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Analysis of the epigenetic landscape in murine macrophages
Analysis of the epigenetic landscape in murine macrophages
Macrophages are cells of the innate immune system and play essential roles in the regulation of inflammatory responses in all parts of the body. Furthermore, macrophages are also involved in different tissue–specific functions and maintenance of the tissue homeostasis. These functions are controlled by the epigenetic landscape, consisting of promoters and enhancers that together regulate gene expression. Enhancers are stretches of regulatory genomic sequences in the non–coding regions of the genome that can be bound by lineage– determining transcription factors. These enhancers can loop in three–dimensional space to be in close proximity to promoters and contribute to the regulation of gene expression. Previous studies suggest that there are about 1 million enhancers in the mammalian genome, of which only about 30,000 – 40,000 are selected in each specific cell type. This dissertation studies the regulation of the epigenetic landscape of murine macrophages by utilizing different tissue macrophages, different complex and simple stimuli, as well as natural genetic variation as a mutagenesis screen. The overarching research question of this dissertation is to understand how the enhancer landscape in macrophages gets selected and regulated in order to control gene expression. In more detail, the main questions answered in this dissertation are: What are the epigenetic mechanisms that are responsible for tissue–specific functions? How do complex stimuli change the epigenetic landscape of macrophages in comparison to simple stimuli? How does natural genetic variation influence the epigenetic landscape and gene expression in murine macrophages? In Chapter 1 (Gosselin, D., Link, V. M., Romanoski, C. E. et al. (2014) appeared in Cell) we investigate the influence of the tissue environment on the epigenetic landscape in mouse macrophages. We compare macrophages residing in the brain (microglia) with macrophages from the peritoneal cavity by measuring mRNA expression, as well as enhancer activation (H3K4me2, H3K27ac, and PU.1). We find highly expressed genes unique to one population of macrophages, which correlates well with the activity signature at enhancers in the corresponding cells. By analyzing the enhancer landscape, we find that the macrophage lineage–determining transcription factor PU.1 plays a key role in establishing the enhancer repertoire, creating a common, macrophage–specific enhancer landscape. Furthermore, expression of tissue–specific transcription factors in collaboration with PU.1 drives a subset of tissue–specific enhancers regulating the differences in gene expression between different tissue–specific macrophage populations. In Chapter 2 (Eichenfield, D. Z., Troutman, D. T., Link, V. M. et al. (2016) appeared in eLife) we investigate the effect of complex stimuli onto the epigenetic landscape in macrophages on the example of wounds. Stimulation of macrophages with homogenated tissue to mimic a wound environment shows a unique pattern of gene expression, which is different from gene expression patterns found after single stimuli (e.g. LPS, IL–4 etc.). To gain insight into the regulation of the enhancer landscape after complex stimuli, we compare the epigenome after single stimuli and tissue homogenate and find substantial differences in enhancer selection and activation. We find that the complex damage signal promotes co–localization of several signal–dependent transcription factors to enhancers not observed under the single stimuli. Therefore, more complex polarizations of cells lead to new combinations of signal–dependent transcription factors and an epigenetic landscape different than observed with single stimuli. In Chapter 3 (Link et al. (2018b) appeared in bioRxiv) MARGE (Mutation Analysis for Regulatory Genomic Elements) is presented, a new method to analyze the effect of natural genetic variation on transcription factor binding and open chromatin. MARGE provides a suite of software tools that integrates genome–wide genetic variation data (including insertions and deletions) with epigenetic data. It provides software to create custom genomes based on a reference genome and variation data, to shift coordinates between different custom genomes, as well as do downstream ChIP–seq analysis. The main algorithm in MARGE analyzes if mutations in transcription factor binding motifs are significantly affecting transcription factor binding or open chromatin. MARGE provides a pairwise comparison, in which the significance of each motif is calculated with a student’s t–test. It compares the transcription factor binding distribution of each mutated motif in individual one with the distribution in individual two. For a more general approach that allows comparisons of many individuals MARGE implements a linear mixed model, modeling transcription factor binding with fixed effects motif existence and random effects locus and genotype. The development of this software allows in depth analysis of genetic variation data in combination with epigenetic data. In Chapter 4 (Link et al. (2018a) under review in Cell) we analyze the effect of natural genetic variation in five diverse strains of mice on the epigenetic landscape. We choose three well–known laboratory inbred mouse strains, as well as two very diverse wild–derived inbred mouse strains. We investigate the enhancer landscape, open chromatin and binding of the most important macrophage lineage–determining transcription factors. We observe substantial strain–specific differences in gene expression of which the majority can be explained by cis–regulatory elements. Application of MARGE onto the transcription factor binding data reveals roles of about 100 transcription factors in establishing the enhancer repertoire in macrophages. Unexpectedly, we find that a substantial fraction of strain– specific DNA binding of transcription factors cannot be explained by local mutations. Investigation of this phenomenon in more detail shows highly interconnected clusters of transcription factors that reside within topologically associating domains. These interconnected clusters are highly correlated with activation of enhancers and gene expression of the nearest gene, uncovering a new layer of transcriptional regulation. In Chapter 5, I briefly discuss additional contributions to the field of macrophage biology I made during my Ph.D. Namely, I was involved in two additional projects. In the first project (Pirzgalska et al. (2017) appeared in Nature Medicine) we identify sympathetic neuron–associated macrophages (SAM) that import and degrade norepinephrine via expression of solute carrier family 6 member 2 (Slc6a2) and monoamine oxidase A (MAOa). We demonstrate that SAM–mediated clearance of extracellular norepinephrine contributes to obesity and we show the relevance of this finding in humans, as we found that SAMs are also present in human tissues. The second project (Oishi et al. (2017) appeared in Cell Metabolism) studies the role of nuclear receptors (LXR and SREBP) in induction of anti–inflammatory fatty acids. We find that right after stimulation of TLR4 (during the induction phase) NF–kB dependent genes are upregulated, whereas LXR dependent genes are repressed. This leads to activation of SREBP1, which drives the expression of enzymes involved in mono–unsaturated and omega–3 polyunsaturated fatty acid biosynthesis. The fatty acids produced by these enzymes repress inflammatory genes under the control of NF–kB and the inflammatory signal gets resolved. In summary, my studies used a combination of experimental and computational approaches to investigate the effect of tissue–environment and factors, complex stimuli and natural genetic variation on the epigenetic landscape in macrophages. These studies broadened our understanding of the regulation of gene expression by the epigenetic landscape substantially. We showed that there is a core set of lineage–determining transcription factors in macrophages, which require diverse signal–dependent transcription factors to establish the enhancer landscape. Not only did we show that transcription factors regulated by the local environment play essential roles in establishing and maintaining tissue–specific functions of macrophages, but also that more complex stimuli can re–direct and combine signal–dependent transcription factors to establish new enhancers, not observed under the single stimuli. Using natural genetic variation as a mutagenesis screen allowed us to estimate the involvement of about 100 transcription factors in shaping the enhancer landscape, as well as to uncover a new layer of transcription regulation due to highly interconnected clusters of concordantly bound transcription factors.
macrophages, enhancers, natural genetic variation, transcription factor, next-generation sequencing
Link, Verena Maria
2018
Englisch
Universitätsbibliothek der Ludwig-Maximilians-Universität München
Link, Verena Maria (2018): Analysis of the epigenetic landscape in murine macrophages. Dissertation, LMU München: Fakultät für Biologie
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Abstract

Macrophages are cells of the innate immune system and play essential roles in the regulation of inflammatory responses in all parts of the body. Furthermore, macrophages are also involved in different tissue–specific functions and maintenance of the tissue homeostasis. These functions are controlled by the epigenetic landscape, consisting of promoters and enhancers that together regulate gene expression. Enhancers are stretches of regulatory genomic sequences in the non–coding regions of the genome that can be bound by lineage– determining transcription factors. These enhancers can loop in three–dimensional space to be in close proximity to promoters and contribute to the regulation of gene expression. Previous studies suggest that there are about 1 million enhancers in the mammalian genome, of which only about 30,000 – 40,000 are selected in each specific cell type. This dissertation studies the regulation of the epigenetic landscape of murine macrophages by utilizing different tissue macrophages, different complex and simple stimuli, as well as natural genetic variation as a mutagenesis screen. The overarching research question of this dissertation is to understand how the enhancer landscape in macrophages gets selected and regulated in order to control gene expression. In more detail, the main questions answered in this dissertation are: What are the epigenetic mechanisms that are responsible for tissue–specific functions? How do complex stimuli change the epigenetic landscape of macrophages in comparison to simple stimuli? How does natural genetic variation influence the epigenetic landscape and gene expression in murine macrophages? In Chapter 1 (Gosselin, D., Link, V. M., Romanoski, C. E. et al. (2014) appeared in Cell) we investigate the influence of the tissue environment on the epigenetic landscape in mouse macrophages. We compare macrophages residing in the brain (microglia) with macrophages from the peritoneal cavity by measuring mRNA expression, as well as enhancer activation (H3K4me2, H3K27ac, and PU.1). We find highly expressed genes unique to one population of macrophages, which correlates well with the activity signature at enhancers in the corresponding cells. By analyzing the enhancer landscape, we find that the macrophage lineage–determining transcription factor PU.1 plays a key role in establishing the enhancer repertoire, creating a common, macrophage–specific enhancer landscape. Furthermore, expression of tissue–specific transcription factors in collaboration with PU.1 drives a subset of tissue–specific enhancers regulating the differences in gene expression between different tissue–specific macrophage populations. In Chapter 2 (Eichenfield, D. Z., Troutman, D. T., Link, V. M. et al. (2016) appeared in eLife) we investigate the effect of complex stimuli onto the epigenetic landscape in macrophages on the example of wounds. Stimulation of macrophages with homogenated tissue to mimic a wound environment shows a unique pattern of gene expression, which is different from gene expression patterns found after single stimuli (e.g. LPS, IL–4 etc.). To gain insight into the regulation of the enhancer landscape after complex stimuli, we compare the epigenome after single stimuli and tissue homogenate and find substantial differences in enhancer selection and activation. We find that the complex damage signal promotes co–localization of several signal–dependent transcription factors to enhancers not observed under the single stimuli. Therefore, more complex polarizations of cells lead to new combinations of signal–dependent transcription factors and an epigenetic landscape different than observed with single stimuli. In Chapter 3 (Link et al. (2018b) appeared in bioRxiv) MARGE (Mutation Analysis for Regulatory Genomic Elements) is presented, a new method to analyze the effect of natural genetic variation on transcription factor binding and open chromatin. MARGE provides a suite of software tools that integrates genome–wide genetic variation data (including insertions and deletions) with epigenetic data. It provides software to create custom genomes based on a reference genome and variation data, to shift coordinates between different custom genomes, as well as do downstream ChIP–seq analysis. The main algorithm in MARGE analyzes if mutations in transcription factor binding motifs are significantly affecting transcription factor binding or open chromatin. MARGE provides a pairwise comparison, in which the significance of each motif is calculated with a student’s t–test. It compares the transcription factor binding distribution of each mutated motif in individual one with the distribution in individual two. For a more general approach that allows comparisons of many individuals MARGE implements a linear mixed model, modeling transcription factor binding with fixed effects motif existence and random effects locus and genotype. The development of this software allows in depth analysis of genetic variation data in combination with epigenetic data. In Chapter 4 (Link et al. (2018a) under review in Cell) we analyze the effect of natural genetic variation in five diverse strains of mice on the epigenetic landscape. We choose three well–known laboratory inbred mouse strains, as well as two very diverse wild–derived inbred mouse strains. We investigate the enhancer landscape, open chromatin and binding of the most important macrophage lineage–determining transcription factors. We observe substantial strain–specific differences in gene expression of which the majority can be explained by cis–regulatory elements. Application of MARGE onto the transcription factor binding data reveals roles of about 100 transcription factors in establishing the enhancer repertoire in macrophages. Unexpectedly, we find that a substantial fraction of strain– specific DNA binding of transcription factors cannot be explained by local mutations. Investigation of this phenomenon in more detail shows highly interconnected clusters of transcription factors that reside within topologically associating domains. These interconnected clusters are highly correlated with activation of enhancers and gene expression of the nearest gene, uncovering a new layer of transcriptional regulation. In Chapter 5, I briefly discuss additional contributions to the field of macrophage biology I made during my Ph.D. Namely, I was involved in two additional projects. In the first project (Pirzgalska et al. (2017) appeared in Nature Medicine) we identify sympathetic neuron–associated macrophages (SAM) that import and degrade norepinephrine via expression of solute carrier family 6 member 2 (Slc6a2) and monoamine oxidase A (MAOa). We demonstrate that SAM–mediated clearance of extracellular norepinephrine contributes to obesity and we show the relevance of this finding in humans, as we found that SAMs are also present in human tissues. The second project (Oishi et al. (2017) appeared in Cell Metabolism) studies the role of nuclear receptors (LXR and SREBP) in induction of anti–inflammatory fatty acids. We find that right after stimulation of TLR4 (during the induction phase) NF–kB dependent genes are upregulated, whereas LXR dependent genes are repressed. This leads to activation of SREBP1, which drives the expression of enzymes involved in mono–unsaturated and omega–3 polyunsaturated fatty acid biosynthesis. The fatty acids produced by these enzymes repress inflammatory genes under the control of NF–kB and the inflammatory signal gets resolved. In summary, my studies used a combination of experimental and computational approaches to investigate the effect of tissue–environment and factors, complex stimuli and natural genetic variation on the epigenetic landscape in macrophages. These studies broadened our understanding of the regulation of gene expression by the epigenetic landscape substantially. We showed that there is a core set of lineage–determining transcription factors in macrophages, which require diverse signal–dependent transcription factors to establish the enhancer landscape. Not only did we show that transcription factors regulated by the local environment play essential roles in establishing and maintaining tissue–specific functions of macrophages, but also that more complex stimuli can re–direct and combine signal–dependent transcription factors to establish new enhancers, not observed under the single stimuli. Using natural genetic variation as a mutagenesis screen allowed us to estimate the involvement of about 100 transcription factors in shaping the enhancer landscape, as well as to uncover a new layer of transcription regulation due to highly interconnected clusters of concordantly bound transcription factors.