False Discovery Rate Estimation in Proteomics

PgxSAVy: A tool for comprehensive evaluation of variant peptide quality in proteogenomics - catching the (un)usual suspects

Variant peptides resulting from single nucleotide polymorphisms (SNPs) can lead to aberrant protein functions and have translational potential for disease diagnosis and personalized therapy. Variant peptides detected by proteogenomics are fraught with high number of false positives, but there is no uniform and comprehensive approach to assess variant quality across analysis pipelines. Despite class-specific FDR along with ad-hoc filters, the problem is far from solved. These protocols are typically manual and tedious, and thus not uniform across labs. We demonstrate that variant peptide rescoring, integrated with intensity, variant event information and search result features, allows better discrimination of correct variant peptides. Implemented into PgxSAVy - a tool for quality control of variant peptides, this method can tackle the high rate of false positives. PgxSAVy provides a rigorous framework for quality control and annotations of variant peptides on the basis of (i) variant quality, (ii) isobaric masses, and (iii) disease annotation. PgxSAVy demonstrated high accuracy by identifying true variants with 98.43% accuracy on simulated data. Large-scale proteogenomic reanalysis of ∼2.8 million spectra (PXD004010 and PXD001468) resulted in 12,705 variant peptide spectrum matches (PSMs), of which PgxSAVy evaluated 3028 (23.8%), 1409 (11.1%) and 8268 (65.1%) as confident, semi-confident and doubtful respectively. PgxSAVy also annotates the variants based on their pathogenicity and provides support for assisted manual validation. The analysis of proteins carrying variants can provide fine granularity in discovering important pathways. PgxSAVy will advance personalized medicine by providing a comprehensive framework for quality control and prioritization of proteogenomics variants. PgxSAVy is freely available at https://pgxsavy.igib.res.in/ as a webserver and https://github.com/anuragraj/PgxSAVy as a stand-alone tool.

Using SWATH-MS to identify new molecular biomarkers in gingival crevicular fluid for detecting periodontitis and its response to treatment

AIM

To identify new biomarkers to detect untreated and treated periodontitis in gingival crevicular fluid (GCF) using sequential window acquisition of all theoretical mass spectra (SWATH-MS).

MATERIALS AND METHODS

GCF samples were collected from 44 periodontally healthy subjects and 40 with periodontitis (Stages III-IV). In the latter, 25 improved clinically 2 months after treatment. Samples were analysed using SWATH-MS, and proteins were identified by the UniProt human-specific database. The diagnostic capability of the proteins was determined with generalized additive models to distinguish the three clinical conditions.

RESULTS

In the untreated periodontitis vs. periodontal health modelling, five proteins showed excellent or good bias-corrected (bc)-sensitivity/bc-specificity values of >80%. These were GAPDH, ZG16B, carbonic anhydrase 1, plasma protease inhibitor C1 and haemoglobin subunit beta. GAPDH with MMP-9, MMP-8, zinc-α-2-glycoprotein and neutrophil gelatinase-associated lipocalin and ZG16B with cornulin provided increased bc-sensitivity/bc-specificity of >95%. For distinguishing treated periodontitis vs. periodontal health, most of these proteins and their combinations revealed a predictive ability similar to previous modelling. No model obtained relevant results to differentiate between periodontitis conditions.

CONCLUSIONS

New single and dual GCF protein biomarkers showed outstanding results in discriminating untreated and treated periodontitis from periodontal health. Periodontitis conditions were indistinguishable. Future research must validate these findings.

An algorithm for decoy-free false discovery rate estimation in XL-MS/MS proteomics

MOTIVATION

Cross-linking tandem mass spectrometry (XL-MS/MS) is an established analytical platform used to determine distance constraints between residues within a protein or from physically interacting proteins, thus improving our understanding of protein structure and function. To aid biological discovery with XL-MS/MS, it is essential that pairs of chemically linked peptides be accurately identified, a process that requires: (i) database search, that creates a ranked list of candidate peptide pairs for each experimental spectrum and (ii) false discovery rate (FDR) estimation, that determines the probability of a false match in a group of top-ranked peptide pairs with scores above a given threshold. Currently, the only available FDR estimation mechanism in XL-MS/MS is the target-decoy approach (TDA). However, despite its simplicity, TDA has both theoretical and practical limitations that impact the estimation accuracy and increase run time over potential decoy-free approaches (DFAs).

RESULTS

We introduce a novel decoy-free framework for FDR estimation in XL-MS/MS. Our approach relies on multi-sample mixtures of skew normal distributions, where the latent components correspond to the scores of correct peptide pairs (both peptides identified correctly), partially incorrect peptide pairs (one peptide identified correctly, the other incorrectly), and incorrect peptide pairs (both peptides identified incorrectly). To learn these components, we exploit the score distributions of first- and second-ranked peptide-spectrum matches for each experimental spectrum and subsequently estimate FDR using a novel expectation-maximization algorithm with constraints. We evaluate the method on ten datasets and provide evidence that the proposed DFA is theoretically sound and a viable alternative to TDA owing to its good performance in terms of accuracy, variance of estimation, and run time.

AVAILABILITY AND IMPLEMENTATION

https://github.com/shawn-peng/xlms.

Critical steps in an environmental metaproteomics workflow

Environmental metaproteomics is a rapidly advancing field that provides insights into the structure, dynamics, and metabolic activity of microbial communities. As the field is still maturing, it lacks consistent workflows, making it challenging for non-expert researchers to navigate. This review aims to introduce the workflow of environmental metaproteomics. It outlines the standard practices for sample collection, processing, and analysis, and offers strategies to overcome the unique challenges presented by common environmental matrices such as soil, freshwater, marine environments, biofilms, sludge, and symbionts. The review also highlights the bottlenecks in data analysis that are specific to metaproteomics samples and provides suggestions for researchers to obtain high-quality datasets. It includes recent benchmarking studies and descriptions of software packages specifically built for metaproteomics analysis. The article is written without assuming the reader's familiarity with single-organism proteomic workflows, making it accessible to those new to proteomics or mass spectrometry in general. This primer for environmental metaproteomics aims to improve accessibility to this exciting technology and empower researchers to tackle challenging and ambitious research questions. While it is primarily a resource for those new to the field, it should also be useful for established researchers looking to streamline or troubleshoot their metaproteomics experiments.

Proteogenomic Data Integration Reveals CXCL10 as a Potentially Downstream Causal Mediator for IL-6 Signaling on Atherosclerosis

BACKGROUND

Genetic and experimental studies support a causal involvement of IL-6 (interleukin-6) signaling in atheroprogression. Although trials targeting IL-6 signaling are underway, any benefits must be balanced against an impaired host immune response. Dissecting the mechanisms that mediate the effects of IL-6 signaling on atherosclerosis could offer insights about novel drug targets with more specific effects.

METHODS

Leveraging data from 522 681 individuals, we constructed a genetic instrument of 26 variants in the gene encoding the IL-6R (IL-6 receptor) that proxied for pharmacological IL-6R inhibition. Using Mendelian randomization, we assessed its effects on 3281 plasma proteins quantified with an aptamer-based assay in the INTERVAL cohort (n=3301). Using mediation Mendelian randomization, we explored proteomic mediators of the effects of genetically proxied IL-6 signaling on coronary artery disease, large artery atherosclerotic stroke, and peripheral artery disease. For significant mediators, we tested associations of their circulating levels with incident cardiovascular events in a population-based study (n=1704) and explored the histological, transcriptomic, and cellular phenotypes correlated with their expression levels in samples from human atherosclerotic lesions.

RESULTS

We found significant effects of genetically proxied IL-6 signaling on 70 circulating proteins involved in cytokine production/regulation and immune cell recruitment/differentiation, which correlated with the proteomic effects of pharmacological IL-6R inhibition in a clinical trial. Among the 70 significant proteins, genetically proxied circulating levels of CXCL10 (C-X-C motif chemokine ligand 10) were associated with risk of coronary artery disease, large artery atherosclerotic stroke, and peripheral artery disease, with up to 67% of the effects of genetically downregulated IL-6 signaling on these end points mediated by decreases in CXCL10. Higher midlife circulating CXCL10 levels were associated with a larger number of cardiovascular events over 20 years, whereas higher CXCL10 expression in human atherosclerotic lesions correlated with a larger lipid core and a transcriptomic profile reflecting immune cell infiltration, adaptive immune system activation, and cytokine signaling.

CONCLUSIONS

Integrating multiomics data, we found a proteomic signature of IL-6 signaling activation and mediators of its effects on cardiovascular disease. Our analyses suggest the interferon-γ-inducible chemokine CXCL10 to be a potentially causal mediator for atherosclerosis in 3 vascular compartments and, as such, could serve as a promising drug target for atheroprotection.

Metabolomic profile of neuroendocrine tumors identifies methionine, porphyrin, and tryptophan metabolisms as key dysregulated pathways associated with patient survival

OBJECTIVE

Metabolic profiling is a valuable tool to characterize tumor biology but remains largely unexplored in neuroendocrine tumors (NETs). Our aim was to comprehensively assess the metabolomic profile of NETs and identify novel prognostic biomarkers and dysregulated molecular pathways.

DESIGN AND METHODS

Multiplatform untargeted metabolomic profiling (GC-MS, CE-MS, and LC-MS) was performed in plasma from 77 patients with G1-2 extra-pancreatic NETs enrolled in the AXINET trial (NCT01744249) (study cohort) and from 68 non-cancer individuals (control). The prognostic value of each differential metabolite (n = 155) in NET patients (P < .05) was analyzed by univariate and multivariate analyses adjusted for multiple testing and other confounding factors. Related pathways were explored by Metabolite Set Enrichment Analysis (MSEA) and Metabolite Pathway Analysis (MPA).

RESULTS

Thirty-four metabolites were significantly associated with progression-free survival (PFS) (n = 16) and/or overall survival (OS) (n = 27). Thirteen metabolites remained significant independent prognostic factors in multivariate analysis, 3 of them with a significant impact on both PFS and OS. Unsupervised clustering of these 3 metabolites stratified patients in 3 distinct prognostic groups (1-year PFS of 71.1%, 47.7%, and 15.4% (P = .012); 5-year OS of 69.7%, 32.5%, and 27.7% (P = .003), respectively). The MSEA and MPA of the 13-metablolite signature identified methionine, porphyrin, and tryptophan metabolisms as the 3 most relevant dysregulated pathways associated with the prognosis of NETs.

CONCLUSIONS

We identified a metabolomic signature that improves prognostic stratification of NET patients beyond classical prognostic factors for clinical decisions. The enriched metabolic pathways identified reveal novel tumor vulnerabilities that may foster the development of new therapeutic strategies for these patients.

Optimizing the PROTREC network-based missing protein prediction algorithm

This article summarizes the PROTREC method and investigates the impact that the different hyper-parameters have on the task of missing protein prediction using PROTREC. We evaluate missing protein recovery rates using different PROTREC score selection approaches (MAX, MIN, MEDIAN, and MEAN), different PROTREC score thresholds, as well as different complex size thresholds. In addition, we included two additional cancer datasets in our analysis and introduced a new validation method to check both the robustness of the PROTREC method as well as the correctness of our analysis. Our analysis showed that the missing protein recovery rate can be improved by adopting PROTREC score selection operations of MIN, MEDIAN, and MEAN instead of the default MAX. However, this may come at a cost of reduced numbers of proteins predicted and validated. The users should therefore choose their hyper-parameters carefully to find a balance in the accuracy-quantity trade-off. We also explored the possibility of combining PROTREC with a p-value-based method (FCS) and demonstrated that PROTREC is able to perform well independently without any help from a p-value-based method. Furthermore, we conducted a downstream enrichment analysis to understand the biological pathways and protein networks within the cancerous tissues using the recovered proteins. Missing protein recovery rate using PROTREC can be improved by selecting a different PROTREC score selection method. Different PROTREC score selection methods and other hyper-parameters such as PROTREC score threshold and complex size threshold introduce accuracy-quantity trade-off. PROTREC is able to perform well independently of any filtering using a p-value-based method. Verification of the PROTREC method on additional cancer datasets. Downstream Enrichment Analysis to understand the biological pathways and protein networks in cancerous tissues.

Proteomics analysis of circulating small extracellular vesicles: Focus on the contribution of EVs to tumor metabolism

The term small extracellular vesicle (sEV) is a comprehensive term that includes any type of cell-derived, membrane-delimited particle that has a diameter < 200 nm, and which includes exosomes and smaller microvesicles. sEVs transfer bioactive molecules between cells and are crucial for cellular homeostasis and particularly during tumor development, where sEVs provide important contributions to the formation of the premetastic niche and to their altered metabolism. sEVs are thus legitimate targets for intervention and have also gained increasing interest as an easily accessible source of biomarkers because they can be rapidly isolated from serum/plasma and their molecular cargo provides information on their cell-of origin. To target sEVs that are specific for a given cell/disease it is essential to identify EV surface proteins that are characteristic of that cell/disease. Mass-spectrometry based proteomics is widely used for the identification and quantification of sEV proteins. The methods used for isolating the sEVs, preparing the sEV sample for proteomics analysis, and mass spectrometry analysis, can have a strong influence on the results and requires careful consideration. This review provides an overview of the approaches used for sEV proteomics and discusses the inherent compromises regarding EV purity versus depth of coverage. Additionally, it discusses the practical applications of the methods to unravel the involvement of sEVs in regulating the metabolism of pancreatic ductal adenocarcinoma (PDAC). The metabolic reprogramming in PDAC includes enhanced glycolysis, elevated glutamine metabolism, alterations in lipid metabolism, mitochondrial dysfunction and hypoxia, all of which are crucial in promoting tumor cell growth. A thorough understanding of these metabolic adaptations is imperative for the development of targeted therapies to exploit PDAC's vulnerabilities.

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.

Recent advances in predicting and modeling protein-protein interactions

Protein-protein interactions (PPIs) drive biological processes, and disruption of PPIs can cause disease. With recent breakthroughs in structure prediction and a deluge of genomic sequence data, computational methods to predict PPIs and model spatial structures of protein complexes are now approaching the accuracy of experimental approaches for permanent interactions and show promise for elucidating transient interactions. As we describe here, the key to this success is rich evolutionary information deciphered from thousands of homologous sequences that coevolve in interacting partners. This covariation signal, revealed by sophisticated statistical and machine learning (ML) algorithms, predicts physiological interactions. Accurate artificial intelligence (AI)-based modeling of protein structures promises to provide accurate 3D models of PPIs at a proteome-wide scale.

Proteogenomic integration reveals CXCL10 as a potentially downstream causal mediator for IL-6 signaling on atherosclerosis

Background

Genetic and experimental studies support a causal involvement of interleukin-6 (IL-6) signaling in atheroprogression. While trials targeting IL-6 signaling are underway, any benefits must be balanced against an impaired host immune response. Dissecting the mechanisms that mediate the effects of IL-6 signaling on atherosclerosis could offer insights about novel drug targets with more specific effects.

Methods

Leveraging data from 522,681 individuals, we constructed a genetic instrument of 26 variants in the gene encoding the IL-6 receptor (IL-6R) that proxied for pharmacological IL-6R inhibition. Using Mendelian randomization (MR), we assessed its effects on 3,281 plasma proteins quantified with an aptamer-based assay in the INTERVAL cohort (n=3,301). Using mediation MR, we explored proteomic mediators of the effects of genetically proxied IL-6 signaling on coronary artery disease (CAD), large artery atherosclerotic stroke (LAAS), and peripheral artery disease (PAD). For significant mediators, we tested associations of their circulating levels with incident cardiovascular events in a population-based study (n=1,704) and explored the histological, transcriptomic, and cellular phenotypes correlated with their expression levels in samples from human atherosclerotic lesions.

Results

We found significant effects of genetically proxied IL-6 signaling on 70 circulating proteins involved in cytokine production/regulation and immune cell recruitment/differentiation, which correlated with the proteomic effects of pharmacological IL-6R inhibition in a clinical trial. Among the 70 significant proteins, genetically proxied circulating levels of CXCL10 were associated with risk of CAD, LAAS, and PAD with up to 67% of the effects of genetically downregulated IL-6 signaling on these endpoints mediated by decreases in CXCL10. Higher midlife circulating CXCL10 levels were associated with a larger number of cardiovascular events over 20 years, whereas higher CXCL10 expression in human atherosclerotic lesions correlated with a larger lipid core and a transcriptomic profile reflecting immune cell infiltration, adaptive immune system activation, and cytokine signaling.

Conclusions

Integrating multiomics data, we found a proteomic signature of IL-6 signaling activation and mediators of its effects on cardiovascular disease. Our analyses suggest the interferon-γ-inducible chemokine CXCL10 to be a potentially causal mediator for atherosclerosis in three vascular compartments and as such could serve as a promising drug target for atheroprotection.

Nucleos'ID: A New Search Engine Enabling the Untargeted Identification of RNA Post-transcriptional Modifications from Tandem Mass Spectrometry Analyses of Nucleosides

As RNA post-transcriptional modifications are of growing interest, several methods were developed for their characterization. One of them established for their identification, at the nucleosidic level, is the hyphenation of separation methods, such as liquid chromatography or capillary electrophoresis, to tandem mass spectrometry. However, to our knowledge, no software is yet available for the untargeted identification of RNA post-transcriptional modifications from MS/MS data-dependent acquisitions. Thus, very long and tedious manual data interpretations are required. To meet the need of easier and faster data interpretation, a new user-friendly search engine, called Nucleos'ID, was developed for CE-MS/MS and LC-MS/MS users. Performances of this new software were evaluated on CE-MS/MS data from nucleoside analyses of already well-described Saccharomyces cerevisiae transfer RNA and Bos taurus total tRNA extract. All samples showed great true positive, true negative, and false discovery rates considering the database size containing all modified and unmodified nucleosides referenced in the literature. The true positive and true negative rates obtained were above 0.94, while the false discovery rates were between 0.09 and 0.17. To increase the level of sample complexity, untargeted identification of several RNA modifications from Pseudomonas aeruginosa 70S ribosome was achieved by the Nucleos'ID search following CE-MS/MS analysis.

Rapid Proteomic Characterization of Bacteriocin-Producing Enterococcus faecium Strains from Foodstuffs

Enterococcus belongs to a group of microorganisms known as lactic acid bacteria (LAB), which constitute a broad heterogeneous group of generally food-grade microorganisms historically used in food preservation. Enterococci live as commensals of the gastrointestinal tract of warm-blooded animals, although they also are present in food of animal origin (milk, cheese, fermented sausages), vegetables, and plant materials because of their ability to survive heat treatments and adverse environmental conditions. The biotechnological traits of enterococci can be applied in the food industry; however, the emergence of enterococci as a cause of nosocomial infections makes their food status uncertain. Recent advances in high-throughput sequencing allow the subtyping of bacterial pathogens, but it cannot reflect the temporal dynamics and functional activities of microbiomes or bacterial isolates. Moreover, genetic analysis is based on sequence homologies, inferring functions from databases. Here, we used an end-to-end proteomic workflow to rapidly characterize two bacteriocin-producing Enterococcus faecium (Efm) strains. The proteome analysis was performed with liquid chromatography coupled to a trapped ion mobility spectrometry-time-of-flight mass spectrometry instrument (TimsTOF) for high-throughput and high-resolution characterization of bacterial proteins. Thus, we identified almost half of the proteins predicted in the bacterial genomes (>1100 unique proteins per isolate), including quantifying proteins conferring resistance to antibiotics, heavy metals, virulence factors, and bacteriocins. The obtained proteomes were annotated according to function, resulting in 22 complete KEGG metabolic pathway modules for both strains. The workflow used here successfully characterized these bacterial isolates and showed great promise for determining and optimizing the bioengineering and biotechnology properties of other LAB strains in the food industry.

Sequence, structure, and function of the Dps DNA-binding protein from Deinococcus wulumuqiensis R12

Deinococcus wulumuqiensis R12, which was isolated from arid irradiated soil in Xinjiang province of China, belongs to a genus that is well-known for its extreme resistance to ionizing radiation and oxidative stress. The DNA-binding protein Dps has been studied for its great contribution to oxidative resistance. To explore the role of Dps in D. wulumuqiensis R12, the Dps sequence and homology-modeled structure were analyzed. In addition, the dps gene was knocked out and proteomics was used to verify the functions of Dps in D. wulumuqiensis R12. Docking data and DNA binding experiments in vitro showed that the R12 Dps protein has a better DNA binding ability than the Dps1 protein from D. radiodurans R1. When the dps gene was deleted in D. wulumuqiensis R12, its resistance to H₂O₂ and UV rays was greatly reduced, and the cell envelope was destroyed by H₂O₂ treatment. Additionally, the qRT-PCR and proteomics data suggested that when the dps gene was deleted, the catalase gene was significantly down-regulated. The proteomics data indicated that the metabolism, transport and oxidation–reduction processes of D. wulumuqiensis R12 were down-regulated after the deletion of the dps gene. Overall, the data conformed that Dps protein plays an important role in D. wulumuqiensis R12.

False discovery rate: the Achilles' heel of proteogenomics

Proteogenomics refers to the integrated analysis of the genome and proteome that leverages mass-spectrometry (MS)-based proteomics data to improve genome annotations, understand gene expression control through proteoforms and find sequence variants to develop novel insights for disease classification and therapeutic strategies. However, proteogenomic studies often suffer from reduced sensitivity and specificity due to inflated database size. To control the error rates, proteogenomics depends on the target-decoy search strategy, the de-facto method for false discovery rate (FDR) estimation in proteomics. The proteogenomic databases constructed from three- or six-frame nucleotide database translation not only increase the search space and compute-time but also violate the equivalence of target and decoy databases. These searches result in poorer separation between target and decoy scores, leading to stringent FDR thresholds. Understanding these factors and applying modified strategies such as two-pass database search or peptide-class-specific FDR can result in a better interpretation of MS data without introducing additional statistical biases. Based on these considerations, a user can interpret the proteogenomics results appropriately and control false positives and negatives in a more informed manner. In this review, first, we briefly discuss the proteogenomic workflows and limitations in database construction, followed by various considerations that can influence potential novel discoveries in a proteogenomic study. We conclude with suggestions to counter these challenges for better proteogenomic data interpretation.

In-Depth Matrisome and Glycoproteomic Analysis of Human Brain Glioblastoma Versus Control Tissue

Glioblastoma (GBM) is the most common and malignant primary brain tumor. The extracellular matrix, also known as the matrisome, helps determine glioma invasion, adhesion, and growth. Little attention, however, has been paid to glycosylation of the extracellular matrix components that constitute the majority of glycosylated protein mass and presumed biological properties. To acquire a comprehensive understanding of the biological functions of the matrisome and its components, including proteoglycans (PGs) and glycosaminoglycans (GAGs), in GBM tumorigenesis, and to identify potential biomarker candidates, we studied the alterations of GAGs, including heparan sulfate (HS) and chondroitin sulfate (CS), the core proteins of PGs, and other glycosylated matrisomal proteins in GBM subtypes versus control human brain tissue samples. We scrutinized the proteomics data to acquire in-depth site-specific glycoproteomic profiles of the GBM subtypes that will assist in identifying specific glycosylation changes in GBM. We observed an increase in CS 6-O sulfation and a decrease in HS 6-O sulfation, accompanied by an increase in unsulfated CS and HS disaccharides in GBM versus control samples. Several core matrisome proteins, including PGs (decorin, biglycan, agrin, prolargin, glypican-1, and chondroitin sulfate proteoglycan 4), tenascin, fibronectin, hyaluronan link protein 1 and 2, laminins, and collagens, were differentially regulated in GBM versus controls. Interestingly, a higher degree of collagen hydroxyprolination was also observed for GBM versus controls. Further, two PGs, chondroitin sulfate proteoglycan 4 and agrin, were significantly lower, about 6-fold for isocitrate dehydrogenase-mutant, compared to the WT GBM samples. Differential regulation of O-glycopeptides for PGs, including brevican, neurocan, and versican, was observed for GBM subtypes versus controls. Moreover, an increase in levels of glycosyltransferase and glycosidase enzymes was observed for GBM when compared to control samples. We also report distinct protein, peptide, and glycopeptide features for GBM subtypes comparisons. Taken together, our study informs understanding of the alterations to key matrisomal molecules that occur during GBM development. (Data are available via ProteomeXchange with identifier PXD028931, and the peaks project file is available at Zenodo with DOI 10.5281/zenodo.5911810).

Towards Building a Quantitative Proteomics Toolbox in Precision Medicine: A Mini-Review

Precision medicine as a framework for disease diagnosis, treatment, and prevention at the molecular level has entered clinical practice. From the start, genetics has been an indispensable tool to understand and stratify the biology of chronic and complex diseases in precision medicine. However, with the advances in biomedical and omics technologies, quantitative proteomics is emerging as a powerful technology complementing genetics. Quantitative proteomics provide insight about the dynamic behaviour of proteins as they represent intermediate phenotypes. They provide direct biological insights into physiological patterns, while genetics accounting for baseline characteristics. Additionally, it opens a wide range of applications in clinical diagnostics, treatment stratification, and drug discovery. In this mini-review, we discuss the current status of quantitative proteomics in precision medicine including the available technologies and common methods to analyze quantitative proteomics data. Furthermore, we highlight the current challenges to put quantitative proteomics into clinical settings and provide a perspective to integrate proteomics data with genomics data for future applications in precision medicine.

Protein Targets of Acetaminophen Covalent Binding in Rat and Mouse Liver Studied by LC-MS/MS

Acetaminophen (APAP) is a mild analgesic and antipyretic used commonly worldwide. Although considered a safe and effective over-the-counter medication, it is also the leading cause of drug-induced acute liver failure. Its hepatotoxicity has been linked to the covalent binding of its reactive metabolite, N-acetyl p-benzoquinone imine (NAPQI), to proteins. The aim of this study was to identify APAP-protein targets in both rat and mouse liver, and to compare the results from both species, using bottom-up proteomics with data-dependent high resolution mass spectrometry and targeted multiple reaction monitoring (MRM) experiments. Livers from rats and mice, treated with APAP, were homogenized and digested by trypsin. Digests were then fractionated by mixed-mode solid-phase extraction prior to liquid chromatography-tandem mass spectrometry (LC-MS/MS). Targeted LC-MRM assays were optimized based on high-resolution MS/MS data from information-dependent acquisition (IDA) using control liver homogenates treated with a custom alkylating reagent yielding an isomeric modification to APAP on cysteine residues, to build a modified peptide database. A list of putative in vivo targets of APAP were screened from data-dependent high-resolution MS/MS analyses of liver digests, previous in vitro studies, as well as selected proteins from the target protein database (TPDB), an online resource compiling previous reports of APAP targets. Multiple protein targets in each species were found, while confirming modification sites. Several proteins were modified in both species, including ATP-citrate synthase, betaine-homocysteine S-methyltransferase 1, cytochrome P450 2C6/29, mitochondrial glutamine amidotransferase-like protein/ES1 protein homolog, glutamine synthetase, microsomal glutathione S-transferase 1, mitochondrial-processing peptidase, methanethiol oxidase, protein/nucleic acid deglycase DJ-1, triosephosphate isomerase and thioredoxin. The targeted method afforded better reproducibility for analysing these low-abundant modified peptides in highly complex samples compared to traditional data-dependent experiments.

Peptidomics: A Review of Clinical Applications and Methodologies

Improvements in both liquid chromatography (LC) and mass spectrometry (MS) instrumentation have greatly enhanced proteomic and small molecule metabolomic analysis in recent years. Less focus has been on the improved capability to detect and quantify small bioactive peptides, even though the exact sequences of the peptide species produced can have important biological consequences. Endogenous bioactive peptide hormones, for example, are generated by the targeted and regulated cleavage of peptides from their prohormone sequence. This process may include organ specific variants, as proglucagon is converted to glucagon in the pancreas but glucagon-like peptide-1 (GLP-1) in the small intestine, with glucagon raising, whereas GLP-1, as an incretin, lowering blood glucose. Therefore, peptidomics workflows must preserve the structure of the processed peptide products to prevent the misidentification of ambiguous peptide species. The poor in vivo and in vitro stability of peptides in biological matrices is a major factor that needs to be considered when developing methods to study them. The bioinformatic analysis of peptidomics data sets requires the inclusion of specific post-translational modifications, which are critical for the function of many bioactive peptides. This review aims to discuss and contrast the various extraction, analytical, and bioinformatics approaches used for human peptidomics studies in a multitude of matrices.

Effect of Cryoablation Treatment on the Protein Expression Profile of Low-Grade Central Chondrosarcoma Identified by LC-ESI-MS/MS

The use of cryoablation/cryosurgery in treating solid tumors has been proven as a unique technique that uses lethal temperatures to destroy the tumors and impart better functions for the affected organs. This novel technique recently demonstrated the best clinical results in chondrosarcoma (CSA) with faster recovery, less recurrence, and metastasis. Due to the resistant nature of CSA to chemo and radiation therapy, cryoablation comes to light as the best alternative approach. Therefore, for the first time, we aimed to compare CSA-untreated with cryoablation treated samples to discover some potential markers that may provide various clues in terms of diagnosis and pathophysiology and may facilitate the development of novel methods to treat sarcoma efficiently. To find the altered proteins among both groups, a mass-based label-free approach was employed and identified a total of 160 significantly altered proteins. Among these, 138 proteins were dysregulated with <1- to -0.1-fold, 18 proteins were up-regulated with >3 folds, and four proteins were similarly expressed in the untreated group compared to the treated. Interestingly, the differential expressions of proteins from the untreated group showed contrast expressions in the treated group. Furthermore, the functional enrichment analysis revealed that most of the identified proteins from this study were associated with various significant pathways such as glycolysis, MAPK activation, PI3K-Akt signaling, extracellular matrix degradation, etc. In addition, two protein expressions, such as fibronectin and annexin-1, were validated by immunoblot analysis. Therefore, this study signifies the most comprehensive discovery of altered protein expressions to date and the first large-scale detection of protein profiles from CSA-cryoablation treated compared to untreated. This work may serve as the basis for future research to open novel treatment options for chondrosarcoma.

Big data, integrative omics and network biology

A cell integrates various signals through a network of biomolecules that crosstalk to synergistically regulate the replication, transcription, translation and other metabolic activities of a cell. These networks regulate signal perception and processing that drives biological functions. The biological complexity cannot be fully captured by a single -omics discipline. The holistic study of an organism-in health, perturbation, exposure to environment and disease, is studied under systems biology. The bottom-up molecular approaches (genes, mRNA, protein, metabolite, etc.) have laid the foundation of current biological knowledge covering the horizon from viruses, bacteria, fungi, plants and animals. Yet, these techniques provide a rather myopic view of biology at the molecular level. To understand how the interconnected molecular components are formed and rewired in disease or exposure to environmental stimuli is the holy grail of modern biology. The omics era was heralded by the genomics revolution but advanced sequencing techniques are now also ubiquitous in transcriptomics, proteomics, metabolomics and lipidomics. Multi-omics data analysis and integration techniques are driving the quest for deeper insights into how the different layers of biomolecules talk to each other in diverse contexts.

Posttranslational modifications in systems biology

The biological complexity cannot be captured by genes or proteins alone. The protein posttranslational modifications (PTMs) impart functional diversity to the proteome and regulate protein structure, activity, localization and interactions. Their dynamics drive cellular signaling, growth and development while their dysregulation causes many diseases. Mass spectrometry based quantitative profiling of PTMs and bioinformatics analysis tools allow systems level insights into their network architecture. High-resolution profiling of PTM networks will advance disease understanding and precision medicine. It can accelerate the discovery of biomarkers and drug targets. This requires better tools for unbiased, high-throughput and accurate PTM identification, site localization and automated annotation on a systems level.

Use of a Recombinant Biomarker Protein DDA Library Increases DIA Coverage of Low Abundance Plasma Proteins

Credible detection and quantification of low abundance proteins from human blood plasma is a major challenge in precision medicine biomarker discovery when using mass spectrometry (MS). In this proof-of-concept study, we employed a mixture of selected recombinant proteins in DDA libraries to subsequently identify (not quantify) cancer-associated low abundance plasma proteins using SWATH/DIA. The exemplar DDA recombinant protein spectral library (rPSL) was derived from tryptic digestion of 36 recombinant human proteins that had been previously implicated as possible cancer biomarkers from both our own and other studies. The rPSL was then used to identify proteins from nondepleted colorectal cancer (CRC) EDTA plasmas by SWATH-MS. Most (32/36) of the proteins used in the rPSL were reliably identified from CRC plasma samples, including 8 proteins (i.e., BTC, CXCL10, IL1B, IL6, ITGB6, TGFα, TNF, TP53) not previously detected using high-stringency protein inference MS according to PeptideAtlas. The rPSL SWATH-MS protocol was compared to DDA-MS using MARS-depleted and postdigestion peptide fractionated plasmas (here referred to as a human plasma DDA library). Of the 32 proteins identified using rPSL SWATH, only 12 could be identified using DDA-MS. The 20 additional proteins exclusively identified using the rPSL SWATH approach were almost exclusively lower abundance (i.e., <10 ng/mL) proteins. To mitigate justified FDR concerns, and to replicate a more typical library creation approach, the DDA rPSL library was merged with a human plasma DDA library and SWATH identification repeated using such a merged library. The majority (33/36) of the low abundance plasma proteins added from the rPSL were still able to be identified using such a merged library when high-stringency HPP Guidelines v3.0 protein inference criteria were applied to our data set. The MS data set has been deposited to ProteomeXchange Consortium via the PRIDE partner repository (PXD022361).

Software Options for the Analysis of MS-Proteomic Data

Mass spectrometry (MS)-based proteomics is currently the most successful approach to measure and compare peptides and proteins in a large variety of biological samples. Modern mass spectrometers, equipped with high-resolution analyzers, provide large amounts of data output. This is the case of shotgun/bottom-up proteomics, which consists in the enzymatic digestion of protein into peptides that are then measured by MS-instruments through a data dependent acquisition (DDA) mode. Dedicated bioinformatic tools and platforms have been developed to face the increasing size and complexity of raw MS data that need to be processed and interpreted for large-scale protein identification and quantification. This chapter illustrates the most popular bioinformatics solution for the analysis of shotgun MS-proteomics data. A general description will be provided on the data preprocessing options and the different search engines available, including practical suggestions on how to optimize the parameters for peptide search, based on hands-on experience.

New mixture models for decoy-free false discovery rate estimation in mass spectrometry proteomics

MOTIVATION

Accurate estimation of false discovery rate (FDR) of spectral identification is a central problem in mass spectrometry-based proteomics. Over the past two decades, target-decoy approaches (TDAs) and decoy-free approaches (DFAs) have been widely used to estimate FDR. TDAs use a database of decoy species to faithfully model score distributions of incorrect peptide-spectrum matches (PSMs). DFAs, on the other hand, fit two-component mixture models to learn the parameters of correct and incorrect PSM score distributions. While conceptually straightforward, both approaches lead to problems in practice, particularly in experiments that push instrumentation to the limit and generate low fragmentation-efficiency and low signal-to-noise-ratio spectra.

RESULTS

We introduce a new decoy-free framework for FDR estimation that generalizes present DFAs while exploiting more search data in a manner similar to TDAs. Our approach relies on multi-component mixtures, in which score distributions corresponding to the correct PSMs, best incorrect PSMs and second-best incorrect PSMs are modeled by the skew normal family. We derive EM algorithms to estimate parameters of these distributions from the scores of best and second-best PSMs associated with each experimental spectrum. We evaluate our models on multiple proteomics datasets and a HeLa cell digest case study consisting of more than a million spectra in total. We provide evidence of improved performance over existing DFAs and improved stability and speed over TDAs without any performance degradation. We propose that the new strategy has the potential to extend beyond peptide identification and reduce the need for TDA on all analytical platforms.

AVAILABILITYAND IMPLEMENTATION

https://github.com/shawn-peng/FDR-estimation.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Exploring the Proteomic Alterations from Untreated and Cryoablation and Irradiation Treated Giant Cell Tumors of Bone Using Liquid-Chromatography Tandem Mass Spectrometry

Giant cell tumors of bone (GCT) are benign tumors that show a locally aggressive nature and affect bones' architecture. Recently, cryoablation and irradiation treatments have shown promising results in GCT patients with faster recovery and less recurrence and metastasis. Therefore, it became a gold standard surgical treatment for patients. Hence, we have compared GCT-untreated, cryoablation, and irradiation-treated samples to identify protein alterations using high-frequency liquid chromatography-electrospray ionization tandem mass spectrometry (LC-ESI-MS/MS). Our label-free quantification analysis revealed a total of 107 proteins (p < 0.01) with 26 up-regulated (< 2-folds to 5-fold), and 81 down-regulated (> 0.1 to 0.5 folds) proteins were identified from GCT-untreated and treated groups. Based on pathway analysis, most of the identified up-regulated proteins involved in critical metabolic functions associated with tumor proliferation, angiogenesis, and metastasis. On the other hand, the down-regulated proteins involved in glycolysis, tumor microenvironment, and apoptosis. The observed higher expressions of matrix metalloproteinase 9 (MMP9) and TGF-beta in the GCT-untreated group associated with bones' osteolytic process. Interestingly, both the proteins showed reduced expressions after cryoablation treatment, and contrast expressions identified in the irradiation treated group. Therefore, these expressions were confirmed by immunoblot analysis. In addition to these, several glycolytic enzymes, immune markers, extracellular matrix (ECM), and heat shock proteins showed adverse expressions in the GCT-untreated group were identified with favorable regulations after treatment. Therefore, the identified expression profiles will provide a better picture of treatment efficacy and effect on the molecular environment of GCT.

Saliva proteomics from children with caries at different severity stages

OBJECTIVE

To perform a comparative analysis of saliva protein profile of patients with early childhood caries at different levels of severity and caries-free individuals.

MATERIALS AND METHODS

Stimulated saliva samples were collected from 126 children (2-6 years old), classified according to the ICDAS II, and divided into 3 groups (n = 42): caries-free (CF), enamel caries (EC), and dentine caries (DC). Samples were digested and analyzed by nanoUPLC coupled with a mass spectrometry. Data analyses were conducted with Progenesis QI for Proteomics Software v2.0. Gene Ontology (GO) terms and protein-protein interaction analysis were obtained.

RESULTS

A total of 306 proteins (≈6 peptides) were identified. Among them, 122 were differentially expressed in comparisons among children with different caries status. Out of the 122 proteins, the proteins E2AK4 and SH3L2 were exclusively present in groups CF and EC, respectively, and 8 proteins (HAUS4, CAH1, IL36A, IL36G, AIMP1, KLHL8, KLH13, and SAA1) were considered caries-related proteins when compared to caries-free children; they were up-regulated proteins in the caries groups (EC and DC).

CONCLUSION

The identification of exclusive proteins for caries-free or carious-related conditions may help in understanding the mechanisms of caries and predicting risk as well as advancing in caries control or anti-caries approaches.

HyperQuant-A Computational Pipeline for Higher Order Multiplexed Quantitative Proteomics

Quantitative proteomics has evolved considerably over the last decade with the advent of higher order multiplexing (HOM) techniques. With the development of methods such as-multitagging, cPILOT, hyperplexing, BONPlex, and MITNCAT, the HOM technique is rapidly taking the center stage in multiplexed quantitative proteomics. These studies combined MS¹ and MS² labels in a single experiment enabling higher sample throughput. While HOM is highly promising, the computational analysis is still a big challenge, as the available tools cannot harness its power completely. We have developed a new quantitative pipeline, HyperQuant to aid in accurately quantitating complex HOM data. The pipeline uses identification results from either MaxQuant or any other search engine and quantitation results from QuantWiz^IQ. The Mapper and Combiner modules of HyperQuant allow facile integration of the labeled data, along with peptide spectrum match (PSM) intensity/ratio integration for proteins, respectively, for each PSM label combination. This also includes appropriate combination of replicates/fractions before summarizing the protein intensity/ratio, leading to robust quantitation. To the best of our knowledge, this is the first tool for the quantitation of HOM data with flexibility for any combination of MS¹ and MS² labels. We demonstrate its utility in analyzing two 18-plex data sets from the hyperplexing and the BONplex studies. The tool is open source and freely available for noncommercial use. HyperQuant is a highly valuable tool that will help in advancing the field of multiplexed quantitative proteomics.

Proteomic profiling and identification of significant markers from high-grade osteosarcoma after cryotherapy and irradiation

Biological reconstruction of allografts and recycled autografts have been widely implemented in high-grade osteogenic sarcoma. For treating tumor-bearing autografts, extracorporeal irradiation (ECIR) and liquid nitrogen (LN) freezing techniques are being used worldwide as a gold standard treatment procedure. Both the methods aim to eradicate the tumor cells from the local recurrence and restore the limb function. Therefore, it is essential and crucial to find, and compare the alterations at molecular and physiological levels of the treated and untreated OGS recycled autografts to obtain valuable clinical information for better clinical practice. Thus, we aimed to investigate the significantly expressed altered proteins from ECIR-and cryotherapy/freezing- treated OGS (n = 12) were compared to untreated OGS (n = 12) samples using LC-ESI-MS/MS analysis, and the selected proteins from this protein panel were verified using immunoblot analysis. From our comparative proteomic analysis identified a total of 131 differentially expressed proteins (DEPs) from OGS. Among these, 91 proteins were up-regulated (2.5 to 3.5-folds), and 40 proteins were down-regulated (0.2 to 0.5 folds) (p < 0.01 and 0.05). The functional enrichment analysis revealed that the identified DEPs have belonged to more than 10 different protein categories include cytoskeletal, extracellular matrix, immune, enzyme modulators, and cell signaling molecules. Among these, we have confirmed two potential candidates’ expressions levels such as Fibronectin and Protein S100 A4 using western blot analysis. Our proteomic study revealed that LN-freezing and ECIR treatments are effectively eradicating tumor cells, and reducing the higher expressions of DEPs at molecular levels which may help in restoring the limb functions of OGS autografts effectively. To the best of our knowledge, this is the first proteomic study that compared proteomic profiles among freezing, ECIR treated with untreated OGS in recycled autografts. Moreover, the verified proteins could be used as prognostic or diagnostic markers that reveal valuable scientific information which may open various therapeutic avenues in clinical practice to improve patient outcomes.

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.

Dietary Intake of β-Glucans Can Prolong Lifespan and Exert an Antioxidant Action on Aged Fish Nothobranchius guentheri

One of the widely accepted conjectures regarding mechanisms of aging is probably the oxidative stress hypothesis. β-1,3-Glucans, well-known immunostimulants, have been shown to increase nonspecific immunity and resistance against infections or pathogenic bacteria in several fish species, but its antiaging function remains poorly understood. By feeding of β-1,3-glucans to the annual fish, Nothobranchius guentheri, we detected the survivorship of the fish and estimated the development of age-related biomarkers at different stages. We first showed that administration of β-1,3-glucans was able to prolong the lifespan of the fish (p < 0.05). We then showed that β-1,3-glucans clearly reduced the accumulation of lipofuscin in the gills and the senescence-associated β-galactosidase in the caudal fins. Moreover, β-1,3-glucans were able to lower the levels of protein oxidation, lipid peroxidation, and reactive oxygen species (ROS) in the muscles. Finally, β-1,3-glucans could promote the activities of the antioxidant enzymes, including catalase, superoxide dismutase, and glutathione peroxidase in the fish, and slow down the increase of P66shc, a critical factor involved in the regulation of intracellular ROS contents. These data together suggest for the first time that β-1,3-glucans can extend the lifespan, delay the onset of age-related biomarkers and exert an antioxidant action of the aged fish, N. guentheri. It also implies that β-1,3-glucans may be potentially useful for health care in the elderly, including extension of the lifespan.

DNA methylation exploration for ARDS: a multi-omics and multi-microarray interrelated analysis

Background

Despite advances in clinical management, there are currently no novel therapeutic targets for acute respiratory distress syndrome (ARDS). DNA methylation, as a reversible process involved in the development and progression of many diseases, would be used as potential therapeutic targets to improve the treatment strategies of ARDS. However, the meaningful DNA methylation sites associated with ARDS still remain largely unknown. We sought to determine the difference in DNA methylation between ARDS patients and healthy participants, and simultaneously, the feasible DNA methylation markers for potential therapeutic targets were also explored.

Methods

Microarray data of human blood samples for ARDS and healthy participants up to June 2019 was searched in GEO database. The difference analyses between ARDS and healthy population were performed through limma R package, and furthermore, interrelated analyses of DNA methylation and transcript were accomplished by VennDiagram R package. Perl and sva R package were used to merge microarray data and decrease heterogeneities among different studies. The biological function of screened methylation sites and their regulating genes were annotated according to UniProt database and Pubmed database. GO term and KEGG pathway enrichment analyses were conducted using DAVID 6.8 and KOBAS 3.0. The meaningful DNA methylation markers to distinguish ARDS from healthy controls were explored through ROC (receiver operating characteristic curves) analyses.

Results

Five datasets in GEO databases (one DNA methylation dataset, three mRNA datasets, and one mRNA dataset of healthy people) were enrolled in present analyses finally, and the series were GSE32707, GSE66890, GSE10474, GSE61672, and GSE67530. These databases included 99 patients with ARDS (within 48 h of onset) and 136 healthy participants. Difference analyses indicated 44,439 DNA methylation alterations and 29 difference mRNAs between ARDS and healthy controls. 40 methylation variations regulated transcription of 16 genes was explored via interrelated analysis. According to the functional annotations, 30 DNA methylation sites were related to the imbalance of inflammation or immunity, endothelial function, epithelial function and/or coagulation function. cg03341377, cg24310395, cg07830557 and cg08418670, with AUC up to 0.99, might be the meaningful characteristics with the highest performance to distinguish ARDS from healthy controls.

Conclusions

44,439 DNA methylation alterations and 29 difference mRNAs exist between ARDS and healthy controls. 30 DNA methylation sites may regulate transcription of 10 genes, which take part in pathogenesis of ARDS. These findings could be intervention targets, with validation experiments to be warranted to assess these further.

SPECTRUM - A MATLAB Toolbox for Proteoform Identification from Top-Down Proteomics Data

Top-Down Proteomics (TDP) is an emerging proteomics protocol that involves identification, characterization, and quantitation of intact proteins using high-resolution mass spectrometry. TDP has an edge over other proteomics protocols in that it allows for: (i) accurate measurement of intact protein mass, (ii) high sequence coverage, and (iii) enhanced identification of post-translational modifications (PTMs). However, the complexity of TDP spectra poses a significant impediment to protein search and PTM characterization. Furthermore, limited software support is currently available in the form of search algorithms and pipelines. To address this need, we propose 'SPECTRUM', an open-architecture and open-source toolbox for TDP data analysis. Its salient features include: (i) MS2-based intact protein mass tuning, (ii) de novo peptide sequence tag analysis, (iii) propensity-driven PTM characterization, (iv) blind PTM search, (v) spectral comparison, (vi) identification of truncated proteins, (vii) multifactorial coefficient-weighted scoring, and (viii) intuitive graphical user interfaces to access the aforementioned functionalities and visualization of results. We have validated SPECTRUM using published datasets and benchmarked it against salient TDP tools. SPECTRUM provides significantly enhanced protein identification rates (91% to 177%) over its contemporaries. SPECTRUM has been implemented in MATLAB, and is freely available along with its source code and documentation at https://github.com/BIRL/SPECTRUM/.

An improved scoring method for the identification of endogenous peptides based on the Mascot MS/MS ion search

To identify endogenous peptides using MS/MS analysis and searching against a polypeptide sequence database, a non-enzyme specific (NES) search considering all of the possible proteolytic cleavages is required. However, the use of a NES search generates more false positive hits than an enzyme specific search, and therefore shows lower identification performance. In this study, the use of the sub-ranked matches for improving the identification performance of the Mascot NES search was investigated and a new scoring method was developed that considered the contribution of all sub-ranked random match probabilities, named the contribution score (CS). The CS showed the highest identification sensitivity using the Mascot NES search with a full protein database when compared to the use of the Mascot first ranked score and the delta score (DS). The confident peptides identified by DS and CS were shown to be complementary. When applied to plant endogenous peptide identification, the identification numbers of tomato endogenous peptides using DS and CS were 176.3% and 184.2%, respectively, higher than the use of the first ranked score of Mascot. The combination of DS and CS identified 200.0% and 8.6% more tomato endogenous peptides compared to the use of Mascot and DS, respectively. This method by combining the CS and DS can significantly improve the identification performance of endogenous peptides without complex computational steps and is also able to improve the identification performance of the enzyme specific search. In addition to the application in the plant peptidomics analysis, this method may be applied to the improvement of peptidomics studies in different species. A web interface for calculating the DS and CS based on Mascot search results was developed herein.

Middle-down proteomics: a still unexploited resource for chromatin biology

INTRODUCTION

Analysis of histone post-translational modifications (PTMs) by mass spectrometry (MS) has become a fundamental tool for the characterization of chromatin composition and dynamics. Histone PTMs benchmark several biological states of chromatin, including regions of active enhancers, active/repressed gene promoters and damaged DNA. These complex regulatory mechanisms are often defined by combinatorial histone PTMs; for instance, active enhancers are commonly occupied by both marks H3K4me1 and H3K27ac. The traditional bottom-up MS strategy identifies and quantifies short (aa 4-20) tryptic peptides, and it is thus not suitable for the characterization of combinatorial PTMs. Areas covered: Here, we review the advancement of the middle-down MS strategy applied to histones, which consists in the analysis of intact histone N-terminal tails (aa 50-60). Middle-down MS has reached sufficient robustness and reliability, and it is far less technically challenging than PTM quantification on intact histones (top-down). However, the very few chromatin biology studies applying middle-down MS resulting from PubMed searches indicate that it is still very scarcely exploited, potentially due to the apparent high complexity of method and analysis. Expert commentary: We will discuss the state-of-the-art workflow and examples of existing studies, aiming to highlight its potential and feasibility for studies of cell biologists interested in chromatin and epigenetics.

Dataset generated using hyperplexing and click chemistry to monitor temporal dynamics of newly synthesized macrophage secretome post infection by mycobacterial strains

Here we provide data for SILAC and iTRAQ based hyperplexing combined with BONCAT based click chemistry for selective enrichment of newly synthesized proteins secreted by THP1 macrophages at various time points after infection with four different strains of Mycobacterium tuberculosis. The macrophages were infected with H37Ra, H37Rv, BND433 and JAL2287 strains of M. tuberculosis. Newly-synthesized secreted host proteins were observed, starting from six hours post-infection till 26 h, at 4 h intervals. We have combined BONCAT with hyperplexing (18-plex), which blends SILAC and iTRAQ, for the first time. Two sets of triplex SILAC were used to encode the strains of M. tuberculosis - H37Ra & H37Rv in one and BND433 & JAL2287 in another with a control in each. BONCAT was used to enrich the secretome for newly synthesized proteins while 6-plex iTRAQ labeling was employed to quantify the temporal changes in the captured proteome. Each set of 18-plex was run in 4 MS replicates with two linear and two non-linear separation modes. This new variant of hyperplexing method, combining triplex SILAC with 6-plex iTRAQ, achieves 18-plex quantitation in a single MS run. Hyperplexing enables large scale spatio-temporal systems biology studies where large number of samples can be processed simultaneously and in quantitative manner. Data are available via ProteomeXchange with identifier ProteomeXchange: PXD004281.

Bacterial Electron Transfer Chains Primed by Proteomics

Electron transport phosphorylation is the central mechanism for most prokaryotic species to harvest energy released in the respiration of their substrates as ATP. Microorganisms have evolved incredible variations on this principle, most of these we perhaps do not know, considering that only a fraction of the microbial richness is known. Besides these variations, microbial species may show substantial versatility in using respiratory systems. In connection herewith, regulatory mechanisms control the expression of these respiratory enzyme systems and their assembly at the translational and posttranslational levels, to optimally accommodate changes in the supply of their energy substrates. Here, we present an overview of methods and techniques from the field of proteomics to explore bacterial electron transfer chains and their regulation at levels ranging from the whole organism down to the Ångstrom scales of protein structures. From the survey of the literature on this subject, it is concluded that proteomics, indeed, has substantially contributed to our comprehending of bacterial respiratory mechanisms, often in elegant combinations with genetic and biochemical approaches. However, we also note that advanced proteomics offers a wealth of opportunities, which have not been exploited at all, or at best underexploited in hypothesis-driving and hypothesis-driven research on bacterial bioenergetics. Examples obtained from the related area of mitochondrial oxidative phosphorylation research, where the application of advanced proteomics is more common, may illustrate these opportunities.