ID: 14632076
Title: A statistical model for identifying proteins by tandem mass spectrometry.
Abstract: A statistical model is presented for computing probabilities that proteins are present in a sample on the basis of peptides assigned to tandem mass (MS/MS) spectra acquired from a proteolytic digest of the sample. Peptides that correspond to more than a single protein in the sequence database are apportioned among all corresponding proteins, and a minimal protein list sufficient to account for the observed peptide assignments is derived using the expectation-maximization algorithm. Using peptide assignments to spectra generated from a sample of 18 purified proteins, as well as complex H. influenzae and Halobacterium samples, the model is shown to produce probabilities that are accurate and have high power to discriminate correct from incorrect protein identifications. This method allows filtering of large-scale proteomics data sets with predictable sensitivity and false positive identification error rates. Fast, consistent, and transparent, it provides a standard for publishing large-scale protein identification data sets in the literature and for comparing the results obtained from different experiments.

ID: 20101609
Title: Maximizing the sensitivity and reliability of peptide identification in large-scale proteomic experiments by harnessing multiple search engines.
Abstract: Despite recent advances in qualitative proteomics, the automatic identification of peptides with optimal sensitivity and accuracy remains a difficult goal. To address this deficiency, a novel algorithm, Multiple Search Engines, Normalization and Consensus is described. The method employs six search engines and a re-scoring engine to search MS/MS spectra against protein and decoy sequences. After the peptide hits from each engine are normalized to error rates estimated from the decoy hits, peptide assignments are then deduced using a minimum consensus model. These assignments are produced in a series of progressively relaxed false-discovery rates, thus enabling a comprehensive interpretation of the data set. Additionally, the estimated false-discovery rate was found to have good concordance with the observed false-positive rate calculated from known identities. Benchmarking against standard proteins data sets (ISBv1, sPRG2006) and their published analysis, demonstrated that the Multiple Search Engines, Normalization and Consensus algorithm consistently achieved significantly higher sensitivity in peptide identifications, which led to increased or more robust protein identifications in all data sets compared with prior methods. The sensitivity and the false-positive rate of peptide identification exhibit an inverse-proportional and linear relationship with the number of participating search engines.

ID: 20816881
Title: A survey of computational methods and error rate estimation procedures for peptide and protein identification in shotgun proteomics.
Abstract: This manuscript provides a comprehensive review of the peptide and protein identification process using tandem mass spectrometry (MS/MS) data generated in shotgun proteomic experiments. The commonly used methods for assigning peptide sequences to MS/MS spectra are critically discussed and compared, from basic strategies to advanced multi-stage approaches. A particular attention is paid to the problem of false-positive identifications. Existing statistical approaches for assessing the significance of peptide to spectrum matches are surveyed, ranging from single-spectrum approaches such as expectation values to global error rate estimation procedures such as false discovery rates and posterior probabilities. The importance of using auxiliary discriminant information (mass accuracy, peptide separation coordinates, digestion properties, and etc.) is discussed, and advanced computational approaches for joint modeling of multiple sources of information are presented. This review also includes a detailed analysis of the issues affecting the interpretation of data at the protein level, including the amplification of error rates when going from peptide to protein level, and the ambiguities in inferring the identifies of sample proteins in the presence of shared peptides. Commonly used methods for computing protein-level confidence scores are discussed in detail. The review concludes with a discussion of several outstanding computational issues.

ID: 36328188
Title: Reanalysis of ProteomicsDB Using an Accurate, Sensitive, and Scalable False Discovery Rate Estimation Approach for Protein Groups.
Abstract: Estimating false discovery rates (FDRs) of protein identification continues to be an important topic in mass spectrometry-based proteomics, particularly when analyzing very large datasets. One performant method for this purpose is the Picked Protein FDR approach which is based on a target-decoy competition strategy on the protein level that ensures that FDRs scale to large datasets. Here, we present an extension to this method that can also deal with protein groups, that is, proteins that share common peptides such as protein isoforms of the same gene. To obtain well-calibrated FDR estimates that preserve protein identification sensitivity, we introduce two novel ideas. First, the picked group target-decoy and second, the rescued subset grouping strategies. Using entrapment searches and simulated data for validation, we demonstrate that the new Picked Protein Group FDR method produces accurate protein group-level FDR estimates regardless of the size of the data set. The validation analysis also uncovered that applying the commonly used Occam's razor principle leads to anticonservative FDR estimates for large datasets. This is not the case for the Picked Protein Group FDR method. Reanalysis of deep proteomes of 29 human tissues showed that the new method identified up to 4% more protein groups than MaxQuant. Applying the method to the reanalysis of the entire human section of ProteomicsDB led to the identification of 18,000 protein groups at 1% protein group-level FDR. The analysis also showed that about 1250 genes were represented by ≥2 identified protein groups. To make the method accessible to the proteomics community, we provide a software tool including a graphical user interface that enables merging results from multiple MaxQuant searches into a single list of identified and quantified protein groups.

ID: 36648107
Title: Quality Control for the Target Decoy Approach for Peptide Identification.
Abstract: Reliable peptide identification is key in mass spectrometry (MS) based proteomics. To this end, the target decoy approach (TDA) has become the cornerstone for extracting a set of reliable peptide-to-spectrum matches (PSMs) that will be used in downstream analysis. Indeed, TDA is now the default method to estimate the false discovery rate (FDR) for a given set of PSMs, and users typically view it as a universal solution for assessing the FDR in the peptide identification step. However, the TDA also relies on a minimal set of assumptions, which are typically never verified in practice. We argue that a violation of these assumptions can lead to poor FDR control, which can be detrimental to any downstream data analysis. We here therefore first clearly spell out these TDA assumptions, and introduce TargetDecoy, a Bioconductor package with all the necessary functionality to control the TDA quality and its underlying assumptions for a given set of PSMs.

ID: 36962508
Title: Modeling Lower-Order Statistics to Enable Decoy-Free FDR Estimation in Proteomics.
Abstract: One of the chief objectives in mass spectrometry-based peptide identification in proteomics is the statistical validation of top-scoring peptide-spectrum matches (PSMs) in the form of false discovery rate (FDR) estimation. Existing methods construct a null model that captures the characteristics of incorrect target PSMs to estimate the FDR, most often with the help of decoys. Decoy-based methods, however, increase the computational cost and rely on the difficult-to-verify assumption that decoy PSMs constitute a sufficient and representative sample of the population of possible incorrect target PSMs. On the other hand, the possibility of FDR estimation assisted by the plentiful non-top-scoring PSMs, which are almost always incorrect, has been scarcely explored. In this work, we propose a novel decoy-free procedure for developing null models for top-scoring PSMs using the transformed e-value (TEV) score and the distributions of non-top-scoring target PSMs. The method relies on a theoretically derivable relationship between the parameters of the distributions of lower-order statistics of the TEV score and a necessary empirical optimization to fit a single parameter to actual data. The framework was tested on multiple different data sets and two search engines. We present evidence that our method is comparable to and occasionally outperforms popular decoy-free and decoy-based methods in FDR estimation.

ID: 37080984
Title: DeepFLR facilitates false localization rate control in phosphoproteomics.
Abstract: Protein phosphorylation is a post-translational modification crucial for many cellular processes and protein functions. Accurate identification and quantification of protein phosphosites at the proteome-wide level are challenging, not least because efficient tools for protein phosphosite false localization rate (FLR) control are lacking. Here, we propose DeepFLR, a deep learning-based framework for controlling the FLR in phosphoproteomics. DeepFLR includes a phosphopeptide tandem mass spectrum (MS/MS) prediction module based on deep learning and an FLR assessment module based on a target-decoy approach. DeepFLR improves the accuracy of phosphopeptide MS/MS prediction compared to existing tools. Furthermore, DeepFLR estimates FLR accurately for both synthetic and biological datasets, and localizes more phosphosites than probability-based methods. DeepFLR is compatible with data from different organisms, instruments types, and both data-dependent and data-independent acquisition approaches, thus enabling FLR estimation for a broad range of phosphoproteomics experiments.

ID: 37261867
Title: Bridging the False Discovery Gap.
Abstract: Controlling the false discovery rate (FDR) among discoveries from a tandem mass spectrometry proteomics experiment using target decoy competition (TDC) controls only the proportion of false discoveries in an average sense. Thus, for any particular analysis, even with a valid FDR control procedure, the proportion of false discoveries (the FDP) may be higher than the specified FDR threshold. We demonstrate this phenomenon using real data and describe two recently developed methods that help bridge the gap between controlling the expected or average rate of false discoveries and the empirical rate (FDP). The FDP Stepdown method controls the FDP at any desired confidence level, and the TDC Uniform Band provides a confidence, or upper prediction bound, on the FDP in TDC's list of discoveries.

ID: 37906674
Title: Data-Driven Tool for Cross-Run Ion Selection and Peak-Picking in Quantitative Proteomics with Data-Independent Acquisition LC-MS/MS.
Abstract: Proteomics provides molecular bases of biology and disease, and liquid chromatography-tandem mass spectrometry (LC-MS/MS) is a platform widely used for bottom-up proteomics. Data-independent acquisition (DIA) improves the run-to-run reproducibility of LC-MS/MS in proteomics research. However, the existing DIA data processing tools sometimes produce large deviations from true values for the peptides and proteins in quantification. Peak-picking error and incorrect ion selection are the two main causes of the deviations. We present a cross-run ion selection and peak-picking (CRISP) tool that utilizes the important advantage of run-to-run consistency of DIA and simultaneously examines the DIA data from the whole set of runs to filter out the interfering signals, instead of only looking at a single run at a time. Eight datasets acquired by mass spectrometers from different vendors with different types of mass analyzers were used to benchmark our CRISP-DIA against other currently available DIA tools. In the benchmark datasets, for analytes with large content variation among samples, CRISP-DIA generally resulted in 20 to 50% relative decrease in error rates compared to other DIA tools, at both the peptide precursor level and the protein level. CRISP-DIA detected differentially expressed proteins more efficiently, with 3.3 to 90.3% increases in the numbers of true positives and 12.3 to 35.3% decreases in the false positive rates, in some cases. In the real biological datasets, CRISP-DIA showed better consistencies of the quantification results. The advantages of assimilating DIA data in multiple runs for quantitative proteomics were demonstrated, which can significantly improve the quantification accuracy.

ID: 38426325
Title: Ion entropy and accurate entropy-based FDR estimation in metabolomics.
Abstract: Accurate metabolite annotation and false discovery rate (FDR) control remain challenging in large-scale metabolomics. Recent progress leveraging proteomics experiences and interdisciplinary inspirations has provided valuable insights. While target-decoy strategies have been introduced, generating reliable decoy libraries is difficult due to metabolite complexity. Moreover, continuous bioinformatics innovation is imperative to improve the utilization of expanding spectral resources while reducing false annotations. Here, we introduce the concept of ion entropy for metabolomics and propose two entropy-based decoy generation approaches. Assessment of public databases validates ion entropy as an effective metric to quantify ion information in massive metabolomics datasets. Our entropy-based decoy strategies outperform current representative methods in metabolomics and achieve superior FDR estimation accuracy. Analysis of 46 public datasets provides instructive recommendations for practical application.

ID: 38491400
Title: On the use of tandem mass spectra acquired from samples of evolutionarily distant organisms to validate methods for false discovery rate estimation.
Abstract: Estimating the false discovery rate (FDR) of peptide identifications is a key step in proteomics data analysis, and many methods have been proposed for this purpose. Recently, an entrapment-inspired protocol to validate methods for FDR estimation appeared in articles showcasing new spectral library search tools. That validation approach involves generating incorrect spectral matches by searching spectra from evolutionarily distant organisms (entrapment queries) against the original target search space. Although this approach may appear similar to the solutions using entrapment databases, it represents a distinct conceptual framework whose correctness has not been verified yet. In this viewpoint, we first discussed the background of the entrapment-based validation protocols and then conducted a few simple computational experiments to verify the assumptions behind them. The results reveal that entrapment databases may, in some implementations, be a reasonable choice for validation, while the assumptions underpinning validation protocols based on entrapment queries are likely to be violated in practice. This article also highlights the need for well-designed frameworks for validating FDR estimation methods in proteomics.

ID: 38895431
Title: Assessment of false discovery rate control in tandem mass spectrometry analysis using entrapment.
Abstract: A pressing statistical challenge in the field of mass spectrometry proteomics is how to assess whether a given software tool provides accurate error control. Each software tool for searching such data uses its own internally implemented methodology for reporting and controlling the error. Many of these software tools are closed source, with incompletely documented methodology, and the strategies for validating the error are inconsistent across tools. In this work, we identify three different methods for validating false discovery rate (FDR) control in use in the field, one of which is invalid, one of which can only provide a lower bound rather than an upper bound, and one of which is valid but under-powered. The result is that the field has a very poor understanding of how well we are doing with respect to FDR control, particularly for the analysis of data-independent acquisition (DIA) data. We therefore propose a theoretical formulation of entrapment experiments that allows us to rigorously characterize the behavior of the various entrapment methods. We also propose a more powerful method for evaluating FDR control, and we employ that method, along with other existing techniques, to characterize a variety of popular search tools. We empirically validate our entrapment analysis in the fairly well-understood DDA setup before applying it in the DIA setup. We find that none of the DIA search tools consistently controls the FDR at the peptide level, and the tools struggle particularly with analysis of single cell datasets.

ID: 39840643
Title: PeptideForest: Semisupervised Machine Learning Integrating Multiple Search Engines for Peptide Identification.
Abstract: The first step in bottom-up proteomics is the assignment of measured fragmentation mass spectra to peptide sequences, also known as peptide spectrum matches. In recent years novel algorithms have pushed the assignment to new heights; unfortunately, different algorithms come with different strengths and weaknesses and choosing the appropriate algorithm poses a challenge for the user. Here we introduce PeptideForest, a semisupervised machine learning approach that integrates the assignments of multiple algorithms to train a random forest classifier to alleviate that issue. Additionally, PeptideForest increases the number of peptide-to-spectrum matches that exhibit a q-value lower than 1% by 25.2 ± 1.6% compared to MS-GF+ data on samples containing mixed HEK and Escherichia coli proteomes. However, an increase in quantity does not necessarily reflect an increase in quality and this is why we devised a novel approach to determine the quality of the assigned spectra through TMT quantification of samples with known ground truths. Thereby, we could show that the increase in PSMs below 1% q-value does not come with a decrease in quantification quality and as such PeptideForest offers a possibility to gain deeper insights into bottom-up proteomics. PeptideForest has been integrated into our pipeline framework Ursgal and can therefore be combined with a wide array of algorithms.

ID: 39905949
Title: PyViscount: Validating False Discovery Rate Estimation Methods via Random Search Space Partition.
Abstract: Validating false discovery rate (FDR) estimation is an essential but surprisingly understudied aspect of method development in shotgun proteomics. Currently available validation protocols mostly rely on ground truth data sets, which typically involve manipulating the properties of the search space or query spectra used. As a result, comparing estimated FDR and ground truth-based false discovery proportion values may not be representative of the scenarios involving natural data sets encountered in practice. In this study, we introduce PyViscount─a Python tool implementing a novel validation protocol based on random search space partition, which enables generating a quasi ground-truth using unaltered search spaces of unique candidate peptides and generic data sets of experimental query spectra. Furthermore, validation of existing FDR estimation methods by PyViscount is consistent with alternative validation protocols. The presented novel approach to validation free from the need for synthetic data sets or dubious manipulation of the data may be an attractive alternative for proteomics practitioners, allowing them to obtain deeper insights into the performance of existing and new FDR estimation methods.

ID: 40252226
Title: Deep Learning-Based Prediction of Decoy Spectra for False Discovery Rate Estimation in Spectral Library Searching.
Abstract: With the advantage of extensive coverage, predicted spectral libraries are becoming an attractive alternative in proteomic data analysis. As a popular false discovery rate estimation method, target decoy search has been adopted in library search workflows. While existing decoy methods for curated experimental libraries have been tested, their performance in predicted library scenarios remains unknown. Current methods rely on perturbing real spectra templates, limiting the diversity and number of decoy spectra that can be generated for a given library. In this study, we explore the shuffle-and-predict decoy library generation approach, which can generate decoy spectra without the need for template spectra. Our experiments shed light on decoy method performance for predicted library scenarios and demonstrate the quality of predicted decoys in FDR estimation.

ID: 40398240
Title: Compositional profiling of protein hydrolysates by high resolution liquid chromatography-mass spectrometry and chemometric analysis.
Abstract: Protein hydrolysates have attracted growing research and commercial attention due to their numerous nutritional, functional, and biological activities. However, only a limited range of proximate properties are determined routinely due to their substantial structural complexity and compositional variability. From both a manufacturing and functional perspective, it is of critical importance to monitor the compositional variations and identify potential similar or disparate features between different protein hydrolysates. In the current study, a single-approached method employing reverse phase ultra-high performance liquid chromatography coupled to high resolution electrospray ionization tandem mass spectrometry (RP-UHPLC-HR-ESI-MS/MS) was developed, optimized, and cross-validated for comprehensive structural and compositional profiling of a range of protein hydrolysates of varying raw materials, including soy, cotton, wheat, rice, and meat. Untargeted chemometric analysis and feature-based molecular network demonstrated potential for large-scale compositional assessment of protein hydrolysates without the need of prior component annotation. Signature features were identified to differentiate soy hydrolysates prepared from different batches of raw material and by different manufacturing processes. A hybrid approach combining de novo sequencing and target-decoy database homology search for peptide annotation is also described. Short peptides of 2 to 5 amino acids represented the most abundant components in soy protein hydrolysates (SPHs). A simple yet reliable integrated workflow for comprehensive structural and compositional profiling of protein hydrolysates was developed to enable an eventual correlation between their structure and function.

ID: 40466863
Title: UniScore, a Unified and Universal Measure for Peptide Identification by Multiple Search Engines.
Abstract: We propose UniScore as a metric for integrating and standardizing the outputs of multiple search engines in the analysis of data-dependent acquisition (DDA) data from LC/MS/MS-based bottom-up proteomics. UniScore is calculated from the annotation information attached to the product ions alone by matching the amino acid sequences of candidate peptides suggested by the search engine with the product ion spectrum. The acceptance criteria are controlled independently of the score values by using the false discovery rate based on the target-decoy approach. Compared to other rescoring methods that use deep learning-based spectral prediction, larger amounts of data can be processed using minimal computing resources. When applied to large-scale global proteome data and phosphoproteome data, the UniScore approach outperformed each of the conventional single search engines examined (Comet, X! Tandem, Mascot, and MaxQuant). Furthermore, UniScore could also be directly applied to peptide matching in chimeric spectra without any additional filters.

ID: 40524023
Title: Assessment of false discovery rate control in tandem mass spectrometry analysis using entrapment.
Abstract: A critical challenge in mass spectrometry proteomics is accurately assessing error control, especially given that software tools employ distinct methods for reporting errors. Many tools are closed-source and poorly documented, leading to inconsistent validation strategies. Here we identify three prevalent methods for validating false discovery rate (FDR) control: one invalid, one providing only a lower bound, and one valid but under-powered. The result is that the proteomics community has limited insight into actual FDR control effectiveness, especially for data-independent acquisition (DIA) analyses. We propose a theoretical framework for entrapment experiments, allowing us to rigorously characterize different approaches. Moreover, we introduce a more powerful evaluation method and apply it alongside existing techniques to assess existing tools. We first validate our analysis in the better-understood data-dependent acquisition setup, and then, we analyze DIA data, where we find that no DIA search tool consistently controls the FDR, with particularly poor performance on single-cell datasets.

ID: 40993657
Title: Proteomic profiling identifies miR-423-5p as a modulator of oncogenic metabolism in HCC.
Abstract: Hepatocellular carcinoma (HCC) remains a significant clinical challenge due to limited diagnostic and therapeutic options. Non-coding RNAs (ncRNAs), such as microRNAs (miRNAs), play key roles in cancer biology. Our previous findings showed that miR-423-5p enhances anti-cancer effects on HCC patients treated with sorafenib by promoting autophagy. Here, we investigated the molecular mechanisms underlying miR-423-5p function through a comprehensive proteomic approach. We generated an HCC cell line stably overexpressing miR-423-5p via lentiviral transduction. Total proteins were extracted from SNU-387 cells, enzymatically digested into peptides, and subsequently analysed by liquid chromatography-tandem mass spectrometry (LC-MS/M). Raw spectral data were processed and quantified using MaxQuant. Differentially expressed proteins (DEPs) were defined based on fold-change (|log2FC| ≥ 1) and false discovery rate (FDR < 0.05). The full proteomic dataset is available via the ProteomeXchange repository (identifier: PXD064869). Functional enrichment analysis of DEPs were performed using DAVID and Reactome. To assess clinical relevance, predicted and validated miR-423-5p targets were integrated with The Cancer Genome Atlas (TCGA) Liver Hepatocellular Carcinoma (LIHC) dataset using GEPIA platform. Survival analyses were performed using the Kaplan-Meier method. Proteomic profiling identified 698 DEPs in miR-423-5p-overexpressing cells compared to controls with significant enrichment in metabolic pathways, related to purine/pyrimidine metabolism and gluconeogenesis. Integration with bioinformatic predictions and miRTarBase validation identified 43 DEPs as potential direct targets of miR-423-5p. Among these, seven proteins (ACACA, ANKRD52, DVL3, MCM5, MCM7, RRM2, SPNS1, and SRM) were significantly associated with patient prognosis in the TCGA-LIHC cohort. These targets were downregulated in miR-423-5p-overexpressing cells but upregulated in advanced-stage HCC tissues, suggesting a potential role for miR-423-5p in the regulation of HCC pathogenesis. Stage-specific expression analysis showed increased levels from stage I to III, followed by a decline at stage IV. Notably, we experimentally confirmed miR-423-5p-mediated suppression of MCM7, DVL3, IMPDH1, and SRM (SPEE), supporting their functional involvement in HCC progression. Overall, our findings support a tumour-suppressive role for miR-423-5p in HCC, mediated by modulation of metabolic pathways and suppression of oncogenic proteins. These results suggest that miR-423-5p and its downstream effectors may serve as promising biomarkers and potential therapeutic targets in HCC. miR-423-5p acts as a tumor suppressor in HCC by targeting key nodes of pro-tumorigenic signalling. miR-423-5p significantly altered metabolic pathways, including purine/pyrimidine metabolism and gluconeogenesis. Seven miR-423-5p targets correlate with poor prognosis in TCGA-LIHC patients and are downregulated in miR-423-5p overexpressing HCC cells. miR-423-5p over-expression induces a significant downregulation of MCM7, DVL3, IMPDH1, SPEE in HCC cell models. miR-423-5p limits tumor metabolic plasticity, suggesting therapeutic potential.

ID: 41030776
Title: Investigating the Mechanism of Jiawei Weijin Decoction in Treating Non-Small Cell Lung Cancer Using Network Pharmacology, Bioinformatics Analysis and Experimental Validation.
Abstract: Non-small cell lung cancer (NSCLC) is a leading cause of cancer-related mortality worldwide. While Qianjin Weijin Decoction is widely used in China for lung cancer treatment, Jiawei Qianjin Weijin Decoction (JWWJD), a modified version, has shown enhanced anti-metastatic effects. However, its active components and underlying mechanisms remain unclear. The effect of JWWJD against NSCLC was evaluated in vitro and in vivo, and the mechanisms were identified in combination with transcriptomics. Network pharmacology and bioinformatics were used to construct an anti-NSCLC prognostic model with JWWJD. The correlation between the expression of the prognostic gene and clinicopathological features was evaluated. The main active components of JWWJD were identified by LC-MS/MS and its anticancer effect and mechanism were investigated in vitro and in vivo. JWWJD-containing serum significantly suppressed cell proliferation and migration, and induced apoptosis in NCI-A549 and NCI-H23 cells. Among different concentrations tested, 20% drug-containing serum showed the most potent inhibitory effect on NSCLC progression (all P-values < 0.05). In a BALB/c-nu mouse xenograft model, oral administration of high-dose JWWJD reduced tumor volume by 27.76% compared to control (P < 0.001). Transcriptomic analysis revealed that JWWJD treatment led to significant downregulation of SPP1 (Fold Change = 0.687, FDR < 0.05), a gene highly associated with poor prognosis in NSCLC patients. Using LC-MS/MS, curcumol was identified as the key active component in JWWJD. Molecular studies demonstrated that curcumol directly binds to SPP1 with strong affinity (KD = 4.55×10-6 M), downregulates its expression, and inhibits NSCLC cell migration and invasion. In vivo experiments showed that curcumol reduced tumor volume by 24.88% (P < 0.001). Our study, integrating transcriptomics, bioinformatics, LC-MS/MS, and experimental validation, revealed that JWWJD alleviates NSCLC metastasis by directly targeting SPP1. JWWJD and its active compound curcumol show promise as alternative therapies for NSCLC patients.

ID: 41086960
Title: Plasma profiles of carnitine and acylcarnitines in first-diagnosed, drug-naïve patients with depression: A case-control analysis.
Abstract: Acylcarnitines, critical intermediates in mitochondrial fatty acid β-oxidation, may serve as promising diagnostic biomarkers for depression. However, current research on depression-associated acylcarnitine metabolism exhibits significant heterogeneity in both methodology and findings. The case-control study included a total of 100 first-diagnosed, drug-naïve depressed patients and 50 healthy controls matched with age, sex and body mass index. Plasma acylcarnitines were identified using ultra-high performance liquid chromatography-quadrupole time-of-flight mass spectrometry, and then quantified by the liquid chromatography-tandem mass spectrometry. This analysis quantified 33 acylcarnitine species and carnitine in plasma samples. For patients with depression, most medium-chain acylcarnitines and C0/ (C16:0 +C18:0) ratio (an index of carnitine palmitoyltransferase I) were decreased, while long-chain acylcarnitine levels were increased. The changes in levels of acylcarnitines C10:1, C11:0, C18:1 and C20:2 remain significant after false discovery rate correction. Receiver operating characteristic curve analysis identified three dysregulated acylcarnitines C11:0, C20:2, C18:1 as potential depression biomarkers, with their combined panel showing promising discriminative power (area under the curve =0.831). These findings revealed significant alterations in acylcarnitine metabolism associated with depression, suggesting their potential utility as metabolic biomarkers. While the observed dysregulation provides new insights into depression pathophysiology, further studies will need to establish diagnostic applicability through mechanistic investigation and clinical validation.

ID: 41135998
Title: DIATAGeR: Triacylglycerol annotation of data-independent acquisition based lipidomics.
Abstract: Triacylglycerols (TGs) are the most abundant lipids in the human body and the primary source of energy storage. TGs are comprised of three fatty acyls with various lengths and double bond composition, complicating structural annotation when performing lipidomics by LCMS. Data-independent acquisition (DIA) based lipidomics enables a continuous and unbiased acquisition of all TGs, creating the potential for more comprehensive TG analysis. However, TG identification in DIA lipidomics data is challenging due to the difficulty analyzing multiplexed tandem mass spectra (MS2). In this study, we present DIATAGeR, an R package aimed to improve and automate TG identifications to the molecular species level in DIA-based lipidomics. With DIATAGeR, TGs are identified using a TG-centric approach, where each TG in the reference database is considered as an analysis target, searched in DIA spectra, and scored using a logistic regression machine learning algorithm. Additionally, DIATAGeR uses a false discovery rate (FDR) correction calculated by a target-decoy approach to improve the confidence of TG identification and limit false positives due to interference from unrelated ions. The performance of DIATAGeR was validated in a lipidomic study of liver and plasma samples from mice with metabolic dysfunction-associated steatohepatitis (MASH) and healthy controls. All 9 TG standards were annotated at an FDR <0.1 in both datasets. When benchmarked against MS-DIAL, TGs identified by DIATAGeR contained 18 % and 12 % more even-carbon fatty acyls in liver and plasma datasets, respectively. DIATAGeR is a valuable tool for streamlining complex TG annotation in DIA-lipidomics data. It supports vendor-neutral MS spectra data formats and offers a customizable reference database. By combining TG-centric and target-decoy approaches, DIATAGeR showed improvements in TG identification by addressing primary challenges associated with multiplexed MS2 spectra. DIATAGeR is freely available at https://github.com/Velenosi-Lab/DIATAGeR.

ID: 41221370
Title: Disc-Hub: a python package for benchmarking machine learning strategies in DIA-MS identification.
Abstract: Accurate analysis of data-independent acquisition (DIA) mass spectrometry data relies on machine learning to distinguish target peptides from decoy peptides. Different DIA identification engines adopt distinct binary classifiers and training workflows to accomplish this learning task. However, systematic comparisons of how different machine learning strategies affect identification performance are lacking. This absence of evaluation hinders optimal learning strategy selection, increases the risk of model underfitting or overfitting, and ultimately undermines the effectiveness and reliability of false discovery rate (FDR) control. In this study, we benchmarked three training strategies and four classifiers on representative DIA datasets. Among them, K-fold training combined with a multilayer perceptron achieved the best balance between identification depth and FDR control. We have released the datasets and code through the Python package Disc-Hub, enabling rapid selection of optimal machine learning configurations for developing DIA identification algorithms. Disc-Hub is released as an open source software and can be installed from PyPi as a python module. The source code is available on GitHub at https://github.com/yuyiwen-yiyuwen/Disc_Hub.

ID: 41571719
Title: Preventing Proteomics Data Tombs Through Collective Responsibility and Community Engagement.
Abstract: Public proteomics repositories now host vast amounts of mass spectrometry data, yet much of it remains difficult to reuse, risking "data tombs" that are open access but not practically re-analyzable. In spring 2025, a graduate-level course at the University of Helsinki tasked six student teams with reanalyzing six projects from the Proteomics Identification Database (label-free quantification only) using a common R-based workflow (rpx, mzR, QFeatures, DEP/MSqRob2/limma/OmicsQ packages) that was shared across all teams. The teams reproduced identification, optional quantification, normalization, imputation, and differential expression analyses, and compared the outcomes to the original studies. As expected, systemic barriers recurred across cases: (i) no sample and data relationship format for proteomics metadata in any of the cases; (ii) missing details regarding decoy sets for false discovery rate assessment; (iii) proprietary-only outputs or software (e.g., Thermo.msf, Progenesis) that impeded open reanalysis in interoperable, community-standard formats; (iv) missing data-independent acquisition spectral libraries or protein sequences database files (FASTA); (v) absent or vague normalization/imputation/statistical parameters; (vi) inconsistent file naming; and (vii) insufficient biological/technical replication in at least one project. These shortcomings yielded large discrepancies in the analysis results (e.g., 13,068 vs. 4,923 proteins; 108 vs. 11 differentially expressed proteins), and, in one instance, a highlighted protein lacked robust support in the deposited identifications. We observed that reproducibility in mass spectrometry-based proteomics hinges less on instruments than on transparent metadata, open formats, and executable analysis provenance. We propose that data creators provide a minimum re-analysis package, including raw data and open formats, community standards, basic quality control summaries, data-independent acquisition spectral libraries, and complete parameter/code sets with pinned versions or containers. Moreover, we recommend repository-level nudges toward making such packages mandatory. This educational exercise simultaneously trains the students as well as stress-tests the community data practices to prevent proteomics "data tombs".

ID: 41601673
Title: Plasma immune proteome-based risk score predicts survival in advanced gastric cancer treated with PD-1 inhibitors and chemotherapy.
Abstract: Blood-based biomarkers that capture systemic immunity could complement tissue-based assays for prognostication in advanced gastric cancer receiving programmed cell death protein 1 (PD-1)-based chemoimmunotherapy. We evaluated whether baseline plasma immune proteomics can stratify clinical outcomes and be operationalized into a clinically usable model. In a prospective cohort (n=40) treated with first-line PD-1 inhibitor plus chemotherapy, nano-ultra-high-performance liquid chromatography (nano-UHPLC) coupled with Orbitrap data-independent acquisition liquid chromatography-tandem mass spectrometry (DIA LC-MS/MS) was used to profile baseline plasma. Quality control (QC)-filtered protein intensities were median-normalized, log2-transformed, and batch-adjusted as needed. Group structure was assessed by principal component analysis (PCA). Differential expression (two-sided testing; Benjamini-Hochberg false discovery rate [FDR] correction) and functional enrichment were performed, with an immune focus defined using Immunology Database and Analysis Portal (ImmPort) sets. Prognostic screening used univariate Cox proportional hazards regression; features were reduced by least absolute shrinkage and selection operator (LASSO)-Cox and entered into multivariable models. A risk score (linear predictor of z-scaled abundances) was evaluated by Kaplan-Meier analysis and time-dependent receiver operating characteristic (ROC) analysis. A prognostic nomogram integrating the proteomic score with clinical variables was calibrated by bootstrap resampling. PCA showed outcome-associated separation. Differential testing identified 322 proteins (179 up, 143 down in long-term survivors), including 36 immune-related differentially expressed proteins (DEPs). Penalized modeling selected a five-protein prognostic panel-LTB4R, GBP2, HLA-G, CYBB, HLA-B. The risk score, dichotomized at the cohort median, stratified overall survival (OS) and progression-free survival (PFS) with clear separation. Time-dependent ROC area under the curve (AUC) values for OS at 6/12/18/24 months were 0.850/0.838/0.911/0.844, exceeding age, sex, grade, and programmed death-ligand 1 (PD-L1) combined positive score (CPS). In multivariable Cox models adjusting for clinical covariates, the score remained independently associated with OS. A nomogram combining the score with clinicopathologic factors yielded individualized 6-, 12-, and 18-month OS estimates with good calibration. Median PFS and OS for the overall cohort were 5.5 and 10.0 months, respectively. Baseline plasma immune proteomics supports a compact, interpretable five-protein risk score that augments clinicopathologic variables for prognostic stratification under PD-1-based chemoimmunotherapy. The model is amenable to targeted assay translation and prospective validation for clinical deployment.

ID: 41636803
Title: Quantifying the ∼75-95% of Peptides in DIA-MS Data Sets that Were Not Previously Quantified.
Abstract: We have developed a novel algorithm termed GoldenHaystack (GH) that was designed for enhanced peptide quantification of data-independent acquisition liquid chromatography mass spectrometry (DIA-LC-MS) data files regardless of whether the amino acid sequences are subsequently assigned to the quantified peptide. The two central ideas behind GH are: (a) for sufficiently sized projects (e.g., ≥∼30 LC-MS files), pairs of peptides that coelute exactly in one subset of LC-MS files do not necessarily coelute exactly in a different subset of files, and (b) the ion intensity ratios between MS2 ions for any given peptide tend to stay the same across samples, but the ion intensity ratios of MS2 ions between different peptides tend to differ substantially across different samples. GH thus analyzes a project holistically: It uses multi-partite matching to match both MS2 (primarily) and MS1 (secondarily) ions across all samples, separates and regroups the MS ions into unique analyte quantifiable signatures (UAQS), reduces stochastic noise, and then quantifies those UAQS. In this paper, GH is compared to DIA-NN, a common algorithm used in DIA-MS proteomic analysis, and we demonstrate that GH (a) quantifies and identifies with better FDR accuracy known peptides found in FASTA search spaces (∼5-25% of analytes in DIA-MS data sets), (b) quantifies the remaining ∼75-95% of unassigned peptides that would be typically unquantified and unreported, and (c) runs ∼40-200× faster (or ∼1-10× faster than the LC-MS). Specifically, without a FASTA or spectral library, GH can deconvolute and accurately quantify chimeric LC-MS spectra. The use of a FASTA file occurs during an optional peptide identification step and is deployed only after the analytes in the MS files have already been quantified. We provide details of GH performance on several existing proteomics data sets, including plasma, cerebrospinal fluid, and cells.

ID: 41797989
Title: A label-free microLC-SWATH-MS methodology with immunoaffinity depletion of highly abundant serum proteins for quantitative proteomic comparison of fresh-frozen human normal breast tissue and tumor clinical specimens.
Abstract: Mass spectrometry (MS)-based proteomics can provide deep insights into protein-driven molecular processes and signaling pathways in breast cancer, thereby contributing to improvements in disease diagnosis, treatment, and prevention. This study focuses on the development of a label-free quantitative proteomic profiling approach for the analysis of fresh-frozen human normal breast tissue (BTIS) and breast tumor (BTUM) samples. A pilot set of BTIS and BTUM samples obtained from eight patients diagnosed with luminal B (Lum B) or triple-negative breast cancer (TNBC) was analyzed using micro-liquid chromatography coupled to tandem mass spectrometry (microLC-MS/MS) in a data-independent acquisition sequential windowed acquisition of all theoretical fragment ion spectra (SWATH) mode. To expand proteome coverage during SWATH data extraction, an experimental spectral ion library was generated from the MS/MS spectra of a pooled sample comprising aliquots from all analyzed BTIS and BTUM samples. To expand the spectral library, the pooled sample was immunodepleted of the 14 most abundant serum proteins, enabling deeper proteome coverage. A total of 562 proteins were identified at a false discovery rate (FDR) of <1%, of which 299 were successfully quantified across all samples. Among these, 158 proteins showed statistically significant differences (p < 0.05) between breast tumor and normal breast tissue samples, including 59 proteins that were upregulated and 23 that were downregulated by at least 1.5-fold. Functional enrichment analysis revealed that the quantified proteins were associated with cellular structures and compartments relevant to breast cancer biology, such as the extracellular matrix (ECM), extracellular exosomes, and nucleosomes. These proteins were also involved in biological processes implicated in disease development and progression, including ECM organization, focal adhesion, mRNA splicing via the spliceosome, interleukin-12-mediated signaling, platelet activation, and metabolic pathways related to amino acid metabolism and gluconeogenesis/glycolysis. This proof-of-concept study demonstrates that the developed microLC-SWATH-MS approach, combined with a custom spectral library generated from pooled breast tissue and tumor samples immunoaffinity-depleted of 14 high-abundance serum proteins, enables robust and high-throughput proteomic profiling of breast tissue and tumors. Further expansion of high-quality spectral libraries may enhance proteome coverage and improve the clinical applicability of this approach. While the methodology supports the discovery of candidate biomarkers and therapeutic targets relevant to translational research and precision oncology, the biological conclusions drawn from this study should be interpreted with caution due to the limited sample size. Validation in larger patient cohorts using orthogonal methods will be required to confirm the potential clinical utility of the identified proteins.

ID: 41822590
Title: Proteomic signatures of cervical mucus associated with fertility in Bali heifers (Bos javanicus): Implications for biomarker-based selection in artificial insemination programs.
Abstract: Despite strong adaptive traits, the reproductive efficiency of Bali cattle (Bos javanicus) remains suboptimal, with low conception rates following artificial insemination (AI). Cervical mucus (CM) is a critical factor in sperm transport and fertilization; however, its molecular basis in relation to fertility has not been elucidated in this indigenous breed. This study aimed to characterize the proteomic profile of CM in Bali heifers and to identify protein biomarkers associated with fertility-related mucus quality. The study was conducted between February and August 2024 in South Sulawesi, Indonesia. Forty clinically healthy Bali heifers (2-3 years old) were sampled during natural oestrus and divided into good CM (GCM; n = 20) and poor CM (PCM; n = 20) groups using a validated five-parameter biophysical scoring system. CM proteins were extracted and analyzed using one-dimensional sodium dodecyl sulfate-polyacrylamide gel electrophoresis followed by liquid chromatography-tandem mass spectrometry. High-confidence protein identification was achieved at <1% false discovery rate, and differential abundance was evaluated using Benjamini-Hochberg correction (p < 0.05). Functional enrichment, correlation analysis with mucus traits, and receiver-operating-characteristic (ROC) analyses with cross-validation were performed. Significant differences (p < 0.05) were observed between GCM and PCM groups for appearance, viscosity, spinnbarkeit, and ferning pattern, while pH did not differ. A total of 52 proteins were identified after quality control, of which 13 showed significant differential abundance. GCM was characterized by higher levels of NT5E, lactoferrin, SCGB1D, and lactotransferrin, whereas PCM showed enrichment of complement factor I (CFI), haptoglobin (HP), MUC5AC, FAIM2, TIMP2, PEBP4, SAA3, GRP, and IGL. Functional enrichment analysis indicated anti-inflammatory and epithelial-protective pathways in GCM, in contrast to complement activation, proteolysis, and oxidative remodeling in PCM. ROC analysis demonstrated excellent discriminative performance for NT5E (GCM) and CFI and haptoglobin (PCM), each achieving an area under the curve of 1.00 in this cohort. This study offers the first proteomic evidence connecting CM composition to fertility-related traits in Bali heifers. NT5E, CFI, and HP stand out as promising biomarkers for fertility screening, providing a molecular framework to improve AI efficiency and selection strategies in indigenous cattle.

ID: 42097574
Title: Plasma proteomic profiling identifies apolipoprotein A4 as a downregulated biomarker of adrenocortical carcinoma: a multi-platform discovery and validation study.
Abstract: Adrenocortical carcinoma (ACC) is a rare, aggressive malignancy associated with heterogeneous prognosis. Preoperative differentiation from adrenocortical adenoma (ACA) remains challenging, and no serum tumor marker has been established. We aimed to identify circulating protein biomarkers that distinguish ACC from ACA using a stepwise, multiplatform proteomics strategy. We assembled discovery (ACC = 10, ACA = 67) and verification (ACC = 7, ACA = 11) cohorts from a tertiary center and profiled fasting plasma using liquid chromatography-mass spectrometry (LC-MS/MS) with data-independent acquisition. Differentially expressed proteins (DEPs) were defined by t-tests with P < .05 and |fold-change| >1.2; DEPs common to both cohorts were prioritized. Targeted validation by parallel reaction monitoring (PRM) used an expanded, two-center cohort including additional cases from Asan Medical Center (ACC = 31; ACA = 78). Orthogonal validation employed the Olink Explore 384 Inflammation II panel in an independent set (ACC = 15; ACA = 24). The discovery cohort yielded 67 DEPs (22 upregulated and 45 downregulated in ACC), and the verification cohort identified 17 DEPs. Three proteins, CD44, proteoglycan 4, and apolipoprotein A4 (APOA4), were common to both analyses and were underexpressed in ACC compared with ACA. In PRM, CD44 and APOA4 showed directionally concordant, significant decreases in ACC, prioritizing these markers for further evaluation. In the Olink analysis, 40 proteins differed between ACC and ACA after false discovery rate correction; APOA4 remained significantly lower in ACC. Across discovery, targeted, and orthogonal platforms, APOA4 consistently exhibited lower circulating levels in ACC, supporting its potential as a serum biomarker for the preoperative differentiation of ACC from ACA. External, multiethnic validation and clinically deployable assays, alone or within multimarker panels, are warranted.

ID: 42133180
Title: Plasma proteomic signatures improve risk stratification and personalized screening for gastric cancer.
Abstract: Accurate identification of individuals at high risk of gastric cancer (GC) remains a major challenge for effective screening. We aimed to identify plasma proteomic signatures and develop a risk prediction model for GC risk stratification. Plasma proteomic profiling was performed using liquid chromatography-tandem mass spectrometry in a case-control discovery set (100 GC cases and 94 controls). Candidate proteins were evaluated in 52,552 UK Biobank participants with a median follow-up of 13.63 years, during which 92 incident GC cases were identified. Risk models integrating clinical, genetic, and proteomic factors were developed using LASSO-penalized Cox regression with stability selection and internally validated using bootstrap resampling. Among 2306 differentially expressed proteins in discovery, 25 were replicated in validation at nominal significance (P < 0.05) with consistent directions. Two proteins (CTSD and GGH) remained significant after false discovery rate correction. A primary proteomic model (clinical factors plus five proteins) improved discrimination versus clinical model (optimism-corrected C-index: 0.745 vs. 0.732, P = 0.046). Risk stratification revealed a clear GC risk gradient: hazard ratios were 6.08 (95% CI 2.15-17.20) for moderate-risk and 23.88 (95% CI 8.66-65.87) for high-risk groups. The risk score was also associated with GC risk as continuous variable (HR per standard deviation: 1.09, 95% CI 1.08-1.11). The 15-year cumulative incidence ranged from 0.02 to 0.56% across risk groups. Decision curve analysis indicated improved clinical utility. Plasma proteomic signatures may improve GC risk stratification beyond traditional clinical factors and could support more targeted screening strategies. Further validation is warranted.

ID: 42173302
Title: Targeted LC-MS/MS validation of unsaturated fatty acid dysregulation and PI3K-Akt-related metabolic signatures in immune thrombocytopenia.
Abstract: Immune thrombocytopenia (ITP) is an acquired autoimmune bleeding disorder characterized by immune dysregulation and thrombocytopenia. Metabolic reprogramming has been implicated in the pathogenesis of immune-mediated diseases, while the PI3K-Akt signaling pathway acts as a critical link between immune response and metabolic regulation.Based on our previously published untargeted metabolomics findings, this study aimed to validate selected lipid metabolites in ITP and explore their potential association with PI3K-Akt-related metabolic signatures. Twenty adults with newly diagnosed active ITP and 17 healthy controls were enrolled. Candidate metabolites were selected from our previously published untargeted metabolomics dataset and prioritized through metabolite annotation and KEGG pathway enrichment analysis. Serum oleic acid, docosahexaenoic acid (DHA), and eicosapentaenoic acid (EPA) were quantified by liquid chromatography-tandem mass spectrometry (LC-MS/MS). Statistical analyses were conducted to compare metabolite levels between the two groups, with P values adjusted for multiple comparisons using the Benjamini-Hochberg false discovery rate (FDR) method. Exploratory receiver operating characteristic (ROC) analyses were performed for individual metabolites, and a multivariable logistic regression model incorporating oleic acid, DHA, and EPA was constructed to evaluate their combined discriminative performance. Untargeted metabolomics showed clear metabolic separation between the ITP and control groups. KEGG analysis indicated enrichment in the PI3K-Akt signaling pathway and multiple lipid metabolism-related pathways. Targeted LC-MS/MS further confirmed that serum oleic acid, DHA, and EPA levels were all significantly higher in patients with ITP than in healthy controls (all FDR-adjusted P = 0.0008). Exploratory ROC analysis showed that oleic acid, EPA, and DHA individually yielded AUC values of 0.841, 0.829, and 0.826, respectively, while the combined logistic regression model incorporating all three metabolites achieved an AUC of 0.879. Patients with ITP exhibit measurable lipid metabolic abnormalities characterized by elevated oleic acid, DHA, and EPA levels. These findings provide targeted quantitative support for lipid metabolic dysregulation in ITP and suggest that these alterations may be associated with PI3K-Akt-related metabolic signatures inferred from pathway enrichment analysis.

ID: 42218224
Title: Metabolic subtypes and biomarkers in preterm and term neonates via targeted screening.
Abstract: Preterm infants exhibit metabolic immaturity, yet metabolic heterogeneity within this population remains underexplored. We performed targeted metabolomics on dried blood spots from 448 preterm (32-36 weeks) and 351 term neonates (37-40 weeks of gestation) using tandem mass spectrometry. Compared with term infants, preterm neonates showed significantly elevated tyrosine, leucine/isoleucine, arginine, and hydroxyoctadecenoylcarnitine (C18:1-OH), along with reduced glutamate (false discovery rate < 0.05). Multivariate analyses, including principal component analysis and partial least squares-discriminant analysis, identified three distinct metabolic clusters associated with gestational maturity and redox-related pathway signals. Pathway enrichment analysis highlighted disruptions in the urea cycle, ammonia recycling, purine metabolism, and mitochondrial fatty acid oxidation. Notably, C18:1-OH emerged as a key discriminatory metabolite and a potential biomarker of mitochondrial immaturity and altered fatty acid oxidation in preterm neonates. These findings support the presence of metabolically distinct subtypes within preterm infants and suggest that metabolomic profiling may contribute to precision neonatal risk stratification, although longitudinal validation is required.

ID: 42277741
Title: Peripheral S-(PGJ2)-glutathione as a diagnostic biomarker for depression-anxiety comorbidity: development and validation.
Abstract: Comorbidity of depression and anxiety disorders (DAs) is as high as 50%, and diagnosis remains heavily reliant on subjective symptomatic assessments due to the lack of validated objective biomarkers. Neuroinflammation and oxidative stress are well-recognized core pathophysiological features of DAs. Prostaglandins (PGs), a class of lipid mediators closely linked to neuroinflammation and oxidative stress, have been implicated as key mediators in the pathogenesis of mood and anxiety disorders. S-(PGJ₂)-glutathione, a covalent conjugate of 15d-PGJ₂ and glutathione (GSH), integrates PG-mediated inflammatory signaling and GSH-dependent antioxidant defense, suggesting its potential as a candidate biomarker for DAs. The case-control study enrolled 77 participants, including 39 patients with comorbid depression and anxiety disorders (DAs) and 38 healthy controls (HCs) matched for gender, age, and body mass index (BMI). The cohort was randomly stratified into training and test sets at a 7:3 ratio. Serum levels of PG-related metabolites were quantified using liquid chromatography-tandem mass spectrometry (LC-MS/MS). Univariate and multivariate logistic regression analyses were performed in the training set to identify independent biomarkers. Receiver operating characteristic (ROC) analysis was employed to assess diagnostic performance in the training cohort, test cohort, and overall population, while decision curve analysis (DCA) was used to evaluate clinical utility. A total of 21 PG-related metabolites were detected, of which five were significantly dysregulated in DAs patients and remained significant after FDR correction for multiple testing. Multivariate logistic regression identified S-(PGJ₂)-glutathione as an independent biomarker associated with DAs, both before and after adjustment for confounding factors including education level, systolic blood pressure (SBP), and diastolic blood pressure (DBP). ROC analysis in the total cohort showed that S-(PGJ₂)-glutathione yielded an AUC of 0.949, with a sensitivity of 0.789 and specificity of 0.949. Consistent results were observed in the training and internal test sets. DCA suggested that using S-(PGJ₂)-glutathione for diagnosis may provide a higher net benefit than conventional "Treat All" or "Treat None" strategies over a wide range of threshold probabilities. The PG metabolic pathway is dysregulated in patients with DAs. S-(PGJ₂)-glutathione is significantly downregulated and exhibits favorable preliminary diagnostic efficacy based on internal training and test set validation. Given the relatively small sample size and the absence of external cohort validation, these findings should be interpreted as preliminary.

ID: 42301584
Title: Urinary organic acid levels and their associations with clinical characteristics in patients with schizophrenia.
Abstract: Schizophrenia is a chronic psychiatric disorder characterized by substantial biological and clinical heterogeneity. Beyond classical neurotransmitter-based models, increasing evidence suggests that systemic metabolic alterations may contribute to its pathophysiology. This study aimed to characterize urinary organic acid profiles in patients with schizophrenia and investigate their associations with clinical characteristics and pathway-level metabolic alterations. In this cross-sectional study, urinary organic acids were quantified using liquid chromatography-tandem mass spectrometry (LC-MS/MS) in 55 patients with schizophrenia and 30 age- and sex-matched healthy controls. Organic acid concentrations were normalized to urinary creatinine levels. Clinical severity was evaluated using the Positive and Negative Syndrome Scale and the Clinical Global Impressions-Severity scale. Differential metabolite analysis, subgroup comparisons, principal component analysis, correlation analyses, and pathway enrichment analyses were performed. Patients with schizophrenia demonstrated widespread alterations in urinary organic acid profiles compared with healthy controls, with 40 metabolites remaining significantly different after false discovery rate correction. Subgroup analyses identified additional metabolomic variation according to symptom severity, treatment adherence, family history, and current treatment status. Principal component analysis demonstrated partial separation between patients and controls, whereas subgroup distributions showed substantial overlap. Correlation analyses revealed predominantly weak-to-moderate associations between clinical variables and urinary metabolite concentrations. Pathway enrichment analysis identified propanoate metabolism as the only pathway that remained statistically significant after multiple testing correction, while several additional pathways demonstrated nominal enrichment. These findings suggest that schizophrenia is associated with broad alterations in urinary metabolomic profiles and support the possibility that intermediary metabolic pathways may contribute to the biological complexity and heterogeneity of the disorder. Further longitudinal and validation studies are needed to clarify the biological and clinical relevance of these observations.

ID: 42352332
Title: Metabolic Remodeling of the Parkinson's Disease Frontal Cortex Revealed by LC-MS/MS Metabolomics.
Abstract: Parkinson's disease (PD) is a progressive neurodegenerative disorder traditionally defined by dopaminergic neuronal loss and Lewy body pathology; however, increasing evidence indicates that metabolic dysfunction contributes to both motor and non-motor manifestations of disease. While metabolomics studies in PD have largely focused on peripheral biofluids or subcortical brain regions, metabolic remodeling within cortical regions critical for cognition remains poorly characterized. Here, we applied LC-MS/MS-based untargeted metabolomics to post-mortem frontal cortex tissue from PD and neurologically normal control donors, with statistical models adjusted for age, sex, and post-mortem interval. A total of 893 metabolites were quantified, of which 234 exhibited significant differential abundance following false discovery rate correction. Pathway enrichment and network-based integration revealed coordinated metabolic remodeling characterized by predicted inhibition of β-alanine metabolism and pantothenate-dependent coenzyme A biosynthesis alongside activation of amino acid, vitamin B-dependent, cofactor-related, redox-associated, oxidative stress, and inflammatory pathways. Recurrent alterations in pantothenic acid, β-alanine-related intermediates, arginine- and histidine-derived metabolites, lumichrome, and vitamin B6-associated species may reflect cortical metabolic perturbations associated with mitochondrial bioenergetic vulnerability and oxidative stress. Together, these findings indicate selective metabolic vulnerability in the PD frontal cortex rather than diffuse metabolic collapse.

ID: 42380053
Title: From Chronic Atrophic Gastritis to Low-Grade Intraepithelial Neoplasia: A Proteomic Study on the Sequential Progression of Gastric Precancerous Lesions.
Abstract: This study aimed to identify differentially expressed proteins (DEPs) in the gastric mucosa of patients with gastric precancerous lesions, establish a differential protein expression profile, and investigate the associated biological processes. Quantitative proteomic analysis of gastric mucosal tissues from 60 patients-including 20 each diagnosed with chronic atrophic gastritis (CAG), intestinal metaplasia (IM), and low-grade intraepithelial neoplasia (LGIN)-was performed using data-independent acquisition liquid chromatography-tandem mass spectrometry (DIA LC-MS/MS). DEPs were identified using stringent statistical criteria (|log2fold change [FC]| > 1.2, false discovery rate [FDR] < 0.05). Subsequent bioinformatic analyses included Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment, as well as receiver operating characteristic (ROC) curve assessments. A total of 591 proteins were identified across the CAG, IM, and LGIN groups. Comparative analysis revealed 21 statistically significantly DEPs primarily associated with metabolic pathways, signal transduction, cytoskeletal organization, viral infection, carcinogenesis, endocytosis, and the spliceosome. Notably, Parkinson's disease protein 7 (PARK7) was consistently downregulated and exhibited differential expression across all three pathological stages. This study delineates characteristic protein alterations in the gastric mucosa throughout the progression of gastric precancerous lesions along the CAG-IM-LGIN sequence. PARK7 demonstrates high diagnostic potential and may serve as a promising biomarker for monitoring disease progression in gastric precancerous conditions.

ID: 42473157
Title: Improved Protein Identification in Shotgun Proteomics with a Group-Level Extension of the LPGF Model.
Abstract: Shotgun proteomics relies on robust statistical methods to increase the confidence of the protein identifications. Previously, we introduced LPGF, a protein probability model based on unique peptides. In this study, we extend LPGF to protein groups that share peptides by considering only peptides that are unique to each group. This strategy preserves the principle of unique evidence while enabling the probability estimation at the protein-group level. Applying our method to three tissues from the Human Proteome Map (HPM), we evaluated the gain in protein identifications obtained with group-unique peptides compared to those obtained using only protein-unique peptides and different scores. The accuracy of false discovery rate (FDR) estimation was further validated using a standard data set for protein inference based on human Protein Epitope Signature Tags (PrESTs), as well as through an entrapment experiment using the substantially larger HPM data set. To facilitate adoption, we developed an R package (b10prot) that integrates the full workflow, including protein-level and group-level LPGF score computation and protein grouping via an R port of our previous tool PAnalyzer. Our results demonstrate that extending LPGF to protein groups increases sensitivity while maintaining robust FDR control, thus providing the proteomics community with a practical framework for more comprehensive protein identification.

ID: 42575280
Title: Fusion entrapment enables unbiased assessment of false discovery rate control in cascaded database searches.
Abstract: Cascaded database searches boost identification sensitivity in vast proteomic search spaces but challenge false discovery rate (FDR) control. The standard target-decoy approach to FDR control relies on decoy matches providing an exchangeable and properly scaled representation of incorrect target matches. This assumption can be disrupted when protein-level filtering is used to define a reduced search space, because target and decoy entries may no longer undergo symmetric retention during database reduction. Although entrapment provides an external benchmark for assessing FDR control, conventional separate-entrapment implementations can become invalid in cascaded searches because entrapment sequences may be disproportionately discarded during protein-level filtering. Here we introduce Fusion Entrapment, a strategy that computationally fuses entrapment sequences with target proteins to preserve identical selection pressure during database reduction. Simulations show that this strategy provides accurate entrapment-based false discovery proportion (FDP) estimation in cascaded searches involving protein-level filtering. Applying Fusion Entrapment to human gut metaproteomic datasets, we further observed that conventional separate target-decoy database reduction led to substantial inflation of the entrapment-estimated FDP relative to the reported FDR threshold. In contrast, fusion target-decoy reduction maintained empirical FDR control under Fusion Entrapment assessment while retaining substantial sensitivity gains over single-step analysis.

ID: 42589138
Title: Plasma Proteomic Signatures in Alkaptonuria.
Abstract: Alkaptonuria (AKU) is a rare metabolic disorder caused by homogentisic acid accumulation and characterised by ochronosis, oxidative stress, chronic inflammation, and progressive connective tissue damage. This study aimed to define the circulating proteomic alterations associated with AKU and assess their relationship with nitisinone treatment. Plasma samples from 11 patients with AKU and 6 age- and sex-matched healthy controls were analysed by liquid chromatography coupled to tandem mass spectrometry using label-free quantification. Differentially abundant proteins were identified using thresholds of |log2FC| ≥ 1 and Benjamini-Hochberg false discovery rate ≤ 0.01, followed by functional enrichment and treatment-stratified analyses. Twenty-two proteins were differentially abundant between AKU patients and controls. Complement components (C1R, C1S, C9, C4BPA, CPN2), fibronectin, clusterin, PGLYRP2, and haemoglobin subunits showed increased abundance, whereas most immunoglobulin chains, kallikrein, apolipoprotein A2, and alpha-1-antitrypsin showed decreased abundance. Functional enrichment highlighted complement activation, B-cell-mediated and humoral immune responses, immunoglobulin-related functions, platelet activation, and erythrocyte gas-exchange pathways. Correlation analysis linked several proteins, particularly CPN2, APOA2, C1R and C1S, to core biochemical parameters of disease activity. Treatment-stratified analysis identified fourteen proteins that remained significantly altered in both treated and untreated patients, forming a treatment-resistant core of the signature, while several complement-, coagulation-, and lipid-related proteins were significant only in one treatment subgroup. These findings define an AKU plasma proteomic signature dominated by complement activation and humoral immune alterations, together with extracellular matrix, erythrocyte-, and coagulation-associated changes. The persistence of most alterations across treatment groups suggests that residual systemic proteomic dysregulation remains despite nitisinone treatment.