A data-driven Boolean model explains memory subsets and evolution in CD8+ T cell exhaustion

Introduction:: T cells play a key role in a variety of immune responses, including infection and cancer. Upon stimulation, naïve CD8+ T cells proliferate and differentiate into a variety of memory and effector cell types; however, failure to clear antigens causes prolonged stimulation of CD8+ T cells, ultimately leading to T cell exhaustion (TCE). The functional and phenotypic changes that occur during CD8+ T cell differentiation are well characterized, but the underlying gene expression state changes are not completely understood. Various models have been proposed to account for the progression of T cell states towards terminal differentiation, from the acute phase of immune responses to exhaustion. Two core differentiation models have been proposed to reflect the transcriptional profiles of T cell subsets: the “linear” and “circular” models. The circular model results in cycling memory and exhausted patterns of transcriptional changes while the linear model results in gene expression patterns that change gradually. Here, we utilize a previously published data-driven Boolean model of gene regulatory interactions to quantitively investigate the evolution of T cell states of gene regulation along the path to exhaustion.

Materials and Methods:: We used a large-scale network model of gene regulation to investigate the progression of T cell states and understand the evolution of gene expression patterns that lead to terminally exhausted T cells. We adapted a previously published Boolean model (Bolouri et al. 2020), which was constructed through an extensive literature-based network of gene regulatory interactions underpinning TCE in CD8+ T cells. Each gene node in the 25-node network is color-coded based on its impact along the path to TCE, labeled as either “proliferative pro-memory” (PP, pink) or “effector exhausted” (EE, green). Ten thousand model simulations were implemented as a series of logic statements that are executed in an asynchronous update paradigm.

Results, Conclusions, and Discussions:: Results: We applied the model to quantitively investigate the evolution of T cell states of gene regulation along the path to exhaustion. Our systems analysis identifies eight attractor states (ES; Figure 1A) comprised of distinct patterns of gene expression corresponding to T cell exhaustion, in response to T cell activation, even when starting from identical underlying networks. The sequence of changes in gene expression that gives rise to these eight cell states (Figure 1B, example from ES1) include four paths represented by the circular model and four paths represented by the linear differentiation model (Figure 1C, example from ES1).



Conclusions: We have shown that a consensus model of CD8+ T cell gene activation can display a variety of behaviors seen experimentally and can predict novel targets for modulating CD8+ T cell exhaustion. Overall, our work provides a solid foundation for future work to study the regulatory pathways that drive T cell decisions and response.


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