Understanding the molecular mechanisms regulating the early placental development using Next-Generation Sequencing (NGS) datasets

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2020-12
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Jain, Ashish
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Tuteja, Geetu
Dickerson, Julie
Dorman, Karin
Hofmann, Heike
Gu, Xun
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Genetics, Development and Cell Biology
Abstract
The placenta is a temporary organ that plays a significant role in the transportation of vital nutrients, gases, pregnancy hormones, and other essential products between the mother and the fetus during pregnancy. It is also involved in various other critical functions, including anchoring the conceptus and fetus immune protection. Throughout pregnancy, the placenta alters its function to cater to the needs of the growing fetus. Aberration in the expression of placental development genes during pregnancy leads to its abnormal structure and function. These abnormalities result in several pregnancy-related complications, including intrauterine growth restriction (IUGR), preterm labor, low birth weight, and preeclampsia (PE). PE causes severe damage to both mother and baby. Many studies suggest that abnormalities leading to early-onset PE start during the first few weeks after conception but can only be detected after 20 weeks. Therefore, it is crucial to study regulatory mechanisms during early placental development. However, this area is not explored very well due to restrictions and lack of early placental cell model. In my thesis, I studied the regulatory mechanisms behind early placenta development by developing computational methods and tools to analyze various next-generation sequencing (NGS) datasets. First, I analyzed transcriptomics data generated in early trophoblast cells derived from human embryonic stem cells and identified a gene regulatory network specific for the early trophoblast development using co-expression network analysis. Second, I developed a bioinformatics tool that carries out tissue-specific gene enrichment using the hypergeometric test. Third, I developed a statistical method to integrate and cluster gene expression and DNA methylation datasets simultaneously to identify PE subtypes using factor analysis and Gaussian mixture models. Altogether, these studies and methods will help understand the molecular mechanisms regulating critical processes during early placental development.
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