Mechanisms Of Age-related Changes in Transcriptional Regulation in lympphocytes

赖氨酸特异性甲基转移酶2D（Kmt 2d）催化组蛋白3赖氨酸4（H3 K4 me 1）的单甲基化，并通过调节Foxp 3基因表达在调节性T细胞生成中起关键作用。在这里，我们报告了Kmt 2d在幼稚CD 8 + T细胞生成和存活中的作用。在不存在Kmt 2d的情况下，与Kmt 2dfl/flCD 4cre-（WT）小鼠相比，Kmt 2dfl/flCD 4cre+（KO）小鼠脾脏中的CD 8 + T细胞，特别是幼稚CD 8 + T细胞（CD 62 Lhi/CD 44 lo）的数量大大减少，并且体外活化相关死亡显著增加。此外，ChIPseq、RNAseq和scRNAseq的分析显示，在不存在Kmt 2d的情况下，增强子中的H3 K4 me 1水平降低，活化的原始CD 8 + T细胞中的凋亡相关基因表达降低。最后，我们证实了在用单核细胞增生李斯特菌感染攻击后，Kmt 2d KO小鼠体内抗原特异性幼稚CD 8 + T细胞的激活诱导的死亡。这些发现表明，Kmt 2d通过调节细胞凋亡和免疫功能相关基因增强子区域中的H3 K4 me 1水平来调节活化诱导的原始CD 8 + T细胞存活。 T细胞响应于抗原刺激而经历稳健的细胞分裂的能力对于有能力的T细胞功能是必不可少的。然而，这种能力随着年龄的增长而降低，导致老年人对传染病，癌症和其他疾病的易感性增加。为了更好地了解T细胞老化，有必要改进T细胞中与年龄相关的细胞变化的测量。最近开发的基于机器学习辅助转录组的个体CD 8 + T细胞年龄定量代表了这方面的重要一步。它揭示了基因表达的显着和微妙的变化，并指出随着衰老，CD 8 + T细胞的潜在功能改变。通过捕捉细胞分化状态和细胞分裂历史的差异，该算法为CD 8 + T细胞分配了不同的年龄，这使研究人员能够更好地理解年龄对单个CD 8 + T细胞功能的影响。我们认为，基于单细胞转录组的免疫系统年龄预测可能有很好的应用前景。

Lysine specific methyltransferase 2D (Kmt2d) catalyzes the mono-methylation of histone 3 lysine 4 (H3K4me1) and plays a critical role in regulatory T cell generation via modulating Foxp3 gene expression. Here we report a role of Kmt2d in nave CD8+ T cell generation and survival. In the absence of Kmt2d, the number of CD8+ T cells, particularly nave CD8+ T cells (CD62Lhi/CD44lo), in spleen was greatly decreased and in vitro activation-related death significantly increased from Kmt2dfl/flCD4cre+ (KO) compared to Kmt2dfl/flCD4cre- (WT) mice. Furthermore, analyses by ChIPseq, RNAseq, and scRNAseq showed reduced H3K4me1 levels in enhancers and reduced expression of apoptosis-related genes in activated nave CD8+ T cells in the absence of Kmt2d. Finally, we confirmed the activation-induced death of antigen-specific nave CD8+ T cells in vivo in Kmt2d KO mice upon challenge with Listeria monocytogenes infection. These findings reveal that Kmt2d regulates activation-induced nave CD8+ T cell survival via modulating H3K4me1 levels in enhancer regions of apoptosis and immune function-related genes. The ability of T cells to undergo robust cell division in response to antigenic stimulation is essential for competent T cell function. However, this ability is reduced with aging, contributing to an increased susceptibility to infectious diseases, cancers, and other diseases among older adults. To better understand T cell aging, improved measurements of age-related cellular changes in T cells are necessary. The recent development of a machine-learning assisted transcriptome-based quantification of individual CD8+ T cell age represents a significant step forward in this regard. It reveals both prominent and subtle changes in gene expression and points to potential functional alterations of CD8+ T cells with aging. By capturing differences in the status of cell differentiation and history of cell divisions, this algorithm assigns different ages to CD8+ T cells, which enables researchers to better comprehend the impact of age on the functions of individual CD8+ T cells. We argue that single cell transcriptome-based age prediction in the immune system may have promising future applications.