Metilação diferencial de DNA no envelhecimento: exploração in silico utilizando dados de elevado rendimento

The emergence of high-throughput methodologies after the conclusion of the Human Genome Project has brought genomic and epigenomic wide studies to the forefront of current research of biological and biomedical knowledge. Currently, the focus in genetic mutations as primary cause of certain disorders is not so relevant as before, since it was demonstrated that epigenetic mechanisms are involved in cellular programming and gene regulation providing adaptive variants of a given gene to a changing environment with an association to cellular differentiation. The research in the DNA methylation field has already revealed essential facts as the existence of methylation in CpG islands and alternative contexts that influence gene expression in tissue-specific manner. The influence of lifestyle choices in aging processes has also been related to methylome variations. And, in the case of cancer, the cooperation of epigenetic and genetic information is essential to understand the progress of cancer development as well as the silencing of key regulatory genes. An overall hypomethylation in cancer genome leads to oncogene activation whereas hypermethylation in specific regions is associated with silencing of tumour suppressor genes. For that reason, the research for new therapeutic approaches to cancer and aging is a current issue of the scientific community that work in the epigenomic field. In order to contribute to the study of mammalian epigenomes during lifespans, this research focused on the usage of public databases datasets to further investigation about DNA methylation across aged individuals in order to extract tissue-specific markers related with healthy aging. The validation of results was made through the usage of samples, form healthy individuals with good or bad cognitive performances, available in iBiMED. In both situations the genes ELOVL2 (cg16867657) and FHL2 (cg06639320) were identified as good markers of ageO aparecimento de metodologias de sequenciação de elevado rendimento após a conclusão do Projeto do Genoma Humano foi um avanço fundamental para a pesquisa biológica e biomédica na área da genómica. Embora as mutações genéticas tenham sido durante décadas o foco principal na causa de certas desordens, atualmente demonstrou-se que os mecanismos epigenéticos estão envolvidos na programação celular e na regulação genética, providenciando variações adaptativas do mesmo gene a um determinado ambiente e possuindo ainda uma associação direta com a diferenciação celular. O desenvolvimento científico no campo da metilação de DNA revela atualmente factos essenciais na biologia molecular, como a existência de metilação nas ilhas CpG e em contextos alternativos que influenciam a expressão genética nos diferentes tecidos humanos. Para além disso, a influência dos estilos de vida no processo de envelhecimento já demonstrou estar relacionada com o estado do epigenoma, nomeadamente com as variações no metiloma humano. No caso do cancro, a cooperação dos fatores genéticos e epigenéticos é essencial para a compreensão do desenvolvimento desta patologia no organismo humano nomeadamente através do silenciamento de genes reguladores essenciais. Uma hipometilação global no genoma do cancro conduz geralmente a uma ativação de oncogenes enquanto que hipermetilações localizadas estão associadas com o silenciamento de genes supressores de tumores. Por estes motivos, o desenvolvimento de novas terapias para o cancro ou o envelhecimento torna-se um tópico de interesse pela comunidade científica da área da epigenómica. Com o objetivo de desenvolver estes temas e melhorar a determinação de variações globais no epigenoma humano, esta investigação desenvolveu-se com base na utilização de dados de bases de dados públicas de indivíduos saudaveis de forma a extrair marcadores de metilação diferenciada em variados tecidos ao longo do envelhecimento saudável. O projeto foi validado através da utilização de amostras saúdaveis e de indivíduos com boas ou más performances cognitivas disponíveis no iBiMED. Em ambas as situações os genes ELOVL2 (cg16867657) e FHL2 (cg06639320) foram identificados como bons marcadores da idade dos indivíduosMestrado em Biotecnologi

Cracking the Code of Human Diseases Using Next-Generation Sequencing: Applications, Challenges, and Perspectives

Next-generation sequencing (NGS) technologies have greatly impacted on every field of molecular research mainly because they reduce costs and increase throughput of DNA sequencing. These features, together with the technology's flexibility, have opened the way to a variety of applications including the study of the molecular basis of human diseases. Several analytical approaches have been developed to selectively enrich regions of interest from the whole genome in order to identify germinal and/or somatic sequence variants and to study DNA methylation. These approaches are now widely used in research, and they are already being used in routine molecular diagnostics. However, some issues are still controversial, namely, standardization of methods, data analysis and storage, and ethical aspects. Besides providing an overview of the NGS-based approaches most frequently used to study the molecular basis of human diseases at DNA level, we discuss the principal challenges and applications of NGS in the field of human genomics

BRCA1 promoter methylation: the influence on gene expression and the effect of long term drug treatment

Breast cancer is the most common type of cancer among woman all over the world, with over 1.67 million new cases in 2012. Heritable breast cancer is closely linked to mutations in the tumor suppressor gene BRCA1, with up to 80% lifetime risk for developing breast cancer among women harboring a mutation in this gene. However, most breast cancer cases are sporadic and somatic mutations of the BRCA1 gene are rare. Furthermore, some tumors show BRCAness, despite being BRCA1 wild-type. Thus, it is of great interest to assess alternative mechanisms for inactivation of the BRCA1 gene, and addressing the missing causality of many breast cancers. Furthermore, it is of great interest to assess the mechanisms of drug resistance, a major challenge in cancer treatment today, where BRCA1 may play an important role. The overall aim of this thesis is to increase the understanding of the biological role of BRCA1 promoter methylation in breast cancer. Three sub aims for the present project were outlined; 1) Quantify the BRCA1 α and β transcripts and the total BRCA1 protein levels and relate the expression data to the methylation pattern in the BRCA1 promoter region in a panel of breast cancer cell lines. 2) Investigate how the total expression levels, as well as the ratio between the α and β transcripts are affected by alterations in the α and β promoter region of BRCA1, including methylation of specific CpGs as well as the polymorphisms rs71361504 and rs799905. 3) Investigate the effect of long term treatment with the drugs olaparib and doxorubicin on the BRCA1 promoter methylation in SKBR3 breast cancer cells as a potential cause of drug resistance. The study showed a weak correlation between BRCA1 methylation pattern and BRCA1 mRNA expression. No correlation was observed between the methylation pattern and protein expressed or between mRNA levels and protein expression. Analysis of polymorphisms rs71361504 and rs799905 found in the BRCA1 promoter showed that the two variants seemed to counter-balance each other, giving equal luciferase expression levels when differing in two positions and lower expression levels when intermediate variants were studied. Finally, long term drug treatment of the cell line SKBR3 did not alter the methylation levels in the BRCA1 promoter, consequently demethylation seems not to be a mechanism for drug resistance in the experimental setup tested in this study.Masteroppgåve i molekylærbiologiMAMN-MOLMOL39

The mapping task and its various applications in next-generation sequencing

The aim of this thesis is the development and benchmarking of computational methods for the analysis of high-throughput data from tiling arrays and next-generation sequencing. Tiling arrays have been a mainstay of genome-wide transcriptomics, e.g., in the identification of functional elements in the human genome. Due to limitations of existing methods for the data analysis of this data, a novel statistical approach is presented that identifies expressed segments as significant differences from the background distribution and thus avoids dataset-specific parameters. This method detects differentially expressed segments in biological data with significantly lower false discovery rates and equivalent sensitivities compared to commonly used methods. In addition, it is also clearly superior in the recovery of exon-intron structures. Moreover, the search for local accumulations of expressed segments in tiling array data has led to the identification of very large expressed regions that may constitute a new class of macroRNAs. This thesis proceeds with next-generation sequencing for which various protocols have been devised to study genomic, transcriptomic, and epigenomic features. One of the first crucial steps in most NGS data analyses is the mapping of sequencing reads to a reference genome. This work introduces algorithmic methods to solve the mapping tasks for three major NGS protocols: DNA-seq, RNA-seq, and MethylC-seq. All methods have been thoroughly benchmarked and integrated into the segemehl mapping suite. First, mapping of DNA-seq data is facilitated by the core mapping algorithm of segemehl. Since the initial publication, it has been continuously updated and expanded. Here, extensive and reproducible benchmarks are presented that compare segemehl to state-of-the-art read aligners on various data sets. The results indicate that it is not only more sensitive in finding the optimal alignment with respect to the unit edit distance but also very specific compared to most commonly used alternative read mappers. These advantages are observable for both real and simulated reads, are largely independent of the read length and sequencing technology, but come at the cost of higher running time and memory consumption. Second, the split-read extension of segemehl, presented by Hoffmann, enables the mapping of RNA-seq data, a computationally more difficult form of the mapping task due to the occurrence of splicing. Here, the novel tool lack is presented, which aims to recover missed RNA-seq read alignments using de novo splice junction information. It performs very well in benchmarks and may thus be a beneficial extension to RNA-seq analysis pipelines. Third, a novel method is introduced that facilitates the mapping of bisulfite-treated sequencing data. This protocol is considered the gold standard in genome-wide studies of DNA methylation, one of the major epigenetic modifications in animals and plants. The treatment of DNA with sodium bisulfite selectively converts unmethylated cytosines to uracils, while methylated ones remain unchanged. The bisulfite extension developed here performs seed searches on a collapsed alphabet followed by bisulfite-sensitive dynamic programming alignments. Thus, it is insensitive to bisulfite-related mismatches and does not rely on post-processing, in contrast to other methods. In comparison to state-of-the-art tools, this method achieves significantly higher sensitivities and performs time-competitive in mapping millions of sequencing reads to vertebrate genomes. Remarkably, the increase in sensitivity does not come at the cost of decreased specificity and thus may finally result in a better performance in calling the methylation rate. Lastly, the potential of mapping strategies for de novo genome assemblies is demonstrated with the introduction of a new guided assembly procedure. It incorporates mapping as major component and uses the additional information (e.g., annotation) as guide. With this method, the complete mitochondrial genome of Eulimnogammarus verrucosus has been successfully assembled even though the sequencing library has been heavily dominated by nuclear DNA. In summary, this thesis introduces algorithmic methods that significantly improve the analysis of tiling array, DNA-seq, RNA-seq, and MethylC-seq data, and proposes standards for benchmarking NGS read aligners. Moreover, it presents a new guided assembly procedure that has been successfully applied in the de novo assembly of a crustacean mitogenome.Diese Arbeit befasst sich mit der Entwicklung und dem Benchmarken von Verfahren zur Analyse von Daten aus Hochdurchsatz-Technologien, wie Tiling Arrays oder Hochdurchsatz-Sequenzierung. Tiling Arrays bildeten lange Zeit die Grundlage für die genomweite Untersuchung des Transkriptoms und kamen beispielsweise bei der Identifizierung funktioneller Elemente im menschlichen Genom zum Einsatz. In dieser Arbeit wird ein neues statistisches Verfahren zur Auswertung von Tiling Array-Daten vorgestellt. Darin werden Segmente als exprimiert klassifiziert, wenn sich deren Signale signifikant von der Hintergrundverteilung unterscheiden. Dadurch werden keine auf den Datensatz abgestimmten Parameterwerte benötigt. Die hier vorgestellte Methode erkennt differentiell exprimierte Segmente in biologischen Daten bei gleicher Sensitivität mit geringerer Falsch-Positiv-Rate im Vergleich zu den derzeit hauptsächlich eingesetzten Verfahren. Zudem ist die Methode bei der Erkennung von Exon-Intron Grenzen präziser. Die Suche nach Anhäufungen exprimierter Segmente hat darüber hinaus zur Entdeckung von sehr langen Regionen geführt, welche möglicherweise eine neue Klasse von macroRNAs darstellen. Nach dem Exkurs zu Tiling Arrays konzentriert sich diese Arbeit nun auf die Hochdurchsatz-Sequenzierung, für die bereits verschiedene Sequenzierungsprotokolle zur Untersuchungen des Genoms, Transkriptoms und Epigenoms etabliert sind. Einer der ersten und entscheidenden Schritte in der Analyse von Sequenzierungsdaten stellt in den meisten Fällen das Mappen dar, bei dem kurze Sequenzen (Reads) auf ein großes Referenzgenom aligniert werden. Die vorliegende Arbeit stellt algorithmische Methoden vor, welche das Mapping-Problem für drei wichtige Sequenzierungsprotokolle (DNA-Seq, RNA-Seq und MethylC-Seq) lösen. Alle Methoden wurden ausführlichen Benchmarks unterzogen und sind in der segemehl-Suite integriert. Als Erstes wird hier der Kern-Algorithmus von segemehl vorgestellt, welcher das Mappen von DNA-Sequenzierungsdaten ermöglicht. Seit der ersten Veröffentlichung wurde dieser kontinuierlich optimiert und erweitert. In dieser Arbeit werden umfangreiche und auf Reproduzierbarkeit bedachte Benchmarks präsentiert, in denen segemehl auf zahlreichen Datensätzen mit bekannten Mapping-Programmen verglichen wird. Die Ergebnisse zeigen, dass segemehl nicht nur sensitiver im Auffinden von optimalen Alignments bezüglich der Editierdistanz sondern auch sehr spezifisch im Vergleich zu anderen Methoden ist. Diese Vorteile sind in realen und simulierten Daten unabhängig von der Sequenzierungstechnologie oder der Länge der Reads erkennbar, gehen aber zu Lasten einer längeren Laufzeit und eines höheren Speicherverbrauchs. Als Zweites wird das Mappen von RNA-Sequenzierungsdaten untersucht, welches bereits von der Split-Read-Erweiterung von segemehl unterstützt wird. Aufgrund von Spleißen ist diese Form des Mapping-Problems rechnerisch aufwendiger. In dieser Arbeit wird das neue Programm lack vorgestellt, welches darauf abzielt, fehlende Read-Alignments mit Hilfe von de novo Spleiß-Information zu finden. Es erzielt hervorragende Ergebnisse und stellt somit eine sinnvolle Ergänzung zu Analyse-Pipelines für RNA-Sequenzierungsdaten dar. Als Drittes wird eine neue Methode zum Mappen von Bisulfit-behandelte Sequenzierungsdaten vorgestellt. Dieses Protokoll gilt als Goldstandard in der genomweiten Untersuchung der DNA-Methylierung, einer der wichtigsten epigenetischen Modifikationen in Tieren und Pflanzen. Dabei wird die DNA vor der Sequenzierung mit Natriumbisulfit behandelt, welches selektiv nicht methylierte Cytosine zu Uracilen konvertiert, während Methylcytosine davon unberührt bleiben. Die hier vorgestellte Bisulfit-Erweiterung führt die Seed-Suche auf einem reduziertem Alphabet durch und verifiziert die erhaltenen Treffer mit einem auf dynamischer Programmierung basierenden Bisulfit-sensitiven Alignment-Algorithmus. Das verwendete Verfahren ist somit unempfindlich gegenüber Bisulfit-Konvertierungen und erfordert im Gegensatz zu anderen Verfahren keine weitere Nachverarbeitung. Im Vergleich zu aktuell eingesetzten Programmen ist die Methode sensitiver und benötigt eine vergleichbare Laufzeit beim Mappen von Millionen von Reads auf große Genome. Bemerkenswerterweise wird die erhöhte Sensitivität bei gleichbleibend guter Spezifizität erreicht. Dadurch könnte diese Methode somit auch bessere Ergebnisse bei der präzisen Bestimmung der Methylierungsraten erreichen. Schließlich wird noch das Potential von Mapping-Strategien für Assemblierungen mit der Einführung eines neuen, Kristallisation-genanntes Verfahren zur unterstützten Assemblierung aufgezeigt. Es enthält Mapping als Hauptbestandteil und nutzt Zusatzinformation (z.B. Annotationen) als Unterstützung. Dieses Verfahren ermöglichte die erfolgreiche Assemblierung des kompletten mitochondrialen Genoms von Eulimnogammarus verrucosus trotz einer vorwiegend aus nukleärer DNA bestehenden genomischen Bibliothek. Zusammenfassend stellt diese Arbeit algorithmische Methoden vor, welche die Analysen von Tiling Array, DNA-Seq, RNA-Seq und MethylC-Seq Daten signifikant verbessern. Es werden zudem Standards für den Vergleich von Programmen zum Mappen von Daten der Hochdurchsatz-Sequenzierung vorgeschlagen. Darüber hinaus wird ein neues Verfahren zur unterstützten Genom-Assemblierung vorgestellt, welches erfolgreich bei der de novo-Assemblierung eines mitochondrialen Krustentier-Genoms eingesetzt wurde

Epigenetic characterization of human hepatocyte subpopulations in context of complex metabolic diseases and during in vitro differentiation of hepatocyte-like cells

The comprehensive transcriptional and epigenetic characterization of human hepatocyte subpopulations is necessary to achieve a better understanding of regulatory processes in health and complex metabolic diseases as well as during in vitro differentiation. Based on integrative analysis of genome-wide sequencing data, this thesis aims to unravel hepatocyte heterogeneity in different biological contexts. A deeper understanding of spatial organization of cells in human tissues is an important challenge. Using a unique experimental set-up based on laser capture microdissection coupled to next generation sequencing, which preserves spatial orientation and still provides genome-wide data of well defined subpopulations, the first combined spatial analysis of transcriptomes and methylomes across three micro-dissected zones of human liver provides a wealth of new positional insights, both in health and in context of fatty liver disease. In addition, these spatial maps serve as reference for projection of single cell data into hepatic pseudospace, which is still a major challenge. Hence, a novel pseudospace inference approach, which considerably improves spatial reconstruction of single cells into tissue context, is demonstrated for human liver. Finally, the identification of underlying regulatory networks by integrative epigenomic analysis of in vitro differentiated hepatocyte-like cells contributes to the development of reasonable cell culture interventions to improve differentiation.Die umfassende transkriptionelle und epigenetische Charakterisierung humaner Leberzellsubpopulationen ist notwendig für die Aufklärung regulatorischer Prozesse in gesundem Gewebe, sowie im Zusammenhang mit komplexen metabolischen Erkrankungen und während der in vitro Differenzierung. Ziel dieser Arbeit ist es, basierend auf der integrativen Analyse genomweiter Sequenzierungsdaten, die Heterogenität von Leberzellen besser zu verstehen. Die räumliche Organisation von Zellen in humanem Gewebe stellt eine große Herausforderung dar. Mit Hilfe von Lasermikrodissektion gekoppelt an Hochdurchsatzsequenzierung ist es möglich definierte Subpopulationen hinsichtlich ihres Gewebekontextes zu analysieren. Somit konnte die erste räumliche Analyse von Transkriptom und Methylom dreier Zonen der humanen Leber erstellt werden, die eine Vielzahl neuer Erkenntnisse sowohl in gesundem Lebergewebe als auch in Zusammenhang mit Fettlebererkrankungen liefert. Außerdem wurde auf Grundlage dieser räumlichen Karten ein neuer Ansatz zur Projektion von Einzelzelldaten in den räumlichen Gewebekontext etabliert. Schließlich konnte durch die integrative Analyse der ausschlaggebenden regulatorischen Netzwerke während der in vitro Differenzierung von Hepatozyten-ähnlichen Zellen neue Strategien zur Verbesserung der Differenzierung entwickelt werden

Targeted Epigenetic Editing to Increase Adult Pancreatic Β-Cell Proliferation

β-cell replacement therapy is potentially a curative approach in treating diabetes, as demonstrated by the success of pancreatic islet transplantation in type 1 diabetes. However, there are an insufficient number of organ donors to meet the demand of this disease, which is increasing in prevalence. One strategy to increase the supply of human β-cells for transplantation in type 1 diabetics, or to increase residual β-cell mass in type 2 diabetics, is to induce human β-cell replication. This strategy has not been implemented clinically because adult human β-cells are largely quiescent and the capacity for proliferation decreases with age. I hypothesized that changes in DNA methylation contribute to the age-related decline in proliferative capacity in human β-cells, and that altering the DNA methylome in a targeted manner could improve proliferative capacity. To investigate this hypothesis, I sought to profile the β-cell across the human lifespan, and to develop tools that permit targeted DNA methylation modifications and efficiency in measuring DNA methylation. I conducted RNA-Seq and whole-genome bisulfite sequencing (WGBS) to profile the aging human β-cell transcriptome and DNA methylome. I found that there are significant changes in gene expression with age, and in DNA methylation, particularly at islet-specific active enhancers. Further, I developed transcription activator-like effector (TALE) fusion proteins conjugated to DNA methyltransferases (DNMTs) and demonstrated that targeting TALE-DNMTs to the promoter of the CDKN2A locus, encoding the cell cycle inhibitor p16, increases proliferation in primary human fibroblasts. Finally, I developed BisPCR2, a novel technique for preparing targeted bisulfite next-generation sequencing libraries, which greatly improves the efficiency in which DNA methylation can be measured at target regions. I demonstrated the utility of this tool to validate genome-wide findings of type 2 diabetes CpG risk loci. Together, these novel datasets and epigenetic tools poise the β-cell regeneration field to investigate targeted epigenetic modifications as a strategy to improve proliferative capacity of adult human β-cells

Analyzing Modern Biomolecules: The Revolution of Nucleic-Acid Sequencing-Review

Recent developments have revolutionized the study of biomolecules. Among them are molecular markers, amplification and sequencing of nucleic acids. The latter is classified into three generations. The first allows to sequence small DNA fragments. The second one increases throughput, reducing turnaround and pricing, and is therefore more convenient to sequence full genomes and transcriptomes. The third generation is currently pushing technology to its limits, being able to sequence single molecules, without previous amplification, which was previously impossible. Besides, this represents a new revolution, allowing researchers to directly sequence RNA without previous retrotranscription. These technologies are having a significant impact on different areas, such as medicine, agronomy, ecology and biotechnology. Additionally, the study of biomolecules is revealing interesting evolutionary information. That includes deciphering what makes us human, including phenomena like non-coding RNA expansion. All this is redefining the concept of gene and transcript. Basic analyses and applications are now facilitated with new genome editing tools, such as CRISPR. All these developments, in general, and nucleic-acid sequencing, in particular, are opening a new exciting era of biomolecule analyses and applications, including personalized medicine, and diagnosis and prevention of diseases for humans and other animals

Deciphering Organoids: High-Dimensional Analysis of Biomimetic Cultures

Organoids are self-organising stem cell-derived ex vivo cultures widely adopted as biomimetic models of healthy and diseased tissues. Traditional low-dimensional experimental methods such as microscopy and bulk molecular analysis have generated remarkable biological insights from organoids. However, as complex heterocellular systems, organoids are especially well-positioned to take advantage of emerging high-dimensional technologies. In particular, single-cell methods offer considerable opportunities to analyse organoids at unprecedented scale and depth, enabling comprehensive characterisation of cellular processes and spatial organisation underpinning organoid heterogeneity. This review evaluates state-of-the-art analytical methods applied to organoids, discusses the latest advances in single-cell technologies, and speculates on the integration of these two rapidly developing fields