Mesenchymal stromal cells with chimaeric antigen receptors for enhanced immunosuppression.

Allogeneic mesenchymal stromal cells (MSCs) are a safe treatment option for many disorders of the immune system. However, clinical trials using MSCs have shown inconsistent therapeutic efficacy, mostly owing to MSCs providing insufficient immunosuppression in target tissues. Here we show that antigen-specific immunosuppression can be enhanced by genetically modifying MSCs with chimaeric antigen receptors (CARs), as we show for E-cadherin-targeted CAR-MSCs for the treatment of graft-versus-host disease in mice. CAR-MSCs led to superior T-cell suppression and localization to E-cadherin+ colonic cells, ameliorating the animals' symptoms and survival rates. On antigen-specific stimulation, CAR-MSCs upregulated the expression of immunosuppressive genes and receptors for T-cell inhibition as well as the production of immunosuppressive cytokines while maintaining their stem cell phenotype and safety profile in the animal models. CAR-MSCs may represent a widely applicable therapeutic technology for enhancing immunosuppression.

Characterization of Integrase and Excisionase Activity in a Cell-Free Protein Expression System Using a Modeling and Analysis Pipeline.

We present a full-stack modeling, analysis, and parameter identification pipeline to guide the modeling and design of biological systems starting from specifications to circuit implementations and parametrizations. We demonstrate this pipeline by characterizing the integrase and excisionase activity in a cell-free protein expression system. We build on existing Python tools─BioCRNpyler, AutoReduce, and Bioscrape─to create this pipeline. For enzyme-mediated DNA recombination in a cell-free system, we create detailed chemical reaction network models from simple high-level descriptions of the biological circuits and their context using BioCRNpyler. We use Bioscrape to show that the output of the detailed model is sensitive to many parameters. However, parameter identification is infeasible for this high-dimensional model; hence, we use AutoReduce to automatically obtain reduced models that have fewer parameters. This results in a hierarchy of reduced models under different assumptions to finally arrive at a minimal ODE model for each circuit. Then, we run sensitivity analysis-guided Bayesian inference using Bioscrape for each circuit to identify the model parameters. This process allows us to quantify integrase and excisionase activity in cell extracts enabling complex-circuit designs that depend on accurate control over protein expression levels through DNA recombination. The automated pipeline presented in this paper opens up a new approach to complex circuit design, modeling, reduction, and parametrization.