A proteomics search algorithm specifically designed for high-resolution tandem mass spectra

Generational Diet-Induced Obesity Remodels the Omental Adipose Proteome in Female Mice

Obesity, a complex condition that involves genetic, environmental, and behavioral factors, is a non-infectious pandemic that affects over 650 million adults worldwide with a rapidly growing prevalence. A major contributor is the consumption of high-fat diets, an increasingly common feature of modern diets. Maternal obesity results in an increased risk of offspring developing obesity and related health problems; however, the impact of maternal diet on the adipose tissue composition of offspring has not been evaluated. Here, we designed a generational diet-induced obesity study in female C57BL/6 mice that included maternal cohorts and their female offspring fed either a control diet (10% fat) or a high-fat diet (45% fat) and examined the visceral adipose proteome. Solubilizing proteins from adipose tissue is challenging due to the need for high concentrations of detergents; however, the use of a detergent-compatible sample preparation strategy based on suspension trapping (S-Trap) enabled label-free quantitative bottom-up analysis of the adipose proteome. We identified differentially expressed proteins related to lipid metabolism, inflammatory disease, immune response, and cancer, providing valuable molecular-level insight into how maternal obesity impacts the health of offspring. Data are available via ProteomeXchange with the identifier PXD042092.

Rescoring Peptide Spectrum Matches: Boosting Proteomics Performance by Integrating Peptide Property Predictors Into Peptide Identification

Rescoring of peptide spectrum matches originating from database search engines enabled by peptide property predictors is exceeding the performance of peptide identification from traditional database search engines. In contrast to the peptide spectrum match scores calculated by traditional database search engines, rescoring peptide spectrum matches generates scores based on comparing observed and predicted peptide properties, such as fragment ion intensities and retention times. These newly generated scores enable a more efficient discrimination between correct and incorrect peptide spectrum matches. This approach was shown to lead to substantial improvements in the number of confidently identified peptides, facilitating the analysis of challenging datasets in various fields such as immunopeptidomics, metaproteomics, proteogenomics, and single-cell proteomics. In this review, we summarize the key elements leading up to the recent introduction of multiple data-driven rescoring pipelines. We provide an overview of relevant post-processing rescoring tools, introduce prominent data-driven rescoring pipelines for various applications, and highlight limitations, opportunities, and future perspectives of this approach and its impact on mass spectrometry-based proteomics.

Boosting the Sensitivity of Quantitative Single-Cell Proteomics with Infrared-Tandem Mass Tags

Single-cell proteomics is a powerful approach to precisely profile protein landscapes within individual cells toward a comprehensive understanding of proteomic functions and tissue and cellular states. The inherent challenges associated with limited starting material demand heightened analytical sensitivity. Just as advances in sample preparation maximize the amount of material that makes it from the cell to the mass spectrometer, we strive to maximize the number of ions that make it from ion source to the detector. In isobaric tagging experiments, limited reporter ion generation limits quantitative accuracy and precision. The combination of infrared photoactivation and ion parking circumvents the m/z dependence inherent in HCD, maximizing reporter generation and avoiding unintended degradation of TMT reporter molecules in infrared-tandem mass tags (IR-TMT). The method was applied to single-cell human proteomes using 18-plex TMTpro, resulting in 4-5-fold increases in reporter signal compared to conventional SPS-MS3 approaches. IR-TMT enables faster duty cycles, higher throughput, and increased peptide identification and quantification. Comparative experiments showcase 4-5-fold lower injection times for IR-TMT, providing superior sensitivity without compromising accuracy. In all, IR-TMT enhances the dynamic range of proteomic experiments and is compatible with gas-phase fractionation and real-time searching, promising increased gains in the study of cellular heterogeneity.

Plasmodium falciparum J-dot localized J domain protein A8iJp modulates the chaperone activity of human HSPA8

Plasmodium falciparum renovates the host erythrocyte to survive during intraerythrocytic development. This renovation requires many parasite proteins to unfold and move outside the parasitophorous vacuolar membrane, and chaperone-regulated protein folding becomes essential for the exported proteins to function. We report on a type-IV J domain protein (JDP), PF3D7_1401100, which we found to be processed before export and trafficked inside the lumen of parasite-derived structures known as J-dots. We found this protein to have holdase activity, as well as stimulate the ATPase and aggregation suppression activity of the human HSP70 chaperone HsHSPA8; thus, we named it "HSPA8-interacting J protein" (A8iJp). Moreover, we found a subset of HsHSPA8 to co-localize with A8iJp inside the infected human erythrocyte. Our results suggest that A8iJp modulates HsHSPA8 chaperone activity and may play an important role in host erythrocyte renovation.

AlphaPept: a modern and open framework for MS-based proteomics

In common with other omics technologies, mass spectrometry (MS)-based proteomics produces ever-increasing amounts of raw data, making efficient analysis a principal challenge. A plethora of different computational tools can process the MS data to derive peptide and protein identification and quantification. However, during the last years there has been dramatic progress in computer science, including collaboration tools that have transformed research and industry. To leverage these advances, we develop AlphaPept, a Python-based open-source framework for efficient processing of large high-resolution MS data sets. Numba for just-in-time compilation on CPU and GPU achieves hundred-fold speed improvements. AlphaPept uses the Python scientific stack of highly optimized packages, reducing the code base to domain-specific tasks while accessing the latest advances. We provide an easy on-ramp for community contributions through the concept of literate programming, implemented in Jupyter Notebooks. Large datasets can rapidly be processed as shown by the analysis of hundreds of proteomes in minutes per file, many-fold faster than acquisition. AlphaPept can be used to build automated processing pipelines with web-serving functionality and compatibility with downstream analysis tools. It provides easy access via one-click installation, a modular Python library for advanced users, and via an open GitHub repository for developers.

Advances in neuroproteomics for neurotrauma: unraveling insights for personalized medicine and future prospects

Neuroproteomics, an emerging field at the intersection of neuroscience and proteomics, has garnered significant attention in the context of neurotrauma research. Neuroproteomics involves the quantitative and qualitative analysis of nervous system components, essential for understanding the dynamic events involved in the vast areas of neuroscience, including, but not limited to, neuropsychiatric disorders, neurodegenerative disorders, mental illness, traumatic brain injury, chronic traumatic encephalopathy, and other neurodegenerative diseases. With advancements in mass spectrometry coupled with bioinformatics and systems biology, neuroproteomics has led to the development of innovative techniques such as microproteomics, single-cell proteomics, and imaging mass spectrometry, which have significantly impacted neuronal biomarker research. By analyzing the complex protein interactions and alterations that occur in the injured brain, neuroproteomics provides valuable insights into the pathophysiological mechanisms underlying neurotrauma. This review explores how such insights can be harnessed to advance personalized medicine (PM) approaches, tailoring treatments based on individual patient profiles. Additionally, we highlight the potential future prospects of neuroproteomics, such as identifying novel biomarkers and developing targeted therapies by employing artificial intelligence (AI) and machine learning (ML). By shedding light on neurotrauma's current state and future directions, this review aims to stimulate further research and collaboration in this promising and transformative field.

Targeted Proteomic Quantitation of NRF2 Signaling and Predictive Biomarkers in HNSCC

The NFE2L2 (NRF2) oncogene and transcription factor drives a gene expression program that promotes cancer progression, metabolic reprogramming, immune evasion, and chemoradiation resistance. Patient stratification by NRF2 activity may guide treatment decisions to improve outcome. Here, we developed a mass spectrometry-based targeted proteomics assay based on internal standard-triggered parallel reaction monitoring to quantify 69 NRF2 pathway components and targets, as well as 21 proteins of broad clinical significance in head and neck squamous cell carcinoma (HNSCC). We improved an existing internal standard-triggered parallel reaction monitoring acquisition algorithm, called SureQuant, to increase throughput, sensitivity, and precision. Testing the optimized platform on 27 lung and upper aerodigestive cancer cell models revealed 35 NRF2 responsive proteins. In formalin-fixed paraffin-embedded HNSCCs, NRF2 signaling intensity positively correlated with NRF2-activating mutations and with SOX2 protein expression. Protein markers of T-cell infiltration correlated positively with one another and with human papilloma virus infection status. CDKN2A (p16) protein expression positively correlated with the human papilloma virus oncogenic E7 protein and confirmed the presence of translationally active virus. This work establishes a clinically actionable HNSCC protein biomarker assay capable of quantifying over 600 peptides from frozen or formalin-fixed paraffin-embedded archived tissues in under 90 min.

Database Creator for Mass Analysis of Peptides and Proteins, DC-MAPP: A Standalone Tool for Simplifying Manual Analysis of Mass Spectral Data to Identify Peptide/Protein Sequences

Proteomic studies typically involve the use of different types of software for annotating experimental tandem mass spectrometric data (MS/MS) and thereby simplifying the process of peptide and protein identification. For such annotations, these softwares calculate the m/z values of the peptide/protein precursor and fragment ions, for which a database of protein sequences must be provided as an input file. The calculated m/z values are stored as another database, which the user usually cannot view. Database Creator for Mass Analysis of Peptides and Proteins (DC-MAPP) is a novel standalone software that can create custom databases for "viewing" the calculated m/z values of precursor and fragment ions, prior to the database search. It contains three modules. Peptide/Protein sequences as per user's choice can be entered as input to the first module for creating a custom database. In the second module, m/z values must be queried-in, which are searched within the custom database to identify protein/peptide sequences. The third module is suited for peptide mass fingerprinting, which can be used to analyze both ESI and MALDI mass spectral data. The feature of "viewing" the custom database can be helpful not only for better understanding the search engine processes, but also for designing multiple reaction monitoring (MRM) methods. Post-translational modifications and protein isoforms can also be analyzed. Since, DC-MAPP relies on the protein/peptide "sequences" for creating custom databases, it may not be applicable for the searches involving spectral libraries. Python language was used for implementation, and the graphical user interface was built with Page/Tcl, making this tool more user-friendly. It is freely available at https://vit.ac.in/DC-MAPP/.

Microglia mediate contact-independent neuronal pruning via secreted Neuraminidase-3 associated with extracellular vesicles

Neurons communicate with each other through electrochemical transmission at synapses. Microglia, the resident immune cells of the central nervous system, can prune these synapses through a variety of contact-dependent and -independent means. Microglial secretion of active sialidase enzymes upon exposure to inflammatory stimuli is one unexplored mechanism of pruning. Recent work from our lab showed that treatment of neurons with bacterial sialidases disrupts neuronal network connectivity. Here, we find that activated microglia secrete Neuraminidase-3 (Neu3) associated with fusogenic extracellular vesicles. Furthermore, we show Neu3 mediates contact-independent pruning of neurons and subsequent disruption of neuronal networks through neuronal glycocalyx remodeling. We observe that NEU3 is transcriptionally upregulated upon exposure to inflammatory stimuli, and that a genetic knock-out of NEU3 abrogates the sialidase activity of inflammatory microglial secretions. Moreover, we demonstrate that Neu3 is associated with a subpopulation of extracellular vesicles, possibly exosomes, that are secreted by microglia upon inflammatory insult. Finally, we demonstrate that Neu3 is both necessary and sufficient to both desialylate neurons and decrease neuronal network connectivity. These results implicate Neu3 in remodeling of the glycocalyx leading to aberrant network-level activity of neurons, with implications in neuroinflammatory diseases such as Parkinson's disease and Alzheimer's disease.

Identification of Splice Variants and Isoforms in Transcriptomics and Proteomics

Alternative splicing is pivotal to the regulation of gene expression and protein diversity in eukaryotic cells. The detection of alternative splicing events requires specific omics technologies. Although short-read RNA sequencing has successfully supported a plethora of investigations on alternative splicing, the emerging technologies of long-read RNA sequencing and top-down mass spectrometry open new opportunities to identify alternative splicing and protein isoforms with less ambiguity. Here, we summarize improvements in short-read RNA sequencing for alternative splicing analysis, including percent splicing index estimation and differential analysis. We also review the computational methods used in top-down proteomics analysis regarding proteoform identification, including the construction of databases of protein isoforms and statistical analyses of search results. While many improvements in sequencing and computational methods will result from emerging technologies, there should be future endeavors to increase the effectiveness, integration, and proteome coverage of alternative splicing events.

The PRMT5/WDR77 complex restricts hepatitis E virus replication

Hepatitis E virus (HEV) is one of the main pathogenic agents of acute hepatitis in the world. The mechanism of HEV replication, especially host factors governing HEV replication is still not clear. Here, using HEV ORF1 trans-complementation cell culture system and HEV replicon system, combining with stable isotope labelling with amino acids in cell culture (SILAC) and mass spectrometry (MS), we aimed to identify the host factors regulating HEV replication. We identified a diversity of host factors associated with HEV ORF1 protein, which were putatively responsible for viral genomic RNA replication, in these two cell culture models. Of note, the protein arginine methyltransferase 5 (PRMT5)/WDR77 complex was identified in both cell culture models as the top hit. Furthermore, we demonstrated that PRMT5 and WDR77 can specifically inhibit HEV replication, but not other viruses such as HCV or SARS-CoV-2, and this inhibition is conserved among different HEV strains and genotypes. Mechanistically, PRMT5/WDR77 can catalyse methylation of ORF1 on its R458, impairing its replicase activity, and virus bearing R458K mutation in ORF1 relieves the restriction of PRMT5/WDR77 accordingly. Taken together, our study promotes more comprehensive understanding of viral infections but also provides therapeutic targets for intervention.

Spatial analysis of the ancient proteome of archeological teeth using mass spectrometry imaging

RATIONALE

Proteins extracted from archaeological bone and teeth are utilised for investigating the phylogeny of extinct and extant species, the biological sex and age of past individuals, as well as ancient health and physiology. However, variable preservation of proteins in archaeological materials represents a major challenge.

METHODS

To better understand the spatial distribution of ancient proteins preserved within teeth, we applied matrix assisted laser desorption/ionisation mass spectrometry imaging (MALDI-MSI) for the first time to bioarchaeological samples to visualise the intensity of proteins in archaeological teeth thin sections. We specifically explored the spatial distribution of four proteins (collagen type I, of which the chains alpha-1 and alpha-2, alpha-2-HS-glycoprotein, haemoglobin subunit alpha and myosin light polypeptide 6).

RESULTS

We successfully identified ancient proteins in archaeological teeth thin sections using mass spectrometry imaging. The data are available via ProteomeXchange with identifier PXD038114. However, we observed that peptides did not always follow our hypotheses for their spatial distribution, with distinct differences observed in the spatial distribution of several proteins, and occasionally between peptides of the same protein.

CONCLUSIONS

While it remains unclear what causes these differences in protein intensity distribution within teeth, as revealed by MALDI-MSI in this study, we have demonstrated that MALDI-MSI can be successfully applied to mineralised bioarchaeological tissues to detect ancient peptides. In future applications, this technique could be particularly fruitful not just for understanding the preservation of proteins in a range of archaeological materials, but making informed decisions on sampling strategies and the targeting of key proteins of archaeological and biological interest.

Mzion enables deep and precise identification of peptides in data-dependent acquisition proteomics

Sensitive and reliable identification of proteins and peptides pertains the basis of proteomics. We introduce Mzion, a new database search tool for data-dependent acquisition (DDA) proteomics. Our tool utilizes an intensity tally strategy and achieves generally a higher performance in terms of depth and precision across 20 datasets, ranging from large-scale to single-cell proteomics. Compared to several other search engines, Mzion matches on average 20% more peptide spectra at tryptic enzymatic specificity and 80% more at no enzymatic specificity from six large-scale, global datasets. Mzion also identifies more phosphopeptide spectra that can be explained by fewer proteins, demonstrated by six large-scale, local datasets corresponding to the global data. Our findings highlight the potential of Mzion for improving proteomic analysis and advancing our understanding of protein biology.

Modeling Lower-Order Statistics to Enable Decoy-Free FDR Estimation in Proteomics

One of the chief objectives in mass spectrometry-based peptide identification in proteomics is the statistical validation of top-scoring peptide-spectrum matches (PSMs) in the form of false discovery rate (FDR) estimation. Existing methods construct a null model that captures the characteristics of incorrect target PSMs to estimate the FDR, most often with the help of decoys. Decoy-based methods, however, increase the computational cost and rely on the difficult-to-verify assumption that decoy PSMs constitute a sufficient and representative sample of the population of possible incorrect target PSMs. On the other hand, the possibility of FDR estimation assisted by the plentiful non-top-scoring PSMs, which are almost always incorrect, has been scarcely explored. In this work, we propose a novel decoy-free procedure for developing null models for top-scoring PSMs using the transformed e-value (TEV) score and the distributions of non-top-scoring target PSMs. The method relies on a theoretically derivable relationship between the parameters of the distributions of lower-order statistics of the TEV score and a necessary empirical optimization to fit a single parameter to actual data. The framework was tested on multiple different data sets and two search engines. We present evidence that our method is comparable to and occasionally outperforms popular decoy-free and decoy-based methods in FDR estimation.

Quantitative structural proteomics in living cells by covalent protein painting

The fold and conformation of proteins are key to successful cellular function, but all techniques for protein structure determination are performed in an artificial environment with highly purified proteins. While protein conformations have been solved to atomic resolution and modern protein structure prediction tools rapidly generate near accurate models of proteins, there is an unmet need to uncover the conformations of proteins in living cells. Here, we describe Covalent Protein Painting (CPP), a simple and fast method to infer structural information on protein conformation in cells with a quantitative protein footprinting technology. CPP monitors the conformational landscape of the 3D proteome in cells with high sensitivity and throughput. A key advantage of CPP is its' ability to quantitatively compare the 3D proteomes between different experimental conditions and to discover significant changes in the protein conformations. We detail how to perform a successful CPP experiment, the factors to consider before performing the experiment, and how to interpret the results.

Fast, Free, and Flexible Peptide and Protein Quantification with FlashLFQ

The rapid and accurate quantification of peptides is a critical element of modern proteomics that has become increasingly challenging as proteomic data sets grow in size and complexity. We present here FlashLFQ, a computer program for high-speed label-free quantification of peptides and proteins following a search of bottom-up mass spectrometry data. FlashLFQ is approximately an order of magnitude faster than established label-free quantification methods and can quantify data-dependent analysis (DDA) search results from any proteomics search program. It is available as a graphical user interface program, a command line tool, a Docker image, and integrated into the MetaMorpheus search software.

Deciphering O-glycoprotease substrate preferences with O-Pair Search

O-Glycoproteases are an emerging class of enzymes that selectively digest glycoproteins at positions decorated with specific O-linked glycans. O-Glycoprotease substrates range from any O-glycoprotein (albeit with specific O-glycan modifications) to only glycoproteins harboring specific O-glycosylated sequence motifs, such as those found in mucin domains. Their utility for multiple glycoproteomic applications is driving the search to both discover new O-glycoproteases and to understand how structural features of characterized O-glycoproteases influence their substrate specificities. One challenge of defining O-glycoprotease specificity restraints is the need to characterize O-glycopeptides with site-specific analysis of O-glycosites. Here, we demonstrate how O-Pair Search, a recently developed O-glycopeptide-centric identification platform that enables rapid searches and confident O-glycosite localization, can be used to determine substrate specificities of various O-glycoproteases de novo from LC-MS/MS data of O-glycopeptides. Using secreted protease of C1 esterase inhibitor (StcE) from enterohemorrhagic Escherichia coli and O-endoprotease OgpA from Akkermansia mucinophila, we explore numerous settings that effect O-glycopeptide identification and show how non-specific and semi-tryptic searches of O-glycopeptide data can produce candidate cleavage motifs. These putative motifs can be further used to define new protease cleavage settings that lower search times and improve O-glycopeptide identifications. We use this platform to generate a consensus motif for the recently characterized immunomodulating metalloprotease (IMPa) from Pseudomonas aeruginosa and show that IMPa is a favorable O-glycoprotease for characterizing densely O-glycosylated mucin-domain glycoproteins.

Characterization of core fucosylation via sequential enzymatic treatments of intact glycopeptides and mass spectrometry analysis

Core fucosylation of N-linked glycoproteins has been linked to the functions of glycoproteins in physiological and pathological processes. However, quantitative characterization of core fucosylation remains challenging due to the complexity and heterogeneity of N-linked glycosylation. Here we report a mass spectrometry-based method that employs sequential treatment of intact glycopeptides with enzymes (STAGE) to analyze site-specific core fucosylation of glycoproteins. The STAGE method utilizes Endo F3 followed by PNGase F treatment to generate mass signatures for glycosites that are formerly modified by core fucosylated N-linked glycans. We benchmark the STAGE method and use it to characterize site specific core fucosylation of glycoproteins from human hepatocellular carcinoma and pancreatic ductal adenocarcinoma, resulting in the identification of 1130 and 782 core fucosylated glycosites, respectively. These results indicate that our STAGE method enables quantitative characterization of core fucosylation events from complex protein mixtures, which may benefit our understanding of core fucosylation functions in various diseases.

Aedes aegypti Aag-2 Cell Proteome Modulation in Response to Chikungunya Virus Infection

Chikungunya virus (CHIKV) is a single-stranded positive RNA virus that belongs to the genus Alphavirus and is transmitted to humans by infected Aedes aegypti and Aedes albopictus bites. In humans, CHIKV usually causes painful symptoms during acute and chronic stages of infection. Conversely, virus–vector interaction does not disturb the mosquito’s fitness, allowing a persistent infection. Herein, we studied CHIKV infection of Ae. aegypti Aag-2 cells (multiplicity of infection (MOI) of 0.1) for 48 h through label-free quantitative proteomic analysis and transmission electron microscopy (TEM). TEM images showed a high load of intracellular viral cargo at 48 h postinfection (hpi), as well as an unusual elongated mitochondria morphology that might indicate a mitochondrial imbalance. Proteome analysis revealed 196 regulated protein groups upon infection, which are related to protein synthesis, energy metabolism, signaling pathways, and apoptosis. These Aag-2 proteins regulated during CHIKV infection might have roles in antiviral and/or proviral mechanisms and the balance between viral propagation and the survival of host cells, possibly leading to the persistent infection.

Internal Fragment Ions Disambiguate and Increase Identifications in Top-Down Proteomics

A large fraction of observed fragment ion intensity remains unidentified in top-down proteomics. The elucidation of these unknown fragment ions could enable researchers to identify additional proteoforms and reduce proteoform ambiguity in their analyses. Internal fragment ions have received considerable attention as a major source of these unidentified fragment ions. Internal fragments are product ions that contain neither protein terminus, in contrast with terminal ions that contain a single terminus. There are many more possible internal fragments than terminal fragments, and the resulting computational complexity has historically limited the application of internal fragment ions to low-complexity samples containing only one or a few proteins of interest. We implemented internal fragment ion functionality in MetaMorpheus to allow the proteome-wide annotation of internal fragment ions. MetaMorpheus first uses terminal fragment ions to identify putative proteoforms and then employs internal fragment ions to disambiguate similar proteoforms. In the analysis of mammalian cell lysates, we found that MetaMorpheus could disambiguate over half of its previously ambiguous proteoforms while also providing up to a 7% increase in proteoform-spectrum matches identified at a 1% false discovery rate.

Time-Resolved Analysis of N-RNA Interactions during RVFV Infection Shows Qualitative and Quantitative Shifts in RNA Encapsidation and Packaging

Rift Valley fever virus (RVFV) is a negative-sense, tripartite RNA virus that is endemic to Africa and the Arabian Peninsula. It can cause severe disease and mortality in humans and domestic livestock and is a concern for its potential to spread more globally. RVFV's nucleocapsid protein (N) is an RNA-binding protein that is necessary for viral transcription, replication, and the production of nascent viral particles. We have conducted crosslinking, immunoprecipitation, and sequencing (CLIP-seq) to characterize N interactions with host and viral RNAs during infection. In parallel, to precisely measure intracellular N levels, we employed multiple reaction monitoring mass spectrometry (MRM-MS). Our results show that N binds mostly to host RNAs at early stages of infection, yielding nascent virus particles of reduced infectivity. The expression of N plateaus 10 h post-infection, whereas the intracellular viral RNA concentration continues to increase. Moreover, the virions produced later in infection have higher infectivity. Taken together, the detailed examination of these N-RNA interactions provides insight into how the regulated expression of N and viral RNA produces both infectious and incomplete, noninfectious particles.

Deep Convolutional Neural Networks Help Scoring Tandem Mass Spectrometry Data in Database-Searching Approaches

Spectrum annotation is a challenging task due to the presence of unexpected peptide fragmentation ions as well as the inaccuracy of the detectors of the spectrometers. We present a deep convolutional neural network, called Slider, which learns an optimal feature extraction in its kernels for scoring mass spectrometry (MS)/MS spectra to increase the number of spectrum annotations with high confidence. Experimental results using publicly available data sets show that Slider can annotate slightly more spectra than the state-of-the-art methods (BoltzMatch, Res-EV, Prosit), albeit 2-10 times faster. More interestingly, Slider provides only 2-4% fewer spectrum annotations with low-resolution fragmentation information than other methods with high-resolution information. This means that Slider can exploit nearly as much information from the context of low-resolution spectrum peaks as the high-resolution fragmentation information can provide for other scoring methods. Thus, Slider can be an optimal choice for practitioners using old spectrometers with low-resolution detectors.

Constitutive expression and discovery of antimicrobial peptides in Zygogramma bicolorata (Coleoptera: Chrysomelidae)

The expression, identification, and discovery of less toxic antimicrobial peptides (AMPs) are significant in managing infectious pathogens. AMPs triggered in response to the immune system have evolved to defend against pathogens and wounding. The protein composition of Zygogramma bicolorata hemolymph is of diagnostic importance as the open circulatory systems of the insects involve signaling through hemolymph. They have conserved many ancestral vertebrate genes that may help better understand the evolution of innate immunity. The present work describes the isolation, purification, identification, and bioinformatics analysis of AMPs from the immunized hemolymph of Z. bicolorata. Thirty-nine peptides were isolated from reverse-phase high-performance liquid chromatography and sequenced via mass spectrometry analysis. The immunization process recorded a threefold higher protein concentration in immunized hemolymph when compared with nonimmunized one. For the first time, the proteomic study on Z. bicolorata hemolymph unveils the three novel proteins in the family Chrysomelidae with no homology in the database, indicating its novelty and the expression of the rest of 36 well-known proteins, including heat-shock, immune, structural, signaling proteins, and others speak for its method validity. Combining the expression of novel AMPs, detoxifying enzymes, hemolytic, and cytotoxic assays, and this work can elucidate new pathways to immune response mechanisms. Its molecular basis also holds the potential applicability in the future drug development process against pathogenic fungi such as Aspergillus niger and Candida albicans.

Proteogenomic characterization of pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive cancer with poor patient survival. Toward understanding the underlying molecular alterations that drive PDAC oncogenesis, we conducted comprehensive proteogenomic analysis of 140 pancreatic cancers, 67 normal adjacent tissues, and 9 normal pancreatic ductal tissues. Proteomic, phosphoproteomic, and glycoproteomic analyses were used to characterize proteins and their modifications. In addition, whole-genome sequencing, whole-exome sequencing, methylation, RNA sequencing (RNA-seq), and microRNA sequencing (miRNA-seq) were performed on the same tissues to facilitate an integrated proteogenomic analysis and determine the impact of genomic alterations on protein expression, signaling pathways, and post-translational modifications. To ensure robust downstream analyses, tumor neoplastic cellularity was assessed via multiple orthogonal strategies using molecular features and verified via pathological estimation of tumor cellularity based on histological review. This integrated proteogenomic characterization of PDAC will serve as a valuable resource for the community, paving the way for early detection and identification of novel therapeutic targets.

MAPLE: A Microbiome Analysis Pipeline Enabling Optimal Peptide Search and Comparative Taxonomic and Functional Analysis

Metaproteomics by mass spectrometry (MS) is a powerful approach to profile a large number of proteins expressed by all organisms in a highly complex biological or ecological sample, which is able to provide a direct and quantitative assessment of the functional makeup of a microbiota. The human gastrointestinal microbiota has been found playing important roles in human physiology and health, and metaproteomics has been shown to shed light on multiple novel associations between microbiota and diseases. MS-powered proteomics generally relies on genome data to define search space. However, metaproteomics, which simultaneously analyzes all proteins from hundreds to thousands of species, faces significant challenges regarding database search and interpretation of results. To overcome these obstacles, we have developed a user-friendly microbiome analysis pipeline (MAPLE, freely downloadable at http://maple.rx.umaryland.edu/), which is able to define an optimal search space by inferring proteomes specific to samples following the principle of parsimony. MAPLE facilitates highly comparable or better peptide identification compared to a sample-specific metagenome-guided search. In addition, we implemented an automated peptide-centric enrichment analysis function in MAPLE to address issues of traditional protein-centric comparison, enabling straightforward and comprehensive comparison of taxonomic and functional makeup between microbiota.

Binary Classifier for Computing Posterior Error Probabilities in MetaMorpheus

MetaMorpheus is a free, open-source software program for the identification of peptides and proteoforms from data-dependent acquisition tandem MS experiments. There is inherent uncertainty in these assignments for several reasons, including the limited overlap between experimental and theoretical peaks, the m/z uncertainty, and noise peaks or peaks from coisolated peptides that produce false matches. False discovery rates provide only a set-wise approximation for incorrect spectrum matches. Here we implemented a binary decision tree calculation within MetaMorpheus to compute a posterior error probability, which provides a measure of uncertainty for each peptide-spectrum match. We demonstrate its utility for increasing identifications and resolving ambiguities in bottom-up, top-down, proteogenomic, and nonspecific digestion searches.

Secreted Peptides for Diagnostic Trajectory Assessments in Brain Injury Rehabilitation

BACKGROUND

Rehabilitation following traumatic brain injury (TBI) significantly improves outcomes; yet TBI heterogeneity raises the need for molecular evidence of brain recovery processes to better track patient progress, evaluate therapeutic efficacy, and provide prognostication.

OBJECTIVE

Here, we assessed whether the trajectory of TBI-responsive peptides secreted into urine can produce a predictive model of functional recovery during TBI rehabilitation.

METHODS

The multivariate urinary peptidome of 12 individuals with TBI was examined using quantitative peptidomics. Measures were assessed upon admission and discharge from inpatient rehabilitation. A combination of Pavlidis template matching and partial least-squares discriminant analysis was used to build models on Disability Rating Scale (DRS) and Functional Independence Measure (FIM) scores, with participants bifurcated into more or less functional improvement groups.

RESULTS

The produced models exhibited high sensitivity and specificity with the area under the receiver operator curve being 0.99 for DRS- and 0.95 for FIM-based models using the top 20 discriminant peptides. Predictive ability for each model was assessed using robust leave-one-out cross-validation with Q² statistics of 0.64 (P = .00012) and 0.62 (P = .011) for DRS- and FIM-based models, respectively, both with a high predictive accuracy of 0.875. Identified peptides that discriminated improved functional recovery reflected heightened neuroplasticity and synaptic refinement and diminished cell death and neuroinflammation, consistent with postacute TBI pathobiology.

CONCLUSIONS

Produced models of urine-based peptide measures reflective of ongoing recovery pathobiology can inform on rehabilitation progress after TBI, warranting further study to assess refined stratification across a larger population and efficacy in assessing therapeutic interventions.

Fast Deisotoping Algorithm and Its Implementation in the MSFragger Search Engine

Deisotoping, or the process of removing peaks in a mass spectrum resulting from the incorporation of naturally occurring heavy isotopes, has long been used to reduce complexity and improve the effectiveness of spectral annotation methods in proteomics. We have previously described MSFragger, an ultrafast search engine for proteomics, that did not utilize deisotoping in processing input spectra. Here, we present a new, high-speed parallelized deisotoping algorithm, based on elements of several existing methods, that we have incorporated into the MSFragger search engine. Applying deisotoping with MSFragger reveals substantial improvements to database search speed and performance, particularly for complex methods like open or nonspecific searches. Finally, we evaluate our deisotoping method on data from several instrument types and vendors, revealing a wide range in performance and offering an updated perspective on deisotoping in the modern proteomics environment.

Efficient Confirmation of Plant Viral Proteins and Identification of Specific Viral Strains by nanoLC-ESI-Q-TOF Using Single-Leaf-Tissue Samples

Plant viruses are important pathogens that cause significant crop losses. A plant protein extraction protocol that combines crushing the tissue by a pestle in liquid nitrogen with subsequent crushing by a roller-ball crusher in urea solution, followed by RuBisCO depletion, reduction, alkylation, protein digestion, and ZipTip purification allowed us to substantially simplify the sample preparation by removing any other precipitation steps and to detect viral proteins from samples, even with less than 0.2 g of leaf tissue, by a medium resolution nanoLC-ESI-Q-TOF. The presence of capsid proteins or polyproteins of fourteen important viruses from seven different families (Geminiviridae, Luteoviridae, Bromoviridae, Caulimoviridae, Virgaviridae, Potyviridae, and Secoviridae) isolated from ten different economically important plant hosts was confirmed through many identified pathogen-specific peptides from a protein database of host proteins and potential pathogen proteins assembled separately for each host and based on existing online plant virus pathogen databases. The presented extraction protocol, combined with a medium resolution LC-MS/MS, represents a cost-efficient virus protein confirmation method that proved to be effective at identifying virus strains (as demonstrated for PPV, WDV) and distinct disease species of BYDV, as well as putative new viral protein sequences from single-plant-leaf tissue samples. Data are available via ProteomeXchange with identifier PXD022456.

O-Pair Search with MetaMorpheus for O-glycopeptide characterization

We report O-Pair Search, an approach to identify O-glycopeptides and localize O-glycosites. Using paired collision- and electron-based dissociation spectra, O-Pair Search identifies O-glycopeptides via an ion-indexed open modification search and localizes O-glycosites using graph theory and probability-based localization. O-Pair Search reduces search times more than 2,000-fold compared to current O-glycopeptide processing software, while defining O-glycosite localization confidence levels and generating more O-glycopeptide identifications. Beyond the mucin-type O-glycopeptides discussed here, O-Pair Search also accepts user-defined glycan databases, making it compatible with many types of O-glycosylation. O-Pair Search is freely available within the open-source MetaMorpheus platform at https://github.com/smith-chem-wisc/MetaMorpheus .

O-Pair Search with MetaMorpheus for O-glycopeptide characterization

We report O-Pair Search, a new approach to identify O-glycopeptides and localize O-glycosites. Using paired collision- and electron-based dissociation spectra, O-Pair Search identifies O-glycopeptides using an ion-indexed open modification search and localizes O-glycosites using graph theory and probability-based localization. O-Pair Search reduces search times more than 2,000-fold compared to current O-glycopeptide processing software, while defining O-glycosite localization confidence levels and generating more O-glycopeptide identifications. Beyond the mucin-type O-glycopeptides discussed here, O-Pair Search also accepts user-defined glycan databases, making it compatible with many types of O-glycosylation. O-Pair Search is freely available within the open-source MetaMorpheus platform at https://github.com/smith-chem-wisc/MetaMorpheus.

Integrated Proteomic and Glycoproteomic Characterization of Human High-Grade Serous Ovarian Carcinoma

Many gene products exhibit great structural heterogeneity because of an array of modifications. These modifications are not directly encoded in the genomic template but often affect the functionality of proteins. Protein glycosylation plays a vital role in proper protein functions. However, the analysis of glycoproteins has been challenging compared with other protein modifications, such as phosphorylation. Here, we perform an integrated proteomic and glycoproteomic analysis of 83 prospectively collected high-grade serous ovarian carcinoma (HGSC) and 23 non-tumor tissues. Integration of the expression data from global proteomics and glycoproteomics reveals tumor-specific glycosylation, uncovers different glycosylation associated with three tumor clusters, and identifies glycosylation enzymes that were correlated with the altered glycosylation. In addition to providing a valuable resource, these results provide insights into the potential roles of glycosylation in the pathogenesis of HGSC, with the possibility of distinguishing pathological outcomes of ovarian tumors from non-tumors, as well as classifying tumor clusters.

Identification of modified peptides using localization-aware open search

Identification of post-translationally or chemically modified peptides in mass spectrometry-based proteomics experiments is a crucial yet challenging task. We have recently introduced a fragment ion indexing method and the MSFragger search engine to empower an open search strategy for comprehensive analysis of modified peptides. However, this strategy does not consider fragment ions shifted by unknown modifications, preventing modification localization and limiting the sensitivity of the search. Here we present a localization-aware open search method, in which both modification-containing (shifted) and regular fragment ions are indexed and used in scoring. We also implement a fast mass calibration and optimization method, allowing optimization of the mass tolerances and other key search parameters. We demonstrate that MSFragger with mass calibration and localization-aware open search identifies modified peptides with significantly higher sensitivity and accuracy. Comparing MSFragger to other modification-focused tools (pFind3, MetaMorpheus, and TagGraph) shows that MSFragger remains an excellent option for fast, comprehensive, and sensitive searches for modified peptides in shotgun proteomics data.

CIRFESS: An Interactive Resource for Querying the Set of Theoretically Detectable Peptides for Cell Surface and Extracellular Enrichment Proteomic Studies

Cell surface transmembrane, extracellular, and secreted proteins are high value targets for immunophenotyping, drug development, and studies related to intercellular communication in health and disease. As the number of specific and validated affinity reagents that target this subproteome are limited, mass spectrometry (MS)-based approaches will continue to play a critical role in enabling discovery and quantitation of these molecules. Given the technical considerations that make MS-based cell surface proteome studies uniquely challenging, it can be difficult to select an appropriate experimental approach. To this end, we have integrated multiple prediction strategies and annotations into a single online resource, Compiled Interactive Resource for Extracellular and Surface Studies (CIRFESS). CIRFESS enables rapid interrogation of the human proteome to reveal the cell surface proteome theoretically detectable by current approaches and highlights where current prediction strategies provide concordant and discordant information. We applied CIRFESS to identify the percentage of various subsets of the proteome which are expected to be captured by targeted enrichment strategies, including two established methods and one that is possible but not yet demonstrated. These results will inform the selection of available proteomic strategies and development of new strategies to enhance coverage of the cell surface and extracellular proteome. CIRFESS is available at www.cellsurfer.net/cirfess.

Annotation of tandem mass spectrometry data using stochastic neural networks in shotgun proteomics

MOTIVATION

The discrimination ability of score functions to separate correct from incorrect peptide-spectrum-matches in database-searching-based spectrum identification is hindered by many superfluous peaks belonging to unexpected fragmentation ions or by the lacking peaks of anticipated fragmentation ions.

RESULTS

Here, we present a new method, called BoltzMatch, to learn score functions using a particular stochastic neural networks, called restricted Boltzmann machines, in order to enhance their discrimination ability. BoltzMatch learns chemically explainable patterns among peak pairs in the spectrum data, and it can augment peaks depending on their semantic context or even reconstruct lacking peaks of expected ions during its internal scoring mechanism. As a result, BoltzMatch achieved 50% and 33% more annotations on high- and low-resolution MS2 data than XCorr at a 0.1% false discovery rate in our benchmark; conversely, XCorr yielded the same number of spectrum annotations as BoltzMatch, albeit with 4-6 times more errors. In addition, BoltzMatch alone does yield 14% more annotations than Prosit (which runs with Percolator), and BoltzMatch with Percolator yields 32% more annotations than Prosit at 0.1% FDR level in our benchmark.

AVAILABILITY AND IMPLEMENTATION

BoltzMatch is freely available at: https://github.com/kfattila/BoltzMatch.

CONTACT

akerteszfarkas@hse.ru.

SUPPORTING INFORMATION

Supplementary data are available at Bioinformatics online.

The Skyline ecosystem: Informatics for quantitative mass spectrometry proteomics

Skyline is a freely available, open-source Windows client application for accelerating targeted proteomics experimentation, with an emphasis on the proteomics and mass spectrometry community as users and as contributors. This review covers the informatics encompassed by the Skyline ecosystem, from computationally assisted targeted mass spectrometry method development, to raw acquisition file data processing, and quantitative analysis and results sharing.

Anatomy and evolution of database search engines-a central component of mass spectrometry based proteomic workflows

Sequence database search engines are bioinformatics algorithms that identify peptides from tandem mass spectra using a reference protein sequence database. Two decades of development, notably driven by advances in mass spectrometry, have provided scientists with more than 30 published search engines, each with its own properties. In this review, we present the common paradigm behind the different implementations, and its limitations for modern mass spectrometry datasets. We also detail how the search engines attempt to alleviate these limitations, and provide an overview of the different software frameworks available to the researcher. Finally, we highlight alternative approaches for the identification of proteomic mass spectrometry datasets, either as a replacement for, or as a complement to, sequence database search engines.

Correlation of Venom Toxinome Composition of Indian Red Scorpion (Mesobuthus tamulus) with Clinical Manifestations of Scorpion Stings: Failure of Commercial Antivenom to Immune-Recognize the Abundance of Low Molecular Mass Toxins of This Venom

The Indian red scorpion (Mesobuthus tamulus), with its life-threatening sting, is the world's most dangerous species of scorpion. The toxinome composition of M. tamulus venom was determined by tandem mass spectrometry (MS) analysis of venom protein bands separated by SDS-PAGE. A total of 110 venom toxins were identified from searching the MS data against the Buthidae family (taxid: 6855) of toxin entries in nonredundant protein databases. The Na⁺ and K⁺ ion channel toxins taken together are the most abundant toxins (76.7%) giving rise to the neurotoxic nature of this venom. The other minor toxin classes in the M. tamulus venom proteome are serine protease-like protein (2.9%), serine protease inhibitor (2.2%), antimicrobial peptide (2.3%), hyaluronidase (2.2%), makatoxin (2.1%), lipolysis potentiating peptides (1.2%), neurotoxin affecting Cl^- channel (1%), parabutoporin (0.6%), Ca²⁺ channel toxins (0.8%), bradykinin potentiating peptides (0.2%), HMG CoA reductase inhibitor (0.1%), and other toxins with unknown pharmacological activity (7.7%). Several of these toxins have been shown to be promising drug candidates. M. tamulus venom does not show enzymatic activity (phospholipase A₂, l-amino acid oxidase, adenosine tri-, di-, and monophosphatase, hyaluronidase, metalloproteinase, and fibrinogenolytic), in vitro hemolytic activity, interference with blood coagulation, or platelet modulation properties. The clinical manifestations post M. tamulus sting have been described in the literature and are well correlated with its venom proteome composition. An abundance of low molecular mass toxins (3-15 kDa) are responsible for exerting the major pharmacological effects of M. tamulus venom, though they are poorly immune-recognized by commercial scorpion antivenom. This is a major concern for the development of effective antivenom therapy against scorpion stings.

Enterococcus durans with mosquito larvicidal toxicity against Culex quinquefasciatus, elucidated using a Proteomic and Metabolomic approach

Various bacteria from the Bacillus species have been used as pesticides against mosquito larvae for more than a decade. The prolonged use of these bacterial species by little alteration within their genome, using various permutations and combinations of mosquito-cidal toxins, has proven unsuccessful in controlling the mosquito population. In our current study we report Enterococcus sp. to be exhibiting similar kind of mosquito-cidal toxins alike those which are present in the mainly used Bacillus strains. Three Enterococcus species were isolated on a rich media selective for gram- positive bacteria from the mid-gut of dead mosquito larvae which were collected from the wild locations within and around the city of Mumbai, India. Their surface morphologies were studied by Scanning Electron Microscopy (SEM) and their identity was confirmed using the standard 16S rRNA sequencing method. Upon performing several repetitive toxicity assays of these three strains on the laboratory cultured third instar stage of Culex quinquefasciatus larvae, showed differential toxicities from a minimum of 20% (LC₅₀: 59.6 CFU/ml), intermediate 35% (LC₅₀: 48.4 CFU/ml) and a maximum of 60% (LC₅₀: 35.7 CFU/ml). To justify the data in all the three similar strains of Enterococcus durans, we followed the differential proteomics using LCMS 6540 UHD Accurate Mass QTOF and differential metabolomics approach using both LCMS 6540 UHD Accurate Mass QTOF and ¹H-NMR. The presence and significance of the obtained toxins were studied to elucidate the plausible reason for showing differential toxicities. This work helped in identifying Enterococcus durans as a new, potential and alternative strain to the Bacillus species in terms of mosquito larvicidal toxicity against Culex quinquefasciatus.

Proteomic analysis of affinity-purified 26S proteasomes identifies a suite of assembly chaperones in Arabidopsis

The 26S proteasome is an essential protease that selectively eliminates dysfunctional and short-lived regulatory proteins in eukaryotes. To define the composition of this proteolytic machine in plants, we tagged either the core protease (CP) or the regulatory particle (RP) sub-complexes in Arabidopsis to enable rapid affinity purification followed by mass spectrometric analysis. Studies on proteasomes enriched from whole seedlings, with or without ATP needed to maintain the holo-proteasome complex, identified all known proteasome subunits but failed to detect isoform preferences, suggesting that Arabidopsis does not construct distinct proteasome sub-types. We also detected a suite of proteasome-interacting proteins, including likely orthologs of the yeast and mammalian chaperones Pba1, Pba2, Pba3, and Pba4 that assist in CP assembly; Ump1 that helps connect CP half-barrels; Nas2, Nas6, and Hsm3 that assist in RP assembly; and Ecm29 that promotes CP-RP association. Proteasomes from seedlings exposed to the proteasome inhibitor MG132 accumulated assembly intermediates, reflecting partially built proteasome sub-complexes associated with assembly chaperones, and the CP capped with the PA200/Blm10 regulator. Genetic analyses of Arabidopsis UMP1 revealed that, unlike in yeast, this chaperone is essential, with mutants lacking the major UMP1a and UMP1b isoforms displaying a strong gametophytic defect. Single ump1 mutants were hypersensitive to conditions that induce proteotoxic, salt and osmotic stress, and also accumulated several proteasome assembly intermediates, consistent with its importance for CP construction. Insights into the chaperones reported here should enable study of the assembly events that generate the 26S holo-proteasome in Arabidopsis from the collection of 64 or more subunits.

MS1 ion current-based quantitative proteomics: A promising solution for reliable analysis of large biological cohorts

The rapidly-advancing field of pharmaceutical and clinical research calls for systematic, molecular-level characterization of complex biological systems. To this end, quantitative proteomics represents a powerful tool but an optimal solution for reliable large-cohort proteomics analysis, as frequently involved in pharmaceutical/clinical investigations, is urgently needed. Large-cohort analysis remains challenging owing to the deteriorating quantitative quality and snowballing missing data and false-positive discovery of altered proteins when sample size increases. MS1 ion current-based methods, which have become an important class of label-free quantification techniques during the past decade, show considerable potential to achieve reproducible protein measurements in large cohorts with high quantitative accuracy/precision. Nonetheless, in order to fully unleash this potential, several critical prerequisites should be met. Here we provide an overview of the rationale of MS1-based strategies and then important considerations for experimental and data processing techniques, with the emphasis on (i) efficient and reproducible sample preparation and LC separation; (ii) sensitive, selective and high-resolution MS detection; iii)accurate chromatographic alignment; (iv) sensitive and selective generation of quantitative features; and (v) optimal post-feature-generation data quality control. Prominent technical developments in these aspects are discussed. Finally, we reviewed applications of MS1-based strategy in disease mechanism studies, biomarker discovery, and pharmaceutical investigations.

Proteomics of neurodegenerative diseases: analysis of human post-mortem brain

Dementias are prevalent brain disorders in the aged population. Dementias pose major socio-medical burden, but currently there is no cure available. Novel proteomics approaches hold promise to identify alterations of the brain proteome that could provide clues on disease etiology, and identify candidate proteins to develop further as a biomarker. In this review, we focus on recent proteomics findings from brains affected with Alzheimer's Disease, Parkinson Disease Dementia, Frontotemporal Dementia, and Amyotrophic Lateral Sclerosis. These studies confirmed known cellular changes, and in addition identified novel proteins that may underlie distinct aspects of the diseases. This article is part of the special issue "Proteomics".

pClean: An Algorithm To Preprocess High-Resolution Tandem Mass Spectra for Database Searching

Database searches of MS/MS spectra are the main approach to peptide/protein identification in proteomics. Since most database search engines only utilize a small portion of the original MS/MS signals for peptide detection, how to improve the quality of MS/MS signals is a primary concern for enhancement of the peptide/protein identification rate. A fundamental issue is that some noise MS signals, informative or uninformative, have to be filtered out prior to database searching. Herein, an integrative preprocessing algorithm was designed, termed pClean, which incorporates three modules to preprocess MS/MS spectra, such as the removal of isobaric-labeling related ions, the reduction in isotopic peaks, the deconvolution of ions with higher charges, and the clearance of uninformative MS/MS signals. In contrast to the currently available approaches to MS/MS data preprocessing, pClean enables treatment of MS/MS spectra with high mass accuracy and favors filtering for the labeling or nonlabeling of peptides. Data sets at various scales gained from mass spectrometers with high resolution were used to assess the quality of peptides identified after pClean treatment and to compare the pClean improvement with those of other software programs. On the basis of the analysis of peptides identified and the Mascot ion score, pClean was proven to be effective in the removal of mass spectral noise and the reduction of random matching. Compared with other software programs, pClean appeared to be beneficial in terms of preprocessing performances for the enhancement of confidence scores and the increase in peptides identified. pClean is available at https://github.com/AimeeD90/pClean_release .

Nanoparticle exposure driven circulating bioactive peptidome causes systemic inflammation and vascular dysfunction

Background

The mechanisms driving systemic effects consequent pulmonary nanoparticle exposure remain unclear. Recent work has established the existence of an indirect process by which factors released from the lung into the circulation promote systemic inflammation and cellular dysfunction, particularly on the vasculature. However, the composition of circulating contributing factors and how they are produced remains unknown. Evidence suggests matrix protease involvement; thus, here we used a well-characterized multi-walled carbon nanotube (MWCNT) oropharyngeal aspiration model with known vascular effects to assess the distinct contribution of nanoparticle-induced peptide fragments in driving systemic pathobiology.

Results

Data-independent mass spectrometry enabled the unbiased quantitative characterization of 841 significant MWCNT-responses within an enriched peptide fraction, with 567 of these factors demonstrating significant correlation across animal-paired bronchoalveolar lavage and serum biofluids. A database search curated for known matrix protease substrates and predicted signaling motifs enabled identification of 73 MWCNT-responsive peptides, which were significantly associated with an abnormal cardiovascular phenotype, extracellular matrix organization, immune-inflammatory processes, cell receptor signaling, and a MWCNT-altered serum exosome population. Production of a diverse peptidomic response was supported by a wide number of upregulated matrix and lysosomal proteases in the lung after MWCNT exposure. The peptide fraction was then found bioactive, producing endothelial cell inflammation and vascular dysfunction ex vivo akin to that induced with whole serum. Results implicate receptor ligand functionality in driving systemic effects, exemplified by an identified 59-mer thrombospondin fragment, replete with CD36 modulatory motifs, that when synthesized produced an anti-angiogenic response in vitro matching that of the peptide fraction. Other identified peptides point to integrin ligand functionality and more broadly to a diversity of receptor-mediated bioactivity induced by the peptidomic response to nanoparticle exposure.

Conclusion

The present study demonstrates that pulmonary-sequestered nanoparticles, such as multi-walled carbon nanotubes, acutely upregulate a diverse profile of matrix proteases, and induce a complex peptidomic response across lung and blood compartments. The serum peptide fraction, having cell-surface receptor ligand properties, conveys peripheral bioactivity in promoting endothelial cell inflammation, vasodilatory dysfunction and inhibiting angiogenesis. Results here establish peptide fragments as indirect, non-cytokine mediators and putative biomarkers of systemic health outcomes from nanoparticle exposure.

One minute analysis of 200 histone posttranslational modifications by direct injection mass spectrometry

DNA and histone proteins define the structure and composition of chromatin. Histone posttranslational modifications (PTMs) are covalent chemical groups capable of modeling chromatin accessibility, mostly due to their ability in recruiting enzymes responsible for DNA readout and remodeling. Mass spectrometry (MS)-based proteomics is the methodology of choice for large-scale identification and quantification of protein PTMs, including histones. High sensitivity proteomics requires online MS coupling with relatively low throughput and poorly robust nano-liquid chromatography (nanoLC) and, for histone proteins, a 2-d sample preparation that includes histone purification, derivatization, and digestion. We present a new protocol that achieves quantitative data on about 200 histone PTMs from tissue or cell lines in 7 h from start to finish. This protocol includes 4 h of histone extraction, 3 h of derivatization and digestion, and only 1 min of MS analysis via direct injection (DI-MS). We demonstrate that this sample preparation can be parallelized for 384 samples by using multichannel pipettes and 96-well plates. We also engineered the sequence of a synthetic "histone-like" peptide to spike into the sample, of which derivatization and digestion benchmarks the quality of the sample preparation. We ensure that DI-MS does not introduce biases in histone peptide ionization as compared to nanoLC-MS/MS by producing and analyzing a library of synthetically modified histone peptides mixed in equal molarity. Finally, we introduce EpiProfileLite for comprehensive analysis of this new data type. Altogether, our workflow is suitable for high-throughput screening of >1000 samples per day using a single mass spectrometer.

Diagnostic potential of saliva proteome analysis: a review and guide to clinical practice

Proteomic techniques have become popular in medicine and dentistry because of their widespread use in analyzing bodily fluids such as blood, saliva, urine, and gingival crevicular fluids as well as hard tissues such as enamel, dentine, and cementum. This review is a guide to proteomic techniques in general dentistry, summarizing techniques and their clinical application in understanding and diagnosing diseases and their use in identifying biomarkers of various diseases.

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.

Binding of a Naja naja venom acidic phospholipase A2 cognate complex to membrane-bound vimentin of rat L6 cells: Implications in cobra venom-induced cytotoxicity

An acidic phospholipase A₂ enzyme (NnPLA₂-I) interacts with three finger toxins (cytotoxin and neurotoxin) from Naja naja venom to form cognate complexes to enhance its cytotoxicity towards rat L6 myogenic cells. The cytotoxicity was further enhanced in presence of trace quantity of venom nerve growth factor. The purified rat myoblast cell membrane protein showing interaction with NnPLA₂-I was identified as vimentin by LC-MS/MS analysis. The ELISA, immunoblot and spectrofluorometric analyses showed greater binding of NnPLA₂-I cognate complex to vimentin as compared to the binding of individual NnPLA₂-I. The immunofluorescence and confocal microscopy studies evidenced the internalization of NnPLA₂-I to partially differentiated myoblasts post binding with vimentin in a time-dependent manner. Pre-incubation of polyvalent antivenom with NnPLA₂-I cognate complex demonstrated better neutralization of cytotoxicity towards L6 cells as compared to exogenous addition of polyvalent antivenom 60-240 min post treatment of L6 cells with cognate complex suggesting clinical advantage of early antivenom treatment to prevent cobra venom-induced cytotoxicity. The in silico analysis showed that 19-22 residues, inclusive of Asp48 residue, of NnPLA₂-I preferentially binds with the rod domain (99-189 and 261-335 regions) of vimentin with a predicted free binding energy (ΔG) and dissociation constant (K_D) values of -12.86 kcal/mol and 3.67 × 10^-10 M, respectively; however, NnPLA₂-I cognate complex showed greater binding with the same regions of vimentin indicating the pathophysiological significance of cognate complex in cobra venom-induced cytotoxicity.