Journal of Proteome Research最新文献

A proteomic analysis of rat liver and kidney was performed following exposure to morphine (10 mg/kg, 10 days) versus untreated controls. Our data revealed alterations in numerous protein expression. Differences were observed in pro- and antiapoptotic signaling, oxidative stress response, transport mechanisms, nucleic acid metabolism, and regulators of alternative splicing, among others. Liver tissue exhibited primarily adaptive changes, including upregulation of metabolic enzymes and subtle markers of oxidative stress. In contrast, the kidneys displayed a broader spectrum of proteomic alterations, affecting a wider array of functional pathways, suggesting that this organ may be more susceptible to morphine-induced toxicity or that of its metabolites. We employed also MALDI–MSI to examine lipidomic alterations: while no significant changes were detected in liver tissue, important lipidomic alterations were observed in the kidneys, further supporting their increased vulnerability to morphine exposure. Our results should be regarded as preliminary; however, they highlight a promising direction for identifying early stage protein markers of toxicity using proteomic approaches. Monitoring such markers could prove valuable in multidrug therapies, particularly for patients with a history of hepatic or renal impairment, contributing to the development of safer therapeutic strategies. Data are available via ProteomeXchange under the identifiers: PXD067999 (DDA) and PXD068084 (DIA).

A crucial step in processing mass spectrometry-based proteomics data is identifying and quantifying the proteins in the sample. While the existing search engines can easily match tandem mass spectra to peptide sequences, selecting the most appropriate search parameters can often be challenging and time-consuming due to the diversity of the data sets and the long list of available parameter values to choose from. This study introduces QuickSearchProt─an algorithm aimed at assisting in the selection of search parameter values across search engines, considering not only the data set specifications but also the properties of the search algorithms. By relying on a small representative subset of the spectra, the algorithm can process most data sets within minutes, largely independent of the size of the original data set. The current implementation supports two common search engines, X! Tandem and Sage, and is designed to process data-dependent acquisition (DDA) proteomics data sets but due to its adaptability and scalability can easily be extended to additional search engines. QuickSearchProt, including a graphical user interface, the complete source code, and additional details are freely available at http://www.github.com/barsnes-group/QuickSearchProt.

Neurosyphilis (NS), caused by Treponema pallidum, results in irreversible neurological damage. This study elucidated NS pathogenesis via cerebrospinal fluid (CSF) proteomic analysis comparing NS, suspected neurosyphilis (spNS), and non-neurosyphilis (nNS) controls by using liquid chromatography–tandem mass spectrometry (LC–MS/MS), public data set PXD033034, and ELISA validation. Of 1261 quantified proteins in the CSF, 234 were differentially expressed in NS, with 189 downregulated and enriched in lysosomal, axon guidance, and neurodegeneration pathways. Three previously identified CSF biomarkers of neurosyphilis, i.e., SEMA7A, SERPINA3, and ITIH4, were replicated. Key proteins in the lysosome pathway, including PSAP, CTSL, NPC2, and DNASE2, were significantly downregulated in the CSF of NS patients, and spNS patients presented a high CSF IgG index (spNS-hi-IgG-i). We also identified EPHA4, a key protein in the axon guidance pathway, as significantly downregulated in the CSF of NS patients. A positive correlation between PSAP and EPHA4 suggested a potential impact of lysosomal function on axonostasis in NS. ROC analysis revealed PSAP and DNASE2 as potential biomarkers for assessing neurodegeneration in NS and spNS-hi-IgG-i. These findings suggest that disruptions in lysosomal and axonal processes may contribute to neurodegeneration in NS, and the identified biomarkers hold potential as diagnostic indicators and therapeutic targets.

Intervertebral disc degeneration (IDD) represents a significant health concern affecting a large portion of the population and leads to chronic pain and disability. Despite the prevalence of this condition, the underlying biological mechanisms and potential biomarkers for early diagnosis remain inadequately understood. Advances in proteomics have opened new avenues for identifying blood biomarkers that can facilitate better diagnostic and therapeutic strategies. The primary aim of this study was to identify and validate specific plasma proteins associated with varying grades of IDD. This case–control study compared plasma samples from patients with grade II (n = 10) and grade V (n = 10) IDD to assess differential protein expression. Proteomic analysis was conducted via the SomaScan Assay to screen and identify candidate proteins. Six differential proteins─COL6α3, REG1β, ATF5, CAP1, MAGEA4, and LILRB3─were identified with the highest fold changes and recognized as biomarkers. Subsequent validation of these biomarkers was performed using enzyme-linked immunosorbent assay (ELISA) technology in a validation cohort of 50 patients. A final six-protein combined model achieved an optimal predictive efficacy (AUC = 0.8700). This study provides several noninvasive and rapid plasma biomarkers for the early diagnosis of IDD.

The C-termini often regulate protein biological functions through specific structures or modifications. Small open reading frame-encoded peptides (SEPs) make up a novel class of gene expression products that participate in various biological activities. Their C-termini have also been found to affect their function, but the polymorphism of SEPs’ C-termini has not yet been systematically elucidated. Using C-terminal proteomics, we identified 3636 C-terminal peptides from 2168 proteins in three human cancer cell lines, including 3364 peptides from 1901 classical proteins and 272 peptides from 267 SEPs. Approximately 20% of all of the identified C-terminal peptides had been reported in previous studies, originating from mRNA alternative splicing or protease cleavage, while more than 85% of the C-terminal peptides from SEPs were novel. Bioinformatics analysis revealed that most new SEP C-termini are likely produced by protease cleavage by the KLK, MMP, and CAT protease families. Others without accurately predicted hydrolysis sites may originate from alternative splicing or protein trimming. The intact and hydrolysis products of some SEPs were verified by immunoblotting. Some cleavage occurs in the predicted domain, which might affect SEPs’ function. This study enriches the SEP sequence information, provides experimental evidence for SEP in vivo processing, and supports the subsequent functional analysis of SEP.

Tau is the primary protein studied in Alzheimer’s disease (AD), and its post-translational modifications (PTMs) play a critical role in disease progression. However, conventional closed-search PTM workflows are limited to predefined modification types, which constrains the detection of unexpected Tau PTMs in AD. In this study, we generated a high-resolution delta-mass table by combining known PTMs from Open-pFind with a dense series of additional mass delta values and applied an open-search proteomics workflow to analyze Alzheimer’s disease data. This approach identified 23 Tau PTM sites in AD, primarily involving ubiquitination (Ub) and deamidation (Asn → Asp). These characteristic PTM sites enabled more accurate discrimination of Braak stage V–VI samples not only from controls but also from earlier Braak stages. We further established a unified AD-identifying indicator based on ubiquitination, deamidation, and characteristic mass shifts, which accurately classified AD samples in data set PXD020517 & PXD020538 and was validated in PXD038901. In addition, we detected some previously unreported Tau PTMs and characteristic mass shifts through open-search analysis. In conclusion, we have successfully employed Open-pFind along with a high-resolution delta-mass table to achieve a comprehensive analysis of both known Tau PTMs and characteristic mass shifts to accurately distinguish Alzheimer’s disease samples.

Escherichia coli is a widely used model organism in molecular biology. Despite its pivotal role, a comprehensive proteome resource covering the E. coli pan-proteome and its post-translational modifications (PTMs) has been lacking. Here we present the E. coli PeptideAtlas build, the first comprehensive pan-proteome analysis of E. coli, generated from 40 high-quality public and in-house data sets spanning a broad diversity of strains, sample types, and experimental conditions, and comprising over 73 million MS/MS spectra. All data sets were reprocessed using both a closed search (Trans-Proteomic Pipeline using MSFragger) and an open search (ionbot). The E. coli PeptideAtlas build provides evidence for 4755 proteins, including 1376 previously lacking protein-level support in UniProtKB. The resource offers protein coverage, modification sites, raw spectra with matched peptides, and manually annotated metadata for the E. coli pan-proteome. PTM profiling identified over 10,000 modification sites, including phosphorylation (3806), acetylation (754), methylation (730), glutathionylation (352) and phosphoribosylation (226). Analysis of the glutathionylation sites revealed potential links to metal binding regulation. We also detected proteins likely stemming from phages, underscoring the value of pan-proteomic approaches for studying host-phage interactions. All identifications are publicly accessible and traceable through the PeptideAtlas interface. We expect that the E. coli PeptideAtlas build will provide a useful resource for the community, which supports, for example, targeted MS experiment design, PTM enrichment method development, and strain typing. It allows straightforward lookups of protein and peptide identifications and facilitates comparative proteomic analyses by enabling the assessment of protein presence and variability across different E. coli strains. The build is available at https://peptideatlas.org/builds/ecoli/.

Snakes of the Bothrops neuwiedi complex are widely distributed and represent medically important species in Brazil. Here, we report compositional and functional profiles of the venom of seven species of Bothrops neuwiedi group: Bothrops mattogrossensis, Bothrops pauloensis, Bothrops pubescens, Bothrops diporus, Bothrops neuwiedi, Bothrops marmoratus, and Bothrops erythromelas. Toxin composition of individual and pooled venoms showed remarkable inter- and intraspecific variability of the relative abundance of toxins (evidenced by SDS-PAGE and RP-HPLC) and enzymatic activities (proteolytic, PLA₂, and thrombin-like activities). In vivo analyses showed that B. erythromelas venom is the most hemorrhagic, B. diporus was the most lethal, and B. pubescens showed the highest myotoxic activity. Histopathological analysis showed that all venoms induced edema, hemorrhage, inflammatory infiltrate, and necrosis of muscle fibers. Consistent with large research evidence on the paraspecificity of various commercial antivenoms generated in Latin America, the pentabothropic antivenom produced by Instituto Butantan showed a high profile of immunoreactivity and lethality neutralization capability toward the venoms of the seven species of the B. neuwiedi clade. Interpreted through the prism of evolution, our data revealed a PIII-SVMP/K49-PLA₂ compositional dichotomy and a remarkable conservation of immunological cross-reactivity across congeneric venoms throughout the 12–16 million years of Bothrops phylogeny.

The proteomics community has fully embraced data sharing, yet data set metadata provision remains limited, especially at the level of the biological samples and experimental design. This hampers large-scale data reuse, as comprehensive and structured sample context and study design information are often essential for confident, automatic reuse, and (re)interpretation. Although standards such as Sample and Data Relationship Format for Proteomics (SDRF-Proteomics) and supporting tools are already available, their adoption remains limited. Many researchers lack incentives, and enforcement by journals and repositories remains challenging in practice. Still, metadata defines a data set’s long-term value. We propose a coordinated plan to dramatically improve metadata annotation of publicly disseminated proteomics data. Funders can drive progress by investing in a sustainable, scalable metadata infrastructure. HUPO-PSI plays a central role in setting community standards and enabling validation. ProteomeXchange repositories are key to implementing and supporting metadata adoption. Data producers must treat metadata as a part of their scientific output. Instrument vendors can contribute by enabling the automatic capture of technical metadata. Software developers should embed SDRF-Proteomics metadata into analysis workflows. Finally, journals and reviewers are well positioned to shape expectations and enforce compliance. By aligning efforts across these stakeholders, we can build the road to large-scale, context-aware reuse and unlock the full value of public proteomics data sets.

Protein quantification is a crucial data processing step that combines quantitative values at the peptide or fragment level into protein levels in mass spectrometry-based proteomics. However, many of the current algorithms, including the state-of-the-art method MaxLFQ, do not scale well with the increasing number of samples, because of the limited system memory and algorithmic complexities. Here we introduce the iq format, a novel data structure designed to support very large data sets. We optimize existing quantification methods for both speed and memory usage. In particular, the new algorithms maxlfq-bit and rlm-cd significantly improve the base methods, MaxLFQ and the robust linear model, respectively, achieving orders of magnitude speed improvements for a large number of samples. The experimental result shows that the MaxLFQ algorithm achieves the highest accuracy, despite its comparatively higher computational cost. We also introduce a generic algorithm to boost the quantification accuracy of all methods by reducing the effect of noisy ion intensity traces. The experimental results show that the weighting approach improves the performance of all tested methods on a spike-in data set and a mixed species data set. The software implementation is publicly available in the R package iq from version 2.