Identification of differentially expressed proteins in follicular fluid from patients with polyendocrine metabolic ovarian syndrome using 4D label-free proteomics
This study employed 4D label-free quantitative proteomics to identify differentially expressed proteins (DEPs) in follicular fluid (FF) from patients with polyendocrine metabolic ovarian syndrome (PMOS). FF samples were collected from the PMOS group ( n = 20) and the control group ( n = 10), followed by whole-proteome identification and quantitative analysis. DEPs were screened using a fold change > 1.5 and P < 0.05, after which bioinformatic analyses were performed on the identified proteins. Compared with the control group, a total of 33 DEPs were identified in the PMOS group, of which 24 were upregulated and 9 were downregulated. GO functional enrichment analysis revealed that these DEPs were primarily involved in immunoglobulin production, humoral immune response, leukocyte migration, glycolysis, and pyruvate metabolism. KEGG pathway enrichment analysis indicated that DEPs were predominantly enriched in glycolysis/gluconeogenesis, pyruvate metabolism, the hypoxia-inducible factor-1 (HIF-1) signalling pathway, and extracellular matrix-receptor interactions. Protein interaction network analysis suggested a synergistic relationship between metabolic reprogramming and immune dysregulation in PMOS. Enzyme-linked immunosorbent assay (ELISA) validation of L-lactate dehydrogenase A chain (LDHA), protein S100-A9 (S100A9) and serum amyloid A1 (SAA1) levels in FF confirmed that all three were significantly higher in the PMOS group than in the control group. At the proteomic level, FF from PMOS patients exhibits substantial glycolytic metabolic reprogramming, immune dysregulation, and matrix remodelling, providing a new perspective on the pathogenesis of this condition. The identified DEPs may serve as diagnostic biomarkers or therapeutic targets for PMOS, and future studies should expand the sample size and validate these findings by integrating clinical phenotypes with functional experiments.
The comparison of adopter and non-adopter sample reveals three potential adoption inhibitor, security, data privacy, and portability, which underlines the importance of the technical and security perspectives for research investigating the adoption of technology.
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It is demonstrated that linker-free PROTACs can outperform traditional designs, marking a paradigm shift in PROTAC development for targeted protein degradation.
Pinal, a 16-billion-parameter foundation model that produces protein candidates from natural-language functional descriptions, supports natural language as a high-level interface for candidate generation in protein design, enabling programmable exploration with reduced reliance on manually specified structural or seque...
A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.