Zac1 and the Imprinted Gene Network program juvenile NAFLD in response to maternal metabolic syndrome

Hepatology. 2022 Jan 26. doi: 10.1002/hep.32363. Online ahead of print.


Background and aims: Within the next decade, NAFLD is predicted to become the most prevalent cause of childhood liver failure in developed countries. Predisposition to juvenile NAFLD can be programmed during early life in response to maternal metabolic syndrome (MetS), but the underlying mechanisms are poorly understood. We hypothesized that imprinted genes, defined by expression from a single parental allele, play a key role in maternal MetS-induced NAFLD, due to their susceptibility to environmental stressors and their functions in liver homeostasis. We aimed to test this hypothesis and determine the critical periods of susceptibility to maternal MetS.

Approach and results: We established a mouse model to compare the effects of MetS during prenatal and postnatal development on NAFLD. Postnatal but not prenatal MetS exposure is associated with histological, biochemical, and molecular signatures of hepatic steatosis and fibrosis in juvenile mice. Using RNA sequencing, we show that the Imprinted Gene Network (IGN), including its regulator Zac1, is up-regulated and overrepresented among differentially expressed genes, consistent with a role in maternal MetS-induced NAFLD. In support of this, activation of the IGN in cultured hepatoma cells by overexpressing Zac1 is sufficient to induce signatures of profibrogenic transformation. Using chromatin immunoprecipitation, we demonstrate that Zac1 binds the TGF-β1 and COL6A2 promoters, forming a direct pathway between imprinted genes and well-characterized pathophysiological mechanisms of NAFLD. Finally, we show that hepatocyte-specific overexpression of Zac1 is sufficient to drive fibrosis in vivo.

Conclusions: Our findings identify a pathway linking maternal MetS exposure during postnatal development to the programming of juvenile NAFLD, and provide support for the hypothesis that imprinted genes play a central role in metabolic disease programming.