CD4+ T Cell Regulatory Network Underlies the Decrease in Th1 and the Increase in Anergic and Th17 Subsets in Severe COVID-19

In this model we use a dynamic and multistable Boolean regulatory network to provide a mechanistic explanation of the lymphopenia and dysregulation of CD4+ T cell subsets in COVID-19 and provide therapeutic targets. Using a previous model, the cytokine micro-environments found in mild, moderate, and severe COVID-19 with and without TGF-beta and IL-10 was we simulated. It shows that as the severity of the disease increases, the number of antiviral Th1 cells decreases, while the the number of Th1-like regulatory and exhausted cells and the proportion between Th1 and Th1R cells increases. The addition of the regulatory cytokines TFG-beta and IL-10 makes the Th1 attractor unstable and favors the Th17 and regulatory subsets. This is associated with the contradictory signals in the micro-environment that activate SOCS proteins that block the signaling pathways. Furthermore, it determined four possible therapeutic targets that increase the Th1 compartment in severe COVID-19: the activation of the IFN-gamma pathway, or the inhibition of TGF-beta or IL-10 pathways or SOCS1 protein; from these, inhibiting SOCS1 has the lowest number of predicted collateral effects. Finally, a tool is provided that allows simulations of specific cytokine environments and predictions of CD4 T cell subsets and possible interventions, as well as associated secondary effects.

Automated summary

We used a dynamic and multistable Boolean regulatory network to explain the lymphopenia and dysregulation of CD4+ T cell subsets in COVID-19 and identified potential therapeutic targets. Simulations of cytokine micro-environments in different severity levels of COVID-19 revealed a decrease in antiviral Th1 cells and an increase in Th1-like regulatory and exhausted cells. Addition of TGF-beta and IL-10 cytokines shifted the balance towards Th17 and regulatory subsets by destabilizing the Th1 attractor.