A cell topography-based mechanism for ligand discrimination by the T cell receptor

Abstract

The T cell receptor (TCR) initiates the elimination of pathogens and tumors by T cells. To avoid damage to the host, the receptor must be capable of discriminating between wild-type and mutated self and nonself peptide ligands presented by host cells. Exactly how the TCR does this is unknown. In resting T cells, the TCR is largely unphosphorylated due to the dominance of phosphatases over the kinases expressed at the cell surface. However, when agonist peptides are presented to the TCR by major histocompatibility complex proteins expressed by antigen-presenting cells (APCs), very fast receptor triggering, i.e., TCR phosphorylation, occurs. Recent work suggests that this depends on the local exclusion of the phosphatases from regions of contact of the T cells with the APCs. Here, we developed and tested a quantitative treatment of receptor triggering reliant only on TCR dwell time in phosphatase-depleted cell contacts constrained in area by cell topography. Using the model and experimentally derived parameters, we found that ligand discrimination likely depends crucially on individual contacts being ∼200 nm in radius, matching the dimensions of the surface protrusions used by T cells to interrogate their targets. The model not only correctly predicted the relative signaling potencies of known agonists and nonagonists but also achieved this in the absence of kinetic proofreading. Our work provides a simple, quantitative, and predictive molecular framework for understanding why TCR triggering is so selective and fast and reveals that, for some receptors, cell topography likely influences signaling outcomes.

ERM-Dependent Assembly of T Cell Receptor Signaling and Co-stimulatory Molecules on Microvilli prior to Activation

“…We found that at both temperatures L-selectin was concentrated on microvilli, 1,2 while CD45 was distributed uniformly throughout the membrane. 1,4,5 Next, we stained T cells with Alexa-647 labelled anti human TCRαβ antibodies at the two temperatures. At 4°C the structure of microvilli on almost all the T cells was intact, and the TCRαβ molecules localized on them (Fig.…”

Surfing on Membrane Waves: Microvilli, Curved Membranes, and Immune Signaling

“…Therefore, multiple groups have investigated the localization of CD45 in the context of microvilli and showed that the segregation between TCR and CD45 occurs a few seconds after contacts are established (58)(59)(60)(61). Interestingly, although it has been assumed and supported by experimental data that CD45 is evenly distributed on the cell surface in resting T cells (19,60), a new study revealed, using expansion microscopy, that CD45 is excluded from the microvilli tip even before contacts are established with APC (53). These discrepancies could be caused by differences in T cell subtypes, activation methods, and resolution of individual imaging techniques.…”

Predicting Cross-Reactivity and Antigen Specificity of T Cell Receptors

“…Indeed, several groups have started to use modeling approaches to test various hypotheses on TCR:pMHC interaction propensities (38,71,72). For instance, Xu and Jo utilized a simple string model to evaluate a trade-off between rapid screening and dissociation penalty, and have shown that while a highly crossreactive TCR detects correct peptides in a short period of time with the help of its degeneracy, it takes much longer to release from an incorrectly bound peptide (71).…”

“…In addition to models predicting TCR:pMHC interactions, models to relate TCR:pMHC binding parameters and antigen doses to T cell response have also been proposed [reviewed in (73)]. Recently, Fernandes et al utilized partial differential equations to study the underlying mechanism of ligand discrimination and TCR triggering based on two physical properties, (i) TCR dwell time in the absence of large tyrosine phosphatase, and (ii) spatial constraints on the contact area, and found that topographically constrained T cell contacts allow, and may even be essential, for ligand discrimination by T cells (72). Although these mathematical models are built upon underlying assumptions e.g., a positive correlation between binding affinity and the extent of TCR cross-reactivity, provided that assumptions are evidence-based and reasonable, such modeling approaches will be a valuable strategy to quickly test hypothesis on cross-recognition potential.…”