Journal of Proteome Research最新文献

Gastric cancer (GC) is a leading cause of cancer-related mortality globally. Histone lactylation, an emerging post-translational modification, holds promise as a therapeutic target and prognostic biomarker, though its expression patterns and clinical relevance in GC are underexplored. Pan-lactylation was quantified by IHC in tumor/adjacent normal tissues and validated via Western blot in GC/normal cell lines. Systematic profiling of lactylome and proteome of tumor-normal pairs (n = 3) identified 127 lactylation-regulated genes. Four prognostic models (stepCOX, LASSO, RSF, GBM) were built using these lactylation-regulated genes from GPL6947 data sets and validated independently in GPL570 data sets. IHC results confirmed elevated pan-lactylation in GC tissues and high pan-lactylation level correlated with larger tumor diameter (P = 0.041) and vascular invasion (P = 0.032). In addition, pan-lactylation level was an independent prognostic predictor (HR = 2.06, 95%CI: 1.51-2.80). Four prognostic models were constructed with different advantages, while the RSF model achieved superior training performance (5-year C-index = 0.837), while COX regression demonstrated optimal validation robustness (5-year C-index = 0.624). Pan-lactylation is a novel prognostic biomarker for GC. The lactylation-driven prognostic signature facilitates precise risk stratification in GC patients, providing a framework for personalized therapeutic intervention.

Mass spectrometry-based proteomics of blood faces significant challenges due to the high dynamic range of proteins, with abundant proteins masking detection of low-abundance biomarkers. This study evaluated a novel volumetric absorptive microsampling (VAMS) method for blood proteome analysis compared with conventional sample processing. Plasma and whole blood samples were processed using three different methods: liquid sample analysis, VAMS-direct processing, and our VAMS-optimized protocol involving selective washing steps. All samples were analyzed using shotgun LC-MS/MS. The VAMS-optimized method demonstrated superior performance, increasing protein identifications 4-fold for plasma (745 vs 3024 proteins) and 2.1-fold for whole blood compared to liquid samples, while improving quantitative reproducibility with mean coefficients of variation below 11%. Further, the VAMS-optimized approach produced more protein identifications and lower %CVs for single-spun plasma and whole blood when compared with a commercially available protein corona bead assay. Whole blood samples showed greater robustness than plasma, with reduced variability from preanalytical processing steps and improved storage stability at room temperature for up to 14 days. The VAMS-optimized approach addresses key limitations in current blood proteomics workflows, enabling patient-centric sample collection for longitudinal studies while maintaining analytical depth and reproducibility, essential for biomarker discovery applications.

Lepidopteran-infecting baculoviruses represent one of the most promising alternatives for pest biocontrol. In particular, Spodoptera frugiperda granulovirus (SfGV) infects the fall armyworm Spodoptera frugiperda, an increasingly important pest for maize and other crops around the world. SfGV has been investigated due to its capacity to enhance other baculovirus infectivity and as a biopesticide itself. Although the proteome of SfGV occlusion bodies (OB) is fundamental for their infectivity properties, we still lack a detailed understanding of its protein components and how they compare with other baculoviruses. To tackle this problem, we performed mass spectrometry-based proteomics analysis on SfGV OB obtained from infected larvae. We could detect 72 proteins included in SfGV OB and confirm the presence of two enhancins. EmPAI-based semiquantification highlighted the presence of two granulovirus proteins, ORF101 and ORF141, shared with other GV proteomic studies. ORF101 exhibits sequence and structural features reminiscent of the well-characterized AcMNPV P10 protein, including a coiled-coil domain and phosphorylation sites, and we confirmed its cytoplasmic aggregation through GFP-tagged expression in insect cells, suggesting a potential role in virion assembly or release. Prediction and comparison of OB protein structures allowed the detection of a common structural fold among 6 proteins PEP-1, PEP-2, PEP-P10, ORF007, ORF025, and Bro-f. Finally, comparison of four reported ODV proteomic studies of different GVs indicated that ORF040, ORF101, ORF141, and Bro-f are common components in GV OBs.

Radioactive iodine therapy (RAIT) is a common postsurgery treatment for papillary thyroid carcinoma (PTC), but some patients respond poorly. This study aimed to develop a noninvasive serological assay to predict RAIT efficacy. We examined levels of 141 inflammatory/immune-related proteins and lymphocyte subsets in 28 PTC patients before, at 30 days, and at 90 days after RAIT. Patients were categorized into excellent response (ER) and nonexcellent response (NER) groups. Pre-RAIT HGF levels were significantly higher in the NER group and predicted a disease-free survival in an independent cohort. Bioinformatics analysis revealed a strong correlation between high HGF expression and immune cell infiltration in the tumor microenvironment. This study proposes a pre-RAIT serum protein (PSP) panel comprising FASLG, CXCL12, and HGF for risk stratification, with higher scores indicating a poorer prognosis, although this requires validation in larger cohorts. It underscores the importance of dynamic immune monitoring and proposes a novel, noninvasive strategy that leverages the role of systemic immunity for clinical patient stratification and outcome prediction.

Temozolomide (TMZ) is a frontline chemotherapeutic agent for glioblastoma multiforme (GBM); however, approximately half of patients develop resistance to therapy. This study investigates the role of altered cellular bioenergetics and metabolism in the acquired TMZ resistance. Using untargeted metabolomics, we explored the metabolic rewiring in TMZ-resistant GBM cells and identified key alterations in glycolysis, the tricarboxylic acid (TCA) cycle, fatty acid metabolism, and amino acid metabolism, all might be linked to cellular proliferation. Our findings suggest that while glycolysis remains important, increased TCA cycle activity contributes to the drug resistance, supported by increased levels of mitochondrial mass and mitochondrial membrane potential. We observed significantly elevated glutamine levels, which may enhance mitochondrial activity, thereby supporting increased energy production. Furthermore, resistant cells exhibited enhanced NRF2 level in parallel with higher levels of antioxidants, including glutathione and catalase enzyme, and a concomitant decrease in the level of its negative regulator, KEAP1. These factors collectively may contribute to drug resistance by mitigating oxidative stress. These findings indicate that mitochondrial metabolic reprogramming and NRF2/KEAP1-mediated antioxidant defense mechanisms play a crucial role in TMZ resistance, and targeting these pathways may offer a novel strategy to overcome resistance in GBM therapy.

Nanoflow liquid chromatography capable of delivering consistent and reliable results across extensive sample sets is essential for the advancement of mass spectrometry-based proteomics. Micro-Pillar Array Columns (μPACs) represent a significant breakthrough, offering durability and performance stability. Here, we evaluate the robustness of μPACs by comparing a column used continuously for over 16 months with more than 7000 injections (μPAC1) to a nearly new column (μPAC2). Analysis of two TMT-labeled yeast TKO standards (TKOpro10u, a 10-plex unit-resolved standard; and TKOpro12, a 12-plex isotopolog-inclusive standard) showed that μPAC1 and μPAC2 yielded comparable numbers of unique peptides and proteins, exhibited similar reproducibility, and delivered equivalent chromatographic and spectral quality. Notably, these data showed that μPAC1 maintained high performance with minimal degradation of data quality, highlighting the exceptional durability of the μPAC technology. These findings underscore that μPAC can contribute to reducing workflow disruptions in high-throughput analytical workflows, particularly in proteomics workflows that utilize mass spectrometry.

Vascular Ehlers-Danlos syndrome (vEDS) is a rare connective tissue disorder caused by mutations in the COL3A1 gene, leading to life-threatening vascular complications. This study presents a proteomic analysis of aortic wall tissue from a 60 year-old vEDS patient with a confirmed COL3A1 mutation, who died from an aortic dissection consequent to an abdominal aortic aneurysm. Compared to healthy controls, the patient's tissue showed an imbalance in collagen isoforms such as elevated types I and VI and reduced types VIII, XIV, and XVIII, with no significant change in type III collagen. Alterations were also found in collagen-processing enzymes, nidogens, and matricellular proteins, such as THBS1, THBS2, and fibulins. These findings reveal specific vessel extracellular matrix remodeling and suggest compensatory mechanisms that may contribute to the vascular fragility in vEDS.

Proteins play essential functions through their complex regulations on cell-type-specific expression, localization, and molecular complexes. Protein complexity is further enhanced by proteoforms, which are the diverse molecular forms that each gene can produce through genomic alterations, transcriptional variations, translational regulations, and protein modifications. Profiling of proteoforms is a promising method for gaining a deeper understanding of the role of proteins in biological pathways and disease mechanisms. Here, we developed ProteoformDB, an application tool for generating proteoform databases, and we cataloged a total of over one million unique single-site human proteoforms. We showed that ProteoformDB can serve as a valuable resource to document the experimentally identified proteoforms in a database, supporting protein characterization in quantitative proteomics for both total protein abundances and modified protein forms.

Colorectal cancer (CRC) is an aggressive malignant tumor of the digestive system that poses a serious threat to human health. Therefore, there is an urgent need to discover early diagnostic markers and effective therapeutic targets for CRC. In this study, data independent acquisition (DIA) mass spectrometry quantitative technology combined with bioinformatics analysis was used to carry out personalized quantitative proteomics research on abundant protein depletion plasma samples from 48 CRC patients at different TNM stages and healthy individuals. A total of 1089 Protein Groups were identified. By comparing the plasma protein expression profiles between CRC patients and healthy individuals, differentially expressed proteins (DEPs) CRP, FABP1, FABP4 and OSTP with significant changes were screened out, and GO functional, KEGG pathway, and GSEA enrichment analysis were performed. Mfuzz clustering analysis categorized the DEPs in CRC plasma into six expression patterns. Among them, the OSTP protein level in proteomics data and the mRNA level of the gene spp1 in TCGA database both showed an upward trend with the progression of the disease, suggesting that it may serve as a diagnostic and prognostic marker in plasma to reflect the disease progression of CRC patients. ROC analysis showed robust predictive performance, and PRM validation cohort correlated well with DIA results, providing potential insights for CRC research.

Understanding intratumoral heterogeneity is essential for elucidating tumor biology. Compared to RNA expression, omics-level characterization of cell-type-specific protein expression remains a technical challenge. Bulk mass spectrometry (MS) provides abundant proteomics resources to infer cell-type specificity via data deconvolution; however, it is unclear which proteomic quantification formats are optimal, as they differ from the data types for which most deconvolution methods were designed. Here, leveraging recently generated large-cohort proteogenomics data, we systematically evaluated different MS proteomics quantification formats and preprocessing strategies to resolve cell-type-specific protein expression. Our results indicate that while label-free spectral counts can be used directly, TMT MS1 intensities and MS2 ratios are less suitable and require appropriate data transformation. We demonstrate that a 'min-score' transformation significantly improves MS1 intensity-based deconvolution, providing useful insights for subtyping pancreatic cancer. Moreover, we identified the coefficient of variation (CV) as a robust statistical indicator of deconvolution suitability. Finally, we developed "ProTransDeconv", an R package integrating data transformation, deconvolution, and quality checks for major MS proteomics data formats. This work provides practical guidance for deconvolving bulk proteomics to study cell-type-specific protein-level dysregulation.