Modeling cell-specific dynamics and regulation of the common gamma chain cytokinesGraphical abstract Highlights d A dynamical model of the common g-chain cytokines accurately predicts response d Receptor trafficking is necessary for predicting ligand response in new contexts d Tensor factorization maps responses across cell populations, receptors, and cytokines d Pathway model provides design criteria for ligands with greater cell type selectivity

11Many receptor families exhibit both pleiotropy and redundancy in their regulation, 12 with multiple ligands, receptors, and responding cell populations. Any intervention, 13 therefore, has multiple effects and is context specific, confounding intuition about 14 how to carry out precise therapeutic manipulation. The common γ-chain cytokine 15 receptor dimerizes with complexes of the cytokines interleukin (IL)-2, IL-4, IL-7, IL-9, 16 IL-15, and IL-21 and their corresponding "private" receptors. These cytokines have 17 existing uses and future potential as immune therapies due to their ability to regu-18 late the abundance and function of specific immune cell populations. Here, we build 19 a binding-reaction model for the ligand-receptor interactions of common γ-chain cy-20 tokines enabling quantitative predictions of response. We show that accounting for 21 receptor-ligand trafficking is essential to accurately model cell response. Using this 22 model, we visualize regulation across the family and immune cell types by tensor 23 factorization. This model accurately predicts ligand response across a wide panel 24 of cell types under diverse experimental designs. Further, we can predict the effect 25 of ligands across cell types. In total, these results present a more accurate model of 26 ligand response validated across a panel of immune cell types, and demonstrate an 27 1 approach for generating interpretable guidelines to manipulate the cell type-specific 28 targeting of engineered ligands. 29 Summary points 30 • A dynamical model of the γ-chain cytokines accurately models responses to IL-2, 31 IL-15, IL-4, and IL-7. 32 • Receptor trafficking is necessary for capturing ligand response. 33 • Tensor factorization maps responses across cell populations, receptors, cytokines, 34 and dynamics. 35 • An activation model coupled with tensor factorization creates design specifica-36 tions for engineering cell-specific responses. 37 42 therapies. 1,2 Each ligand binds to its specific private receptors before interacting with 43 the common γ c receptor to induce signaling. 3 γ c receptor signaling induces lymphopro-44 liferation, offering a mechanism for selectively expanding or repressing immune cell 45 types. 4,5 Consequently, loss-of-function or reduced activity mutations in the γ c recep-46 tor can cause severe combined immunodeficiency (SCID) due to insufficient T and NK 47 48 to more selective effects, including diminished regulatory T cell (T reg ) proliferation and 49 loss of self-tolerance. 7-9 Deficiency in the IL-2 receptor IL-2Rα also causes hyperpro-50 liferation in CD8+ T cells, but diminished antigen response. 10 These examples show 51 how γ c receptor cytokines coordinate a dynamic balance of immune cell abundance 52 and function. 53 2The γ c cytokines' ability to regulate lymphocytes can impact both solid and hemato-54 logical tumors. 11 IL-2 is an approved, effective therapy for metastatic melanoma, and 55 the antitumor effects of IL-2 and -15 have been explored in combination with other ...

The common γ-chain receptor cytokines are promising immune therapies due to their central role in coordinating the proliferation and activity of various immune cell populations. One of these cytokines, interleukin (IL)-2, has potential as a therapy in autoimmunity but is limited in effectiveness by its modest specificity toward regulatory T cells (Tregs). Therapeutic ligands are often made dimeric as antibody Fc fusions to confer desirable pharmacokinetic benefits, with under-explored consequences on signaling. Here, we systematically profiled the signaling responses to a panel of wild type and mutein IL-2 molecules in various Fc fusion configurations. We used a tensor-structured dimensionality reduction scheme to decompose the responses of each cell population to each ligand over a range of time points and cytokine concentrations. We found that dimeric muteins are uniquely specific for Tregs at intermediate ligand concentrations. We then compared signaling response across all treatments to a simple, two-step multivalent binding model. Our model was able to predict cellular responses with high accuracy. Bivalent Fc fusions display enhanced specificity and potency for Tregs through avidity effects toward IL-2Rα. We then utilize our model to identify the potential benefits conferred by valency engineering as an additional mechanism for cytokines with optimized therapeutic benefits. In total, these findings represent a comprehensive analysis of how ligand properties, and their consequent effects on surface receptor-ligand interactions, translate to selective activation of immune cell populations. It also identifies a new route toward engineering even more selective therapeutic cytokines.

Eral1 is a GTPase and ribosomal assembly factor of the mitochondrial small subunit (SSU). Eral1 binds 12SrRNA at a critical juncture, and its association and dissociation are necessary for mitoribosome maturation. Previous work in the literature indicated the proteolytic complex mitochondrial ClpXP (mtClpXP) as a regulator of dissociation: mtClpXP physically associates with Eral1 in vivo and knockout of ClpP results in Eral1 stabilization, accumulation of Eral1‐bound small subunit precursors, and reduced levels of mature mitoribosomes. In order for mtClpXP to both permit Eral1 association and enact its dissociation from the assembling small subunit, it must either recognize a specific Eral1 conformation that indicates completion of its assembly factor function or precisely tune Eral1 levels to achieve a metastable state. We aim to describe the recognition strategy that mtClpXP utilizes to unfold and degrade Eral1 and enable ribosome assembly. We have demonstrated that mtClpXP directly unfolds and degrades Eral1 in vitro. Evolutionary coupling analysis between human Eral1 and mtClpX has pointed to select residues that may mediate the interaction between these proteins. We have shown that mutation of at least one such Eral1 residue blocks its unfolding and degradation. We are utilizing a peptide SPOT array to characterize additional Eral1 motifs that assist in recognition and unfolding by mtClpX and hypothesize that the exposure of these sequences aligns with the Eral1 state that is recognized by mtClpX.