Differential placental methylation and expression of VEGF, FLT-1 and KDR genes in human term and preterm preeclampsia

BACKGROUND: Preeclampsia, a pregnancy complication of placental origin is associated with altered expression of angiogenic factors and their receptors. Recently, there is considerable interest in understanding the role of adverse intrauterine conditions in placental dysfunction and adverse pregnancy outcomes. Since we have observed changes in placental global DNA methylation levels in preeclampsia, this study was undertaken to examine gene promoter CpG methylation and expression of several angiogenic genes. We recruited 139 women comprising, 46 normotensive women with term delivery (≥37 weeks), 45 women with preeclampsia delivering preterm (<37 weeks) and 48 women with preeclampsia delivering at term. Expression levels and promoter CpG methylation of VEGF, FLT-1 and KDR genes in placentae from respective groups were determined by Taqman-based quantitative real time PCR and by the Sequenom® EpiTYPER™ technology respectively. RESULTS: We observed several differentially methylated CpG sites in the promoter regions of VEGF, FLT-1 and KDR between the normotensive and preeclampsia groups. We specifically observed hypomethylated CpGs in the promoter region and an increased expression of VEGF gene between term and preterm preeclampsia. However, mean promoter CpG methylation could not account for the higher expression of FLT-1 and KDR in preterm preeclampsia as compared to normotensive group. CONCLUSIONS: Our data indicates altered DNA methylation patterns in the VEGF, FLT-1 and KDR genes in preeclampsia as compared to the normotensive group, which could be involved in the pathophysiology of preeclampsia. Hypomethylation of VEGF promoter and consequent upregulation of VEGF mRNA levels could be a compensatory mechanism to restore normal angiogenesis and blood flow in preterm preeclampsia. This study suggests a role of altered DNA methylation in placental angiogenesis and in determining adverse pregnancy outcomes

High Resolution Methylome Map of Rat Indicates Role of Intragenic DNA Methylation in Identification of Coding Region

DNA methylation is crucial for gene regulation and maintenance of genomic stability. Rat has been a key model system in understanding mammalian systemic physiology, however detailed rat methylome remains uncharacterized till date. Here, we present the first high resolution methylome of rat liver generated using Methylated DNA immunoprecipitation and high throughput sequencing (MeDIP-Seq) approach. We observed that within the DNA/RNA repeat elements, simple repeats harbor the highest degree of methylation. Promoter hypomethylation and exon hypermethylation were common features in both RefSeq genes and expressed genes (as evaluated by proteomic approach). We also found that although CpG islands were generally hypomethylated, about 6% of them were methylated and a large proportion (37%) of methylated islands fell within the exons. Notably, we obeserved significant differences in methylation of terminal exons (UTRs); methylation being more pronounced in coding/partially coding exons compared to the non-coding exons. Further, events like alternate exon splicing (cassette exon) and intron retentions were marked by DNA methylation and these regions are retained in the final transcript. Thus, we suggest that DNA methylation could play a crucial role in marking coding regions thereby regulating alternative splicing. Apart from generating the first high resolution methylome map of rat liver tissue, the present study provides several critical insights into methylome organization and extends our understanding of interplay between epigenome, gene expression and genome stability

Migration and DNA methylation: a comparison of methylation patterns in type 2 diabetes susceptibility genes between indians and europeans

BACKGROUND: Type 2 diabetes is a global problem that is increasingly prevalent in low and middle income countries including India, and is partly attributed to increased urbanisation. Genotype clearly plays a role in type 2 diabetes susceptibility. However, the role of DNA methylation and its interaction with genotype and metabolic measures is poorly understood. This study aimed to establish whether methylation patterns of type 2 diabetes genes differ between distinct Indian and European populations and/or change following rural to urban migration in India. METHODS: Quantitative DNA methylation analysis in Indians and Europeans using Sequenom(®) EpiTYPER(®) technology was undertaken in three genes: ADCY5, FTO and KCNJ11. Metabolic measures and genotype data were also analysed. RESULTS: Consistent differences in DNA methylation patterns were observed between Indian and European populations in ADCY5, FTO and KCNJ11. Associations were demonstrated between FTO rs9939609 and BMI and between ADCY5rs17295401 and HDL levels in Europeans. However, these observations were not linked to local variation in DNA methylation levels. No differences in methylation patterns were observed in urban-dwelling migrants compared to their non-migrant rural-dwelling siblings in India. CONCLUSIONS: Analysis of DNA methylation at three type 2 diabetes susceptibility loci highlighted geographical and ethnic differences in methylation patterns. These differences may be attributed to genetic and/or region-specific environmental factors

Genomic distribution of methylated and unmethylated CGI.

<p>CpG Island of each methylated and unmethylated Islands were classified in different bins on the basis of size. A – Number of methylated CpG Islands in a particular bin was calculated in different regions like intron, exon, promoter (5 kb upstream from the transcription start site) and rest was put in others category. The count was then normalized by the total number of CpG Island in that bin. B – Number of unmethylated CpG Island of bin was calculated in different regions like intron, exon, promoter (5 kb upstream from the transcription start site) and others, and the count was then normalized by the total number of CpG Islands in that bin.</p

Methylation marks the coding region.

<p>Third exon of the <i>Ccdc 75</i> gene shows methylation in MeDIP-Seq data as visualized in UCSC genome browser.</p

Chromosomal distribution of DNA methylation.

<p>Graphical representation of chromosome wide distribution of methylation peaks of chromosome 1, 2 and 3 along with their GC percentage (dark black color), Refseq genes (blue color), CpG Islands (green color), and chromosome band in UCSC Genome Browser.</p

Average methylation density around transcription start site (TSS).

<p>A - Distribution of peak summit count in 100 bp sliding window, 5 kb upstream and downstream from the start site was calculated for all RefSeq genes and identified liver proteins. Count was normalized by dividing individual count with total number of genes in that category. The plot obtained of RefSeq and identified liver proteins were further smoothened by taking a moving average of 5. B – Similar distribution of peak summit count in 100 bp sliding window, 5 kb upstream and downstream from the transcription start site was calculated for up regulated and down regulated genes in normal rat liver tissue. Smoothing of peaks was done by taking moving average of 5.</p

Average methylation density at the intron-exon-intron junctions.

<p>Distribution of peak summit count in 10 bp sliding window, 200 bp upstream and downstream from the start site and end site of exons was calculated for all RefSeq gene exons, first exon and all last exons. Smoothing of peaks was done by taking moving average of 5.</p

Methylation in alternate splice events.

<p>Methylation in genomic features along with Intron retention class of alternative splicing events was calculated. Genomic features include RefSeq exons, introns, identified liver expressed gene exons and introns. Three bins were created: 1) 200 bp upstream from start site of the event, 2) from start site to end of the event, 3) 200 bp downstream from the end. Peak summit count obtained in all bins was normalized by dividing the count with the area of that bin. Distribution of peak summit count in 10 bp sliding window, 200 bp upstream and downstream from the start site of all RefSeq exons and cassette exons.</p

Methylation distribution of first and last exons based on presence and absence of coding region.

<p>The first and last exons were further classified as coding exons and non-coding exons based on the fact that they contain coding region within them or not. (a), (b) represents the methylation of rat RefSeq first exon and last exons while (c), (d) represent the methylation pattern in Human RefSeq first exon and last exons plotted using the MeDIP-Seq data from Human brain tissue. Distribution of peak summit count in 10 bp sliding window, 200 bp upstream and downstream from the start site and end site of exons was calculated for first exon and all last exons. Smoothing of peaks was done by taking moving average of 5.</p