Journal of Proteome Research最新文献

Postpartum depression (PPD) poses significant risks to maternal and infant health, yet proteomic analyses of PPD-risk women remain limited. This study analyzed plasma samples from 30 healthy postpartum women and 30 PPD-risk women using mass spectrometry, identifying 98 differentially expressed proteins (29 upregulated and 69 downregulated). Principal component analysis revealed distinct protein expression profiles between the groups. Functional enrichment and PPI analyses further explored the biological functions of these proteins. Machine learning models (XGBoost and LASSO regression) identified 17 key proteins, with the optimal logistic regression model comprising P13797 (PLS3), P56750 (CLDN17), O43173 (ST8SIA3), P01593 (IGKV1D-33), and P43243 (MATR3). The model demonstrated excellent predictive performance through ROC curves, calibration, and decision curves. These findings suggest potential biomarkers for early PPD risk assessment, paving the way for personalized prediction. However, limitations include the lack of diagnostic interviews, such as the Structured Clinical Interview for DSM-V (SCID), to confirm PPD diagnosis, a small sample size, and limited ethnic diversity, affecting generalizability. Future studies should expand sample diversity, confirm diagnoses with SCID, and validate biomarkers in larger cohorts to ensure their clinical applicability.

A combination of pathway enrichment and metabolite clustering analysis is used to interpret untargeted ¹H NMR metabolomics data, enabling a biochemically informative comparison of the effects induced by a panel of known cytotoxic gold(I) and gold(III) compounds in A2780 ovarian cancer cells. The identification of the most dysregulated pathways for the major classes of compounds highlights specific chemical features that lead to common biological effects. The proposed approach may have broader applicability to the screening of metal-based drug candidate libraries, which is always complicated by their multitarget nature, and support the comprehensive interpretation of their metabolic actions.

Comprehensive protein profiling in intestinal tissues provides detailed information about the pathogenesis of colorectal cancer (CRC). This study quantified the expression levels of 92 oncology-related proteins in tumors, paired para-carcinoma tissues, and remote normal tissues from a cohort of 52 CRC patients utilizing the Olink technology. The proteomic profile of normal tissues closely resembled that of para-carcinoma tissues while distinctly differing from that of tumors. Among the 68 differentially expressed proteins (DEPs) identified between the tumor and normal tissues, WISP-1, ESM-1, and TFPI-2 showed the most pronounced alterations and exhibited relatively strong correlations. These markers also presented the highest AUC values for distinguishing between tissue types. Bioinformatic analysis of the DEPs revealed that the plasma membrane and the PI3K-AKT signaling pathway were among the most enriched GO terms and KEGG pathways. Furthermore, although TFPI-2 is typically recognized as a tumor suppressor, both Olink and enzyme linked immunosorbent assay (ELISA) analyses have demonstrated that its expression is significantly elevated in tumors compared with paired normal tissues. To the best of our knowledge, this is the first study to profile the proteome of intestinal tissue using the Olink technology. This work offers valuable insights into potential biomarkers and therapeutic targets for CRC, complementing the Olink profiling of circulating proteins.

Patients with lateral lymph node metastasis (LLNM) may experience higher locoregional recurrence rates and poorer prognoses compared to those without LLNM, highlighting the need for effective preoperative stratification to reliably assess risk LLNM. In this study, we collected PTMC samples from Peking Union Medical College Hospital and employed data-independent acquisition mass spectrometry proteomics technique to identify protein profiles in PTMC tissues with and without LLNM. Pseudo temporal analysis and single sample gene set enrichment analysis were conducted in combination with The Cancer Genome Atlas Thyroid Carcinoma for functional coordination analysis and the construction of a prediction model based on random forest. Non-negative matrix factorization (NMF) clustering was utilized to classify molecular subtypes of PTMC. Our findings revealed that the differential activation of pathways such as MAPK and PI3K was critical in enhancing the lateral lymph node metastatic potential of PTMC. We successfully screened biomarkers via machine learning and public databases, creating an effective prediction model for metastasis. Additionally, we explored the mechanism of metastasis-associated PTMC subtypes via NMF clustering. These insights into LLNM mechanisms in PTMC may contribute to future biomarker screening and the identification of therapeutic targets.

The prevalence rate of colorectal cancer (CRC) has dramatically increased in recent decades. However, robust CRC biomarkers with therapeutic value for early diagnosis are still lacking. To comprehensively reveal the molecular characteristics of CRC development, we employed a multiomics strategy to investigate eight different types of CRC samples. Proteomic analysis revealed 2022 and 599 differentially expressed tissue proteins between CRC and control groups in CRC patients and CRC mice, respectively. In patients with colorectal precancerous lesions, 25 and 34 significantly changed proteins were found between patients and healthy controls in plasma and white blood cells, respectively. Notably, vesicle-associated membrane protein-associated protein A (VAPA) was found to be consistently and significantly decreased in most types of CRC samples, and its level was also significantly correlated with increased overall survival of CRC patients. Furthermore, 37 significantly enriched pathways in CRC were further validated via metabolomics analysis. Ten VAPA-related pathways were found to be significantly enriched in CRC samples, among which PI3K-Akt signaling, central carbon metabolism in cancer, cholesterol metabolism, and ABC transporter pathways were also enriched in the premalignant stage. Our study identified VAPA and its associated pathways as key regulators, suggesting their potential applications in the early diagnosis and prognosis of CRC.

Spontaneous Achilles tendon rupture (SATR) predominantly affects middle-aged and elderly individuals with chronic injuries. However, the exact cause and mechanism of SATR remain elusive, and potential therapeutic intervention or prevention is still insufficient. The present study aimed to uncover the key pathological molecules by using iTRAQ proteomics. The results identified 2432 candidate proteins in SATR patients using iTRAQ proteomic analysis. A total of 307 differentially expressed proteins (DEPs) were identified and linked to 211 KEGG signaling pathways including Coronavirus disease (COVID-19), focal adhesion, and ribosomes. GO enrichment analysis highlighted significant enrichment in processes such as biological adhesion, ossification, lipid (APOA4) processes, and extracellular matrix (ECM) organization (collagen). PPI network analysis identified hub genes such as serum albumin (ALB), fibronectin (FN1), and actin cytoplasmic 1. The WB analysis confirmed that FN1 and the receptor for activated C kinase (RACK1) were downregulated in the SATR tendon. Immunohistochemical staining revealed that collagen I and III were suppressed, while collagen II and APOA4 expression were higher in the SATR pathological tissue (P < 0.05). However, the primary cultured tenocytes (PCTs) from SATR patients showed enhanced proliferation and, consistent with tissue staining, reduced collagen I and III and increased collagen II. Our findings reveal vital targets and pathways in SATR's etiological progression, offering a new perspective on the diagnosis, treatment, and prognosis of this complex disorder.

Post-translational modifications are crucial in regulating biological functions across both prokaryotes and eukaryotes. In Aeromonas hydrophila, CobQ, a recently identified novel deacetylase, plays a significant role in lysine deacetylation, influencing bacterial metabolism and stress responses. The present study utilized quantitative proteomics to investigate the impact of cobQ deletion on the global protein expression profile in A. hydrophila. Through data-independent acquisition mass spectrometry, we identified 233 upregulated and 41 downregulated proteins in the cobQ deletion mutant (ΔahcobQ) strain compared to the wild-type (WT) strain. Key differentially expressed proteins were involved in oxidative phosphorylation, bacterial secretion, and ribosomal function. Additionally, phenotypic assays demonstrated that the ΔahcobQ strain exhibited an increased resistance to oxidative phosphorylation inhibitors, suggesting a pivotal role for AhCobQ in energy metabolism. Outer membrane proteins and efflux pumps also showed altered expression, indicating potential implications for membrane permeability and antibiotic resistance. These results suggested that AhCobQ plays a vital regulatory role in maintaining metabolic homeostasis and responding to environmental stress, highlighting its potential as a target for therapeutic interventions against A. hydrophila infections.

Oxylipins, diverse lipid mediators derived from fatty acids, play key roles in respiratory physiology, but the contribution of lung structural cells to this diverse profile is not well understood. This study aimed to characterize the oxylipin profiles of airway smooth muscle (ASM), lung fibroblasts (HLF), and epithelial (HBE) cells and define how they shift when they are exposed to stimuli related to contractility, fibrosis, and inflammation. Using HPLC-MS/MS, 162 oxylipins were measured in baseline media from cultured human ASM, HLF, and HBE cells as well as after stimulation with modulators of contractility and central regulators of fibrosis/inflammation. At the baseline, ASM and HLF cells had the most similar oxylipin profiles, dominated by oxylipins from cytochrome P450 (CYP450) epoxygenase metabolites. TGFβ stimulation of HLF suppressed CYP450-derived oxylipins, while ASM stimulation increased prostaglandin production. HBE showed the most distinct baseline profile enriched with cyclooxygenase (COX)-derived oxylipins. TGFβ stimulation of HBE increased the level of several oxylipins from CYP450 epoxygenases. These findings highlight the importance of CYP450 oxylipins, which are relatively unexplored in the context of respiratory physiology. By resolving these oxylipin profiles, we enable future respiratory research to understand the function of these oxylipins in regulating physiology, especially in the context of modifying contraction and inflammation.

Top-down mass spectrometry is widely used for proteoform identification, characterization, and quantification owing to its ability to analyze intact proteoforms. In the past decade, top-down proteomics has been dominated by top-down data-dependent acquisition mass spectrometry (TD-DDA-MS), and top-down data-independent acquisition mass spectrometry (TD-DIA-MS) has not been well studied. While TD-DIA-MS produces complex multiplexed tandem mass spectrometry (MS/MS) spectra, which are challenging to confidently identify, it selects more precursor ions for MS/MS analysis and has the potential to increase proteoform identifications compared with TD-DDA-MS. Here we present TopDIA, the first software tool for proteoform identification by TD-DIA-MS. It generates demultiplexed pseudo MS/MS spectra from TD-DIA-MS data and then searches the pseudo MS/MS spectra against a protein sequence database for proteoform identification. We compared the performance of TD-DDA-MS and TD-DIA-MS using Escherichia coli K-12 MG1655 cells and demonstrated that TD-DIA-MS with TopDIA increased proteoform and protein identifications compared with TD-DDA-MS.

Diagnosis of non-small cell lung cancer (NSCLC) currently relies on imaging; however, these methods are not effective for detecting early stage disease. Investigating blood-based protein biomarkers aims to simplify the diagnostic process and identify disease-associated changes before they can be seen by using imaging techniques. In this study, plasma and frozen whole blood cell pellets from NSCLC patients and healthy controls were processed using both classical and novel techniques to produce a unique set of four sample types from a single blood draw. These samples were analyzed using 12 immunoassays and liquid chromatography-mass spectrometry to collectively screen 3974 proteins. Analysis of all fractions produced a set of 522 differentially expressed proteins, with conventional blood analysis (proteomic analysis of plasma) accounting for only 7 of the total. Boosted regression tree analysis of the differentially expressed proteins produced a panel of 13 proteins that were able to discriminate between controls and NSCLC patients, with an area under the ROC curve (AUC) of 0.864 for the set. Our rapid and reproducible (<10% CV for technical replicates) blood preparation and analysis methods enabled the production of high-quality data from only 30 μL of complex samples that typically require significant fractionation prior to proteomic analysis. With our methods, almost 4000 proteins were identified from a single fraction over a 62.5 min gradient by LC-MS/MS.