FoxO1, A2M, and TGF-β1: three novel genes predicting depression in gene X environment interactions are identified using cross-species and cross-tissues transcriptomic and miRNomic analyses. Issue 11 (November 2018) - Cattaneo, Annamaria; Cattane, Nadia; Malpighi, Chiara; Czamara, Darina; Suarez, Anna; Mariani, Nicole; Kajantie, Eero; Luoni, Alessia; Eriksson, Johan; Lahti, Jari; Mondelli, Valeria; Dazzan, Paola; et al.
Abstract To date, gene-environment (GxE) interaction studies in depression have been limited to hypothesis-based candidate genes, since genome-wide (GWAS)-based GxE interaction studies would require enormous datasets with genetics, environmental, and clinical variables. We used a novel, cross-species and cross-tissues "omics " approach to identify genes predicting depression in response to stress in GxE interactions. We integrated the transcriptome and miRNome profiles from the hippocampus of adult rats exposed to prenatal stress (PNS) with transcriptome data obtained from blood mRNA of adult humans exposed to early life trauma, using a stringent statistical analyses pathway. Network analysis of the integrated gene lists identified the Forkhead box protein O1 (FoxO1 ), Alpha-2-Macroglobulin (A2M ), and Transforming Growth Factor Beta 1 (TGF-β1) as candidates to be tested for GxE interactions, in two GWAS samples of adults either with a range of childhood traumatic experiences (Grady Study Project, Atlanta, USA) or with separation from parents in childhood only (Helsinki Birth Cohort Study, Finland). After correction for multiple testing, a meta-analysis across both samples confirmed sixFoxO1 SNPs showing significant GxE interactions with early life emotional stress in predicting depressive symptoms. Moreover, in vitro experiments in a human hippocampal progenitor cell line confirmed a functional role ofFoxO1 in stress responsivity. In secondary analyses, A2M andTGF-β1 showed significant GxE interactions with emotional, physical, and sexual abuse in the Grady Study. We therefore provide a successful 'hypothesis-free' approach for the identification and prioritization of candidate genes for GxE interaction studies that can be investigated in GWAS datasets.