Paradoxical Signaling by a Secreted Molecule Leads to Homeostasis of Cell Levels

Abstract

A widespread feature of extracellular signaling in cell circuits is paradoxical pleiotropy: the same secreted signaling molecule can induce opposite effects in the responding cells. For example, the cytokine IL-2 can promote proliferation and death of T cells. The role of such paradoxical signaling remains unclear. To address this, we studied CD4(+) T cell expansion in culture. We found that cells with a 30-fold difference in initial concentrations reached a homeostatic concentration nearly independent of initial cell levels. Below an initial threshold, cell density decayed to extinction (OFF-state). We show that these dynamics relate to the paradoxical effect of IL-2, which increases the proliferation rate cooperatively and the death rate linearly. Mathematical modeling explained the observed cell and cytokine dynamics and predicted conditions that shifted cell fate from homeostasis to the OFF-state. We suggest that paradoxical signaling provides cell circuits with specific dynamical features that are robust to environmental perturbations.

T Cells Integrate Local and Global Cues to Discriminate between Structurally Similar Antigens

“…2). Conventional understanding of T cell activation would dictate that IL-2 acts as a differentiating/mitogenic signal at intermediate or late timescales, after T cells are already fully committed to the activated states and ready to “read” cytokine cues to drive differentiation (Pipkin et al, 2010), to accelerate cell proliferation or to abrogate apoptosis (Hart et al, 2014). Our experiments demonstrate that blocking CD25 in the first hours of antigen activation does abrogate the synergetic effect of IL-2 even at times when key surface proteins associated with activation (e.g.…”

“…4). Such back-and-forth between biochemically explicit model and experimental validation is expanding our quantitative understanding of the immune system (Hart et al, 2014; Tkach et al, 2014). Future research will need to include spatio-temporal heterogeneities that are the hallmark of cytokine communication in vivo (while this study focused on establishing a well-mixed model that matches our experimental settings in vitro ).…”

“…Such modeling approach has recently provided valuable insights about different functions of the immune system, with theoretical efforts addressing how the TCR signaling machinery achieves ligand discrimination (François et al, 2013; Stepanek et al, 2014), how T cells regulate their differentiation and cell lineage commitment (Buchholz et al, 2013; Gerlach et al, 2013; Schulz et al, 2009), how populations of T cells respond collectively to antigens and cytokines (Hart et al, 2014; Tkach et al, 2014) etc. Computational models of the immune response serve three purposes: 1) testing the sufficiency of our biological understanding; 2) tackling the combinatorial and dynamic complexity of immune regulations; 3) designing new perturbations for immunotherapeutic manipulations.…”

The Case for Absolute Ligand Discrimination: Modeling Information Processing and Decision by Immune T Cells

“…Deriving an intensive output (i.e. an output that is independent from the size of the system) for a population of T cells has been observed in very related [19,29,60,67]. In particular, T cells can rely on such cell-to-cell communications to achieve higher levels of immune recognition.…”

“…b Tkach et al found a surprising scaling law, whereby the maximum concentration ([I L − 2] max ) of the IL-2 cytokine released by a population of T cells scales almost linearly with the amount of antigens that is present in the system, practically independently of the number of T cells present in the system. c Such analog scaling at the population level was found to derive through a coherent feed-forward loop of Type 4, using the nomenclature introduced by Alon and Mangan [45] experiments [29]. Hart et al measured the cell expansion of a population of CD4 + T cells after activation through their antigen receptor pathway.…”