Nascent RNA sequencing reveals a dynamic global transcriptional response at genes and enhancers to the natural medicinal compound celastrol

Most studies of responses to transcriptional stimuli measure changes in cellular mRNA concentrations. By sequencing nascent RNA instead, it is possible to detect changes in transcription in minutes rather than hours, and thereby distinguish primary from secondary responses to regulatory signals. Here, we describe the use of PRO-seq to characterize the immediate transcriptional response in human cells to celastrol, a compound derived from traditional Chinese medicine that has potent anti-inflammatory, tumor-inhibitory and obesity-controlling effects. Our analysis of PRO-seq data for K562 cells reveals dramatic transcriptional effects soon after celastrol treatment at a broad collection of both coding and noncoding transcription units. This transcriptional response occurred in two major waves, one within 10 minutes, and a second 40-60 minutes after treatment. Transcriptional activity was generally repressed by celastrol, but one distinct group of genes, enriched for roles in the heat shock response, displayed strong activation. Using a regression approach, we identified key transcription factors that appear to drive these transcriptional responses, including members of the E2F and RFX families. We also found sequence-based evidence that particular TFs drive the activation of enhancers. We observed increased polymerase pausing at both genes and enhancers, suggesting that pause release may be widely inhibited during the celastrol response. Our study demonstrates that a careful analysis of PRO-seq time course data can disentangle key aspects of a complex transcriptional response, and it provides new insights into the activity of a powerful pharmacological agent

MapCap allows high-resolution detection and differential expression analysis of transcription start sites

The position, shape and number of transcription start sites (TSS) are critical determinants of gene regulation. Most methods developed to detect TSSs and study promoter usage are, however, of limited use in studies that demand quantification of expression changes between two or more groups. In this study, we combine high-resolution detection of transcription start sites and differential expression analysis using a simplified TSS quantification protocol, MAPCap (Multiplexed Affinity Purification of Capped RNA) along with the software icetea . Applying MAPCap on developing Drosophila melanogaster embryos and larvae, we detected stage and sex-specific promoter and enhancer activity and quantify the effect of mutants of maleless (MLE) helicase at X-chromosomal promoters. We observe that MLE mutation leads to a median 1.9 fold drop in expression of X-chromosome promoters and affects the expression of several TSSs with a sexually dimorphic expression on autosomes. Our results provide quantitative insights into promoter activity during dosage compensation

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

Computational studies of RNA modification-dependent RNA binding protein networks

The covalent modification of RNA nucleotides is a powerful layer of post-transcriptional control of gene expression across the tree of life. Historically, only abundant modifications on abundant RNAs such as tRNA and rRNA could be studied, due to methodological limitations. In the past decade, leaps forward in biochemistry and high throughput sequencing methods have enabled mapping of RNA modifications across all RNA species. In particular this thesis focuses on the most abundant internal modification of mRNA, N6-methyladenosine (m6A), and how RNA binding proteins (RBPs) interact with RNA modifications to impact RNA life cycle. Alongside these experimental developments have come new computational challenges. Integration of many datasets must be approached carefully, with a view to extract as much biological information as possible. Throughout this work I describe the development of open source computational tools for the analysis and visualisation of CLIP data. A computational pipeline based on hierarchical pre-mapping steps enables accurate quantification of non-coding RNAs from individual nucleotide resolution crosslinking and immunoprecipitation (iCLIP) datasets. Using the pipeline I describe novel tRNA binding for the DEAH-box helicase DDX3X and identify widespread binding of NSun2 and Trmt2A to pre-tRNAs. In collaboration with the lab of Prof. Folkert van Werven, I integrate m6A miCLIP with m6A-reader protein iCLIP data, alongside functional datasets in WT and methyltransferase deletion conditions in order to uncover the role of m6A in early budding yeast meiosis. Surprisingly, we find that the sole yeast m6A-binding protein, Pho92p, binds in both an m6A-dependent and an m6A-independent manner. m6A-dependent Pho92p binding partners are implicated in mRNA decay coupled to translation. Taken together I present powerful computational tools that will be of use to the wider community, alongside the interesting biological insights they have already enabled

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

Post-transcriptional Regulation through Long Noncoding RNAs (lncRNAs)

This book is a collection of eight articles, of which seven are reviews and one is a research paper, that together form a Special Issue that describes the roles that long noncoding RNAs (lncRNA) play in gene regulation at a post-transcriptional level

Computational Analysis of the Post-Transcriptional Gene Regulatory Network.

Across eukaryotic organisms, specific and coordinated interactions between protein-coding mRNAs, small regulatory RNAs, and a growing collection of RNA-binding proteins (RBPs) have emerged as major components orchestrating post-transcriptional gene regulation (PTGR). High-throughput sequencing technologies have dramatically accelerated our ability to probe the vast number of RNA:RNA and RBP:RNA interactions, which are the molecular drivers of PTGR. These efforts have generated an unprecedented quantity of data, placing higher demands on computational analyses to inform biological mechanisms and define underlying rules of PTGR. In this dissertation, I apply well-established computational tools and develop novel bioinformatic approaches to mine deep sequencing datasets to achieve the following aims. 1) I elucidate the biogenesis mechanism and downstream targets of the conserved piRNA class of small RNAs, which are required for fertility in Caenorhabditis elegans and higher metazoans. I define sex-specific piRNA subclasses that target unique sets of genes required for germline development. 2) I characterize the global dynamics of RBP:RNA interactions in the budding yeast Saccharomyces cerevisiae. I reveal that RBP binding explains over 40% of conservation at 3' untranslated regions, and I uncover pervasive binding of RBPs to not only single-stranded RNAs but also double-stranded RNAs, supporting a novel paradigm of RBPs targeting highly structured RNAs. Over one-third of RBP:RNA interactions are significantly altered under two environmental stress conditions, suggesting that PTGR is highly responsive to stress adaptation. 3) I identify RNA targets and propose biological mechanisms of PTGR for the conserved Pumilio family of RBPs in yeast. For example, I discovered a dual-regulatory mode of binding for Puf3p and Puf4p that is linked to both sequence and structure motifs. 4) Finally, I propose PTGR mechanisms for PUF-9- and microRNA-mediated co-regulation of developmental timing in C. elegans, and how LARP1 binding to translation machinery-encoding genes regulates mTOR signaling in human cell lines. Dysregulation of small RNA pathways and RBP-mediated processes has emerged as an important determinant in human disorders including cancer and neuromuscular disorders. Therefore, characterization of fundamental mechanisms of PTGR promises to enrich our understanding of the complex interactions governing eukaryotic gene expression and offers insights into the development of targeted therapies.PHDBioinformaticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/111339/1/mafree_1.pd

Genome-scale transcriptomic and epigenomic analysis of stem cells

Embryonic stem cells (ESCs) are a special type of cell marked by two key properties: The capacity to create an unlimited number of identical copies of themselves (self-renewal) and the ability to give rise to differentiated progeny that can contribute to all tissues of the adult body (pluripotency). Decades of past research have identified many of the genetic determinants of the state of these cells, such as the transcription factors Pou5f1, Sox2 and Nanog. Many other transcription factors and, more recently, epigenetic determinants like histone modifications, have been implicated in the establishment, maintenance and loss of pluripotent stem cell identity. The study of these regulators has been boosted by technological advances in the field of high-throughput sequencing (HTS) that have made it possible to investigate the binding and modification of many proteins on a genome-wide level, resulting in an explosion of the amount of genomic data available to researchers. The challenge is now to effectively use these data and to integrate the manifold measurements into coherent and intelligible models that will actually help to better understand the way in which gene expression in stem cells is regulated to maintain their precarious identity. In this thesis, I first explore the potential of HTS by describing two pilot studies using the technology to investigate global differences in the transcriptional profiles of different cell populations. In both cases, I was able to identify a number of promising candidates that mark and, possibly, explain the phenotypic and functional differences between the cells studied. The pilot studies highlighted a strong requirement for specialised software to deal with the analysis of HTS data. I have developed GeneProf, a powerful computational framework for the integrated analysis of functional genomics experiments. This software platform solves many recurring data analysis challenges and streamlines, simplifies and standardises data analysis work flows promoting transparent and reproducible methodologies. The software offers a graphical, user-friendly interface and integrates expert knowledge to guide researchers through the analysis process. All primary analysis results are supplemented with a range of informative plots and summaries that ease the interpretation of the results. Behind the scenes, computationally demanding tasks are handled remotely on a distributed network of high-performance computers, removing rate-limiting requirements on local hardware set-up. A flexible and modular software design lays the foundations for a scalable and extensible framework that will be expanded to address an even wider range of data analysis tasks in future. Using GeneProf, billions of data points from over a hundred published studies have been re-analysed. The results of these analyses are stored in an web-accessible database as part of the GeneProf system, building up an accessible resource for all life scientists. All results, together with details about the analysis procedures used, can be browsed and examined in detail and all final and intermediate results are available and can instantly be reused and compared with new findings. In an attempt to elucidate the regulatory mechanisms of ESCs, I use this knowledge base to identify high-confidence candidate genes relevant to stem cell characteristics by comparing the transcriptional profiles of ESCs with those of other cell types. Doing so, I describe 229 genes with highly ESC-specific transcription. I then integrate the expression data for these ES-specific genes with genome-wide transcription factor binding and histone modification data. After investigating the global characteristics of these "regulatory inputs", I employ machine learning methods to first cluster subgroups of genes with ESC-specific expression patterns and then to define a "regulatory code" that marks one of the subgroups based on their regulatory signatures. The tightly co-regulated core cluster of genes identified in this analysis contains many known members of the transcriptional circuitry of ESCs and a number of novel candidates that I deem worthy of further investigations thanks to their similarity to their better known counterparts. Integrating these candidates and the regulatory code that drives them into our models of the workings of ESCs might eventually help to refine the ways in which we derive, culture and manipulate these cells - with all its prospective benefits to research and medicine

Putting the Pieces Together: Exons and piRNAs: A Dissertation

Analysis of gene expression has undergone a technological revolution. What was impossible 6 years ago is now routine. High-throughput DNA sequencing machines capable of generating hundreds of millions of reads allow, indeed force, a major revision toward the study of the genome’s functional output—the transcriptome. This thesis examines the history of DNA sequencing, measurement of gene expression by sequencing, isoform complexity driven by alternative splicing and mammalian piRNA precursor biogenesis. Examination of these topics is framed around development of a novel RNA-templated DNA-DNA ligation assay (SeqZip) that allows for efficient analysis of abundant, complex, and functional long RNAs. The discussion focuses on the future of transcriptome analysis, development and applications of SeqZip, and challenges presented to biomedical researchers by extremely large and rich datasets