Tissue macrophages are essential for the maintenance of organ homeostasis, but the cellular mechanisms specifying their phenotype are poorly understood. Here, we leverage natural genetic variation between inbred mouse strains as a tool to perturb Kupffer cell transcriptional regulation. We show that natural genetic variation disrupts Kupffer cell gene expression through cis-mediated disruption of transcription factor binding motifs and via trans-acting differences in the activity of gene regulatory machinery. We further divide trans-acting genetic variation into non-cell autonomous variation driven by changes in the Kupffer cell environment, and cell autonomous variation driven by changes in intracellular pathway activity. We show that careful evaluation of each mode of genetic variation can reveal signaling pathways specifying Kupffer cell identity in vivo. Collectively, this work demonstrates a novel approach for understanding how genetic diversity impacts tissue macrophage behavior.

The following work uses multi-level genomics approaches coupled with mouse models to study the gene regulatory networks in hepatic macrophages during both healthy conditions and liver disease. Chapter 1 will serve as an introduction, reviewing what is currently known about regulation of gene expression by sequence specific transcription factors and enhancers, how niche specific environmental signals are relayed through this regulatory machinery to produce the unique phenotypes of different populations of tissue resident macrophages, and how Kupffer cells and recruited macrophages function in the liver during nonalcoholic steatohepatitis. Chapter 2 utilizes a model system of experimental Kupffer cell ablation and repopulation to dissect the pathways that allow bone marrow derived monocytes to differentiate into Kupffer cell-like macrophages. Using this system, we found that TGF/SMAD, Notch/RBPJ, and LXR are critical pathways underlying Kupffer cell identity. Chapter 3 investigates the changes that occur to macrophages during experimentally induced fatty liver disease in mice. Bone marrow derived monocytes were recruited to both liver sinusoids (the Kupffer cell niche) and large diameter vessels and displayed divergent patterns of open chromatin and gene expression based on their destination. Further, embryonically derived Kupffer cells lost enhancer activity disproportionately at regulatory regions important for Kupffer cell identity. This loss of enhancer activity correlated with a selective loss of LXR binding at these regions. Liver disease also selectively resulted in apoptosis of embryonically derived Kupffer cells and their replacement with bone marrow derived precursors. Chapter 4 will discuss the broader implications of these studies.

Liver X Receptors (LXRs), composed of LXRα and LXRβ, are ligand activated nuclear receptors that are important regulators of cholesterol and lipid metabolism. LXRs control expression of genes involved in cholesterol efflux, fatty acid metabolism, and inflammation in tissues such as the liver and intestine. LXRα is highly expressed in liver resident macrophages, called Kupffer cells, which are involved in clearing pathogens from the portal circulation, as well as sensing tissue injury. During liver disease, such as nonalcoholic steatohepatitis (NASH), Kupffer cells are responsible for initiating a proinflammatory response program; therefore, I sought to understand the LXR-dependent responses modulating cholesterol accumulation and inflammation in Kupffer cells at both the steady state and during disease. I found a decreased expression of cholesterol efflux and inflammatory genes in Kupffer cells of mice lacking LXRs throughout all tissues but aimed to understand the function of LXRs intrinsic to Kupffer cells. Therefore, I studied a Lyz2 Cre and a novel Clec4f Kupffer cell specific Cre mouse lacking LXRs and saw an expansion of a new F4/80HiCd11bIntTim4Neg Kupffer cell population that is associated with fatty liver disease. Consequently, LXR activation may have beneficial properties, so pharmacological activation with a synthetic mimetic of the natural LXR ligand desmosterol was analyzed. Results suggest that this desmosterol mimetic dampens hepatic inflammation, lipid accumulation, and fibrosis, and may not only be targeting the liver, but also altering gene expression in the intestine as well. Thus, pharmacological activation of LXRs may act therapeutically in patients with fatty liver disease.

TREM2 (Triggering Receptor Expressed on Myeloid Cells 2) is a transmembrane receptor that has recently been established as a key regulator of macrophages in multiple disease models. It is highly expressed in distinct macrophage populations in models of Alzheimer’s Disease, non-alcoholic steatohepatitis, and obesity. In this study, we interrogated the effect of deletion of TREM2 or treatment with a TREM2-activating antibody in myocardial injury models. To understand how TREM2 itself is regulated and how it mediates its effects on transcription, we established a conditionally immortalized monocyte progenitor cell line that constitutively expresses Cas9 and is able to differentiate into macrophages. We used this cell line to delete the intergenic enhancer of TREM2 and to knock out TREM2 as well as transcription factors. Thereby, we identified transcription factors mediating the effects of TREM2 on gene expression. Overall, in this work we demonstrate the role of TREM2 in myocardial injury, interrogate regulation of Trem2 expression, and identify transcription factors downstream of TREM2 signaling.

Spalt-Like Transcription Factor 1 (Sall1) is a critical regulator of microglia identity. Despite its importance, the transcriptional function of SALL1 and mechanisms regulating its expression are not fully understood. Here, we demonstrate that Sall1 is physically connected to a microglia-specific, environment-dependent super enhancer (SE) containing conserved binding sites for SMAD TFs downstream of TGF-beta signaling. Deletion of the SE in mice (EKO) led to selective loss of Sall1 transcript and SALL1 protein in microglia, resulting in downregulation of microglial identity genes and activation of inflammation- and aging-associated genes. Using ChIP-seq, we defined binding sites of SALL1 and leveraged EKO mice to probe how SALL1 shapes the regulatory landscape of microglia. We found thousands of putative enhancers whose activity was increased or abrogated by loss of SALL1; we further classified these enhancers as being ‘directly’ or ‘indirectly’ regulated by SALL1 based on overlap with SALL1 binding sites. Unexpectedly, motifs for SMADs are enriched within enhancers predicted to be directly activated by SALL1, suggesting that collaborative interactions between SALL1 and SMADs are required to establish microglia-specific gene expression. To test this hypothesis, we generated a conditional KO of the common co-SMAD Smad4 and determined the binding sites of SMAD4 in WT and EKO microglia. These studies demonstrated that SMAD4 binds to the Sall1 SE and controls Sall1 expression, and that its ability to activate other major microglia genes is dependent on co-binding with SALL1. Collectively, these results suggest a molecular basis for many of the transcriptional characteristics of yolk sac-derived microglia.

Despite the advent of high throughput genomics technology and the wealth of data characterizing transcription that followed, it remains difficult to relate genomic sequence to transcriptional activity. Next generation sequencing techniques, including ChIP-seq, RNA-seq, and ATAC-seq, have enabled high resolution mapping of transcriptional activity, including RNA expression and histone modifications, as well as the localization of transcription factors and DNA binding proteins that regulate transcription. By integrating of these activity maps using statistical methods and high-performance computing, a model has emerged in which transcription factors recognize and bind to short DNA sequence motifs (“words”) to recruit cellular machinery such as RNA polymerase, which is necessary for transcription. Previous studies have also demonstrated that transcription factors often bind together in a cell type and context specific manner, setting the foundation for a genomic grammar in which combinations of transcription factors recognize "sentences" that specify cell type and context specific transcriptional activity. Using this foundational model as our starting point, we devised a machine learning framework named TBA (a Transcription factor Binding Analysis), for investigating the sequence specificity of transcription factors by jointly weighing the contributions of hundreds of DNA motifs. We applied TBA to a systematic map of the binding profiles for the AP-1 transcription factor family, which share a conserved DNA binding domain. We observed that each family member demonstrated interactions with distinct sets of motifs, which varied from cell type to cell type, and in different cellular states. Next we applied the TBA framework to hundreds of transcription factor ChIP-seq data sets, demonstrating that like AP-1, transcription factors generally interact with dozens of other transcription factors genome-wide and with 3-4 transcription factors at a given locus in a cell-type specific manner. We used these findings describing transcription factor behavior to devise a neural network with an attention mechanism that calculates locus specific maps of how motifs interact to predict transcriptional activity. These studies demonstrate machine learning approaches that reveal additional insight into a transcriptional grammar that coordinates eukaryotic gene expression.