Modulation of antigen discrimination by duration of immune contacts in a kinetic proofreading model of T cell activation with extreme statistics
Modulation of antigen discrimination by duration of immune contacts in a kinetic proofreading model of T cell activation with extreme statistics
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DOI：
10.1101/2023.05.30.542789
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发表时间：
2023-06
影响因子：
4.3
通讯作者：
J. Morgan;A. Lindsay
中科院分区：
文献类型：
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作者：
J. Morgan;A. Lindsay
T细胞与抗原呈递细胞（APCs）形成短暂的细胞间接触，以促进膜结合T细胞受体（tcr）的表面询问。在识别病原体的分子特征（抗原）后，T细胞可以启动适应性免疫反应。观察到T细胞/APC接触的持续时间变化很大，但尚不清楚这种变化可能在免疫信号传导中发挥什么建设性作用，如果有的话。描述抗原区分的建模工作通常集中在稳态近似上，而不考虑细胞接触的瞬态性质。在动态校对（KP）机制的框架内，我们开发了一个随机第一受体激活模型（FRAM），描述了在接触到期之前产生有效免疫信号的可能性。通过极端统计的使用，我们描述了第一次TCR触发是由罕见的激动剂抗原诱导的概率，而不是由丰富的自身抗原诱导的概率。我们表明，将积极的免疫结果定义为对极端统计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，我们的模型能够产生单一激动剂敏感性，同时对大量自身抗原保持无反应性，即使当自身和激动剂抗原的解离率与TCR相似，但表达差异很大时也是如此。此外，即使激动剂阳性apc遭遇罕见，我们的模型也能达到高水平的准确性。最后，我们讨论了与高分类准确性相关的潜在生物成本，特别是在具有挑战性的T细胞环境中。T细胞和抗原呈递细胞（APC）之间的物理接触对于产生免疫信号至关重要。人们观察到这一接触时间存在很大差异，但对控制这一关键时间尺度的机制知之甚少，也不知道其持续时间如何影响抗原辨别。我们开发并分析了T细胞激活的概率数学模型，该模型结合了动力学校正（KP）和有限接触持续时间。我们的模型能够抑制大量的自身配体，同时在T细胞/APC细胞接触中仅对单一激动剂保持敏感。此外，我们探讨了两种具有挑战性的情况，其中一种是自我和激动剂抗原相似，另一种是激动剂阳性apc罕见。我们发现我们的模型可以通过增加动态校对步骤的数量来克服这些环境挑战。最后，我们讨论了实现这种准确性的潜在生物成本。我们的工作证明了动态校对在时间背景下的极端有效性，同时也证明了这种模型在生物实施中可能面临的挑战。
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. Author summary Physical contact between the T cell and antigen presenting cell (APC) is essential for productive immune signaling. Wide variations in this contact time have been observed yet little is known of mechanisms controlling this crucial timescale, nor how its duration may impact antigen discrimination. We develop and analyze a probabilistic mathematical model of T cell activation which combines kinetic proofreading (KP) with a finite contact duration. Our model is capable of suppressing large populations of self ligands while remaining sensitive to only a single agonist in T cell/APC cellular contacts. Additionally, we explored two challenging cases, one in which self and agonist antigen are similar and one in which agonist positive APCs are rare. We found that our model could overcome these environmental challenges by increasing the number of kinetic proofreading steps. Finally, we discuss the potential biological costs of achieving such accuracy. Our work demonstrates the extreme effectiveness of kinetic proofreading in a temporal context while also demonstrating the possible challenges in biological implementation of such a model.