ABSTRACT
Non-coding genetic variation is a major driver of phenotypic diversity and allows the investigation of mechanisms that control gene expression. Here, we systematically investigated the effects of >50 million variations from five strains of mice on mRNA, nascent transcription, transcription start sites, and transcription factor binding in resting and activated macrophages. We observed substantial differences associated with distinct molecular pathways. Evaluating genetic variation provided evidence for roles of â¼100 TFs in shaping lineage-determining factor binding. Unexpectedly, a substantial fraction of strain-specific factor binding could not be explained by local mutations. Integration of genomic features with chromatin interaction data provided evidence for hundreds of connected cis-regulatory domains associated with differences in transcription factor binding and gene expression. This system and the >250 datasets establish a substantial new resource for investigation of how genetic variation affects cellular phenotypes.
Subject(s)
Genetic Variation , Macrophages/metabolism , Transcription Factors/metabolism , Animals , Binding Sites , Bone Marrow Cells/cytology , CCAAT-Enhancer-Binding Protein-beta/genetics , CCAAT-Enhancer-Binding Protein-beta/metabolism , Cluster Analysis , Enhancer Elements, Genetic/genetics , Female , Gene Expression Regulation/drug effects , Lipopolysaccharides/pharmacology , Macrophages/cytology , Macrophages/drug effects , Male , Mice , Mice, Inbred BALB C , Mice, Inbred C57BL , Mice, Inbred NOD , Promoter Regions, Genetic , Protein Binding , Proto-Oncogene Proteins/genetics , Proto-Oncogene Proteins/metabolism , Trans-Activators/genetics , Trans-Activators/metabolism , Transcription Factors/geneticsABSTRACT
Macrophages reside in essentially all tissues of the body and play key roles in innate and adaptive immune responses. Distinct populations of tissue macrophages also acquire context-specific functions that are important for normal tissue homeostasis. To investigate mechanisms responsible for tissue-specific functions, we analyzed the transcriptomes and enhancer landscapes of brain microglia and resident macrophages of the peritoneal cavity. In addition, we exploited natural genetic variation as a genome-wide "mutagenesis" strategy to identify DNA recognition motifs for transcription factors that promote common or subset-specific binding of the macrophage lineage-determining factor PU.1. We find that distinct tissue environments drive divergent programs of gene expression by differentially activating a common enhancer repertoire and by inducing the expression of divergent secondary transcription factors that collaborate with PU.1 to establish tissue-specific enhancers. These findings provide insights into molecular mechanisms by which tissue environment influences macrophage phenotypes that are likely to be broadly applicable to other cell types.
Subject(s)
Enhancer Elements, Genetic , Macrophages/metabolism , Animals , Genetic Variation , Histone Code , Macrophages/cytology , Macrophages/immunology , Male , Mice, Inbred C57BL , Mice, Inbred NOD , Mice, Inbred Strains , Transcription Factors/metabolismABSTRACT
Recent studies suggest a hierarchical model in which lineage-determining factors act in a collaborative manner to select and prime cell-specific enhancers, thereby enabling signal-dependent transcription factors to bind and function in a cell-type-specific manner. Consistent with this model, TLR4 signaling primarily regulates macrophage gene expression through a pre-existing enhancer landscape. However, TLR4 signaling also induces priming of â¼3,000 enhancer-like regions de novo, enabling visualization of intermediates in enhancer selection and activation. Unexpectedly, we find that enhancer transcription precedes local mono- and dimethylation of histone H3 lysine 4 (H3K4me1/2). H3K4 methylation at de novo enhancers is primarily dependent on the histone methyltransferases Mll1, Mll2/4, and Mll3 and is significantly reduced by inhibition of RNA polymerase II elongation. Collectively, these findings suggest an essential role of enhancer transcription in H3K4me1/2 deposition at de novo enhancers that is independent of potential functions of the resulting eRNA transcripts.
Subject(s)
Enhancer Elements, Genetic , Macrophage Activation/genetics , Toll-Like Receptor 4/metabolism , Animals , Base Sequence , CCAAT-Enhancer-Binding Proteins/metabolism , Cells, Cultured , DNA Methylation , Gene Expression , Gene Expression Regulation , Histone-Lysine N-Methyltransferase/metabolism , Histones/genetics , Histones/metabolism , Macrophages/immunology , Macrophages/metabolism , Male , Mice , Mice, Inbred C57BL , Myeloid-Lymphoid Leukemia Protein/metabolism , NF-kappa B/metabolism , Proto-Oncogene Proteins/metabolism , RNA Polymerase II/antagonists & inhibitors , Sequence Analysis, DNA , Signal Transduction , Trans-Activators/metabolism , Transcription Factor RelA/metabolism , Transcription, GeneticABSTRACT
An amendment to this paper has been published and can be accessed via a link at the top of the paper.
ABSTRACT
Microglia are the main immune cells of the brain and express a large genetic pattern of genes linked to Parkinson's disease risk alleles. Monocytes like microglia are myeloid-lineage cells, raising the questions of the extent to which they share gene expression with microglia and whether they are already altered early in the clinical course of the disease. To decipher a monocytic gene expression signature in Parkinson's disease, we performed RNA-seq and applied the two-sample Kolmogorov-Smirnov test to identify differentially expressed genes between controls and patients with Parkinson's disease and changes in gene expression variability and dysregulation. The gene expression profiles of normal human monocytes and microglia showed a plethora of differentially expressed genes. Additionally, we identified a distinct gene expression pattern of monocytes isolated from Parkinson's disease patients at an early disease stage compared to controls using the Kolmogorov-Smirnov test. Differentially expressed genes included genes involved in immune activation such as HLA-DQB1, MYD88, REL, and TNF-α. Our data suggest that future studies of distinct leukocyte subsets are warranted to identify possible surrogate biomarkers and may lead to the identification of novel interventions early in the disease course.