Omics approaches: interactions at the maternal-fetal interface and origins of child health and disease

Abstract

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]).

Fig. 1. Exposome, fetal programming, and representative omics approaches (immunomics, transcriptomics, metabolomics) and analytical methods (supervised, unsupervised machine-learning and neural networks) are illustrated.

However, these are not mutually exclusive (for example, sparse methods are supervised; also supervised methods can be single task or multitask).

Fig. 2. Inflammatory and immune regulation of healthy and pathological pregnancies.

Summary of NF-KB pathway in normal and pathological pregnancies.