Resolving molecular networks and dynamics of individual T cells in chronic infections

持续感染会诱导T细胞，这种T细胞在表型和功能上与急性缓解感染时形成的T细胞类型不同。传统观点认为，这些T细胞功能失调(这种状态通常被称为“衰竭”)。然而，越来越多的证据表明，T细胞在慢性感染中保持着强大的效应能力，通过阻断PD1和其他抑制性受体的信号可以显著增强这种能力。尽管有这些新的见解，但我们仍然远未能够通过治疗重新激活免疫系统来根除既定的慢性感染。此类治疗的发展需要对细胞动力学和分子网络有更多的了解，这些分子网络决定了T细胞在慢性感染中的分化方式。特别是，我们需要清楚地了解决定T细胞是保持效应和再扩增潜力还是变得功能失调的机制。阐明这些方面具有确定新的分子靶点以克服T细胞功能障碍和重建保护性免疫的潜力。促进我们对T细胞生物学的理解的一个主要限制是，T细胞反应的分子网络和细胞动力学通常仅在整个抗原特异性T细胞群体的水平上被描述。基于总体的表达模式评估只会产生平均值，可能会产生严重的偏差，甚至会产生错误的结果。在极端情况下，当两个分子在不重叠的亚群中高数量独占表达时，整个种群的全球基因表达分析将错误地表明，这两个分子在种群中的所有细胞中都低数量地共同表达。一个相关的问题是，一个表型亚群在较早的时间点和另一个表型亚群在较晚的时间点的显性通常被解释为细胞从一种表型向另一种表型转化的证据。然而，另一种可能性(表型稳定的子集的选择性生长)只能通过绘制单个T细胞随时间的命运图来揭示。我们建议结合D.Busch，P.Romero和D.Zehn实验室提供的专业知识和已建立的小鼠模型系统来描述来自单个T细胞的免疫反应模式的特征，以及与现有的基于群体的数据相比，单个T细胞水平上基因表达的相似性和特殊性。我们将通过这种先进的方法获得的知识将用于识别和测试T细胞亚群和确定的分子途径的治疗潜力。我们期待着对决定慢性感染中T细胞关键命运决定的机制有一个新的见解。此外，我们预计我们的发现将刺激新的治疗方法的发展。

Persisting infections induce T cells, which are phenotypically and functionally distinct from the types of T cells formed in acutely resolved infections. The traditional view is that these T cells are dysfunctional (a state often referred to as 'exhaustion'). However, there is increasing evidence that T cells retain substantial effector capacity in chronic infections, which can be significantly enhanced by blocking signaling through PD1 and other inhibitory receptors. Despite these new insights, we are still far away from being able to therapeutically re-activate the immune system to eradicate established chronic infections. The development of such treatments requires significantly more insight into cellular dynamics and molecular networks that determine how T cells differentiate in chronic infections. In particular, we need clear understanding of the mechanisms that decide whether T cells retain effector and re-expansion potential or become dysfunctional. Elucidating these aspects holds the potential of identifying new molecular targets to overcome T cell dysfunction and to re-establish protective immunity. A major limitation in advancing our understanding of T cell biology is that the molecular networks and cellular dynamics of T cell responses are usually described only at the level of the entire population of antigen-specific T cells. Population-based assessments of expression patterns will only generate average values and can create significant biases and even false results. In an extreme situation, when two molecules are exclusively expressed at high quantities in non-overlapping subpopulations, global gene expression analysis across the entire population would incorrectly suggest that both molecules are co-expressed at low quantities by all cells in the population. A related problem is that dominance of one phenotypic subset at an earlier and that of another at a later time point is generally interpreted as evidence for conversion of cells from one phenotype to the other. However, the alternative possibility (the selective outgrowth of a phenotypically stable subset) can only be uncovered by mapping the fate of individual T cells over time. We propose to combine the expertise and established murine model systems available in the laboratories of D. Busch, P. Romero and D. Zehn to describe the characteristics of immune response patterns derived from individual T cells as well as the similarities and particularities of gene expression at the level of individual T cells compared to the existing population-based data. The knowledge we will gain through this advanced approach will be used to identify and test the therapeutic potential of T cell subsets and defined molecular pathways. We anticipate to obtain a new quality of insight into the mechanisms that determine critical fate decisions of T cells in chronic infections. Moreover, we foresee that our findings will stimulate the development of novel therapeutic approaches.