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Liver fibrosis is a determinant-stage process of many chronic liver diseases and affected over 7.9 billion populations worldwide with increasing demands of ideal therapeutic agents. Discovery of active molecules with anti-hepatic fibrosis efficacies presents the most attacking filed. Here, we revealed that hepatic L-aspartate levels were decreased in CCl4-induced fibrotic mice. Instead, supplementation of L-aspartate orally alleviated typical manifestations of liver injury and fibrosis. These therapeutic efficacies were alongside improvements of mitochondrial adaptive oxidation. Notably, treatment with L-aspartate rebalanced hepatic cholesterol-steroid metabolism and reduced the levels of liver-impairing metabolites, including corticosterone (CORT). Mechanistically, L-aspartate treatment efficiently reversed CORT-mediated glucocorticoid receptor β (GRβ) signaling activation and subsequent transcriptional suppression of the mitochondrial genome by directly binding to the mitochondrial genome. Knockout of GRβ ameliorated corticosterone-mediated mitochondrial dysfunction and hepatocyte damage which also weakened the improvements of L-aspartate in suppressing GRβ signaling. These data suggest that L-aspartate ameliorates hepatic fibrosis by suppressing GRβ signaling via rebalancing cholesterol-steroid metabolism, would be an ideal candidate for clinical liver fibrosis treatment.

Abstract The emergency generated by the current COVID-19 pandemic has claimed millions of lives worldwide. There have been multiple waves across the globe that emerged as a result of new variants, due to arising from unavoidable mutations. The existing network toolbox to study epidemic spreading cannot be readily adapted to the study of multiple, coexisting strains. In this context, particularly lacking are models that could elucidate re-infection with the same strain or a different strain—phenomena that we are seeing experiencing more and more with COVID-19. Here, we establish a novel mathematical model to study the simultaneous spreading of two strains over a class of temporal networks. We build on the classical susceptible–exposed–infectious–removed model, by incorporating additional states that account for infections and re-infections with multiple strains. The temporal network is based on the activity-driven network paradigm, which has emerged as a model of choice to study dynamic processes that unfold at a time scale comparable to the network evolution. We draw analytical insight from the dynamics of the stochastic network systems through a mean-field approach, which allows for characterizing the onset of different behavioral phenotypes (non-epidemic, epidemic, and endemic). To demonstrate the practical use of the model, we examine an intermittent stay-at-home containment strategy, in which a fraction of the population is randomly required to isolate for a fixed period of time.