Magee-Womens Research Institute, 204 Craft Avenue, Pittsburgh, PA, 15213, USA.
Department of Obstetrics, Gynecology and Reproductive Sciences, University of Pittsburgh, 300 Halket Street, Pittsburgh, PA, 15213, USA.
BMC Med. 2023 Sep 8;21(1):349. doi: 10.1186/s12916-023-03054-8.
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.
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.
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.
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.
胎盘功能障碍是影响人类妊娠的常见综合征（如子痫前期（PE）、胎儿生长受限（FGR）和自发性早产（sPTD））的根本原因，但目前仍未得到明确界定。这些常见但临床表现不同的产科综合征具有相似的胎盘组织病理学模式，而每个综合征中的个体表现出不同的分子变化，这挑战了我们的认识，也阻碍了我们预防和治疗这些综合征的能力。
我们利用广泛的生物库，鉴定了患有严重 PE（n=75）、FGR（n=40）、FGR 合并高血压疾病（FGR+HDP；n=33）、sPTD（n=72）的女性，以及两个未合并 PE、FGR 或 sPTD 的对照组（足月组 n=113 和早产组 n=16）。我们使用胎盘活检进行转录组学、蛋白质组学和代谢组学数据分析以及组织学评估。在常规两两比较后，我们采用了一种无偏倚的基于人工智能的相似网络融合（SNF）方法来整合不同的数据类型，并识别基于组学的胎盘聚类。我们使用贝叶斯模型选择来比较组织病理学特征与疾病状况与 SNF 聚类之间的关联。
基于疾病的两两比较显示出相对较少的差异，这可能反映了临床综合征的异质性。因此，我们采用了无偏倚的基于组学的 SNF 方法。我们的分析得到了四个不同的聚类，这些聚类主要由特定的综合征主导。值得注意的是，由早发型 PE 主导的聚类表现出强烈的胎盘功能障碍模式，而由 sPTD 主导的聚类表现出较弱的损伤模式。SNF 定义的聚类与组织病理学的相关性优于预定义的疾病组。
我们的研究结果表明，基于整合组学的 SNF 可以独特地重新分类常见产科综合征的胎盘功能障碍模式，增进我们对病理过程的理解，并可能促进更个体化的干预措施的探索。
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