Prenatal alcohol exposure alters expression of genes involved in cell adhesion, immune response, and toxin metabolism in adolescent rat hippocampus
Alcohol exposure during fetal development is associated with a wide range of behavioral and physical symptoms that are observed from childhood throughout adolescence and beyond. It is believed that this exposure may alter gene expression patterns permanently by changing genomic architecture, but the actual changes themselves are still unclear. In this study we examined gene expression patterns in rats exposed to ethanol during gestation. These were compared to control rats where dams were fed a similar liquid diet without ethanol as well as rats fed a normal diet. We identified the top differentially expressed genes and performed gene ontology analysis to identify both genes and pathways important in the response to ethanol during fetal development. We focused on adolescent rats since PAE has been shown at this stage to influence behavior. We indeed found a number of significant changes in gene expression, suggesting that PAE has ongoing consequences throughout and likely beyond adolescence into adulthood. Pathways such as cell adhesion, immune response, and toxin response were all highlighted. Future work will focus on making connections between these gene expression changes and behavioral changes observed at this same life stage. Overall design: RNA was isolated  from the hippocampus of adolescent rats exposed to ethanol during prenatal development and compared gene expression to controls. Dams were  either given free access to standard chow ad libitum (AD), pair-fed a liquid diet (PF) or were given a liquid diet with ethanol (6.7% ethanol, ET) throughout gestation (gestational day (GD) 0-20). All dams were given control diet ad libitum beginning on GD 20 and throughout parturition and lactation. A total of 17 RNA samples were sequenced representing the three treatment groups: 5 ET samples (2 females and 3 males), 6 PF samples (3 Females and 3 Males), 6 AD samples (3 Females and 3 Males) with three technical replicates for each sample. Differential gene expression analysis was performed using the DESeq method as implemented within the Bioinformatics Toolbox in Matlab (R2022a).