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

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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.