Division of Neonatal-Perinatal Medicine, Department of Pediatrics, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Department of Anesthesiology, Pain, and Perioperative Medicine, Stanford University School of Medicine, Stanford, CA, USA.
Pediatr Res. 2023 Jan;93(2):366-375. doi: 10.1038/s41390-022-02335-x. Epub 2022 Oct 10.
Immunoperinatology is an emerging field. Transdisciplinary efforts by physicians, physician-scientists, basic science researchers, and computational biologists have made substantial advancements by identifying unique immunologic signatures of specific diseases, discovering innovative preventative or treatment strategies, and establishing foundations for individualized neonatal intensive care of the most vulnerable neonates. In this review, we summarize the immunobiology and immunopathology of pregnancy, highlight omics approaches to study the maternal-fetal interface, and their contributions to pregnancy health. We examined the importance of transdisciplinary, multiomic (such as genomics, transcriptomics, proteomics, metabolomics, and immunomics) and machine-learning strategies in unraveling the mechanisms of adverse pregnancy, neonatal, and childhood outcomes and how they can guide the development of novel therapies to improve maternal and neonatal health. IMPACT: Discuss immunoperinatology research from the lens of omics and machine-learning approaches. Identify opportunities for omics-based approaches to delineate infection/inflammation-associated maternal, neonatal, and later life adverse outcomes (e.g., histologic chorioamnionitis [HCA]).
免疫围产医学是一个新兴领域。医生、医师科学家、基础科学研究人员和计算生物学家的跨学科努力,通过确定特定疾病的独特免疫特征、发现创新的预防或治疗策略以及为最脆弱新生儿的个体化新生儿重症监护奠定基础,取得了实质性进展。在这篇综述中,我们总结了妊娠的免疫生物学和免疫病理学,强调了用于研究母体-胎儿界面的组学方法及其对妊娠健康的贡献。我们研究了跨学科、多组学(如基因组学、转录组学、蛋白质组学、代谢组学和免疫组学)和机器学习策略在揭示不良妊娠、新生儿和儿童结局的机制中的重要性,以及它们如何指导新型疗法的开发,以改善母婴健康。影响:从组学和机器学习方法的角度讨论免疫围产医学研究。确定基于组学的方法在描绘与感染/炎症相关的母体、新生儿和以后生活中的不良结局(例如组织学绒毛膜羊膜炎[HCA])方面的机会。