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PubMed2025

A Study on Differential Proteomics in Differentiated Gastric Adenocarcinoma With Low-grade Atypia Based on Paraffin-embedded Tissues.

In this study, we analyzed and characterized differentially expressed proteins in differentiated gastric adenocarcinoma with low-grade atypia for screening potential protein markers. We collected gastric tissue specimens from 90 patients treated at the Pathology Department of the First People's Hospital of Yunnan Province, China, between January 2019 and December 2022. These specimens had been fixed in 10% neutral-buffered formalin and embedded in paraffin. We classified these samples into 3 groups: the control group (normal gastric mucosa), the low-grade atypia group (differentiated gastric adenocarcinoma with low-grade atypia), and the high-grade atypia group (differentiated gastric adenocarcinoma with high-grade atypia), consisting of 30 cases in each group. We analyzed differential proteomes with the data-independent acquisition-mass spectrometry (DIA-MS) methodology and selected 4 differentially expressed proteins that were subjected to immunohistochemistry (IHC) staining for validation. A total of 4406 proteins were identified, among which 598 and 357 proteins were statistically different in the low-grade atypia group as compared with the control group and the high-grade atypia group, respectively. IHC staining showed that the expression of FHL3, CSRP2, and FCGR3A was significantly higher in the low-grade atypia group than in the control group ( P <0.05) and significantly higher in the high-grade atypia group than in the low-grade atypia group ( P <0.05). FHL2 expression was negative to weakly positive in the control and low-grade atypia groups and not significantly different between the 2 groups, whereas FHL2 expression in the high-grade atypia group was significantly higher than in the control and low-grade atypia groups ( P <0.05). Proteomic analysis is helpful for discovering new protein markers. Using a combination of FHL3, CSRP2, and FCGR3A can increase the accuracy of the pathologic diagnosis of differentiated gastric adenocarcinoma with low-grade atypia.

Humans↗

PubMed2026

Differential Proteomic Profiling of Responders and Non-responders to Direct-Acting Antivirals Treatment in Chronic Hepatitis C Virus Infection.

Hepatitis C Virus (HCV), particularly genotype 3 (GT-3), is highly prevalent in India and is associated with faster progression to cirrhosis, hepatocellular carcinoma, and higher treatment failure rates. Although Direct-Acting Antivirals (DAAs) have revolutionized HCV therapy, 5-10% of patients fail to achieve sustained virological response (SVR). This proteomic study aimed to identify changes in the proteomic profile before and after treatment of both responders and non-responders to HCV treatment. Paired plasma samples from HCV GT-3 infected patients were collected before and 12 weeks after initiating DAAs treatment, along with healthy controls. Quantitative proteomic analysis was performed on the paired samples. Differentially expressed proteins (DEPs) were identified and subjected to functional analysis including gene set enrichment analysis (GSEA) and protein-protein interaction (PPI) network analysis. GSEA revealed enrichment in extracellular matrix organization and innate immune pathways. Expression patterns of candidate proteins selected based on fold change and false discovery rate (FDR) criteria were further evaluated in an independent cohort. Western blot confirmed key expression trends of candidate proteins. Proteins linked to extracellular matrix remodeling and angiogenesis showed differential expression patterns. Successful validation of these candidate proteins in large independent cohorts holds potential to predict therapeutic outcomes.

Humans↗

PubMed2026

narrowPASEF: A Sample-Aware diaPASEF Method Optimization Strategy Improving Differential Proteomics Performance on Low-Abundance Proteins.

Recent instrumental and computational innovations in mass-spectrometry-based proteomics offer new promise in biomarker discovery, thanks to unprecedented proteome coverage and depth. Data-independent acquisition (DIA) methods are very promising in this context as they allow improved proteome coverage, reduced missing value rates, and enhanced quantification precision. However, DIA methods also suffer from their own challenges, such as increased data complexity, cycle times, and background noise. In this work, we propose a sample-aware diaPASEF method optimization strategy for a timsTOF platform. Thorough method optimizations have first been conducted on standard HeLa lysates. Then, a ground-truth calibrated sample series, consisting of a range of UPS amounts spiked into a complex Arabidopsis background, was used to mimic differential analyses under controlled conditions. These benchmark experiments demonstrate clear benefits of using narrowPASEF for differential protein discovery. Finally, our strategy was applied to real use case biological samples to conduct a differential analysis of purified mouse astrocyte cells across two different conditions. narrowPASEF improved the proteome depth by 13%, considering proteins quantified with a coefficient of variation (CV) of <20%, and led to a 68% (435 vs 729) increase in differentially expressed proteins. These results provide an opportunity for a more precise and comprehensive analysis of the biological functions of biomarkers, offering a more profound understanding of the disease mechanisms. The benefits of our sample-aware narrowPASEF strategy demonstrated the most substantial impact on low-abundance proteins. Overall, these results show promise for more valuable and robust biomarker discoveries in the future.

Proteomics↗

PubMed2026

Machine learning-assisted plasma PEA proteomics enables differential diagnosis of melancholic depression and bipolar disorder.

Differentiating bipolar disorder (BD) from major depressive disorder (MDD) remains a critical unmet need in psychiatry due to overlapping clinical presentations and the absence of reliable biological markers. In this study, we assessed the capacity of multivariate machine learning models to accurately differentiate BD from MDD with melancholic features using plasma proteomic profiles obtained via Proximity Extension Assay (PEA) technology. A total of 67 participants were included (23 BD, 20 MDD, and 24 HC), and plasma protein expression was assessed using the Olink Target 96 Neurology panel. Differential proteomic analysis revealed distinct disorder-specific expression patterns, identifying 21 differentially expressed proteins in BD versus MDD, 18 in BD versus healthy controls, and 7 in MDD versus healthy controls. Using a stepwise feature reduction strategy, machine learning models were trained on three feature sets comprising all proteins, the top 20 most informative proteins, and the top 5 most beneficial proteins, and evaluated across BD-MDD, BD-HC, and MDD-HC classification tasks using five algorithms. For BD-MDD discrimination, the Random Forest model achieved the highest performance when trained on the top 5 protein set (LXN, HAGH, MATN3, PLXNB1, and CTSC), yielding an AUC of 0.905, with similarly strong performance observed using the top 20 protein set. Feature importance analysis highlighted proteins involved in neurodevelopmental processes, immune regulation, and extracellular matrix organization. Overall, these findings demonstrate that integrating plasma proteomics with machine learning enables robust differentiation between BD and MDD with melancholic features, supporting the development of scalable and biologically informed diagnostic tools for precision psychiatry.

Bipolar disorder↗

PubMed2026

Systemic Proteome Profiling to Differentiate Primary Glomerular Diseases.

KEY POINTS: Plasma proteome profiling identified distinct signatures across biopsy-proven primary glomerular disease subtypes. An elastic net model using 93 proteins classified primary glomerular disease subtypes and controls, with external validation. Integrating proteomics with machine learning yields biologically interpretable insights in primary glomerular diseases. BACKGROUND: Primary GN is a heterogeneous group of kidney disorders where understanding of their pathophysiology remains incomplete. Despite the diagnostic potential of high-throughput proteomics, constrained proteomic depth and a reliance on binary comparisons have left the feasibility of using systemic signatures to differentiate multiple GN subtypes largely unexplored. METHODS: To identify protein signatures that noninvasively differentiate major primary glomerular disease subtypes and provide mechanistic insights, we performed large-scale systemic proteome profiling of 5416 plasma proteins via Olink Explore HT in a discovery cohort ( n =147) and an external validation cohort ( n =85) of Korean participants (mean age, 41±13 years; 46% female). The study population included patients with four GN subtypes-focal segmental glomerulosclerosis, IgA nephropathy, minimal change disease, and membranous nephropathy-alongside healthy controls. We developed a machine learning (ML) model using logistic regression with elastic net regularization to classify disease groups based on proteomic profiles and evaluated its performance in the independent validation cohort. RESULTS: Plasma proteome profiles were distinct among disease subtypes, emerging as a significant source of data variation independent of conventional markers such as eGFR or proteinuria levels. The ML model performed robustly in both the discovery and validation cohorts, achieving an area under the receiver operating characteristic curve >0.8 for differentiating minimal change disease, membranous nephropathy, and IgA nephropathy. The model, even without clinical information, correctly identified 93% of minimal change disease cases (14 of 15) and 63% of IgA nephropathy cases (20 of 32), but its performance was limited for focal segmental glomerulosclerosis, with only 21% of cases (three of 14) correctly classified. Functional analysis of key proteins highlighted distinct biologic pathways, such as hemostasis in minimal change disease. CONCLUSIONS: We identified distinct systemic proteome signatures for primary glomerular diseases, where disease subtype served as a major determinant of proteomic variance alongside conventional clinical markers. ML models demonstrated robust discriminatory performance for minimal change disease, membranous nephropathy, and IgA nephropathy, underscoring the potential for proteome-based classification.

Humans↗

PubMed2025

iTRAQ-based quantitative proteomics reveals reduced expression of KRT19, KRT7, and PSTDG in cutaneous specimens after kidney transplantation.

Clinical improvement in pigmentation is frequently observed after kidney transplantation. However, the underlying molecular and histological mechanisms remain unclear. We conducted a study to quantify the skin color change using a handheld reflected light colorimeter and to investigate protein expression changes in the skin before and after kidney transplantation. Paired skin biopsies were obtained from three patients who underwent kidney transplantation before and one month after transplantation. Protein expression was analyzed using iTRAQ-based quantitative proteomics. Differentially expressed proteins were identified and visualized using hierarchical clustering and volcano plots. Histopathological evaluation included hematoxylin and eosin (H&E), Masson's trichrome, and immunohistochemical (IHC) staining for keratin (KRT) 7, KRT19, and MelanA. Skin pigmentation of the arms, ankles, and abdomen had significant L-value improvement after kidney transplantation. Proteomic profiling identified 2148 proteins, with six proteins showing significant differential expression after transplantation. Among them, KRT7, KRT19, and prostaglandin D2 synthase (PTGDS) were significantly downregulated, potentially reflecting reduced epithelial stress and systemic inflammation. H&E and Masson's trichrome staining revealed a post-transplantation reduction in dermal pigmentation and collagen content. IHC showed decreased KRT7, KRT19, and MelanA expression after transplantation. Our results suggest that targeting KRT or prostaglandin pathways may offer new treatments for ESRD-related skin symptoms.

Humans↗

PubMed2025

Reprogramming neuroblastoma by diet-enhanced polyamine depletion.

Neuroblastoma is a highly lethal childhood tumour derived from differentiation-arrested neural crest cells1,2. Like all cancers, its growth is fuelled by metabolites obtained from either circulation or local biosynthesis3,4. Neuroblastomas depend on local polyamine biosynthesis, and the inhibitor difluoromethylornithine has shown clinical activity5. Here we show that such inhibition can be augmented by dietary restriction of upstream amino acid substrates, leading to disruption of oncogenic protein translation, tumour differentiation and profound survival gains in the Th-MYCN mouse model. Specifically, an arginine- and proline-free diet decreases the amount of the polyamine precursor ornithine and enhances tumour polyamine depletion by difluoromethylornithine. This polyamine depletion causes ribosome stalling, unexpectedly specifically at codons with adenosine in the third position. Such codons are selectively enriched in cell cycle genes and low in neuronal differentiation genes. Thus, impaired translation of these codons, induced by combined dietary and pharmacological intervention, favours a pro-differentiation proteome. These results suggest that the genes of specific cellular programmes have evolved hallmark codon usage preferences that enable coherent translational rewiring in response to metabolic stresses, and that this process can be targeted to activate differentiation of paediatric cancers.

Animals↗

PubMed2026

Discovery and validation of novel plasma protein biomarkers for severe tuberculosis patients.

OBJECTIVE: Severe tuberculosis (STB) imposes a substantial disease burden, yet reliable biomarkers for distinguishing STB from mild/moderate tuberculosis (MTB) remain scarce. This study aimed to identify and independently validate plasma protein biomarkers associated with tuberculosis severity. METHODS: In this multicenter prospective study, 298 adults with confirmed pulmonary tuberculosis were enrolled into screening (n = 128) and independent validation (n = 170) cohorts. Plasma samples were analysed using data-independent acquisition proteomics. Differentially expressed proteins were screened via Limma and four machine-learning algorithms, with candidate proteins measured by enzyme-linked immunosorbent assays. Receiver operating characteristic analysis assessed individual and combined diagnostic performance. RESULTS: STB patients were older and presented with lymphopenia, hypoalbuminemia, neutrophilia, and elevated lactate dehydrogenase. Among 166 differentially expressed proteins, HSPA5, HSP90B1, EEF1D, and SULT1A1 were selected for validation. In STB patients, HSPA5, HSP90B1, and EEF1D were upregulated, whereas SULT1A1 was downregulated. The four-protein panel achieved an AUC of 0.908 (95% CI 0.864-0.952), with 87.5% sensitivity and 83.8% specificity, modestly outperforming HSPA5 alone (AUC = 0.894). Functional enrichment implicated cholesterol metabolism, immune-inflammatory pathways, and endoplasmic reticulum stress. CONCLUSIONS: The four-protein panel effectively discriminated STB from MTB; however, its marginal improvement over HSPA5 alone suggests that an HSPA5-based assay may offer a simpler, more practical, and potentially cost-effective strategy for severity stratification.

Humans↗

PubMed2026

Plasma Proteomic Profiling Identifies Candidate Biomarkers for Pancreatic Ductal Adenocarcinoma.

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal malignancy that is often diagnosed after curative treatment is no longer feasible. Existing biomarkers, particularly CA19-9, have limited sensitivity and specificity. Plasma proteins that capture tumor-associated biological alterations may therefore provide useful signals for earlier detection. METHODS: Plasma samples from 99 patients with PDAC and 30 healthy controls were analyzed using data-independent acquisition (DIA) proteomics. Differentially expressed proteins were identified using predefined statistical thresholds and further examined by functional enrichment analysis. Selected candidate biomarkers were validated by ELISA in an independent subset. RESULTS: Among 565 quantified plasma proteins, 52 were differentially expressed between PDAC and controls. These proteins were enriched in extracellular processes, cholesterol metabolism, complement and coagulation cascades, and pancreatic secretion pathways. ELISA validation confirmed higher plasma levels of Cathepsin S, CTRB2, MARCO, PIGR, PRDX6, REG1A, Trypsin-2, and PEP-FAP in patients with PDAC compared with healthy controls. ROC analyses showed moderate-to-good discriminatory performance for several candidates, and the MARCO + PEP-FAP model improved classification compared with either marker alone. CONCLUSION: These findings reveal circulating proteins linked to key PDAC-related biological processes and identify eight candidates for further evaluation in multi-protein diagnostic panels. Larger validation studies incorporating clinically relevant disease control groups are warranted to determine their diagnostic specificity and clinical utility.

Humans↗

PubMed2025

HEPARIN AND DNase I TREAT MYOCARDIAL INJURY IN SEPTIC MICE.

Background: Sepsis is a life-threatening clinical condition often seen in intensive care units, leading to multi-organ dysfunction. Myocardial injury is a prevalent complication, significantly increasing mortality among sepsis patients. Although heparin is used in sepsis management, its specific effects on myocardial injury and the role of neutrophil extracellular traps (NETs) in this context remain insufficiently understood. Aim: This study investigates the role of unfractionated heparin (UFH) combined with DNase I in reducing myocardial injury in a septic mouse model. Methods: A cecal ligation and puncture (CLP)-induced sepsis model was established in C57BL/6 mice to study myocardial injury. The experimental groups included treatments with UFH, UFH with DNase I, and NETs introduction. Myocardial injury was assessed using hematoxylin and eosin staining, enzyme linked immunosorbent assay for injury markers (creatine kinase MB [CK-MB] and lactate dehydrogenase [LDH]), and Western blotting for inflammatory proteins (TNF-α and IL-6). Differential proteomic analysis using data independent acquisition mass spectrometry and pathway enrichment analysis (Gene Ontology and Kyoto Encyclopedia of Genes and Genomes) were conducted to identify molecular pathways and key proteins affected by the treatments. Results: Single UFH treatment increased the formation of NETs, upregulated TNF-α and IL-6, and increased CK-MB and LDH, worsening myocardial injury. The combination of UFH and DNase I significantly reduced myocardial injury, suppressing NETs formation and inflammation. Proteomic analysis identified crucial pathways related to NETs, metabolism, and complement and coagulation cascades, with proteins Ccn1 and Tagln highlighted as potential therapeutic targets. Conclusion: UFH combined with DNase I effectively alleviates myocardial injury in septic mice by modulating NETs formation and associated inflammatory processes. This study may provide new insights and options for the early use of heparin in the treatment of septic patients, particularly in cases with a higher risk of myocardial injury.

Animals↗

PubMed2026

Single-Cell Proteomics Reveals Proteome Remodeling and Cellular Heterogeneity During NGF-Induced PC12 Neuronal Differentiation.

Single-cell proteomics enables direct measurement of cellular heterogeneity during dynamic biological processes, but its application to fragile and highly adherent neuronal models remains challenging. Here, we developed and applied an optimized single-cell proteomics workflow to characterize proteome remodeling during nerve growth factor (NGF)-induced differentiation of PC12 cells. To enable reliable single-cell analysis, we implemented gentle dissociation, antiaggregation strategies, and thermal inkjet-based cell dispensing, achieving high accuracy in single-cell isolation. Inclusion of n-dodecyl-β-d-maltoside (DDM) improved recovery of membrane-associated and low-solubility proteins. Coupled with LC-ion mobility-mass spectrometry, this workflow enabled quantification of 2,000-3,000 proteins per cell across the differentiation time course. Single-cell proteomic analysis revealed progressive and heterogeneous proteome remodeling during differentiation. While undifferentiated cells formed a relatively homogeneous population, later stages (Days 4-6) exhibited increased variability, including multimodal protein abundance distributions and separation into distinct subpopulations. Dimensionality reduction, clustering, and non-negative matrix factorization identified multiple coexisting proteomic states within the same time points, reflecting asynchronous differentiation trajectories. These subpopulations were characterized by coordinated differences in pathways related to intracellular trafficking, protein translation, cytoskeletal organization, and neuronal maturation. Comparison with bulk proteomics demonstrated that proteins associated with differentiated neuronal states, including those involved in neurite formation and structural remodeling, are underrepresented in population-averaged measurements but are enriched within specific single-cell subpopulations. Temporal and cluster-resolved analyses further revealed distinct protein expression trajectories, including early decreases in cell cycle and metabolic pathways and later increases in neuronal structural and regulatory proteins. Together, this study establishes an optimized workflow for single-cell proteomics of neuronal systems and demonstrates that NGF-induced PC12 differentiation proceeds through heterogeneous and divergent proteomic states that are not resolved by bulk analysis.

Animals↗

PubMed2026

A Comprehensive Analysis of Differential Protein Expression in the Plasma of Rheumatoid Arthritis Patients Utilizing Data-Independent Acquisition (DIA) Proteomics Technology.

BACKGROUND: Rheumatoid Arthritis (RA) is a Prevalent Autoimmune Disorder Affecting Millions of People Worldwide. A Thorough Understanding of Its Clinical and Pathological Features Is Essential to Improve Patient Outcomes. METHODS: This Study Combined Data-Independent Acquisition Proteomics and Enzyme-Linked Immunosorbent Assay (ELISA) to Identify and Validate Potential Plasma Protein Biomarkers for the Early Diagnosis of RA. RESULTS: Differential Proteomic Analysis Identified Differentially Expressed Proteins Between Patients With RA and Healthy Controls and Characterized Their Functions. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes Enrichment Analyses Were Performed to Explore Protein Functions and Associated Biological Pathways. The STRING Database and the Metascape Platform Were Used to Conduct an in-Depth Analysis of the Protein-Protein Interaction Network, Highlighting the Functional Attributes and Interconnections of Upregulated Proteins and Identifying Key Protein Complexes Involved in RA. ELISA Analysis of Plasma Samples Revealed Significantly Elevated SERPINA3 Levels in Patients With RA, Which Were Positively Correlated With Disease Activity Indicators-Including Erythrocyte Sedimentation Rate, C-Reactive Protein, and Disease Activity Score 28-But Were Not Correlated With Rheumatoid Factor or Its Subtypes. CONCLUSIONS: This Study Provides New Insights and Identifies Potential Biomarkers for the Early Diagnosis of RA.

Humans↗

PubMed2026

Occupationally relevant vibrations and the brain: frequency-dependent proteomics signatures in a rat model.

INTRODUCTION: Occupational exposure to whole-body vibration (WBV), particularly in agricultural environments, has been associated with adverse cognitive and physiological effects. This study examined the neurophysiological impact of WBV in a rat model at 4 Hz and 30 Hz, frequencies representative of off-road and on-road vehicle operation. METHODOLOGY: Forty-four Sprague-Dawley rats were assigned to control (0 Hz), low-frequency (4 Hz), or high-frequency (30 Hz) vibration conditions. After three days of exposure, brain tissues were collected and analyzed using mass spectrometry-based proteomics to identify differentially expressed proteins. RESULTS: Proteomic profiling revealed distinct, frequency-dependent alterations in brain protein expression. Compared with controls, 32 cognition-related proteins were differentially regulated at 4 Hz and 29 at 30 Hz, with 13 differing between the two vibration conditions. Principal component analysis showed clear separation among groups, indicating unique proteomic signatures for each exposure frequency. Functional enrichment and protein-protein interaction analyses demonstrated involvement of synaptic plasticity, cytoskeletal organization, calcium regulation, and neurotransmitter release. Exposure to 4 Hz was associated with the upregulation of proteins involved in calcium homeostasis and synaptic integrity, suggesting potential disruption of cognitive processes. In contrast, 30 Hz increased the expression of proteins related to axonal guidance and neuroprotection, indicating a less clearly adverse response that may reflect adaptive or potentially beneficial effects. DISCUSSION: These findings provide new insight into biological mechanisms underlying WBV-induced cognitive changes and underscore the importance of vibration frequency in shaping neurophysiological outcomes. They also establish a foundation for future studies integrating proteomics with behavioural assessments in animals and humans.

Animals↗

PubMed2026

Proteomic analysis of cisplatin-induced spermatogenesis defects in mice.

BACKGROUND: Cisplatin is a crucial chemotherapeutic agent used for treating various cancers; however, its excessive use can cause irreversible damage to the reproductive system, and the protein expression profile of cisplatin-induced testicular injury remains unclear. METHODS: Male C57BL/6 mice were treated with cisplatin at various doses, and testes were collected for histological, immunofluorescence, and proteomic analyses. Germ cell loss and apoptosis were assessed using H&E staining, TUNEL assays, and immunofluorescence for LIN28A, SYCP3, MVH, and CDK1. Label-free quantitative proteomics identified differentially expressed proteins, which were analyzed for functional enrichment and protein-protein interactions. RESULTS: We observed that cisplatin treatment led to smaller testes, reduced sperm count, and a significant decrease in the number of spermatocytes and spermatids in mice. Label-free quantitative proteomic analysis revealed that cisplatin significantly reduced the expression of cyclin-dependent kinase 1 (CDK1), a key spermatogenesis regulator, in the testes. Reduction in CDK1 expression is correlated with spermatogenic arrest, particularly in spermatocytes. CONCLUSION: These findings highlight the critical role of CDK1 in cisplatin-induced spermatogenic dysfunction and provide new insights into fertility preservation strategies for patients with cancer undergoing chemotherapy.

Animals↗

PubMed2026

Natural Product Target Identification of Wheldone, a Fungal Metabolite, as a KIF11 Inhibitor in Ovarian Cancer Using the DiffPOP (Differential Protein Precipitation) Method.

Wheldone, a fungal metabolite, was identified as a cytotoxic compound in high-grade serous ovarian cancer (HGSOC). Wheldone induced caspase 3/7-dependent apoptosis and reduced migration, invasion, and spheroid growth. Wheldone stimulated apoptosis in chemoresistant HGSOC models. Wheldone treatment caused significant downregulation of HNRNPD, a DNA repair protein, and increased DNA damage that could be blocked by N-acetyl-L-cysteine. In vivo, wheldone displayed minimal toxicity but was rapidly cleared from circulation, despite in vitro metabolic stability. Wheldone treatment in vivo did not demonstrate significant reduction in tumor burden. Therefore, in order to overcome these liabilities, it was necessary to find the protein target of wheldone so that modifications can be made to improve the drug-like characteristics of the compound. Using the drug-target interaction proteomics method, differential precipitation of proteins, wheldone was found to act as an inhibitor of Kinesin superfamily protein 11 (KIF11), a motor protein essential for mitotic spindle formation. An ATPase biochemical cell-free assay confirmed direct binding and functional inhibition of KIF11. Wheldone resulted in G2/M arrest and downstream regulation of mitotic proteins such as TPX2, AURKA, and phospho-histone H3. Proteomics after treatment of wheldone in four different HGSOC cancer cell lines all supported changes consistent with mitotic spindle assembly disruption. Further, KIF11 was one of only 13 proteins upregulated in all 4 cell lines treated. Overall, wheldone was found to be a fungal metabolite that inhibits KIF11 in chemoresistant ovarian cancer, with future studies needed to improve its pharmacokinetics and delivery.

Female↗

PubMed2026

Metaproteomic profiling reveals viral proteins and associated host proteomic alterations in glioblastoma.

Glioblastoma (GB) is a WHO grade 4 brain cancer with dismal prognosis, yet its aetiology remains poorly defined. Although viral involvement has been proposed, findings across studies remain inconsistent, reflecting inherent limitations of individual technologies and cohort size. Here we applied metaproteomic profiling to a publicly available GB proteome dataset (12 control, 21 adjacent, 159 tumour) and an independent cohort of 81 samples (37 control, 44 tumour) to detect viral proteins in tumour and controls tissues. Across cohorts, we detected viral proteins from diverse species, with human herpesviruses (HHV-1, 2, and 8) more frequently detected in GB tumours compared with control tissues. Analysis of the host tumour proteome revealed differential abundance of proteins related to transcriptional regulation, RNA processing, protein translation, immune responses, and mitochondrial-associated metabolism. Correlation analysis identified associations between viral and human proteins, with several linked to biological processes previously implicated in DNA virus-host interactions. Further stratification of tumour by HHV-1 status showed consistent alterations in proteins associated with mitochondrial-associated metabolism, protein turnover, and cell adhesion/signalling.In summary, this study demonstrates the feasibility of metaproteomics for detecting viral components in archival GB tissues. Using this approach, we observed differences in viral protein landscape across cohorts and identified associations between viral presence and host proteomic features, providing a protein-level framework for future studies of virus-host interactions in GB.

Humans↗

PubMed2026

Proteomics-Based Identification of the Pyroptosis-Related Biomarker PCSK9 and Its Association With the Pathogenesis of Rheumatoid Arthritis.

Rheumatoid arthritis (RA) is a common autoimmune disease, and early diagnosis is critical for effective treatment. This study aims to identify potential biomarkers related to pyroptosis through serum proteomics analysis, offering new insights for the early diagnosis of RA. We enrolled 100 participants, including 50 patients with RA and 50 healthy controls. Serum samples were collected and analyzed using high-resolution liquid chromatography-tandem mass spectrometry (LC-MS/MS) for proteomics profiling. Differential protein expression analysis and functional annotation revealed significant upregulation of pyroptosis-related proteins in the serum of patients with RA. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses, along with protein-protein interaction (PPI) network analysis, showed that these proteins are involved in inflammation and immune pathways, particularly the activation of the NOD-like receptor protein 3 (NLRP3) inflammasome. Enzyme-linked immunosorbent assay (ELISA) validation confirmed a significant increase in PCSK9 levels in patients with RA, suggesting that PCSK9 may play a key role in the pathogenesis of RA. This study provides new directions for biomarker research in RA, particularly regarding the potential involvement of the pyroptosis pathway, with significant clinical application prospects.

Humans↗

PubMed2025

Alterations in DNA Methylation, Proteomic, and Metabolomic Profiles in African Ancestry Populations with APOL1 Risk Alleles.

KEY POINTS: We aimed to elucidate potential methylation, proteomic, and metabolomic mechanisms by which APOL1 variants may be linked to kidney disease. We report distinct methylation profiling between APOL1 risk allele carriers and noncarriers, many near APOL gene family. We report higher APOL1 protein and lower C18:1 cholesteryl ester in two risk allele carriers. BACKGROUND: The APOL1 high-risk haplotype has been associated with CKD and the deterioration of kidney function, particularly in populations with West African ancestry. However, the mechanisms by which APOL1 risk variants increase the risk for kidney disease and its progression have not been fully elucidated. METHODS: We compared methylation (N=3191; 715 [22%] carriers), proteomic (N=1240; 169 [14%] carriers), and metabolomic (N=6309; 674 [11%] carriers) profiles in African and Hispanic/Latino carriers of two APOL1 high-risk alleles (G1/G1, G2/G2, G1/G2) and noncarriers (G0/G0), excluding heterozygotes (G0/G1, G0/G2), from the Population Architecture using Genomics and Epidemiology Consortium and UK Biobank. In each study, the associations between the APOL1 high-risk haplotype and up to 722,719 cytosine-phosphate-guanine (CpG) sites, 2923 proteins, or 836 metabolites were estimated using covariate-adjusted linear regression models, followed by fixed-effects sample size–weighted meta-analyses. RESULTS: Significant associations were observed between APOL1 high-risk haplotype and methylation at 52 CpG sites, with 48 located on chromosome 22 and 18 in the vicinity of APOL1–4 and MYH9. All significant CpG sites near APOL2 were hypomethylated, whereas those near APOL3 and APOL4 were hypermethylated. APOL1-associated CpG sites were also identified in genes involved in ion transport and mitochondrial stress pathways. Sensitivity analyses indicated consistent yet attenuated effects among heterozygotes, supporting an additive effect of APOL1 risk alleles. Further analyses of the 52 CpG sites identified two near APOL4 exhibiting G1-specific effects, eight associated with CKD but none with eGFR, and three showing heterogeneity by CKD status. In addition, carrying two APOL1 risk alleles was associated with higher plasma APOL1 protein (β=1.12, PFDR = 2.26e-70) and lower C18:1 cholesteryl ester metabolite (Z=−4.50, PFDR = 4.83e-3). CONCLUSIONS: Our results demonstrate differential methylation, proteomic, and metabolomic profiles associated with APOL1 high-risk haplotypes.

APOL1↗