MicroRNA Expression in Abdominal and Gluteal Adipose Tissue Is Associated with mRNA Expression Levels and Partly Genetically Driven

Abstract

To understand how miRNAs contribute to the molecular phenotype of adipose tissues and related traits, we performed global miRNA expression profiling in subcutaneous abdominal and gluteal adipose tissue of 70 human subjects and characterised which miRNAs were differentially expressed between these tissues. We found that 12% of the miRNAs were significantly differentially expressed between abdominal and gluteal adipose tissue (FDR adjusted p<0.05) in the primary study, of which 59 replicated in a follow-up study of 40 additional subjects. Further, 14 miRNAs were found to be associated with metabolic syndrome case-control status in abdominal tissue and three of these replicated (primary study: FDR adjusted p<0.05, replication: p<0.05 and directionally consistent effect). Genome-wide genotyping was performed in the 70 subjects to enable miRNA expression quantitative trait loci (eQTL) analysis. Candidate miRNA eQTLs were followed-up in the additional 40 subjects and six significant, independent cis-located miRNA eQTLs (primary study: p<0.001; replication: p<0.05 and directionally consistent effect) were identified. Finally, global mRNA expression profiling was performed in both tissues to enable association analysis between miRNA and target mRNA expression levels. We find 22% miRNAs in abdominal and 9% miRNAs in gluteal adipose tissue with expression levels significantly associated with the expression of corresponding target mRNAs (FDR adjusted p<0.05). Taken together, our results indicate a clear difference in the miRNA molecular phenotypic profile of abdominal and gluteal adipose tissue, that the expressions of some miRNAs are influenced by cis-located genetic variants and that miRNAs are associated with expression levels of their predicted mRNA targets.

A genomic portrait of the genetic architecture and regulatory impact of microRNA expression in response to infection

“…We first showed that the expression of 3% of miRNAs is explained by proximate genetic factors, consistent with several previous estimates (Rantalainen et al 2011;Parts et al 2012;Civelek et al 2013). That 9% of protein-coding genes harbor cis-regulatory variants in the same samples, at an FDR of only 0.01 (Barreiro et al 2012), supports the notion that miRNA expression is under less genetic control than that of mRNAs (Su et al 2011;Civelek et al 2013;Lappalainen et al 2013).…”

“…The extent to which this variation is under genetic control (i.e., miRNA expression quantitative trait loci, miR-eQTLs) has recently begun to be investigated (Borel et al 2011;Rantalainen et al 2011;Gamazon et al 2012;Parts et al 2012;Civelek et al 2013;Gamazon et al 2013;Lappalainen et al 2013). However, as has been shown for mRNAs in yeast and mammals, genetic variants can differentially affect gene expression after perturbation by various treatments or environmental variables (i.e., response eQTLs) (Gargalovic et al 2006;Smith and Kruglyak 2008;Smirnov et al 2009;Yang et al 2009;Dombroski et al 2010;Romanoski et al 2010;Maranville et al 2011;Barreiro et al 2012).…”

Incorporating inter-relationships between different levels of genomic data into cancer clinical outcome prediction

“…In order to identify genes that are associated with gene dosage, many integrative analyses between copy number and gene expression have been conducted [26–29]. In addition, miRNA as one of the important regulators of gene expression can be integrated with gene expression to identify the selective inhibition of translation or selective degradation [30–32]. Furthermore, in terms of epigenetic regulation, histone modification or DNA methylation can serve to regulate gene expression in cancer [33–36].…”

A note on statistical repeatability and study design for high-throughput assays

“…The two technical replicates, each comprising measurements from multiple analytes, are plotted against each other, one point per analyte, and the corresponding sample correlation coefficient, r , is reported as a measure of experimental precision; see for example 9, 10, 11, 12, 13, 14, 15. As illustration, Figure 1a–d displays this method applied to a pair of replicates from each of four representative high‐throughput assays 20, 21, 22, 23.…”

“…Returning to the four data sets 20, 21, 22, 23 introduced in Figure 1a–d, we estimated their corresponding variance components directly on each full set of data (Table 1). We found each assay's average noise variance ${\overline{v}}_e$ to be of a similar magnitude to its signal ${\overline{v}}_b$, but two to three orders of magnitude smaller than its dynamic range v d .…”