Background: Koalas are a vulnerable marsupial species with unique reproductive traits. Efforts to develop assisted breeding technologies have been hindered by a limited understanding of maternal recognition of pregnancy and physiological changes induced by the foeto-placental unit. Differences in the reproductive physiology of pregnant and non-pregnant koalas were examined to investigate the possibility of maternal recognition and identify potential pregnancy and/or embryonic loss biomarkers. Methods: Koalas were separated into three groups: pregnant (n = 4 cycles from three females), mated but non-parturient (n = 4 cycles from three females), and gonadotropin-releasing hormone (GnRH) agonist-treated females (n = 7). Plasma was collected on day of mating/GnRH injection (D0) and on multiple subsequent days. Progesterone concentrations were measured by enzyme immunoassay, and plasma proteomes were analysed using filter-aided sample preparation followed by liquid chromatography-tandem mass spectrometry (LC-MS/MS), employing sequential window acquisition of all theoretical fragment ion spectra. Results: Ovulation induction mechanisms influenced peri-ovulatory progesterone secretion (pregnant 40.7 ± 3.3 ng/mL vs. GnRH-treated 15.7 ± 1.8 ng/mL), with no significant differences in progesterone that occurred later in the luteal phase. LC-MS/MS identified 158 proteins, representing the first koala plasma proteome. Leucine-rich alpha-2-glycoprotein (LRG1) was significantly elevated at D2 in pregnant females compared to GnRH-treated females, and pregnant D9 and D19. In pregnant females, fibronectin (FN1) was significantly more abundant at D19 compared to D9 but not significantly different between treatments. Conclusions: These preliminary findings provide foundational data for further investigation into maternal recognition and pregnancy/embryonic loss in koalas.
Background: Mechanosensitive ion channels PIEZO1 and PIEZO2 are key mediators of mechanotransduction, which converts physical forces into cellular signals involved in proprioception, touch, vascular function, and other physiological processes. Mutations in human PIEZO proteins are linked to various diseases, such as hereditary xerocytosis, lymphatic dysplasia, and proprioceptive dysfunction. However, the role of intrinsic disorder in the regulation of these proteins and their susceptibility for disease-associated mutations remains unclear. Methods: We analyzed canonical human PIEZO1 and PIEZO2 protein sequences using machine learning, neural network, and energy-based disorder predictors, together with the prediction of disorder-mediated binding regions, phase separation propensity, interaction networks, evolutionary conservation, clinically annotated human variants, and peptide structural modeling. Results: Both proteins showed moderate intrinsic disorder, with PIEZO2 having slightly greater disorder propensity and higher predicted phase separation potential. Intrinsically disordered regions frequently overlapped binding-prone segments and post-translational modification sites, supporting regulatory functions. Evolutionary comparisons showed strong conservation of PIEZO proteins, while selected disordered regions retained disorder propensity despite greater sequence variability. Disease-causing variants mainly affected the ordered regions of both proteins, whereas disordered regions contained proportionally more benign variants and relatively few pathogenic mutations. The modeling of mutations within disordered hotspots showed altered local conformational tendencies, indicating that some disease variants may disrupt dynamic interaction interfaces rather than global structure. Interaction network analysis linked both proteins to enriched mechanotransduction, ion transport, and cytoskeletal pathways. Conclusions: Overall, our findings identify intrinsic disorder as an underappreciated feature of PIEZO channel biology and provide a framework for interpreting PIEZO-associated channelopathies. PIEZO proteins also perfectly illustrate the proteoform concept, where one gene yields a highly diverse kit of mechanosensitive molecular tools. While humans only have two primary PIEZO genes (PIEZO1 and PIEZO2), the body generates a vast array of functional variations.
BACKGROUND:Multi-drug resistant Gram-negative bacteria (GNB) are major contributors to the antimicrobial resistance (AMR) burden. AMR mechanisms are primarily mediated by proteoforms; therefore, proteomic analyses of GNB offers a significant advantage in understanding the mechanisms of AMR. A large portion of these mechanisms are mediated by membrane proteins; however, they are often difficult to extract due to their hydrophobic nature and complex interactions with other components of the cell membrane. To extract the greatest number of proteoforms, an efficient homogenisation protocol is required to effectively disrupt the rigid cell wall and membrane. METHODS:Using Escherichia coli, Klebsiella pneumoniae, Acinetobacter baumannii and Pseudomonas aeruginosa, we systematically compared the extraction efficiency of bead-beating with flash frozen and lyophilized cell pellets. RESULTS:We demonstrate that lyophilization improves bead-beating extraction methods by increasing the detection of membrane proteins. We detected numerous unique membrane proteins in each bacterial isolate, including ABC transporters and proteins involved in lipopolysaccharide synthesis, when lyophilizing prior to bead-beating, compared to only flash-freezing. CONCLUSIONS:As membrane proteins play a central role in AMR mechanisms, this improvement in their isolation and identification will aid in understanding the resistance and molecular mechanisms associated with multi-drug resistant GNB.
As global ecosystems and food systems face unprecedented anthropogenic and climatic challenges, there is a demand for an integrated understanding of biological systems. Proteomics has emerged as a definitive approach offering a direct view of the molecular phenotype, yet it is traditionally separated into plant and animal disciplines. With recent advances in mass spectrometry (MS) and bioinformatics tools, this prospective review proposes that combining a One Health proteomics approach with deep-learning data analysis can revolutionize global food security, animal productivity, and ecosystem health by uncovering proteoform signatures that drive resilience across life. The potential of a unified One Health proteomic framework, highlighting major developments, including 4D proteomics, Data-Independent Acquisition (DIA), and single-cell resolution, and emphasizes their capacity to resolve the complex proteoform landscape across kingdoms. Review emphasizes the applications of proteogenomics as a cross-disciplinary tool to improve genome annotations, explain evolutionary differences, discover biomarkers in animals and resolve complex signaling networks in plants under stress. Nevertheless, contemporary proteogenomics methods still show limitations in their ability to comprehensively resolve proteoforms due to the fact that the use of peptide-based approaches makes it difficult to fully appreciate the post-translational modifications specific to each protein isoform. We show that One Health proteomics will provide a transformative roadmap for deciphering the functional proteoform signatures that underpin resilience across the tree of life.
BACKGROUND:Distinguishing genuine kinase-substrate motifs from background noise is a growing challenge, as mass spectrometry (MS)-based global phosphoproteomics identifies a rapidly expanding set of phosphorylation sites. One of the major limitations is selecting an appropriate background model that systematically controls both technical and biological sources of bias. Although using the entire proteome as a background in a FASTA format considers the overall amino acid composition, it is still prone to biases from protein abundance and the uneven distribution of sequence space (particularly around low-abundance proteins). By contrast, internal background methods can control experiment-specific detection biases, but they may not fully capture residue-specific compositions or general trends in phosphorylation. METHODS:I develop a Dual-Background Enrichment (DBE) framework with a position-specific enrichment (PSE) strategy, which involves analyzing motif enrichment against two distinct background models: (1) A residue-heterogeneous internal background composed of phospho-motifs centered on the residue; e.g., phosphoserine (pS) motifs are tested relative to the pool of all detected phosphothreonine (pT) and phosphotyrosine (pY) motifs from the same experiment. (2) A FASTA background that includes all S, T, and Y residues in the UniProtKB proteome sequences. RESULTS:Motifs are classified as high confidence if they meet statistical significance (q ≤ 0.05, fold enrichment > 1.5) against both background models. CONCLUSION:By applying the DBE strategy to a large-scale phosphoproteomics dataset, we distinguish motifs driven by amino acid composition (enriched in FASTA background only) from those reflecting kinase substrate specificity (enriched in both backgrounds). This dual-reference approach reduces false positives arising from sequence composition bias and enriches high-confidence candidate kinase recognition motifs.
BACKGROUND:Autoimmune diseases (AIDs) are associated with increased cardiovascular risk. However, specific protein mediators linking AIDs to major adverse cardiovascular events (MACE) and cardiovascular death (CV death) remain unexplored. This study identifies proteomic mediators linking AIDs to MACE via high-dimensional mediation analysis in the UK Biobank. METHODS:We used UK Biobank data with proteomic profiling by Olink platform. Participants with prevalent myocardial infarction (MI), stroke, and heart failure at baseline were excluded. AIDs were categorized into musculoskeletal (MSK), vasculitis, gastrointestinal (GI), neurologic, and rheumatic fever subsets. Fine-Gray models assessed associations between AIDs and MACE and CV death. Proteome-wide association studies identified proteins associated with both AIDs and cardiovascular outcomes. High-dimensional mediation analysis (HIMA) explored protein-mediated pathways. All models adjusted for age, sex, lipids, BMI, smoking, hypertension, diabetes, chronic kidney disease, atrial fibrillation, and coronary artery disease. RESULTS:Among 400,633 participants (median follow-up 14.5 years, 44.8% male), AIDs were present in 28,754 (7.2%). All AID categories were associated with increased MACE (sHR: MSK 1.34, vasculitis 1.67, GI 1.20, neurologic 1.33, rheumatic fever 1.38; all p < 0.001). For CV death, MSK, vasculitis, and rheumatic fever showed increased risk (sHR 1.34, 1.78, 1.51; all p ≤ 0.004), but not GI or neurologic AIDs. In 43,599 participants with proteomic data, HIMA identified 66 and 32 unique potential mediators linking AIDs to MACE and CV death, respectively. Four proteins (Growth Differentiation Factor 15, Interleukin-15, urokinase plasminogen activator receptor, and Tenascin C) mediated the AID-MACE relationship across multiple AID categories. Growth Differentiation Factor 15 and Interleukin-15 were shared mediators for CV death. CONCLUSIONS:This proteomic analysis identifies specific proteins that may mediate the association between AIDs and adverse cardiovascular outcomes, offering mechanistic insights into immune-related cardiovascular risk. These findings are hypothesis-generating and require replication and validation before the identified proteins can be considered causal mediators or adopted for clinical risk stratification.
Background: cMGVB is a graphical processing unit (GPU)-enabled implementation of the computational proteomics data analysis toolset MGVB. MGVB was released in 2025 as a Linux program designed to run on multi-node servers. It utilizes a novel algorithm for finding combinations of post-translational modification in peptide MS/MS data. The original combinatorial algorithm required a significant amount of resources to be practical. Hence, the aim of the research reported here was to port the algorithm to GPU and thus increase its speed and efficiency. Methods: To accomplish this it was recoded in CUDA C; recursive functions and data structures were re-implemented as non-recursive, and the algorithm was incorporated in a new version of MGVB, now termed cMGVB. Results: The re-implemented algorithm is much faster and, unlike the original program, can run on single CPU workstations equipped with inexpensive GPUs and still be much faster than the original algorithm running on HPC clusters. A typical focused search is completed in about a minute by cMGVB compared to 10-15 min by the original implementation. Illustrative case studies are presented and discussed in this report. Conclusions: cMGVB enables workflows that were not practical or even possible with the original MGVB.
BACKGROUND:Comparative proteome analysis can reveal functional conservation and divergence among orthologous proteins, with important implications for pharmacology and toxicology. Protein language models (PLMs) may capture sequence-derived functional relationships beyond what conventional alignment metrics capture. METHODS:Orthologous proteins from Danio rerio and Danio aesculapii were compared using embeddings generated by the Evolutionary Scale Modeling 2 (ESM-2) protein language model. Reciprocal best-hit inference identified 68,971 high-confidence ortholog pairs, of which 51,086 were available for embedding-based analysis. PLM divergence was quantified using cosine distance and evaluated using length-matched and bitscore-matched random controls, Gene Ontology graph-distance analysis, and localized domain-level comparisons. RESULTS:Ortholog pairs showed strong global conservation, with a median PLM distance of 0.000487, whereas randomized controls exhibited substantially greater divergence. Increasing Gene Ontology graph distance broadened PLM-distance distributions, and leaf-parent comparisons demonstrated significant functional ordering (Wilcoxon p = 2.44 × 10-4). Local analyses revealed increased divergence in pathophysiologically relevant regions of aryl hydrocarbon receptor (AHR) and potassium channel proteins. CONCLUSIONS:PLM embeddings provide a scalable framework for comparative proteome characterization, complement conventional sequence-based analyses, and prioritize orthologs or protein regions with elevated functional divergence for experimental validation in cross-species pharmacology, toxicology, and systems biology.
BACKGROUND:Salinity, which hampers wheat growth and development, is one of the major abiotic stresses. Plant-derived smoke (PDS) solution alleviates salt stress and promotes wheat growth and development; however, the underlying molecular mechanisms have not been completely clarified. METHODS:In this study, nuclear proteomics was employed to reveal the promotive effect of PDS solution on salt-stressed wheat. Nuclear fractions were isolated from wheat roots, and their purity was confirmed via enrichment of histone H3 and reduction of cytosolic ascorbate peroxidase. Using this nuclear purification technique, label-free nano LC-MS/MS-based nuclear proteomics was performed to identify differentially abundant nuclear proteins in salt-stressed wheat with or without PDS solution treatment. RESULTS:Salt stress decreased histone H2A and DNA polymerase levels, whereas PDS solution treatment of salt-stressed wheat increased levels of histone variants (H2A, H2B, H3, and H4), DNA polymerase, and DNA topoisomerase II. In addition, the PDS solution increased the levels of pre-mRNA cleavage factor Im 25 kDa subunit and RNA helicase in salt-stressed wheat. Immunoblot analysis further validated the increase in histone deacetylase levels triggered by the PDS solution treatment in the salt-stressed wheat. CONCLUSIONS:These results suggest that PDS solution alters nuclear proteins in a way that contributes to chromatin remodeling and transcription during salt stress.
The liver is a central metabolic organ that integrates nutrient sensing, lipid handling, and detoxification to maintain systemic homeostasis. In metabolic dysfunction–associated steatotic liver disease (MASLD), chronic metabolic overload accelerates hepatocyte senescence, impairing regenerative capacity and promoting progression toward fibrosis and hepatocellular carcinoma. While transcriptomic studies have provided important insights into stress-responsive pathways, they incompletely capture the proteome remodeling and proteoform-level alterations that govern hepatocyte function during aging and disease. Recent mass spectrometry–based proteomics studies have revealed that disruption of autophagy-dependent proteome homeostasis is a defining feature of senescent hepatocytes. Quantitative analyses demonstrate coordinated alterations in selective autophagy pathways—including lipophagy, mitophagy, ferritinophagy, ER-phagy, and pexophagy—accompanied by organelle-specific protein abundance signatures and remodeling of autophagy-related proteoforms. These findings position proteomics as an essential tool for resolving the spatial and functional reorganization of hepatocyte proteomes that cannot be inferred from transcript abundance alone. In this review, we synthesize proteomics-driven evidence defining selective autophagy dysfunction in aging and MASLD livers, critically evaluate methodological limitations, and propose a conceptual framework in which impaired selective autophagy acts as a proteome-level driver of hepatocyte senescence. We further outline future directions for proteoform-resolved and spatial proteomics approaches aimed at identifying actionable targets for therapeutic intervention in liver disease.
Background: Several studies have investigated the clinical and immunological aspects of medication-related osteonecrosis of the jaw (MRONJ). However, the underlying immunological mechanisms and signaling pathways involved in its pathophysiology remain incompletely understood. This systematic review and meta-analysis, complemented by bioinformatics analyses, aimed to identify proteomic biomarkers associated with MRONJ. Methods: Six databases (PubMed, Embase, Scopus, Web of Science, Cochrane Library, and VHL) were searched, along with gray literature and manual searches. Observational studies in English comparing proteomic profiles of individuals with and without MRONJ were included. Study selection and data management were conducted using EndNote™ X8 and Rayyan.ai, and risk of bias was assessed using the QUADOMICS tool. Functional enrichment analysis was performed using g:Profiler and Reactome, and interaction networks were constructed using GeneMANIA, STRING, and MetaboAnalyst (Cytoscape program; version 3.10.1). Meta-analysis was performed in RStudio (R-4.5, Rstudio extension 2025.05.1+513) (α = 0.05). Results: Three studies were included in the review, and two in the meta-analysis. The meta-analysis showed higher salivary levels of Apolipoprotein B-100 (APOB), Apolipoprotein A-II (APOA2), and Heparin Cofactor 2 (SERPIND1) in MRONJ patients, while the protein Keratin (KRT16) showed reduced levels without statistical significance. Bioinformatics analyses indicated involvement in lipid metabolism, impaired tissue repair, and inflammatory and immune responses. Conclusions: These findings suggest altered salivary proteomic signatures in MRONJ for APOB, APOA2, SERPIND1, and KRT16 proteins.
Background: Fresh frozen tissues are considered the gold standard for proteomic analyses due to their superior preservation of protein integrity; however, their use is limited by the logistical and financial requirements of long-term cold storage. Formaldehyde-fixed paraffin-embedded (FFPE) tissues provide a practical alternative, owing to their stability and widespread availability in clinical settings. A critical step in FFPE proteomics is deparaffinization, which traditionally relies on organic solvents such as xylene, along with the efficient reversal of formaldehyde-induced crosslinks. Methods: In this study, we evaluated multiple FFPE protein extraction and digestion workflows including chaotropic, surfactant-based, and detergent-free approaches in combination with xylene-free deparaffinization strategies, using label-free data-independent acquisition (DIA) LC-MS/MS. Results: Among the tested methods, a chaotropic, reductant, and surfactant-free in-solution digestion workflow demonstrated robust protein and peptide recovery. A modified version of this protocol further improved peptide coverage while maintaining comparable protein depth. The applicability of the optimized workflow was assessed using FFPE needle biopsy samples from control, hepatic steatosis, and liver fibrosis groups. Exploratory proteomic patterns were observed across conditions, with hepatic steatosis associated with early activation of stress-response pathways, while fibrosis showed evidence suggesting altered lipid metabolism. Conclusions: Overall, this study presents a simple, xylene-free, and MS-compatible workflow for FFPE proteomics that is suitable for low-input clinical samples and may support broader application of archival tissues in proteomic research.
Background: Extracellular vesicles (EVs) mediate intercellular communication in the central nervous system and are a major source of biomarkers. This study characterizes the EV-derived proteome secreted by human endothelial brain cells (HEBCs), astrocytes, and neurons to identify cell-specific roles in intercellular communication in the brain. Methods: Mass spectrometry analyses of EVs and corresponding parent cells were performed to identify differentially enriched proteins. Gene Ontology (GO) analysis of statistically significant, abundantly expressed proteins between EVs and parent cells (log2 fold-change ≥ 2.0, p < 0.05) was performed to assess cell-specific functions. Results: Proteome analysis identified on average 932 proteins in astrocyte EVs (versus 1725 in parent cells), 1040 in HEBC EVs (versus 5451 in parent cells), and 470 in neuronal EVs (versus 578 in parent cells). The analysis indicated that astrocytes had the highest number of significantly abundant proteins (118), followed by HEBCs (24) and neurons (25). Astrocyte EVs were enriched in lipoproteins, complement factors, and protease inhibitors; HEBCs EVs in tight junction proteins, adhesion molecules, and protease regulators; and neuronal EVs in chromatin-associated histones, tubulin isoforms, and RNA-binding proteins. Conclusions: The proteomic signatures of EVs from different neurovascular unit cells suggest specialized roles in blood–brain barrier homeostasis, immune regulation, and synaptic and epigenetic signaling under healthy conditions. These baseline signatures provide a framework for future studies to investigate how brain cell-derived EVs may contribute to neurodegenerative disorders.
Background: Untargeted proteomics enables quantitative host cell protein (HCP) determination in biotherapeutics, yet no workflow has been validated under ICH Q2(R2) for regulated quality control. Methods: A prospective total-error (TE) validation of label-free ddaPASEF proteomics was performed. A stable isotope-labeled whole-proteome standard was spiked into NISTmAb at seven levels (20–80 ng) and analyzed in four independent assays (198 injections), supporting one-way random-effects ANOVA with Welch–Satterthwaite adjustment. Peptide-level identification error was evaluated by dual entrapment. Results: Empirical false-discovery proportions were below 1% at q = 0.01. Weighted least-squares regression (R2 = 0.993) confirmed stable proportional compression with 81–85% recovery. Repeatability dominated the variance structure (median CV 2.7%); intermediate precision SD ranged from 0.69% to 3.81%. Both 95% β-expectation and 95/95 content tolerance intervals were contained within ±30% at all levels, defining a validated range of 20–80 ng. Abundance-stratified TE profiling revealed concentration-dependent calibration heterogeneity, with stratum-specific intervals within ±35% defining an abundance-aware LLOQ of 3.6 ppm (P95 = 3.87 ppm). Robustness under independent search software (FragPipe v24.0, CCC = 0.998) and cross-platform acquisition (Astral, CCC = 0.980) remained within ±30% limits. Conclusions: This constitutes the first prospective ICH Q2(R2)-aligned validation of untargeted proteomics for HCP quantification, with a transferable statistical framework for high-dimensional analytical methods.
Background: Maize is a vital crop, supporting 19.5% of global calorie intake. However, maize is vulnerable to even brief periods of drought which substantially reduces seed set and therefore yield. Methods: To identify proteins involved in responses of maize to drought, soluble proteins were extracted from leaf and silk tissues of Zea mays and protein abundance and phosphorylation status were quantified relative to well-watered controls. Label-free quantification and phosphopeptide enrichment were applied to the same biological samples and over 300 proteins were identified with significantly different changes. Results: Proteins known to be involved in drought responses were identified, such as the abscisic acid pathway and transcription factors. Of particular interest is a group of dehydrins quantified at both total protein and phosphopeptide levels, permitting insight into stoichiometry. The biological function of dehydrins in the model plant Arabidopsis thaliana is known to be regulated by phosphorylation. Conclusions: Translation of protein function from model plant to crops remains highly challenging because genome duplication has created complex sets of orthologous and homologous proteins. By focusing on proteomic changes during crop stress responses, this work enables the identification of known and novel proteins, substantially aiding the transfer of knowledge from model plants to crops.
Single-cell proteomics (SCP) is an exciting new field of study with developments in the areas of sample preparation, instrumentation and informatics. SCP has captured the imagination of biologists and clinicians and the critical interest of both academic and commercial mass-spectrometry groups. Currently (i.e., at the time this manuscript was written), SCP is still difficult and slow relative to competing single-cell technologies. What SCP may lose in relative throughput, it trades for direct analysis of protein and proteoforms, albeit with biases toward those of the highest relative concentration in each cell. These strengths may not make SCP the technology of choice for every study. This perspective is intended to identify current and future biological or clinical areas where SCP has or could have the greatest potential to advance human health and knowledge. I will also discuss applications where SCP would be less impactful than other technologies and where SCP, when mature, could play a true role in clinical diagnostics.
Background: During maize anthesis, heat stress severely limits productivity—particularly under humid conditions where high humidity suppresses transpirational cooling, forcing tissues to endure direct thermal load. Methods: Using field enclosures to impose enclosure-imposed humid heat shock (EHS), we screened 135 maize inbred lines for flowering-stage yield resilience, using grain weight per ear at maturity under EHS relative to the corresponding control (CK) condition as the primary selection criterion. Based on this screen, we selected two tolerant (R025, R100) and two sensitive (R133, R135) genotypes for data-independent acquisition mass spectrometry (DIA-MS) profiling of the tassel-subtending leaf. Results: At baseline, the selected tolerant lines exhibited a constitutively distinct proteomic state, including lower abundance of light-harvesting complex components and higher abundance or detection frequency of several regulatory proteins, including SRK2E/OST1 and HSF-B2a. Under sustained EHS, the selected sensitive lines showed extensive proteomic disruption, including reduced abundance of photosynthesis-related proteins and oxidative phosphorylation, together with increased abundance of proteins associated with endoplasmic reticulum stress responses and protein turnover. In contrast, the selected tolerant lines displayed a more constrained acclimation response, characterized by relative maintenance of photosynthesis-related proteins together with selective increases in chaperone systems (HSP90/sHSPs) and benzoxazinoid biosynthesis-related proteins. Several proteins showed switch-like detection patterns between the selected tolerant and sensitive lines, including TMEM97-like and a peptidyl-prolyl isomerase, indicating potentially distinct regulatory states. Conclusions: These findings suggest that tolerant performance under enclosure-imposed heat stress is associated with a pre-conditioned proteomic state and enhanced protein homeostasis (proteostasis) buffering capacity that may help preserve photosynthetic function during flowering-stage stress. The identified proteins should be regarded as candidate markers requiring further functional validation before any application in breeding programs aimed at improving adaptation to increasingly frequent heat-stress events.
Background: Acetification is a complex process driven by acetic acid bacteria (AAB), in which high ethanol and acidity levels require strong microbial metabolic adaptation. Although the microbiota involved in vinegar production has been described, the functional mechanisms that enable these bacteria to maintain metabolic activity remain poorly understood. In this study, the functional dynamics of AAB during Verdejo vinegar acetification were analyzed using a quantitative metaproteomic approach. Methods: Acetification was performed in submerged culture under semi-continuous conditions, and samples were collected at four stages of the cycle (S1-S4). Results: LC-MS/MS analysis led to the identification of 1626 proteins, of which 1409 were assigned to the Acetobacteraceae family. Komagataeibacter europaeus was the dominant species (73.7%). Hierarchical clustering revealed four protein abundance patterns, and differential analysis identified 350 proteins with increased abundance and 169 with decreased abundance, with the greatest changes observed between S1 and S4. Functional annotation and protein-protein interaction analyses indicated that the main metabolic adaptations involve pathways related to energy metabolism, amino acid biosynthesis, membrane-associated functions, cellular homeostasis, and acid stress response. Conclusions: Overall, the results show that K. europaeus concentrates most of the metabolic activity during acetification and that proteome reorganization reflects key molecular strategies for adaptation and survival under high-acidity conditions.
We begin by expressing our sincere thanks to all Editorial Board Members, Guest Editors, Reviewers, Authors, and the staff in the Editorial Office for their dedicated service in support of Proteomes [...].
Recent advances in mass spectrometry, data-independent acquisition, proteoform-resolving workflows, and multi-omics integration have significantly expanded the scale and scope of proteomics. However, the reuse and translational application of these datasets are limited by inconsistent standards, insufficient metadata, and inadequate computational interoperability. Proteoform-centric approaches provide higher molecular resolution by capturing intact protein variants and patterns of post-translational modification. Computational methods, including selected applications of machine learning and large language models (LLMs), are increasingly used for tasks such as spectral prediction and pattern discovery in clinical proteomics datasets. Despite these advancements, FAIR (Findable, Accessible, Interoperable, and Reusable) data practices, proteoform biology, and AI analytics are often pursued independently. This work presents an integrated framework for next-generation proteomics in which standardization and FAIR (Findable, Accessible, Interoperable, and Reusable) principles establish machine-actionable foundations for proteoform-resolved analysis and computational inference. It examines community efforts to promote data sharing and interoperability, as well as strategies for characterizing proteoforms using bottom-up, middle-down, and top-down approaches. It also highlights emerging AI and ML applications within the proteomics workflow. The framework emphasizes the importance of treating proteoforms as primary computational entities and adopting FAIR practices during data collection to enable reproducible and interpretable modeling. Finally, it introduces an architectural model that integrates FAIR infrastructures and proteoform resolution. In addition, practical recommendations for making AI-ready proteomics, including a minimal community checklist to support reproducibility, benchmarking, and translational scalability, are provided.
