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At least 1,279 records · Page 71Linked to original sources
PubMed2026

Plasma proteomics reveal SERPINA1 and CD59 as candidate biomarkers for COVID-19 severity stratification and prognosis prediction.

BACKGROUND: COVID-19 has been closely associated with coagulation abnormalities. However, existing biomarkers, including D-dimer and fibrin degradation products (FDP), exhibit limited accuracy in stratifying disease severity and predicting long-term clinical outcomes. OBJECTIVES: This study aimed to use proteomic analysis to identify plasma biomarkers associated with COVID-19 severity and prognosis, and validate their predictive utility for mortality and thromboembolic complications. METHODS: Plasma proteomic profiles were analyzed across three COVID-19 severity classes. Differential expression analysis and functional analysis were performed. Clustering analysis was used to identify proteins correlated with disease severity. Candidate biomarkers were validated in an independent cohort. Predictive performance of the biomarkers for mortality, sepsis and venous thromboembolism was evaluated using bootstrap-corrected ROC analyses and multivariable regression analyses. RESULTS: Proteomic analysis revealed progressive involvement of the coagulation and complement pathway with increasing disease severity. SERPINA1 and CD59 were identified as candidate biomarkers and exhibited significantly higher plasma levels in severe cases. Bootstrap-corrected ROC analyses demonstrated strong predictive performance: SERPINA1 achieved AUCs of 0.775 and 0.924 for 30-day and 12-month mortality, and CD59 achieved AUCs of 0.720 for sepsis; the combined model further improved prediction of 12-month mortality (AUC 0.946) and sepsis (AUC 0.904), outperforming D-dimer and FDP. Multivariable regression confirmed their independent prognostic value. CONCLUSION: This exploratory study identifies SERPINA1 and CD59 as candidate prognostic biomarkers in COVID-19, highlighting the role of coagulation and complement-related pathways in disease severity and warranting further prospective validation.

Humans↗
PubMed2006

Effect of ionizing radiation on rat tissue: proteomic and biochemical analysis.

Reactive oxygen species (ROS), generated by ionizing radiation, has been implicated in its effect on living tissues. We confirmed the changes in the oxidative stress markers upon irradiation. We characterized the changes in the proteome profile in rat liver after administering irradiation, and the affected proteins were identified by MALDI-TOF-MS and ESI-MS/MS. The identified proteins represent diverse sets of proteins participating in the cellular metabolism. Our results demonstrated that proteomics analysis is a useful method for characterization of a global proteome change caused by ionizing radiation to unravel the molecular mechanisms involved in the cellular responses to ionizing radiation.

Animals↗
PubMed2026

Proteomic insights into Helicobacter pylori infection in stomach cells, revealing host response and host-targeted therapeutics repurposing.

BACKGROUND: Helicobacter pylori (H. pylori) is a globally prevalent gastric pathogen strongly associated with chronic gastritis, peptic ulcers, and gastric cancer. While bacterial factors have been extensively studied, host proteomic responses and their therapeutic potential remain largely underexplored. RESEARCH DESIGN AND METHODS: Current analyses employed a systematic proteomics-based data integration and harmonization approach (retrospective qualitative cohort study) to identify important differentially regulated host proteins. Proteomic datasets were curated from in vitro studies and analyzed for functional enrichment, protein-protein interaction networks, and hub protein identification. To explore therapeutic repurposing, drug repositioning was performed using the DrugBank database. RESULTS: Data summation describing protein differential regulation in human gastric cells as a result of the infection revealed 1672 perturbed host proteins. Bioinformatics analysis revealed 11 proteins including CSK, MET, RELA, MARK2, GRB2, FTO, PLCG1, CRKL, RPS5, RPS9, and RPS27A to be ideal host targets for therapeutic repurposing. Clinically approved drugs such as Dasatinib (targeting CSK) and Crizotinib (targeting MET) emerged as promising candidates due to favorable pharmacokinetics and known bioactivity. CONCLUSIONS: Host-directed therapeutics could offer alternative strategies to conventional antibiotic therapy, addressing challenges such as resistance and infection recurrence, providing a foundation for future experimental validation and development of host-targeted interventions for infection control.

Humans↗
PubMed2026

The proteomic origin of the genetic code.

INTRODUCTION: The origin and evolution of the genetic code is a central problem in molecular biology. Classical models have emphasized stereochemistry, frozen accidents, or adaptive optimization, often treating proteins as passive products of preexisting codes. More recent views instead portray the code as a dynamic, coevolving system shaped by reciprocal interactions among amino acids, RNA, and early catalysts. AREAS COVERED: Here, I review efforts of phylogeny reconstruction of the history of tRNA, protein structural domains, and dipeptide sequences in proteomes. These complementary approaches allow exploration of the entry of amino acids and codons into the code, and the transition from an operational RNA code in the tRNA acceptor arm to the canonical code in the anticodon loop. Evidence for ancestral synthetase enzymes with dual functions in aminoacylation and peptide-bond formation, as well as early bidirectional (sense-antisense) coding reflected in dipeptide-antidipeptide emergence is also discussed. EXPERT OPINION: The genetic code is best viewed as a proteome-driven, evolvable system in which early peptides actively shaped coding rules by stabilizing structure, expanding chemical diversity, and enhancing catalysis. This perspective connects origin-of-life studies with modern efforts of code expansion, translational engineering, and peptide-based therapeutics, highlighting the impact of the code's proteomic origin.

Genetic Code↗
PubMed2006

Organization of brain complexity--synapse proteome form and function.

Proteomic study of the synapse has generated an extensive list of molecular components, revealing one of the most complex functional systems currently known to cell biology. While fundamental to neural information processing, behaviour and disease, the molecular organisation of the synapse and its relation to higher-level function has yet to be clearly understood. Neurotransmitter receptor complexes, such as the N-methyl-D-aspartate receptor complex (NRC/MASC), are major components of the synaptic proteome. We have recently completed a detailed study of MASC, its functional organisation and involvement in behaviour and disease. This pointed to simple design principles underlying synaptic organisation. Drawing together the results of proteomic and analytical study, we sketch out a model for synaptic functional organisation.

Animals↗
PubMed2026

ProMeta: a meta-learning framework for robust disease diagnosis and prediction from plasma proteomics.

MOTIVATION: The plasma proteome offers a dynamic window of human health, capturing the real-time intersections between genetics and physiology. However, the application of deep learning to proteomics is currently hindered by a reliance on large-scale labeled datasets, rendering standard models ineffective for rare or novel diseases where patient samples are inherently scarce. RESULTS: Here, we present ProMeta, a meta-learning framework designed to enable robust disease modeling under extreme data restrictions. By integrating knowledge-guided pathway encoding with bi-level meta-optimization, ProMeta projects unstructured proteomic profiles into biologically interpretable functional tokens. This architecture allows the model to learn a global initialization containing transferable biological priors from biobank-scale data, facilitating rapid adaptation to novel tasks. Through comprehensive benchmark experiments, ProMeta consistently outperformed transfer learning and traditional machine learning baselines in both disease diagnosis and prediction tasks. In the most challenging 4-shot scenarios (utilizing only 2 cases and 2 controls), the model achieved robust generalization with an average AUROC of ∼0.69, representing a 24.6% relative improvement over the best-performing baseline methods. Mechanistic investigation revealed that ProMeta disentangles cases from controls in the latent space prior to task-specific adaptation, confirming the acquisition of universal biological rules rather than rote memorization. Furthermore, gradient-based interpretation identified disease-specific protein biomarkers and functional pathways consistent with known pathophysiology. Collectively, ProMeta overcomes the data-scarcity bottleneck in precision medicine, providing a scalable, interpretable framework for characterizing the full spectrum of human diseases, particularly for rare conditions lacking extensive clinical cohorts. AVAILABILITY AND IMPLEMENTATION: The source code of ProMeta is available at GitHub (https://github.com/lihan97/ProMeta).

Proteomics↗
PubMed2026

Foundation model enables interpretable open and error-tolerant searching for mass spectrometry-based proteomics.

MOTIVATION: Mass spectrometry-based proteomics allows studying all proteins of a sample on a molecular level. However, mass spectra are noisy and contain complex patterns, making them inherently challenging to analyze with algorithmic approaches. In terms of the protein sequence landscape, most recent bottom-up MS-based proteomics studies consider either a diverse pool of post-translational modifications, employ large databases-as in metaproteomics or proteogenomics, study multiple isoforms of proteins, include unspecific cleavage sites or even combinations thereof. All this makes peptide and protein identifications challenging. RESULTS: Here, we present a foundation model, called yHydra, that jointly embeds spectra and peptides. This allows us to implement various downstream tasks and search modes in Euclidean space. We implement an open search which allows querying multiple ten-thousands of spectra against millions of peptides. Furthermore, we implement an error-tolerant search for identifying additional proteoforms that are not included in off-the-shelf reference proteomes. Our foundation model provides meaningful embeddings, as we interpret learned peptide embeddings in comparison to the peptide's physico-chemical properties. Hydra's open search, assigns delta masses to each identification which allows to unrestrictedly characterize post-translational modifications. The error-tolerant mode of yHydra can be used as post-processing to existing search engines or as a standalone. yHydra is evaluated on several real life data sets for the identification of modified peptide sequences and shows up to 25% increase in peptide identification at constant false discovery rate compared to the current state-of-the-art. AVAILABILITY AND IMPLEMENTATION: Code is available on Gitlab: https://gitlab.com/dacs-hpi/yHydra, and https://gitlab.com/dacs-hpi/yHydra_train.

Proteomics↗
PubMed2025

Penalized likelihood optimization for censored missing value imputation in proteomics.

Label-free bottom-up proteomics using mass spectrometry and liquid chromatography has long been established as one of the most popular high-throughput analysis workflows for proteome characterization. However, it produces data hindered by complex and heterogeneous missing values, which imputation has long remained problematic. To cope with this, we introduce Pirat, an algorithm that harnesses this challenge using an original likelihood maximization strategy. Notably, it models the instrument limit by learning a global censoring mechanism from the data available. Moreover, it estimates the covariance matrix between enzymatic cleavage products (ie peptides or precursor ions), while offering a natural way to integrate complementary transcriptomic information when multi-omic assays are available. Our benchmarking on several datasets covering a variety of experimental designs (number of samples, acquisition mode, missingness patterns, etc.) and using a variety of metrics (differential analysis ground truth or imputation errors) shows that Pirat outperforms all pre-existing imputation methods. Beyond the interest of Pirat as an imputation tool, these results pinpoint the need for a paradigm change in proteomics imputation, as most pre-existing strategies could be boosted by incorporating similar models to account for the instrument censorship or for the correlation structures, either grounded to the analytical pipeline or arising from a multi-omic approach.

Proteomics↗
PubMed2005

Signal in noise: evaluating reported reproducibility of serum proteomic tests for ovarian cancer.

Proteomic profiling of serum initially appeared to be dramatically effective for diagnosis of early-stage ovarian cancer, but these results have proven difficult to reproduce. A recent publication reported good classification in one dataset using results from training on a much earlier dataset, but the authors have since reported that they did not perform the analysis as described. We examined the reproducibility of the proteomic patterns across datasets in more detail. Our analysis reveals that the pattern that enabled successful classification is biologically implausible and that the method, properly applied, does not classify the data accurately. We show that the method used in previously published studies does not establish reproducibility and performs no better than chance for classifying the second dataset, in part because the second dataset is easy to classify correctly. We conclude that the reproducibility of the proteomic profiling approach has yet to be established.

Female↗
PubMed2006

PepSeeker: a database of proteome peptide identifications for investigating fragmentation patterns.

Proteome science relies on bioinformatics tools to characterize proteins via their proteolytic peptides which are identified via characteristic mass spectra generated after their ions undergo fragmentation in the gas phase within the mass spectrometer. The resulting secondary ion mass spectra are compared with protein sequence databases in order to identify the amino acid sequence. Although these search tools (e.g. SEQUEST, Mascot, X!Tandem, Phenyx) are frequently successful, much is still not understood about the amino acid sequence patterns which promote/protect particular fragmentation pathways, and hence lead to the presence/absence of particular ions from different ion series. In order to advance this area, we have developed a database, PepSeeker (http://nwsr.smith.man.ac.uk/pepseeker), which captures this peptide identification and ion information from proteome experiments. The database currently contains >185,000 peptides and associated database search information. Users may query this resource to retrieve peptide, protein and spectral information based on protein or peptide information, including the amino acid sequence itself represented by regular expressions coupled with ion series information. We believe this database will be useful to proteome researchers wishing to understand gas phase peptide ion chemistry in order to improve peptide identification strategies. Questions can be addressed to j.selley@manchester.ac.uk.

Databases, Protein↗
PubMed2006

Proteome and transcriptome analysis of 5-fluoro-2'-deoxyuridine-induced cell death mechanisms.

5-Fluoro-2'-deoxyuridine (FUdR) inhibits thymidylate synthase. The inhibition of thymidylate synthase causes an imbalance of intracellular deoxyribonucleoside triphosphate (dNTP) pools which subsequently induced cell death. We have been investigated the molecular mechanisms of cell death in mouse mammary tumor FM3A cells, F28-7 strain and its mutant F28-7-A strain, after treated with FUdR. We have previously been reported that F28-7 strain induced DNA cleavage into chromosomal sized fragments and subsequently develop necrosis, but F28-7-A strain induced DNA cleavage into oligonucleosomal sized fragments and subsequently develop apoptosis after treated with FUdR. In this report, in order to understand the molecular mechanisms of regulate of two differential cell death necrosis and apoptosis, we identify cell death regulator by using proteome and transcriptome analysis. When compared with the proteome from F28-7 strain and F28-7-A strain, it was found that ten proteins were increased and six proteins were decreased in F28-7-A strain. Furthermore, transcriptome analysis shows that 127 genes were increased and 181 genes were decreased in F28-7-A strain. These differentially expressed proteins and genes were involved in various cellular processes such as cell cycle regulation, apoptosis, proliferation, and differentiation. These two techniques clarified numerous features in F28-7 strain and F28-7-A strain. Our results revealed that numerous features indicated the coordinated regulation of molecular networks from various aspects of necrosis or apoptosis at the proteome and transcriptome levels.

Animals↗
PubMed2026

Emerging Trends in Mass Spectrometry-Based Quantitative Proteome and Phosphoproteome Profiling in Maize.

Maize (Zea mays) is both an agronomically important crop and a reference model organism that has enabled the dissection of the molecular basis of plant development and environmental responses. Mass spectrometry-based proteomics provides a powerful approach to identify and quantify proteins and their post-translational modifications, facilitating the discovery of molecular mechanisms underlying complex biological processes. Unlike the study of gene expression using transcriptomics, analysis of the proteome and phosphoproteome provides direct measurement of proteins, which are responsible for driving or regulating nearly all cellular processes, thus offering a more complete picture of the cell's functional state. Over the past two decades, advancements in mass spectrometry have enabled large-scale profiling of protein abundance and phosphorylation sites in maize, improving our understanding of various biological phenomena. Here, we briefly summarize some of the major biological insights gained from maize proteome and phosphoproteome studies, and provide an overview of mass spectrometry sample preparation and acquisition/analysis workflows for the quantitative and reproducible analysis of protein abundance and phosphorylation dynamics in maize.

Zea mays↗
PubMed2003

Mapping the proteome of barrel medic (Medicago truncatula).

A survey of six organ-/tissue-specific proteomes of the model legume barrel medic (Medicago truncatula) was performed. Two-dimensional polyacrylamide gel electrophoresis reference maps of protein extracts from leaves, stems, roots, flowers, seed pods, and cell suspension cultures were obtained. Five hundred fifty-one proteins were excised and 304 proteins identified using peptide mass fingerprinting and matrix-assisted laser desorption ionization time-of-flight mass spectrometry. Nanoscale high-performance liquid chromatography coupled with tandem quadrupole time-of-flight mass spectrometry was used to validate marginal matrix-assisted laser desorption ionization time-of-flight mass spectrometry protein identifications. This dataset represents one of the most comprehensive plant proteome projects to date and provides a basis for future proteome comparison of genetic mutants, biotically and abiotically challenged plants, and/or environmentally challenged plants. Technical details concerning peptide mass fingerprinting, database queries, and protein identification success rates in the absence of a sequenced genome are reported and discussed. A summary of the identified proteins and their putative functions are presented. The tissue-specific expression of proteins and the levels of identified proteins are compared with their related transcript abundance as quantified through EST counting. It is estimated that approximately 50% of the proteins appear to be correlated with their corresponding mRNA levels.

Amino Acid Sequence↗
PubMed2006

'Suicide' of crickets harbouring hairworms: a proteomics investigation.

Despite increasing evidence of host phenotypic manipulation by parasites, the underlying mechanisms causing infected hosts to act in ways that benefit the parasite remain enigmatic in most cases. Here, we used proteomics tools to identify the biochemical alterations that occur in the head of the cricket Nemobius sylvestris when it is driven to water by the hairworm Paragordius tricuspidatus. We characterized host and parasite proteomes during the expression of the water-seeking behaviour. We found that the parasite produces molecules from the Wnt family that may act directly on the development of the central nervous system (CNS). In the head of manipulated cricket, we found differential expression of proteins specifically linked to neurogenesis, circadian rhythm and neurotransmitter activities. We also detected proteins for which the function(s) are still unknown. This proteomics study on the biochemical pathways altered by hairworms has also allowed us to tackle questions of physiological and molecular convergence in the mechanism(s) causing the alteration of orthoptera behaviour. The two hairworm species produce effective molecules acting directly on the CNS of their orthoptera hosts.

Amino Acid Sequence↗
PubMed2005

Mapping the platelet proteome: a report of the ISTH Platelet Physiology Subcommittee.

Proteomic technology has the potential to transform the way we analyze platelet biology, through the determination of platelet protein composition and its modification upon stimulation and with disease. We are a considerable way from achieving these goals, however, because of significant limitations in current methodology. It is therefore important to consider the extent to which these aims can be met and the way that proteomic data should be presented and used. These issues are discussed in the present paper by the Platelet Physiology Subcommittee of the ISTH Scientific Standardisation Committee (SSC). It is recommended that proteomic information be combined with data from other experimental approaches to establish a database on protein expression and function in platelets.

Blood Platelets↗
PubMed2006

Two-dimensional electrophoretic proteome study of serum thermostable fraction from patients with various tumor conditions.

One of the problems of plasma proteomics is a presence of large major components. In this work, we use the thermostable fraction as a way to deplete these major proteins. The thermostable fraction of serum samples from patients with ovarian, uterus, and breast cancers and benign ovarian tumor was analyzed using two-dimensional electrophoresis combined with MALDI-TOF(-TOF)-mass spectrometry. Of them, alpha-1-acid glycoprotein and clusterin are expressly down-regulated in breast cancer, whereas transthyretin is decreased specifically in ovarian cancer. Apolipoprotein A-I forms have decreased spot volumes, while haptoglobin alpha1, in contrast, is elevated in several tumors. These data are partly consistent with previous art studies on cancer proteomics, which involve mass-spectrometry-based serum profiling techniques. Serum thermostable fraction may be recommended as a good tool for medium and small protein proteome investigation, in particular, by 2D-electrophoresis.

Adult↗
PubMed2003

Proteomics.

Fueled by ever-growing DNA sequence information, proteomics-the large scale analysis of proteins-has become one of the most important disciplines for characterizing gene function, for building functional linkages between protein molecules, and for providing insight into the mechanisms of biological processes in a high-throughput mode. It is now possible to examine the expression of more than 1000 proteins using mass spectrometry technology coupled with various separation methods. High-throughput yeast two-hybrid approaches and analysis of protein complexes using affinity tag purification have yielded valuable protein-protein interaction maps. Large-scale protein tagging and subcellular localization projects have provided considerable information about protein function. Finally, recent developments in protein microarray technology provide a versatile tool to study protein-protein, protein-nucleic acid, protein-lipid, enzyme-substrate, and protein-drug interactions. Other types of microarrays, though not fully developed, also show great potential in diagnostics, protein profiling, and drug identification and validation. This review discusses high-throughput technologies for proteome analysis and their applications. Also discussed are the approaches used for the integrated analysis of the voluminous sets of data generated by proteome analysis conducted on a global scale.

Drug Design↗
PubMed2003

Proteome analysis by mass spectrometry.

The coupling of high-performance mass spectrometry instrumentation with highly efficient chromatographic and electrophoretic separations has enabled rapid qualitative and quantitative analysis of thousands of proteins from minute samples of biological materials. Here, we review recent progress in the development and application of mass spectrometry-based techniques for the qualitative and quantitative analysis of global proteome samples derived from whole cells, tissues, or organisms. Techniques such as multidimensional peptide and protein separations coupled with mass spectrometry, accurate mass measurement of peptides from global proteome digests, and mass spectrometric characterization of intact proteins hold great promise for characterization of highly complex protein mixtures. Advances in chemical tagging and isotope labeling techniques have enabled quantitative analysis of proteomes, and highly specific isolation strategies have been developed aimed at selective analysis of posttranslationally modified proteins.

Chromatography, Affinity↗