Studying transcription factor binding networks in activated and dying macrophages

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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.