757 publications from this institution
Macrophages play a crucial role in immune responses and undergo metabolic reprogramming to fulfill their functions. The tetramerization of the glycolytic enzyme pyruvate kinase M2 (PKM2) induces the production of the anti-inflammatory cytokine interleukin (IL)-10 in vivo, but the underlying mechanism remains elusive. Here, we report that PKM2 activation with the pharmacological agent TEPP-46 increases IL-10 production in LPS-activated macrophages by metabolic reprogramming, leading to the production and release of ATP from glycolysis. The effect of TEPP-46 is abolished in PKM2-deficient macrophages. Extracellular ATP is converted into adenosine by ectonucleotidases that activate adenosine receptor A2a (A2aR) to enhance IL-10 production. Interestingly, IL-10 production induced by PKM2 activation is associated with improved mitochondrial health. Our results identify adenosine derived from glycolytic ATP as a driver of IL-10 production, highlighting the role of tetrameric PKM2 in regulating glycolysis to promote IL-10 production.
MicroRNAs (miRNAs) are non-coding RNAs that regulate gene expression by binding to the messenger RNA (mRNA) of protein coding genes. They control gene expression by either inhibiting translation or inducing mRNA degradation. A number of computational techniques have been developed to identify the targets of miRNAs. In this study we used predicted miRNA-gene interactions to analyse mRNA gene expression microarray data to predict miRNAs associated with particular diseases or conditions.Here we combine correspondence analysis, between group analysis and co-inertia analysis (CIA) to determine which miRNAs are associated with differences in gene expression levels in microarray data sets. Using a database of miRNA target predictions from TargetScan, TargetScanS, PicTar4way PicTar5way, and miRanda and combining these data with gene expression levels from sets of microarrays, this method produces a ranked list of miRNAs associated with a specified split in samples. We applied this to three different microarray datasets, a papillary thyroid carcinoma dataset, an in-house dataset of lipopolysaccharide treated mouse macrophages, and a multi-tissue dataset. In each case we were able to identified miRNAs of biological importance.We describe a technique to integrate gene expression data and miRNA target predictions from multiple sources.