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Diffuse grade II IDH−mutant gliomas are slow−growing brain tumors that progress into high−grade gliomas. They present intratumoral cell heterogeneity, and no reliable markers are available to distinguish the different cell subtypes. The molecular mechanisms underlying the formation of this cell diversity is also ill−defined. Here, we report that SOX9 and OLIG1 transcription factors, which specifically label astrocytes and oligodendrocytes in the normal brain, revealed the presence of two largely nonoverlapping tumoral populations in IDH1−mutant oligodendrogliomas and astrocytomas. Astrocyte−like SOX9+ cells additionally stained for APOE, CRYAB, ID4, KCNN3, while oligodendrocyte−like OLIG1+ cells stained for ASCL1, EGFR, IDH1, PDGFRA, PTPRZ1, SOX4, and SOX8. GPR17, an oligodendrocytic marker, was expressed by both cells. These two subpopulations appear to have distinct BMP, NOTCH1, and MAPK active pathways as stainings for BMP4, HEY1, HEY2, p−SMAD1/5 and p−ERK were higher in SOX9+ cells. We used primary cultures and a new cell line to explore the influence of NOTCH1 activation and BMP treatment on the IDH1−mutant glioma cell phenotype. This revealed that NOTCH1 globally reduced oligodendrocytic markers and IDH1 expression while upregulating APOE, CRYAB, HEY1/2, and an electrophysiologically−active Ca2+−activated apamin−sensitive K+ channel (KCNN3/SK3). This was accompanied by a reduction in proliferation. Similar effects of NOTCH1 activation were observed in nontumoral human oligodendrocytic cells, which additionally induced strong SOX9 expression. BMP treatment reduced OLIG1/2 expression and strongly upregulated CRYAB and NOGGIN, a negative regulator of BMP. The presence of astrocyte−like SOX9+ and oligodendrocyte−like OLIG1+ cells in grade II IDH1−mutant gliomas raises new questions about their role in the pathology.

Motivation Long-read sequencing technologies are invaluable for determining complex RNA transcript architectures but are error-prone. Numerous ‘hybrid correction’ algorithms have been developed for genomic data that correct long reads by exploiting the accuracy and depth of short reads sequenced from the same sample. These algorithms are not suited for correcting more complex transcriptome sequencing data. Results We have created a novel reference-free algorithm called Transcript-level Aware Long-Read Correction (TALC) which models changes in RNA expression and isoform representation in a weighted De Bruijn graph to correct long reads from transcriptome studies. We show that transcript-level aware correction by TALC improves the accuracy of the whole spectrum of downstream RNA-seq applications and is thus necessary for transcriptome analyses that use long read technology. Availability and implementation TALC is implemented in C++ and available at https://github.com/lbroseus/TALC. Supplementary information Supplementary data are available at Bioinformatics online.

Comparative analysis of high throughput sequencing data between multiple conditions often involves mapping of sequencing reads to a reference and downstream bioinformatics analyses. Both of these steps may introduce heavy bias and potential data loss. This is especially true in studies where patient transcriptomes or genomes may vary from their references, such as in cancer. Here we describe a novel approach and associated software that makes use of advances in genetic algorithms and feature selection to comprehensively explore massive volumes of sequencing data to classify and discover new sequences of interest without a mapping step and without intensive use of specialized bioinformatics pipelines. We demonstrate that our approach called GECKO for GEnetic Classification using k-mer Optimization is effective at classifying and extracting meaningful sequences from multiple types of sequencing approaches including mRNA, microRNA, and DNA methylome data., Aubin Thomas, Sylvain Barriere et al. present a computational method for classifying and extracting meaningful sequences from high-throughput sequencing data. The method, called GECKO, uses k-mer counts that are able to classify the input data with high accuracy.

Background While intron retention (IR) is now widely accepted as an important mechanism of mammalian gene expression control, it remains the least studied form of alternative splicing. To delineate conserved features of IR, we performed an exhaustive phylogenetic analysis in a highly purified and functionally defined cell type comprising neutrophilic granulocytes from five vertebrate species spanning 430 million years of evolution. Results Our RNA-sequencing-based analysis suggests that IR increases gene regulatory complexity, which is indicated by a strong anti-correlation between the number of genes affected by IR and the number of protein-coding genes in the genome of individual species. Our results confirm that IR affects many orthologous or functionally related genes in granulocytes. Further analysis uncovers new and unanticipated conserved characteristics of intron-retaining transcripts. We find that intron-retaining genes are transcriptionally co-regulated from bidirectional promoters. Intron-retaining genes have significantly longer 3′ UTR sequences, with a corresponding increase in microRNA binding sites, some of which include highly conserved sequence motifs. This suggests that intron-retaining genes are highly regulated post-transcriptionally. Conclusions Our study provides unique insights concerning the role of IR as a robust and evolutionarily conserved mechanism of gene expression regulation. Our findings enhance our understanding of gene regulatory complexity by adding another contributor to evolutionary adaptation. Electronic supplementary material The online version of this article (doi:10.1186/s13059-017-1339-3) contains supplementary material, which is available to authorized users.

International audience; Fever is commonly used to diagnose disease and is consistently associated with increased mortality in critically ill patients. However, the molecular controls of elevated body temperature are poorly understood. We discovered that the expression of RNA-binding motif protein 3 (RBM3), known to respond to cold stress and to modulate microRNA (miRNA) expression, was reduced in 30 patients with fever, and in THP-1-derived macrophages maintained at a fever-like temperature (40 degrees C). Notably, RBM3 expression is reduced during fever whether or not infection is demonstrable. Reduced RBM3 expression resulted in increased expression of RBM3-targeted temperature-sensitive miRNAs, we termed thermomiRs. ThermomiRs such as miR-142-5p and miR-143 in turn target endogenous pyrogens including IL-6, IL6ST, TLR2, PGE2 and TNF to complete a negative feedback mechanism, which may be crucial to prevent pathological hyperthermia. Using normal PBMCs that were exogenously exposed to fever-like temperature (40 degrees C), we further demonstrate the trend by which decreased levels of RBM3 were associated with increased levels of miR-142-5p and miR-143 and vice versa over a 24 h time course. Collectively, our results indicate the existence of a negative feedback loop that regulates fever via reduced RBM3 levels and increased expression of miR-142-5p and miR-143.

Summary: We have developed an online tool called mirEval which can search sequences of up to 10 000 nt for novel microRNAs in multiple organisms. It is a comprehensive tool, easy to use and very informative. It will allow users with no prior knowledge of in-silico detection of microRNAs to take advantage of the most successful approaches to investigate sequences of interest. Availability: The mirEval web server is available at http://tagc.univ-mrs.fr/mireval Contact: W.Ritchie@centenary.org.au Supplementary information: Supplementary data are available at Bioinformatics online.

Most mammalian genes are able to express several splice variants in a phenomenon known as alternative splicing. Serious alterations of alternative splicing occur in cancer tissues, leading to expression of multiple aberrant splice forms. Most studies of alternative splicing defects have focused on the identification of cancer-specific splice variants as potential therapeutic targets. Here, we examine instead the bulk of non-specific transcript isoforms and analyze their level of disorder using a measure of uncertainty called Shannon's entropy. We compare isoform expression entropy in normal and cancer tissues from the same anatomical site for different classes of transcript variations: alternative splicing, polyadenylation, and transcription initiation. Whereas alternative initiation and polyadenylation show no significant gain or loss of entropy between normal and cancer tissues, alternative splicing shows highly significant entropy gains for 13 of the 27 cancers studied. This entropy gain is characterized by a flattening in the expression profile of normal isoforms and is correlated to the level of estimated cellular proliferation in the cancer tissue. Interestingly, the genes that present the highest entropy gain are enriched in splicing factors. We provide here the first quantitative estimate of splicing disruption in cancer. The expression of normal splice variants is widely and significantly disrupted in at least half of the cancers studied. We postulate that such splicing disorders may develop in part from splicing alteration in key splice factors, which in turn significantly impact multiple target genes., Author Summary RNA splicing is the process by which gene products are pieced together to form a mature messenger RNA (mRNA). In normal cells, RNA splicing is a tightly controlled process that leads to production of a well-defined set of mRNAs. Cancer cells, however, often produce aberrant, mis-spliced mRNAs. Such disorders have not been quantified to date. To this end, we use a well-known measure of disorder called Shannon's entropy. We show that overall splicing disorders are highly significant in many cancers, and that the extent of disorder may be correlated to the level of cell proliferation in each tumor. Surprisingly, genes that control the splicing mechanism are unusually frequent among genes affected by splicing disorders. This suggests that cancer cells may withstand harmful chain reactions in which splicing defects in key regulatory genes would in turn cause extensive splicing damage. As mis-spliced mRNAs are widely studied for cancer diagnosis, awareness of these global disorders is important to distinguish reliable cancer markers from background noise.

A large portion of the information contained in next-generation sequencing data is potentially lost through classical bioinformatics analysis. Both the mapping of sequencing reads to a genome or transcriptome and filtering results to focus on known gene regions eliminate useful information. This is especially true in cancer studies where patient transcriptomes or genomes may vary from their references.We created a novel approach that makes use of recent advances in genetic algorithms, neural networks and feature selection to comprehensively explore massive volumes of sequencing data to classify samples without these biases. Our approach, called GECKO for GEnetic Classification using k-mer Optimisation maximizes the sequencing information used when trying to explain the difference between 2 or more samples. Our algorithm has been effective at classifying data from large-scale cancer studies using mRNA-seq, circulating DNA or whole-genome resequencing.iMOKA (interactive multi-objective k-mer analysis) is a software that enables the comprehensive analysis of sequencing data from large cohorts to generate robust classification models or explore specific genetic elements associated with disease etiology. iMOKA uses a fast and accurate feature reduction step that combines a Naïve Bayes classifier augmented by an adaptive entropy filter and a graph-based filter to rapidly reduce the search space. By using a flexible file format and distributed indexing, iMOKA can easily integrate data from multiple experiments and also reduces disk space requirements and identifies changes in transcript levels and single nucleotide variants.Our software could be run on a desktop computer and enable scientists and clinicians to discover novel informative sequences in their own NGS data.Accurate quantification and detection of intron retention levels require specialized software. Building on our previous software, we have created a suite of tools: IRFinder-S, to analyse and explore intron retention events in multiple samples. Specifically, IRFinder-S allows a better identification of true intron retention events using a convolutional neural network, allows the sharing of intron retention results between labs, integrates a dynamic database to explore and contrast available samples and provides a tested method to detect differential levels of intron retention.; Une grande partie des informations contenues dans les données de séquençage de nouvelle génération est potentiellement perdue par l'analyse bioinformatique classique. L'alignement des lectures de séquençage sur un génome ou un transcriptome et le filtrage des résultats pour se concentrer sur des régions génétiques connues éliminent les informations utiles. Cela est particulièrement vrai dans les études sur le cancer où les transcriptomes ou les génomes des patients peuvent différer de leurs références.Nous avons créé une nouvelle approche qui utilise les avancées récentes dans les algorithmes génétiques, les réseaux de neurones et la sélection de caractéristiques pour explorer de manière exhaustive des volumes massifs de données de séquençage afin de classer les échantillons sans ces biais. Notre approche, appelée GECKO pour GEnetic Classification using k-mer Optimization, maximise les informations de séquençage utilisées pour tenter d'expliquer la différence entre 2 échantillons ou plus. Notre algorithme s'est avéré efficace pour classer les données d'études sur le cancer à grande échelle à l'aide du séquençage de l'ARNm, de l'ADN circulant ou du reséquençage du génome entier.iMOKA (interactive multi-objective k-mer analysis) est un logiciel qui permet l'analyse complète des données de séquençage de grandes cohortes pour générer des modèles de classification robustes ou explorer des éléments génétiques spécifiques associés à l'étiologie de la maladie. iMOKA utilise une étape de réduction de caractéristiques rapide et précise qui combine un classificateur Naïve Bayes augmenté d'un filtre d'entropie adaptatif et d'un filtre basé sur un graphique pour réduire rapidement l'espace de recherche. En utilisant un format de fichier flexible et une indexation distribuée, iMOKA peut facilement intégrer les données de plusieurs expériences et réduit également les besoins en espace disque et identifie les changements dans les niveaux de transcription et les variantes de nucléotide unique.Notre logiciel pourrait être exécuté sur un ordinateur de bureau et permettre aux scientifiques et aux cliniciens de découvrir de nouvelles séquences informatives dans leurs propres données NGS.La quantification et la détection précises des niveaux de rétention d'intron nécessitent un logiciel spécialisé. En nous appuyant sur notre logiciel précédent, nous avons créé une suite d'outils : IRFinder-S, pour analyser et explorer les événements de rétention d'intron dans plusieurs échantillons. Plus précisément, IRFinder-S permet une meilleure identification des véritables événements de rétention d'intron à l'aide d'un réseau de neurones convolutifs, permet le partage des résultats de rétention d'intron entre les laboratoires, intègre une base de données dynamique pour explorer et contraster les échantillons disponibles et fournit une méthode testée pour détecter les niveaux différentiels de rétention d'intron.

Alternative splicing is one of the major mechanisms leading to a diversity in the proteome. It has become very clear that this mechanism is playing a role in many genetic diseases including cancer. During oncogenesis, the cellular content of RNA isoforms is highly altered and this phenomenon seems to be context specific. Even in the same tissue, the pool of transcripts can display specific rearrangements corresponding to different subtypes of the disease.As we know now, the majority of deaths from solid tumors are caused by metastases. This metastatic cascade might involve the Epithelial Mesenchymal Transition (EMT) which is a complex biological trans-differentiation process that allows epithelial cells to transiently obtain mesenchymal features. During this process, an alternative splicing program is differentially regulated, and increasing number of studies have started to suggest that a simple isoform switching is sufficient to start an EMT. Stopping the spreading of cancer cells in the human body represent an important challenge in the fight against cancer. In this context, we believe that exploring alternative splicing could add a thinner layer or regulation to classify patients more precisely, help to discover new potential targets for therapy and therefore, improve patient care. As we are in the era of precision medicine, we use a rational approach, focusing a specific subtype to avoid bias due to heterogeneity of breast cancer. We focus on basal-like breast cancer which is one of the most aggressive and deadly among all breast cancers.During this work, we took advantage of datasets from a large-scale initiative (The Cancer Genome Atlas) which provides researchers with cancer genomics and associated clinical follow-up. We analyzed 188 patients with basal-like breast cancer for gene expression and alternative splicing. Based on breast cancer cell lines RNA-sequencing, using a custom machine learning approach based on Random Forest, we succeeded to distinguish two groups of patients with distinct prognosis. Using several public EMT-induced RNA sequencing projects, we confirmed these alternative splicing events were linked to EMT.As a side project, we also got involved in the development of methods of classification using k-mers. We first were involved in a project that test the ability of k-mer to classify breast cancer subtype. Secondly, we were focused in the discovery of biological knowledge that k-mers are bringing in the breast cancer stratification.Finally, our results show that alternative splicing or k-mers can be the source of new valuable information to help in the thinner definition of oncogenic subtypes or identification of biological processes in cancer. In a breast cancer subtype that does not benefit from targeted therapy, we demonstrate that alternative splicing relative to an EMT could be used as potential biomarkers to isolate patients where the tumor progress faster. This work could help to develop new treatments for precision oncology.; L’épissage alternatif est l’un des mécanismes majeurs conduisant à la diversité du protéome. Il est devenu très clair que ce mécanisme joue un rôle dans de nombreuses maladies génétiques, y compris le cancer. Au cours de l’oncogenèse, le contenu cellulaire en isoformes d’ARN est fortement altéré et ce phénomène semble être spécifique au contexte.Comme nous le savons maintenant, la majorité des décès dus à des tumeurs solides sont causés par des métastases. Cette cascade métastatique pourrait impliquer la transition épithélio-mésenchymateuse (EMT) qui est un processus biologique complexe de trans-différenciation qui permet aux cellules épithéliales d’obtenir de manière transitoire des caractéristiques mésenchymateuses. Au cours de ce processus, un programme d’épissage alternatif est régulé de manière différentielle, et un nombre croissant d’études commence à suggérer qu’un simple changement d’isoforme pourrait s’avérer suffisant pour amorcer une EMT. Stopper la propagation des cellules cancéreuses dans le corps humain représente un défi important dans la lutte contre le cancer. Dans ce contexte, nous pensons que l’exploration de l’épissage alternatif pourrait apporter une couche de régulation plus fine pour classer les patients plus précisément, aider à découvrir de nouvelles cibles potentielles pour la thérapie et de ce fait, améliorer les soins des patients.Comme nous sommes à l’ère de la médecine de précision, nous utilisons une approche rationnelle en nous concentrant sur un sous-type spécifique pour éviter les biais dus à l’hétérogénéité du cancer du sein. Nous nous sommes concentrés sur le cancer du sein de type basal qui est l’un des cancers du sein les plus agressifs et les plus meurtriers.Au cours de ce travail, nous avons profité des ensembles de données d’une initiative à grande échelle (The Cancer Genome Atlas) qui fournit aux chercheurs des données génomiques de multiples tumeurs avec les informations cliniques associées à chaque patient. Nous avons analysé l’expression génique et l’épissage alternatif de 188 patientes atteintes d’un cancer du sien de type basal. Sur la base du séquençage d’ARN de lignées cellulaires cancéreuses, en utilisant une approche d’apprentissage automatique originale et basée sur les « forêt d’arbres », nous avons réussi à distinguer deux groupes de patients avec un pronostic distinct. En utilisant plusieurs projets publics de séquençage induisant une EMT dans différents modèles cellulaires, nous avons confirmé que ces évènements d’épissage alternatif étaient liés à l’EMT.En parallèle, nous nous sommes également impliqués dans le développement de méthodes de classification et d’annotation d’événements utilisant des k-mers. Nous avons d’abord été impliqués dans un projet qui teste la capacité des k-mers à classer les sous-types du cancer du sein. Dans un second temps, nous nous sommes focalisés sur la découverte des connaissances biologiques que les k-mers apportent dans la stratification du cancer du sein.Enfin nos résultats montrent que l’épissage alternatif ou les k-mers peuvent être la source de nouvelles informations précieuses pour aider à la définition plus fine des sous-types oncogènes ou pour permettre l’identification de processus biologiques impliqués dans le cancer. Dans un sous-type de cancer du sein qui ne bénéficie pas d’une thérapie ciblée, nous démontrons que l’épissage alternatif en lien avec l’EMT pourrait être utilisé comme biomarqueur potentiel pour isoler les patients ou la tumeur progresse plus rapidement. Ces travaux pourraient aider à développer de nouveaux traitements dans le cadre de l’oncologie de précision.