Morpho-chemistry in secondary sludge filtration cakes: a case study

International audienceLight Microscopy, Transmission Electron Microscopy (TEM, EDXS), and Fourier Transform Infra Red MicroSpectroscopy (FTIRMS) were used to describe the organization and chemical distribution of major constituents in a sludge filtration cake. Samples were obtained from a municipal wastewater treatment plant using conventional ferric chloride and lime sludge conditioning.Various samples collected at different stages of the process were embedded in an Epoxy resin after acetone-dehydration, and sectioned using an ultra-microtome. The thickness of the sections was adapted to the experimental techniques used. TEM showed that in the activated sludge, bacterial colonies, isolated bacteria and debris are trapped within a gel matrix of exocellular polymeric substances, whereas those same components are compacted and distorted in the filtration cake. Furthermore, conditioning chemicals appeared in the cake as amorphous aggregated colloids and acicular particles which do not form inside the colonies. A chemical mapping was obtained by determining and integrating FTIR bands characteristics of specific components of the cake. Preliminary results showed that the amounts of resin can be used to assess the relative compacity at different levels of the cake

Real-time imaging of cotranscriptional splicing reveals a kinetic model that reduces noise: implications for alternative splicing regulation

A combination of several rate-limiting steps allows for efficient control of alternative splicing

A genome-wide approach to identify novel regulatory layers in alternative splicing during EMT.

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.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

Approche à large échelle visant à détecter de nouveaux régulateurs de l'épissage alternatif au cours de la transition épithélio-mésenchymateuse.

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

Transformation Foci in IDH1-mutated Gliomas Show STAT3 Phosphorylation and Downregulate the Metabolic Enzyme ETNPPL, a Negative Regulator of Glioma Growth

International audienceIDH1-mutated gliomas are slow-growing brain tumours which progress into high-grade gliomas. The early molecular events causing this progression are ill-defined. Previous studies revealed that 20% of these tumours already have transformation foci. These foci offer opportunities to better understand malignant progression. We used immunohistochemistry and high throughput RNA profiling to characterize foci cells. These have higher pSTAT3 staining revealing activation of JAK/STAT signaling. They downregulate RNAs involved in Wnt signaling (DAAM2, SFRP2), EGFR signaling (MLC1), cytoskeleton and cell-cell communication (EZR, GJA1). In addition, foci cells show reduced levels of RNA coding for Ethanolamine-Phosphate Phospho-Lyase (ETNPPL/AGXT2L1), a lipid metabolism enzyme. ETNPPL is involved in the catabolism of phosphoethanolamine implicated in membrane synthesis. We detected ETNPPL protein in glioma cells as well as in astrocytes in the human brain. Its nuclear localization suggests additional roles for this enzyme. ETNPPL expression is inversely correlated to glioma grade and we found no ETNPPL protein in glioblastomas. Overexpression of ETNPPL reduces the growth of glioma stem cells indicating that this enzyme opposes gliomagenesis. Collectively, these results suggest that a combined alteration in membrane lipid metabolism and STAT3 pathway promotes IDH1-mutated glioma malignant progression

Endothelial, epithelial, and fibroblast cells exhibit specific splicing programs independently of their tissue of origin

International audienceAlternative splicing is the main mechanism of increasing the proteome diversity coded by a limited number of genes. It is well established that different tissues or organs express different splicing variants. However, organs are composed of common major cell types, including fibroblasts, epithelial, and endothelial cells. By analyzing large-scale data sets gen-erated by The ENCODE Project Consortium and after extensive RT-PCR validation, we demonstrate that each of the three major cell types expresses a specific splicing program independently of its organ origin. Furthermore, by analyzing splicing factor expression across samples, publicly available splicing factor binding site data sets (CLIP-seq), and exon array data sets after splicing factor depletion, we identified several splicing factors, including ESRP1 and 2, MBNL1, NOVA1, PTBP1, and RBFOX2, that contribute to establishing these cell type–specific splicing programs. All of the analyzed data sets are freely available in a user-friendly web interface named FasterDB, which describes all known splicing variants of human and mouse genes and their splicing patterns across several dozens of normal and cancer cells as well as across tissues. Information regarding splicing factors that potentially contribute to individual exon regulation is also provided via a dedicated CLIP-seq and exon array data visualization interface. To the best of our knowledge, FasterDB is the first database integrating such a variety of large-scale data sets to enable functional genomics analyses at exon-level resolution

Influence of advancing age on clinical presentation, treatment efficacy and safety, and long-term outcome of pre-excitation syndromes: a retrospective cohort study of 961 patients included over a 25-year period

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Inferring ligand-receptor cellular networks from bulk and spatial transcriptomic datasets with BulkSignalR

International audienceThe study of cellular networks mediated by ligand-receptor interactions has attracted much attention recently owing to single-cell omics. However, rich collections of bulk data accompanied with clinical information exists and continue to be generated with no equivalent in single-cell so far. In parallel, spatial transcriptomic (ST) analyses represent a revolutionary tool in biology. A large number of ST projects rely on multicellular resolution, for instance the Visium™ platform, where several cells are analyzed at each location, thus producing localized bulk data. Here, we describe BulkSignalR, a R package to infer ligand-receptor networks from bulk data. BulkSignalR integrates ligand-receptor interactions with downstream pathways to estimate statistical significance. A range of visualization methods complement the statistics, including functions dedicated to spatial data. We demonstrate BulkSignalR relevance using different datasets, including new Visium liver metastasis ST data, with experimental validation of protein colocalization. A comparison with other ST packages shows the significantly higher quality of BulkSignalR inferences. BulkSignalR can be applied to any species thanks to its built-in generic ortholog mapping functionality

GECKO is a genetic algorithm to classify and explore high throughput sequencing data

International audienceComparative 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

Mini-Exome Coupled to Read-Depth Based Copy Number Variation Analysis in Patients with Inherited Ataxias

International audienceNext-generation sequencing (NGS) has an established diagnostic value for inherited ataxia. However, the need of a rigorous process of analysis and validation remains challenging. Moreover, copy number variations (CNV) or dynamic expansions of repeated sequence are classically considered not adequately detected by exome sequencing technique. We applied a strategy of mini-exome coupled to read-depth based CNV analysis to a series of 33 patients with probable inherited ataxia and onset <50 years. The mini-exome consisted of the capture of 4,813 genes having associated clinical phenotypes. Pathogenic variants were found in 42% and variants of uncertain significance in 24% of the patients. These results are comparable to those from whole exome sequencing and better than previous targeted NGS studies. CNV and dynamic expansions of repeated CAG sequence were identified in three patients. We identified both atypical presentation of known ataxia genes (ATM, NPC1) and mutations in genes very rarely associated with ataxia (ERCC4, HSD17B4). We show that mini-exome bioinformatics data analysis allows the identification of CNV and dynamic expansions of repeated sequence. Our study confirms the diagnostic value of the proposed genetic analysis strategy. We also provide an algorithm for the multidisciplinary process of analysis, interpretation, and validation of NGS data