Unveiling Multi-Omics and Immune Landscapes in COVID-19 Patients' Ovaries Microenvironment for Oocyte Competency Biomarkers Identification

Background: The outbreak of coronavirus disease 2019 (COVID-19) poses a considerable health threat to humanity, with potential implications for the ovarian microenvironment remaining uncertain. Methods: Transcriptomic and proteomic analyses of ovarian granulosa cells, along with metabolomic and lipidomic profiling of follicular fluid, were conducted on 17 non-COVID-19 cases and 9 COVID-19 cases. This study received approval from the ethics committee (KYLL-2022-581). Generalized estimating equations model was performed to identify oocyte competency biomarkers. Additionally, cell proliferation, apoptosis, and altered pathways were examined following lentivirus transfection. Methods: Transcriptomic and proteomic analyses of ovarian granulosa cells, along with metabolomic and lipidomic profiling of follicular fluid, were conducted on 17 non-COVID-19 cases and 9 COVID-19 cases. This study received approval from the ethics committee (KYLL-2022-581). Generalized estimating equations model was performed to identify oocyte competency biomarkers. Additionally, cell proliferation, apoptosis, and altered pathways were examined following lentivirus transfection. Conclusions: By integrating untargeted metabolomic and lipidomic features, we identified biomarkers indicative of oocyte competency influenced by COVID-19. To investigate the influence of COVID-19 on ovarian granulosa cells, transcriptomic analyses of ovarian granulosa cells were conducted on 17 non-COVID-19 cases and 9 COVID-19 cases.

背景：2019冠状病毒病（coronavirus disease 2019, COVID-19）的暴发对人类健康构成了显著威胁，但其对卵巢微环境的潜在影响仍未明确。
方法：本研究纳入17例非COVID-19病例与9例COVID-19病例，对其卵巢颗粒细胞（ovarian granulosa cells）开展转录组学与蛋白质组学分析，并对卵泡液进行代谢组谱与脂质组谱分析。本研究已通过伦理委员会批准（KYLL-2022-581）。采用广义估计方程模型（generalized estimating equations model）识别卵母细胞发育能力生物标志物。此外，在慢病毒转染（lentivirus transfection）后检测了细胞增殖、凋亡及通路变化情况。
方法：本研究纳入17例非COVID-19病例与9例COVID-19病例，对其卵巢颗粒细胞开展转录组学与蛋白质组学分析，并对卵泡液进行代谢组谱与脂质组谱分析。本研究已通过伦理委员会批准（KYLL-2022-581）。采用广义估计方程模型识别卵母细胞发育能力生物标志物。此外，在慢病毒转染后检测了细胞增殖、凋亡及通路变化情况。
结论：通过整合非靶向代谢组与脂质组特征，我们识别出受COVID-19影响的卵母细胞发育能力相关生物标志物。为探究COVID-19对卵巢颗粒细胞的影响，本研究对17例非COVID-19病例与9例COVID-19病例的卵巢颗粒细胞开展了转录组分析。