Journal of Proteomics

We compared traditional data-dependent acquisition mass spectrometry (DDA-MS) with the increasingly adopted data-independent acquisition (DIA-MS) to evaluate their relative utility for large-scale quantitative biofluid proteomics of lung compartments, specifically paired bronchoalveolar lavage (BAL) cells and bronchoalveolar lavage fluid (BALF). Using beryllium-related granulomatous lung disease as a focused model, we analyzed BALF and BAL cells from beryllium-sensitized (BeS) individuals using both acquisition strategies to assess proteome depth, quantitative completeness, and analytical robustness. In BAL cells, 5,640 proteins were identified by DDA-MS and 5,227 by DIA-MS; however, DIA-MS yielded markedly improved quantitative completeness, with 5,178 proteins ([~]99%) quantified across all samples compared with 3,539 ([~]63%) quantified by DDA-MS. While 3,397 proteins were quantified by both methods, DIA-MS uniquely quantified 1,781 lower-abundance proteins. Proteins identified by both DIA and DDA-MS approaches revealed pathways associated with granulomatous inflammation, including Toll-like receptor, clathrin-mediated endocytosis, sirtuin, and C-type lectin receptor signaling, whereas DIA-MS resolved additional pathways, such as the complement cascade, coagulation system, and JAK/IL-6-type cytokine signaling. In BALF, although more proteins were identified by DDA-MS than by DIA-MS (2,069 vs 1,742), DIA-MS achieved greater quantitative completeness, with 1,695 proteins quantified across all samples compared with 1,050 using DDA-MS, underscoring its suitability for biomarker-oriented analyses in lung fluid compartments. Together, these results support DIA-MS as a robust and sensitive platform for quantitative lung proteomics and discovery of disease-relevant protein signatures.

Parkinsons disease is a progressive neurodegenerative disorder associated with mitochondrial dysfunction, oxidative stress, impaired autophagy, and dysregulated cellular signaling pathways. Although Glycyrrhiza glabra has been reported to exhibit neuroprotective properties, the early phosphorylation-mediated signaling mechanisms underlying its protective effects remain poorly understood. In this study, we employed a Tandem Mass Tag (TMT)-based temporal quantitative phosphoproteomic approach to investigate early signaling events associated with Glycyrrhiza glabra-mediated neuroprotection in a rotenone-induced in vitro PD model. Differentiated IMR-32 neuronal cells were treated with rotenone alone or in combination with Glycyrrhiza glabra extract, and phosphoproteomic alterations were analyzed at 2, 5, 15, and 30 minutes using liquid chromatography coupled with tandem mass spectrometer. Temporal phosphoproteomic analysis identified 6,424 phosphopeptides corresponding to 2,368 phosphoproteins and 5,468 phosphorylation sites. Comparative analysis revealed extensive phosphorylation rewiring induced by rotenone and restoration of several dysregulated phosphorylation events following Glycyrrhiza glabra co-treatment. More than 130 phosphoproteins and multiple kinase-associated signaling pathways were dynamically regulated across the temporal conditions. Kinase enrichment analysis identified restoration of several critical kinases, including AKT1, MTOR, MAPK1/3, PRKACA, PRKCD, and GSK3A/B, which are associated with neuronal survival, stress adaptation, and autophagy. Integrated pathway and kinase-substrate interaction analyses further revealed enrichment of AMPK signaling, FOXO signaling, receptor tyrosine kinase signaling, RNA processing, and cell-cycle regulatory pathways. Notably, several spliceosome-associated phosphoproteins demonstrated dynamic phosphorylation changes during the early neuroprotective response. Collectively, this study provides a detailed temporal phosphoproteomic landscape of early signaling events associated with Glycyrrhiza glabra-mediated neuroprotection and highlights kinase-driven signaling pathways that may represent potential therapeutic targets in Parkinsons disease.

BackgroundThe developmental pattern of the crystalline lens provides a unique model to study biological aging and its effects on the posttranslational modification of long-lived proteins. The orderly differentiation of lens fiber cells leads to a spatiotemporal gradient where mature, organelle-free fiber cells are packed in the lens nucleus surrounded by con-centric rings of successively younger fiber cells in the cortex. MethodsPig lenses were separated into six layers by dissolution in a hypotonic buffer. The changes in protein abundance, oxidation, and phosphorylation that occur across the spatiotemporal gradient of the lens were assessed quantitatively by using data independent acquisition label-free proteomic analysis of these six fractions. ResultsExpected changes in protein abundance of major lens protein which reflect the maturation process of lens fiber cells across the spatiotemporal gradient were found. Significant differences were noted in phosphorylation sites on crystallins, phakinin, and actin. Significant changes in oxidation of residues on essential lens proteins, as well as several glycolytic enzymes, were found across the spatiotemporal gradient. ConclusionDissolution of the lens followed by high resolution data-independent acquisition proteomics is a powerful technique for spatial mapping of protein abundance and posttranslational modification changes in the lens. The oxidation and phosphorylation sites noted in this study may play important roles in both lens development and cataractogenesis.

The axolotl salamander can fully regenerate amputated limbs, yet the systemic consequences underlying this process remain largely understudied. Cerebrospinal fluid is an emerging signaling medium capable of communicating with both the central and peripheral nervous systems, but its composition and potential role in salamander limb regeneration have not yet been examined using modern multi-omics techniques. Here, we developed a protocol for extracting mixed perilymph-cerebrospinal fluid (P-CSF) from axolotl and provided the first proteomic and metabolomic characterization of this biological fluid. We identified 2,626 unique proteins and 173 high-confidence metabolites and quantified them across four time points of early limb regeneration. We demonstrated that limb amputation drives progressive shifts in P-CSF proteins, including an elevation of sarcomeric muscle proteins, regeneration-associated factors, and protease/extracellular matrix proteins. We observed shifts in metabolites involved in oxidative stress, polyunsaturated fatty acid oxidation, and histamine metabolism. Injury-comparison experiments revealed that the observed proteomic changes as a result of limb amputation are different than crush injury, denervation, or tail amputation. This study proposes axolotl P-CSF as a reservoir for limb amputation-associated systemic signaling and as a potential conduit of signals involved in limb regeneration.

Multicenter studies are essential for benchmarking analytical workflows, yet their interpretation is often confounded by the combined effects of experimental protocols and instrumentation. To address this challenge, we introduce a simple normalization-based analytical framework, the recovery metric ({rho}), designed to decouple protocol driven effects from instrument dependent variability. We applied this framework to the 13th Proteomics Multicentric Experiment (PME13), a large multicentric proteomics dataset generated across 27 laboratories using high sensitivity workflows and varying sample preparation protocols. By leveraging a common digested reference sample, {rho} enables direct cross-comparison of all datasets on a unified scale, effectively minimizing instrument-related biases. Using this approach, we demonstrate that apparent instrument dependent trends are largely removed when evaluated through {rho}, revealing consistent protocol driven effects across laboratories. Statistical modeling identified key variables influencing {rho}, including sample input amount, reduction and alkylation, and the use of n-dodecyl-{beta}-D-maltoside (DDM). While DDM was associated with improved {rho}, reduction and alkylation and additional handling steps led to reduced performance, particularly at low input levels. We further highlight practical considerations for the application of ratio based normalization, including the occurrence of values exceeding theoretical bounds, which reflect deviations from underlying assumptions and require appropriate filtering. Overall, this work establishes a generalizable analytical strategy for disentangling confounding factors in multicentric datasets and provides practical guidelines for optimizing high sensitivity proteomics (HSP) workflows. The proposed framework is broadly applicable to other analytical fields where cross laboratory comparability is required.

This study employed a "one-versus-many" (a single pregnant woman compared with multiple non-pregnant women) urinary proteomic comparative framework to examine whether fetal development-related signals can be captured through changes in the urinary proteome under conditions of limited sample size. The experimental group consisted of urinary proteomic data from three women with normal pregnancies (R6, R15, R16) at three gestational time points ([~]6-8 weeks, 22-24 weeks, and 32-34 weeks; Wang et al., 2022), while the control group consisted of urinary proteomic data from six healthy non-pregnant women (Bao & Gao, ChinaXiv: 202302.00108v2). A total of nine independent "1 vs. 6" differential protein analyses and DAVID GO Biological Process enrichment analyses (P < 0.05) were performed. The results showed that all three pregnant women exhibited a large number of differentially expressed proteins at each time point, and all enriched GO BP terms highly relevant to concurrent fetal organ development (nervous system, lung, eye, ear, kidney, etc.). This suggests that the pregnancy urinary proteome can reflect fetal development signals, corroborating the findings reported by Wang et al. (2025) in a rat model. This study demonstrates that the one-versus-many comparative approach maintains high sensitivity under small-sample conditions and can provide a methodological reference for personalized pregnancy medicine.

O_LIDiatoms are key players in marine ecosystems and are frequently exposed to low-oxygen conditions in sediments and oxygen minimum zones. However, the metabolic strategies that enable their survival under anoxia remain poorly understood. C_LIO_LIUsing the model diatom Thalassiosira pseudonana, we investigated its response to dark anoxia. We first used proteomic approaches to identify differences between dark anoxia and dark oxic conditions combined with a phylogenetic analysis to decipher how the anoxic tolerance was acquired by this lineage. This approach revealed unexpected shunts involving amino acid pathways. We then correlated these findings with targeted metabolomic analysis on amino acids. C_LIO_LIOur results show that the diatom T. pseudonana undergoes a coordinated metabolic reprogramming under anoxia, centered on alanine production and tightly coupled to nitrogen metabolism. This work reveals how carbon and nitrogen fluxes are integrated to maintain cellular homeostasis in the absence of oxygen and provides a framework for understanding the resilience of diatoms in oxygen-depleted environments. C_LIO_LIWe identify three key features for anoxic adaptation in this lineage (i) alanine-centered metabolic reprogramming as a central component of acclimation to anoxia (ii) the involvement of an arginine-succinate shunt; and (iii) a critical contribution of lateral gene transfer (LGT) for anoxic tolerance. C_LI

The plant immune system comprises a complex signaling network that is activated upon perceiving molecules derived from invading pathogens. The first line of defense at the plant cell surface is mediated by receptor-like proteins (RLPs) and receptor-like kinases (RLKs). RLPs, which lack a cytoplasmic signalling domain themselves, constitutively interact with the RLK SUPRESSOR OF BIR1-1 (SOBIR1), which is a key component initiating immune signal transduction upon pathogen perception. Therefore, elucidating the composition of the SOBIR1 protein complex will contribute to understanding the basic molecular mechanisms of plant disease resistance. Most of the studies focused on the identification of SOBIR1-interacting proteins are limited to model plants, due to technical challenges and lack of reliable genome and proteome databases in crop plants. Here, we evaluate the application of the biotin ligase TurboID (TbID)-based proximity-dependent labeling (PL) approach by transiently expressing SOBIR1 from Nicotiana benthamiana (NbSOBIR1), fused to TbID in leaves of the wild potato Solanum microdontum. We show that NbSOBIR1-YFP-TbID properly accumulates in potato and that proximal proteins are biotinylated. Quantitative proteomic analysis yielded over 130 candidate proteins to be in the proximity of the cytoplasmic kinase domain of NbSOBIR1, of which some could be linked to disease resistance by KEGG pathway and gene ontology (GO) molecular function analysis. We also studied the dynamics of the proteome in proximity of NbSOBIR1 upon perception of the INF1 elicitin of Phytophthora infestans that was co-expressed in potato with the elicitin receptor ELR, which is an RLP that constitutively interacts with SOBIR1. We found more than 80 proteins, including the NB-LRR REQUIRED FOR HR-ASSOCIATED CELL DEATH 1 (NRC1), putatively interacting with NbSOBIR1. In conclusion, we were able to successfully apply PL in potato and a future roadmap for further research on deciphering the composition of protein complexes involved in immune signaling has been established.

Plasma proteomics is used for a variety of applications including biomarker discovery, disease monitoring, and drug development. Data-independent acquisition (DIA) has vastly improved the breadth of proteins that are identified from samples; however, given challenges in reproducibility and translation, it is critical that the quantitative performance of these methods is reliable. Analysis of global proteomics data typically incorporates information from all detected peptides. However, some peptides do not reflect their parent protein amount, due to irreproducible digestion, modification, analytical interferences or instability. We hypothesise that including these peptides impacts protein relative quantification, and thus, a refined spectral library containing only quantitatively representative peptides provides superior protein quantification. By analysing a defined multi-species spike-in model, we show that refining a plasma spectral library by removing precursors that fail to meet quality control metrics (25.4% of all identified precursors) reduces noise and variability, improving precision, accuracy and differential abundance analysis by up to [~]11%, with minimal identification losses and substantial reduction in computational demand. This demonstrates proof-of-concept that refining spectral libraries produces results that prioritize quantification quality over quantity. This approach could enable development of universal tissue-specific refined spectral libraries able to improve quantification quality with easy implementation and minimal processing time. Significance of the StudyAs DIA mass spectrometry proteome depth increases, the quality of the associated protein quantifications must be considered alongside identification breadth, particularly in complex matrices such as plasma, which presents additional technical challenges. The spectral library used for protein identification and quantification is a critical determinant of DIA performance, and its composition requires considerable consideration. This work illustrates an initial step toward improving protein quantification starting at the spectral library level by filtering precursors which are poor quantitative representatives of their parent proteins. In doing so, the resulting data is more reliable for downstream and biological interpretation, with fewer false differential abundance assignments and reduced quantitative noise. As such, this work represents a broader shift away from the habitual focus of MS workflows on maximising the number of protein and differential abundance identifications and instead prioritises the quality of quantification over quantity. These initial findings lay the groundwork for further development of spectral library refinement strategies, with the potential to continue improving the accuracy and precision of protein quantification in DIA-based proteomics.

BackgroundSepsis-associated acute kidney injury (S-AKI) is a major contributor to morbidity and mortality in critically ill patients. However, the molecular mechanisms underlying its temporal progression remain poorly understood because conventional biomarkers primarily reflect renal dysfunction rather than disease pathogenesis. Urinary extracellular vesicles (uEVs), which carry kidney-derived molecular cargo, provide a promising platform for monitoring renal-specific biological alterations during disease progression. MethodsWe conducted a longitudinal multi-omics study of uEVs collected from 81 patients with sepsis, including 48 patients with S-AKI and 33 sepsis-only controls. Patients were randomly assigned to a discovery cohort (n = 52) and an independent validation cohort (n = 29). Urine samples were collected at Day 1, Day 4, and Day 8 after AKI diagnosis. High-resolution proteomic and metabolomic profiling was performed to characterize temporal molecular alterations. Enriched pathways identified in the discovery cohort were evaluated in the validation cohort using pathway-level concordance analysis. ResultsComparative analysis between S-AKI and sepsis-only patients identified distinct stage-specific molecular alterations throughout disease progression. At the early stage (Day 1), validated pathways included complement and coagulation cascades, ferroptosis, HIF-1 signaling, sphingolipid metabolism, and arachidonic acid metabolism, highlighting coordinated inflammatory, hypoxic, and lipid metabolic responses. During the mid-stage (Day 4), persistent activation of complement and coagulation cascades, ferroptosis, and HIF-1 signaling was accompanied by metabolic reprogramming involving alanine, aspartate and glutamate metabolism and tyrosine metabolism. Although limited sample availability reduced statistical power at Day 8, phenylalanine metabolism remained validated in the metabolomic analysis, suggesting persistent metabolic dysregulation during late-stage disease progression. ConclusionsThis study provides the first longitudinal, independently validated multi-omics characterization of human uEVs in S-AKI. By integrating proteomic and metabolomic profiling, we reveal the temporal evolution of renal-specific molecular pathways from early inflammatory and hypoxic responses to subsequent metabolic reprogramming. These findings establish uEV-based multi-omics as a promising strategy for molecular phenotyping of S-AKI beyond conventional clinical biomarkers and provide a valuable resource for future biomarker discovery and therapeutic target identification.

Melittin, the principal bioactive peptide of bee venom, exhibits promising antitumor activity, whereas its molecular mechanisms in cervical cancer remain incompletely understood. In this study, the biological effects and molecular responses of melittin in U14 cervical cancer cells were investigated using Astral data-independent acquisition (Astral-DIA)-based quantitative proteomics combined with molecular validation. The effects of melittin on cell migration, invasion, and cell death were evaluated by Transwell assays and PI/Hoechst staining. Differentially expressed proteins (DEPs) were screened and subjected to Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and protein-protein interaction (PPI) analyses. Representative oxidative stress-related genes and proteins were further validated by RT-qPCR and Western blotting. Melittin significantly inhibited the migration and invasion of U14 cervical cancer cells and increased cell death. Quantitative proteomics identified 9,782 protein groups and 187 DEPs, including 71 up- and 116 down-regulated proteins. KEGG pathway enrichment analysis revealed oxidative phosphorylation (OXPHOS) as the most significantly enriched pathway, together with glutathione metabolism, ferroptosis-related pathways, reactive oxygen species signaling, and mitophagy. GO term enrichment analysis indicated that DEGs were mainly engaged in mitochondrial function, electron transport, oxidoreductase activity, and energy metabolism. RT-qPCR assay demonstrated altered expression of Duox1, Gpx4, Gsx2, Nfe2l2, and Gstp2. Additionally, PPI analysis identified Gstp2 and ODC1 as representative hub proteins involved in redox regulation and metabolic adaptation. Furthermore, Western blotting confirmed increased GSTP2 expression following melittin treatment. Overall, these findings provide a comprehensive proteomic landscape of melittin-treated U14 cervical cancer cells and suggest that mitochondrial OXPHOS remodeling and redox-associated pathways may contribute to the antitumor activity of melittin.

BackgroundIgA nephropathy (IgAN) is increasingly recognized as a systemic immune-mediated disease characterized by aberrant IgA1 glycosylation, circulating immune complex formation, complement activation, and emerging metabolic perturbations. However, clinical diagnosis still relies on invasive renal biopsy, and non-invasive biomarkers capable of capturing both systemic immune activation and kidney-specific alterations remain lacking. Extracellular vesicles (EVs), as biologically active carriers of proteins and metabolites, provide a unique opportunity to interrogate compartment-specific molecular signatures underlying IgAN pathophysiology. MethodsWe performed an integrated, untargeted multi-omics analysis of plasma- and urine-derived EVs from 60 individuals (24 IgAN, 21 chronic kidney disease [CKD], and 15 controls). Differentially expressed proteins (DEPs) and metabolite features (DEFs) discriminating IgAN from CKD and controls were identified using Venn diagram analysis, followed by pathway enrichment and receiver operating characteristic (ROC) evaluation. ResultsVenn analysis identified 22 and 3 candidate DEPs in plasma EVs (pEVs) and urinary EVs (uEVs), respectively, revealing broader systemic proteomic alterations relative to renal EV cargo. Notably, complement and coagulation regulators, including C4b-binding protein alpha chain (C4BPA) and vitamin K-dependent protein S (PROS1), demonstrated strong discriminatory performance between IgAN and CKD (AUC = 0.826 and 0.795), suggesting EV-associated complement-coagulation crosstalk in IgAN. Metabolomic profiling revealed 1,006 and 540 candidate DEFs in pEVs and uEVs, respectively. Enrichment analyses highlighted steroid biosynthesis and fatty acid metabolism pathways in both compartments, indicating immune-metabolic reprogramming. Three metabolite features (C27H44O, C30H50O, and C28H46O) distinguished IgAN from CKD with high accuracy (AUC = 0.942-0.877). ConclusionsThis study provides the first compartment-resolved, plasma- and urine-derived EV multi-omics landscape of IgAN. Our findings suggest that EV cargo reflects coordinated complement dysregulation and metabolic alterations, extending current understanding of IgAN beyond glomerular immune complex deposition. These EV-associated proteins and metabolites offer a mechanistically informed framework for non-invasive biomarker development and for exploring immune-metabolic pathways involved in IgAN progression.

Data-independent acquisition (DIA) mass spectrometry has emerged as a powerful tool for neuropeptidomics, but its success relies heavily on the quality of spectral libraries used for peptide identification. There are inherent challenges to mass spectrometry analysis of crustacean neuropeptides, including the endogenous nature in which they are analyzed, extensive post-translational modification (PTM), and atypical fragmentation patterns. Thus, general-purpose proteomic spectral prediction tools may not perform optimally in the endogenous peptide domain. In this study, we benchmark four widely used spectral prediction platforms, Prosit, MS2PIP, AlphaPeptDeep, and UniSpec, to evaluate their performance in predicting the fragmentation of neuropeptides. Using an empirically derived spectral library from crustacean tissues as reference, we assess model compatibility, dot-product similarity, Pearson correlation, and DIA-based identifications across brain, sinus gland, and pericardial organ samples. Our results reveal that no single model comprehensively captures neuropeptide fragmentation characteristics. While UniSpec showed unexpected strengths due to its inclusion of neutral loss ions, AlphaPeptDeep demonstrated the highest spectral similarity, and MS2PIP and Prosit outperformed in DIA-NN identifications. We further highlight the critical impact of neutral loss fragments, present in over 50% of empirical spectra, and emphasize the need for hybrid spectral libraries that integrate complementary strengths across models. This work provides a foundational framework for optimizing spectral library selection in neuropeptidomics and underscores the importance of model-specific biases when analyzing structurally diverse endogenous peptides.

BackgroundThe commercialization of Nigerias single-line pod borer-resistant (PBR) cowpea (IT97KT), the first transgenic cowpea variety in the world expressing Cry1Ab gene, has raised questions about potential unintended molecular changes and their ecological implications. This study employed integrated proteomic and metabolomic profiling to compare the transgenic line with its non-transgenic isoline (IT97KN) and assess molecular indicators associated with ecosystem resilience. MethodsProteomic analyses were conducted using LC-MS/MS following filter-assisted sample preparation, while metabolomic profiling employed GC-MS and UHPLC-MS/MS platforms. Differential protein and metabolite abundance were assessed using label-free quantification, volcano plot analysis, principal component analysis (PCA), hierarchical clustering, and Gene Ontology (GO) enrichment analyses. ResultsProteomic profiling revealed substantial overlap between IT97KT and IT97KN, with only a limited subset of proteins exhibiting significant differential abundance. Upregulated proteins in IT97KT were primarily associated with seed storage, redox regulation, oxidative stress mitigation, and defense-related functions, including Late Embryogenesis Abundant Protein 1 (LEA1), vicilins, thioredoxin, and iron superoxide dismutase. Among 37 proteins linked to ecological adaptation, only LEA1, CPRD22, and Bg7S showed significant differences. Similarly, only carbonic anhydrase II displayed differential abundance among proteins associated with potential ecological risk. PCA and clustering analyses demonstrated high proteomic similarity between genotypes. Metabolomic analyses identified sixteen major metabolites, predominantly fatty acids, with no statistically significant differences in abundance or composition between transgenic and non-transgenic lines ConclusionsThe transgenic PBR cowpea exhibited minimal unintended proteomic and metabolomic alterations relative to its non-transgenic isoline. These findings indicate that Cry1Ab insertion did not substantially disrupt molecular pathways associated with ecological adaptation, environmental risk, or metabolic homeostasis, providing molecular evidence supporting the environmental and biosafety equivalence of PBR cowpea.

Novel serum peptidomics offers a direct insight into proteolytic activity, tissue injury, and systemic signaling. Nevertheless, existing workflows suffer from low peptide yields, low throughput, and limited recovery of low-abundance species. Here we present a native serum peptidomics protocol that integrates mild acid treatment, solid-phase extraction with molecular weight cutoff filtration and data-independent acquisition mass spectrometry (DIA-MS). The protocol requires less than 100 {micro}l of serum or plasma, is completed within hours, time-cost-effective and compatible with 96-well formats without specialized equipment. Applied to a proof-of-concept cohort of patients with acute ischemic stroke (AIS), intracranial haemorrhage (ICH), and healthy controls, the workflow identified over 12,000 peptides, exceeding the three-fold threshold of existing peptidomics approaches. DIA-MS analysis across independent batches demonstrated 78-83% peptide overlap and consistent fold-change directionality. We further introduce peptide locus analysis, which aggregates overlapping peptides within defined protein regions. This approach revealed bidirectional regulation within individual precursor proteins such as the fibrinogen alpha chain (FIBA), resolving intraprotein proteolytic dynamics. Three candidate peptides from TYB4, CO4B, and ITIH4 proteins accurately distinguished stroke subtypes and controls, while characteristic shifts in peptide physicochemical properties were observed across strokes. This workflow substantially advances the sensitivity, throughput, and biological resolution of serum peptidomics for quantitative multi-biomarker discovery, validation and its output promises effective implementation of AI/ML models aiming for new dimensions in diagnostics, prognostics, prediction and monitoring.

Experimental bacterial meningitis is often analyzed as microbial burden and inflammation, but the tissue-level organization of host-pathogen interplay remains poorly resolved. Here, we use multimodal imaging to map Escherichia coli meningoencephalitis as a spatially organized process across infected rat brain tissue and cerebrospinal fluid (CSF). A low-dose intracerebral inoculum expands within 24 h into an anatomically structured infection involving ventricular, meningeal, and perivascular interfaces. Scanning electron microscopy reveals biofilm-like multicellular E. coli aggregates embedded in extracellular material, consistent with neutrophil extracellular traps. MALDI mass spectrometry imaging detects aerobactin and salmochelin-derived metabolites, whereas intact enterobactin is not detected despite favorable analytical sensitivity. These bacterial iron-acquisition signals partially overlap with calprotectin proteoforms, defining ventricular and periventricular metal-conflict microenvironments. Spatial peptidomics identifies infection-enriched antimicrobial territories dominated by rat neutrophil peptides RatNP-2, RatNP-3, and RatNP-4. Endogenous proenkephalin-derived neuropeptides are reduced in basal ganglia regions. Finally, cerebrospinal fluid captures both bacterial siderophores and host antimicrobial peptides with lipocalin-2 protein, linking tissue-resolved host-pathogen chemistry to a proximal diagnostic fluid. Together, these data show that experimental E. coli meningoencephalitis is a spatially organized host-pathogen process. Bacterial communities, nutritional immunity, antimicrobial peptides, and neuropeptide networks occupy distinct but connected CNS niches, components of which are recoverable in CSF.

Microglial inflammatory activation is accompanied by extensive molecular remodeling, yet proteomic, lipidomic, and metabolomic responses are often analyzed independently. Here, we applied an integrated mass spectrometry-based multiomic workflow to characterize proteomic, lipidomic, and polar metabolomic remodeling from matched BV-2 biological samples following stimulation with interferon-{gamma} and lipopolysaccharide (IFN-{gamma} and LPS). Inflammatory activation was confirmed by increased nitrite accumulation, elevated TNF- and IL-6 secretion, and treatment-associated morphological changes. Discovery proteomics quantified 8,676 proteins and identified 562 significantly altered proteins, including 344 increased and 218 decreased proteins. Increased proteins were enriched for interferon-responsive, innate immune, inflammatory effector, and antigen-associated pathways, whereas decreased proteins were associated with cellular organization, protein biogenesis, vesicular trafficking, and metabolic regulation. Targeted lipidomics identified 237 significantly altered lipid features out of 356 measured lipids, including increased triacylglycerols and diacylglycerols and broad remodeling of glycerophospholipids, lysophospholipids, and sphingolipid-related species. Targeted polar metabolomics identified 75 significantly altered metabolites out of 98 measured metabolites, including changes in nucleotide/NAD-related metabolism, amino acid metabolism, methylation-associated metabolites, acylcarnitine abundance, phospholipid precursors, polyamine metabolism, arginine/nitric oxide-associated metabolism, and redox-associated metabolites. Process-level integration of significant features revealed coordinated remodeling of inflammatory protein programs with lipid storage, membrane remodeling, nucleotide metabolism, amino acid availability, phospholipid precursor abundance, nitric oxide-associated metabolism, and redox/osmolyte pathways. These findings demonstrate that IFN-{gamma} and LPS-induced activation of BV-2 cells involves integrated immune, lipid, and metabolic adaptation rather than isolated induction of canonical inflammatory mediators. This integrated multiomic framework provides a resource for investigating how lipid and metabolic remodeling regulate microglial inflammatory states. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=121 SRC="FIGDIR/small/741024v1_ufig1.gif" ALT="Figure 1"> View larger version (23K): org.highwire.dtl.DTLVardef@11dd431org.highwire.dtl.DTLVardef@155f107org.highwire.dtl.DTLVardef@1430e89org.highwire.dtl.DTLVardef@16f4a25_HPS_FORMAT_FIGEXP M_FIG C_FIG

Abstract Background: Intraductal papillary mucinous neoplasms (IPMNs) are recognized as precursor lesions to pancreatic ductal adenocarcinoma (PDAC). However, the molecular programs underlying progression from low-grade dysplasia to advanced disease remain incompletely characterized. Herein, we performed an integrated plasma and tissue-proteomic analyses coupled with spatial and single-cell transcriptomics to identify biologically coherent remodeling programs reflected in circulation that distinguish IPMN by dysplasia grade and invasive disease. Methods: Using the O-link proximity extension assay platform, a panel of 1,104 proteins were quantified in plasma samples collected from patients with low-grade (LG) IPMN (n=30), high-grade (HG) IPMN with or without associated PDAC (IPMN/PDAC; n=40) and PDAC without IPMN (n=8). Predictive performance of individual biomarkers were assessed; likelihood ratio testing was performed to identify protein biomarkers that were complementarity with CA19-9 for risk of malignancy of IPMN. Findings were intersected with available spatial (N= 13) and single-cell (N= 6) transcriptomic datasets of IPMN tissues as well as mass spectrometry-based proteomic profiles of an independent set of resected human IPMN tissues (N= 9). Results: A total of 28, 43, and 35 circulating proteins were found to be differential in HG, IPMN/PDAC, and HG + IPMN/PDAC cases compared to LG IPMN. Among differential proteins were known PDAC-associated markers CEACAM5, CTRC, and REG3A as well as several biomarkers reflecting cytoskeletal and extracellular matrix remodeling and inflammatory processes. Focusing on cytoskeletal and ECM-related proteins and using likelihood ratio testing, an OR rule considering CA19-9, BGN, and ITGB1BP1 achieved overall sensitivity of 48.7% for HG + IPMN/PDAC, including 38.1% sensitivity for HG IPMN, at an overall specificity of 90%, which was improved compared to that of CA19-9 alone (overall sensitivity of 28.2%; McNemar Exact test 1-sided p-value: 0.011). Integrated proteomic and spatial transcriptomic datasets of IPMN tissues revealed coordinated alterations cytoskeletal and ECM remodeling and elevated matrix stiffness as prominent features associated with IPMN/PDAC, which paralleled concordant increases in BGN and ITGB1BP1. Cell-type of origin analyses based on spatial and single-cell data further revealed fibroblasts and myeloid cells as primary contributors to expression levels of BGN whereas ITGB1BP1 was primarily expressed in neoplastic epithelium. Conclusion: Advanced IPMN dysplasia and invasive disease are characterized by coordinated tissue remodeling programs that are systemically reflected in circulating proteomic profiles. Blood-based biomarkers identified through our study, such as BGN and ITB1BP1, have potential to improve upon CA19-9 for risk stratification of IPMN to better guide clinical management.

Serum proteomics is increasingly used for minimally invasive biomarker discovery and disease phenotyping, and the choice of serum preprocessing workflow can shape proteome depth, quantitative characteristics, and downstream biological readouts. However, disease-oriented comparisons within a single cohort remain limited. Here, we compared four serum preprocessing workflows--Top14 depletion (TOP14D), tomato lectin affinity purification (TomAP), and two nanoparticle-based enrichment workflows (NPA and NPB)--using serum from six patients with systemic juvenile idiopathic arthritis (sJIA) and six age- and sex-matched healthy controls, and analyzed them using unified data-independent acquisition mass spectrometry (DIA-MS) and a statistical pipeline. We evaluated proteome depth, missingness, quantitative characteristics, group separation, differential abundance signatures, pathway enrichment, curated sJIA-related gene set coverage, pre-ranked gene set enrichment analysis (GSEA) results, and detection of inflammasome/interferon-related proteins. TomAP yielded the greatest proteome depth (7612 proteins), followed by NPB (6735 proteins) and NPA (6602 proteins), whereas TOP14D yielded the smallest protein set (3303 proteins). Principal component analysis (PCA) showed a separation between the sJIA and control groups for all workflows. Differentially expressed proteins (DEPs) showed limited overlap, with only 75 DEPs common to all four workflows. Functional enrichment patterns were workflow-dependent; TOP14D and TomAP mainly captured neutrophil/myeloid and inflammatory processes, whereas NPA and NPB captured RNA processing- and translation-related signals. TomAP showed relatively broad coverage and positive enrichment of curated sJIA-related gene sets associated with inflammation, innate immunity, and macrophage activation syndrome (MAS). Inflammasome/interferon-related proteins, including NLRC4, PYCARD, GSDMD, MEFV, IL-18, OAS3, and MYD88, showed workflow-dependent detectability and differential abundance. These findings support a disease-oriented benchmark for fit-for-purpose workflow selection according to the disease axis and analytical objective rather than proteome depth alone.

The natural variation that distinguishes living organisms within a single species is currently being studied intensively, primarily at the genetic level. Unfortunately, studies of natural variants at the level of protein gene products are not very common, mainly due to the lack of appropriate databases and bioinformatics tools. The main research technique used to study proteomes/peptidomes is mass spectrometry (MS). A classic method for interpreting raw mass spectrometry data in proteomic/peptidomic studies involves the use of databases containing representative (canonical) sequences that define the proteome of the organism under study. In this paper, we present the AliceDB database, which contains information on over 7 million natural variants of protein sequences described in the scientific literature for Homo sapiens. The data contained in the AliceDB database can be utilized using widely available and commonly used software for interpreting proteomic data. Test results regarding the use of the AliceDB database for the interpretation of proteomic data indicate that accounting for the presence of natural variants increases both the number and quality of identified proteins. Furthermore, it is easy to identify protein sequence variants that may, for example, be of significance in medicine.