Exosomal microRNA miR-92a concentration in serum reflects human brown fat activity.

Brown adipose tissue (BAT) dissipates energy and its activity correlates with leanness in human adults. (18)F-fluorodeoxyglucose ((18)F-FDG) positron emission tomography coupled with computer tomography (PET/CT) is still the standard for measuring BAT activity, but exposes subjects to ionizing radiation. To study BAT function in large human cohorts, novel diagnostic tools are needed. Here we show that brown adipocytes release exosomes and that BAT activation increases exosome release. Profiling miRNAs in exosomes released from brown adipocytes, and in exosomes isolated from mouse serum, we show that levels of miRNAs change after BAT activation in vitro and in vivo. One of these exosomal miRNAs, miR-92a, is also present in human serum exosomes. Importantly, serum concentrations of exosomal miR-92a inversely correlate with human BAT activity measured by (18)F-FDG PET/CT in two unique and independent cohorts comprising 41 healthy individuals. Thus, exosomal miR-92a represents a potential serum biomarker for BAT activity in mice and humans.

Large scale statistical inference of signaling pathways from RNAi and microarray data.

BACKGROUND: The advent of RNA interference techniques enables the selective silencing of biologically interesting genes in an efficient way. In combination with DNA microarray technology this enables researchers to gain insights into signaling pathways by observing downstream effects of individual knock-downs on gene expression. These secondary effects can be used to computationally reverse engineer features of the upstream signaling pathway. RESULTS: In this paper we address this challenging problem by extending previous work by Markowetz et al., who proposed a statistical framework to score networks hypotheses in a Bayesian manner. Our extensions go in three directions: First, we introduce a way to omit the data discretization step needed in the original framework via a calculation based on p-values instead. Second, we show how prior assumptions on the network structure can be incorporated into the scoring scheme using regularization techniques. Third and most important, we propose methods to scale up the original approach, which is limited to around 5 genes, to large scale networks. CONCLUSION: Comparisons of these methods on artificial data are conducted. Our proposed module network is employed to infer the signaling network between 13 genes in the ER-alpha pathway in human MCF-7 breast cancer cells. Using a bootstrapping approach this reconstruction can be found with good statistical stability. The code for the module network inference method is available in the latest version of the R-package nem, which can be obtained from the Bioconductor homepage.