miRNA profiling of granulosa cell-derived exosomes reveals their role in promoting follicle development.

To explore whether granulosa cell (GC)-derived exosomes (GC-Exos) and follicular fluid-derived exosomes (FF-Exos) have functional similarities in follicle development and to establish relevant experiments to validate whether GC-Exos could serve as a potential substitute for follicular fluid-derived exosomes to improve folliculogenesis. GC-Exos were characterized. MicroRNA (miRNA) profiles of exosomes from human GCs and follicular fluid were analyzed in depth. The signature was associated with folliculogenesis, such as phosphatidylinositol 3 kinases-protein kinase B signal pathway, mammalian target of rapamycin signal pathway, mitogen-activated protein kinase signal pathway, Wnt signal pathway, and cyclic adenosine monophosphate signal pathway. A total of five prominent miRNAs were found to regulate the above five signaling pathways. These miRNAs include miRNA-486-5p, miRNA-10b-5p, miRNA-100-5p, miRNA-99a-5p, and miRNA-21-5p. The exosomes from GCs and follicular fluid were investigated to explore the effect on folliculogenesis by injecting exosomes into older mice. The proportion of follicles at each stage is counted to help us understand folliculogenesis. Exosomes derived from GCs were isolated successfully. miRNA profiles demonstrated a remarkable overlap between the miRNA profiles of FF-Exos and GC-Exos. The shared miRNA signature exhibited a positive influence on follicle development and activation. Furthermore, exosomes derived from GCs and follicular fluid promoted folliculogenesis in older female mice. Exosomes derived from GCs had similar miRNA profiles and follicle-promoting functions as follicular fluid exosomes. Consequently, GC-Exos are promising for replacing FF-Exos and developing new commercial reagents to improve female fertility.

Identification of energy metabolism anomalies and serum biomarkers in the progression of premature ovarian failure via extracellular vesicles' proteomic and metabolomic profiles.

BACKGROUND: Premature ovarian failure (POF) is a clinical condition characterized by the cessation of ovarian function, leading to infertility. The underlying molecular mechanisms remain unclear, and no predictable biomarkers have been identified. This study aimed to investigate the protein and metabolite contents of serum extracellular vesicles to investigate underlying molecular mechanisms and explore potential biomarkers. METHODS: This study was conducted on a cohort consisting of 14 POF patients and 16 healthy controls. The extracellular vesicles extracted from the serum of each group were subjected to label-free proteomic and unbiased metabolomic analysis. Differentially expressed proteins and metabolites were annotated. Pathway network clustering was conducted with further correlation analysis. The biomarkers were confirmed by ROC analysis and random forest machine learning. RESULTS: The proteomic and metabolomic profiles of POF patients and healthy controls were compared. Two subgroups of POF patients, Pre-POF and Pro-POF, were identified based on the proteomic profile, while all patients displayed a distinguishable metabolomic profile. Proteomic analysis suggested that inflammation serves as an early factor contributing to the infertility of POF patients. For the metabolomic analysis, despite the dysfunction of metabolism, oxidative stress and hormone imbalance were other key factors appearing in POF patients. Signaling pathway clustering of proteomic and metabolomic profiles revealed the progression of dysfunctional energy metabolism during the development of POF. Moreover, correlation analysis identified that differentially expressed proteins and metabolites were highly associated, with six of them being selected as potential biomarkers. ROC curve analysis, together with random forest machine learning, suggested that AFM combined with 2-oxoarginine was the best diagnostic biomarker for POF. CONCLUSIONS: Omics analysis revealed that inflammation, oxidative stress, and hormone imbalance are factors that damage ovarian tissue, but the progressive dysfunction of energy metabolism might be the critical pathogenic pathway contributing to the development of POF. AFM combined with 2-oxoarginine serves as a precise biomarker for clinical POF diagnosis.