Auswirkungen ausgewählter Einflussfaktoren auf die genomweite DNA-Methylierung beim Menschen

Umweltfaktoren können über epigenetische Mechanismen das Expressionsmuster von Genen verändern. So kann eine Veränderung des Lebensstils, z.B. Zigarettenkonsum, durch epigenetische Modifikationen, wie die der DNA-Methylierung, wesentlich zur Pathogenese einiger Erkrankungen beitragen. In der hier vorliegenden Arbeit sollte der Einfluss des Alters, eines hohen Body Mass Index (BMI), des Rauchens und der Schichtarbeit auf das DNA-Methylierungsmuster von Zwillingen mittels arraybasierter Technologie analysiert und charakterisiert werden. Die Untersuchung von monozygoten Zwillingen hat dabei den Vorteil, dass aufgrund des gleichen genetischen Hintergrundes der Zwillinge, Veränderungen im DNA-Methylierungsmuster den analysierten Parametern zugeschrieben werden können. Aufgrund des auf der primären Fragestellung dieser Arbeit basierenden Studiendesigns, konnte ein zwillingsbasierter Ansatz nur für die Analyse des Einflusses der Schichtarbeit gewählt werden. Für die Parameter Alter, BMI und Rauchen wurde für die Identifizierung einer veränderten DNA-Methylierung eine gruppenbasierte Analyse mittels der sog. False Discovery Rate (FDR, q) bzw. eines definierten Signifikanzwertes bei einer gleichzeitigen Differenz der mittleren Methylierungswerte zwischen zwei Gruppen von mindestens 5% (│∆β│>0,05) durchgeführt. Zusammengefasst wurden folgende Befunde erzielt: 1. Der Infinium HumanMethylation450 BeadChip konnte in einem Vorexperiment erfolgreich etabliert und somit für die anschließenden genomweiten hypothesenfreien DNA-Methylierungsanalysen eingesetzt werden. Diese wurden an aus Zellen des peripheren Blutes isolierter genomischer DNA von monozygoten und dizygoten Zwillingen durchgeführt. 2. Für die Identifizierung altersabhängig methylierter CpG-Loci wurden insgesamt 53 mono- und 63 dizygote DNA-Proben von Zwillingen im Alter von 20–63 Jahren einbezogen. Mittels linearer Regression konnten 111 CpGs bzw. 63 Gene identifiziert werden, deren Methylierung signifikant mit dem Alter korrelierte (q<1x10-11). 47 Gene bzw. 58 Loci konnten dabei die Befunde altersabhängiger Methylierungsveränderungen aus anderen Studien bestätigen, während 16 erstmals neu identifizierte Gene ermittelt werden konnten. Über eine Genontologie-Analyse konnten unter den insgesamt 63 altersabhängig methylierten Genen statistisch signifikante Anreicherungen für Gene ermittelt werden, die an Prozessen des Blutdrucks, der Herzkontraktion und des Nervensystems beteiligt sind. 3. Der Vergleich von fünf adipösen und 29 normalgewichtigen Zwillingen lieferte bei einer FDR q0,05). 3/43 differentiell methylierte Gene (ADORA1, LPGAT1, POM121C) wurden bereits im Zusammenhang mit Adipositas publiziert, während zwei weitere Gene (ADCY5, SYN2) mit einer damit verbundenen Folgeerkrankung, dem Typ-2-Diabetes, assoziiert werden konnten. 4. Der Vergleich von 12 Rauchern und 25 Nichtrauchern resultierte bei einer FDR q0,05). Mit beiden Ansätzen konnten Loci und Gene bestätigt werden, für die andere Studien bereits eine mit dem Rauchen veränderte DNA-Methylierung gezeigt hatten. Insgesamt konnten unter den identifizierten Genen mittels verschiedener Anreicherungsanalysen Gene ermittelt werden, die z.B. in Herzerkrankungen (CACNA1C, GATA4, EVC2, NODAL) und Thrombose (F2RL3, F10), deren Entstehung teilweise durch das Rauchen begünstigt wird, involviert sind. 5. Bei sechs für Nachtarbeit diskordanten Zwillingspaaren konnten insgesamt 21 bzw. elf differentiell methylierte Loci bzw. Gene ermittelt werden (p<0,05; │∆β│≥0,05). Von diesen konnten zehn ausgewählte Loci nicht erfolgreich validiert werden, was primär der geringen statistischen Power zugeschrieben wurde. Dennoch wurden 2/21 Loci bzw. 3/11 Gene (SLC11A1, TMEM132D, TRIM10) bereits in anderen Studien mit einer veränderten Methylierung bei Nachtschichtarbeitern identifiziert, was eine mögliche Auswirkung der Nachtarbeit auf den veränderten Methylierungsstatus dieser Gene unterstreicht. Weiterhin konnten für einige der von differentieller Methylierung betroffenen Gene Assoziationen zu bekannten Risikofaktoren von Schichtarbeit, z.B. dem metabolischen Syndrom (CREB5), erhöhter Anfälligkeit für Infektionen (SLC11A1) sowie Angststörungen (SLC6A1, TMEM132D) gefunden werden. Insgesamt lässt sich schlussfolgern, dass die untersuchten Einflussfaktoren mit spezifischen Veränderungen in der DNA-Methylierung korrelieren. Dabei konnten Loci und Gene aus den einzelnen Teilstudien bestätigt und zumindest die Beteiligung einiger der betroffenen Gene in einen entsprechenden plausiblen biologischen Kontext gebracht werden.Through epigenetic mechanisms environmental factors are able to change the expression pattern of genes. Thus, a change in lifestyle, such as cigarette consumption, may significantly contribute amongst others to the pathogenesis of some diseases through epigenetic modifications, such as DNA methylation. In the present thesis the influence of age as well as of a high body mass index (BMI), smoking and shiftwork on the DNA methylation pattern of twins should be analyzed and characterized through an array-based approach. Investigation of monozygotic twins offers the advantage that due to the identical genetic background of these twins, changes in DNA methylation patterns can be attributed to the analyzed parameters. Due to the study design based on the primary question of this thesis, a twin-based approach could be used only for the analysis of the influence of shiftwork. For the parameters age, BMI and smoking a group-based approach was used for the identification of changes in DNA methylation by applying the false discovery rate (FDR, q) and a defined level of significance with a simultaneously difference of the mean methylation values between two groups of at least 5% (│∆β│>0.05), respectively. Summarized the following results were obtained: 1. The Infinium HumanMethylation450 BeadChip could be successfully validated in a pre-experiment and could therefore be used for subsequent genome-wide hypothesis free DNA methylation analyses. These were performed in whole blood genomic DNA from monozygotic and dizygotic twins. 2. For identification of age dependent methylated CpG loci a total of 53 monozygotic and 63 dizygotic DNA samples of twins at age of 20–63 years were included. By using the linear regression model 111 CpGs and 63 genes, respectively, could be identified whose methylation significantly correlated with age (q<1x10-11). 47 genes and 58 loci, respectively, could be confirmed that have already been related to an age-dependent methylation by previous studies whereas 16 newly identified genes could be detected. Performing gene ontology analysis for the 63 age-dependent methylated genes, statistically significant enrichments could be found for genes involved in processes of blood pressure, heart contraction and nervous system. 3. The comparison of five obese and 29 normal weighted twins resulted in three differentially methylated CpGs associated with the genes TREML4 and POM121C by applying a FDR q0.05). 3/43 differentially methylated genes (ADORA1, LPGAT1, POM121C) were already published with association to adiposity whereas two further genes (ADCY5, SYN2) could be associated with a secondary disease, type 2 diabetes. 4. The comparison of 12 smokers and 25 nonsmokers resulted in a total of 22 differentially methylated CpGs and 14 genes, respectively, by applying a FDR q0.05). With both approaches loci and genes could be confirmed of which other studies have already reported an altered DNA methylation due to tobacco smoking. All in all, with different enrichment analyses differentially methylated genes could be detected to be involved in e.g. heart diseases (CACNA1C, GATA4, EVC2, NODAL) and thrombosis (F2RL3, F10) whose development is partially promoted by smoking. 5. In six twin pairs discordant for night work a total of 21 and eleven differentially methylated loci and genes, respectively, could be detected (p<0.05; │∆β│≥0.05). For ten selected loci validation failed which may be attributed to low statistical power. However, 2/21 loci and 3/11 genes (SLC11A1, TMEM132D, TRIM10), respectively, have already been identified in night shift workers with an altered DNA methylation by other studies, emphasizing a possible effect of night work on the methylation of these genes. Furthermore, for some of the differentially methylated genes associations to known risk factors of shiftwork such as the metabolic syndrome (CREB5), increased susceptibility to infections (SLC11A1) and anxiety disorders (SLC6A1, TMEM132D) could be found. All in all it can be concluded that the investigated influence factors correlate with specific alterations in DNA methylation. Loci and genes from substudies could be confirmed and at least involvement of some of the affected genes may be plausible in the relevant biological context

DNA methylome analysis in Burkitt and follicular lymphomas identifies differentially methylated regions linked to somatic mutation and transcriptional control

Although Burkitt lymphomas and follicular lymphomas both have features of germinal center B cells, they are biologically and clinically quite distinct. Here we performed whole-genome bisulfite, genome and transcriptome sequencing in 13 IG-MYC translocation-positive Burkitt lymphoma, nine BCL2 translocation-positive follicular lymphoma and four normal germinal center B cell samples. Comparison of Burkitt and follicular lymphoma samples showed differential methylation of intragenic regions that strongly correlated with expression of associated genes, for example, genes active in germinal center dark-zone and light-zone B cells. Integrative pathway analyses of regions differentially methylated in Burkitt and follicular lymphomas implicated DNA methylation as cooperating with somatic mutation of sphingosine phosphate signaling, as well as the TCF3-ID3 and SWI/SNF complexes, in a large fraction of Burkitt lymphomas. Taken together, our results demonstrate a tight connection between somatic mutation, DNA methylation and transcriptional control in key B cell pathways deregulated differentially in Burkitt lymphoma and other germinal center B cell lymphomas

Quantitative cross-validation and content analysis of the 450k DNA methylation array from Illumina, Inc.

<p>Abstract</p> <p>Background</p> <p>The newly released 450k DNA methylation array from Illumina, Inc. offers the possibility to analyze more than 480,000 individual CpG sites in a user friendly standardized format. In this study the relationship between the β-values provided by the Illumina, Inc. array for each individual CpG dinucleotide and the quantitative methylation levels obtained by pyrosequencing were analyzed. In addition, the representation of microRNA genes and imprinted loci on the Illumina, Inc. array was assessed in detail. Genomic DNA from 4 human breast cancer cell lines (IPH-926, HCC1937, MDA-MB-134, PMC42) and 18 human breast cancer specimens as well as 4 normal mammary epithelial fractions was analyzed on 450k DNA methylation arrays. The β-values for 692 individual CpG sites from 62 different genes were cross-validated using conventional quantitative pyrosequencing.</p> <p>Findings</p> <p>The newly released 450k methylation array from Illumina, Inc. shows a high concordance with quantitative pyrosequencing if identical CpG sites are analyzed in cell lines (Spearman r = 0.88, p ≪ 0.0001), which is somewhat reduced in primary tumor specimens (Spearman r = 0.86, p ≪ 0.0001). 80.7% of the CpG sites show an absolute difference in methylation level of less than 15 percentage points. If different CpG sites in the same CpG islands are targeted the concordance is lower (r = 0.83 in cell lines and r = 0.7 in primary tumors). The number of CpG sites representing microRNA genes and imprinted loci is very heterogeneous (range: 1 – 70 CpG sites for microRNAs and 1 – 288 for imprinted loci).</p> <p>Conclusions</p> <p>The newly released 450k methylation array from Illumina, Inc. provides a genome-wide quantitative representation of DNA methylation aberrations in a convenient format. Overall, the congruence with pyrosequencing data is very good. However, for individual loci one should be careful to translate the β-values directly into percent methylation levels.</p

Die Rolle der Antileukoproteinase SLPI bei der Infektion mit HPV bei Kopf-Hals-Karzinomen

<div><p>Treatment with recombinant human growth hormone (rhGH) has been consistently reported to induce transcriptional changes in various human tissues including peripheral blood. For other hormones it has been shown that the induction of such transcriptional effects is conferred or at least accompanied by DNA-methylation changes. To analyse effects of short term rhGH treatment on the DNA-methylome we investigated a total of 24 patients at baseline and after 4-day rhGH stimulation. We performed array-based DNA-methylation profiling of paired peripheral blood mononuclear cell samples followed by targeted validation using bisulfite pyrosequencing. Unsupervised analysis of DNA-methylation in this short-term treated cohort revealed clustering according to individuals rather than treatment. Supervised analysis identified 239 CpGs as significantly differentially methylated between baseline and rhGH-stimulated samples (p<0.0001, unadjusted paired t-test), which nevertheless did not retain significance after adjustment for multiple testing. An individualized evaluation strategy led to the identification of 2350 CpG and 3 CpH sites showing methylation differences of at least 10% in more than 2 of the 24 analyzed sample pairs. To investigate the long term effects of rhGH treatment on the DNA-methylome, we analyzed peripheral blood cells from an independent cohort of 36 rhGH treated children born small for gestational age (SGA) as compared to 18 untreated controls. Median treatment interval was 33 months. In line with the groupwise comparison in the short-term treated cohort no differentially methylated targets reached the level of significance in the long-term treated cohort. We identified marked intra-individual responses of DNA-methylation to short-term rhGH treatment. These responses seem to be predominately associated with immunologic functions and show considerable inter-individual heterogeneity. The latter is likely the cause for the lack of a rhGH induced homogeneous DNA-methylation signature after short- and long-term treatment, which nevertheless is well in line with generally assumed safety of rhGH treatment.</p></div

The CpG island methylator phenotype in breast cancer is associated with the lobular subtype

Background: Aberrations in DNA methylation patterns are well-described in human malignancies. However, the existence of the 'CpG island methylator phenotype' (CIMP) in human breast cancer is still controversial. Materials & methods: Illumina's HumanMethylation 450K BeadChip was used to analyze genome-wide DNA methylation patterns. Chromosomal abnormalities were determined by array-based CGH. Results: Invasive lobular breast carcinomas exhibit the highest number of differentially methylated CpG sites and a strong inverse correlation of aberrant DNA hypermethylation and copy number alterations. Nine differentially methylated regions within seven genes discriminating the investigated subgroups were identified and validated in an independent validation cohort and correlated to a better relapse-free survival. Conclusion: These results depict a clear difference between genetically and epigenetically unstable breast carcinomas indicating different ways of tumor progression and/or initiation, which strongly supports the association of CIMP with the lobular subtype and provide new options for detection and therapy

DNA-methylation results in the short-term rhGH treatment study cohort using an individualised evaluation approach.

<p>Differences of avg.beta values between stimulated and baseline samples (delta beta) of each pair were calculated. Depicted are those loci, in which at least 3/24 analysed sample pairs showed absolute delta beta values below -0.1 or above 0.1 (roughly corresponding to a difference of DNA-methylation of +/- 10% between each analysed sample pair) and in which at least 3 CpGs were affected per gene (n = 259). Red: delta beta values above 0.2, pale red: delta beta values between 0.1 and 0.2. Blue: delta beta values below -0.2, pale blue: delta beta values between -0.1 and -0.2. Samples are sorted according to <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0120463#pone.0120463.t001" target="_blank">Table 1</a> with P1 at the left margin and P26 at the right margin. Those loci used for bisulfite pyrosequencing validation are zoomed out and shown at the right side of the figure.</p

Increase of IGF1 concentration upon rhGH treatment in the IGFGT.

<p>Scatter plot of the IGF1 concentrations (ng/ml) at baseline and rhGH stimulated blood sampling among the short-term rhGH treatment study cohort. The two measurements per sample are connected. Sample numbering corresponds to the sample identifier in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0120463#pone.0120463.t001" target="_blank">Table 1</a>. A significant increase in IGF1 concentrations after rhGH treatment is observed (paired t-test, p = 1.93 x 10–6).</p