Utilising an in silico model to predict outcomes in senescence-driven acute liver injury

Abstract

Currently liver transplantation is the only treatment option for liver disease, but organ availability cannot meet demand and transplant recipients require lifelong immunosuppression. The identification of alternative treatments, e.g. cell therapies, able to tip resolution of injury from inflammation to regeneration requires an understanding of the host response to the degree of injury. We adopt a combined in vivo-in silico approach and develop a mathematical model of acute liver disease able to predict the host response to injury. We utilise the Mdm2fl/fl mouse model together with a single Cre induction through intravenous injection of the hepatotropic Adeno-associated Virus Serotype 8 Cre (AAV8.Cre) to model acute liver injury. We derive a complementary ordinary differential equation model to capture the dynamics of the key cell players in the injury response together with the extracellular matrix. We show that the mathematical model is able to predict the host response to moderate injury via qualitative comparison of the model predictions with the experimental data. We then use the model to predict the host response to mild and severe injury, and test these predictions in vivo, obtaining good qualitative agreement.

Liver macrophages revisited: The expanding universe of versatile responses in a spatiotemporal context

“…As such, Mdm2 inducible deficiency was used as a model of acute senescence-driven liver injury in mice, and to collect data on the associated microenvironment changes. [87] These data were then integrated to build an in silico mechanistic model. This approach led the authors to the conclusion that there is a threshold for the initial senescence that, once passed, leads to irreversible injury due to unresolved macrophage-driven inflammation.…”

“…Noteworthy, the model was also able to predict senescence-induced proinflammatory signals originating from endothelial cells, myofibroblast activation, and extracellular matrix deposition in a dosedependent manner. [87] Machine-learning has been used to generate a predictive algorithm for macrophage reactivity and tolerance that relies on a 338-gene signature correlating with disease stage and outcome in a variety of organs, including the liver. [40] Mouse interstrain immune cell transcriptional variations are long known.…”

“…This approach led the authors to the conclusion that there is a threshold for the initial senescence that, once passed, leads to irreversible injury due to unresolved macrophage-driven inflammation. Noteworthy, the model was also able to predict senescence-induced proinflammatory signals originating from endothelial cells, myofibroblast activation, and extracellular matrix deposition in a dose-dependent manner 87 …”

“…More elaborate models allow for the prediction of liver macrophage changes over time in defined settings. As such, Mdm2 inducible deficiency was used as a model of acute senescence-driven liver injury in mice, and to collect data on the associated microenvironment changes 87 . These data were then integrated to build an in silico mechanistic model.…”