Decoding in vivo regulatory programs of CD4+ T lymphocyte populations in inflamma

描述（由申请人提供）：在多细胞生物中，分化的细胞表现出由不同的转录和表观遗传程序指定的特殊功能。调控元件的差异使用定义了大多数先前研究的谱系特异性基因表达程序。然而，应激、损伤或感染等挑战可引起分化细胞的适应性或致病性反应，导致其功能状态和转录输出的变化。免疫系统细胞提供了一个强大的实验模型，用于解剖不同分化和激活状态下的基因组机制。在本提案中，我们研究了不同的CD4+ T细胞群，当它们从“幼稚”（或静止）状态转变为具有相反功能的激活状态：效应T细胞促进

DESCRIPTION (provided by applicant): In multicellular organisms, differentiated cells exhibit specialized functions that are specified by distinct transcriptional and epigenetic programs. Differential use of regulatory elements defines most previously studied lineage specific gene expression programs. However, challenges such as stress, injury, or infection can elicit adaptive or pathogenic responses in differentiated cells leading to a change in their functional state and transcriptional output. Cells of the immune system offer a powerful experimental model for dissection of genomic mechanisms underlying establishment of distinct differentiation and activation states. In this proposal, we study distinct CD4+ T cell populations as they transition from "na�ve" (or resting) to activated states with opposing function: effector T cells that promote - and activated regulatory T cells (Treg) that suppress - immune response and associated inflammation. Resting Treg cells emerge during differentiation as a stable lineage of T lymphocytes distinct from na�ve CD4+ T cells. We will use sophisticated genetic mouse models to generate a short-term inflammatory disorder and investigate genomic features of activated Treg and T effector cells and their resting counterparts in an inflammatory context in vivo. We will profile the enhancer and transcriptional landscapes of the four cell states using DNase-seq, TF and histone modification ChIP-seq, and bulk and single-cell RNA-seq profiles from ex vivo isolated cells. Using these comprehensive data sets, we will: (1) decode the changes in the enhancer landscape that govern the activation of distinct CD4+ T lymphocyte populations; (2) model the differential transcriptional output of genes in these cells as a function of the sequence and activity of their enhancers; and (3) model the expression distribution of individual genes over a population of cells as a function of the state space of their enhancers. Given the central role that Treg cells play in suppressing immune-mediated inflammation in diverse biological contexts ranging from autoimmunity, injury, and infection to pregnancy and metabolic disease as well as emerging understanding of their pivotal role in cancer, our study has broad relevance to human health and major practical significance.