T-cell Receptor Is a Threshold Detector: Sub- and Supra-Threshold Stochastic Resonance in TCR-MHC Clusters on the...

Area of Science:

• Immunology
• Biophysics
• Computational Biology

Background:

• Major histocompatibility molecules (MHC) and T-cell receptors (TCR) are crucial for adaptive immunity.
• Cellular signaling relies on complex molecular interactions and threshold dynamics.
• Stochastic resonance (SR) is a phenomenon where noise enhances signal detection.

Purpose of the Study:

• To extend the understanding of stochastic resonance in MHC clusters.
• To apply suprathreshold stochastic resonance (SSR) as a model for transmembrane signaling.
• To explore SSR's potential in optimizing molecular prostheses for cancer therapy.

Main Methods:

• Detailed description of adaptive thresholding in MHC clusters.
• Application of suprathreshold stochastic resonance (SSR) to model signal transduction.
• Analysis of TCR structure and kinetic-segregation models.
• Comparison of SSR with Förster Resonance Energy Transfer (FRET).

Main Results:

• SSR acts as a stochastically quantizing encoder for transmembrane signals downstream of MHC and TCR.
• Adaptive thresholding mirrors the TCR's noncognate-cognate dichotomy and kinetic-segregation model.
• MHC and TCR clusters are proposed as sites for temporal signal encoding via SSR.
• An analogy is drawn between FRET and SSR for information transfer, with overlap integrals paralleling mutual information.

Conclusions:

• Suprathreshold stochastic resonance provides a framework for understanding transmembrane signaling in immune cells.
• This framework offers potential optimization strategies for engineered immunotherapies, such as chimeric antigen receptors (CARs).
• The information transfer mechanisms of SSR and FRET share mathematical parallels, highlighting conserved principles in biological energy and signal transfer.