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MotivationWhile many pipelines have been developed for calling genotypes using RNA-sequencing data, they all have adapted DNA genotype callers that do not model biases specific to RNA-sequencing such as reference panel bias or allele specific expression.ResultsHere, we present BBmix, a Bayesian Beta-Binomial mixture model that first learns the expected distribution of read counts for each genotype, and then deploys those learned parameters to call genotypes probabilistically. We benchmarked our model on a wide variety of datasets and showed that our method generally performed better than competitors, mainly due to an increase of up to 1.4% in the accuracy of heterozygous calls. Moreover, BBmix can be easily incorporated into standard pipelines for calling genotypes. We further show that parameters are generally transferable within datasets, such that a single learning run of less than one hour is sufficient to call genotypes in a large number of samples.AvailabilityWe implemented BBmix as an R package that is available for free under a GPL-2 licence athttps://gitlab.com/evigorito/bbmixand accompanying pipeline athttps://gitlab.com/evigorito/bbmix_pipeline.

The datasets uploaded in this repository allows to reproduce the figures (main and supplementary except diagrams) and data tables for the manuscript "Detection of quantitative trait loci from RNA-seq data with or without genotypes using BaseQTL" The zip files (fig_data.zip and source_data.z) contain the external data to run the figures. fig_data.zip contains the files necessary to make the main figures without further processing, while source_data.zip containsthe raw data for reproducing the figures and tables and files in fig_data. The tar.gz files contain the gitlab repository for baseqtl ( baseqtl_master, commit id 8fca4c829f408fbb93b4a7bc8445a2fd9c919016), for baseqtl_pipeline (baeqtl_pipeline, commit id0802ccd687c0c75198e83864f35d1a99d9c7671c) and for baseqtl_paper (baseqtl_paper_master, commit id e5b4518a741cebee532a643d594e0e8a9b0bc3a2). The code for running the figures is athttps://gitlab.com/evigorito/baseqtl_paper. &nbsp

Population-based genome wide association studies have identified a locus at 9p22.2 associated with ovarian cancer risk, which also modifies ovarian cancer risk in BRCA1 and BRCA2 mutation carriers. We conducted fine-scale mapping at 9p22.2 to identify potential causal variants in BRCA1 and BRCA2 mutation carriers. Genotype data were available for 15,252 (2,462 ovarian cancer cases) BRCA1 and 8,211 (631 ovarian cancer cases) BRCA2 mutation carriers. Following genotype imputation, ovarian cancer associations were assessed for 4,873 and 5,020 SNPs in BRCA1 and BRCA 2 mutation carriers respectively, within a retrospective cohort analytical framework. In BRCA1 mutation carriers one set of eight correlated candidate causal variants for ovarian cancer risk modification was identified (top SNP rs10124837, HR: 0.73, 95%CI: 0.68 to 0.79, p-value 2× 10-16). These variants were located up to 20 kb upstream of BNC2. In BRCA2 mutation carriers one region, up to 45 kb upstream of BNC2, and containing 100 correlated SNPs was identified as candidate causal (top SNP rs62543585, HR: 0.69, 95%CI: 0.59 to 0.80, p-value 1.0 × 10-6). The candidate causal in BRCA1 mutation carriers did not include the strongest associated variant at this locus in the general population. In sum, we identified a set of candidate causal variants in a region that encompasses the BNC2 transcription start site. The ovarian cancer association at 9p22.2 may be mediated by different variants in BRCA1 mutation carriers and in the general population. Thus, potentially different mechanisms may underlie ovarian cancer risk for mutation carriers and the general population.

Population-based genome wide association studies have identified a locus at 9p22.2 associated with ovarian cancer risk, which also modifies ovarian cancer risk in BRCA1 and BRCA2 mutation carriers. We conducted fine-scale mapping at 9p22.2 to identify potential causal variants in BRCA1 and BRCA2 mutation carriers. Genotype data were available for 15,252 (2,462 ovarian cancer cases) BRCA1 and 8,211 (631 ovarian cancer cases) BRCA2 mutation carriers. Following genotype imputation, ovarian cancer associations were assessed for 4,873 and 5,020 SNPs in BRCA1 and BRCA 2 mutation carriers respectively, within a retrospective cohort analytical framework. In BRCA1 mutation carriers one set of eight correlated candidate causal variants for ovarian cancer risk modification was identified (top SNP rs10124837, HR: 0.73, 95%CI: 0.68 to 0.79, p-value 2× 10−16). These variants were located up to 20 kb upstream of BNC2. In BRCA2 mutation carriers one region, up to 45 kb upstream of BNC2, and containing 100 correlated SNPs was identified as candidate causal (top SNP rs62543585, HR: 0.69, 95%CI: 0.59 to 0.80, p-value 1.0 × 10−6). The candidate causal in BRCA1 mutation carriers did not include the strongest associated variant at this locus in the general population. In sum, we identified a set of candidate causal variants in a region that encompasses the BNC2 transcription start site. The ovarian cancer association at 9p22.2 may be mediated by different variants in BRCA1 mutation carriers and in the general population. Thus, potentially different mechanisms may underlie ovarian cancer risk for mutation carriers and the general population.

The production of high-affinity antibodies by B cells is essential for pathogen clearance. Antibody affinity for antigen is increased through the affinity maturation in germinal centers (GCs). This is an iterative process in which B cells cycle between proliferation coupled with the acquisition of mutations and antigen-based positive selection, resulting in retention of the highest-affinity B cell clones. The posttranscriptional regulator microRNA-155 (miR-155) is critical for efficient affinity maturation and the maintenance of the GCs; however, the cellular and molecular mechanism by which miR-155 regulates GC responses is not well understood. Here, we utilized a miR-155 reporter mouse strain and showed that miR-155 is coexpressed with the proto-oncogene encoding c-MYC in positively selected B cells. Functionally, miR-155 protected positively selected c-MYC+ B cells from apoptosis, allowing clonal expansion of this population, providing an explanation as to why Mir155 deletion impairs affinity maturation and promotes the premature collapse of GCs. We determined that miR-155 directly inhibits the Jumonji family member JARID2, which enhances B cell apoptosis when overexpressed, and thereby promotes GC B cell survival. Our findings also suggest that there is cooperation between c-MYC and miR-155 during the normal GC response, a cooperation that may explain how c-MYC and miR-155 can collaboratively function as oncogenes., R. Nakagawa was supported by a Marie Curie Incoming Fellowship from the European Union’s Seventh Framework Programme for research, technological development, and demonstration. E. Vigorito was supported by the Medical Research Council grants G1001781 and G0700287 and by the Biotechnology and Biological Sciences Research Council. M. Meyer-Hermann was supported by the German Federal Ministry of Education and Research within the Measures for the Establishment of Systems Medicine, project SYSIMIT (BMBF eMed project SYSIMIT, FKZ: 01ZX1308B) and by the Human Frontier Science Program (RGP0033/2015).

A single microRNA (miRNA) can regulate the expression of many genes, though the level of repression imparted on any given target is generally low. How then is the selective pressure for a single miRNA/target interaction maintained across long evolutionary distances? We addressed this problem by disrupting in vivo the interaction between miR-155 and PU.1 in mice. Remarkably, this interaction proved to be key to promoting optimal T cell-dependent B cell responses, a previously unrecognized role for PU.1. Mechanistically, miR-155 inhibits PU.1 expression, leading to Pax5 down-regulation and the initiation of the plasma cell differentiation pathway. Additional PU.1 targets include a network of genes whose products are involved in adhesion, with direct links to B-T cell interactions. We conclude that the evolutionary adaptive selection of the miR-155-PU.1 interaction is exercised through the effectiveness of terminal B cell differentiation.

Detecting genetic variants associated with traits (quantitative trait loci, QTL) requires genotyped study individuals. Here we describe BaseQTL, a Bayesian method that exploits allele-specific expression to map molecular QTL from sequencing reads (eQTL for gene expression) even when no genotypes are available. When used with genotypes to map eQTL, BaseQTL has lower error rates and increased power compared with existing QTL mapping methods. Running without genotypes limits how many tests can be performed, but due to the proximity of QTL variants to gene bodies, the 2.8% of variants within a 100 kB window that could be tested contained 26% of eQTL detectable with genotypes. eQTL effect estimates were invariably consistent between analyses performed with and without genotypes. Often, sequencing data may be generated in the absence of genotypes on patients and controls in differential expression studies, and we identified an apparent psoriasis-specific eQTL for GSTP1 in one such dataset, providing new insights into disease-dependent gene regulation. BaseQTL is a Bayesian method to map molecular QTL affecting allele-specific expression even when no genotypes are available. It is well suited to discover eQTLs hidden in a wealth of RNA-seq data to unravel molecular mechanisms underpinning disease.

The production of high-affinity antibodies by B cells is essential for pathogen clearance. Antibody affinity for antigen is increased through the affinity maturation in germinal centers (GCs). This is an iterative process in which B cells cycle between proliferation coupled with the acquisition of mutations and antigen-based positive selection, resulting in retention of the highest-affinity B cell clones. The posttranscriptional regulator microRNA-155 (miR-155) is critical for efficient affinity maturation and the maintenance of the GCs; however, the cellular and molecular mechanism by which miR-155 regulates GC responses is not well understood. Here, we utilized a miR-155 reporter mouse strain and showed that miR-155 is coexpressed with the proto-oncogene encoding c-MYC in positively selected B cells. Functionally, miR-155 protected positively selected c-MYC+ B cells from apoptosis, allowing clonal expansion of this population, providing an explanation as to why Mir155 deletion impairs affinity maturation and promotes the premature collapse of GCs. We determined that miR-155 directly inhibits the Jumonji family member JARID2, which enhances B cell apoptosis when overexpressed, and thereby promotes GC B cell survival. Our findings also suggest that there is cooperation between c-MYC and miR-155 during the normal GC response, a cooperation that may explain how c-MYC and miR-155 can collaboratively function as oncogenes.

BACKGROUND: miRNAs are a class of small non-coding RNAs that regulate gene expression and have critical functions in various biological processes. Hundreds of miRNAs have been identified in mammalian genomes but only a small number of them have been functionally characterized. Recent studies also demonstrate that some miRNAs have important roles in reprogramming somatic cells to induced pluripotent stem cells (iPSCs). METHODS: We screened 52 miRNAs cloned in a piggybac (PB) vector for their roles in reprogramming of mouse embryonic fibroblast cells to iPSCs. To identify targets of miRNAs, we made Dgcr8-deficient embryonic stem (ES) cells and introduced miRNA mimics to these cells, which lack miRNA biogenesis. The direct target genes of miRNA were identified through global gene expression analysis and target validation. RESULTS AND CONCLUSION: We found that over-expressing miR-25 or introducing miR-25 mimics enhanced production of iPSCs. We identified a number of miR-25 candidate gene targets. Of particular interest were two ubiquitin ligases, Wwp2 and Fbxw7, which have been proposed to regulate Oct4, c-Myc and Klf5, respectively. Our findings thus highlight the complex interplay between miRNAs and transcription factors involved in reprogramming, stem cell self-renewal and maintenance of pluripotency.

MicroRNAs (miRNAs) play an important role in diverse physiological processes and are potential therapeutic agents. Synthetic oligonucleotides (ONs) of different chemistries have proven successful for blocking miRNA expression. However, their specificity and efficiency have not been fully evaluated. Here, we show that peptide nucleic acids (PNAs) efficiently block a key inducible miRNA expressed in the haematopoietic system, miR-155, in cultured B cells as well as in mice. Remarkably, miR-155 inhibition by PNA in primary B cells was achieved in the absence of any transfection agent. In mice, the high efficiency of the treatment was demonstrated by a strong overlap in global gene expression between B cells isolated from anti-miR-155 PNA-treated and miR-155-deficient mice. Interestingly, PNA also induced additional changes in gene expression. Our analysis provides a useful platform to aid the design of efficient and specific anti-miRNA ONs for in vivo use.