Search Results (388)

Although Hylocereus polyrhizus pulp residues polysaccharides (HPPP) have shown potential in improving metabolic disorders and intestinal barrier function, the mechanism by which they exert their effects through regulating O-glycosylation modifications in the mucus layer remains unclear. Therefore, this study established a HFD-induced obese colitis mouse model (n = 5 per group) and combined nano-capillary liquid chromatography-tandem mass spectrometry (nanoLC-MS/MS) technology to quantitatively analyze the dynamic changes in O-glycosylation. Additionally, through quantitative O-glycosylation proteomics and whole-proteome analysis, we identified 155 specifically altered O-glycosylation sites in colon tissue, with the glycosylation modification level of the MUC2 core protein increased by approximately 2.1-fold. The results indicate that HPPP alleviates colonic mucosal damage by regulating interactions between mucus O-glycosylation. Overall, we demonstrated that HPPP increases HFD-induced O-glycosylation sites, improves intestinal mucosal structure in obese mice, and provides protective effects against obesity-induced intestinal mucosal damage. Full article

Background/Objectives: Degenerative joint diseases (DJDs) involve intricate molecular disruptions within bone, cartilage, and synovial tissues, often preceding overt radiographic changes. These tissues exhibit complex biomolecular architectures and their degeneration leads to microstructural disorganization and inflammation that are challenging to detect with conventional imaging techniques. This review aims to synthesize recent advances in imaging, computational modeling, and sequencing technologies that enable high-resolution, non-invasive characterization of joint tissue health. Methods: We examined advanced modalities including high-resolution MRI (e.g., T1ρ, sodium MRI), quantitative and dual-energy CT (qCT, DECT), and ultrasound elastography, integrating them with radiomics, deep learning, and multi-scale modeling approaches. We also evaluated RNA-seq, spatial transcriptomics, and mass spectrometry-based proteomics for omics-guided imaging biomarker discovery. Results: Emerging technologies now permit detailed visualization of proteoglycan content, collagen integrity, mineralization patterns, and inflammatory microenvironments. Computational frameworks ranging from convolutional neural networks to finite element and agent-based models enhance diagnostic granularity. Multi-omics integration links imaging phenotypes to gene and protein expression, enabling predictive modeling of tissue remodeling, risk stratification, and personalized therapy planning. Conclusions: The convergence of imaging, AI, and molecular profiling is transforming musculoskeletal diagnostics. These synergistic platforms enable early detection, multi-parametric tissue assessment, and targeted intervention. Widespread clinical integration requires robust data infrastructure, regulatory compliance, and physician education, but offers a pathway toward precision musculoskeletal care. Full article

Background: Rheumatoid arthritis (RA) is a chronic autoimmune disorder that predominantly affects synovial joints, leading to inflammation, pain, and progressive joint damage. Despite therapeutic advancements, the molecular basis of established RA remains poorly defined. Methods: In this study, we conducted an untargeted plasma proteomic analysis using two-dimensional differential gel electrophoresis (2D-DIGE) and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) in samples from RA patients and healthy controls in the discovery phase. Results: Significantly (ANOVA, p ≤ 0.05, fold change > 1.5) differentially abundant proteins (DAPs) were identified. Notably, upregulated proteins included mitochondrial dicarboxylate carrier, hemopexin, and 28S ribosomal protein S18c, while CCDC124, osteocalcin, apolipoproteins A-I and A-IV, and haptoglobin were downregulated. Receiver operating characteristic (ROC) analysis identified CCDC124, osteocalcin, and metallothionein-2 with high diagnostic potential (AUC = 0.98). Proteins with the highest selected frequency were quantitatively verified by multiple reaction monitoring (MRM) analysis in the validation cohort. Bioinformatic analysis using Ingenuity Pathway Analysis (IPA) revealed the underlying molecular pathways and key interaction networks involved STAT1, TNF, and CD40. These central nodes were associated with immune regulation, cell-to-cell signaling, and hematological system development. Conclusions: Our combined proteomic and bioinformatic approaches underscore the involvement of dysregulated immune pathways in RA pathogenesis and highlight potential diagnostic biomarkers. The utility of these markers needs to be evaluated in further studies and in a larger cohort of patients. Full article

Radiotherapy resistance represents a critical aspect of cancer treatment, and molecular characterization is needed to explore the pathways and mechanisms involved. DNA repair, hypoxia, metabolic reprogramming, apoptosis, tumor microenvironment modulation, and activation of cancer stem cells are the primary mechanisms that regulate radioresistance, and understanding their complex interactions is essential for planning the correct therapeutic strategy. Proteomics has emerged as a key approach in precision medicine to study tumor heterogeneity and treatment response in cancer patients. The integration of mass spectrometry-based techniques with bioinformatics has enabled high-throughput, quantitative analyses to identify biomarkers, pathways, and new potential therapeutic targets. This review highlights recent advances in proteomic technologies and their application in identifying biomarkers predictive of radiosensitivity and radioresistance in different tumors, including head and neck, breast, lung, and prostate cancers. Sample variability, data interpretation, and the translation of findings into clinical practice remain challenging elements of proteomics. However, technological advancements support its application in a wide range of topics, allowing a comprehensive approach to radiobiology, which helps overcome radiation resistance. Ultimately, incorporating proteomics into the radiotherapy workflow offers significant potential for enhancing treatment efficacy, minimizing toxicity, and guiding precision oncology strategies. Full article

Background/Objectives: Ruptured abdominal aortic aneurysm (RAAA) remains a leading cause of vascular death, with mortality rates approaching 90%. Biomarkers capable of identifying the most at-risk population are urgently needed in the clinic. We aimed to identify potential alterations in the urine proteome that can enable non-invasive detection of abdominal aortic aneurysms (AAA) at high risk of rupture. Methods: We used multiplexed kinase inhibitor beads (MIBs) and quantitative mass spectrometry (MIB/MS) to examine potential biomarkers in urine samples. Quantitative proteomic profiling was conducted using iTRAQ labeling and LC-TEMPO MALDI-TOF/TOF analysis, revealing several dysregulated proteins in the urinary proteome between the two groups. MS and MS/MS data were generated using MALDI TOF/TOF instruments (models 5800 or 4800; AB SCIEX). MS/MS spectra were processed with ProteinPilot™ software version 3.0 (AB SCIEX) and matched against the UniProt/Swiss-Prot database for identification of proteins with an Unused ProtScore >1.3. Statistical tests were performed using R/Bioconductor software and bioinformatics analysis using open-source software. Results: We quantitatively measured activity over 130 kinases from various kinase families using MIB/MS with a threshold of 1.5-fold change in expression. Statistical analysis assigned significance to EPHB6, AXL, EPHB4, DDR1, EPHA2 and EPHB3. All were tyrosine kinases, and the Ephrin receptor type was dominant. The reduced expression of specific kinases identified by MIB/MS analysis was validated by Western blot. Conclusions: This pilot study presents a promising breakthrough in the diagnosis and surveillance of AAA. We identified six dysregulated tyrosine kinases in the urine proteome of patients with RAAAs, suggesting their potential as urinary biomarkers for early detection of AAA at high risk of rupture. However, these preliminary findings require confirmation in larger, prospective cohorts to validate their diagnostic utility and generalizability. Full article

Background: Deinococcus radiodurans, renowned for its exceptional resistance to radiation, provides a robust model for elucidating cellular stress responses and DNA repair mechanisms. Previous studies have established PprI as a key regulator contributing to radiation resistance through its involvement in DNA damage repair pathways, oxidative stress response, and metabolic regulation. Methods: Building upon these foundations, our study employs label-free quantitative (LFQ) proteomics coupled with high-resolution mass spectrometry to systematically map pprI deletion protein networks by comparing the global proteomic profiles of pprI knockout and wild-type D. radiodurans strains. Results: Under stringent screening criteria, we identified 719 significantly higher and 281 significantly lower abundant proteins in the knockout strain compared to wild-type strains. Functional analysis revealed that PprI deficiency disrupts homologous recombination (HR) repair, activates nucleotide excision repair (NER) and base excision repair (BER) as a compensatory mechanism, and impairs Mn/Fe homeostasis and carotenoid biosynthesis, leading to increased oxidative stress. Furthermore, PprI deficiency induces significant metabolic reprogramming, including impaired purine synthesis, compromised cell wall integrity, etc. Conclusions: These proteomic findings delineate the extensive regulatory network influenced by PprI, revealing coordinated perturbations across multiple stress response systems when PprI is absent. Full article

Phytopathogenic fungi are important causative agents of many plant diseases, resulting in substantial economic losses in agriculture. Proteomics has become one of the most relevant high-throughput technologies, and current advances in proteomic methodologies have been helpful in obtaining massive biological information about several organisms. This review outlines recent advances in mass spectrometry-based proteomics applied to the study of phytopathogenic fungi, including analytical platforms such as LC-MS/MS and MALDI-TOF, as well as quantitative strategies including TMT, iTRAQ, and label-free quantification. Key findings are presented from studies exploring infection-related protein expression, virulence-associated factors, post-translational modifications, and fungal adaptation to chemical fungicides, antimicrobial peptides, and biological control agents. Proteomic analyses have also elucidated mechanisms of resistance, oxidative stress response, and metabolic disruption following exposure to natural products, including essential oils and volatile organic compounds. The proteomic approach enables a comprehensive understanding of fungal biology by identifying proteins related to pathogenicity, stress adaptation, and antifungal resistance, while also facilitating the discovery of molecular targets and natural compounds for the development of sustainable antifungal strategies that reduce risks to human health and the environment. Full article

The disruption of microglial homeostasis and cytokine release are critical for neuroinflammation post-injury and strongly implicated in retinal neurodegenerative diseases like glaucoma. This study examines microglial responses to chemical hypoxia induced by cobalt chloride (CoCl₂) in BV-2 murine microglial cells, focusing on signaling pathways and proteomic alterations. We assessed the protective effects of monoclonal antibodies against TNFα and IL-1β. CoCl₂ exposure led to decreased cell viability, reduced mitochondrial membrane potential, increased lactate dehydrogenase release, elevated reactive oxygen species generation, and activation of inflammatory pathways, including nitric oxide synthase (iNOS), STAT1, and NF-κB/NLRP3. These responses were significantly mitigated by treatment with anti-TNFα and anti-IL-1β, suggesting their dual role in reducing microglial damage and inhibiting inflammatory reactivity. Additionally, these treatments reduced apoptosis by modulating ATF4 and the p38 MAPK/caspase-3 pathways. Label-free quantitative mass spectrometry-based proteomics and Gene Ontology revealed that CoCl₂ exposure led to the upregulation of proteins primarily involved in endoplasmic reticulum and catabolic processes, while downregulated proteins are associated with biosynthesis. Anti-TNFα and anti-IL-1β treatments partially restored the proteomic profile toward normalcy, with network analysis identifying heat shock protein family A member 8 (HSPA8) as a central mediator in recovery. These findings offer insights into the pathogenesis of hypoxic microglial impairment and suggest potential therapeutic targets. Full article

Background: Systemic sclerosis (SSc) is a multisystem autoimmune disease characterised by fibrosis, vasculopathy, and immune dysfunction. Silica exposure has been associated with a more aggressive phenotype of the disease, including diffuse cutaneous involvement and interstitial lung disease. This study aims to identify proteomic differences between SSc patients exposed to silica and those not exposed to silica. Methods: An observational study of 32 SSc patients (11 silica-exposed and 21 non-exposed) was performed, with occupational history and quantitative proteomic analysis using SWATH-MS mass spectrometry. Differentially expressed proteins were analysed, and functional pathway enrichment was performed. Results: Eight proteins showed significant differences between groups, all with reduced levels in silica-exposed patients: adiponectin, immunoglobulins (IGLV3-19, IGLV2-18), complement C2, alpha-2-macroglobulin, vitronectin, cytoplasmic actin 2, and pigment epithelium-derived factor. Alterations in pathways related to fibrinolysis, complement activation, and inflammation were highlighted, suggesting that silica exposure may influence the pathogenesis of SSc and worsen its clinical course. Conclusions: This study supports the hypothesis that silica exposure is not only a triggering factor for SSc, but is also modulating its progression through inflammatory, procoagulant, and fibrotic pathways. The identification of proteomic biomarkers could contribute to the phenotypic classification of patients and the development of personalised therapies. Future studies should expand the cohort and further investigate the functional mechanisms of these proteins in SSc. Full article

Melanoma is an aggressive cancer with rising incidence and high mortality rates, largely due to chemotherapy resistance and molecular dysregulation. Nanotechnology, particularly silver nanoparticles (AgNPs), has emerged as a promising therapeutic avenue because of the nanoparticles’ ability to induce oxidative stress and apoptosis in cancer cells. However, conventional colloidal AgNPs lack selectivity, often causing significant damage to healthy cells. In this study, we introduce a green synthesis of AgNPs using plant extracts, providing an eco-friendly alternative with improved antitumor selectivity compared to traditional colloidal AgNPs. Leveraging label-free Data-Independent Acquisition/Sequential Window Acquisition of All Theoretical Mass Spectrometry (DIA/SWATH MS) quantitative proteomics, we investigated the antitumor effects of green-synthesized versus traditional AgNPs on A375 melanoma cells at 24 and 48 h. Our findings reveal that green AgNPs selectively reduced melanoma cell viability while sparing healthy keratinocytes (HaCaT), a benefit not observed with colloidal AgNPs. Proteomic analysis highlighted that green AgNPs significantly downregulated oncogenes, enhanced carbohydrate metabolism, and disrupted copper homeostasis in melanoma cells. This marks the first study to explore the differential effects of green and traditional AgNPs on melanoma using an integrated proteomic approach, underscoring the molecular potential of green AgNPs as a targeted and sustainable option for cancer therapy. Full article

It is unknown whether there are differentially expressed proteins (DEPs) in the circulating exosomes of appropriate- vs. small-for-gestational-age (AGA vs. SGA) infants, and if so, whether such DEPs relate to measures of endocrine–metabolic health and body composition in childhood. Proteomic analysis in cord-blood-derived exosomes was performed by label-free quantitative mass spectrometry in AGA (n = 20) and SGA infants (n = 20) and 91 DEPs were identified. Enrichment analysis revealed that they were related to complement and coagulation cascades, lipid metabolism, neural development, PI3K/Akt and RAS/RAF/MAPK signaling pathways, phagocytosis and focal adhesion. Protein–protein interaction (PPI) analysis identified 39 DEPs involved in the pathways enriched by the KEGG and Reactome. Those DEPs were associated with measures of adiposity and insulin resistance and with liver fat at age 7 (all p < 0.01). Multivariate linear regression analysis uncovered that two DEPs (up-regulated in SGA), namely PCYOX1 (related to adipogenesis) and HSP90AA1 (related to lipid metabolism and metabolic-dysfunction-associated steatotic liver disease progression), were independent predictors of the hepatic fat fraction at age 7 (β = 0.634; p = 0.002; R² = 52% and β = 0.436; p = 0.009; R² = 24%, respectively). These data suggest that DEPs at birth may predict insulin resistance, adrenarche and/or ectopic adiposity in SGA children at age 7, when an early insulin-sensitizing intervention could be considered. Full article

Background: Tear fluid, rich in proteins, is a promising source of novel biomarkers for ocular and systemic health. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) is the primary method for biomarker discovery. Still, factors such as limited sample volume, extracellular protein contamination, and reflex tearing can significantly impact results. Glass microcapillary tubes minimize these issues. Schirmer strips remain the most common collection method due to existing LC-MS/MS protocol optimization. Methods: In this study, we evaluated multiple digestion protocols for the shotgun quantitative LC-MS/MS analysis of small-volume tear fluid samples collected using glass capillary tubes. Protocol optimization was performed using pooled samples and then compared with the analysis of individual samples. Results: Using the optimized protocol, one μL samples were processed using a timsTOF Pro 2 mass spectrometer (Bruker) coupled online with an Evosep One liquid chromatography system (Evosep), leading to the identification of an average of 361 ± 63 proteins in pooled samples and 525 ± 123 proteins in individual small-volume tear fluid samples. Conclusions: This protocol highlights the practicality of using glass capillary tubes for comprehensive LC-MS/MS-based tear proteomics analysis, paving the way for detailed proteomics characterization of individual tear fluid samples rather than pooled samples. By shifting from pooled to individual samples, this approach greatly accelerates tear biomarker discovery, advancing precision and personalized medicine. Full article

Protein phosphorylation is a crucial regulatory mechanism in cellular homeostasis. The human cytomegalovirus (HCMV) incorporates protein phosphatase 1 (PP1) into its tegument, yet the biological relevance and mechanisms of this incorporation remain unclear. Our study offers the first characterization of the PP1 interactome during HCMV infection and its alterations. Using co-immunoprecipitation, mass spectrometry, and quantitative proteomics, we identified 159 high-confidence interacting proteins (HCIPs) in the PP1 interactome, consisting of 126 human and 33 viral proteins. We observed significant temporal changes in the PP1 interactome following HCMV infection, including the altered interactions of PP1 regulatory subunits. Further analysis highlighted the central roles of these PP1 interacting proteins in intracellular trafficking, with particular emphasis on the trafficking protein particle complex and Rab GTPases, which are crucial for the virus’s manipulation of host cellular processes in virion assembly and egress. Additionally, our study on the noncatalytic PP1 inhibitor 1E7-03 revealed a decrease in PP1’s interaction with key HCMV proteins, supporting its potential as an antiviral agent. Our findings suggest that PP1 docking motifs are critical in viral–host interactions and offer new insights for antiviral strategies. Full article

Mass-spectrometry-based proteomics frequently utilizes label-free quantification strategies due to their cost-effectiveness, methodological simplicity, and capability to identify large numbers of proteins within a single analytical run. Despite these advantages, the prevalence of missing values (MV), which can impact up to 50% of the data matrix, poses a significant challenge by reducing the accuracy, reproducibility, and interpretability of the results. Consequently, effective handling of missing values is crucial for reliable quantitative analysis in proteomic studies. This study systematically evaluated the performance of selected imputation methods for addressing missing values in proteomic dataset. Two protein identification algorithms, FragPipe and MaxQuant, were employed to generate datasets, enabling an assessment of their influence on im-putation efficacy. Ten imputation methods, representing three methodological categories—single-value (LOD, ND, SampMin), local-similarity (kNN, LLS, RF), and global-similarity approaches (LSA, BPCA, PPCA, SVD)—were analyzed. The study also investigated the impact of data logarithmization on imputation performance. The evaluation process was conducted in two stages. First, performance metrics including normalized root mean square error (NRMSE) and the area under the receiver operating characteristic (ROC) curve (AUC) were applied to datasets with artificially introduced missing values. The datasets were designed to mimic varying MV rates (10%, 25%, 50%) and proportions of values missing not at random (MNAR) (0%, 20%, 40%, 80%, 100%). This step enabled the assessment of data characteristics on the relative effectiveness of the imputation methods. Second, the imputation strategies were applied to real proteomic datasets containing natural missing values, focusing on the true-positive (TP) classification of proteins to evaluate their practical utility. The findings highlight that local-similarity-based methods, particularly random forest (RF) and local least-squares (LLS), consistently exhibit robust performance across varying MV scenarios. Furthermore, data logarithmization significantly enhances the effectiveness of global-similarity methods, suggesting it as a beneficial preprocessing step prior to imputation. The study underscores the importance of tailoring imputation strategies to the specific characteristics of the data to maximize the reliability of label-free quantitative proteomics. Interestingly, while the choice of protein identification algorithm (FragPipe vs. MaxQuant) had minimal influence on the overall imputation error, differences in the number of proteins classified as true positives revealed more nuanced effects, emphasizing the interplay between imputation strategies and downstream analysis outcomes. These findings provide a comprehensive framework for improving the accuracy and reproducibility of proteomic analyses through an informed selection of imputation approaches. Full article

Primary sclerosing cholangitis (PSC) and Primary biliary cholangitis (PBC) are chronic inflammatory biliary diseases characterized by progressive damage of the bile ducts, resulting in hepatobiliary fibrosis and cirrhosis. Currently, specific biomarkers that allow to distinguish between PSC and PBC do not exist. In this study, we examined the salivary proteome by carrying out a comprehensive and non-invasive screening aimed at highlighting possible quali-quantitative protein deregulations that could be the starting point for the identification of effective biomarkers in future. Saliva samples collected from 6 PBC patients were analyzed using a liquid chromatography–tandem mass spectrometry technique, and the results were compared with those previously obtained in the PSC group. We identified 40 proteins as significantly deregulated in PSC patients compared to the PBC group. The Gene Ontology and pathway analyses highlighted that several proteins (e.g., small integral membrane protein 22, cofilin-1, macrophage-capping protein, plastin-2, and biliverdin reductase A) were linked to innate immune responses and actin cytoskeleton remodeling, which is a critical event in liver fibrosis and cancer progression. These findings provide new foundations for a deeper understanding of the pathophysiology of PSC and demonstrate that saliva is a suitable biological sample for obtaining proteomic fingerprints useful in the search for biomarkers capable of discriminating between the two cholestatic diseases. Full article