Data-Mining Approach on Transcriptomics and Methylomics Placental Analysis Highlights Genes in Fetal Growth Restriction.

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ID: 88267
2019
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Abstract
Intrauterine Growth Restriction (IUGR) affects 8% of newborns and increases morbidity and mortality for the offspring even during later stages of life. Single omics studies have evidenced epigenetic, genetic, and metabolic alterations in IUGR, but pathogenic mechanisms as a whole are not being fully understood. An in-depth strategy combining methylomics and transcriptomics analyses was performed on 36 placenta samples in a case-control study. Data-mining algorithms were used to combine the analysis of more than 1,200 genes found to be significantly expressed and/or methylated. We used an automated text-mining approach, using the bulk textual gene annotations of the discriminant genes. Machine learning models were then used to explore the phenotypic subgroups (premature birth, birth weight, and head circumference) associated with IUGR. Gene annotation clustering highlighted the alteration of cell signaling and proliferation, cytoskeleton and cellular structures, oxidative stress, protein turnover, muscle development, energy, and lipid metabolism with insulin resistance. Machine learning models showed a high capacity for predicting the sub-phenotypes associated with IUGR, allowing a better description of the IUGR pathophysiology as well as key genes involved.
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chabrun2019dataminingfrontiers Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Chabrun, Floris;Huetz, Noémie;Dieu, Xavier;Rousseau, Guillaume;Bouzillé, Guillaume;Chao de la Barca, Juan Manuel;Procaccio, Vincent;Lenaers, Guy;Blanchet, Odile;Legendre, Guillaume;Mirebeau-Prunier, Delphine;Cuggia, Marc;Guardiola, Philippe;Reynier, Pascal;Gascoin, Geraldine;
Journal Frontiers in genetics
Year 2019
DOI
10.3389/fgene.2019.01292
URL
Keywords
Data mining transcriptomics intrauterine growth restriction multi-omics text-mining methylomics

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