A systems biology approach reveals the physiological origin of increased plasma HDL levels and hepatic steatosis induced by liver X receptor activation

Liver X receptor (LXR) agonists increase plasma high density lipoprotein cholesterol (HDLc) rendering LXR an attractive drug target for anti-atherosclerotic therapies. However, LXR agonism simultaneously induces excessive hepatic triglyceride (TG) accumulation. This is currently a major drawback for the clinical application of LXR agonists. To obtain detailed insight into the underlying mechanisms, we employed a novel computational modeling approach called Analysis of Dynamic Adaptations in Parameter Trajectories (ADAPT). Our model predicted that both peripheral cholesterol efflux to HDL and hepatic HDLc uptake increase over time in LXR-agonist treated C57Bl6/J mice. A small imbalance in both fluxes leads to elevated plasma HDLc. Interestingly, and unexpectedly, the model also predicted that despite an increment in hepatic HDLc uptake, the affinity for HDLc decreased over time. This prediction was experimentally tested by analyzing scavenger receptor class B1 (SR-B1) protein expression in hepatic membranes. SR-B1 contributes to the hepatic uptake of cholesterol and SR-B1 levels were found to be decreased, confirming the model prediction. Analysis of TG fluxes furthermore showed that both input and output fluxes to hepatic TG content are strongly induced upon LXR activation, and that hepatic TG accumulation in the early phase of LXR agonist treatment results from only a minor imbalance between the two. It is generally believed that LXR-induced liver steatosis results from increased de novo lipogenesis (DNL). ADAPT, however, predicted that the hepatic influx of free fatty acids (FFA) is the major contributor to early hepatic TG accumulation upon LXR activation. Validation of this prediction indeed showed a 5-fold increase in FFA flux from plasma to hepatic monounsaturated fatty acids upon acute LXR activation, while DNL was not yet significantly increased at this time. This study illustrates that application of state of the art computational modeling produces unexpected predictions that are amenable to validation and leads to novel insights into the complex biological network that coordinates systemic lipid metabolism. Funded by EC (FP7 no. 305707 RESOLVE).