SUMMARY MicroRNAs (miRNAs) are crucial for normal embryonic stem (ES) cell self-renewal and cellular differentiation, but how miRNA gene expression is controlled by the key transcriptional regulators of ES cells has not been established. We describe here a new map of the transcriptional regulatory circuitry of ES cells that incorporates both protein-coding and miRNA genes, and which is based on high-resolution ChIP-seq data, systematic identification of miRNA promoters, and quantitative sequencing of short transcripts in multiple cell types. We find that the key ES cell transcription factors are associated with promoters for most miRNAs that are preferentially expressed in ES cells and with promoters for a set of silent miRNA genes. This silent set of miRNA genes is co-occupied by Polycomb Group proteins in ES cells and expressed in a tissue-specific fashion in differentiated cells. These data reveal how key ES cell transcription factors promote the miRNA expression program that contributes to normal self-renewal and cellular differentiation, and integrate miRNAs and their targets into an expanded model of the regulatory circuitry controlling ES cell identity.

BackgroundAssays of the abundance of immune cell populations in the tumor microenvironment promise to inform immune oncology research and the choice of immunotherapy for individual patients. We propose to measure the intratumoral abundance of various immune cell populations with gene expression. In contrast to IHC and flow cytometry, gene expression assays yield high information content from a clinically practical workflow. Previous studies of gene expression in purified immune cells have reported hundreds of genes showing enrichment in a single cell type, but the utility of these genes in tumor samples is unknown. We use co-expression patterns in large tumor gene expression datasets to evaluate previously reported candidate cell type marker genes lists, eliminate numerous false positives and identify a subset of high confidence marker genes.MethodsUsing a novel statistical tool, we use co-expression patterns in 9986 samples from The Cancer Genome Atlas (TCGA) to evaluate previously reported cell type marker genes. We compare immune cell scores derived from these genes to measurements from flow cytometry and immunohistochemistry. We characterize the reproducibility of our cell scores in replicate runs of RNA extracted from FFPE tumor tissue.ResultsWe identify a list of 60 marker genes whose expression levels measure 14 immune cell populations. Cell type scores calculated from these genes are concordant with flow cytometry and IHC readings, show high reproducibility in replicate RNA samples from FFPE tissue and enable detailed analyses of the anti-tumor immune response in TCGA. In an immunotherapy dataset, they separate responders and non-responders early on therapy and provide an intricate picture of the effects of checkpoint inhibition. Most genes previously reported to be enriched in a single cell type have co-expression patterns inconsistent with cell type specificity.ConclusionsDue to their concise gene set, computational simplicity and utility in tumor samples, these cell type gene signatures may be useful in future discovery research and clinical trials to understand how tumors and therapeutic intervention shape the immune response.Electronic supplementary materialThe online version of this article (doi:10.1186/s40425-017-0215-8) contains supplementary material, which is available to authorized users.

MicroRNAs are important negative regulators of protein-coding gene expression and have been studied intensively over the past years. Several measurement platforms have been developed to determine relative miRNA abundance in biological samples using different technologies such as small RNA sequencing, reverse transcription-quantitative PCR (RT-qPCR) and (microarray) hybridization. In this study, we systematically compared 12 commercially available platforms for analysis of microRNA expression. We measured an identical set of 20 standardized positive and negative control samples, including human universal reference RNA, human brain RNA and titrations thereof, human serum samples and synthetic spikes from microRNA family members with varying homology. We developed robust quality metrics to objectively assess platform performance in terms of reproducibility, sensitivity, accuracy, specificity and concordance of differential expression. The results indicate that each method has its strengths and weaknesses, which help to guide informed selection of a quantitative microRNA gene expression platform for particular study goals.

Modification of microRNA sequences by the 3′ addition of nucleotides to generate so-called “isomiRs” adds to the complexity of miRNA function, with recent reports showing that 3′ modifications can influence miRNA stability and efficiency of target repression. Here, we show that the 3′ modification of miRNAs is a physiological and common post-transcriptional event that shows selectivity for specific miRNAs and is observed across species ranging from C. elegans to human. The modifications result predominantly from adenylation and uridylation and are seen across tissue types, disease states, and developmental stages. To quantitatively profile 3′ nucleotide additions, we developed and validated a novel assay based on NanoString Technologies' nCounter platform. For certain miRNAs, the frequency of modification was altered by processes such as cell differentiation, indicating that 3′ modification is a biologically regulated process. To investigate the mechanism of 3′ nucleotide additions, we used RNA interference to screen a panel of eight candidate miRNA nucleotidyl transferases for 3′ miRNA modification activity in human cells. Multiple enzymes, including MTPAP, PAPD4, PAPD5, ZCCHC6, ZCCHC11, and TUT1, were found to govern 3′ nucleotide addition to miRNAs in a miRNA-specific manner. Three of these enzymes–MTPAP, ZCCHC6, and TUT1–have not previously been known to modify miRNAs. Collectively, our results indicate that 3′ modification observed in next-generation small RNA sequencing data is a biologically relevant process, and identify enzymatic mechanisms that may lead to new approaches for modulating miRNA activity in vivo.