Fine-scale population epigenetic structure in relation to gastrointestinal parasite load in red grouse (Lagopus lagopus scotica)

Acknowledgements This study was funded by a BBSRC studentship (MA Wenzel) and NERC grants NE/H00775X/1 and NE/D000602/1 (SB Piertney). The authors are grateful to Mario Röder and Keliya Bai for fieldwork assistance; Alex Douglas for statistical advice; Tyler Stevenson, Heather Ritchie and three anonymous reviewers for helpful comments on manuscript drafts; and all estate owners, factors and keepers for access to field sites, most particularly MJ Taylor and Mike Nisbet (Airlie), Neil Brown (Allargue), RR Gledson and David Scrimgeour (Delnadamph), Andrew Salvesen and John Hay (Dinnet), Stuart Young and Derek Calder (Edinglassie), Kirsty Donald and David Busfield (Glen Dye), Neil Hogbin and Ab Taylor (Glen Muick), Alistair Mitchell (Glenlivet), Simon Blackett, Jim Davidson and Liam Donald (Invercauld) Richard Cooke and Fred Taylor (Invermark), Shaila Rao and Christopher Murphy (Mar Lodge), and Ralph Peters and Philip Astor (Tillypronie).Peer reviewedPublisher PD

Modeling DNA methylation dynamics with approaches from phylogenetics

Methylation of CpG dinucleotides is a prevalent epigenetic modification that is required for proper development in vertebrates, and changes in CpG methylation are essential to cellular differentiation. Genome-wide DNA methylation assays have become increasingly common, and recently distinct stages across differentiating cellular lineages have been assayed. How- ever, current methods for modeling methylation dynamics do not account for the dependency structure between precursor and dependent cell types. We developed a continuous-time Markov chain approach, based on the observation that changes in methylation state over tissue differentiation can be modeled similarly to DNA nucleotide changes over evolutionary time. This model explicitly takes precursor to descendant relationships into account and enables inference of CpG methylation dynamics. To illustrate our method, we analyzed a high-resolution methylation map of the differentiation of mouse stem cells into several blood cell types. Our model can successfully infer unobserved CpG methylation states from observations at the same sites in related cell types (90% correct), and this approach more accurately reconstructs missing data than imputation based on neighboring CpGs (84% correct). Additionally, the single CpG resolution of our methylation dynamics estimates enabled us to show that DNA sequence context of CpG sites is informative about methylation dynamics across tissue differentiation. Finally, we identified genomic regions with clusters of highly dynamic CpGs and present a likely functional example. Our work establishes a framework for inference and modeling that is well-suited to DNA methylation data, and our success suggests that other methods for analyzing DNA nucleotide substitutions will also translate to the modeling of epigenetic phenomena.Comment: 8 pages, 5 figure

Linking the Epigenome with Exposure Effects and Susceptibility: The Epigenetic Seed and Soil Model

The epigenome is a dynamic mediator of gene expression that shapes the way that cells, tissues, and organisms respond to their environment. Initial studies in the emerging field of “toxicoepigenetics” have described either the impact of an environmental exposure on the epigenome or the association of epigenetic signatures with the onset or progression of disease; however, the majority of these pioneering studies examined the relationship between discrete epigenetic modifications and the effects of a single environmental factor. Although these data provide critical blocks with which we construct our understanding of the role of the epigenome in susceptibility and disease, they are akin to individual letters in a complex alphabet that is used to compose the language of the epigenome. Advancing the use of epigenetic data to gain a more comprehensive understanding of the mechanisms underlying exposure effects, identify susceptible populations, and inform the next generation risk assessment depends on our ability to integrate these data in a way that accounts for their cumulative impact on gene regulation. Here we will review current examples demonstrating associations between the epigenetic impacts of intrinsic factors, such as such as age, genetics, and sex, and environmental exposures shape the epigenome and susceptibility to exposure effects and disease. We will also demonstrate how the “epigenetic seed and soil” model can be used as a conceptual framework to explain how epigenetic states are shaped by the cumulative impacts of intrinsic and extrinsic factors and how these in turn determine how an individual responds to subsequent exposure to environmental stressors

Bioinformatic tools for analyzing epigenomic profiling data

Epigenetik, die Erforschung der biologischen Information in Genomen ausserhalb der DNA Sequenz, hat durch die rasche Entwicklung der Hochdurchsatz-Techniken besonders viele Impulse bekommen. Deshalb spielt die Bioinformatik eine wichtige Rolle bei der Analyse der ausserordentlich grossen Datenmengen und der Formulierung biologischer Hypothesen in der Epigenetik. DNA Methylierung ist ein wichtiger epigenetischer Parameter in der normalen und pathologischen Entwicklungsbiologie. Genomweite DNA Methylierungsprofile werden hauptsächlich durch Bisulfit-Konversion genomischer DNA erstellt, bei der unmethyliertes Cytosin (C) in Thymin (T) umgewandelt wird, gefolgt von Hochdurchsatz-Sequenzierung (BS-Seq). Die Umwandlung von C zu T erschwert die Zuordnung der Einzelsequenzen zum Referenzgenom in mehrerer Hinsicht. Ausserdem kann mit der herkömmlichen Technik die Heterogenität der DNA Methylierung in Material aus mehreren Zellen oder Geweben nicht berücksichtigt werden. Das beeinträchtigt die Genauigkeit bei der Bestimmung der genomischen Methylierungsmuster. Deshalb sind neue bioinformatische Methoden erforderlich, um zellspezifische DNA Methylierung zu erkennen. Aufgrund der schnell wachsenden Datenmengen ist die gleichzeitige Erfassung mehrerer epigenetischer Parameter in Form von Chromatineigenschaften in verschiedenen Proben, Bedingungen oder Organismen eine weitere Herausforderung und ein wenig bearbeitetes Gebiet der Bioinformatik, jedoch Voraussetzung zur Entdeckung eines chromatin-basierten epigenetischen Codes. Vergleichende bioinformatische Ansätze werden hierbei durch unterschiedliche Verteilung und/oder Spannweite der Parameter erschwert. In dieser Dissertation stelle ich von mir entwickelte bioinformatische Methoden zu diesen Themenbereichen vor und zeige deren Anwendung auf Daten aus dem Modellorganismus Arabidopsis thaliana. Als erstes habe ich ein neues und hochauflösendes Verfahren zur Analyse von BS-Seq Daten entwickelt, welches auf dem „Smith-Waterman local alignment“ Prinzip beruht. Zweitens habe ich einen effizienten Algorithmus konzipiert, um den Grad der Heterogenität in BS-Seq Daten zu bestimmen. Drittens habe ich eine Methode entworfen, mit der man zahlreiche epigenetische Parameter und deren genomweite Profile zusammenfassen, vergleichen und optisch darstellen kann, um die weitere Analyse und Interpretation zu erleichtern.Epigenetics, investigating the biological information of genomes not only encoded in the DNA sequence, has become a hot topic boosted by rapid development of high-throughput technologies. In the light of that, bioinformatics plays an important role in analyzing the massive datasets to further examine the data and to formulate biological hypotheses. DNA methylation is one important epigenetic mark in developmental and disease bi- ology. One widely-used technique to profile genome-wide DNA methylation is based on bisulfite conversion of unmethylated cytosines (C) to thymines (T), followed by deep sequencing technology, called BS-Seq data. The C-T conversion raises a number of challenges in mapping the bisulfite-converted short reads to the reference genome. Besides, the current technology cannot consider the heterogeneity of DNA methylation from mixtures of cells. This affects the accuracy of estimating the DNA methylation patterns in the genome. Hence, new bioinformatics methods are required to estimate the cell-type specific DNA methylation. Integrating multiple datasets of profiling epigenetic/chromatin marks for many different samples, conditions and organisms is also an underdeveloped field in bioinformatics, given the rapid growth of biological data. It is essential for further studies to find epigenomic patterns like a chromatin-based epigenetic code. However, comparative bioinformatics procedure is difficult because of different distributions or different scales of the marks. In this thesis, I have developed bioinformatics tools and applied them to the model organism, Arabidopsis thaliana. First, I have implemented a new and sensitive analysis tool for analyzing BS-Seq data based on Smith-Waterman local alignment mapping. Second, I have developed an efficient algorithm to deal with heterogeneity in DNA methylation data derived from BS-Seq. Finally, I have suggested a method to integrate epigenomic signals from multiple genome-wide profiling data for further data mining purpose, e.g. epigenetic signature discovery

Analyzing epigenomic data in a large-scale context

While large amounts of epigenomic data are publicly available, their retrieval in a form suitable for downstream analysis is a bottleneck in current research. In a typical analysis, users are required to download huge files that span the entire genome, even if they are only interested in a small subset (e.g., promoter regions) or an aggregation thereof. Moreover, complex operations on genome-level data are not always feasible on a local computer due to resource limitations. The DeepBlue Epigenomic Data Server mitigates this issue by providing a robust server that affords a powerful API for searching, filtering, transforming, aggregating, enriching, and downloading data from several epigenomic consortia. Furthermore, its main component implements scalable data storage and Manipulation methods that scale with the increasing amount of epigenetic data, thereby making it the ideal resource for researchers that seek to integrate epigenomic data into their analysis workflow. This work also presents companion tools that utilize the DeepBlue API to enable users not proficient in scripting or programming languages to analyze epigenomic data in a user-friendly way: (i) an R/Bioconductor package that integrates DeepBlue into the R analysis workflow. The extracted data are automatically converted into suitable R data structures for downstream analysis and visualization within the Bioconductor frame- work; (ii) a web portal that enables users to search, select, filter and download the epigenomic data available in the DeepBlue Server. This interface provides elements, such as data tables, grids, data selections, developed for empowering users to find the required epigenomic data in a straightforward interface; (iii) DIVE, a web data analysis tool that allows researchers to perform large-epigenomic data analysis in a programming-free environment. DIVE enables users to compare their datasets to the datasets available in the DeepBlue Server in an intuitive interface, which summarizes the comparison of hundreds of datasets in a simple chart. Furthermore, these tools are integrated, being capable of sharing results among themselves, creating a powerful large-scale epigenomic data analysis environment. The DeepBlue Epigenomic Data Server and its ecosystem was well received by the International Human Epigenome Consortium and already attracted much attention by the epigenomic research community with currently 160 registered users and more than three million anonymous workflow processing requests since its release.Während große Mengen epigenomischer Daten öffentlich verfügbar sind, ist ihre Abfrage in einer für die Downstream-Analyse geeigneten Form ein Engpass in der aktuellen Forschung. Bei einer typischen Analyse müssen Benutzer riesige Dateien herunterladen, die das gesamte Genom umfassen, selbst wenn sie nur an einer kleinen Teilmenge (z.B., Promotorregionen) oder einer Aggregation davon interessiert sind. Darüber hinaus sind komplexe Vorgänge mit Daten auf Genomebene aufgrund von Ressourceneinschränkungen auf einem lokalen Computer nicht immer möglich. Der DeepBlue Epigenomic Data Server behebt dieses Problem, indem er eine leistungsstarke API zum Suchen, Filtern, Umwandeln, Aggregieren, Anreichern und Herunterladen von Daten verschiedener epigenomischer Konsortien bietet. Darüber hinaus implementiert der DeepBlue-Server skalierbare Datenspeicherungs- und manipulationsmethoden, die der zunehmenden Menge epigenetischer Daten gerecht werden. Dadurch ist der DeepBlue Server ideal für Forscher geeignet, die die aktuellen epigenomischen Ressourcen in ihren Analyse-Workflow integrieren möchten. In dieser Arbeit werden zusätzlich Begleittools vorgestellt, die die DeepBlue-API verwenden, um Benutzern, die sich mit Scripting oder Programmiersprachen nicht auskennen, die Möglichkeit zu geben, epigenomische Daten auf benutzerfreundliche Weise zu analysieren: (i) ein R/ Bioconductor-Paket, das DeepBlue in den R-Analyse-Workflow integriert. Die extrahierten Daten werden automatisch in geeignete R-Datenstrukturen für die Downstream-Analyse und Visualisierung innerhalb des Bioconductor-Frameworks konvertiert; (ii) ein Webportal, über das Benutzer die auf dem DeepBlue Server verfügbaren epigenomischen Daten suchen, auswählen, filtern und herunterladen können. Diese Schnittstelle bietet Elemente wie Datentabellen, Raster, Datenselektionen, mit denen Benutzer die erforderlichen epigenomischen Daten in einer einfachen Schnittstelle finden können; (iii) DIVE, ein Webdatenanalysetool, mit dem Forscher umfangreiche epigenomische Datenanalysen in einer programmierungsfreien Umgebung durchführen können. Mit DIVE können Benutzer ihre Datensätze mit den im Deep- Blue Server verfügbaren Datensätzen in einer intuitiven Benutzeroberfläche vergleichen. Dabei kann der Vergleich hunderter Datensätze in einem Diagramm ausgedrückt werden. Aufgrund der großen Datenmenge, die in DIVE verfügbar ist, werden Methoden bereitgestellt, mit denen die ähnlichsten Datensätze für eine vergleichende Analyse vorgeschlagen werden können. Alle zuvor genannten Tools sind miteinander integriert, so dass sie die Ergebnisse untereinander austauschen können, wodurch eine leistungsstarke Umgebung für die Analyse epigenomischer Daten entsteht. Der DeepBlue Epigenomic Data Server und sein Ökosystem wurden vom International Human Epigenome Consortium äußerst gut aufgenommen und erreichten seit ihrer Veröffentlichung große Aufmerksamkeit bei der epigenomischen Forschungsgemeinschaft mit derzeit 160 registrierten Benutzern und mehr als drei Millionen anonymen Verarbeitungsanforderungen

Linking epigenetics and biological conservation: Towards a conservation epigenetics perspective

International audience1. Biodiversity conservation is a global issue where the challenge is to integrate all levels of biodiversity to ensure the long-term evolutionary potential and resilience of biological systems. Genetic approaches have largely contributed to conservation biology by defining "conservation entities" accounting for their evolutionary history and adaptive potential, the so-called evolutionary significant units (ESUs). Yet, these approaches only loosely integrate the short-term ecological history of organisms. 2. Here, we argue that epigenetic variation, and more particularly DNA methylation, represents a molecular component of biodiversity that directly links the genome to the environment. As such, it provides the required information on the ecological background of organisms for an integrative field of conservation biology. 3. We synthesize knowledge about the importance of epigenetic mechanisms in (a) orchestrating fundamental development alternatives in organisms, (b) enabling individuals to respond in real-time to selection pressures and (c) improving ecosystem stability and functioning. 4. Using practical examples in conservation biology, we illustrate the relevance of DNA methylation (a) as biomarkers of past and present environmental stress events as well as biomarkers of physiological conditions of individuals; (b) for documenting the ecological structuring/clustering of wild populations and hence for better integrating ecology into ESUs; (c) for improving conservation transloca-tions; and (d) for studying landscape functional connectivity. 5. We conclude that an epigenetic conservation perspective will provide environmental managers the possibility to refine ESUs, to set conservation plans taking into account the capacity of organisms to rapidly cope with environmental changes, and hence to improve the conservation of wild populations. K E Y W O R D S conservation, DNA methylation, ecological timescales, epigenetic, evolutionary significant unit

G9a inhibition potentiates the anti-tumour activity of DNA double-strand break inducing agents by impairing DNA repair independent of p53 status.

Cancer cells often exhibit altered epigenetic signatures that can misregulate genes involved in processes such as transcription, proliferation, apoptosis and DNA repair. As regulation of chromatin structure is crucial for DNA repair processes, and both DNA repair and epigenetic controls are deregulated in many cancers, we speculated that simultaneously targeting both might provide new opportunities for cancer therapy. Here, we describe a focused screen that profiled small-molecule inhibitors targeting epigenetic regulators in combination with DNA double-strand break (DSB) inducing agents. We identify UNC0638, a catalytic inhibitor of histone lysine N-methyl-transferase G9a, as hypersensitising tumour cells to low doses of DSB-inducing agents without affecting the growth of the non-tumorigenic cells tested. Similar effects are also observed with another, structurally distinct, G9a inhibitor A-366. We also show that small-molecule inhibition of G9a or siRNA-mediated G9a depletion induces tumour cell death under low DNA damage conditions by impairing DSB repair in a p53 independent manner. Furthermore, we establish that G9a promotes DNA non-homologous end-joining in response to DSB-inducing genotoxic stress. This study thus highlights the potential for using G9a inhibitors as anti-cancer therapeutic agents in combination with DSB-inducing chemotherapeutic drugs such as etoposide.Research in the S.P.J. laboratory is funded by Cancer Research UK Program Grant C6/A18796 and the European Research Council (DDREAM) grant 268536-DDRREAM. Core infrastructure funding was provided by Cancer Research UK Grant C6946/ A14492 and Wellcome Trust Grant WT092096. S.P.J. receives a salary from the University of Cambridge, supplemented by Cancer Research UK. P.A. was financially supported by CRUK grant C6/ A11224 and ERC grant DDREAM.This is the final version of the article. It first appeared from Elsevier via http://dx.doi.org/10.1016/j.canlet.2016.07.00

Conserved epigenetic mechanisms could play a key role in regulation of photosynthesis and development-related genes during needle development of Pinus radiata

Needle maturation is a complex process that involves cell growth, differentiation and tissue remodelling towards the acquisition of full physiological competence. Leaf induction mechanisms are well known; however, those underlying the acquisition of physiological competence are still poorly understood, especially in conifers. We studied the specific epigenetic regulation of genes defining organ function (PrRBCS and PrRBCA) and competence and stress response (PrCSDP2 and PrSHMT4) during three stages of needle development and one de-differentiated control. Gene-specific changes in DNA methylation and histone were analysed by bisulfite sequencing and chromatin immunoprecipitation (ChIP). The expression of PrRBCA and PrRBCS increased during needle maturation and was associated with the progressive loss of H3K9me3, H3K27me3 and the increase in AcH4. The maturation-related silencing of PrSHMT4 was correlated with increased H3K9me3 levels, and the repression of PrCSDP2, to the interplay between AcH4, H3K27me3, H3K9me3 and specific DNA methylation. The employ of HAT and HDAC inhibitors led to a further determination of the role of histone acetylation in the regulation of our target genes. The integration of these results with high-throughput analyses in Arabidopsis thaliana and Populus trichocarpa suggests that the specific epigenetic mechanisms that regulate photosynthetic genes are conserved between the analysed species

The Human Blood Transcriptome in a Large Population Cohort and Its Relation to Aging and Health

Background: The blood transcriptome is expected to provide a detailed picture of an organism’s physiological state with potential outcomes for applications in medical diagnostics and molecular and epidemiological research.We here present the analysis of blood specimens of 3,388 adult individuals, together with phenotype characteristics such as disease history, medication status, lifestyle factors, and body mass index (BMI). The size and heterogeneity of this data challenges analytics in terms of dimension reduction, knowledge mining, feature extraction, and data integration. Methods: Self-organizing maps (SOM)-machine learning was applied to study transcriptional states on a population-wide scale. This method permits a detailed description and visualization of the molecular heterogeneity of transcriptomes and of their association with different phenotypic features. Results: The diversity of transcriptomes is described by personalized SOM-portraits, which specify the samples in terms of modules of co-expressed genes of different functional context. We identified two major blood transcriptome types where type 1 was found more in men, the elderly, and overweight people and it upregulated genes associated with inflammation and increased heme metabolism, while type 2 was predominantly found in women, younger, and normal weight participants and it was associated with activated immune responses, transcriptional, ribosomal, mitochondrial, and telomere-maintenance cell-functions. We find a striking overlap of signatures shared by multiple diseases, aging, and obesity driven by an underlying common pattern, which was associated with the immune response and the increase of inflammatory processes. Conclusions: Machine learning applications for large and heterogeneous omics data provide a holistic view on the diversity of the human blood transcriptome. It provides a tool for comparative analyses of transcriptional signatures and of associated phenotypes in population studies and medical applications