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

模拟人类树突状细胞对病原体反应的基因表达规则

• 批准号: 8770761
• 负责人: Manuel Garber
• 金额: $ 202.88万
• 依托单位: UNIV OF MASSACHUSETTS MED SCH WORCESTER
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-01-05 至 2017-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8770761
• 关键词: Antigen-Presenting Cells; Antigens; Autoimmunity; Binding Sites; Biology; Cell Differentiation process; Cell surface; Cells; ChIP-seq; Chromatin; Clustered Regularly Interspaced Short Palindromic Repeats; Code; Common Epitope; Competence; Complement; Computer Simulation; DNA; DNA Binding; DNA Footprint; DNA Polymerase II; DNA Sequence; DNA-Protein Interaction; Data; Dendritic Cells; Development; Educational process of instructing; Elements; Engineering; Environment; Enzymes; Gene Expression; Gene Expression Regulation; Genes; Genetic; Genetic Determinism; Genetic Polymorphism; Genetic Transcription; Genome; Genotype; Human; Immune; Immune response; Individual; Leukocytes; Link; Lipopolysaccharides; Mammalian Cell; Maps; Measurement; Methods; Modeling; Molecular Conformation; Monitor; Morphology; Mutation; Nucleic Acid Regulatory Sequences; Other Genetics; Output; Pathogen detection; Prevention; Printing; Publishing; RNA; Regulator Genes; Resolution; Sampling; Signal Transduction; Stimulus; T-Cell Proliferation; T-Lymphocyte; Technology; Time; Transcript; base; cytokine; foot; genetic element; genetic variant; genome-wide; genome-wide analysis; knock-down; pathogen; programs; public health relevance; receptor; research study; response; reverse genetics; stem cell biology; tool; transcription factor; vaccine development

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.

描述（由申请人提供）：当细胞对环境刺激响应的响应时，发生的发育转移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（TFS），染色质修饰酶和顺式作用DNA元素有助于基于此类程序的网络。将这些变量链接的代码以某种方式可以根据特定组件的存在来预测给定基因的表达。此类代码的模型将基于人类树突细胞（DC）的全基因组分析，因为它们响应病原体而成熟。 DC是抗原呈递细胞，可以启动并确定宿主免疫反应的质量和大小。这里将利用干细胞生物学，原代人细胞的反遗传学工具以及对转录本的全基因组评估，局部染色质特征和远程染色质相互作用的最新技术进步，以构建人类DC中病原体检测和成熟的转录调节网络的模型。在AIM 1中，DC将在用LPS刺激后在时间课程中采样。刺激反应性基因将通过RNA-seq鉴定。染色质特征将使用芯片序列，高分辨率全局DNA脚打印和HI-C映射。所有响应基因及其相关的调节区域的系统图将与已知的DNA结合基序相结合，以生成基因调节网络的初始模型。在AIM 2中，将从AIM 1中的模型中鉴定出在LPS刺激的前2个小时内驱动转录的60个转录因子。这些因素将在HDC中击倒，并且将使用RNA-Seq评估对LPS响应的影响。每个TF的功能数据将通过使用Chip-Seq测量蛋白-DNA相互作用来补充。后来的将利用CRISPR技术将常见的表位标签融合到这些TFS的内源性编码序列中。通过查明关键转录因子，它们的结合位点和转录响应基因，该分析将用于 优化基因调节网络的模型。在AIM 3中，将使用先前目标中收集的特征来确定对控制HDC转录响应至关重要的一个子集调节区域。将使用CRISPR技术对选定的基因座进行干扰，并将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-beio进行基因分型的300个人类样本来评估对相关基因座中等位基因变体基因表达的影响。同时，将通过5C评估50个关键调节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型。该分析的结果将是一个模型，该模型突出了驱动LPS刺激的人类DC中基因表达的关键转录因子和顺式作用成分。我们的发现有望提供一种更通用的方法来识别原代哺乳动物细胞中基因表达的遗传决定因素。