Receptor Pre-Clustering and T cell Responses: Insights into Molecular Mechanisms

Abstract

T cell activation, initiated by T cell receptor (TCR) mediated recognition of pathogen-derived peptides presented by major histocompatibility complex class I or II molecules (pMHC), shows exquisite specificity and sensitivity, even though the TCR–pMHC binding interaction is of low affinity. Recent experimental work suggests that TCR pre-clustering may be a mechanism via which T cells can achieve such high sensitivity. The unresolved stoichiometry of the TCR makes TCR–pMHC binding and TCR triggering, an open question. We formulate a mathematical model to characterize the pre-clustering of T cell receptors (TCRs) on the surface of T cells, motivated by the experimentally observed distribution of TCR clusters on the surface of naive and memory T cells. We extend a recently introduced stochastic criterion to compute the timescales of T cell responses, assuming that ligand-induced cross-linked TCR is the minimum signaling unit. We derive an approximate formula for the mean time to signal initiation. Our results show that pre-clustering reduces the mean activation time. However, additional mechanisms favoring the existence of clusters are required to explain the difference between naive and memory T cell responses. We discuss the biological implications of our results, and both the compatibility and complementarity of our approach with other existing mathematical models.

Three-dimensional localization of T-cell receptors in relation to microvilli using a combination of superresolution microscopies

“…Indeed, there is evidence for fast motion of microvilli following interaction with model surfaces (50). Furthermore, the concentration of TCRs on a relatively small area of highly exposed cell projections might significantly increase the avidity of these receptors to peptide-MHC complexes on APC surfaces (45,51). Finally, these actin-enriched projections may also serve as physical barriers for the diffusion and random mixing of nonmicrovillar surface proteins with microvillar proteins.…”

CCR5 deficiency impairs CD4+T cell memory responses and antigenic sensitivity through increased ceramide synthesis

“…To define the differences between OT-II WT and CCR5 -/cells, we used a model that accounts for receptor clustering dynamics (Castro et al, 2014), a Bayesian inference method that estimates the so-called clustering parameter, b. Based on this model, we concluded that the probability of a chance nanocluster distribution similar to that observed for naïve and activated OT-II WT and CCR5 -/cells approaches 0% (Fig.…”

“…To quantify the mechanistic relevance of cluster size between random distributions of clusters and clusters in WT and in CCR5 -/-CD4 + T cells, we used a Bayesian inference model on top of a mechanistic model (Castro et al, 2014). The model assumes that TCR aggregates by incorporating one receptor at a time, with on and off rates that depend on the diffusion properties of the receptor on the membrane, but not existing cluster size.…”

Trial Watch: Peptide-based anticancer vaccines

“…These epitopes are expected to bind MHC molecules on the surface of antigen-presenting cells (APCs) and hence be directly presented to T cells. [143][144][145] In addition, a few clinical teams tested the therapeutic profile of fulllength TAAs, 146 "synthetic long peptides" (SLPs), i.e., TAA-derived peptides of 25-35 amino acids; [147][148][149][150] or "carbohydrate-mimetic peptides" (CMPs), i.e., synthetic polypeptides that mimic the structure of carbohydrate TAAs. 151 The use of SLPs as therapeutic anticancer vaccines presents several advantages as compared to that of short TAA-derived peptides.…”