Engineering in vitro models of non-alcoholic fatty liver disease
Decreased resources and a scarcity of affordable, healthy food is contributing to rising obesity rates throughout the world. Consequentially, non-alcoholic fatty liver disease (NAFLD), which is highly correlated with obesity, rates are also increasing with greater than 30% of the US population currently diagnosed. NAFLD starts as a benign state of fat accumulation within liver hepatocytes but often progresses to more detrimental conditions such as non-alcoholic steatohepatitis (NASH), fibrosis/cirrhosis and hepatocellular carcinoma (HCC). There is no cure for NAFLD or its downstream complications and questions still remain about what factors contribute to disease progression. Specifically, the cause(s) of insulin resistance, lipid accumulation, inflammation, and fibrosis are not completely understood. Many of these questions cannot be elucidated in animal models due to confounding contributions from other organs, differences in animal disease pathology (relative to humans) and dietary restrictions. Additionally, if therapies are to be identified for NALFD, human-relevant systems will need to be used due to species differences in drug metabolism enzymes. Primary human hepatocytes (PHHs) are the gold standard for assessing drug metabolism in vitro, but these cells rapidly lose their liver phenotype in vitro. Here we show that micropatterned co-cultures (MPCCs) of PHHs and stromal cells maintain glucose and lipid metabolism in hepatocytes, which suggests their utility for in vitro disease models of NAFLD. Major advances in culturing methods were developed to increase the insulin sensitivity and overall health of hepatocytes in MPCCs prior to carrying out studies regarding NAFLD-related insulin resistance. The highly insulin sensitive MPCC model was then used to develop models of fatty acid-induced NAFLD and hepatic stellate cell induced NASH phenotypes. Potential disease mechanisms and treatments for fatty acid-induced insulin resistance and NASH disease progression were identified using these models.
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