Integrated unbiased multiomics defines disease-independent placental clusters in common obstetrical syndromes.
Integrated unbiased multiomics defines disease-independent placental clusters in common obstetrical syndromes.
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DOI：
10.1186/s12916-023-03054-8
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发表时间：
2023-09-08
期刊：
影响因子：
9.3
通讯作者：
Sadovsky, Yoel
中科院分区：
文献类型：
--
作者：
Barak, Oren;Lovelace, Tyler;Piekos, Samantha;Chu, Tianjiao;Cao, Zhishen;Sadovsky, Elena;Mouillet, Jean-Francois;Ouyang, Yingshi;Parks, W. Tony;Hood, Leroy;Price, Nathan D.;Benos, Panayiotis V.;Sadovsky, Yoel
胎盘功能障碍是影响人类妊娠的常见综合征的根本原因，例如先兆子痫 (PE)、胎儿生长受限 (FGR) 和自发性早产 (sPTD)，但其定义仍然不明确。这些常见但临床上不同的产科综合征具有相似的胎盘组织病理学模式，而每种综合征中的个体表现出独特的分子变化，挑战我们的理解并阻碍我们预防和治疗这些综合征的能力。利用我们广泛的生物库，我们确定了患有严重 PE (n = 75)、FGR (n = 40)、FGR 合并高血压疾病 (FGR + HDP; n = 33)、sPTD (n = 72) 和两个不复杂的对照组、足月 (n = 113) 和早产的女性没有 PE、FGR 或 sPTD (n = 16)。我们使用胎盘活检进行转录组学、蛋白质组学、代谢组学数据和组织学评估。经过传统的成对比较后，我们部署了基于人工智能的无偏相似性网络融合（SNF）来整合数据类型并识别组学定义的胎盘簇。我们使用贝叶斯模型选择来比较组织病理学特征和疾病状况与 SNF 簇之间的关联。基于疾病的成对比较显示出相对较小的差异，可能反映了临床综合征的异质性。因此，我们部署了无偏倚、基于组学的 SNF 方法。我们的分析得出了四个不同的簇，它们主要由特定的综合征主导。值得注意的是，以早发性 PE 为主的簇表现出强烈的胎盘功能障碍模式，而以 sPTD 为主的簇中的损伤模式较弱。与预先定义的疾病组相比，SNF 定义的簇与组织病理学表现出更好的相关性。我们的结果表明，基于组学的综合 SNF 独特地重新分类了常见产科综合征背后的胎盘功能障碍模式，提高了我们对病理过程的理解，并可以促进对更个性化干预措施的探索。在线版本包含可在 10.1186/s12916-023-03054-8 获取的补充材料。
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. The online version contains supplementary material available at 10.1186/s12916-023-03054-8.
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影响因子：
14.9
作者：
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D'Eustachio P
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Chu T;Mouillet JF;Cao Z;Barak O;Ouyang Y;Sadovsky Y
通讯作者：
Sadovsky Y
影响因子：
3.8
作者：
Challier, J;Galtier, M;Hauguel-de Mouzon, S
通讯作者：
Hauguel-de Mouzon, S
影响因子：
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影响因子：
13.6
作者：
Espinosa C;Becker M;Marić I;Wong RJ;Shaw GM;Gaudilliere B;Aghaeepour N;Stevenson DK;Prematurity Research Center at Stanford
通讯作者：
Prematurity Research Center at Stanford