R tools for MicroRNA pathway analysis

In the early 2000s, microRNAs (miRNAs) were discovered as segments of a new class of highly conserved and small non-coding RNA molecules of 20-25 nucleotides that are transcribed from DNA.
They do not translation into proteins, rather they inhibit protein expression by binding to the 3’untranslated regions (3’ UTRs) of specific mRNA targets (that is/are complementary to them) and guiding their translational repression or complete degradation and gene silencing. With this, miRNAs provide a second level of regulation beyond primary gene expression. Integrative study of cellular pathways is pivotal to understanding the functions of individual genes and proteins in terms of systems and processes that contribute to normal physiology and to disease. "WikiPathways":http://wikipathways.org is an open, collaborative platform dedicated to the curation of biological pathways by and for the scientific community. The collection of pathways is publicly available to the researchers. The miRNA’s predicted by TargetScan in cardiomyocytes hypertrophy pathway has already been visualized on WikiPathways (WP1560). Since more studies investigate miRNAs using microarray technologies it would be desirable to be able to use information about miRNA’s in that analysis. One way to do that is to add the miRNA’s to all pathways. Therefore, we are integrating both validated and predicted miRNA information into biological pathways and making them available in WikiPathways. Initially, we focused on pathways related to the heart because miRNAs created a true revolution in the cardiovascular research field. The validated miRNAs have been downloaded from miRNA databases such as TarBase or miRTarbase. In order to link the validated miRNA targets to the genes in the pathways of our interest, we use "BridgeDb":http://www.bridgedb.org for identifier mapping. BridgeDb is a middleware between the relational databases, files and mapping services. BridgeDb is available in two forms. The first is a framework suitable for integration in Java applications. The other is based on Representational State Transfer (REST) webservices and is suitable for all other programming languages. The identifier mapping has been done in the R statistical environment as the connected Bioconductor repository has many pre-existing packages for microarray data analysis. For now we used the REST interface from R but we will also submit BridgeDb R package to Bioconductor.
Predicted miRNA targets by different prediction algorithms were verified by co evaluating miRNA and mRNA expression using microarray analysis. Quality control and normalization of the microarray datasets was done using the current functionality of the arrayanalysis.org web portal. Statistical analysis was done using Limma and the miRNAs were visualized in the pathways of interest using "PathVisio":http://www.pathvisio.org. Modules for statistical and pathway analysis have been developed which will be added to the "arrayanalysis.org":http://www.arrayanalysis.org portal. This also required connecting R to PathVisio, for which a new XMLRPC interface was developed. Through this PathVisio can be controlled by R scripts.
In conclusion, these R tools can help to integrate information about miRNAs with other knowledge about biological pathways and used for research purposes

Effects of Methyl-Group Metabolism and Lifestyle Factors on Genome-Wide DNA Methylation

The aim of this thesis is to study the association of methyl-group metabolism, nutritional and lifestyle factors with genome-wide DNA methylation. The first section of this thesis includes two literature reviews. Chapter 2 is a literature review of Hcy and its role in DNA methylation. Chapter 3 is a systematic literature review of the relation between micro- and macro- nutrients and DNA methylation in humans across the life course. The second section of this thesis focuses on a key metabolite of the methyl-group metabolism, Hcy. Chapter 4 is a meta-analysis of EWASs to investigate the association between plasma Hcy and DNA methylation in leukocytes of 2,035 individuals from six cohorts. In Chapter 5, we used genetically defined elevated Hcy as an instrument, i.e. the MTHFR 677C>T variant and the combined weighted genetic risk score of 18 previously studied Hcy-associated variants, to test whether genetically defined elevated Hcy levels are associated with DNA methylation changes in leukocytes of 9,894 individuals from 12 cohorts. In Chapter 6, we conducted an interaction study to investigate the effect of elevated Hcy in individuals by MTHFR 677C>T genotype on genome-wide DNA methylation in leukocytes of 1280 individuals from 2 cohorts. The third section of this thesis focuses on nutrition and lifestyle factors. Chapter 7 is a meta-analysis of EWASs to investigate the association of folate intake and vitamin B12 intake with DNA methylation in leukocytes of 5,841 participants from 10 cohorts. Chapter 8 focuses on association between cigarette smoking as a lifestyle factor and DNA methylation in leukocyte assessed in 15,907 individuals (2,433 current, 6,518 former, and 6,956 never smokers) from 16 cohorts

Genomic and phenotypic insights from an atlas of genetic effects on DNA methylation

DNA methylation quantitative trait locus (mQTL) analyses on 32,851 participants identify genetic variants associated with DNA methylation at 420,509 sites in blood, resulting in a database of >270,000 independent mQTLs. Characterizing genetic influences on DNA methylation (DNAm) provides an opportunity to understand mechanisms underpinning gene regulation and disease. In the present study, we describe results of DNAm quantitative trait locus (mQTL) analyses on 32,851 participants, identifying genetic variants associated with DNAm at 420,509 DNAm sites in blood. We present a database of >270,000 independent mQTLs, of which 8.5% comprise long-range (trans) associations. Identified mQTL associations explain 15-17% of the additive genetic variance of DNAm. We show that the genetic architecture of DNAm levels is highly polygenic. Using shared genetic control between distal DNAm sites, we constructed networks, identifying 405 discrete genomic communities enriched for genomic annotations and complex traits. Shared genetic variants are associated with both DNAm levels and complex diseases, but only in a minority of cases do these associations reflect causal relationships from DNAm to trait or vice versa, indicating a more complex genotype-phenotype map than previously anticipated.Peer reviewe

Altered DNA methylation in children born to mothers with rheumatoid arthritis during pregnancy

Objectives The main objective of this study was to determine whether the DNA methylation profile of children born to mothers with rheumatoid arthritis (RA) is different from that of children born to mothers from the general population. In addition, we aimed to determine whether any differences in methylation are associated with maternal RA disease activity or medication use during pregnancy. Methods For this study, genome-wide DNA methylation was measured at cytosine-phosphateguanine (CpG) sites, using the Infinium Illumina HumanMethylation 450K BeadChip, in 80 blood samples from children (mean age=6.8 years) born to mothers with RA. As controls, blood samples from 354 children (mean age=6.0 years) from the population-based Generation R Study were used. Linear mixed models were performed to investigate differential methylation between the groups, corrected for relevant confounders. Results A total of 147 CpGs were differentially methylated between blood samples of children born to mothers with RA and the control blood samples. The five most significantly associated CpGs were cg06642177, cg08867893, cg06778273, cg07786668 and cg20116574. The differences in methylation were not associated with maternal RA disease activity or medication use during pregnancy. Conclusions DNA methylation at 147 CpGs differed between children born to mothers with RA and children born to mothers from the general population. It remains unknown whether the identified associations are causal, and if so whether they are caused by the disease or treatment. More research, including replication of these results, is necessary in order to strengthen the relevance of our findings for the later-life health of children born to mothers with R

Differentially methylated regions in T cells identify kidney transplant patients at risk for de novo skin cancer

Abstract Background Cutaneous squamous cell carcinoma (cSCC) occurs 65–200 times more in immunosuppressed organ transplant patients than in the general population. T cells, which are targeted by the given immunosuppressive drugs, are involved in anti-tumor immune surveillance and are functionally regulated by DNA methylation. Prior to kidney transplantation, we aim to discover differentially methylated regions (DMRs) in T cells involved in de novo post-transplant cSCC development. Methods We matched 27 kidney transplant patients with a future de novo cSCC after transplantation to 27 kidney transplant patients without cSCC and studied genome-wide DNA methylation of T cells prior to transplantation. From 11 out of the 27 cSCC patients, the DNA methylation of T cells after transplantation was also examined to assess stability of the observed differences in DNA methylation. Raw methylation values obtained with the 450k array were confirmed with pyrosequencing. Results We found 16 DMRs between patients with a future cSCC and those who do not develop this complication after transplantation. The majority of the DMRs were located in regulatory genomic regions such as flanking bivalent transcription start sites and bivalent enhancer regions, and most of the DMRs contained CpG islands. Examples of genes annotated to the DMRs are ZNF577, coding for a zinc-finger protein, and FLOT1, coding for a protein involved in T cell migration. The longitudinal analysis revealed that DNA methylation of 9 DMRs changed significantly after transplantation. DNA methylation of 5 out of 16 DMRs was relatively stable, with a variation in beta-value lower than 0.05 for at least 50% of the CpG sites within that region. Conclusions This is the first study demonstrating that DNA methylation of T cells from patients with a future de novo post-transplant cSCC is different from patients without cSCC. These results were obtained before transplantation, a clinically relevant time point for cSCC risk assessment. Several DNA methylation profiles remained relatively stable after transplantation, concluding that these are minimally affected by the transplantation and possibly have a lasting effect on post-transplant cSCC development

An Integrative Cross-Omics Analysis of DNA Methylation Sites of Glucose and Insulin Homeostasis

Despite existing reports on differential DNA methylation in type 2 diabetes (T2D) and obesity, our understanding of its functional relevance remains limited. Here we show the effect of differential methylation in the early phases of T2D pathology by a blood-based epigenome-wide association study of 4808 non-diabetic Europeans in the discovery phase and 11,750 individuals in the replication. We identify CpGs in LETM1, RBM20, IRS2, MAN2A2 and the 1q25.3 region associated with fasting insulin, and in FCRL6, SLAMF1, APOBEC3H and the 15q26.1 region with fasting glucose. In silico cross-omics analyses highlight the role of differential methylation in the crosstalk between the adaptive immune system and glucose homeostasis. The differential methylation explains at least 16.9% of the association between obesity and insulin. Our study sheds light on the biological interactions between genetic variants driving differential methylation and gene expression in the early pathogenesis of T2D

Newborn DNA-methylation, childhood lung function, and the risks of asthma and COPD across the life course

Rationale: We aimed to identify differentially methylated regions (DMRs) in cord blood DNA associated with childhood lung function, asthma and chronic obstructive pulmonary disease (COPD) across the life course. Methods: We meta-analysed epigenome-wide data of 1688 children from five cohorts to identify cord blood DMRs and their annotated genes, in relation to forced expiratory volume in 1 s (FEV1), FEV1/forced vital capacity (FVC) ratio and forced expiratory flow at 75% of FVC at ages 7-13 years. Identified DMRs were explored for associations with childhood asthma, adult lung function and COPD, gene expression and involvement in biological processes. Results: We identified 59 DMRs associated with childhood lung function, of which 18 were associated with childhood asthma and nine with COPD in adulthood. Genes annotated to the top 10 identified DMRs were HOXA5, PAOX, LINC00602, ABCA7, PER3, CLCA1, VENTX, NUDT12, PTPRN2 and TCL1A. Differential gene expression in blood was observed for 32 DMRs in childhood and 18 in adulthood. Genes related with 16 identified DMRs were associated with respiratory developmental or pathogenic pathways. Interpretation: Our findings suggest that the epigenetic status of the newborn affects respiratory health and disease across the life course

Genetically defined elevated homocysteine levels do not result in widespread changes of DNA methylation in leukocytes

BACKGROUND:DNA methylation is affected by the activities of the key enzymes and intermediate metabolites of the one-carbon pathway, one of which involves homocysteine. We investigated the effect of the well-known genetic variant associated with mildly elevated homocysteine: MTHFR 677C>T independently and in combination with other homocysteine-associated variants, on genome-wide leukocyte DNA-methylation. METHODS:Methylation levels were assessed using Illumina 450k arrays on 9,894 individuals of European ancestry from 12 cohort studies. Linear-mixed-models were used to study the association of additive MTHFR 677C>T and genetic-risk score (GRS) based on 18 homocysteine-associated SNPs, with genome-wide methylation. RESULTS:Meta-analysis revealed that the MTHFR 677C>T variant was associated with 35 CpG sites in cis, and the GRS showed association with 113 CpG sites near the homocysteine-associated variants. Genome-wide analysis revealed that the MTHFR 677C>T variant was associated with 1 trans-CpG (nearest gene ZNF184), while the GRS model showed association with 5 significant trans-CpGs annotated to nearest genes PTF1A, MRPL55, CTDSP2, CRYM and FKBP5. CONCLUSIONS:Our results do not show widespread changes in DNA-methylation across the genome, and therefore do not support the hypothesis that mildly elevated homocysteine is associated with widespread methylation changes in leukocytes