Birth defects are the leading cause of infant mortality, and they are caused by a combination of genetic and environmental factors. Environmental risk factors may contribute to birth defects in genetically susceptible infants by altering critical molecular pathways during embryogenesis, but experimental evidence for gene-environment interactions is limited. Fetal hyperglycemia associated with maternal diabetes results in a 5-fold increased risk of congenital heart disease (CHD), but the molecular basis for this correlation is unknown. Here, we show that the effects of maternal hyperglycemia on cardiac development are sensitized by haploinsufficiency of Notch1, a key transcriptional regulator known to cause CHD. Using ATAC-seq, we found that hyperglycemia decreased chromatin accessibility at the endothelial NO synthase (Nos3) locus, resulting in reduced NO synthesis. Transcription of Jarid2, a regulator of histone methyltransferase complexes, was increased in response to reduced NO, and this upregulation directly resulted in inhibition of Notch1 expression to levels below a threshold necessary for normal heart development. We extended these findings using a Drosophila maternal diabetic model that revealed the evolutionary conservation of this interaction and the Jarid2-mediated mechanism. These findings identify a gene-environment interaction between maternal hyperglycemia and Notch signaling and support a model in which environmental factors cause birth defects in genetically susceptible infants.
Madhumita Basu, Jun-Yi Zhu, Stephanie LaHaye, Uddalak Majumdar, Kai Jiao, Zhe Han, Vidu Garg
This article was first published October 19, 2017. Usage data is cumulative from October 2017 through July 2018.
Usage information is collected from two different sources: this site (JCI) and Pubmed Central (PMC). JCI information (compiled daily) shows human readership based on methods we employ to screen out robotic usage. PMC information (aggregated monthly) is also similarly screened of robotic usage.
Various methods are used to distinguish robotic usage. For example, Google automatically scans articles to add to its search index and identifies itself as robotic; other services might not clearly identify themselves as robotic, or they are new or unknown as robotic. Because this activity can be misinterpreted as human readership, data may be re-processed periodically to reflect an improved understanding of robotic activity. Because of these factors, readers should consider usage information illustrative but subject to change.