Implication of miR-612 and miR-1976 in the regulation of <i>TP53</i> and <i>CD40</i> and their relationship in the response to specific weight-loss diets

<div><p>Background</p><p>Non-coding RNAs (i.e., miRNAs) play a role in the development of obesity and related comorbidities and the regulation of body weight.</p><p>Objective</p><p>To identify candidate miRNA biomarkers throughout omics approaches in order to predict the response to specific weight-loss dietary treatments.</p><p>Design</p><p>Genomic DNA and cDNA isolated from white blood cells of a subset from the RESMENA nutritional intervention study (Low-responders (LR) vs High-responders (HR)) was hybridized in Infinium Human Methylation450 BeadChip and in Illumina Human HT-12 v4 gene expression BeadChips arrays respectively. A bioinformatic prediction of putative target sites of selected miRNAs was performed by applying miRBase algorithms. HEK-293T cells were co-transfected with expression vectors containing the 3’-UTR of candidate genes to validate the binding of miRNAs to its target sites.</p><p>Results</p><p>134 miRNAs were differentially methylated between HR and LR in the methylation array, whereas 44 miRNAs were differentially expressed between both groups in the expression array. Specifically, miR-1237, miR-1976, miR-642, miR-636, miR-612 and miR-193B were simultaneously hypomethylated and overexpressed in HR. miR-612 and miR-1976 showed greatest differences in methylation and expression levels, respectively. The bioinformatic prediction revealed that <i>TP53</i> was a putative target gene of miR-612 and <i>CD40</i> of miR-1976. Moreover, <i>TP53</i> was downregulated in the expression array when comparing HR vs LR expression levels adjusted by sex, diet, age and baseline weight, and <i>CD40</i> showed a statistical trend. Furthermore, gene expression levels of <i>TP53</i> and <i>CD40</i> in white blood cells, when measured by qPCR, were also downregulated in HR. Finally, miR-612 and miR-1976 potently repressed <i>TP53</i> and <i>CD40</i> respectively by targeting its 3’-UTR regions.</p><p>Conclusion</p><p>miR-612 and miR-1976 levels could be prospective biomarkers of response to specific weight-loss diets and might regulate the gene expression of <i>TP53</i> and <i>CD40</i>.</p></div

Validation of miR-1976 and its target gene <i>CD40</i>.

<p>A) miR-612 expression levels by qPCR in HR and LR, adjusted for age, sex, diet and baseline body weight. * p < 0.05 from ANCOVA test. B) Pearson’s correlation between miR-1976 expression levels in the microarray and by qPCR, adjusted by sex, age, baseline body weight and diet. C) Gene expression of <i>CD40</i> in HR and LR in the microarray. t p = 0.069. D) Pearson’s correlations between miR-1976 expression levels and its target gene (<i>CD40</i>) in the microarray, adjusted by sex, age, baseline body weight and diet. E) Validation of <i>CD40</i> expression profile in HR and LR white blood cells by qPCR adjusted for age, sex, diet and baseline body weight. * p < 0.05 from ANCOVA test. F) Luciferase activity assays of pmiR-GLO-<i>CD40</i>-3’-UTR after co-transfection with miR-1976. Normalized luciferase activity is presented as the mean ± SEM of three separate triplicate experiments. * p < 0.05 from Student t-test.</p

Validation of miR-612 and its target gene <i>TP53</i>.

<p>A) Correlation between miR-612 methylation levels by microarray and EpiTyper. B) Gene expression of <i>TP53</i> in HR and LR in the microarray. C) Validation of <i>TP53</i> expression profile in HR and LR white blood cells by qPCR, adjusted for age, sex, diet and baseline body weight. * p < 0.05 from ANCOVA test. D) Luciferase activity assay of pmiR-GLO-<i>TP53</i>-3’-UTR after co-transfection with miR-612. Normalized luciferase activity is presented as the mean ± SEM of three separate triplicate experiments. *** p < 0.001 from Student t-test.</p

Identification of miR-612 and miR-1976 in the methylation and expression microarrays.

<p>A) Volcano plot of miRNAs differentially methylated between HR and LR. B) Volcano plot of miRNAs differentially expressed between HR and LR.</p

MC3R polymorphisms genotyped in this study and linkage disequilibrium (r²) measures.

<p>MC3R polymorphisms genotyped in this study and linkage disequilibrium (r²) measures.</p

Basic statistical analysis of <i>MC3R</i> variants.

<p>Basic statistical analysis of <i>MC3R</i> variants.</p

Drop out according to <i>MC3R</i> genotypes and type of diet.

<p>*Per-allele OR's for drop-outs and <i>MC3R</i> genotypes were calculated using logistic regression with the least frequent allele for each SNP coded a 0, 1 or 2 alleles (see statistical methods).</p

Flow chart of the NUGENOB trial and genotyping success of MC3R variants.

<p>Legend to <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0019934#pone-0019934-g001" target="_blank">Figure 1</a>: weight loss is expressed as mean ± standard deviation.</p

<i>MC3R</i> variants, fat mass and resting energy expenditure (REE) change in the NUGENOB trial.

<p>*Per-allele changes were calculated using the number of the least frequent alleles for each SNP (coded a 0, 1 or 2 alleles) and under the co-dominant model adjusted for covariates (see statistical methods). Changes in REE were assessed in a subset of the sample (440 participants).</p

<i>MC3R</i> variants, weight loss and waist circumference change in the NUGENOB trial.

<p>*Per-allele changes were calculated using the number of the least frequent alleles for each SNP (coded a 0, 1 or 2 alleles) and under the co-dominant model adjusted for covariates (see statistical methods).</p