Studying transcription factor binding networks in activated and dying macrophages

背景：基因表达和细胞反应的调控是通过转录因子的结合在DNA上协调进行的。转铁蛋白结合是一个受细胞外信号修饰的动态而特异的过程。然而，人们对转录因子调控基因表达的复杂相互作用知之甚少。在这里，我们将集中在下一代测序数据集上使用机器学习来解释骨髓来源的巨噬细胞(BMDM)中的TF结合模式，并研究细胞环境的变化如何改变TF结合。目的：研究骨髓间充质干细胞核因子-B转铁蛋白结合网络的细微差别。在这里，我将使用脂多糖激活的BMDM中的Chip-seq和RNA-seq来定义指定NF-？家族成员结合的TF。然后，我将使用CRISPR-Cas9在全局范围内测试我们的模型，以有选择地删除预计将选择Nf-？ü家庭成员占用的Tf。然后，我将使用注意力神经网络和ElasticNet回归建立这个模型，以定义每个NF-？家族成员及其控制的基因集所特有的转录因子基序集。我还将通过CRISPR-Cas9删除特定站点的基序来更具体地测试我们的模型。目的：从细胞因子激活的角度研究骨髓基质细胞与转铁蛋白结合的动力学。为了研究TF结合的上下文变化，我们将用一组细胞因子处理BMDM，并进行ATAC和RNA测序。我们将使用在AIM 1中开发的分析工具来定义每个上下文中的TF结合模式以及与基因相关的TF组合。由相同的转铁蛋白或转铁蛋白组合控制的基因组将接受通路分析，以确定每个转铁蛋白和一组转铁蛋白的作用。我们还将研究不同细胞因子治疗引起的细胞环境变化如何通过TF靶向的重排改变基因集。利用这些数据，我们将建立定义BMDM细胞因子治疗的基因和转铁蛋白集合。然后，我们将使用CRISPR/Cas9敲除在细胞因子环境中的基因组中预测重要的TF，以确认我们的结果并构建上下文特定的人工启动子。目的：使用TF网络在单细胞RNA-SEQ数据中定义活/死的BMDM细胞群。在这里，我们将诱导小鼠骨髓基质细胞的凋亡、坏死和下垂，并进行单细胞多组体测序(ATAC和RNA)。将提取RNA表达和转铁蛋白结合部位，并基于假时间对细胞进行比对，以确定细胞死亡的进程。然后，我们将使用目标2中的ElasticNet回归为每个细胞定义每种类型的细胞死亡的转录调控网络。然后，我们将使用这些数据来制作细胞死亡图谱，并在之前发表的单细胞实验中预测细胞死亡的数量和类型，这些实验得到了显微镜的证实。这将使我们能够在单细胞数据集中分离死亡细胞，并在实验中确定细胞死亡的类型。

Background: Control of gene expression and cell responses is coordinated on the DNA through the binding of transcription factors (TFs). TF binding is a dynamic and specific process that is modified by extracellular signals. However, the complex interplay of TFs to control gene expression is poorly understood. Here, we will focus on the use of machine learning on next generation sequencing datasets to interpret TF binding patterns in bone marrow derived macrophages (BMDMs) and study how changes in the cellular environment modify TF binding. Objective A: Studying the nuances in Nf-?B TF binding networks in BMDMs. Here, I will use ChIP-seq and RNA-seq in BMDMs activated with LPS to define the TFs which specifies Nf-?ß family member binding. I will then test our model globally by using CRISPR-Cas9 to selectively remove TFs which are predicted to select Nf-?ß family members for occupancy. I will then build upon this model using an attention neural network and elasticnet regression to define the transcriptional factor motif sets specific to each Nf-?ß family members and the gene sets they control. I will also test our model more specifically by CRISPR-Cas9 deletion of motifs at specific sites. Objective B: Studying the dynamics of TF binding using cytokine activation of BMDMs. To study contextual changes in TF binding, we will treat BMDMs with a panel of cytokines and perform ATAC and RNA sequencing. We will use the analysis tools developed in Aim 1 to define the TF binding patterns in each context and TF combinations associated with genes. Gene groups which are controlled by the same TF or combination of TFs will undergo pathways analysis to define the role of each TF and set of TFs. We will also study how changes in cellular context caused by different cytokine treatments change the gene sets through a rearrangement of TF targeting. Using this data, we will build gene and TF sets which define cytokine treatments in BMDMs. We will then use CRISPR/Cas9 to knockout TFs which are predicted to be important in gene sets in cytokine contexts to confirm our results and build context specific artificial promoters. Objective C: Defining live/dead BMDM cell populations in single cell RNA-seq data using TF networks. Here, we will induce apoptosis, necrosis and pyroptosis in mouse BMDMs and perform single-cell multi-omic sequencing (ATAC and RNA). RNA expression and TF binding sites will be extracted and cells will be aligned based on pseudotime to define the progression of cell death. We will then use elasticnet regression as in Aim 2 for each cell to define transcriptional regulatory networks for each type of cell death. We will then use this data to produce a cell death atlas and predict the amount and type of cell death in previously published single cell experiments which have microscopy confirmation. This will allow us to isolate dead cells in single cell datasets and determine the type of cell death in experiments.