PT  - JOURNAL ARTICLE
AU  - Ali M. Farhat
AU  - Adam C. Weiner
AU  - Cori Posner
AU  - Zoe S. Kim
AU  - Scott M. Carlson
AU  - Aaron S. Meyer
TI  - Modeling Cell-Specific Dynamics and Regulation of the Common Gamma Chain Cytokines
AID  - 10.1101/778894
DP  - 2019 Jan 01
TA  - bioRxiv
PG  - 778894
4099  - http://biorxiv.org/content/early/2019/09/23/778894.short
4100  - http://biorxiv.org/content/early/2019/09/23/778894.full
AB  - Many receptor families exhibit both pleiotropy and redundancy in their regulation, with multiple ligands, receptors, and responding cell populations. Any intervention, therefore, has multiple effects and is context specific, confounding intuition about how to carry out precise therapeutic manipulation. The common γ-chain cytokine receptor dimerizes with complexes of the cytokines interleukin (IL)-2, IL-4, IL-7, IL-9, IL-15, and IL-21 and their corresponding “private” receptors. These cytokines have existing uses and future potential as immune therapies due to their ability to regulate the abundance and function of specific immune cell populations. Here, we build a binding-reaction model for the ligand-receptor interactions of common γ-chain cytokines enabling quantitative predictions of response. We show that accounting for receptor-ligand trafficking is essential to accurately model cell response. Using this model, we visualize regulation across the family and immune cell types by tensor factorization. This model accurately predicts ligand response across a wide panel of cell types under diverse experimental designs. Further, we can predict the effect of ligands across cell types. In total, these results present a more accurate model of ligand response validated across a panel of immune cell types, and demonstrate an approach for generating interpretable guidelines to manipulate the cell type-specific targeting of engineered ligands.Summary pointsA dynamical model of the γ-chain cytokines accurately models responses to IL-2, IL-15, IL-4, and IL-7.Receptor trafficking is necessary for capturing ligand response.Tensor factorization maps responses across cell populations, receptors, cytokines, and dynamics.An activation model coupled with tensor factorization creates design specifications for engineering cell-specific responses.