Department of Applied and Computational Mathematics and Statistics, University of Notre Dame, South Bend, Indiana, United States of America.
Biophysics Graduate Program, University of Notre Dame, South Bend, Indiana, United States of America.
PLoS Comput Biol. 2023 Aug 30;19(8):e1011216. doi: 10.1371/journal.pcbi.1011216. eCollection 2023 Aug.
T cells form transient cell-to-cell contacts with antigen presenting cells (APCs) to facilitate surface interrogation by membrane bound T cell receptors (TCRs). Upon recognition of molecular signatures (antigen) of pathogen, T cells may initiate an adaptive immune response. The duration of the T cell/APC contact is observed to vary widely, yet it is unclear what constructive role, if any, such variations might play in immune signaling. Modeling efforts describing antigen discrimination often focus on steady-state approximations and do not account for the transient nature of cellular contacts. Within the framework of a kinetic proofreading (KP) mechanism, we develop a stochastic First Receptor Activation Model (FRAM) describing the likelihood that a productive immune signal is produced before the expiry of the contact. Through the use of extreme statistics, we characterize the probability that the first TCR triggering is induced by a rare agonist antigen and not by that of an abundant self-antigen. We show that defining positive immune outcomes as resilience to extreme statistics and sensitivity to rare events mitigates classic tradeoffs associated with KP. By choosing a sufficient number of KP steps, our model is able to yield single agonist sensitivity whilst remaining non-reactive to large populations of self antigen, even when self and agonist antigen are similar in dissociation rate to the TCR but differ largely in expression. Additionally, our model achieves high levels of accuracy even when agonist positive APCs encounters are rare. Finally, we discuss potential biological costs associated with high classification accuracy, particularly in challenging T cell environments.
T 细胞与抗原呈递细胞 (APCs) 形成短暂的细胞间接触，以促进细胞膜结合的 T 细胞受体 (TCRs) 对面部的检查。在识别病原体的分子特征（抗原）后，T 细胞可能会启动适应性免疫反应。T 细胞/APC 接触的持续时间观察到变化范围很广，但尚不清楚这种变化会在免疫信号中起到何种建设性作用（如果有的话）。描述抗原识别的建模工作通常侧重于稳态近似，而不考虑细胞接触的瞬态性质。在动力学校验 (KP) 机制的框架内，我们开发了一种随机第一受体激活模型 (FRAM)，用于描述在接触结束之前产生有效免疫信号的可能性。通过使用极端统计学，我们描述了第一个 TCR 触发是由罕见的激动剂抗原引起的，而不是由丰富的自身抗原引起的可能性。我们表明，将积极的免疫结果定义为对极端统计数据的弹性和对罕见事件的敏感性，可以减轻与 KP 相关的经典权衡。通过选择足够数量的 KP 步骤，我们的模型能够在保持对大量自身抗原不反应的情况下，产生单个激动剂的敏感性，即使自身和激动剂抗原与 TCR 的解离速率相似，但表达差异很大。此外，即使激动剂阳性 APC 相遇的频率很低，我们的模型也能达到很高的准确性水平。最后，我们讨论了与高分类准确性相关的潜在生物学成本，特别是在具有挑战性的 T 细胞环境中。
