Lever Melissa, Lim Hong-Sheng, Kruger Philipp, Nguyen John, Trendel Nicola, Abu-Shah Enas, Maini Philip Kumar, van der Merwe Philip Anton, Dushek Omer
Sir William Dunn School of Pathology, University of Oxford, Oxford OX1 3RE, United Kingdom.
Wolfson Centre for Mathematical Biology, Mathematical Institute, University of Oxford, Oxford OX2 6GG, United Kingdom.
Proc Natl Acad Sci U S A. 2016 Oct 25;113(43):E6630-E6638. doi: 10.1073/pnas.1608820113. Epub 2016 Oct 4.
T cells must respond differently to antigens of varying affinity presented at different doses. Previous attempts to map peptide MHC (pMHC) affinity onto T-cell responses have produced inconsistent patterns of responses, preventing formulations of canonical models of T-cell signaling. Here, a systematic analysis of T-cell responses to 1 million-fold variations in both pMHC affinity and dose produced bell-shaped dose-response curves and different optimal pMHC affinities at different pMHC doses. Using sequential model rejection/identification algorithms, we identified a unique, minimal model of cellular signaling incorporating kinetic proofreading with limited signaling coupled to an incoherent feed-forward loop (KPL-IFF) that reproduces these observations. We show that the KPL-IFF model correctly predicts the T-cell response to antigen copresentation. Our work offers a general approach for studying cellular signaling that does not require full details of biochemical pathways.
T细胞必须对以不同剂量呈现的不同亲和力抗原做出不同反应。先前将肽-主要组织相容性复合体(pMHC)亲和力映射到T细胞反应上的尝试产生了不一致的反应模式,阻碍了T细胞信号传导规范模型的构建。在此,对T细胞对pMHC亲和力和剂量的100万倍变化的反应进行的系统分析产生了钟形剂量反应曲线,以及在不同pMHC剂量下不同的最佳pMHC亲和力。使用顺序模型拒绝/识别算法,我们确定了一个独特的、最小的细胞信号传导模型,该模型结合了动力学校对和与非相干前馈环(KPL-IFF)耦合的有限信号传导,再现了这些观察结果。我们表明,KPL-IFF模型正确预测了T细胞对抗原共呈递的反应。我们的工作提供了一种研究细胞信号传导的通用方法,该方法不需要生化途径的全部细节。