N6-methyladenosine (m6A) is a common modification of mRNA with potential roles in fine-tuning the RNA life cycle. Here, we identify a dense network of proteins interacting with METTL3, a component of the methyltransferase complex, and show that three of them (WTAP, METTL14, and KIAA1429) are required for methylation. Monitoring m6A levels upon WTAP depletion allowed the definition of accurate and near single-nucleotide resolution methylation maps and their classification into WTAP-dependent and -independent sites. WTAP-dependent sites are located at internal positions in transcripts, topologically static across a variety of systems we surveyed, and inversely correlated with mRNA stability, consistent with a role in establishing "basal" degradation rates. WTAP-independent sites form at the first transcribed base as part of the cap structure and are present at thousands of sites, forming a previously unappreciated layer of transcriptome complexity. Our data shed light on the proteomic and transcriptional underpinnings of this RNA modification.
Extracellular vesicles (EVs) are the collective term for the various vesicles that are released by cells into the extracellular space. Such vesicles include exosomes and microvesicles, which vary by their size and/or protein and genetic cargo. With the discovery that EVs contain genetic material in the form of RNA (evRNA) has come the increased interest in these vesicles for their potential use as sources of disease biomarkers and potential therapeutic agents. Rapid developments in the availability of deep sequencing technologies have enabled the study of EV-related RNA in detail. In October 2012, the International Society for Extracellular Vesicles (ISEV) held a workshop on "evRNA analysis and bioinformatics." Here, we report the conclusions of one of the roundtable discussions where we discussed evRNA analysis technologies and provide some guidelines to researchers in the field to consider when performing such analysis.
The phosphoinositide 3-kinase (PI3-kinase)-protein kinase B (Akt) signaling pathway is essential in the induction of physiological cardiac hypertrophy. In contrast, protein kinase C beta2 (PKCbeta2) is implicated in the development of pathological cardiac hypertrophy and heart failure. Thus far, no clear association has been demonstrated between these two pathways. In this study, we examined the potential interaction between the PI3-kinase and PKCbeta2 pathways by crossing transgenic mice with cardiac specific expression of PKCbeta2, constitutively active (ca) PI3-kinase, and dominant-negative (dn) PI3-kinase. In caPI3-kinase/PKCbeta2 and dnPI3-kinase/PKCbeta2 double-transgenic mice, the heart weight-to-body weight ratios and cardiomyocyte sizes were similar to those observed in caPI3-kinase and dnPI3-kinase transgenic mice, respectively, suggesting that the regulation of physiological developmental hypertrophy via modulation of cardiomyocyte size proceeds through the PI3-kinase pathway. In addition, we observed that caPI3-kinase/PKCbeta2 mice showed improved cardiac function while the function of dnPI3-kinase/PKCbeta2 mice was similar to that of the PKCbeta2 group. PKCbeta2 protein levels in both dnPI3-kinase/PKCbeta2 and PKCbeta2 mice were significantly upregulated. Interestingly, however, PKCbeta2 protein expression was significantly attenuated in caPI3-kinase/PKCbeta2 mice. PI3-kinase activity measured by Akt phosphorylation was not affected by PKCbeta2 overexpression. These data suggest a potential interaction between these two pathways in the heart, where PI3-kinase is predominantly responsible for the regulation of physiological developmental hypertrophy and may act as an upstream modulator of PKCbeta2 with the potential for rescuing the pathological cardiac dysfunction induced by overexpression of PKCbeta.
Next-generation sequencing offers many advantages over other methods of microRNA (miRNA) expression profiling, such as sample throughput and the capability to discover novel miRNAs. As the sequencing depth of current sequencing platforms exceeds what is necessary to quantify miRNAs, multiplexing several samples in one sequencing run offers a significant cost advantage. Although previous studies have achieved this goal by adding bar codes to miRNA libraries at the ligation step, this was recently shown to introduce significant bias into the miRNA expression data. This bias can be avoided, however, by bar coding the miRNA libraries at the PCR step instead. Here, we describe a user-friendly PCR bar-coding method of preparing multiplexed microRNA libraries for Illumina-based sequencing. The method also prevents the production of adapter dimers and can be completed in one day.
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