Journal of Proteome Research最新文献

MGVB is a collection of tools for proteomics data analysis. It covers data processing from in silico digestion of protein sequences to comprehensive identification of post-translational modifications and solving the protein inference problem. The toolset is developed with efficiency in mind. It enables analysis at a fraction of the resources cost typically required by existing commercial and free tools. MGVB, as it is a native application, is faster than existing proteomics tools such as MaxQuant and, at the same time, finds very similar, in some cases even larger, numbers of peptides at a chosen level of statistical significance. It implements a probabilistic scoring function to match spectra to sequences, a novel combinatorial search strategy for finding post-translational modifications, and a Bayesian approach to locate modification sites. This report describes the algorithms behind the tools, presents benchmarking data sets analysis comparing MGVB performance to MaxQuant/Andromeda, and provides step by step instructions for using it in typical analytical scenarios.

Identifying novel biomarkers is crucial for early detection of colorectal cancer (CRC). Saliva, as a noninvasive sample, holds promise for CRC detection. Here, we used Olink proteomics and untargeted metabolomics to analyze saliva samples from CRC patients and healthy controls with the aim of identifying candidate biomarkers in CRC saliva. Univariate and multivariate analyses revealed 16 differentially expressed proteins (DEPs) and 40 differentially accumulated metabolites (DAMs). Pathway enrichment showed DEPs were mainly involved in cancer transcriptional dysregulation, Toll-like receptor signaling, and chemokine signaling. Metabolomics analysis highlighted significant changes in amino acid metabolites, particularly in pathways such as arginine biosynthesis, histidine metabolism, and cysteine and methionine metabolism. Random forest analysis and ELISA validation identified four potential biomarkers: succinate, l-methionine, GZMB, and MMP12. A combined protein-metabolite diagnostic model was developed using logistic regression, achieving an area under the curve of 0.933 (95% CI: 0.871-0.996) for the discovery cohort and 0.969 (95% CI: 0.918-1.000) for the validation cohort, effectively distinguishing CRC patients from healthy individuals. In conclusion, our study identified and validated a panel of noninvasive saliva-based biomarkers that could improve CRC screening and provide new insights into clinical CRC diagnosis.

The renin-angiotensin system (RAS) is involved in kidney fibrosis. We previously identified six RAS-regulated proteins (RHOB, BST1, LYPA1, GLNA, TSP1, and LAMB2) that were increased in the urine of patients with kidney allograft fibrosis, compared to patients without fibrosis. We hypothesized that these urinary RAS-regulated proteins predicted primary outcomes in kidney transplant recipients enrolled in the largest RAS inhibitor randomized controlled trial. Urine excretion of 10 peptides corresponding to the six RAS-regulated proteins was quantified using parallel reaction monitoring mass spectrometry assays (normalized by urine creatinine) in a subset of patients in the trial. Machine learning models predicting outcomes based on urine peptide excretion rates were developed and evaluated. Urine samples (n = 111) from 56 patients were collected at 0, 6, 12, and 24 months. Twenty-four primary outcomes (doubling of serum creatinine, graft loss, or death) occurred in 17 patients. Logistic regression utilizing eight peptides of TSP1, BST1, LAMB2, LYPA1, and RHOB, from the last urine sample prior to outcomes, predicted a graft loss with an AUC of 0.78 (p = 0.00001). A random forest classifier utilizing BST1 and LYPA1 peptides predicted death with an AUC of 0.80 (p = 0.0016). Urine measurements of RAS-regulated proteins may predict outcomes in kidney transplant recipients, although further prospective studies are required.

Proteomics data-dependent acquisition data sets collected with high-resolution mass-spectrometry (MS) can achieve very high-quality results, but nearly every analysis yields results that are thresholded at some accepted false discovery rate, meaning that a substantial number of results are incorrect. For study conclusions that rely on a small number of peptide-spectrum matches being correct, it is thus important to examine at least some crucial spectra to ensure that they are not one of the incorrect identifications. We present Quetzal, a peptide fragment ion spectrum annotation tool to assist researchers in annotating and examining such spectra to ensure that they correctly support study conclusions. We describe how Quetzal annotates spectra using the new Human Proteome Organization (HUPO) Proteomics Standards Initiative (PSI) mzPAF standard for fragment ion peak annotation, including the Python-based code, a web-service end point that provides annotation services, and a web-based application for annotating spectra and producing publication-quality figures. We illustrate its functionality with several annotated spectra of varying complexity. Quetzal provides easily accessible functionality that can assist in the effort to ensure and demonstrate that crucial spectra support study conclusions. Quetzal is publicly available at https://proteomecentral.proteomexchange.org/quetzal/.

Metabolic perturbations of the gut microbiome have been implicated in the pathogenesis of multiple human diseases, including type-2 diabetes (T2D). However, our understanding of the global metabolic alterations of the gut microbiota in T2D and their functional roles remains limited. To address this, we conducted a high-coverage metabolomics profiling analysis of serum samples from 1282 Chinese individuals with and without T2D. Among the 220 detected microbiota-associated compounds detected, 111 were significantly altered, forming a highly interactive regulatory network associated with T2D development. Pathway enrichment and correlation analyses revealed aberrant metabolic pathways, primarily including the activation of pyrimidine metabolism, unsaturated fatty acid biosynthesis, and diverse amino acid metabolisms such as Tryptophan metabolism, Lysine metabolism, and Branched-chain amino acid biosynthesis. A microbiota-dependent biomarker panel, comprising pipecolinic acid, methoxysalicylic acid, N-acetylhistamine, and 3-hydroxybutyrylcarnitine, was defined and validated with satisfactory sensitivity (>78%) for large-scale, population-based T2D screening. The functional role of a gut microbial product, N-acetylhistamine, was further elucidated in T2D progression through its inhibition of adenosine monophosphate-activated protein kinase phosphorylation. Overall, this study expands our understanding of gut microbiota-driven metabolic dysregulation in T2D and suggests that monitoring these metabolic changes could facilitate the diagnosis and treatment of T2D.

Variation in parenting behavior is widespread across the animal kingdom, both within and between species. There are two ecotypes of the three-spined stickleback fish (Gasterosteus aculeatus) that exhibit dramatic differences in their paternal behavior. Males of the common ecotype are highly attentive fathers, tending to young from eggs to fry, while males of the white ecotype desert offspring as eggs. As the pituitary is a key regulator in the hypothalamic-pituitary-adrenal (HPA) axis and the hypothalamic-pituitary-gonadal (HPG) axis between the brain and body, its peptides may influence parenting behaviors. Here, we utilized matrix-assisted laser desorption ionization mass spectrometry (MALDI-MS) for high-throughput peptide analysis in single cells of pituitaries from both three-spined stickleback ecotypes. Peptide mass fingerprinting was performed using an in silico generated peptide library to identify detected prohormones. Differential analysis revealed POMC-derived peptides, MCH-derived peptides, and oxytocin as significantly different between the two ecotypes, with higher oxytocin levels in the common ecotype. Interestingly, these subtle chemical differences were not captured by Leiden clustering of the cellular phenotypes. These results call for further investigation of the neurochemical basis for parenting in sticklebacks.

Although amino acid (AA) metabolism is linked to tumor progression and could serve as an attractive intervention target, its association with neuroblastoma (NB) is unknown. Based on AA metabolism-related genes, we established three NB subtypes associated with distinct prognoses and specific functions, with C1 and C2 having better outcomes. The C1 displayed enhanced metabolic activity and recruited metabolism-associated cells. The C2 exhibited an activated immune microenvironment and was more vulnerable to immunotherapy. The C3, characterized by cell cycle peculiarity, possessed a dismal prognosis and high frequency of gene mutations and was susceptible to chemotherapy. Furthermore, single-cell RNA sequencing analysis revealed that the C3-associated Scissor+ cell subpopulation was characterized by notorious functional states and orchestrated an immunosuppressive microenvironment. Additionally, we identified that ALK and BIRC5 contributed to the shorter lifespan of C3 and their corresponding inhibitors were potential interventions. In conclusion, we identified three distinct subtypes of NB, which help us foster individualized therapeutic strategies to improve the prognosis of NB.

The accurate and rapid identification of bacterial pathogens poses a significant challenge in clinical diagnostics, environmental monitoring, and microbial research. Lipidomics and proteomics serve as powerful methodologies for bacterial characterization; however, the complexity of biological matrices and the low abundance of bacterial lipids often limit effective detection. This study introduces graphene-polyglycerol amine (G-PGA) as a novel nanomaterial that enhances the selective trapping and detection of Escherichia coli (E. coli) using desorption electrospray ionization mass spectrometry and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS). The antimicrobial properties of G-PGA reveal a minimum inhibitory concentration (MIC) of 250 μg/μL and a minimum bactericidal concentration (MBC) of 500 μg/μL. Optimal sonication conditions (10 min) maximize G-PGA's surface activity, facilitating effective bacterial trapping while maintaining cellular integrity, as confirmed by scanning electron microscopy and atomic force microscopy. Molecular docking simulations show a strong affinity between G-PGA and the β-barrel assembly machinery (BAM) proteins of E. coli, suggesting potential disruption of critical bacterial processes. Preconcentration with G-PGA significantly improves detection sensitivity and signal-to-noise ratio in mass spectrometry analyses, highlighting its potential as a transformative tool for rapid, sensitive, and highly specific bacterial identification in lipidomics and proteomics.

Pulmonary embolism (PE) is a life-threatening disease. Our aim was to search for potential biomarkers by using modern high-throughput metabolomics methods to improve diagnostic efficacy. The discovery cohort included 60 participants, including 30 PE patients and 30 healthy individuals. The validation cohort included 40 participants, including 20 PE patients and 20 healthy individuals. Gas chromatography-mass spectrometry (GC-MS) was combined with multivariate data analysis to determine serum metabolic profiles in patients with PE and healthy controls. The distribution of metabolic profiles in the two cohorts was assessed by unsupervised principal component analysis (PCA) and supervised partial least-squares discriminant analysis (PLS-DA). Sixteen metabolites were initially selected from the ranked variable of predictive importance (VIP) scores and applied to the correlation analysis of PE-related clinical indicators. Four metabolites that were correlated with D-dimer levels were selected, including l-tryptophan, N-alpha-acetyl-l-lysine, dopamine, and N²-acetylornithine. Finally, the AUC values were calculated to be 0.958 (95% CI: 0.9072-1) for the combined biomarker panel, including the 4 specific metabolites in the discovery cohort, and 0.963 (95% CI: 0.9122-1) in the validation cohort. The results suggest that these four specific metabolites can be used as diagnostic biomarkers to improve diagnostic efficacy in pulmonary embolism.

Proteoforms are distinct molecular forms of proteins that act as building blocks of organisms, with post-translational modifications (PTMs) being one of the key changes that generate these variations. Mass spectrometry (MS)-based top-down proteomics (TDP) is the leading technology for proteoform identification due to its preservation of intact proteoforms for analysis, making it well-suited for comprehensive PTM characterization. A crucial step in TDP is searching MS data against a database of candidate proteoforms. To extend the reach of TDP to organisms with limited PTM annotations, we developed Proteoform-predictor, an open-source tool that integrates homology-based PTM site prediction into proteoform database creation. The new tool creates databases of proteoform candidates after registration of homologous sequences, transferring PTM sites from well-characterized species to those with less comprehensive proteomic data. Our tool features a user-friendly interface and intuitive workflow, making it accessible to a wide range of researchers. We demonstrate that Proteoform-predictor expands proteoform databases with tens of thousands of proteoforms for three bacterial strains by comparing them to the reference proteome of Escherichia coli (E. coli) K12. Subsequent TDP analysis for Serratia marcescens (S. marcescens) and Salmonella typhimurium (S. typhimurium) demonstrated significant improvement in protein and proteoform identification, even for proteins with variant sequences. As TDP technology advances, Proteoform-predictor will become an important tool for expanding the applicability of proteoform identification and PTM biology to more diverse species across the phylogenetic tree of life.