Data-Mining Approach on Transcriptomics and Methylomics Placental Analysis Highlights Genes in Fetal Growth Restriction - 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; et al. (Frontiers in Genetics, 2020)
Intrauterine Growth Restriction (IUGR) affects 8% of new-borns 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 1200 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 signalling 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.