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Abstract Background Placental dysfunction, a root cause of common syndromes affecting human pregnancy, such as preeclampsia (PE), fetal growth restriction (FGR), and spontaneous preterm delivery (sPTD), remains poorly defined. These common, yet clinically disparate obstetrical syndromes share similar placental histopathologic patterns, while individuals within each syndrome present distinct molecular changes, challenging our understanding and hindering our ability to prevent and treat these syndromes. Methods Using our extensive biobank, we identified women with severe PE (n = 75), FGR (n = 40), FGR with a hypertensive disorder (FGR + HDP; n = 33), sPTD (n = 72), and two uncomplicated control groups, term (n = 113), and preterm without PE, FGR, or sPTD (n = 16). We used placental biopsies for transcriptomics, proteomics, metabolomics data, and histological evaluation. After conventional pairwise comparison, we deployed an unbiased, AI-based similarity network fusion (SNF) to integrate the datatypes and identify omics-defined placental clusters. We used Bayesian model selection to compare the association between the histopathological features and disease conditions vs SNF clusters. Results Pairwise, disease-based comparisons exhibited relatively few differences, likely reflecting the heterogeneity of the clinical syndromes. Therefore, we deployed the unbiased, omics-based SNF method. Our analysis resulted in four distinct clusters, which were mostly dominated by a specific syndrome. Notably, the cluster dominated by early-onset PE exhibited strong placental dysfunction patterns, with weaker injury patterns in the cluster dominated by sPTD. The SNF-defined clusters exhibited better correlation with the histopathology than the predefined disease groups. Conclusions Our results demonstrate that integrated omics-based SNF distinctively reclassifies placental dysfunction patterns underlying the common obstetrical syndromes, improves our understanding of the pathological processes, and could promote a search for more personalized interventions.

In the human placenta, two trophoblast cell layers separate the maternal blood from the villous basement membrane and fetal capillary endothelial cells. The inner layer, which is complete early in pregnancy and later becomes discontinuous, comprises the proliferative mononuclear cytotrophoblasts, which fuse together and differentiate to form the outer layer of multinucleated syncytiotrophoblasts. Because the syncytiotrophoblasts are responsible for key maternal-fetal exchange functions, tight regulation of this differentiation process is critical for the proper development and the functional role of the placenta. The molecular mechanisms regulating the fusion and differentiation of trophoblasts during human pregnancy remain poorly understood. To decipher the interactions of non-coding RNAs (ncRNAs) in this process, we exposed cultured primary human trophoblasts to standard in vitro differentiation conditions or to conditions known to hinder this differentiation process, namely exposure to hypoxia (O2 < 1%) or to the addition of dimethyl sulfoxide (DMSO, 1.5%) to the culture medium. Using next generation sequencing technology, we analyzed the differential expression of trophoblastic lncRNAs, miRNAs, and mRNAs that are concordantly modulated by both hypoxia and DMSO. Additionally, we developed a model to construct a lncRNA-miRNA-mRNA co-expression network and inferred the functions of lncRNAs and miRNAs via indirect gene ontology analysis. This study improves our knowledge of the interactions between ncRNAs and mRNAs during trophoblast differentiation and identifies key biological processes that may be impaired in common gestational diseases, such as fetal growth restriction or preeclampsia.