mRNA PGC-1α levels in blood samples reliably correlates with its myocardial expression: study in patients undergoing cardiac surgery

et al.[Objective]: Peroxisome proliferator-activated receptor-γ coactivator-1α (PGC-1α) is a transcriptional coactivator that has been proposed to play a protective role in mouse models of cardiac ischemia and heart failure, suggesting that PGC-1α could be relevant as a prognostic marker. Our previous studies showed that the estimation of peripheral mRNA PGC-1α expression was feasible and that its induction correlated with the extent of myocardial necrosis and left ventricular remodeling in patients with myocardial infarction. In this study, we sought to determine if the myocardial and peripheral expressions of PGC-1α are well correlated and to analyze the variability of PGC-1α expression depending on the prevalence of some metabolic disorders. [Methods]: This was a cohort of 35 consecutive stable heart failure patients with severe aortic stenosis who underwent an elective aortic valve replacement surgery. mRNA PGC-1α expression was simultaneously determined from myocardial biopsy specimens and blood samples obtained during surgery by quantitative PCR, and a correlation between samples was made using the Kappa index. Patients were divided into two groups according to the detection of baseline expression levels of PGC-1α in blood samples, and comparisons between both groups were made by chi-square test or unpaired Student’s t-test as appropriate. [Results]: Based on myocardial biopsies, we found that mRNA PGC-1α expression in blood samples showed a statistically significant correlation with myocardial expression (Kappa index 0.66, p<0.001). The presence of higher systemic PGC-1α expression was associated with a greater expression of some target genes such as silent information regulator 2 homolog-1 (x-fold expression in blood samples: 4.43±5.22 vs. 1.09±0.14, p=0.044) and better antioxidant status in these patients (concentration of Trolox: 0.40±0.05 vs. 0.34±0.65, p=0.006). [Conclusions]: Most patients with higher peripheral expression also had increased myocardial expression, so we conclude that the non-invasive estimation of mRNA PGC-1α expression from blood samples provides a good approach of the constitutive status of the mitochondrial protection system regulated by PGC-1α and that this could be used as prognostic indicator in cardiovascular disease.Grant from Sociedad Valenciana de Cardiología, 2013 to Óscar Fabregat-Andrés.Peer Reviewe

Number of imputed SNPs, number and percentage of significant SNPs with missings imputed, mean, median and maximum of the fraction of missing information () for multinomial logit models with five different sets of predictors.

<p>The last column (% reversal) indicates the percentage of SNPs whose test results changed status (from significant to non-significant or the reverse) in comparison with a test omitting missings.</p

Scatter plot of inbreeding coefficients for 1070 non-monomorphic SNPs with missings obtained by multiple imputation (MICE) and single imputation (IMPUTE2).

<p>Scatter plot of inbreeding coefficients for 1070 non-monomorphic SNPs with missings obtained by multiple imputation (MICE) and single imputation (IMPUTE2).</p

Significance tests of equal mean intensities for missing and non-missing genotyping results.

<p>Number and percentage of significance tests are given for 140 non-monomorphic SNPs with between 10 and 50% missing values (). Results are given for tests with and without homocedasticity assumption ( is the intensity variance of the completely observed genotypes, is the intensity variance of the missing genotypes).</p

Estimation of inbreeding coefficients by multiple imputation and by omitting missings.

<p>Left panel: using allele intensities only. Right panel: using allele intensities and covariate SNPs in LD (complete and incomplete) with . Symbols indicate the result of two significance tests: a test for HWP discarding missings and a test for HWP with imputation of missings. Circles: SNPs with both tests non-significant; Diamonds: SNPs with both tests significant; Upward triangles: SNPs with a significant chi-square test when missings are omitted, but an insignificant test when missings are imputed. Downward triangles: SNPs with a non-significant chi-square test when missings are omitted, but a significant test when missings are imputed.</p

Ternary plots and Q-Q plots for Hardy-Weinberg proportions.

<p>Curves in the ternary plots indicate the HW parabola, and the limits of the 95% acceptance region of a test for HWP. Top row plots are for 545 fully observed SNPs. Bottom row plots are for 140 SNPs with 10 to 50% missings (missings were discarded in these plots). The Q-Q plots show two lines, a solid reference line and an estimate of the linear tendency in the cloud of points (dashed).</p

Inbreeding coefficients, confidence intervals, <i>p</i>-values and missing data statistics (relative increase in variance (), and fraction of missing information ()) for multiple imputation with different multinomial logit models, and for single imputation with IMPUTE2.

<p>Inbreeding coefficients, confidence intervals, <i>p</i>-values and missing data statistics (relative increase in variance (), and fraction of missing information ()) for multiple imputation with different multinomial logit models, and for single imputation with IMPUTE2.</p

Simulation results.

<p>Overall percentage of missing data, percentage of SNPs with missings, probabilities of missingness for the three genotypes and the root mean squared error (RSME) for the inbreeding coefficient () when missings are discarded, imputed by MICE or imputed by IMPUTE2, under MCAR and MNAR.</p

Intensity plot of a G/T polymorphism for 146 individuals.

<p>Missing values (NA, 33% of the data) indicated by black crosses occur mainly at the boundaries of homozygotes and heterozygotes.</p

Ternary plots of <i>m</i> = 50 imputed data set for the G/T polymorphism of Figure 1.

<p>Curves in the ternary plots indicate the HW parabola, and the limits of the 95% acceptance region of a test for HWP. Left panel: imputed data sets with allele intensities as covariates (model 3). Right panel: imputed data sets with allele intensities and 1 covariate SNP (model 5).</p