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Carlson, S. M.

Publications and source records attributed to Carlson, S. M..

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Multivalency enhances the specificity of Fc-cytokine fusions

The common {gamma}-chain receptor cytokines coordinate the proliferation and function of immune cell populations. One of these cytokines, interleukin (IL)-2, has potential as a therapy in autoimmune disease but is limited in effectiveness by its modest specificity toward regulatory T cells (Tregs). Engineering Treg-selective IL-2 has primarily focused on retaining binding to the high-affinity receptor, expressed more highly on Tregs, while reducing binding to the lower affinity receptor with broader expression. However, other parameters, such as the orientation and valency of Fc fusion, have signaling effects that have never been systematically explored. Here, we systematically profiled the signaling responses to a panel of wild type and mutein IL-2-Fc fusions across time, cell types, and concentrations. Exploring these responses, we found that dimeric muteins have unique specificity for Tregs through binding avidity. A mechanistic model of receptor interactions could capture these effects and directed the design of tetravalent IL-2-Fc fusions with greater Treg specificity than possible with current design strategies. Exploration of other surface targets on Tregs revealed that there are no other binding moieties that could be fused to IL-2 for greater selectivity. Instead, IL2R itself is a maximally unique surface target for Tregs, and so avidity is likely the only route to more selective Treg interaction. However, the binding model revealed that asymmetrical, multivalent IL-2 fusions can bias avidity effects toward IL2R for even further enhanced Treg selectivity. These findings present a comprehensive analysis of how ligand properties and their effects on surface receptor-ligand interactions translate to selective activation of immune cell populations, and consequently reveals two new routes toward therapeutic cytokines with superior Treg selectivity that can be exploited for designing selective therapies in many other contexts. Significance StatementSignaling in off-target immune cells has hindered the effectiveness of IL-2 as an immunotherapy. We show that IL-2-Fc fusions with higher valency can exhibit enhanced regulatory T cell selectivity. This altered selectivity is explained by the kinetics of surface receptor-ligand binding and can be quantitatively predicted using a multivalent binding model. Using these insights, we successfully develop two new strategies for IL-2 therapies with unprecedented selectivity. HighlightsO_LICurrent IL-2 therapies are limited by a selectivity/target potency tradeoff. C_LIO_LIMultivalency enhances selectivity for Tregs through IL2R avidity. C_LIO_LITreg selectivity cannot be enhanced by targeting other surface protein markers. C_LIO_LIMultivalency can decouple selectivity from signaling using asymmetric cytokine fusions. C_LI

immunology↗

Modeling Cell-Specific Dynamics and Regulation of the Common Gamma Chain Cytokines

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, confounding intuition about how to precisely manipulate signaling for therapeutic purposes. The common {gamma}-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. However, engineering cell specificity into a therapy is confounded by the complexity of the family across responsive cell types. Here, we build a binding-reaction model for the ligand-receptor interactions of common {gamma}-chain cytokines enabling quantitative predictions of response. We show that accounting for receptor-ligand trafficking is essential to accurately model cell response. This model accurately predicts ligand response across a wide panel of cell types under diverse experimental designs. Further, we can predict the effect and specificity of natural or engineered ligands across cell types. We then show that tensor factorization is a uniquely powerful tool to visualize changes in the input-output behavior of the family across time, cell types, ligands, and concentration. 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. These techniques will in turn help to study and therapeutically manipulate many other complex receptor-ligand families. Summary pointsO_LIA dynamical model of the {gamma}-chain cytokines accurately models responses to IL-2, IL-15, IL-4, and IL-7. C_LIO_LIReceptor trafficking is necessary for capturing ligand response. C_LIO_LITensor factorization maps responses across cell populations, receptors, cytokines, and dynamics to visualize cytokine specificity. C_LIO_LIAn activation model coupled with tensor factorization provides design specifications for engineering cell-specific responses. C_LI

immunology↗