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8 results on '"Clara Benoit-Pilven"'

1. X-chromosome inactivation in human iPSCs provides insight into X-regulated gene expression in autosomes

Author

Hande Topa, Clara Benoit-Pilven, Taru Tukiainen, and Olli Pietiläinen

Subjects
X chromosome inactivation, hiPSC, Sex differences, RNA-seq, Allele-specific expression, XIST-bound autosomal genes, Biology (General), QH301-705.5, Genetics, QH426-470
Abstract

Abstract Background Variation in X chromosome inactivation (XCI) in human-induced pluripotent stem cells (hiPSCs) can impact their ability to model biological sex biases. The gene-wise landscape of X chromosome gene dosage remains unresolved in female hiPSCs. To characterize patterns of de-repression and escape from inactivation, we performed a systematic survey of allele specific expression in 165 female hiPSC lines. Results XCI erosion is non-random and primarily affects genes that escape XCI in human tissues. Individual genes and cell lines vary in the frequency and degree of de-repression. Bi-allelic expression increases gradually after modest decrease of XIST in cultures, whose loss is commonly used to mark lines with eroded XCI. We identify three clusters of female lines at different stages of XCI. Increased XCI erosion amplifies female-biased expression at hypomethylated sites and regions normally occupied by repressive histone marks, lowering male-biased differences in the X chromosome. In autosomes, erosion modifies sex differences in a dose-dependent way. Male-biased genes are enriched for hypermethylated regions, and de-repression of XIST-bound autosomal genes in female lines attenuates normal male-biased gene expression in eroded lines. XCI erosion can compensate for a dominant loss of function effect in several disease genes. Conclusions We present a comprehensive view of X chromosome gene dosage in hiPSCs and implicate a direct mechanism for XCI erosion in regulating autosomal gene expression in trans. The uncommon and variable reactivation of X chromosome genes in female hiPSCs can provide insight into X chromosome’s role in regulating gene expression and sex differences in humans.

Published
2024
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2. Altered splicing of ATG16-L1 mediates acquired resistance to tyrosine kinase inhibitors of EGFR by blocking autophagy in non-small cell lung cancer

Author

Anne‐Sophie Hatat, Clara Benoit‐Pilven, Amélie Pucciarelli, Florence de Fraipont, Lucie Lamothe, Pascal Perron, Amandine Rey, Matteo Giaj Levra, Anne‐Claire Toffart, Didier Auboeuf, Beatrice Eymin, and Sylvie Gazzeri

Subjects
Cancer Research, EGF Family of Proteins, Lung Neoplasms, Autophagy-Related Proteins, Antineoplastic Agents, Gefitinib, General Medicine, ErbB Receptors, Oncology, Drug Resistance, Neoplasm, Carcinoma, Non-Small-Cell Lung, Cell Line, Tumor, Genetics, Autophagy, Molecular Medicine, Humans, Protein Kinase Inhibitors
Abstract

Despite the initial efficacy of using tyrosine kinase inhibitors of epidermal growth factor receptors (EGFR-TKIs) for treating patients with non-small cell lung cancer (NSCLC), resistance inevitably develops. Recent studies highlight a link between alternative splicing and cancer drug response. Therefore, we aimed to identify deregulated splicing events that play a role in resistance to EGFR-TKI. By using RNA sequencing, reverse-transcription PCR (RT-PCR), and RNA interference, we showed that overexpression of a splice variant of the autophagic gene ATG16-L1 that retains exon 8 and encodes the β-isoform of autophagy-related protein 16-1 (ATG16-L1 β) concurs acquired resistance to EGFR-TKI in NSCLC cells. Using matched biopsies, we found increased levels of ATG16-L1 β at the time of progression in 3 of 11 NSCLC patients treated with EGFR-TKI. Mechanistically, gefitinib-induced autophagy was impaired in resistant cells that accumulated ATG16-L1 β. Neutralization of ATG16-L1 β restored autophagy in response to gefitinib, induced apoptosis, and inhibited the growth of in ovo tumor xenografts. Conversely, overexpression of ATG16-L1 β in parental sensitive cells prevented gefitinib-induced autophagy and increased cell survival. These results support a role of defective autophagy in acquired resistance to EGFR-TKIs and identify splicing regulation of ATG16-L1 as a therapeutic vulnerability that could be explored for improving EGFR-targeted cancer therapy.

Published
2022

3. Influenza virus infection induces widespread alterations of host cell splicing

Author

U. Ashraf, Sandie Munier, Vincent Navratil, Vincent Lacroix, Guillaume Fournier, Nadia Naffakh, Clara Benoit-Pilven, Odile Sismeiro, Cécile Ligneau, Jean-Yves Coppée, Génétique Moléculaire des Virus à ARN - Molecular Genetics of RNA Viruses (GMV-ARN (UMR_3569 / U-Pasteur_2)), Centre National de la Recherche Scientifique (CNRS)-Institut Pasteur [Paris]-Université de Paris (UP), Equipe de recherche européenne en algorithmique et biologie formelle et expérimentale (ERABLE), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Baobab, Département PEGASE [LBBE] (PEGASE), Laboratoire de Biométrie et Biologie Evolutive - UMR 5558 (LBBE), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire de Biométrie et Biologie Evolutive - UMR 5558 (LBBE), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS), Centre de recherche en neurosciences de Lyon (CRNL), Université de Lyon-Université de Lyon-Université Jean Monnet [Saint-Étienne] (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Pôle Rhône-Alpin de BioInformatique [Lyon] (PRABI), Université de Lyon-Université de Lyon, European Virus Bioinformatics Center [Jena], Institut Français de Bioinformatique (IFB-CORE), Institut National de Recherche en Informatique et en Automatique (Inria)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Transcriptome et Epigénome (PF2), Institut Pasteur [Paris], French National Research Agency [ANR-16-CE23-0001 to V.L.], LabEx IBEID [10-LABX-0062 to N.N.], Horizon 2020—Research and Innovation Framework Programme [665807 to U.A. as a participant in the Pasteur-Paris University International PhD Program], Institut Carnot Pasteur Microbes & Santé [to U.A. as a participant in the Pasteur-Paris University International PhD Program], ANR-16-CE23-0001,ASTER,Algorithmes et outils logiciels pour le séquençage d'ARN de troisième génération(2016), ANR-10-LABX-0062,IBEID,Integrative Biology of Emerging Infectious Diseases(2010), European Project: 665807,H2020,H2020-MSCA-COFUND-2014,PASTEURDOC(2015), Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), Centre de recherche en neurosciences de Lyon - Lyon Neuroscience Research Center (CRNL), Université de Lyon-Université de Lyon-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Institut Pasteur [Paris] (IP), Lacroix, Vincent, Algorithmes et outils logiciels pour le séquençage d'ARN de troisième génération - - ASTER2016 - ANR-16-CE23-0001 - AAPG2016 - VALID, Integrative Biology of Emerging Infectious Diseases - - IBEID2010 - ANR-10-LABX-0062 - LABX - VALID, Institut Pasteur International Docotal Program - PASTEURDOC - - H20202015-10-01 - 2020-10-01 - 665807 - VALID, Genomics of Sex Differences, and Institute for Molecular Medicine Finland

Subjects
AcademicSubjects/SCI01140, AcademicSubjects/SCI01060, AcademicSubjects/SCI00030, Standard Article, [SDV.BC.BC]Life Sciences [q-bio]/Cellular Biology/Subcellular Processes [q-bio.SC], Biology, AcademicSubjects/SCI01180, Virus, 03 medical and health sciences, Exon, 0302 clinical medicine, Structural Biology, Interferon, [SDV.BBM.GTP]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Genomics [q-bio.GN], Gene expression, 111 Mathematics, Genetics, medicine, [SDV.BC.BC] Life Sciences [q-bio]/Cellular Biology/Subcellular Processes [q-bio.SC], TRANSCRIPTION, Molecular Biology, 030304 developmental biology, [INFO.INFO-BI] Computer Science [cs]/Bioinformatics [q-bio.QM], [SDV.MP.VIR] Life Sciences [q-bio]/Microbiology and Parasitology/Virology, 0303 health sciences, Messenger RNA, Applied Mathematics, Alternative splicing, 1184 Genetics, developmental biology, physiology, Intron, 3. Good health, Computer Science Applications, Cell biology, RNA splicing, [SDV.MP.VIR]Life Sciences [q-bio]/Microbiology and Parasitology/Virology, [SDV.BBM.GTP] Life Sciences [q-bio]/Biochemistry, Molecular Biology/Genomics [q-bio.GN], AcademicSubjects/SCI00980, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], 030217 neurology & neurosurgery, medicine.drug
Abstract

Influenza A viruses (IAVs) use diverse mechanisms to interfere with cellular gene expression. Although many RNA-seq studies have documented IAV-induced changes in host mRNA abundance, few were designed to allow an accurate quantification of changes in host mRNA splicing. Here, we show that IAV infection of human lung cells induces widespread alterations of cellular splicing, with an overall increase in exon inclusion and decrease in intron retention. Over half of the mRNAs that show differential splicing undergo no significant changes in abundance or in their 3′ end termination site, suggesting that IAVs can specifically manipulate cellular splicing. Among a randomly selected subset of 21 IAV-sensitive alternative splicing events, most are specific to IAV infection as they are not observed upon infection with VSV, induction of interferon expression or induction of an osmotic stress. Finally, the analysis of splicing changes in RED-depleted cells reveals a limited but significant overlap with the splicing changes in IAV-infected cells. This observation suggests that hijacking of RED by IAVs to promote splicing of the abundant viral NS1 mRNAs could partially divert RED from its target mRNAs. All our RNA-seq datasets and analyses are made accessible for browsing through a user-friendly Shiny interface (http://virhostnet.prabi.fr:3838/shinyapps/flu-splicing or https://github.com/cbenoitp/flu-splicing).

Published
2020
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4. Clinical interpretation of variants identified in RNU4ATAC, a non-coding spliceosomal gene

Author

Claire Benetollo, Gabriel Sala, Sylvie Mazoyer, Anne-Louise Leutenegger, Alicia Besson, Justine Guguin, Clément Saccaro, Marion Delous, Gaetan Lesca, Vincent Lacroix, Clara Benoit-Pilven, Audrey Putoux, Patrick Edery, Richard A. Padgett, Audric Cologne, Laboratoire de Biométrie et Biologie Evolutive - UMR 5558 (LBBE), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS), Equipe de recherche européenne en algorithmique et biologie formelle et expérimentale (ERABLE), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Centre de recherche en neurosciences de Lyon - Lyon Neuroscience Research Center (CRNL), Université de Lyon-Université de Lyon-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Cleveland Clinic, Maladies neurodéveloppementales et neurovasculaires (NeuroDiderot (UMR_S_1141 / U1141)), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Cité (UPCité), This work was supported by CNRS, Inserm, Université Paris 7 and Université Lyon 1 through recurrent funding, the ANR Aster (no. ANR-16-CE23-0001) and U4ATAC-BRAIN (no. ANR-18CE12-0007-01) grants and an Inserm/ Hospices Civils de Lyon grant to P.E. (Contrat d’Interface pour Hospitaliers). A.C. was supported by a grant from Inria (Thèse Inria-Inserm 'Médecine Numérique' - 2016) and C.B.P. by a grant from the Fondation pour la Recherche Médicale to P.E. (Financement d’un ingénieur - ING20160435660)., ANR-16-CE23-0001,ASTER,Algorithmes et outils logiciels pour le séquençage d'ARN de troisième génération(2016), ANR-18-CE12-0007,U4ATAC-BRAIN,Rôle de l'épissage mineur dans le développement cérébral(2018), Bodescot, Myriam, Centre de recherche en neurosciences de Lyon (CRNL), Université de Lyon-Université de Lyon-Université Jean Monnet [Saint-Étienne] (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Plateau NeuroGénétique FOnctionnelle [Bron], Université de Lyon-Université de Lyon-Université Jean Monnet [Saint-Étienne] (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), and Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Paris (UP)

Subjects
Molecular biology, [SDV.BBM.BM] Life Sciences [q-bio]/Biochemistry, Molecular Biology/Molecular biology, Biochemistry, Exon, Database and Informatics Methods, Minor spliceosome, Animal Cells, RNA, Small Nuclear, Databases, Genetic, Medicine and Health Sciences, Child, Connective Tissue Cells, 0303 health sciences, Multidisciplinary, Fetal Growth Retardation, medicine.diagnostic_test, Small nuclear RNA, Computer-Aided Drug Design, 030305 genetics & heredity, Genomics, Genomic Databases, 3. Good health, Nucleic acids, Connective Tissue, RNA splicing, Microcephaly, Medicine, Cellular Types, Anatomy, Research Article, Spliceosome, Drug Research and Development, Bioinformatics, Science, RNA Splicing, Dwarfism, Computational biology, Biology, DNA construction, Osteochondrodysplasias, Genome Complexity, Research and Analysis Methods, 03 medical and health sciences, medicine, Genetics, Humans, Non-coding RNA, Gene, 030304 developmental biology, Genetic testing, Pharmacology, Intron, Genetic Variation, Biology and Life Sciences, Computational Biology, [SDV.BBM.BM]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Molecular biology, Cell Biology, Fibroblasts, Genome Analysis, Introns, Biological Tissue, Biological Databases, Molecular biology techniques, Drug Design, Plasmid Construction, Spliceosomes, Nucleic Acid Conformation, RNA
Abstract

International audience; Biallelic variants in RNU4ATAC, a non-coding gene transcribed into the minor spliceosome component U4atac snRNA, are responsible for three rare recessive developmental diseases, namely Taybi-Linder/MOPD1, Roifman and Lowry-Wood syndromes. Next-generation sequencing of clinically heterogeneous cohorts (children with either a suspected genetic disorder or a congenital microcephaly) recently identified mutations in this gene, illustrating how profoundly these technologies are modifying genetic testing and assessment. As RNU4ATAC has a single non-coding exon, the bioinformatic prediction algorithms assessing the effect of sequence variants on splicing or protein function are irrelevant, which makes variant interpretation challenging to molecular diagnostic laboratories. In order to facilitate and improve clinical diagnostic assessment and genetic counseling, we present i) an update of the previously reported RNU4ATAC mutations and an analysis of the genetic variations affecting this gene using the Genome Aggregation Database (gnomAD) resource; ii) the pathogenicity prediction performances of scores computed based on an RNA structure prediction tool and of those produced by the Combined Annotation Dependent Depletion tool for the 285 RNU4ATAC variants identified in patients or in large-scale sequencing projects; iii) a method, based on a cellular assay, that allows to measure the effect of RNU4ATAC variants on splicing efficiency of a minor (U12-type) reporter intron. Lastly, the concordance of bioinformatic predictions and cellular assay results was investigated.

Published
2020
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5. Les programmes d'épissage médiés par ZRANB2 et SYF2 et convergeant sur ECT2 sont impliqués dans la résistance des cellules de cancer du sein à la doxorubicine

Author

Martin Dutertre, Didier Auboeuf, Iris Tanaka, Olivier Saulnier, Clara Benoit-Pilven, Ivan Bièche, Alina Chakraborty, Frederic Pouzoulet, Stéphan Vagner, Dalila Labiod, Olivier Delattre, Sophie Vacher, Eric Lam, Cancer Research UK, Breast Cancer Care & Breast Cancer Now, Medical Research Council (MRC), Stress génotoxiques et cancer, Université Paris-Sud - Paris 11 (UP11)-Institut Curie [Paris]-Centre National de la Recherche Scientifique (CNRS), Equipe labelisée Ligue Contre le Cancer, Unité de génétique et biologie des cancers (U830), Université Paris Descartes - Paris 5 (UPD5)-Institut Curie [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM), Laboratoire de biologie et modélisation de la cellule (LBMC UMR 5239), École normale supérieure - Lyon (ENS Lyon)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Institut Curie [Paris], Imperial College London, and DUTERTRE, Martin

Subjects
medicine.medical_treatment, 05 Environmental Sciences, [SDV.BBM.BM] Life Sciences [q-bio]/Biochemistry, Molecular Biology/Molecular biology, S Phase, Exon, 0302 clinical medicine, Breast cancer chemotherapy, RNA interference, Protein Isoforms, Aged, 80 and over, 0303 health sciences, RNA-Binding Proteins, Exons, Middle Aged, 3. Good health, Gene Expression Regulation, Neoplastic, 030220 oncology & carcinogenesis, RNA splicing, MCF-7 Cells, [SDV.BBM.GTP] Life Sciences [q-bio]/Biochemistry, Molecular Biology/Genomics [q-bio.GN], Female, medicine.drug, Adult, RNA Splicing Factors, Epithelial-Mesenchymal Transition, Mice, Nude, [SDV.CAN]Life Sciences [q-bio]/Cancer, Breast Neoplasms, Biology, 03 medical and health sciences, Breast cancer, [SDV.CAN] Life Sciences [q-bio]/Cancer, [SDV.BBM.GTP]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Genomics [q-bio.GN], Cell Line, Tumor, Proto-Oncogene Proteins, RNA and RNA-protein complexes, Genetics, medicine, Animals, Humans, Doxorubicin, Aged, Cell Proliferation, 030304 developmental biology, Alternative splicing, [SDV.BBM.BM]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Molecular biology, 06 Biological Sciences, medicine.disease, Alternative Splicing, MicroRNAs, Drug Resistance, Neoplasm, Spliceosomes, Cancer research, RNA Splice Sites, 08 Information and Computing Sciences, Developmental Biology
Abstract

Besides analyses of specific alternative splicing (AS) variants, little is known about AS regulatory pathways and programs involved in anticancer drug resistance. Doxorubicin is widely used in breast cancer chemotherapy. Here, we identified 1723 AS events and 41 splicing factors regulated in a breast cancer cell model of acquired resistance to doxorubicin. An RNAi screen on splicing factors identified the little studied ZRANB2 and SYF2, whose depletion partially reversed doxorubicin resistance. By RNAi and RNA-seq in resistant cells, we found that the AS programs controlled by ZRANB2 and SYF2 were enriched in resistance-associated AS events, and converged on the ECT2 splice variant including exon 5 (ECT2-Ex5+). Both ZRANB2 and SYF2 were found associated with ECT2 pre-messenger RNA, and ECT2-Ex5+ isoform depletion reduced doxorubicin resistance. Following doxorubicin treatment, resistant cells accumulated in S phase, which partially depended on ZRANB2, SYF2 and the ECT2-Ex5+ isoform. Finally, doxorubicin combination with an oligonucleotide inhibiting ECT2-Ex5 inclusion reduced doxorubicin-resistant tumor growth in mouse xenografts, and high ECT2-Ex5 inclusion levels were associated with bad prognosis in breast cancer treated with chemotherapy. Altogether, our data identify AS programs controlled by ZRANB2 and SYF2 and converging on ECT2, that participate to breast cancer cell resistance to doxorubicin.

Published
2019
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6. New insights into minor splicing-a transcriptomic analysis of cells derived from TALS patients

Author

Amandine Campan-Fournier, Sylvie Mazoyer, Aimee D C Paulussen, Alicia Besson, Audric Cologne, Vincent Lacroix, Annick Toutain, Anne-Louise Leutenegger, Audrey Putoux, Patrick Edery, Clara Benoit-Pilven, Chaim M. Roifman, Christine E. M. de Die-Smulders, Lucile Pinson, Michael B. Bober, Equipe de recherche européenne en algorithmique et biologie formelle et expérimentale (ERABLE), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Centre de recherche en neurosciences de Lyon (CRNL), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Université Jean Monnet [Saint-Étienne] (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Hospices Civils de Lyon (HCL), Nemours/Alfred I. du Pont Hospital for Children, Maastricht University Medical Centre (MUMC), Maastricht University [Maastricht], Service de génétique médicale [Montpellier], Centre Hospitalier Régional Universitaire [Montpellier] (CHRU Montpellier)-Hôpital Arnaud de Villeneuve, Inserm CIC 202, France, François Rabelais University Tours, France, Tours University Hospital, France, Institut National de la Santé et de la Recherche Médicale (INSERM), Imagerie et cerveau (iBrain - Inserm U1253 - UNIV Tours ), Université de Tours (UT)-Institut National de la Santé et de la Recherche Médicale (INSERM), Department of Immunology, University of Toronto, Neuroprotection du Cerveau en Développement / Promoting Research Oriented Towards Early Cns Therapies (PROTECT), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpital Robert Debré-Université Paris Diderot - Paris 7 (UPD7)-Institut National de la Santé et de la Recherche Médicale (INSERM), Baobab, Département PEGASE [LBBE] (PEGASE), Laboratoire de Biométrie et Biologie Evolutive - UMR 5558 (LBBE), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire de Biométrie et Biologie Evolutive - UMR 5558 (LBBE), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS), Maastricht University Medical Center (MUMC), Science et Technologie du Lait et de l'Oeuf (STLO), Institut National de la Recherche Agronomique (INRA)-AGROCAMPUS OUEST, Assistance publique - Hôpitaux de Paris (AP-HP) (APHP)-Hôpital Robert Debré-Université Paris Diderot - Paris 7 (UPD7)-Institut National de la Santé et de la Recherche Médicale (INSERM), Centre de recherche en neurosciences de Lyon - Lyon Neuroscience Research Center (CRNL), Université de Lyon-Université de Lyon-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), ANR-16-CE23-0001,ASTER,Algorithmes et outils logiciels pour le séquençage d'ARN de troisième génération(2016), ANR-18-CE12-0007,U4ATAC-BRAIN,Rôle de l'épissage mineur dans le développement cérébral(2018), Sagot, Marie-France, Université de Tours-Institut National de la Santé et de la Recherche Médicale (INSERM), RS: GROW - R4 - Reproductive and Perinatal Medicine, MUMC+: DA KG Polikliniek (9), Klinische Genetica, and MUMC+: DA KG Lab Centraal Lab (9)

Subjects
Male, intron retention, U12-DEPENDENT INTRONS, DEVELOPMENTAL DISORDER, Minor spliceosome, RNA, Small Nuclear, Genetics, 0303 health sciences, [SDV.BIBS] Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], [SDV.MHEP] Life Sciences [q-bio]/Human health and pathology, Fetal Growth Retardation, MOPD1, 030302 biochemistry & molecular biology, RNU4ATAC, RNA sequencing, Middle Aged, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], Child, Preschool, RNA splicing, Microcephaly, Female, MESSENGER-RNA, Adult, Spliceosome, RNA Splicing, SPLICEOSOME, Dwarfism, Biology, Osteochondrodysplasias, Article, 03 medical and health sciences, Young Adult, Consensus Sequence, TWINTRON, Humans, RNA, Messenger, Twintron, Molecular Biology, Gene, 030304 developmental biology, Aged, Base Sequence, MUTATIONS, Gene Expression Profiling, Intron, RNA, Infant, COMPONENT, Introns, minor splicing, Spliceosomes, Transcriptome, U12-type introns, Small nuclear RNA, [SDV.MHEP]Life Sciences [q-bio]/Human health and pathology
Abstract

Minor intron splicing plays a central role in human embryonic development and survival. Indeed, biallelic mutations in RNU4ATAC, transcribed into the minor spliceosomal U4atac snRNA, are responsible for three rare autosomal recessive multimalformation disorders named Taybi–Linder (TALS/MOPD1), Roifman (RFMN), and Lowry–Wood (LWS) syndromes, which associate numerous overlapping signs of varying severity. Although RNA-seq experiments have been conducted on a few RFMN patient cells, none have been performed in TALS, and more generally no in-depth transcriptomic analysis of the ∼700 human genes containing a minor (U12-type) intron had been published as yet. We thus sequenced RNA from cells derived from five skin, three amniotic fluid, and one blood biosamples obtained from seven unrelated TALS cases and from age- and sex-matched controls. This allowed us to describe for the first time the mRNA expression and splicing profile of genes containing U12-type introns, in the context of a functional minor spliceosome. Concerning RNU4ATAC-mutated patients, we show that as expected, they display distinct U12-type intron splicing profiles compared to controls, but that rather unexpectedly mRNA expression levels are mostly unchanged. Furthermore, although U12-type intron missplicing concerns most of the expressed U12 genes, the level of U12-type intron retention is surprisingly low in fibroblasts and amniocytes, and much more pronounced in blood cells. Interestingly, we found several occurrences of introns that can be spliced using either U2, U12, or a combination of both types of splice site consensus sequences, with a shift towards splicing using preferentially U2 sites in TALS patients’ cells compared to controls.

Published
2019
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7. Annotation and differential analysis of alternative splicing using de novo assembly of RNAseq data

Author

Amandine E. Rey, Clara Benoit-Pilven, Vincent Lacroix, Camille Marchet, Marie-Pierre Lambert, Gustavo Sacomoto, Leandro Lima, Emilie Chautard, Cyril F. Bourgeois, Didier Auboeuf, Laboratoire de biologie et modélisation de la cellule (LBMC UMR 5239), École normale supérieure - Lyon (ENS Lyon)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Scalable, Optimized and Parallel Algorithms for Genomics (GenScale), Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-GESTION DES DONNÉES ET DE LA CONNAISSANCE (IRISA-D7), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes 1 (UR1), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Centre de Recherche en Cancérologie de Lyon (UNICANCER/CRCL), Centre Léon Bérard [Lyon]-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), Equipe de recherche européenne en algorithmique et biologie formelle et expérimentale (ERABLE), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), École normale supérieure - Lyon (ENS Lyon), École normale supérieure de Lyon (ENS de Lyon)-Université Claude Bernard Lyon 1 (UCBL), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), École normale supérieure de Lyon (ENS de Lyon), Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-CentraleSupélec-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), and Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1)

Subjects
Genetics, 0303 health sciences, Alternative splicing, Sequence assembly, Computational biology, Biology, de novo assembly, ENCODE, Exon skipping, 03 medical and health sciences, Exon, alternative splicing, 0302 clinical medicine, [SDV.BBM.GTP]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Genomics [q-bio.GN], RNA splicing, Human genome, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], transcriptome, 030217 neurology & neurosurgery, 030304 developmental biology, Reference genome
Abstract

Genome-wide analyses reveal that more than 90% of multi exonic human genes produce at least two transcripts through alternative splicing (AS). Various bioinformatics methods are available to analyze AS from RNAseq data. Most methods start by mapping the reads to an annotated reference genome, but some start by a de novo assembly of the reads. In this paper, we present a systematic comparison of a mapping-first approach (FaRLine) and an assembly-first approach (KisSplice). These two approaches are event-based, as they focus on the regions of the transcripts that vary in their exon content. We applied these methods to an RNAseq dataset from a neuroblastoma SK-N-SH cell line (ENCODE) differentiated or not using retinoic acid. We found that the predictions of the two pipelines overlapped (70% of exon skipping events were common), but with noticeable differences. The assembly-first approach allowed to find more novel variants, including novel unannotated exons and splice sites. It also predicted AS in families of paralog genes. The mapping-first approach allowed to find more lowly expressed splicing variants, and was better in predicting exons overlapping repeated elements. This work demonstrates that annotating AS with a single approach leads to missing a large number of candidates. We further show that these candidates cannot be neglected, since many of them are differentially regulated across conditions, and can be validated experimentally. We therefore advocate for the combine use of both mapping-first and assembly-first approaches for the annotation and differential analysis of AS from RNAseq data.

Published
2017

8. Identification of protein features encoded by alternative exons using Exon Ontology

Author

Didier Auboeuf, Hussein Mortada, Arnaud Poret, Léon-Charles Tranchevent, Fatima Zahra Chakrama, Amandine Rey, Fabien Aubé, Louis Dulaurier, Evelyne Goillot, Maira Alejandra Moreno-Garcia, Emilie Chautard, Cyril F. Bourgeois, Franck Mortreux, Stéphane Janczarski, François-Olivier Desmet, and Clara Benoit-Pilven

Subjects
0301 basic medicine, Resource, Computational biology, Ontology (information science), Biology, Bioinformatics, Genome, 03 medical and health sciences, Exon, 0302 clinical medicine, Exon trapping, Cell Line, Tumor, Genetics, Autophagy, Humans, Gene, Genetics (clinical), Genome, Human, Gene Expression Profiling, Alternative splicing, Molecular Sequence Annotation, Exons, Alternative Splicing, 030104 developmental biology, Gene Ontology, RNA splicing, MCF-7 Cells, Tandem exon duplication, RNA Splicing Factors, Transcriptome, Proto-Oncogene Proteins c-akt, 030217 neurology & neurosurgery, Software, Genome-Wide Association Study, Signal Transduction
Abstract

Transcriptomic genome-wide analyses demonstrate massive variation of alternative splicing in many physiological and pathological situations. One major challenge is now to establish the biological contribution of alternative splicing variation in physiological- or pathological-associated cellular phenotypes. Toward this end, we developed a computational approach, named “Exon Ontology,” based on terms corresponding to well-characterized protein features organized in an ontology tree. Exon Ontology is conceptually similar to Gene Ontology-based approaches but focuses on exon-encoded protein features instead of gene level functional annotations. Exon Ontology describes the protein features encoded by a selected list of exons and looks for potential Exon Ontology term enrichment. By applying this strategy to exons that are differentially spliced between epithelial and mesenchymal cells and after extensive experimental validation, we demonstrate that Exon Ontology provides support to discover specific protein features regulated by alternative splicing. We also show that Exon Ontology helps to unravel biological processes that depend on suites of coregulated alternative exons, as we uncovered a role of epithelial cell-enriched splicing factors in the AKT signaling pathway and of mesenchymal cell-enriched splicing factors in driving splicing events impacting on autophagy. Freely available on the web, Exon Ontology is the first computational resource that allows getting a quick insight into the protein features encoded by alternative exons and investigating whether coregulated exons contain the same biological information.

Published
2016

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