental designs. Results can be viewed within the Trans-Proteomic Pipeline graphical user interfaces and exported in standard formats for downstream statistical analysis. By integrating StPeter into the freely available Trans-Proteomic Pipeline, users can now obtain high-quality label-free quantification of any data set in seconds by adding a single command to the workflow.

A Proteomics Workflow for Dual Labeling Biotin Switch Assay to Detect and Quantify Protein S-Nitroylation

S-nitrosylation (or S-nitrosation, SNO) is an oxidative posttranslational modification to the thiol group of a cysteine amino acid residue. There are several methods to detect SNO modifications, mostly based on the classic biotin-switch assay, where the labile SNO sites are replaced with a stable biotin moiety to facilitate enrichment of the modified proteins. As the technique has evolved, new and more advanced thiol-reactive reagents have been introduced in the protocol to improve the identification of modified peptides or to quantify the level of modification at individual cysteine residues. However, the growing diversity of thiol-reactive affinity tags has not produced a consistent set of protein modifications, suggesting incomplete coverage using a single tag. Here, we present a parallel dual labeling strategy followed by an optimized proteomics workflow, which maximizes the overall detection of SNO by reducing the labeling bias derived from the use of a single tag-capture approach.

FZD4 Marks Lateral Plate Mesoderm and Signals with NORRIN to Increase Cardiomyocyte Induction from Pluripotent Stem Cell-Derived Cardiac Progenitors

The identification of cell surface proteins on stem cells or stem cell derivatives is a key strategy for the functional characterization, isolation, and understanding of stem cell population dynamics. Here, using an integrated mass spectrometry- and microarray-based approach, we analyzed the surface proteome and transcriptome of cardiac progenitor cells (CPCs) generated from the stage-specific differentiation of mouse and human pluripotent stem cells. Through bioinformatics analysis, we have identified and characterized FZD4 as a marker for lateral plate mesoderm. Additionally, we utilized FZD4, in conjunction with FLK1 and PDGFRA, to further purify CPCs and increase cardiomyocyte (CM) enrichment in both mouse and human systems. Moreover, we have shown that NORRIN presented to FZD4 further increases CM output via proliferation through the canonical WNT pathway. Taken together, these findings demonstrate a role for FZD4 in mammalian cardiac development.

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Identified and characterized FZD4 as a new marker for lateral plate mesoderm

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FZD4, in conjunction with FLK1 and PDGFRA, increases cardiomyocyte enrichment

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FZD4 is expressed in the human system and shows enrichment in cardiomyocytes

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NORRIN addition shows increase in cardiomyocyte output from FZD4 progenitor cells

The Human Plasma Proteome Draft of 2017: Building on the Human Plasma PeptideAtlas from Mass Spectrometry and Complementary Assays

Human blood plasma provides a highly accessible window to the proteome of any individual in health and disease. Since its inception in 2002, the Human Proteome Organization's Human Plasma Proteome Project (HPPP) has been promoting advances in the study and understanding of the full protein complement of human plasma and on determining the abundance and modifications of its components. In 2017, we review the history of the HPPP and the advances of human plasma proteomics in general, including several recent achievements. We then present the latest 2017-04 build of Human Plasma PeptideAtlas, which yields ∼43 million peptide-spectrum matches and 122,730 distinct peptide sequences from 178 individual experiments at a 1% protein-level FDR globally across all experiments. Applying the latest Human Proteome Project Data Interpretation Guidelines, we catalog 3509 proteins that have at least two non-nested uniquely mapping peptides of nine amino acids or more and >1300 additional proteins with ambiguous evidence. We apply the same two-peptide guideline to historical PeptideAtlas builds going back to 2006 and examine the progress made in the past ten years in plasma proteome coverage. We also compare the distribution of proteins in historical PeptideAtlas builds in various RNA abundance and cellular localization categories. We then discuss advances in plasma proteomics based on targeted mass spectrometry as well as affinity assays, which during early 2017 target ∼2000 proteins. Finally, we describe considerations about sample handling and study design, concluding with an outlook for future advances in deciphering the human plasma proteome.