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2. The control of transcriptional memory by stable mitotic bookmarking

Author

Maëlle Bellec, Jérémy Dufourt, George Hunt, Hélène Lenden-Hasse, Antonio Trullo, Amal Zine El Aabidine, Marie Lamarque, Marissa M. Gaskill, Heloïse Faure-Gautron, Mattias Mannervik, Melissa M. Harrison, Jean-Christophe Andrau, Cyril Favard, Ovidiu Radulescu, and Mounia Lagha

Subjects
Science
Abstract

Using quantitative imaging and monitoring transcription in living embryos, Bellec et al., provide evidence that the pioneer factor GAF acts as a stable mitotic bookmarker during early Drosophila development.

Published
2022
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3. Ras/MAPK signalling intensity defines subclonal fitness in a mouse model of hepatocellular carcinoma

Author

Anthony Lozano, Francois-Régis Souche, Carine Chavey, Valérie Dardalhon, Christel Ramirez, Serena Vegna, Guillaume Desandre, Anaïs Riviere, Amal Zine El Aabidine, Philippe Fort, Leila Akkari, Urszula Hibner, and Damien Grégoire

Subjects
oncogenic dosage, tumour heterogeneity, microenvironment, NK cells, liver, Medicine, Science, Biology (General), QH301-705.5
Abstract

Quantitative differences in signal transduction are to date an understudied feature of tumour heterogeneity. The MAPK Erk pathway, which is activated in a large proportion of human tumours, is a prototypic example of distinct cell fates being driven by signal intensity. We have used primary hepatocyte precursors transformed with different dosages of an oncogenic form of Ras to model subclonal variations in MAPK signalling. Orthotopic allografts of Ras-transformed cells in immunocompromised mice gave rise to fast-growing aggressive tumours, both at the primary location and in the peritoneal cavity. Fluorescent labelling of cells expressing different oncogene levels, and consequently varying levels of MAPK Erk activation, highlighted the selection processes operating at the two sites of tumour growth. Indeed, significantly higher Ras expression was observed in primary as compared to secondary, metastatic sites, despite the apparent evolutionary trade-off of increased apoptotic death in the liver that correlated with high Ras dosage. Analysis of the immune tumour microenvironment at the two locations suggests that fast peritoneal tumour growth in the immunocompromised setting is abrogated in immunocompetent animals due to efficient antigen presentation by peritoneal dendritic cells. Furthermore, our data indicate that, in contrast to the metastatic-like outgrowth, strong MAPK signalling is required in the primary liver tumours to resist elimination by NK (natural killer) cells. Overall, this study describes a quantitative aspect of tumour heterogeneity and points to a potential vulnerability of a subtype of hepatocellular carcinoma as a function of MAPK Erk signalling intensity.

Published
2023
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5. GOLT1B Activation in Hepatitis C Virus-Infected Hepatocytes Links ER Trafficking and Viral Replication

Author

Jacqueline Butterworth, Damien Gregoire, Marion Peter, Armando Andres Roca Suarez, Guillaume Desandré, Yannick Simonin, Alessia Virzì, Amal Zine El Aabidine, Marine Guivarch, Jean-Christophe Andrau, Edouard Bertrand, Eric Assenat, Joachim Lupberger, and Urszula Hibner

Subjects
hepatitis C, transcriptomic profiling, hepatocellular carcinoma, smiFISH, Medicine
Abstract

Chronic hepatitis C carries a high risk of development of hepatocellular carcinoma (HCC), triggered by both direct and indirect effects of the virus. We examined cell-autonomous alterations in gene expression profiles associated with hepatitis C viral presence. Highly sensitive single molecule fluorescent in situ hybridization applied to frozen tissue sections of a hepatitis C patient allowed the delineation of clusters of infected hepatocytes. Laser microdissection followed by RNAseq analysis of hepatitis C virus (HCV)-positive and -negative regions from the tumoral and non-tumoral tissues from the same patient revealed HCV-related deregulation of expression of genes in the tumor and in the non-tumoral tissue. However, there was little overlap between both gene sets. Our interest in alterations that increase the probability of tumorigenesis prompted the examination of genes whose expression was increased by the virus in the non-transformed cells and whose level remained high in the tumor. This strategy led to the identification of a novel HCV target gene: GOLT1B, which encodes a protein involved in ER-Golgi trafficking. We further show that GOLT1B expression is induced during the unfolded protein response, that its presence is essential for efficient viral replication, and that its expression is correlated with poor outcome in HCC.

Published
2021
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6. The control of transcriptional memory by stable mitotic bookmarking

Author

Mounia Lagha, Melissa M. Harrison, George Hunt, Jeremy Dufourt, Antonio Trullo, Maelle Bellec, Cyril Favard, Jean-Christophe Andrau, Ovidiu Radulescu, Mattias Mannervik, Hélène Lenden-Hasse, Marissa M Gaskill, Marie Lamarque, Heloïse Faure-Gautron, Amal Zine El Aabidine, Institut de Génétique Moléculaire de Montpellier (IGMM), and Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)

Subjects
[SDV]Life Sciences [q-bio], General Physics and Astronomy, Mitosis, General Biochemistry, Genetics and Molecular Biology, Histones, 03 medical and health sciences, Gene expression, Animals, Transcription factor, 030304 developmental biology, 0303 health sciences, Multidisciplinary, biology, Bookmarking, 030302 biochemistry & molecular biology, Acetylation, General Chemistry, Chromatin, Cell biology, Histone, Mitotic exit, biology.protein, Maternal to zygotic transition, Drosophila, Transcription Factors
Abstract

To maintain cellular identities during development, gene expression profiles must be faithfully propagated through cell generations. The reestablishment of gene expression patterns upon mitotic exit is thought to be mediated, in part, by mitotic bookmarking by transcription factors (TF). However, the mechanisms and functions of TF mitotic bookmarking during early embryogenesis remain poorly understood. In this study, taking advantage of the naturally synchronized mitoses of Drosophila early embryos, we provide evidence that the pioneer-like transcription factor GAF acts as stable mitotic bookmarker during zygotic genome activation. We report that GAF remains associated to a large fraction of its interphase targets including at cis-regulatory sequences of key developmental genes, with both active and repressive chromatin signatures. GAF mitotic targets are globally accessible during mitosis and are bookmarked via histone acetylation (H4K8ac). By monitoring the kinetics of transcriptional activation in living embryos, we provide evidence that GAF binding establishes competence for rapid activation upon mitotic exit.

Published
2021

8. Fra-1 regulates its target genes via binding to remote enhancers without exerting major control on chromatin architecture in triple negative breast cancers

Author

Hughes Parrinello, Tony Kaoma, Raphaël Romero, Laurent Brehelin, Damien J. Downes, Charles-Henri Lecellier, Marc Piechaczyk, Kazem Zibara, Amal Zine El Aabidine, Fabienne Bejjani, Jim R. Hughes, Muhammad Ahmad Maqbool, Mathias Boulanger, Claire Tolza, Laurent Vallar, Jean-Christophe Andrau, Sophie Lèbre, Isabelle Jariel-Encontre, Marine Rohmer, Institut de Génétique Moléculaire de Montpellier (IGMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), The Weatherall Institute of Molecular Medicine, University of Oxford [Oxford], Méthodes et Algorithmes pour la Bioinformatique (MAB), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Institut Montpelliérain Alexander Grothendieck (IMAG), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Institut de Génomique Fonctionnelle - Montpellier GenomiX (IGF MGX), Institut de Génomique Fonctionnelle (IGF), Université de Montpellier (UM)-Université Montpellier 1 (UM1)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Montpellier 2 - Sciences et Techniques (UM2)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Université Montpellier 1 (UM1)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Montpellier 2 - Sciences et Techniques (UM2)-Centre National de la Recherche Scientifique (CNRS), Luxembourg Institute of Health (LIH), and Lebanese University [Beirut] (LU)

Subjects
AcademicSubjects/SCI00010, Triple Negative Breast Neoplasms, Fos-Related Antigen-2, Biology, Epigenesis, Genetic, Transcriptome, 03 medical and health sciences, 0302 clinical medicine, Transcription (biology), Cell Line, Tumor, [SDV.BBM.GTP]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Genomics [q-bio.GN], Genetics, Humans, p300-CBP Transcription Factors, [SDV.BBM]Life Sciences [q-bio]/Biochemistry, Molecular Biology, Nucleotide Motifs, Enhancer, Promoter Regions, Genetic, Gene, Transcription factor, 030304 developmental biology, 0303 health sciences, Binding Sites, Gene regulation, Chromatin and Epigenetics, Promoter, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], Chromatin, Cell biology, Gene Expression Regulation, Neoplastic, Transcription Factor AP-1, Enhancer Elements, Genetic, 030220 oncology & carcinogenesis, Candidate Disease Gene, Proto-Oncogene Proteins c-fos
Abstract

The ubiquitous family of dimeric transcription factors AP-1 is made up of Fos and Jun family proteins. It has long been thought to operate principally at gene promoters and how it controls transcription is still ill-understood. The Fos family protein Fra-1 is overexpressed in triple negative breast cancers (TNBCs) where it contributes to tumor aggressiveness. To address its transcriptional actions in TNBCs, we combined transcriptomics, ChIP-seqs, machine learning and NG Capture-C. Additionally, we studied its Fos family kin Fra-2 also expressed in TNBCs, albeit much less. Consistently with their pleiotropic effects, Fra-1 and Fra-2 up- and downregulate individually, together or redundantly many genes associated with a wide range of biological processes. Target gene regulation is principally due to binding of Fra-1 and Fra-2 at regulatory elements located distantly from cognate promoters where Fra-1 modulates the recruitment of the transcriptional co-regulator p300/CBP and where differences in AP-1 variant motif recognition can underlie preferential Fra-1- or Fra-2 bindings. Our work also shows no major role for Fra-1 in chromatin architecture control at target gene loci, but suggests collaboration between Fra-1-bound and -unbound enhancers within chromatin hubs sometimes including promoters for other Fra-1-regulated genes. Our work impacts our view of AP-1.

Published
2021
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9. QTL mapping of flowering and fruiting traits in olive.

Author

Inès Ben Sadok, Jean-Marc Celton, Laila Essalouh, Amal Zine El Aabidine, Gilbert Garcia, Sebastien Martinez, Naziha Grati-Kamoun, Ahmed Rebai, Evelyne Costes, and Bouchaib Khadari

Subjects
Medicine, Science
Abstract

One of the challenge fruit growers are facing is to balance between tree production and vegetative growth from year to year. To investigate the existence of genetic determinism for reproductive behaviour in olive tree, we studied an olive segregating population derived from a cross between 'Olivière' and 'Arbequina' cultivars. Our strategy was based on (i) an annual assessment of individual trees yield, and (ii) a decomposition of adult growth units at the crown periphery into quantitative variables related to both flowering and fruiting process in relation to their growth and branching. Genetic models, including the year, genotype effects and their interactions, were built with variance function and correlation structure of residuals when necessary. Among the progeny, trees were either 'ON' or 'OFF' for a given year and patterns of regular vs. irregular bearing were revealed. Genotype effect was significant on yield but not for flowering traits at growth unit (GU) scale, whereas the interaction between genotype and year was significant for both traits. A strong genetic effect was found for all fruiting traits without interaction with the year. Based on the new constructed genetic map, QTLs with small effects were detected, revealing multigenic control of the studied traits. Many were associated to alleles from 'Arbequina'. Genetic correlations were found between Yield and Fruit set at GU scale suggesting a common genetic control, even though QTL co-localisations were in spe`cific years only. Most QTL were associated to flowering traits in specific years, even though reproductive traits at GU scale did not capture the bearing status of the trees in a given year. Results were also interpreted with respect to ontogenetic changes of growth and branching, and an alternative sampling strategy was proposed for capturing tree fruiting behaviour. Regular bearing progenies were identified and could constitute innovative material for selection programs.

Published
2013
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10. Alternative Enhancer Usage and Targeted Polycomb Marking Hallmark Promoter Choice during T Cell Differentiation

Author

Frederic Koch, Olivier Joffre, Amal Zine El Aabidine, Tom Sexton, Romain Fenouil, Jean-Christophe Andrau, Anne Molitor, Lan T.M. Dao, Muhammad Ahmad Maqbool, Nezih Karasu, Léo Pioger, Georges Lacaud, Salvatore Spicuglia, Francois van Laethem, Ivo Gut, Guillaume Charbonnier, Marta Gut, Sebastian Amigorena, University of Manchester [Manchester], Institut de Génétique Moléculaire de Montpellier (IGMM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC), Université de Strasbourg (UNISTRA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Theories and Approaches of Genomic Complexity (TAGC), Aix Marseille Université (AMU)-Institut National de la Santé et de la Recherche Médicale (INSERM), Universitat Pompeu Fabra [Barcelona] (UPF), Barcelona Institute of Science and Technology (BIST), Institut Curie [Paris], Immunité et cancer (U932), Institut Curie [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM), Centre de Physiopathologie Toulouse Purpan (CPTP), Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA), and Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Male, 0301 basic medicine, Gene isoform, CapSTARR-seq, T-Lymphocytes, [SDV]Life Sciences [q-bio], Polycomb-Group Proteins, macromolecular substances, Computational biology, General Biochemistry, Genetics and Molecular Biology, Mice, enhancer and promoter usage, long-distance enhancer-promoter interactions, 03 medical and health sciences, 0302 clinical medicine, enhancer redundancy, [SDV.BBM.GTP]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Genomics [q-bio.GN], Animals, Enhancer, Gene, Epigenomics, biology, Manchester Cancer Research Centre, ResearchInstitutes_Networks_Beacons/mcrc, Cell Differentiation, Promoter, Phenotype, 3. Good health, 030104 developmental biology, biology.protein, [SDV.IMM]Life Sciences [q-bio]/Immunology, PRC2, 030217 neurology & neurosurgery, Function (biology), T cell enhancerome
Abstract

During thymic development and upon peripheral activation, T cells undergo extensive phenotypic and functional changes coordinated by lineage-specific developmental programs. To characterize the regulatory landscape controlling T cell identity, we perform a wide epigenomic and transcriptional analysis of mouse thymocytes and naive CD4 differentiated T helper cells. Our investigations reveal a dynamic putative enhancer landscape, and we could validate many of the enhancers using the high-throughput CapStarr sequencing (CapStarr-seq) approach. We find that genes using multiple promoters display increased enhancer usage, suggesting that apparent "enhancer redundancy" might relate to isoform selection. Furthermore, we can show that two Runx3 promoters display long-range interactions with specific enhancers. Finally, our analyses suggest a novel function for the PRC2 complex in the control of alternative promoter usage. Altogether, our study has allowed for the mapping of an exhaustive set of active enhancers and provides new insights into their function and that of PRC2 in controlling promoter choice during T cell differentiation. This work was supported in J.-C.A.’s lab by grants from Fondation pour la Recherche Médicale (FRM) “Amorçage Jeunes Equipes” AJE20130728183, Agence Nationale de la Recherche (ANR) isplice (ANR-11-BSV8-0013), ANR ChromaTin (ANR-10-BLAN-1326), ITMO INCA “Dys3Dpoly,” ESGI 2011 (FP7 funding for high-throughput sequencing), and Ligue Régionale contre le Cancer (Hérault). L.P. was supported by a grant from FRM (4eme année thèse FDT201904007910). The T.S. lab was supported by funds from the European Research Council (ERC) (H2020, Starting Grant 678624 - CHROMTOPOLOGY), the ATIP-Avenir program, and the grant ANR-10-LABX-0030-INRT, under program ANR-10-IDEX-0002-02. A.M.M. is supported by funds from IDEX (University of Strasbourg) and the Institut National Du Cancer.

Published
2020
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11. De novo assembly of viral quasispecies using overlap graphs

Author

Jasmijn A. Baaijens, Amal Zine El Aabidine, Eric Rivals, Alexander Schönhuth, Centrum Wiskunde & Informatica (CWI), Méthodes et Algorithmes pour la Bioinformatique (MAB), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Life Sciences [Amsterdam] (MAC4), and Centrum Wiskunde & Informatica, Amsterdam (CWI), The Netherlands

Subjects
0301 basic medicine, 0206 medical engineering, Sequence assembly, Method, 02 engineering and technology, Viral quasispecies, Computational biology, Genome, Viral, Hepacivirus, Biology, Genome, 03 medical and health sciences, Contig Mapping, Zika, 0302 clinical medicine, Genetics, Genomics/standards, Genetics (clinical), Selection (genetic algorithm), 030304 developmental biology, Sequence (medicine), 0303 health sciences, Polymorphism, Genetic, Contig, Genomics, Sequence Analysis, DNA, Zika Virus, Reference Standards, Data structure, Virus, 3. Good health, Algorithm, 030104 developmental biology, Haplotypes, Mutation (genetic algorithm), [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], 020602 bioinformatics, 030217 neurology & neurosurgery, Software, Reference genome
Abstract

A viral quasispecies, the ensemble of viral strains populating an infected person, can be highly diverse. For optimal assessment of virulence, pathogenesis and therapy selection, determining the haplotypes of the individual strains can play a key role. As many viruses are subject to high mutation and recombination rates, high-quality reference genomes are often not available at the time of a new disease outbreak. We present SAVAGE, a computational tool for reconstructing individual haplotypes of intrahost virus strains without the need for a high-quality reference genome. SAVAGE makes use of either FM-index based data structures or ad-hoc consensus reference sequence for constructing overlap graphs from patient sample data. In this overlap graph, nodes represent reads and/or contigs, while edges reflect that two reads/contigs, based on sound statistical considerations, represent identical haplotypic sequence. Following an iterative scheme, a new overlap assembly algorithm that is based on the enumeration of statistically well-calibrated groups of reads/contigs then efficiently reconstructs the individual haplotypes from this overlap graph. In benchmark experiments on simulated and on real deep coverage data, SAV-AGE drastically outperforms generic de novo assemblers as well as the only specialized de novo viral quasispecies assembler available so far. When run on ad-hoc consensus reference sequence, SAVAGE performs very favorably in comparison with state-of-the-art reference genome guided tools. We also apply SAVAGE on two deep coverage samples of patients infected by the Zika and the hepatitis C virus, respectively, which sheds light on the genetic structures of the respective viral quasispecies.

Published
2017
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12. The Landscape of L1 Retrotransposons in the Human Genome Is Shaped by Pre-insertion Sequence Biases and Post-insertion Selection

Author

Simona Saccani, Marc Bailly-Bechet, Dominic van Essen, Nicolas Gilbert, Oliver Siol, Pilvi Nigumann, Tania Sultana, Léo Pioger, Amal Zine El Aabidine, Claude Philippe, Jean-Christophe Andrau, Gaël Cristofari, Institut de Recherche sur le Cancer et le Vieillissement (IRCAN), Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015 - 2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015 - 2019) (COMUE UCA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA), Institut de génétique humaine (IGH), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Université Côte d'Azur (UCA), Institut Gilbert-Laustriat : Biomolécules, Biotechnologie, Innovation Thérapeutique, Université Louis Pasteur - Strasbourg I-Centre National de la Recherche Scientifique (CNRS), Institut de Génétique Moléculaire de Montpellier (IGMM), Cellules Souches, Plasticité Cellulaire, Médecine Régénératrice et Immunothérapies (IRMB), Centre Hospitalier Régional Universitaire [Montpellier] (CHRU Montpellier)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Montpellier (UM), Fondation pour la Recherche Medicale [FRM-DEP20131128533], European Research Council [ERC-2009-StG 243312], Agence Nationale de la Recherche (LABEX SIGNALIFE) [ANR-11-LABX-0028-01], Agence Nationale de la Recherche (RETROMET) [ANR-16-CE12-0020], Canceropole PACA (Projet Emergence), CNRS [GDR 3546], University Hospital Federation (FHU) OncoAge, FEDER, Inserm, Universite Cote d'Azur (France), University of Dhaka (Bangladesh), Agence Nationale de la Recherche (RETROGENO) [ANR-12-BSV6-0003], Region Provence Alpes Cote d'Azur, Conseil Departemental 06, ITMO Cancer Aviesan [plan cancer], ANR: 11-LABX-0028,SIGNALIFE,program 'Investments for the Future' LABEX SIGNALIFE, ANR-16-CE12-0020,RETROMET,Rendre unique l'ADN répété ou comment révéler la régulation épigénétique des rétrotransposons L1 dans les cellules somatiques humaines à une résolution inégalée.(2016), ANR-12-BSV6-0003,RETROGENO,Complexes de rétrotransposition du LINE-1 humain et instabilité génomique(2012), Université Nice Sophia Antipolis (1965 - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA), Institut Sophia Agrobiotech (ISA), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Côte d'Azur (UCA), ANR-11-LABX-0028,SIGNALIFE,Réseau d'Innovation sur les Voies de Signalisation en Sciences de la Vie(2011), CCSD, Accord Elsevier, Centres d'excellences - Réseau d'Innovation sur les Voies de Signalisation en Sciences de la Vie - - SIGNALIFE2011 - ANR-11-LABX-0028 - LABX - VALID, Rendre unique l'ADN répété ou comment révéler la régulation épigénétique des rétrotransposons L1 dans les cellules somatiques humaines à une résolution inégalée. - - RETROMET2016 - ANR-16-CE12-0020 - AAPG2016 - VALID, BLANC - Complexes de rétrotransposition du LINE-1 humain et instabilité génomique - - RETROGENO2012 - ANR-12-BSV6-0003 - BLANC - VALID, Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Centre d'Immunologie de Marseille - Luminy (CIML), Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU), Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Nice Sophia Antipolis (... - 2019) (UNS), Université Côte d'Azur (UCA)-Université Côte d'Azur (UCA), and Université de Montpellier (UM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre Hospitalier Régional Universitaire [Montpellier] (CHRU Montpellier)

Subjects
DNA Replication, replication, Retroelements, [SDV]Life Sciences [q-bio], x-chromosome incactivation, Retrotransposon, Biology, dna, Genome, 03 medical and health sciences, 0302 clinical medicine, read alignement, [SDV.BBM.GTP]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Genomics [q-bio.GN], [SDV.BBM] Life Sciences [q-bio]/Biochemistry, Molecular Biology, Humans, [SDV.BBM]Life Sciences [q-bio]/Biochemistry, Molecular Biology, Insertion sequence, Molecular Biology, ComputingMilieux_MISCELLANEOUS, 030304 developmental biology, Whole genome sequencing, 0303 health sciences, Replication timing, Natural selection, Genome, Human, line-1 retrotransposition, Chromosome Mapping, Genomics, Cell Biology, LINE1, reverse transcription, [SDV] Life Sciences [q-bio], Long Interspersed Nucleotide Elements, Evolutionary biology, DNA Transposable Elements, human-cells, [SDV.BBM.GTP] Life Sciences [q-bio]/Biochemistry, Molecular Biology/Genomics [q-bio.GN], Human genome, Mobile genetic elements, transposable elements, rplication, reveals, intergration site selection, 030217 neurology & neurosurgery, HeLa Cells
Abstract

International audience; L1 retrotransposons are transposable elements and major contributors of genetic variation in humans. Where L1 integrates into the genome can directly impact human evolution and disease. Here, we experimentally induced L1 retrotransposition in cells and mapped integration sites at nucleotide resolution. At local scales, L1 integration is mostly restricted by genome sequence biases and the specificity of the L1 machinery. At regional scales, L1 shows a broad capacity for integration into all chromatin states, in contrast to other known mobile genetic elements. However, integration is influenced by the replication timing of target regions, suggesting a link to host DNA replication. The distribution of new L1 integrations differs from those of preexisting L1 copies, which are significantly reshaped by natural selection. Our findings reveal that the L1 machinery has evolved to efficiently target all genomic regions and underline a predominant role for post-integrative processes on the distribution of endogenous L1 elements.

Published
2019
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13. Critical role for TRIM28 and HP1β/γ in the epigenetic control of T cell metabolic reprograming and effector differentiation

Author

Marianne Burbage, Pablo J. Sáez, Deborah Lefevre, Thomas Hoyler, Amal Zine El Aabidine, Rébecca Panes, Guadalupe Suarez, Christel Goudot, Sebastian Amigorena, Jean-Marie Carpier, Mengliang Ye, Fanny Aprahamian, Claire Hivroz, Florence Cammas, Olivier Joffre, Véronique Adoue, Elina Zueva, Jean-Christophe Andrau, Angelique Bellemare-Pelletier, Sylvère Durand, Leonel Joannas, Etienne Gagnon, Maqbool Muhammad Ahmad, Guido Kroemer, Cyril Esnault, Ulf Gehrmann, Sandrine Heurtebise-Chrétien, Nina Burgdorf, Centre de Physiopathologie Toulouse Purpan (CPTP), Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Immunité et cancer (U932), Institut Curie [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM), AstraZeneca, Institut de Génétique Moléculaire de Montpellier (IGMM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Métabolisme, Cancer et Immunité (CRC - UMR_S 1138), Institut Gustave Roussy (IGR)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPCité)-Centre de Recherche des Cordeliers (CRC (UMR_S_1138 / U1138)), École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPCité)-École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Cité (UPCité), Université de Montréal (UdeM), Institut de Recherche en Immunologie et en Cancérologie [UdeM-Montréal] (IRIC), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Institut de Recherche en Cancérologie de Montpellier (IRCM - U1194 Inserm - UM), CRLCC Val d'Aurelle - Paul Lamarque-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Montpellier (UM), Karolinska University Hospital [Stockholm], ANR-10-BLAN-1326,chromaTin,Dynamique de la chromatine au cours de l'activation des lympohocytes T: role de HP1(2010), ANR-11-LABX-0043,DCBIOL,Biologie des cellules dendritiques(2011), ANR-10-IDEX-0001,PSL,Paris Sciences et Lettres(2010), ANR-14-CE14-0021,EpiTreg,Régulation épigénétique du développement et de l'activité des lymphocytes T (régulateurs) par HP1 et son interactome(2014), ANR-10-INBS-0009,France-Génomique,Organisation et montée en puissance d'une Infrastructure Nationale de Génomique(2010), ANR-11-LABX-0038,CelTisPhyBio,Des cellules aux tissus: au croisement de la Physique et de la Biologie(2011), ANR-16-CE18-0023,Healskin,Matrices pour la régénération et la cicatrisation cutanée(2016), ANR-10-EQPX-0003,ICGex,Equipement de biologie intégrative du cancer pour une médecine personnalisée(2010), ANR-11-LABX-0044,DEEP,Développement, Epigénèse, Epigénétique et potentiel de vie(2011), and European Project: 340046,EC:FP7:ERC,ERC-2013-ADG,DCBIOX(2014)

Subjects
CD4-Positive T-Lymphocytes, Chromosomal Proteins, Non-Histone, MESH: Cellular Reprogramming / genetics, T-Lymphocytes, MESH: Cellular Reprogramming / physiology, [SDV]Life Sciences [q-bio], Cell Plasticity, MESH: Chromobox Protein Homolog 5, [SDV.BC.BC]Life Sciences [q-bio]/Cellular Biology/Subcellular Processes [q-bio.SC], Tripartite Motif-Containing Protein 28, T-Lymphocytes, Regulatory, [SDV.IMM.II]Life Sciences [q-bio]/Immunology/Innate immunity, Epigenesis, Genetic, Histones, immunology, Mice, Phosphatidylinositol 3-Kinases, Immunology and Inflammation, 0302 clinical medicine, MESH: Animals, ComputingMilieux_MISCELLANEOUS, Mice, Knockout, 0303 health sciences, Multidisciplinary, Effector, autoimmunity, Cell Differentiation, MESH: DNA-Binding Proteins / metabolism, Biological Sciences, MESH: Cell Differentiation / physiology, Cellular Reprogramming, MESH: Gene Expression Regulation, MESH: Cell Differentiation / genetics, Chromatin, Cell biology, DNA-Binding Proteins, medicine.anatomical_structure, PNAS Plus, Regulatory sequence, 030220 oncology & carcinogenesis, Cytokines, [SDV.IMM]Life Sciences [q-bio]/Immunology, MESH: Chromosomal Proteins, Non-Histone / metabolism, MESH: Epigenesis, Genetic / physiology, MESH: Cell Plasticity / physiology, Colon, T cell, Receptors, Antigen, T-Cell, T cells, Biology, 03 medical and health sciences, MESH: CD4-Positive T-Lymphocytes / metabolism, [SDV.BBM.GTP]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Genomics [q-bio.GN], medicine, Animals, Gene silencing, Gene Silencing, Epigenetics, 030304 developmental biology, Histone binding, MESH: DNA-Binding Proteins / genetics, epigenetics, TRIM28, MESH: Cytokines / metabolism, T-cell receptor, Gene Expression Regulation, Chromobox Protein Homolog 5, MESH: Autoimmunity / physiology, MESH: Colon / pathology, Transcriptome
Abstract

Significance CD4 T cells are major regulators of immune responses against both self and pathogens. Understanding pathways that govern CD4 T cell differentiation and regulation are thus key for the discovery of new immunoregulatory drug targets. Here, we have identified an epigenetic pathway that regulates the expression of a set of proteins that determine T cell responsiveness. By silencing enhancers distal to a set of genes known to be involved in regulatory T cell function, the epigenetic modifiers TRIM28 and HP1β/γ regulate T cell receptor signaling. This leads to defective metabolic reprograming and inefficient effector differentiation of naive T cells. This mechanism provides an exciting opportunity to regulate T cell responsivity in both autoimmunity and T cell-based immunodeficiencies., Naive CD4+ T lymphocytes differentiate into different effector types, including helper and regulatory cells (Th and Treg, respectively). Heritable gene expression programs that define these effector types are established during differentiation, but little is known about the epigenetic mechanisms that install and maintain these programs. Here, we use mice defective for different components of heterochromatin-dependent gene silencing to investigate the epigenetic control of CD4+ T cell plasticity. We show that, upon T cell receptor (TCR) engagement, naive and regulatory T cells defective for TRIM28 (an epigenetic adaptor for histone binding modules) or for heterochromatin protein 1 β and γ isoforms (HP1β/γ, 2 histone-binding factors involved in gene silencing) fail to effectively signal through the PI3K–AKT–mTOR axis and switch to glycolysis. While differentiation of naive TRIM28−/− T cells into cytokine-producing effector T cells is impaired, resulting in reduced induction of autoimmune colitis, TRIM28−/− regulatory T cells also fail to expand in vivo and to suppress autoimmunity effectively. Using a combination of transcriptome and chromatin immunoprecipitation-sequencing (ChIP-seq) analyses for H3K9me3, H3K9Ac, and RNA polymerase II, we show that reduced effector differentiation correlates with impaired transcriptional silencing at distal regulatory regions of a defined set of Treg-associated genes, including, for example, NRP1 or Snai3. We conclude that TRIM28 and HP1β/γ control metabolic reprograming through epigenetic silencing of a defined set of Treg-characteristic genes, thus allowing effective T cell expansion and differentiation into helper and regulatory phenotypes.

Published
2019
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14. Tyrosine-1 of RNA Polymerase II CTD Controls Global Termination of Gene Transcription in Mammals

Author

Amal Zine El Aabidine, Dirk Eick, David C. Martin, Cyril Esnault, Yousra Yahia, Jean-Christophe Andrau, Nilay Shah, Roland Schüller, Stefan Krebs, Tim-Michael Decker, Helmut Blum, Axel Imhof, Muhammad Ahmad Maqbool, Ignasi Forné, Institut de Génétique Moléculaire de Montpellier (IGMM), and Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)

Subjects
0301 basic medicine, Transcription, Genetic, viruses, [SDV]Life Sciences [q-bio], RNA polymerase II, read-through transcription, MESH: Tyrosine, MESH: RNA, Small Nuclear, chemistry.chemical_compound, 0302 clinical medicine, divergent transcription, Transcription (biology), RNA polymerase, RNA, Small Nuclear, Promoter Regions, Genetic, biology, Chromatin, Cell biology, Tyrosine1, RNA splicing, MESH: Cell Line, Tumor, MESH: Mutation, promoter-proximal pausing, MESH: Chromatin, 03 medical and health sciences, MESH: Transcription Termination, Genetic, Cell Line, Tumor, MESH: Promoter Regions, Genetic, Humans, RNA, Messenger, Enhancer, Molecular Biology, Gene, transcription termination, MESH: RNA, Messenger, MESH: Humans, MESH: Transcription, Genetic, Promoter, Cell Biology, CTD, MESH: RNA Polymerase II, 030104 developmental biology, chemistry, Transcription Termination, Genetic, Mutation, biology.protein, Tyrosine, 030217 neurology & neurosurgery
Abstract

International audience; The carboxy-terminal domain (CTD) of RNA polymerase (Pol) II is composed of a repetition of YSPTSPS heptads and functions as a loading platform for protein complexes that regulate transcription, splicing, and maturation of RNAs. Here, we studied mammalian CTD mutants to analyze the function of tyrosine1 residues in the transcription cycle. Mutation of 3/4 of the tyrosine residues (YFFF mutant) resulted in a massive read-through transcription phenotype in the antisense direction of promoters as well as in the 3' direction several hundred kilobases downstream of genes. The YFFF mutant shows reduced Pol II at promoter-proximal pause sites, a loss of interaction with the Mediator and Integrator complexes, and impaired recruitment of these complexes to chromatin. Consistent with these observations, Pol II loading at enhancers and maturation of snRNAs are altered in the YFFF context genome-wide. We conclude that tyrosine1 residues of the CTD control termination of transcription by Pol II.

Published
2018
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15. Fra-1 regulates its target genes via binding to remote enhancers without exerting major control on chromatin architecture in triple negative breast cancers.

Author

Bejjani, Fabienne, Tolza, Claire, Boulanger, Mathias, Downes, Damien, Romero, Raphaël, Maqbool, Muhammad Ahmad, Zine El Aabidine, Amal, Andrau, Jean-Christophe, Lebre, Sophie, Brehelin, Laurent, Parrinello, Hughes, Rohmer, Marine, Kaoma, Tony, Vallar, Laurent, Hughes, Jim R, Zibara, Kazem, Lecellier, Charles-Henri, Piechaczyk, Marc, and Jariel-Encontre, Isabelle

Published
2021
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16. Colib’read on galaxy: a tools suite dedicated to biological information extraction from raw NGS reads

Author

Eric Rivals, Bastien Cazaux, Gustavo Sacomoto, Leena Salmela, Camille Marchet, Yvan Le Bras, Raluca Uricaru, Olivier Collin, Alexan Andrieux, Cyril Monjeaud, Susete Alves-Carvalho, Amal Zine El Aabidine, Vincent Miele, Claire Lemaitre, Pierre Peterlongo, Vincent Lacroix, Service Expérimentation et Développement (SED [Rennes]), 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)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria), Plateforme bioinformatique GenOuest [Rennes], Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Plateforme Génomique Santé Biogenouest®-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), 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)-Université de Bretagne Sud (UBS)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-CentraleSupélec-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)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Rennes (ENS Rennes)-Télécom Bretagne-CentraleSupélec, 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), 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), Institut de Biologie Computationnelle (IBC), Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Méthodes et Algorithmes pour la Bioinformatique (MAB), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Scalable, Optimized and Parallel Algorithms for Genomics (GenScale), GESTION DES DONNÉES ET DE LA CONNAISSANCE (IRISA-D7), Université de Rennes (UNIV-RENNES)-CentraleSupélec-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UNIV-RENNES)-CentraleSupélec-Inria Rennes – Bretagne Atlantique, Helsinki Institute for Information Technology (HIIT), Aalto University, Laboratoire Bordelais de Recherche en Informatique (LaBRI), Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Université Sciences et Technologies - Bordeaux 1-Université Bordeaux Segalen - Bordeaux 2, Centre de Bioinformatique de Bordeaux (CBIB), CGFB, Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Inria Rennes – Bretagne Atlantique, Université de Rennes (UR)-Plateforme Génomique Santé Biogenouest®-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 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)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-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)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), 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)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-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)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), Helsingin yliopisto = Helsingfors universitet = University of Helsinki-Aalto University, Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS), CentraleSupélec-Télécom Bretagne-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Rennes (ENS Rennes)-Université de Bretagne Sud (UBS)-Centre National de la Recherche Scientifique (CNRS)-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)-CentraleSupélec-Télécom Bretagne-Université de Rennes 1 (UR1), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-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)-Plateforme Génomique Santé Biogenouest®-Inria Rennes – Bretagne Atlantique, Université de Montpellier (UM)-Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-École normale supérieure - Rennes (ENS Rennes)-Université de Bretagne Sud (UBS)-Centre National de la Recherche Scientifique (CNRS)-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), Aalto University-University of Helsinki, Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB), Department of Computer Science, Helsinki Institute for Information Technology, Finnish Centre of Excellence in Algorithmic Data Analysis Research (Algodan), Genome-scale Algorithmics research group / Veli Mäkinen, Bioinformatics, and Algorithmic Bioinformatics

Subjects
0301 basic medicine, Computer science, Molecular Sequence Data, 0206 medical engineering, Information Storage and Retrieval, Health Informatics, 02 engineering and technology, computer.software_genre, Whole-genome assembly-less treatment, De Bruijn graph, Set (abstract data type), 03 medical and health sciences, symbols.namesake, Bloom filter, Software, Variant calling, Technical Note, Cluster Analysis, Genome, Base Sequence, business.industry, Suite, Computational Biology, High-Throughput Nucleotide Sequencing, Reproducibility of Results, Genomics, 113 Computer and information sciences, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], Computer Science Applications, Information extraction, 030104 developmental biology, long read correction, NGS, Memory footprint, symbols, Data mining, Metagenomics, RNA-seq, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], Raw data, business, computer, 020602 bioinformatics
Abstract

Background With next-generation sequencing (NGS) technologies, the life sciences face a deluge of raw data. Classical analysis processes for such data often begin with an assembly step, needing large amounts of computing resources, and potentially removing or modifying parts of the biological information contained in the data. Our approach proposes to focus directly on biological questions, by considering raw unassembled NGS data, through a suite of six command-line tools. Findings Dedicated to ‘whole-genome assembly-free’ treatments, the Colib’read tools suite uses optimized algorithms for various analyses of NGS datasets, such as variant calling or read set comparisons. Based on the use of a de Bruijn graph and bloom filter, such analyses can be performed in a few hours, using small amounts of memory. Applications using real data demonstrate the good accuracy of these tools compared to classical approaches. To facilitate data analysis and tools dissemination, we developed Galaxy tools and tool shed repositories. Conclusions With the Colib’read Galaxy tools suite, we enable a broad range of life scientists to analyze raw NGS data. More importantly, our approach allows the maximum biological information to be retained in the data, and uses a very low memory footprint. Electronic supplementary material The online version of this article (doi:10.1186/s13742-015-0105-2) contains supplementary material, which is available to authorized users.

Published
2016
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17. Correction: QTL Mapping of Flowering and Fruiting Traits in Olive

Author

Bouchaib Khadari, Ahmed Rebai, Gilbert Garcia, Laila Essalouh, Jean-Marc Celton, Amal Zine El Aabidine, Sébastien Martinez, Evelyne Costes, Inès Ben Sadok, and Naziha Grati-Kamoun

Subjects
Multidisciplinary, Science, lcsh:R, Correction, lcsh:Medicine, Quantitative trait locus, Biology, Bioinformatics, Evolutionary biology, Medicine, lcsh:Q, lcsh:Science
Abstract

Only a portion of Figure 5 is included in the article. Please find a full version of Figure 5 here

Published
2014

18. Co2Vis: A Visual Analytics Tool for Mining Co-Expressed and Co-Regulated Genes Implied in HIV Infections

Author

Zine El Aabidine, Amal, Sallaberry, Arnaud, Bringay, Sandra, Fabrègue, Mickaël, Lecellier, Charles-Henri, Hai, Phan Nhat, Poncelet, Pascal, Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), ADVanced Analytics for data SciencE (ADVANSE), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Université Paul-Valéry - Montpellier 3 (UPVM), Laboratoire des sciences de l'ingénieur, de l'informatique et de l'imagerie (ICube), Institut National des Sciences Appliquées - Strasbourg (INSA Strasbourg), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS)-École Nationale du Génie de l'Eau et de l'Environnement de Strasbourg (ENGEES)-Réseau nanophotonique et optique, Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Matériaux et nanosciences d'Alsace (FMNGE), Institut de Chimie du CNRS (INC)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Chimie du CNRS (INC)-Université de Strasbourg (UNISTRA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA), Institut de Génétique Moléculaire de Montpellier (IGMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Université de Montpellier (UM), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Université Paul-Valéry - Montpellier 3 (UM3), Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS)-Matériaux et nanosciences d'Alsace, Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Institut National de la Santé et de la Recherche Médicale (INSERM), Amélioration génétique et adaptation des plantes méditerranéennes et tropicales ( UMR AGAP ), Institut national de la recherche agronomique [Montpellier] ( INRA Montpellier ) -Centre international d'études supérieures en sciences agronomiques ( Montpellier SupAgro ) -Centre de Coopération Internationale en Recherche Agronomique pour le Développement ( CIRAD ) -Institut national d’études supérieures agronomiques de Montpellier ( Montpellier SupAgro ), ADVanced Analytics for data SciencE ( ADVANSE ), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier ( LIRMM ), Université de Montpellier ( UM ) -Centre National de la Recherche Scientifique ( CNRS ) -Université de Montpellier ( UM ) -Centre National de la Recherche Scientifique ( CNRS ), Université Paul-Valéry - Montpellier 3 ( UM3 ), Laboratoire des sciences de l'ingénieur, de l'informatique et de l'imagerie ( ICube ), Institut National des Sciences Appliquées - Strasbourg ( INSA Strasbourg ), Institut National des Sciences Appliquées ( INSA ) -Institut National des Sciences Appliquées ( INSA ) -Université de Strasbourg ( UNISTRA ) -Centre National de la Recherche Scientifique ( CNRS ) -École Nationale du Génie de l'Eau et de l'Environnement de Strasbourg ( ENGEES ) -Réseau nanophotonique et optique, Université de Strasbourg ( UNISTRA ) -Université de Haute-Alsace (UHA) Mulhouse - Colmar ( Université de Haute-Alsace (UHA) ) -Centre National de la Recherche Scientifique ( CNRS ) -Université de Strasbourg ( UNISTRA ) -Université de Haute-Alsace (UHA) Mulhouse - Colmar ( Université de Haute-Alsace (UHA) ) -Centre National de la Recherche Scientifique ( CNRS ) -Matériaux et nanosciences d'Alsace, Université de Strasbourg ( UNISTRA ) -Université de Haute-Alsace (UHA) Mulhouse - Colmar ( Université de Haute-Alsace (UHA) ) -Institut National de la Santé et de la Recherche Médicale ( INSERM ) -Centre National de la Recherche Scientifique ( CNRS ) -Université de Strasbourg ( UNISTRA ) -Institut National de la Santé et de la Recherche Médicale ( INSERM ), Territoires, Environnement, Télédétection et Information Spatiale ( UMR TETIS ), Centre de Coopération Internationale en Recherche Agronomique pour le Développement ( CIRAD ) -AgroParisTech-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture ( IRSTEA ), Institut de Génétique Moléculaire de Montpellier ( IGMM ), Université de Montpellier ( UM ) -Centre National de la Recherche Scientifique ( CNRS ), Université de Montpellier ( UM ), École Nationale du Génie de l'Eau et de l'Environnement de Strasbourg (ENGEES)-Université de Strasbourg (UNISTRA)-Institut National des Sciences Appliquées - Strasbourg (INSA Strasbourg), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Les Hôpitaux Universitaires de Strasbourg (HUS)-Centre National de la Recherche Scientifique (CNRS)-Matériaux et Nanosciences Grand-Est (MNGE), Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Réseau nanophotonique et optique, Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS), and Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)

Subjects
[ INFO ] Computer Science [cs], [INFO]Computer Science [cs]
Abstract

International audience; One of the key challenges in human health is the identification of disease-causing genes like AIDS (Acquired ImmunoDeficiency Syndrome). Numerous studies have addressed this challenge through gene expression analysis. Due to the amount of data available, processing DNA microarrays in a way that makes biomedical sense is still a major issue.Statistical methods and data mining techniques play a key role in discovering previously unknown knowledge. However, applying such techniques in this context is difficult because the number of measurement points (i.e., gene expression levels) is much higher than the number of samples resulting in the well-known curse of dimensionality problem also called the high feature-to-sample ratio.We propose a combination of data mining and visual analytics methods to identify and render groups of genes implied in HIV infections and sharing common behaviors.

Published
2013

19. How can we efficiently characterize genes of agronomic interest in Olive: towards the genetic association studies?

Author

Sandy Contreras, Amal Zine El Aabidine, Evelyne Costes, Inès Ben Sadok, Ahmed El Bakkali, Abdelmajid Moukhli, Laila Essalouh, Bouchaib Khadari, Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Institut National de la Recherche Agronomique (INRA)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Architecture et Fonctionnement des Espèces Fruitières [AGAP] (AFEF), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), UR Amélioration Plantes, Institut national de la recherche agronomique [Maroc] (INRA Maroc), International Society for Horticultural Science (ISHS). INT., Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), and ProdInra, Archive Ouverte

Subjects
0106 biological sciences, Germplasm, [SDV]Life Sciences [q-bio], Population, Horticulture, Quantitative trait locus, Biology, 01 natural sciences, 03 medical and health sciences, Sampling methods, education, Association mapping, 030304 developmental biology, Genetic association, 2. Zero hunger, 0303 health sciences, education.field_of_study, Olea europaea L, business.industry, food and beverages, Biotechnology, [SDV] Life Sciences [q-bio], SSRs, Genetic distance, Core collection, Genetic structure, Gene pool, business, 010606 plant biology & botany
Abstract

Despite the socio-economic importance of olive oil and the need of olive breeding, genetic studies on agronomic traits are restricted to few biparental populations limiting the efficiency of QTL (Quantitative Trait Loci) mapping strategy. Association mapping based on a diversified collection of olive germplasm can be proposed as a complementary strategy to genetically map agronomic traits. Here, we aimed to develop tools for association mapping studies by defining a Mediterranean olive core collection and characterizing a massive set of microsatellite markers (SSRs). The worldwide olive germplasm bank of Marrakech, Morocco (561 accessions from 14 Mediterranean countries) was characterized using 17 nuclear SSRs and cpDNA markers and classified into east, centre and west Mediterranean gene pools. Combining two sampling methods maximizing the capture of diversity and genetic distance, we proposed two core collections of 50 and 94 accessions including all nuclear SSR alleles, cpDNA haplotypes and states of agro-morphology (from Olea databases) from the WOGB Marrakech. These core collections include cultivars considered as the most important in Mediterranean olive producing countries and display a limited genetic structure between east and west/center gene pools. Hence, they are efficient candidates for phenotyping agronomic trait and to explore the largest variability in distinct environmental conditions. Concurrently, we developed a set of genomic and Expressed Sequence Tag (EST)-derived SSRs that were used to complete the genetic map of ‘Oliviere’ × ‘Arbequina’ segregating population. Molecular markers will be used on the proposed core collections to assess the linkage disequilibrium decay according to the genetic distance and to further develop association mapping studies.

Published
2012

20. A genetic linkage map of olive based on amplified fragment length polymorphism, intersimple sequence repeat and simple sequence repeat markers

Author

Cinderella Grout, Nathalie Moutier, Inès Ben Sadok, Evelyne Costes, Bouchaib Khadari, Sylvain Santoni, Amal Zine El Aabidine, Agnès Doligez, Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Institut National de la Recherche Agronomique (INRA)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Développement et amélioration des plantes (UMR DAP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Université Montpellier 2 - Sciences et Techniques (UM2)-Centre National de la Recherche Scientifique (CNRS), Diversité et adaptation des plantes cultivées (UMR DIAPC), Université Montpellier 2 - Sciences et Techniques (UM2)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Department of Genetics, Plant Breeding INRA, and PRAD [03 06]

Subjects
0106 biological sciences, OLEA EUROPAEA, Horticulture, Quantitative trait locus, Biology, CARTOGRAPHIE GENETIQUE, 01 natural sciences, 03 medical and health sciences, ARBEQUINA, Gene mapping, Genetic linkage, OLIVIERE, [SDV.IDA]Life Sciences [q-bio]/Food engineering, Genetics, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, [SPI.GPROC]Engineering Sciences [physics]/Chemical and Process Engineering, OLEA EUROPEA, 030304 developmental biology, Hybrid, 2. Zero hunger, 0303 health sciences, F1 PSEUDO-TEST CROSS, Genetic distance, CONSENSUS MAP, Genetic marker, MICROSATELLITE LOCI, Microsatellite, OLIVIER, Amplified fragment length polymorphism, DOMINANT MARKERS, 010606 plant biology & botany
Abstract

A detailed genomic linkage map of the olive (Olea europaea L. ssp.europaea (2x=2n= 46)) was constructed with a 147 F1 full-sib 'Olivi` · 'Arbequina' progeny in a two-way pseudo-test cross-mapping configuration. Based onalogarithmofoddsthresholdof6andamaximumrecombination fractionof0.4,maternalandpaternalmapswere constructed using 222 makers (178 amplified fragment length polymorphism (AFLP), 37 simple sequence repeat (SSR),sevenintersimplesequencerepeat(ISSR))and219markers(174AFLP,39SSR,6ISSR)markers,respectively. The female map regrouped 36 linkage groups (LGs) defining 2210.2 cM of total map length with an average marker spacing 11.2 cM and a maximum gap of 48.5 cM between adjacent markers. The male map contained 31 LGs and covered a distance of 1966.2 cM with an average and a maximum distance between two adjacent markers of 10.3 and 40.4 cM, respectively. Mean LG size was 61.3 and 63.4 cM in the maternal and paternal maps, respectively. The LGs consisted of two to 17 loci (up to 21 loci in the paternal map) and ranged in length from 2.7 to 182 cM (female map) or from4.1 to 218.1 cM(paternal map).Markers were distributed throughout the mapswithoutanyclustering. Thetotal length of the consensus map was 3823.2 cM containing 436 markers distributed into 42 LGs with a mean distance between two adjacent loci of 8.7 cM. Both parental maps and the consensus maps were compared with previously published olive maps. Although not saturated yet, the present maps offer a promising tool for quantitative trait loci mapping because phenotypic characterization of the cross is currently carried out. Olive is one of the most important Mediterranean fruit species cultivated for oil and canned fruit consumption. With over 2000 cultivars, the olive tree is highly diverse and adapted to Mediterranean agroecological conditions. It plays a major socioeconomical role in traditional agroecosystems, especially in southern Mediterranean areas with an expanding use in oil and cosmetic industries (Aburjai and Natsheh, 2003). Olive is a diploid species (2n = 46 (Green and Wickens, 1989)) with a nuclear DNA content ranging from 2.90 ± 0.020

Published
2010
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21. Construction of a genetic linkage map for the olive based on AFLP and SSR markers

Author

Cherkaoui El Modafar, Abdelmajid Moukhli, Agnès Doligez, Jamal Charafi, Cinderella Grout, Amal Zine El Aabidine, Christian Jay-Allemand, Sylvain Santoni, Bouchaib Khadari, Développement et amélioration des plantes (UMR DAP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Université Montpellier 2 - Sciences et Techniques (UM2)-Centre National de la Recherche Scientifique (CNRS), Diversité et adaptation des plantes cultivées (UMR DIAPC), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Université Montpellier 2 - Sciences et Techniques (UM2), Institut national de la recherche agronomique [Maroc] (INRA Maroc), Université Montpellier 2 - Sciences et Techniques (UM2), Université Cadi Ayyad [Marrakech] (UCA), Centre National de la Recherche Scientifique (CNRS)-Université Montpellier 2 - Sciences et Techniques (UM2)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), and Université Montpellier 2 - Sciences et Techniques (UM2)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)

Subjects
0106 biological sciences, Linkage (software), Genetics, 0303 health sciences, food and beverages, Quantitative trait locus, Biology, CARTOGRAPHIE GENETIQUE, 01 natural sciences, RAPD, 03 medical and health sciences, Gene mapping, Genetic linkage, Genetic marker, [SDV.IDA]Life Sciences [q-bio]/Food engineering, Microsatellite, OLIVIER, Amplified fragment length polymorphism, [SPI.GPROC]Engineering Sciences [physics]/Chemical and Process Engineering, PICHOLINE, OLEA EUROPEA, Agronomy and Crop Science, 030304 developmental biology, 010606 plant biology & botany
Abstract

Correspondance: khadari@supagro.inra.fr Publication Inra prise en compte dans l'analyse bibliométrique des publications scientifiques mondiales sur les Fruits, les Légumes et la Pomme de terre. Période 2000-2012. http://prodinra.inra.fr/record/256699; International audience; Genetic mapping is essential for quantitative trait locus (QTL) identification related to interesting traits. However, only two genetic maps on olive have yet been published, with very few transferable markers. We have constructed a linkage map of the olive according to a “two-way pseudo-testcross” mapping strategy and based on the progeny generated from a cross between ‘Picholine marocaine’ (female parent) × ‘Picholine du Languedoc’ (male parent) cultivars. A total of 592 markers (47 simple sequence repeat [SSR], 509 amplified fragment length polymorphism [AFLP], 27 inter-simple sequence repeat [ISSR], eight random amplified polymorphic DNA [RAPD], and one sequence characterized amplified region [SCAR] marker) were scored in 140 F1 progeny. Linkage groups were defined at a likelihood odds ratio score of 6.00 and a recombination fraction of 0.40. The maternal map consisted of 175 markers clustered into 40 linkage groups and spanning 1547.40 cM, while the paternal map was based on 170 markers clustered into 38 linkage groups and covering 1428.00 cM. For the consensus map, 345 markers were used covering 2366.4 cM and clustered into 49 linkage groups with a mean distance between two adjacent loci of 8.06 cM and the estimated map coverage of 72.6%. This integrated map provides a useful tool for the detection of QTLs controlling economically important traits.

Published
2010
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22. Phenolic compounds of olive-tree leaves and their relationship with the resistance to the leaf-spot disease caused by Spilocaea oleaginea

Author

Rahioui, B., Zine El Aabidine, A., Baissac, Y., El Boustani, E., Khadari, Bouchaib, Jay-Allemand, C., El Modafar, C., Université Cadi Ayyad [Marrakech] (UCA), Université Montpellier 2 - Sciences et Techniques (UM2), Développement et amélioration des plantes (UMR DAP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Université Montpellier 2 - Sciences et Techniques (UM2)-Centre National de la Recherche Scientifique (CNRS), and Centre National de la Recherche Scientifique (CNRS)-Université Montpellier 2 - Sciences et Techniques (UM2)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)

Subjects
TAVELURE DE L'OLIVIER, MALADIE DE L'OEIL DE PAON, [SDV.IDA]Life Sciences [q-bio]/Food engineering, food and beverages, OLIVIER, [SPI.GPROC]Engineering Sciences [physics]/Chemical and Process Engineering, SPILOCAEA OLEAGINEA, PHENOLIC COMPOUNDS, OLEA EUROPEA, MARQUEUR BIOCHIMIQUE, RESISTANCE
Abstract

UMR DAP Equipe AFEF; International audience; Phenolic compounds are associated with the olive tree resistance to the leaf-spot disease caused by Spilocaea oleaginea were studied in different resistant, susceptible and intermediate cultivars. The HPLC analysis highlights 33 phenolic compounds distinguished according to their chromatographic and spectral characteristics into five phenolic families (hydroxycinnamic derivatives, flavonoids, verbascoside derivatives, tyrosol derivatives, oleuropein derivatives). The phenolic extract of the olive-tree is dominated by ten major compounds identified as rutin, luteolin-7-glucoside, oleuropein, versbacoside, tyrosol, apigenin and four other phenolic compounds not completely identified (oleuropein derivative, hydroxycinnamic derivative and two flavonol monoglucosides). No qualitative difference was observed between cultivars. However, the principal components analysis highlights two multifactorial components distinguishing the various cultivars according to their behaviour to the disease. The first component, identified as oleuropein aglycone, a hydroxycinnamic derivative and a flavonol monoglucoside contents, clearly distinguished the resistant cultivars from the susceptible and intermediately resistant cultivars. The resistant cultivars contain higher contents. The second component, identified as tyrosol derivative and an oleuropein derivative contents, distinguished the susceptible cultivars from the intermediately resistant cultivar which presents the highest contents. The role of these phenolic compounds in the defense and their use as biochemical markers in olive-tree resistance to S. oleaginea is discussed

Published
2009

23. GOLT1B Activation in Hepatitis C Virus-Infected Hepatocytes Links ER Trafficking and Viral Replication.

Author

Butterworth, Jacqueline, Gregoire, Damien, Peter, Marion, Roca Suarez, Armando Andres, Desandré, Guillaume, Simonin, Yannick, Virzì, Alessia, Zine El Aabidine, Amal, Guivarch, Marine, Andrau, Jean-Christophe, Bertrand, Edouard, Assenat, Eric, Lupberger, Joachim, and Hibner, Urszula

Subjects
VIRAL replication, CHRONIC hepatitis C, FROZEN tissue sections, UNFOLDED protein response, HEPATITIS C virus, VIRAL hepatitis, HEPATITIS C
Abstract

Chronic hepatitis C carries a high risk of development of hepatocellular carcinoma (HCC), triggered by both direct and indirect effects of the virus. We examined cell-autonomous alterations in gene expression profiles associated with hepatitis C viral presence. Highly sensitive single molecule fluorescent in situ hybridization applied to frozen tissue sections of a hepatitis C patient allowed the delineation of clusters of infected hepatocytes. Laser microdissection followed by RNAseq analysis of hepatitis C virus (HCV)-positive and -negative regions from the tumoral and non-tumoral tissues from the same patient revealed HCV-related deregulation of expression of genes in the tumor and in the non-tumoral tissue. However, there was little overlap between both gene sets. Our interest in alterations that increase the probability of tumorigenesis prompted the examination of genes whose expression was increased by the virus in the non-transformed cells and whose level remained high in the tumor. This strategy led to the identification of a novel HCV target gene: GOLT1B, which encodes a protein involved in ER-Golgi trafficking. We further show that GOLT1B expression is induced during the unfolded protein response, that its presence is essential for efficient viral replication, and that its expression is correlated with poor outcome in HCC. [ABSTRACT FROM AUTHOR]

Published
2022
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24. The Landscape of L1 Retrotransposons in the Human Genome Is Shaped by Pre-insertion Sequence Biases and Post-insertion Selection.

Author

Sultana, Tania, van Essen, Dominic, Siol, Oliver, Bailly-Bechet, Marc, Philippe, Claude, Zine El Aabidine, Amal, Pioger, Léo, Nigumann, Pilvi, Saccani, Simona, Andrau, Jean-Christophe, Gilbert, Nicolas, and Cristofari, Gael

Subjects
*HUMAN genome, *MOBILE genetic elements, *RETROTRANSPOSONS, *DNA replication, *HUMAN genetic variation, *DNA insertion elements
Abstract

L1 retrotransposons are transposable elements and major contributors of genetic variation in humans. Where L1 integrates into the genome can directly impact human evolution and disease. Here, we experimentally induced L1 retrotransposition in cells and mapped integration sites at nucleotide resolution. At local scales, L1 integration is mostly restricted by genome sequence biases and the specificity of the L1 machinery. At regional scales, L1 shows a broad capacity for integration into all chromatin states, in contrast to other known mobile genetic elements. However, integration is influenced by the replication timing of target regions, suggesting a link to host DNA replication. The distribution of new L1 integrations differs from those of preexisting L1 copies, which are significantly reshaped by natural selection. Our findings reveal that the L1 machinery has evolved to efficiently target all genomic regions and underline a predominant role for post-integrative processes on the distribution of endogenous L1 elements. • High-throughput insertion site profiling of a LINE-1 (L1) element by ATLAS-seq • Insertion is influenced strongly by DNA sequence but only weakly by chromatin state • L1 integration preferences suggest a link with host DNA replication • Post-insertion selection reshapes L1 distribution across functional genomic regions Sultana, van Essen, et al. report the genome-wide profiling of new L1 retrotransposon insertions in cultured cells. They uncover the contribution of sequence and genomic contexts on integration site selection, its link with host DNA replication, and the role of post-integration selection in the genomic distribution of L1 elements. [ABSTRACT FROM AUTHOR]

Published
2019
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25. Ras/MAPK signalling intensity defines subclonal fitness in a mouse model of hepatocellular carcinoma.

Author

Lozano A, Souche FR, Chavey C, Dardalhon V, Ramirez C, Vegna S, Desandre G, Riviere A, Zine El Aabidine A, Fort P, Akkari L, Hibner U, and Grégoire D

Subjects
Animals, Humans, Mice, Killer Cells, Natural, MAP Kinase Signaling System, Signal Transduction, Tumor Microenvironment, ras Proteins metabolism, Carcinoma, Hepatocellular genetics, Liver Neoplasms genetics
Abstract

Quantitative differences in signal transduction are to date an understudied feature of tumour heterogeneity. The MAPK Erk pathway, which is activated in a large proportion of human tumours, is a prototypic example of distinct cell fates being driven by signal intensity. We have used primary hepatocyte precursors transformed with different dosages of an oncogenic form of Ras to model subclonal variations in MAPK signalling. Orthotopic allografts of Ras-transformed cells in immunocompromised mice gave rise to fast-growing aggressive tumours, both at the primary location and in the peritoneal cavity. Fluorescent labelling of cells expressing different oncogene levels, and consequently varying levels of MAPK Erk activation, highlighted the selection processes operating at the two sites of tumour growth. Indeed, significantly higher Ras expression was observed in primary as compared to secondary, metastatic sites, despite the apparent evolutionary trade-off of increased apoptotic death in the liver that correlated with high Ras dosage. Analysis of the immune tumour microenvironment at the two locations suggests that fast peritoneal tumour growth in the immunocompromised setting is abrogated in immunocompetent animals due to efficient antigen presentation by peritoneal dendritic cells. Furthermore, our data indicate that, in contrast to the metastatic-like outgrowth, strong MAPK signalling is required in the primary liver tumours to resist elimination by NK (natural killer) cells. Overall, this study describes a quantitative aspect of tumour heterogeneity and points to a potential vulnerability of a subtype of hepatocellular carcinoma as a function of MAPK Erk signalling intensity., Competing Interests: AL, FS, CC, VD, CR, SV, GD, AR, AZ, PF, LA, UH, DG No competing interests declared, (© 2023, Lozano et al.)

Published
2023
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26. GOLT1B Activation in Hepatitis C Virus-Infected Hepatocytes Links ER Trafficking and Viral Replication.

Author

Butterworth J, Gregoire D, Peter M, Roca Suarez AA, Desandré G, Simonin Y, Virzì A, Zine El Aabidine A, Guivarch M, Andrau JC, Bertrand E, Assenat E, Lupberger J, and Hibner U

Abstract

Chronic hepatitis C carries a high risk of development of hepatocellular carcinoma (HCC), triggered by both direct and indirect effects of the virus. We examined cell-autonomous alterations in gene expression profiles associated with hepatitis C viral presence. Highly sensitive single molecule fluorescent in situ hybridization applied to frozen tissue sections of a hepatitis C patient allowed the delineation of clusters of infected hepatocytes. Laser microdissection followed by RNAseq analysis of hepatitis C virus (HCV)-positive and -negative regions from the tumoral and non-tumoral tissues from the same patient revealed HCV-related deregulation of expression of genes in the tumor and in the non-tumoral tissue. However, there was little overlap between both gene sets. Our interest in alterations that increase the probability of tumorigenesis prompted the examination of genes whose expression was increased by the virus in the non-transformed cells and whose level remained high in the tumor. This strategy led to the identification of a novel HCV target gene: GOLT1B, which encodes a protein involved in ER-Golgi trafficking. We further show that GOLT1B expression is induced during the unfolded protein response, that its presence is essential for efficient viral replication, and that its expression is correlated with poor outcome in HCC.

Published
2021
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27. Critical role for TRIM28 and HP1β/γ in the epigenetic control of T cell metabolic reprograming and effector differentiation.

Author

Gehrmann U, Burbage M, Zueva E, Goudot C, Esnault C, Ye M, Carpier JM, Burgdorf N, Hoyler T, Suarez G, Joannas L, Heurtebise-Chrétien S, Durand S, Panes R, Bellemare-Pelletier A, Sáez PJ, Aprahamian F, Lefevre D, Adoue V, Zine El Aabidine A, Muhammad Ahmad M, Hivroz C, Joffre O, Cammas F, Kroemer G, Gagnon E, Andrau JC, and Amigorena S

Subjects
Animals, Autoimmunity physiology, CD4-Positive T-Lymphocytes metabolism, Cell Differentiation genetics, Cell Plasticity physiology, Cellular Reprogramming genetics, Chromobox Protein Homolog 5, Colon pathology, Cytokines metabolism, DNA-Binding Proteins genetics, DNA-Binding Proteins metabolism, Gene Expression Regulation, Gene Silencing, Histones metabolism, Mice, Mice, Knockout, Phosphatidylinositol 3-Kinases metabolism, Receptors, Antigen, T-Cell metabolism, T-Lymphocytes, Regulatory immunology, T-Lymphocytes, Regulatory metabolism, Transcriptome, Tripartite Motif-Containing Protein 28 genetics, Cell Differentiation physiology, Cellular Reprogramming physiology, Chromosomal Proteins, Non-Histone metabolism, Epigenesis, Genetic physiology, T-Lymphocytes metabolism, Tripartite Motif-Containing Protein 28 metabolism
Abstract

Naive CD4 + T lymphocytes differentiate into different effector types, including helper and regulatory cells (Th and Treg, respectively). Heritable gene expression programs that define these effector types are established during differentiation, but little is known about the epigenetic mechanisms that install and maintain these programs. Here, we use mice defective for different components of heterochromatin-dependent gene silencing to investigate the epigenetic control of CD4 + T cell plasticity. We show that, upon T cell receptor (TCR) engagement, naive and regulatory T cells defective for TRIM28 (an epigenetic adaptor for histone binding modules) or for heterochromatin protein 1 β and γ isoforms (HP1β/γ, 2 histone-binding factors involved in gene silencing) fail to effectively signal through the PI3K-AKT-mTOR axis and switch to glycolysis. While differentiation of naive TRIM28 -/- T cells into cytokine-producing effector T cells is impaired, resulting in reduced induction of autoimmune colitis, TRIM28 -/- regulatory T cells also fail to expand in vivo and to suppress autoimmunity effectively. Using a combination of transcriptome and chromatin immunoprecipitation-sequencing (ChIP-seq) analyses for H3K9me3, H3K9Ac, and RNA polymerase II, we show that reduced effector differentiation correlates with impaired transcriptional silencing at distal regulatory regions of a defined set of Treg-associated genes, including, for example, NRP1 or Snai3. We conclude that TRIM28 and HP1β/γ control metabolic reprograming through epigenetic silencing of a defined set of Treg-characteristic genes, thus allowing effective T cell expansion and differentiation into helper and regulatory phenotypes., Competing Interests: Competing interest statement: U.G. is currently employed by AstraZeneca AB (Mölndal, Sweden)., (Copyright © 2019 the Author(s). Published by PNAS.)

Published
2019
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28. Colib'read on galaxy: a tools suite dedicated to biological information extraction from raw NGS reads.

Author

Le Bras Y, Collin O, Monjeaud C, Lacroix V, Rivals É, Lemaitre C, Miele V, Sacomoto G, Marchet C, Cazaux B, Zine El Aabidine A, Salmela L, Alves-Carvalho S, Andrieux A, Uricaru R, and Peterlongo P

Subjects
Base Sequence, Cluster Analysis, Genome genetics, Genomics methods, Molecular Sequence Data, Reproducibility of Results, Computational Biology methods, High-Throughput Nucleotide Sequencing methods, Information Storage and Retrieval methods, Software
Abstract

Background: With next-generation sequencing (NGS) technologies, the life sciences face a deluge of raw data. Classical analysis processes for such data often begin with an assembly step, needing large amounts of computing resources, and potentially removing or modifying parts of the biological information contained in the data. Our approach proposes to focus directly on biological questions, by considering raw unassembled NGS data, through a suite of six command-line tools., Findings: Dedicated to 'whole-genome assembly-free' treatments, the Colib'read tools suite uses optimized algorithms for various analyses of NGS datasets, such as variant calling or read set comparisons. Based on the use of a de Bruijn graph and bloom filter, such analyses can be performed in a few hours, using small amounts of memory. Applications using real data demonstrate the good accuracy of these tools compared to classical approaches. To facilitate data analysis and tools dissemination, we developed Galaxy tools and tool shed repositories., Conclusions: With the Colib'read Galaxy tools suite, we enable a broad range of life scientists to analyze raw NGS data. More importantly, our approach allows the maximum biological information to be retained in the data, and uses a very low memory footprint.

Published
2016
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