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<p>The placenta is essential for pregnancy, and its dysfunction can harm both mother and fetus. To better understand placental physiology and its disruption in disease, we employ a multiomics approach (transcriptomics, metabolomics, and proteomics) combined with clinical data and histopathology from 321 placentas across conditions: severe fetal growth restriction (FGR), FGR with hypertension (FGR + HDP), severe&#xa0;preeclampsia (PE), and spontaneous&#xa0;preterm delivery (PTD). Cellular deconvolution reveals FGR + HDP placentas have more extravillous trophoblasts than controls (p &lt; 0.0001). After adjusting for fetal sex and gestational age, we build condition-specific interomics networks and detect communities (a.k.a. subnetworks). In a control community, <i>miR-365a-3p</i> is the most connected node, whereas in FGR + HDP placentas, it is hypoxia-induced <i>miR-210-3p</i>. From this community, we identify a signature containing mRNAs implicated in placental dysfunction (e.g. <i>FLT1</i>, <i>FSTL3</i>, <i>HTRA4</i>, <i>LEP</i>, and <i>PHYHIP</i>), which distinguishes FGR + HDP placentas from those with other conditions, illustrating the power of interomics in understanding obstetric syndromes.</p>

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Placental network differences among obstetric syndromes identified with an integrated multiomics approach

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月18日

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  • Samantha N. Piekos,
  • Oren Barak,
  • Andrew Baumgartner,
  • Tianjiao Chu,
  • W. Tony Parks,
  • Jennifer Hadlock,
  • Leroy Hood,
  • Nathan D. Price,
  • Yoel Sadovsky

摘要

The placenta is essential for pregnancy, and its dysfunction can harm both mother and fetus. To better understand placental physiology and its disruption in disease, we employ a multiomics approach (transcriptomics, metabolomics, and proteomics) combined with clinical data and histopathology from 321 placentas across conditions: severe fetal growth restriction (FGR), FGR with hypertension (FGR + HDP), severe preeclampsia (PE), and spontaneous preterm delivery (PTD). Cellular deconvolution reveals FGR + HDP placentas have more extravillous trophoblasts than controls (p < 0.0001). After adjusting for fetal sex and gestational age, we build condition-specific interomics networks and detect communities (a.k.a. subnetworks). In a control community, miR-365a-3p is the most connected node, whereas in FGR + HDP placentas, it is hypoxia-induced miR-210-3p. From this community, we identify a signature containing mRNAs implicated in placental dysfunction (e.g. FLT1, FSTL3, HTRA4, LEP, and PHYHIP), which distinguishes FGR + HDP placentas from those with other conditions, illustrating the power of interomics in understanding obstetric syndromes.