Rules of gene expression modeled on human dendritic cell response to pathogens

描述（由申请人提供）：当细胞对环境刺激做出反应时发生的发育转变在很大程度上由涉及数千个基因的基因表达程序控制。转录因子（TF），染色质修饰酶，顺式作用的DNA元件有助于网络，这些程序的基础。将这些变量联系在一起的密码，使得可以根据特定成分的存在来预测给定基因的表达，这一点还有待破译。这种代码的模型将在这里构建基于人类树突状细胞（DC）的全基因组分析，因为它们成熟的病原体。DC是抗原呈递细胞，其启动并决定宿主免疫应答的质量和幅度。干细胞生物学的最新技术进展，原代人类细胞的反向遗传工具，和全基因组评估的成绩单，局部染色质特征和长距离染色质相互作用，将在这里利用构建一个模型的转录调控网络的基础病原体检测和成熟的人树突状细胞。在目标1中，将在用LPS刺激后的时间过程中对DC进行采样。将通过RNA-Seq鉴定刺激应答基因。染色质特征将使用ChIP-Seq、高分辨率全局DNA足迹和Hi-C进行映射。所有响应基因和与它们相关的调控区域的系统图将与已知的DNA结合基序相结合，以生成基因调控网络的初始模型。在目标2中，将从目标1中的模型中鉴定在LPS刺激的前2小时内驱动转录的60个转录因子。这些因子将在hDC中被敲低，并且将使用RNA-Seq评估对LPS应答的影响。每个TF的功能数据将通过使用ChIP-Seq测量蛋白质-DNA相互作用来补充;后者将利用CRISPR技术将共同的表位标签融合到这些TF的内源编码序列。通过精确定位关键的转录因子，它们的结合位点和转录响应基因，这种分析将用于 完善基因调控网络的模型。在目标3中，将使用在先前目标中收集的特征鉴定对于控制hDC对LPS的转录应答重要的顺式作用调节区的子集。将使用CRISPR技术干扰选定的基因座，并检查对靶基因表达的影响。将使用已发表的数据以及将使用Pac- Bio进行基因分型的300份人类样本评估相关基因座中等位基因变体对基因表达的影响。同时，将通过5C评估5个个体中50个关键调控基因座的高阶染色质构象的差异。这些数据将用于进一步完善我们的监管模式。该分析的结果将是一个模型，该模型突出了在LPS刺激的人DC中驱动基因表达的关键转录因子和顺式作用组分。我们的研究结果有望提供一个更普遍的方法来确定在原代哺乳动物细胞中的基因表达的遗传决定因素。

DESCRIPTION (provided by applicant): The developmental shifts that occur when cells respond to environmental stimuli are controlled in large part by gene expression programs involving thousands of genes. Transcription factors (TFs), chromatin modifying enzymes, and cis-acting DNA elements contribute to the networks that underlie such programs. The code that links these variables in such a way that the expression of a given gene can be predicted based on the presence of specific components has yet to be deciphered. A model for such a code will be constructed here based on genome-wide analysis of human dendritic cells (DCs) as they mature in response to pathogens. DCs are antigen-presenting cells that initiate and determine the quality and magnitude of the host immune response. Recent technical advances in stem cell biology, reverse-genetic tools for primary human cells, and genome-wide assessment of transcripts, local chromatin features and long-range chromatin interactions, will be exploited here to construct a model for the transcriptional regulatory network that underlies pathogen detection and maturation in human DCs. In Aim 1, DCs will be sampled in a time-course following stimulation with LPS. Stimulation-responsive genes will be identified by RNA-Seq. Chromatin features will be mapped using ChIP-Seq, high-resolution global DNA foot printing, and Hi-C. This systematic map of all responsive genes and the regulatory regions associated with them will be combined with known DNA-binding motifs to generate an initial model for the gene regulatory network. In Aim 2 sixty transcription factors that drive transcription within the first 2 hrs of LPS stimulation will be identified from the model in Aim 1. These factors will be knocked-down in hDCs and the effect on the LPS response will be assessed using RNA-Seq. This functional data for each TF will be complemented by measurement of protein-DNA interactions using ChIP- Seq; the later will exploit CRISPR technology to fuse a common epitope tag to endogenous coding sequences for these TFs. By pinpointing key transcription factors, their binding sites, and transcriptionally-responsive genes, this analysis will be used to refine the model for the gene regulatory network. In Aim 3, a subset of cis- acting regulatory regions important for controlling the hDC transcriptional response to LPS will be identified using features collected in the previous aims. Selected loci will be perturbed using CRISPR technology and effects on target gene expression will be examined. The effect on gene expression of allelic variants in relevant loci will be assessed, using published data as well as 300 human samples that will be genotyped using Pac- Bio. In conjunction, the variance in the higher-order chromatin conformation for 50 key regulatory loci across five individuals will be assessed by 5C. These data will be used to further refine our regulatory model. The result of this analysis will be a model that highlights the key transcription factors and cis-acting components that drive gene expression in LPS-stimulated human DCs. Our findings are expected to provide a more general method for identifying the genetic determinants of gene expression in primary mammalian cells.