Data-Mining Approach on Transcriptomics and Methylomics Placental Analysis Highlights Genes in Fetal Growth Restriction - Université de Rennes
Journal Articles Frontiers in Genetics Year : 2020

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

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.
Fichier principal
Vignette du fichier
pdf (5.8 Mo) Télécharger le fichier
Origin Publisher files allowed on an open archive

Dates and versions

hal-02469273 , version 1 (06-02-2020)

Licence

Identifiers

Cite

Floris Chabrun, Noemie Huetz, Xavier Dieu, Guillaume Rousseau, Guillaume Bouzillé, et al.. Data-Mining Approach on Transcriptomics and Methylomics Placental Analysis Highlights Genes in Fetal Growth Restriction. Frontiers in Genetics, 2020, 10, pp.1292. ⟨10.3389/fgene.2019.01292⟩. ⟨hal-02469273⟩
303 View
70 Download

Altmetric

Share

More