How T-cells use large deviations to recognize foreign antigens

Zint, Natali; Baake, Ellen; Hollander, Frank den

doi:10.1007/s00285-008-0191-1

Cited by 9 publications

(23 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Earlier attempts have considered probabilistic models of encounters aiming at distinguishing self from not-self, exploiting proofreading to detect long tails in detection events (23). Detection of mixture of ligands is a more challenging problem, which has so far only been considered in refs.…”

Section: Discussionmentioning

confidence: 99%

Chemodetection in fluctuating environments: Receptor coupling, buffering, and antagonism

Lalanne

François

2015

Proc. Natl. Acad. Sci. U.S.A.

View full text Add to dashboard Cite

Variability in the chemical composition of the extracellular environment can significantly degrade the ability of cells to detect rare cognate ligands. Using concepts from statistical detection theory, we formalize the generic problem of detection of small concentrations of ligands in a fluctuating background of biochemically similar ligands binding to the same receptors. We discover that in contrast with expectations arising from considerations of signal amplification, inhibitory interactions between receptors can improve detection performance in the presence of substantial environmental variability, providing an adaptive interpretation to the phenomenon of ligand antagonism. Our results suggest that the structure of signaling pathways responsible for chemodetection in fluctuating and heterogeneous environments might be optimized with respect to the statistics and dynamics of environmental composition. The developed formalism stresses the importance of characterizing nonspecific interactions to understand function in signaling pathways.I nformation transmission within biological networks has recently been subject to intense scrutiny. Quantities such as mutual information appear to be biologically relevant and optimized in many contexts (1), such as neural coding (2) and development (3, 4). In these situations, the nature of the signal is unambiguous: only uncertainty in the value of the input limits information content. In other realistic biological problems, the input consists of a complex mixture, with the signal of interest buried in a sea of nonspecific interactions. For instance, multiple different molecules could bind to a cellular receptor, in which case the signaling pathway downstream needs to find a way to discriminate between correct and spurious signals. An example is immune ligand detection: T cells need to detect foreign ligands at the surface of antigen presenting cells (APC) but there are many other nonagonist ligands interacting with receptors in charge of detection (5).The consideration of heterogeneous environments, with multiple ligand types binding to a receptor, corresponds to a departure from past theoretical analyses of bounds on the performance of cellular measurements (6, 7) and requires specific treatment (8). Predefined mixtures of ligands were previously considered in ref. 9 with an emphasis on optimum decision time for given ligand compositions. Here, we use statistical detection theory to formalize the general problem of detection of a chemical signal in a time-varying mixture. We compare the detection performance of optimal proofreading networks of independent receptors to receptors with inhibitory coupling whose strength increases with the input. We find that despite antagonism and signal attenuation, coupled receptors outperform independent receptors in strongly varying environments. Intuitively, a negative feedback growing with the input size damps rare large fluctuations in the spurious ligand concentration, while retaining sensitivity to situations where few correct ligands ...

show abstract

Section: Discussionmentioning

confidence: 99%

Chemodetection in fluctuating environments: Receptor coupling, buffering, and antagonism

Lalanne

François

2015

Proc. Natl. Acad. Sci. U.S.A.

View full text Add to dashboard Cite

show abstract

“…The graph of this function (P(w i j > ω) in dependence of ω), called the "activation curve," is a visual representation of the degeneracy of the TCR clonotype at hand. It has previously been established how P(w i j > ω) can be calculated as a function of ω by combining three key ingredients: (i) the kinetics; (ii) the triggering probability; and (iii) a distribution for the TCR/pMHCI dissociation rate 8,31,33,34 . Appendix B and Fig.…”

Section: Characterisation Of Tcr Degeneracymentioning

confidence: 99%

Modulation of T-cell receptor functional sensitivity via the opposing actions of protein tyrosine kinases and phosphatases: a mathematical model

Szomolay

Berg

2014

Integr. Biol.

View full text Add to dashboard Cite

Combining receptor kinetics and stochastic modelling of receptor activation, we show that a T-cell can specifically augment its functional sensitivity to one particular peptide ligand while simultaneously decreasing its sensitivity to other ligands, by coordinating the expression levels of the co-receptor CD8 and the relative activities of kinases and phosphatases in the vicinity of the T-cell receptor (TCR). We propose that this focusable degeneracy of epitope recognition allows a TCR to have a wide range of potential ligands but be specifically sensitive to only one or a few of these at any one time, which resolves the paradox of how a relatively small number of clones (∼10(6)) can maintain the potential to respond to a vast space of ligands (∼20(9)) whilst avoiding auto-immunity. We validate the model against experimental data and predict shifts in functional sensitivity following a shift in the kinase/phosphatase balance (which could in principle be induced by experimental means). Moreover, we propose that in vivo, the T-cell gauges ligand quality by monitoring changes in TCR triggering rate concomitant with shifts in this balance, for instance as the immunological synapse matures.

show abstract

“…Computational models of broader scope have helped to interpret how CTL killing depends on cell density within lymph nodes (12,13). Stochastic models have helped to formulate a probabilistic treatment of T-cell activation in terms of the APP: for instance, (14) modelled the idea that infections are detected when the sum of the stimuli received by a T-cell from the interactions between its TCR and the pMHCs on the APC, exceeds a given threshold. The ability of T-cells of distinguishing between self and non-self is a consequence of a higher copy number of foreign peptides in comparison with self on the APC surface (probabilistic recognition).…”

Section: Introductionmentioning

confidence: 99%

A theory for how the antigen presentation profile influences the timing of T-cell detection

Carignano

Dalchau

2018

Preprint

View full text Add to dashboard Cite

T-cells are activated when their receptor molecules recognize complexes of MHC proteins bound to peptides on the surface of neighbouring cells. Each T-cell expresses one variant of many possible receptor molecules, which are generated through a partially random process that culminates in approximately 10 7 possible T-cell receptors. As the peptide sequence bound to an MHC molecule is also highly variable, the optimal strategy of an antigen-presenting cell for displaying peptide-MHC complexes is not obvious. A natural compromise arises between aggressive peptide filtering, displaying a few peptides with high stability MHC binding in high abundance and regularity, and promiscuous peptide binding, which can result in more diverse peptides being presented, but in lower abundance. To study this compromise, we have combined a model of MHC class I peptide filtering with a simple probabilistic description of the interactions between antigen presenting cells (APCs) and cytotoxic T-cells (CTLs). By asking how long it takes, on average, for an APC to encounter a circulating CTL that recognises one of the peptides being presented by its MHC molecules, we found that there often exists an optimal degree of peptide filtering, which minimises this expected time until first encounter. The optimal degree of filtering is often in the range of values that the chaperone molecule tapasin confers on peptide selection, but varies between MHC class I molecules that have different peptide binding properties. Our model-based analysis therefore helps to understand how variations in the antigen presentation profile might be exploited for vaccine design or immunotherapies. antigen presentation | cytotoxic T-cell | probabilistic modelling | peptide filtering | computational immunology

show abstract

How T-cells use large deviations to recognize foreign antigens

Cited by 9 publications

References 24 publications

Chemodetection in fluctuating environments: Receptor coupling, buffering, and antagonism

Chemodetection in fluctuating environments: Receptor coupling, buffering, and antagonism

Modulation of T-cell receptor functional sensitivity via the opposing actions of protein tyrosine kinases and phosphatases: a mathematical model

A theory for how the antigen presentation profile influences the timing of T-cell detection

Contact Info

Product

Resources

About