T Cell Receptor Is Required for Differentiation, but Not Maintenance, of Intestinal CD4+ Intraepithelial Lymphocytes.

The gut epithelium is populated by intraepithelial lymphocytes (IELs), a heterogeneous T cell population with cytotoxic and regulatory properties, which can be acquired at the epithelial layer. However, the role of T cell receptor (TCR) in this process remains unclear. Single-cell transcriptomic analyses revealed distinct clonal expansions between cell states, with CD4+CD8αα+ IELs being one of the least diverse populations. Conditional deletion of TCR on differentiating CD4+ T cells or of major histocompatibility complex (MHC) class II on intestinal epithelial cells prevented CD4+CD8αα+ IEL differentiation. However, TCR ablation on differentiated CD4+CD8αα+ IELs or long-term cognate antigen withdraw did not affect their maintenance. TCR re-engagement of antigen-specific CD4+CD8αα+ IELs by Listeria monocytogenes did not alter their state but correlated with reduced bacterial invasion. Thus, local antigen recognition is an essential signal for differentiation of CD4+ T cells at the epithelium, yet differentiated IELs are able to preserve an effector program in the absence of TCR signaling.

A mid-level approach to modeling scientific communities.

This paper provides an account of mid-level models which calibrate highly theoretical agent-based models of scientific communities by incorporating empirical information from real-world systems. As a result, these models more closely correspond with real-world communities, and are better suited for informing policy decisions than extant how-possibly models. I provide an exemplar of a mid-level model of science funding allocation that incorporates bibliometric data from scientific publications and data generated from empirical studies of peer review into an epistemic landscape model. The results of my model show that on a dynamic epistemic landscape, allocating funding by modified and pure lottery strategies performs comparably to a perfect selection funding allocation strategy. These results support the idea that introducing randomness into a funding allocation process may be a tractable policy worth exploring further through pilot studies. My exemplar shows that agent-based models need not be restricted to the abstract and the apriori; they can also be informed by empirical data.