15 research outputs found

    Association of the CpG Methylation Pattern of the Proximal Insulin Gene Promoter with Type 1 Diabetes

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    The insulin (INS) region is the second most important locus associated with Type 1 Diabetes (T1D). The study of the DNA methylation pattern of the 7 CpGs proximal to the TSS in the INS gene promoter revealed that T1D patients have a lower level of methylation of CpG -19, -135 and -234 (p = 2.10−16) and a higher methylation of CpG -180 than controls, while methylation was comparable for CpG -69, -102, -206. The magnitude of the hypomethylation relative to a control population was 8–15% of the corresponding levels in controls and was correlated in CpGs -19 and -135 (r = 0.77) and CpG -135 and -234 (r = 0.65). 70/485 (14%) of T1D patients had a simultaneous decrease in methylation of CpG -19, -135, -234 versus none in 317 controls. CpG methylation did not correlate with glycated hemoglobin or with T1D duration. The methylation of CpG -69, -102, -180, -206, but not CpG -19, -135, -234 was strongly influenced by the cis-genotype at rs689, a SNP known to show a strong association with T1D. We hypothesize that part of this genetic association could in fact be mediated at the statistical and functional level by the underlying changes in neighboring CpG methylation. Our observation of a CpG-specific, locus-specific methylation pattern, although it can provide an epigenetic biomarker of a multifactorial disease, does not indicate whether the reported epigenetic pattern preexists or follows the establishment of T1D. To explore the effect of chronic hyperglycemia on CpG methylation, we studied non obese patients with type 2 diabetes (T2D) who were found to have decreased CpG-19 methylation versus age-matched controls, similar to T1D (p = 2.10−6) but increased CpG-234 methylation (p = 5.10−8), the opposite of T1D. The causality and natural history of the different epigenetic changes associated with T1D or T2D remain to be determined

    DNA methylation dynamics during pregnancy

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    International audiencePregnancy is a state of multiple physiological adaptations. Since methylation of DNA is an epigenetic mechanism that regulates gene expression and contributes to adaptive phenotypic variations, we investigated methylation changes in maternal blood of a longitudinal cohort of pregnant women from the first trimester of gestation to the third. Interestingly, during pregnancy, we found a gain of methylation in genes involved in morphogenesis, such as ezrin, while we identified a loss of methylation in genes promoting maternal-infant bonding (AVP and PPP1R1B). Together, our results provide insights into the biological mechanisms underlying physiological adaptations during pregnancy

    Fetal growth is associated with CpG methylation in the P2 promoter of the IGF1 gene

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    Abstract Background There are many reasons to think that epigenetics is a key determinant of fetal growth variability across the normal population. Since IGF1 and INS genes are major determinants of intrauterine growth, we examined the methylation of selected CpGs located in the regulatory region of these two genes. Methods Cord blood was sampled in 159 newborns born to mothers prospectively followed during their pregnancy. A 142-item questionnaire was filled by mothers at inclusion, during the last trimester of the pregnancy and at the delivery. The methylation of selected CpGs located in the promoters of the IGF1 and INS genes was measured in cord blood mononuclear cells collected at birth using bisulfite-PCR-pyrosequencing. Results Methylation at IGF1 CpG-137 correlated negatively with birth length (r = 0.27, P = 3.5 × 10−4). The same effect size was found after adjustment for maternal age, parity, and smoking: a 10% increase in CpG-137 methylation was associated with a decrease of length by 0.23 SDS. Conclusion The current results suggest that the methylation of IGF1 CpG-137 contributes to the individual variation of fetal growth by regulating IGF1 expression in fetal tissues

    Identifying blue whiting (micromesistius poutassou) stock structure in the northeast atlantic by otolith shape analysis

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    Information on stock identification and spatial stock structure provide a basis for understanding fish population dynamics and improving fisheries management. In this study, otolith shape analysis was used to study the stock structure of blue whiting (Micromesistius poutassou) in the northeast Atlantic using 1693 samples from mature fish collected between 37 degrees N and 75 degrees N and 20 degrees W and 25 degrees E. The results indicated two stocks located north and south of ICES Divisions VIa and VIb (54 degrees 5N to 60 degrees 5N, 4 degrees W to 11 degrees W). The central area corresponds to the spawning area west of Scotland. Sampling year effects and misclassification in the linear discriminant analysis suggested exchanges between the northern and southern stocks. The results corroborate previous studies indicating a structuring of the blue whiting stock into two stocks, with some degree of mixing in the central overlap area

    CpG methylation in the <i>INS</i> promoter in T2D patients and controls.

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    <p><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0036278#s2" target="_blank">Results</a> are expressed as mean ± sd. <i>P</i>-values are calculated by Wilcoxon rank sum test.</p

    Lack of correlation between age and <i>INS</i> promoter methylation in T1D patients (green) and Controls (red).

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    <p>Only CpG -180 methylation showed a slight downward trend with age (r = 0.09, p = 0.01). The figure also shows the distribution of individual methylation values, and the differences between T1D and control subjects for CpGs -19, -135, -180, and -234.</p

    Boxplot of methylation values for the 7 CpGs in <i>INS</i> promoter with respect to SNPs other than rs689 in T1D patients.

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    <p>Methylation at CpGs -69, −102, −180 and −206 are associated with rs3842748. More distant SNPs showed a much weaker correlation with methylation at CpG -69 and -102 for rs4320932 and with methylation at CpGs -102, -135, -180 and −206 for rs6356. See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0036278#pone.0036278.s001" target="_blank">Figure S1</a> for SNP locations. P-values are calculated by Krukal-Wallis test.</p
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