代谢组学和蛋白质组学分析揭示了甘油磷脂代谢途径在不明原因复发性自然流产中的作用。

Metabolomics and proteomics analyses reveal the role of the glycerophospholipid metabolism pathway in unexplained recurrent spontaneous abortion.

作者信息

Chen Yihong, Zhao Xiumei, Gan Bei, Jin Leiyi, Wei Juanbing, Yan Jianying

机构信息

College of Clinical Medicine for Obstetrics & Gynecology and Pediatrics, Fujian Medical University, Department of Obstetrics and Gynecology, Fujian Maternity and Child Health Hospital, Fujian Clinical Research Center for Maternal-Fetal Medicine, Fuzhou, China.

Department of Obstetrics and Gynecology, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China.

出版信息

PeerJ. 2025 Apr 30;13:e19317. doi: 10.7717/peerj.19317. eCollection 2025.

DOI:10.7717/peerj.19317

PMID:40321821

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12049100/

Abstract

BACKGROUND

Unexplained recurrent spontaneous abortion (URSA) is a complex pregnancy complication with a high miscarriage rate. Incomprehensive understanding of the molecular mechanism in URSA also leads to a lack of effective treatment methods. Hence, the current study aimed to explore the underlying pathogenesis of URSA applying metabonomic and bioinformatics analysis.

METHODS

The decidual tissues of eight URSA samples and eight normal pregnancy (normal control, NC) samples were collected for liquid chromatography-mass spectrometry (LC-MS) analysis using the Progenesis QI metabolomics software. The orthogonal partial least squares discrimination analysis (OPLS-DA) and the Human Metabolome Database (HMDB) were employed for differential metabolite analysis and pathway enrichment analysis, respectively. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway topological analysis was performed to rank the importance of pathways involved in URSA, and differential proteins were identified based on fold change difference. Finally, a metabolic network was visualized by the Cytoscape tool.

RESULTS

After LC-MS analysis and quality control, samples in the same group showed high consistency and reliability. Differential metabolites between NC and URSA groups were mainly enriched to five biological processes, with glycerophospholipid metabolism pathway containing the greatest number of differential metabolites. KEGG enrichment analysis showed significant differences in glycerophospholipid metabolism, bile secretion, and choline metabolism pathways, with glycerophospholipid metabolism showing a higher pathway importance. Proteome and metabolome analysis revealed a total of 65 overlapping pathways involved in the differential proteins and differential metabolites, and finally , and were identified as the key genes in glycerophospholipid metabolism pathway.

CONCLUSION

LC-MS analysis revealed that glycerophospholipid metabolism pathway and its three key genes were crucially involved in URSA progression, providing novel insights into the treatment strategy of URSA.

摘要

背景

不明原因复发性自然流产（URSA）是一种复杂的妊娠并发症，流产率高。对URSA分子机制的不完全理解也导致缺乏有效的治疗方法。因此，本研究旨在应用代谢组学和生物信息学分析来探索URSA的潜在发病机制。

方法

收集8例URSA样本和8例正常妊娠（正常对照，NC）样本的蜕膜组织，使用Progenesis QI代谢组学软件进行液相色谱-质谱（LC-MS）分析。分别采用正交偏最小二乘法判别分析（OPLS-DA）和人类代谢组数据库（HMDB）进行差异代谢物分析和通路富集分析。进行京都基因与基因组百科全书（KEGG）通路拓扑分析以对参与URSA的通路的重要性进行排序，并基于倍数变化差异鉴定差异蛋白。最后，用Cytoscape工具可视化代谢网络。

结果

经过LC-MS分析和质量控制，同一组中的样本显示出高度的一致性和可靠性。NC组和URSA组之间的差异代谢物主要富集到五个生物学过程，其中甘油磷脂代谢途径含有的差异代谢物数量最多。KEGG富集分析显示甘油磷脂代谢、胆汁分泌和胆碱代谢途径存在显著差异，甘油磷脂代谢显示出更高的通路重要性。蛋白质组和代谢组分析揭示了差异蛋白和差异代谢物共涉及65条重叠通路，最后，和被鉴定为甘油磷脂代谢途径中的关键基因。