Measuring microRNA expression in size-limited FACS-sorted and microdissected samples.

MicroRNAs (miRNAs) are small noncoding RNAs of an average length of 22 nucleotides, which repress translation of a large number of target mRNAs. The particular importance of this group of small RNAs arises from the ever growing evidence that they control many biological processes, such as differentiation, proliferation, and apoptosis and that deregulation of individual miRNAs frequently results in cancer. The expression of miRNAs is spatially and temporarily fine-tuned and expression levels can reach more than 50,000 copies of one miRNA within a single cell. It is well documented that the comparison of miRNA signatures of normal and diseased tissues results in a small number of differentially expressed miRNAs, which are consequently of high diagnostic value. However, measuring miRNA expression can easily produce false-positive results, due to the high sequence similarity of the miRNAs within families and because biologically inactive pre-miRNAs as well as contaminating bystander cells may falsify the signal. The application of a quantitative PCR-based method is described here to specifically and reliably detect miRNA expression levels from as little as 50 cells. Pure cell populations were either derived from fluorescence-activated cell sorting (FACS) or laser capture microdissection (LCM). Importantly, a combination of quantitative PCR and LCM can also be applied to measure miRNA expression of cells obtained from formalin-fixed, paraffin-embedded (FFPE) tissues, thereby giving experimental access to archives with large numbers of routinely collected normal and diseased tissue samples

Posttranscriptional regulation of T helper cell fate decisions.

T helper cell subsets orchestrate context- and pathogen-specific responses of the immune system. They mostly do so by secreting specific cytokines that attract or induce activation and differentiation of other immune or nonimmune cells. The differentiation of T helper 1 (Th1), Th2, T follicular helper, Th17, and induced regulatory T cell subsets from naive T cells depends on the activation of intracellular signal transduction cascades. These cascades originate from T cell receptor and costimulatory receptor engagement and also receive critical input from cytokine receptors that sample the cytokine milieu within secondary lymphoid organs. Signal transduction then leads to the expression of subset-specifying transcription factors that, in concert with other transcription factors, up-regulate downstream signature genes. Although regulation of transcription is important, recent research has shown that posttranscriptional and posttranslational regulation can critically shape or even determine the outcome of Th cell differentiation. In this review, we describe how specific microRNAs, long noncoding RNAs, RNA-binding proteins, and ubiquitin-modifying enzymes regulate their targets to skew cell fate decisions

In human glioblastomas transcript elongation by alternative polyadenylation and miRNA targeting is a potent mechanism of MGMT silencing.

Favorable outcome after chemotherapy of glioblastomas cannot unequivocally be linked to promoter hypermethylation of the O (6)-methylguanine-DNA methyltransferase (MGMT) gene encoding a DNA repair enzyme associated with resistance to alkylating agents. This indicates that molecular mechanisms determining MGMT expression have not yet been fully elucidated. We here show that glioblastomas are capable to downregulate MGMT expression independently of promoter methylation by elongation of the 3'-UTR of the mRNA, rendering the alternatively polyadenylated transcript susceptible to miRNA-mediated suppression. While the elongated transcript is poorly expressed in normal brain, its abundance in human glioblastoma specimens is inversely correlated with MGMT mRNA expression. Using a bioinformatically guided experimental approach, we identified miR-181d, miR-767-3p, and miR-648 as significant post-transcriptional regulators of MGMT in glioblastomas; the first two miRNAs induce MGMT mRNA degradation, the latter affects MGMT protein translation. A regression model including the two miRNAs influencing MGMT mRNA expression and the MGMT methylation status reliably predicts The Cancer Genome Atlas MGMT expression data. Responsivity of MGMT expressing T98G glioma cells to temozolomide was significantly enhanced after transfection of miR-181d, miR-767-3p, and miR-648. Taken together, our results uncovered alternative polyadenylation of the MGMT 3'-UTR and miRNA targeting as new mechanisms of MGMT silencing