DNA methylation at the mu-1 opioid receptor gene (OPRM1) promoter predicts preoperative, acute, and chronic postsurgical pain after spine fusion.

INTRODUCTION:The perioperative pain experience shows great interindividual variability and is difficult to predict. The mu-1 opioid receptor gene (OPRM1) is known to play an important role in opioid-pain pathways. Since deoxyribonucleic acid (DNA) methylation is a potent repressor of gene expression, DNA methylation was evaluated at the OPRM1 promoter, as a predictor of preoperative, acute, and chronic postsurgical pain (CPSP). METHODS:A prospective observational cohort study was conducted in 133 adolescents with idiopathic scoliosis undergoing spine fusion under standard protocols. Data regarding pain, opioid consumption, anxiety, and catastrophizing (using validated questionnaires) were collected before and 2-3 months postsurgery. Outcomes evaluated were preoperative pain, acute postoperative pain (area under curve [AUC] for pain scores over 48 hours), and CPSP (numerical rating scale >3/10 at 2-3 months postsurgery). Blood samples collected preoperatively were analyzed for DNA methylation by pyrosequencing of 22 CpG sites at the OPRM1 gene promoter. The association of each pain outcome with the methylation percentage of each CpG site was assessed using multivariable regression, adjusting for significant (P<0.05) nongenetic variables. RESULTS:Majority (83%) of the patients reported no pain preoperatively, while CPSP occurred in 36% of the subjects (44/121). Regression on dichotomized preoperative pain outcome showed association with methylation at six CpG sites (1, 3, 4, 9, 11, and 17) (P<0.05). Methylation at CpG sites 4, 17, and 18 was associated with higher AUC after adjusting for opioid consumption and preoperative pain score (P<0.05). After adjusting for postoperative opioid consumption and preoperative pain score, methylation at CpG sites 13 and 22 was associated with CPSP (P<0.05). DISCUSSION:Novel CPSP biomarkers were identified in an active regulatory region of the OPRM1 gene that binds multiple transcription factors. Inhibition of binding by DNA methylation potentially decreases the OPRM1 gene expression, leading to a decreased response to endogenous and exogenous opioids, and an increased pain experience

DEVELOPMENT OF COMPUTATIONAL TOOLS TO STUDY THE PATTERNING OF DNA AND RNA METHYLATION IN HEALTHY AND DISEASE STATES

Epigenetics can be defined as the set of sequence independent processes that produces heritable changes in cellular information. These chromatin-based events such as covalent modification of DNA and histone tails are laid down by the co-ordinated action of chromatin modifying enzymes, thus altering the organisation of chromatin and its accessibility to the transcriptional machinery. Our understanding of epigenetic intricacies has considerably increased over the last decade owing to rapid development of genomic and proteomic technologies. This has resulted in huge surge in the generation of epigenomics data. Integrative analysis of these epigenomics datasets provides holistic view on the interplay of various epigenetic components and possible aberration in patterns in specific biological or disease states. Although, there are numerous computational tools available catering individually to each epigenomic datatype, a comprehensive computational framework for integrated exploratory analysis of these datasets was missing. We developed a suite of R packages methylPipe and compEpiTools that can efficiently handle whole genome base-resolution DNA methylation datasets and effortlessly integrate them with other epigenomics data. We applied these methods to the study of epigenomics landscape in B-cell lymphoma identifying a putative set of tumor suppressor genes. Moreover, we also applied these methods to explore possible associations between m6A RNA methylation, epigenetic marks and regulatory proteins

Epigenomes in Cardiovascular Disease.

If unifying principles could be revealed for how the same genome encodes different eukaryotic cells and for how genetic variability and environmental input are integrated to impact cardiovascular health, grand challenges in basic cell biology and translational medicine may succumb to experimental dissection. A rich body of work in model systems has implicated chromatin-modifying enzymes, DNA methylation, noncoding RNAs, and other transcriptome-shaping factors in adult health and in the development, progression, and mitigation of cardiovascular disease. Meanwhile, deployment of epigenomic tools, powered by next-generation sequencing technologies in cardiovascular models and human populations, has enabled description of epigenomic landscapes underpinning cellular function in the cardiovascular system. This essay aims to unpack the conceptual framework in which epigenomes are studied and to stimulate discussion on how principles of chromatin function may inform investigations of cardiovascular disease and the development of new therapies

DNA methylation inheritance in Arabidopsis: The next generation

Ons DNA, de drager van onze erfelijkheid, bestaat uit een code die is samengesteld uit vier letters; A, C, G en T. De genetica leert ons dat er mutaties in het DNA kunnen ontstaan (letter A verandert bijvoorbeeld in letter G) die het functioneren van genen beïnvloeden. Onderzoek aan planten maakt duidelijk dat het functioneren van genen ook beïnvloed kan worden door zogenoemde epigenetische veranderingen. Een bekend voorbeeld hiervan is de verandering van cytosine in 5-methylcytosine (de letter C), en vice versa. Men spreekt in dit geval van DNA-methylering. Een fundamentele doelstelling in de plantbiologie is het vaststellen van de stabiliteit van epigenetische veranderingen over verschillende generaties en de mate waarin deze bijdragen aan de variatie in eigenschappen (bijv. de hoogte).In dit proefschrift laten we resultaten zien van onderzoek aan een experimentele plantenpopulatie van de modelplant Arabidopsis thaliana (zandraket). Het unieke kenmerk van deze populatie is dat de planten bijna identieke DNA-sequenties hebben, maar sterke verschillen in DNA-methylatie patronen. We laten zien dat deze patronen doorgegeven worden aan de opeenvolgende generaties en effect hebben op vele belangrijke planteneigenschappen zoals bloeitijd en wortellengte. We laten ook zien dat methyleringsverschillen spontaan kunnen ontstaan in natuurlijke populaties en dat de snelheid hiervan vele malen groter is dan die van DNA-mutaties.Het feit dat veranderingen in DNA-methylatie worden overgeërfd en bijdragen aan variatie in planteneigenschappen laat zien dat erfelijke informatie verder reikt dan de vier letters waaruit ons DNA is samengesteld en levert nieuwe vraagstukken op aangaande de rol hiervan in plantenevolutie en de toepassing in agrarische teeltprogramma’s.Our DNA, the molecule that stores heritable information, consists of a four letter code; A, C, G and T. Text book genetics tell us that DNA can be mutated (e.g. letter A turns into letter G), and that such mutations can change the functions of genes. In plants, it is becoming increasingly clear that heritable alterations in gene function can also be acquired through so-called epigenetic changes. A well-known example of an epigenetic change is the gain or loss of DNA methylation, the chemical modification of a cytosine (the letter C in the DNA code) into 5-methylcytosine. A fundamental goal in plant biology is to assess how stable epigenetic changes are across generations and to which extent they affect observable traits (e.g. plant height).In this thesis we performed extensive bioinformatic and statistical analyses of an experimental population of the model plant Arabidopsis thaliana. The unique feature of this population is that all plants are nearly identical at the DNA level but show strong differences in DNA methylation patterns. We show that these patterns can be inherited for many generations, and affect important plants traits such as flowering time and root length. We also show that DNA methylation changes occur stochastically in natural populations, and at a rate far exceeding the known DNA mutation rate in this species.The fact that DNA methylation changes affect observable plant traits that are transmitted across generations shows that heritable information extends beyond the four letters encoded in our DNA and opens up new questions regarding its role in plant evolution and its use in agricultural breeding programs

Epigenetic modelling: DNA methylation and working towards model parameterisation

The main focus of the research in this thesis is the investigation in DNA methylation mechanisms of epigenetics and the study of a specific database. As part of the latter work, the role of curation is described, and a new knowledge management system, PathEpigen1 , is reported that is currently being developed for colon cancer in the Sci-Sym centre. The database deals with genetic and epigenetic interactions and contains considerable data on molecular events such as genetic and epigenetic events. The data curation includes biomedical and biological information. An efficient method was devised to extract biological information from the literature to process, manage and upgrade data. We present a Deterministic Finite Automata (DFA) model for the DNA methylation mechanism controlled by DNA methyltransferase (DNMT) enzymes. This thesis provides a brief introduction to epigenetics, a survey of ongoing research on computational epigenetics and a description of the DNA methylation database. Furthermore, it also gives an overview of DNA methylation and its importance in cancer. The DFA models three states of methylation frequency (normal, de-novo and hypermethylated) in the cell. It has been executed on input of random strings of size 100. Out of the strings considered, we found that 26%, 37% and 37% correspond to normal, de-novo (cancer initiation) and hypermethylated (cancer) states, respectively

Epigenetic regulation of innate immune responses to infection

L’importance des modifications épigénétiques sur le contrôle de l’expression génique est clairement établie dans la littérature. Il demeure cependant incertain si les marques épigénétiques modulent l’activité transcriptionnelle ou si ce sont plutôt des conséquences découlant de facteurs régulateurs qui modulent préalablement cette activité. Pour ma thèse, j’ai investigué le rôle de la méthylation de l’ADN dans le contexte de l’activation du système immunitaire inné. Plus précisément, j’ai conduit une analyse intégrant des données de méthylation à l’échelle génomique, de modifications d’histones, d’accessibilité à la chromatine et d’expression génique sur des cellules dendritiques avant et après une infection provoquée par Mycobacterium tuberculosis (MTB). Dans le cadre du projet, je montre que la réponse immunitaire à l’infection est associée à la perte de méthylation sur des milliers de sites CpG, indépendamment de la prolifération cellulaire. Les déméthylations actives se trouvent principalement sur des éléments amplificateurs éloignés des sites d’initiation de la transcription et sont fortement associées à l’induction de gènes situés dans leur voisinage. Cependant, une analyse longitudinale indique que la plupart des changements d’expression se produisent avant les changements perceptibles de méthylation. Une analyse de footprint de l’ADN a révélé que le recrutement de facteurs de transcriptions impliqués dans la réponse immunitaire, tel que NF-κB/Rel, précède les pertes de méthylation observées. Il est intéressant de noter que les niveaux de méthylation dans les régions déméthylées ne sont pas rétablis durant l'infection, même pour des gènes dont l’expression retourne à l’état basal. Ces résultats suggèrent que la déméthylation de l’ADN n’est probablement pas cruciale à la mise en place du programme de régulation central enclenché par les cellules du système immunitaire en réponse aux pathogènes. Celle-ci pourrait cependant jouer un rôle dans la mémoire épigénétique et pourrait permettre une réponse plus rapide à une seconde infection. De manière générale, les résultats ouvrent la porte à l’utilisation des régions de méthylation de l’ADN comme bio-marqueur prédictifs d’infections passées et présentes.The importance of epigenetic modifications in the control of gene expression is widely accepted. Yet, it often remains unclear whether altered epigenetic patterns themselves invoke transcriptional modulation or are instead downstream consequences of regulatory factors. During my thesis, I investigated the role of DNA methylation in the regulation of innate immune responses. Specifically, I performed an integrated analysis of data on genome-wide DNA methylation, histone modifications, chromatin accessibility, and gene expression, in dendritic cells (DCs), before and after infection with Mycobacterium tuberculosis (MTB). I demonstrate that the immune response to infection is associated with loss of methylation at thousands of CpG sites, independent of cell proliferation. Active demethylation was specifically targeted to distal enhancer elements and was strongly associated with induction of nearby genes. However, time course analysis further indicates that most changes in gene expression in response to infection occur prior to detectable changes in DNA methylation. Footprinting analysis revealed that the recruitment of immune-related transcription factors, such as NF-κB/Rel, to these regions preceded the observed loss in methylation. Interestingly, levels of methylation at differentially methylated CpG sites never reverted back to higher levels during the course of infection, even among genes for which expression levels return to basal state. Collectively, these results show that DNA demethylation is likely not crucial for the establishment of the core regulatory program engaged by innate immune cells in response to a pathogen. Instead, it might play a role in the establishment of epigenetic memory, which allows for a faster response to a secondary infection. More generally, the results from this thesis opens the door for using DNA methylation marks as a predictive biomarker of past or present infection

DISMISS: detection of stranded methylation in MeDIP-Seq data

BACKGROUND: DNA methylation is an important regulator of gene expression and chromatin structure. Methylated DNA immunoprecipitation sequencing (MeDIP-Seq) is commonly used to identify regions of DNA methylation in eukaryotic genomes. Within MeDIP-Seq libraries, methylated cytosines can be found in both double-stranded (symmetric) and single-stranded (asymmetric) genomic contexts. While symmetric CG methylation has been relatively well-studied, asymmetric methylation in any dinucleotide context has received less attention. Importantly, no currently available software for processing MeDIP-Seq reads is able to resolve these strand-specific DNA methylation signals. Here we introduce DISMISS, a new software package that detects strand-associated DNA methylation from existing MeDIP-Seq analyses. RESULTS: Using MeDIP-Seq datasets derived from Apis mellifera (honeybee), an invertebrate species that contains more asymmetric- than symmetric- DNA methylation, we demonstrate that DISMISS can identify strand-specific DNA methylation signals with similar accuracy as bisulfite sequencing (BS-Seq; single nucleotide resolution methodology). Specifically, DISMISS is able to confidently predict where DNA methylation predominates (plus or minus DNA strands – asymmetric DNA methylation; plus and minus DNA stands – symmetric DNA methylation) in MeDIP-Seq datasets derived from A. mellifera samples. When compared to DNA methylation data derived from BS-Seq analysis of A. mellifera worker larva, DISMISS-mediated identification of strand-specific methylated cytosines is 80 % accurate. Furthermore, DISMISS can correctly (p <0.0001) detect the origin (sense vs antisense DNA strands) of DNA methylation at splice site junctions in A. mellifera MeDIP-Seq datasets with a precision close to BS-Seq analysis. Finally, DISMISS-mediated identification of DNA methylation signals associated with upstream, exonic, intronic and downstream genomic loci from A. mellifera MeDIP-Seq datasets outperforms MACS2 (Model-based Analysis of ChIP-Seq2; a commonly used MeDIP-Seq analysis software) and closely approaches the results achieved by BS-Seq. CONCLUSIONS: While asymmetric DNA methylation is increasingly being found in growing numbers of eukaryotic species and is the predominant pattern observed in some invertebrate genomes, it has been difficult to detect in MeDIP-Seq datasets using existing software. DISMISS now enables more sensitive examinations of MeDIP-Seq datasets and will be especially useful for the study of genomes containing either low levels of DNA methylation or for genomes containing relatively high amounts of asymmetric methylation