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54. Meta-analysis of human genome-microbiome association studies: The MiBioGen consortium initiative

Author

Wang, J. (Jun), Kurilshikov, A. (Alexander), Radjabzadeh, D. (Djawad), Turpin, W. (Williams), Croitoru, K. (Kenneth), Bonder, M.J. (Marc), Jackson, M.A. (Matthew A.), Medina-Gomez, M.C. (Carolina), Frost, F. (Fabian), Homuth, G. (Georg), Rühlemann, M. (Malte), Hughes, D. (David), Kim, H.-N. (Han-na), Spector, T.D. (Timothy), Bell, J.T. (Jordana T.), Steves, C.J. (Claire), Timpson, N. (Nicolas), Franke, A. (Andre), Wijmenga, C. (Cisca), Meyer, K. (Katie), Kacprowski, T. (Tim), Franke, L. (Lude), Paterson, A.D. (Andrew), Raes, J. (Jeroen), Kraaij, R. (Robert), Zhernakova, A. (Alexandra), Wang, J. (Jun), Kurilshikov, A. (Alexander), Radjabzadeh, D. (Djawad), Turpin, W. (Williams), Croitoru, K. (Kenneth), Bonder, M.J. (Marc), Jackson, M.A. (Matthew A.), Medina-Gomez, M.C. (Carolina), Frost, F. (Fabian), Homuth, G. (Georg), Rühlemann, M. (Malte), Hughes, D. (David), Kim, H.-N. (Han-na), Spector, T.D. (Timothy), Bell, J.T. (Jordana T.), Steves, C.J. (Claire), Timpson, N. (Nicolas), Franke, A. (Andre), Wijmenga, C. (Cisca), Meyer, K. (Katie), Kacprowski, T. (Tim), Franke, L. (Lude), Paterson, A.D. (Andrew), Raes, J. (Jeroen), Kraaij, R. (Robert), and Zhernakova, A. (Alexandra)

Abstract

Background: In recent years, human microbiota, especially gut microbiota, have emerged as an important yet complex trait influencing human metabolism, immunology, and diseases. Many studies are investigating the forces underlying the observed variation, including the human genetic variants that shape human microbiota. Several preliminary genome-wide association studies (GWAS) have been completed, but more are necessary to achieve a fuller picture. Results: Here, we announce the MiBioGen consortium initiative, which has assembled 18 population-level cohorts and some 19,000 participants. Its aim is to generate new knowledge for the rapidly developing field of microbiota research. Each cohort has surveyed the gut microbiome via 16S rRNA sequencing and genotyped their participants with full-genome SNP arrays. We have standardized the analytical pipelines for both the microbiota phenotypes and genotypes, and all the data have been processed using identical approaches. Our analysis of microbiome composition shows that we can reduce the potential artifacts introduced by technical differences in generating microbiota data. We are now in the process of benchmarking the association tests and performing meta-analyses of genome-wide associations. All pipeline and summary statistics results will be shared using public data repositories. Conclusion: We present the largest consortium to date devoted to microbiota-GWAS. We have adapted our analytical pipelines to suit multi-cohort analyses and expect to gain insight into host-microbiota cross-talk at the genome-wide level. And, as an open consortium, we invite more cohorts to join us (by contacting one of the corresponding authors) and to follow the analytical pipeline we have developed.

Published
2018
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55. Meta-analysis of human genome-microbiome association studies: the MiBioGen consortium initiative

Author

Wang, Johnny, Kurilshikov, A, Radjabzadeh, Djawad, Turpin, W, Croitoru, K, Bonder, MJ, Jackson, MA, Medina Gomez, Maria, Frost, F, Homuth, G, Ruehlemann, M, Hughes, D, Kim, HN, Spector, TD, Bell, JT, Steves, CJ, Timpson, N, Franke, A, Wijmenga, C, Meyer, K, Kacprowski, T, Franke, L, Paterson, AD, Raes, J, Kraaij, Robert, Zhernakova, A, Wang, Johnny, Kurilshikov, A, Radjabzadeh, Djawad, Turpin, W, Croitoru, K, Bonder, MJ, Jackson, MA, Medina Gomez, Maria, Frost, F, Homuth, G, Ruehlemann, M, Hughes, D, Kim, HN, Spector, TD, Bell, JT, Steves, CJ, Timpson, N, Franke, A, Wijmenga, C, Meyer, K, Kacprowski, T, Franke, L, Paterson, AD, Raes, J, Kraaij, Robert, and Zhernakova, A

Published
2018

56. Identification and assembly of genomes and genetic elements in complex metagenomic samples without using reference genomes

Author

Nielsen, H.B., Almeida, M., Sierakowska Juncker, A., Rasmussen, S., Li, J., Sunagawa, S., Plichta, D.R., Gautier, L., Pedersen, A.G., Le Chatelier, E., Pelletier, E., Bonde, I., Nielsen, T., Manichanh, C., Arumugam, M., Batto, J.M., Quintanilha dos Santos, M.B., Blom, N., Borruel, N., Burgdorf, K.S., Boumezbeur, F., Casellas, F., Doré, J., Dworzynski, P., Guarner, F., Hansen, T., Hildebrand, F., Kaas, R.S., Kennedy, S., Kristiansen, K., Kultima, J.R., Leonard, P., Levenez, F., Lund, O., Moumen, B., Le Paslier, D., Pons, N., Pedersen, O., Prifti, E., Qin, J., Raes, J., Sørensen, S., Tap, J., Tims, S., Ussery, D.W., Yamada, T., Jamet, A., Mérieux, A., Cultrone, A., Torrejon, A., Quinquis, B., Brechot, C., Delorme, C., M'Rini, C., de Vos, W.M., Maguin, E., Varela, E., Guedon, E., Gwen, F., Haimet, F., Artiguenave, F., Vandemeulebrouck, G., Denariaz, G., Khaci, G., Blottière, H., Knol, J., Weissenbach, J., van Hylckama Vlieg, J.E., Torben, J., Parkhil, J., Turner, K., van de Guchte, M., Antolin, M., Rescigno, M., Kleerebezem, M., Derrien, M., Galleron, N., Sanchez, N., Grarup, N., Veiga, P., Oozeer, R., Dervyn, R., Layec, S., Bruls, T., Winogradski, Y., Zoetendal, E.G., Renault, D., Sicheritz-Ponten, Bork, P., Wang, J., Brunak, S., Ehrlich, S.D., Center for Biological Sequence Analysis, Technical University of Denmark [Lyngby] (DTU), Novo Nordisk Foundation Center for Biosustainability, MICrobiologie de l'ALImentation au Service de la Santé (MICALIS), Institut National de la Recherche Agronomique (INRA)-AgroParisTech, Department of Computer Science [Baltimore], Johns Hopkins University (JHU), BGI Hong Kong Researche Institute, BGI Shenzhen, School of Bioscience and Biotechnology, Southern University of Science and Technology [Shenzhen] (SUSTech), European Molecular Biology Laboratory, US 1367 MetaGénoPolis, Institut National de la Recherche Agronomique (INRA)-Département Microbiologie et Chaîne Alimentaire (MICA), Institut National de la Recherche Agronomique (INRA)-MetaGénoPolis (MGP), Genoscope - Centre national de séquençage [Evry] (GENOSCOPE), Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, Université d'Évry-Val-d'Essonne (UEVE), Novo Nordisk Foundation Center for Basic Metabolic Research (CBMR), Faculty of Health and Medical Sciences, University of Copenhagen = Københavns Universitet (KU)-University of Copenhagen = Københavns Universitet (KU), Digestive System Research Unit, Vall d'Hebron University Hospital [Barcelona], Faculty of Health Sciences, University of Southern Denmark (SDU), Department of Structural Biology, Flanders Institute for Biotechnology, Department of Bioscience Engineering, Vrije Universiteit [Brussels] (VUB), 8National Food Institute - Division for Epidemiology and Microbial Genomics, Department of Biology [Copenhagen], Faculty of Science [Copenhagen], Hagedorn Research Institute, Faculty of Health, Aarhus University [Aarhus], BGI Hong Kong research Institute, Rega Institute - Department of Microbiology and Immunology, Université Catholique de Louvain (UCL), VIB Center for the Biology of Disease, Section of Microbiology [Copenhagen], University of Copenhagen = Københavns Universitet (KU)-University of Copenhagen = Københavns Universitet (KU)-Faculty of Science [Copenhagen], Laboratory of Microbiology, Wageningen University and Research Centre [Wageningen] (WUR), Department of Biological Information, Tokyo Institute of Technology [Tokyo] (TITECH), Max-Delbrück Center for Molecular Medicine, Princess Al Jawhara Center of Excellence in the Research of Hereditary Disorders, King Abdulaziz University, Centre for Host-Microbiome Interactions, Dental Institute Central Office, Guy’s Hospital, King‘s College London, Département Microbiologie et Chaîne Alimentaire (MICA), Institut National de la Recherche Agronomique (INRA), European Community's Seventh Framework Programme [FP7-HEALTH-F4-2007-201052, FP7-HEALTH-2010-261376], OpenGPU FUI collaborative research projects, DGCIS, Instituto de Salud Carlos III (Spain), Ministere de la Recherche et de l'Education Nationale (France), [ANR-11-DPBS-0001], Danmarks Tekniske Universitet = Technical University of Denmark (DTU), Beijing Genomics Institute [Shenzhen] (BGI), Southern University of Science and Technology (SUSTech), MetaGenoPolis, Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), University of Copenhagen = Københavns Universitet (UCPH)-University of Copenhagen = Københavns Universitet (UCPH), Vrije Universiteit Brussel (VUB), Université Catholique de Louvain = Catholic University of Louvain (UCL), University of Copenhagen = Københavns Universitet (UCPH)-University of Copenhagen = Københavns Universitet (UCPH)-Faculty of Science [Copenhagen], Wageningen University and Research [Wageningen] (WUR), Max Delbrück Center for Molecular Medicine [Berlin] (MDC), Helmholtz-Gemeinschaft = Helmholtz Association, European Project: 201052,EC:FP7:HEALTH,FP7-HEALTH-2007-A,METAHIT(2008), Department of Systems Biology, Center for Biological Sequence Analysis, Ctr Biol Sequence Anal, National University of Singapore (NUS), European Molecular Biology Laboratory [Heidelberg] (EMBL), Department of Mathematics and Computer Science [Odense] (IMADA), Génomique métabolique (UMR 8030), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université d'Évry-Val-d'Essonne (UEVE)-Centre National de la Recherche Scientifique (CNRS), Vall d’Hebron Research Institute (VHIR), Faculty of Health and Medical Sciences, The Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen = Københavns Universitet (KU), INRA US1367 MetaGenoPolis, European Molecular Biology Laboratory [Grenoble] (EMBL), Unité de Recherche sur les Maladies Cardiovasculaires, du Métabolisme et de la Nutrition = Institute of cardiometabolism and nutrition (ICAN), Université Pierre et Marie Curie - Paris 6 (UPMC)-Assistance publique - Hôpitaux de Paris (AP-HP) (APHP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [APHP], Center for Biological Sequence Analysis [Lyngby], Chinese Academy of Agricultural Mechanization Sciences (CCCME), 1Génétique Microbienne, INRA, Domaine de Vilvert, 78352 Jouy en Josas Cedex, and Department of Bio-engineering Sciences

Subjects
Cellular immunity, polypeptide, [SDV]Life Sciences [q-bio], SHORT READ ALIGNMENT SEQUENCES SYSTEMS ALGORITHMS MICROBIOTA PROTEIN LIFE SETS TREE TOOL, complex metagenomic sample, Applied Microbiology and Biotechnology, Genome, Microbiologie, Databases, Genetic, genetic element, Cluster Analysis, sets, short read alignment, ComputingMilieux_MISCELLANEOUS, Genetics, 0303 health sciences, tool, metagenomic, tree, Lactococcus lactis, IL-12, Molecular Medicine, Biotechnology, life, Microbial Genomes, antigen specific immune response, Biomedical Engineering, Bioengineering, Computational biology, [SDV.BID]Life Sciences [q-bio]/Biodiversity, cellular immunity, Biology, algorithms, Microbiology, 03 medical and health sciences, Genetic variation, microbiota, Microbiome, Gene, genome, 030304 developmental biology, adjuvant activity, VLAG, 030306 microbiology, Metagenomics, WIAS, Microbial genetics, sequences, systems, protein
Abstract

Most current approaches for analyzing metagenomic data rely on comparisons to reference genomes, but the microbial diversity of many environments extends far beyond what is covered by reference databases. De novo segregation of complex metagenomic data into specific biological entities, such as particular bacterial strains or viruses, remains a largely unsolved problem. Here we present a method, based on binning co-abundant genes across a series of metagenomic samples, that enables comprehensive discovery of new microbial organisms, viruses and co-inherited genetic entities and aids assembly of microbial genomes without the need for reference sequences. We demonstrate the method on data from 396 human gut microbiome samples and identify 7,381 co-abundance gene groups (CAGs), including 741 metagenomic species (MGS). We use these to assemble 238 high-quality microbial genomes and identify affiliations between MGS and hundreds of viruses or genetic entities. Our method provides the means for comprehensive profiling of the diversity within complex metagenomic samples.

Published
2014

58. Relationship between genome and epigenome - challenges and requirements for future research

Author

Almouzni, G., Altucci, L., Amati, B., Ashley, N., Baulcombe, D., Beaujean, N., Bock, C., Bongcam-Rudloff, E., Bousquet, J., Braun, S., Bressac-de Paillerets, B., Bussemakers, M., Clarke, L., Conesa, A., Estivill, X., Fazeli, A., Grgurevic, N., Gut, I., Heijmans, B.T., Hermouet, S., Houwing-Duistermaat, J., Iacobucci, I., Ilas, J., Kandimalla, R., Krauss-Etschmann, S., Lasko, P., Lehmann, S., Lindroth, A., Majdic, G., Marcotte, E., Martinelli, G., Martinet, N., Meyer, E., Miceli, C., Mills, K., Moreno-Villanueva, M., Morvan, G., Nickel, D., Niesler, B., Nowacki, M., Nowak, J., Ossowski, S., Pelizzola, M., Pochet, R., Potocnik, U., Radwanska, M., Raes, J., Rattray, M., Robinson, M.D., Roelen, B., Sauer, S., Schinzer, D., Slagboom, E., Spector, T., Stunnenberg, H.G., Tiligada, E., Torres-Padilla, M.E., Tsonaka, R., Soom, A. van, Vidakovic, M., Widschwendter, M., Dynamique du noyau, Institut Curie-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Dip. Patologia generale, Seconda Università di Napoli, Università degli studi di Napoli Federico II, University of Cambridge [UK] (CAM), Biologie du Développement et Reproduction (BDR), Institut National de la Recherche Agronomique (INRA), CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, 1090 Vienna, Austria, Swedish University of Agricultural Sciences (SLU), Centre Hospitalier Régional Universitaire [Montpellier] (CHRU Montpellier), Centro de Genomica, Instituto Valenciano de Investigaciones Agrarias, Universitat Pompeu Fabra [Barcelona], Institute of Biomedicine and Translational Medicine, Department of Pathophysiology, University of Tartu, Centre National de Génotypage (CNG), Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Molecular Mechanisms of Chronic Inflammation in Hematological Diseases ( CRCINA - Département INCIT - Equipe 16), Centre de recherche de Cancérologie et d'Immunologie / Nantes - Angers (CRCINA), Université d'Angers (UA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Centre hospitalier universitaire de Nantes (CHU Nantes)-Université de Nantes - UFR de Médecine et des Techniques Médicales (UFR MEDECINE), Université de Nantes (UN)-Université de Nantes (UN)-Université d'Angers (UA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Centre hospitalier universitaire de Nantes (CHU Nantes)-Université de Nantes - UFR de Médecine et des Techniques Médicales (UFR MEDECINE), Université de Nantes (UN)-Université de Nantes (UN), Comprehensive Pneumology Center, Ludwig Maximilians University and Helmholtz Zentrum Muenchen, Member of the German Research Center for Lung Research, Großhadern, Germany, McGill University, GeoBiosphere Science Centre, Lund University [Lund], Biologie Cellulaire et Moleculaire du Transport des Nutriments, Université Henri Poincaré - Nancy 1 (UHP)-Institut National de la Santé et de la Recherche Médicale (INSERM), Institut National des Langues et Civilisations Orientales (Inalco), School of biosciences and biotechnology, Università di Camerino (UNICAM), Haematology Research Group, Queen's University [Belfast] (QUB)-CCRCB, University of Konstanz, Institute of Cell Biology, University of Bern, University of Bern, Laboratoire de Parasitologie, Université Libre de Bruxelles [Bruxelles] (ULB), Vrije Universiteit [Brussels] (VUB), Faculty of Life Sciences [Manchester], University of Manchester [Manchester], Max Planck Institute for Molecular Genetics (MPIMG), Max-Planck-Gesellschaft, Section Molecular Epidemiology, Leiden University Medical Center (LUMC), Centre for Molecular Life Sciences (NCMLS), Department of Molecular Biology, Radboud university [Nijmegen], Institut de Génétique et de Biologie Moléculaire et Cellulaire, Centre National de la Recherche Scientifique (CNRS), Department of Gynaecological Oncology, Institute for Women's Health, University College of London [London] (UCL), Dynamique du noyau [Institut Curie], Institut Curie [Paris]-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Biologie du développement et reproduction (BDR), École nationale vétérinaire d'Alfort (ENVA)-Institut National de la Recherche Agronomique (INRA)-Centre National de la Recherche Scientifique (CNRS), Centro de Genómica - Centre de Genòmica [IVIA], Instituto Valenciano de Investigaciones Agrarias - Institut Valencià d'Investigacions Agraries - Valencian Institute for agricultural Research (IVIA), Universitat Pompeu Fabra [Barcelona] (UPF), Molecular Mechanisms of Chronic Inflammation in Hematological Diseases (CRCINA-ÉQUIPE 16), Centre de Recherche en Cancérologie et Immunologie Nantes-Angers (CRCINA), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Nantes - UFR de Médecine et des Techniques Médicales (UFR MEDECINE), Université de Nantes (UN)-Université de Nantes (UN)-Centre hospitalier universitaire de Nantes (CHU Nantes)-Centre National de la Recherche Scientifique (CNRS)-Université d'Angers (UA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Nantes - UFR de Médecine et des Techniques Médicales (UFR MEDECINE), Université de Nantes (UN)-Université de Nantes (UN)-Centre hospitalier universitaire de Nantes (CHU Nantes)-Centre National de la Recherche Scientifique (CNRS)-Université d'Angers (UA), McGill University = Université McGill [Montréal, Canada], Università degli Studi di Camerino (UNICAM), Université libre de Bruxelles (ULB), Vrije Universiteit Brussel (VUB), Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC), Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA), LS Voortplanting Inwendige Ziekten, Sub Celbiologisch lab., Biology of Reproductive Cells, ES/FAH BRC, Almouzni, G, Altucci, Lucia, Amati, B, Ashley, N, Baulcombe, D, Beaujean, N, Bock, C, Bongcam Rudloff, E, Bousquet, J, Braun, S, Paillerets, Bb, Bussemakers, M, Clarke, L, Conesa, A, Estivill, X, Fazeli, A, Grgurević, N, Gut, I, Heijmans, Bt, Hermouet, S, Houwing Duistermaat, J, Iacobucci, I, Ilaš, J, Kandimalla, R, Krauss Etschmann, S, Lasko, P, Lehmann, S, Lindroth, A, Majdič, G, Marcotte, E, Martinelli, G, Martinet, N, Meyer, E, Miceli, C, Mills, K, Moreno Villanueva, M, Morvan, G, Nickel, D, Niesler, B, Nowacki, M, Nowak, J, Ossowski, S, Pelizzola, M, Pochet, R, Potočnik, U, Radwanska, M, Raes, J, Rattray, M, Robinson, Md, Roelen, B, Sauer, S, Schinzer, D, Slagboom, E, Spector, T, Stunnenberg, Hg, Tiligada, E, Torres Padilla, Me, Tsonaka, R, Soom, Av, Vidaković, M, Widschwendter, M., University of Naples Federico II = Università degli studi di Napoli Federico II, École nationale vétérinaire - Alfort (ENVA)-Institut National de la Recherche Agronomique (INRA)-Centre National de la Recherche Scientifique (CNRS), Université d'Angers (UA)-Université de Nantes (UN)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Centre hospitalier universitaire de Nantes (CHU Nantes)-Université d'Angers (UA)-Université de Nantes (UN)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Centre hospitalier universitaire de Nantes (CHU Nantes), Università degli Studi di Camerino = University of Camerino (UNICAM), Universität Bern [Bern] (UNIBE), Radboud University [Nijmegen], and Université de Strasbourg (UNISTRA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Epigenomics, [SDV]Life Sciences [q-bio], MESH: Epigenomics, programme de recherche scientifique, Epigenesis, Genetic, Epigenome, Biologie de la reproduction, MESH: Epigenesis, Genetic, epidrugs, génération, DNA METHYLATION, ComputingMilieux_MISCELLANEOUS, Reproductive Biology, évolution de la maladie, Genome, MESH: Genomics, Biologie du développement, DEATH, Genomics, Development Biology, CANCER, MESH: Research, impact environnemental, Technology Platforms, genome, epigenome, microbiome, environment, STEM-CELLS, epigenetic, Biotechnology, durée de vie, programme européen, facteur génétique, INHIBITION, [SDV.BC]Life Sciences [q-bio]/Cellular Biology, Environment, maladie, ddc:570, Correspondence, Genetics, Humans, cancer, MESH: Genome, [SDV.BBM]Life Sciences [q-bio]/Biochemistry, Molecular Biology, Veterinary Sciences, Microbiome, MESH: Humans, Research, santé publique, 570 Life sciences, biology, sensibilité aux maladies, [SDV.MHEP]Life Sciences [q-bio]/Human health and pathology
Abstract

Understanding the links between genetic, epigenetic and non-genetic factors throughout the lifespan and across generations and their role in disease susceptibility and disease progression offer entirely new avenues and solutions to major problems in our society. To overcome the numerous challenges, we have come up with nine major conclusions to set the vision for future policies and research agendas at the European level. ispartof: BMC Genomics vol:15 issue:1 pages:487-487 ispartof: location:England status: published

Published
2014

59. A human gut microbial gene catalogue established by metagenomic sequencing

Author

Qin, J., Li, R., Raes, J., Arumugam, M., Burgdorf, K. S., Manichanh, C., Nielsen, T., Pons, N., Levenez, F., Yamada, Takuji, Mende, D. R., Li, J., Xu, J., Li, S., Li, D., Cao, J., Wang, B., Liang, H., Zheng, H., Xie, Y., Tap, J., Lepage, P., Bertalan, M., Batto, J. M., Hansen, T., Le Paslier, D., Linneberg, A., Nielsen, H. B., Pelletier, E., Renault, P., Sicheritz-Ponten, T., Turner, K., Zhu, H., Yu, C., Jian, M., Zhou, Y., Li, Y., Zhang, X., Qin, N., Yang, H., Wang, J., Brunak, S., Dor�, J., Guarner, F., Kristiansen, K., Pedersen, O., Parkhill, J., Weissenbach, J., Bork, P., Ehrlich, S. D., Consortium, MetaHIT, European Molecular Biology Laboratory [Heidelberg] (EMBL), Beijing Genomics Institute [Shenzhen] (BGI), Hagedorn Research Institute, Vrije Universiteit Brussel (VUB), Vall d'Hebron University Hospital [Barcelona], Institut National de la Recherche Agronomique (INRA), School of Software Engineering, Southern University of Science and Technology [Shenzhen] (SUSTech), Genome Research Institute, Shenzhen University Medical School, Center for Biological Sequence Analysis [Lyngby], Technical University of Denmark [Lyngby] (DTU), Center for Biological Sequence Analysis, Genoscope - Centre national de séquençage [Evry] (GENOSCOPE), Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Research Center for Prevention and Health, Glostrup Hospital, The Wellcome Trust Sanger Institute [Cambridge], Department of Biology [Copenhagen], Faculty of Science [Copenhagen], University of Copenhagen = Københavns Universitet (KU)-University of Copenhagen = Københavns Universitet (KU), Faculty of Health Sciences, Department of Biomedical Sciences [Copenhagen], Faculty of Health and Medical Sciences, MetaHIT Consortium, European Project: 201052,EC:FP7:HEALTH,FP7-HEALTH-2007-A,METAHIT(2008), Vrije Universiteit [Brussels] (VUB), Hospital Universitari Val d'Hebron, CIBERehd, Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, MetaGenoPolis, Southern University of Science and Technology (SUSTech), MICrobiologie de l'ALImentation au Service de la Santé (MICALIS), Institut National de la Recherche Agronomique (INRA)-AgroParisTech, Danmarks Tekniske Universitet = Technical University of Denmark (DTU), and University of Copenhagen = Københavns Universitet (UCPH)-University of Copenhagen = Københavns Universitet (UCPH)

Subjects
intestinal microbiota, Adult, Denmark, Genomics, Bacterial genome size, Biology, Genome, Microbiology, diversity, Cohort Studies, 03 medical and health sciences, Contig Mapping, Feces, Open Reading Frames, Microbiologie, Humans, Obesity, fermentation, 030304 developmental biology, Inner mucus layer, VLAG, Genetics, 0303 health sciences, Multidisciplinary, Genes, Essential, Bacteria, 030306 microbiology, 16s ribosomal-rna, Human microbiome, alignment, Sequence Analysis, DNA, Overweight, Inflammatory Bowel Diseases, communities, Gastrointestinal Tract, [SDV.GEN.GH]Life Sciences [q-bio]/Genetics/Human genetics, host, Metagenomics, Genes, Bacterial, Health, Spain, networks, Metagenome, Enterotype, environment, Genome, Bacterial, Human Microbiome Project
Abstract

Udgivelsesdato: 2010-Mar-4 To understand the impact of gut microbes on human health and well-being it is crucial to assess their genetic potential. Here we describe the Illumina-based metagenomic sequencing, assembly and characterization of 3.3 million non-redundant microbial genes, derived from 576.7 gigabases of sequence, from faecal samples of 124 European individuals. The gene set, approximately 150 times larger than the human gene complement, contains an overwhelming majority of the prevalent (more frequent) microbial genes of the cohort and probably includes a large proportion of the prevalent human intestinal microbial genes. The genes are largely shared among individuals of the cohort. Over 99% of the genes are bacterial, indicating that the entire cohort harbours between 1,000 and 1,150 prevalent bacterial species and each individual at least 160 such species, which are also largely shared. We define and describe the minimal gut metagenome and the minimal gut bacterial genome in terms of functions present in all individuals and most bacteria, respectively.

Published
2010

60. Quantifying environmental adaptation of metabolic pathways in metagenomics

Author

Gianoulis, T. A., Raes, J., Patel, P. V., Bjornson, R., Korbel, J. O., Letunic, I., Yamada, Takuji, Paccanaro, A., Jensen, L. J., Snyder, M., Bork, P., and Gerstein, M. B.

Subjects
0303 health sciences, Multidisciplinary, 030306 microbiology, Ecology, Genomics, Computational biology, Biosensing Techniques, Biological Sciences, Biology, Network dynamics, Lipid Metabolism, Microbiology, Regression, 03 medical and health sciences, 13. Climate action, Metagenomics, Polysaccharides, Geographic site, Multiple correlation, Adaptation, Amino Acids, Canonical correlation, 030304 developmental biology
Abstract

Recently, approaches have been developed to sample the genetic content of heterogeneous environments (metagenomics). However, by what means these sequences link distinct environmental conditions with specific biological processes is not well understood. Thus, a major challenge is how the usage of particular pathways and subnetworks reflects the adaptation of microbial communities across environments and habitats—i.e., how network dynamics relates to environmental features. Previous research has treated environments as discrete, somewhat simplified classes (e.g., terrestrial vs. marine), and searched for obvious metabolic differences among them (i.e., treating the analysis as a typical classification problem). However, environmental differences result from combinations of many factors, which often vary only slightly. Therefore, we introduce an approach that employs correlation and regression to relate multiple, continuously varying factors defining an environment to the extent of particular microbial pathways present in a geographic site. Moreover, rather than looking only at individual correlations (one-to-one), we adapted canonical correlation analysis and related techniques to define an ensemble of weighted pathways that maximally covaries with a combination of environmental variables (many-to-many), which we term a metabolic footprint. Applied to available aquatic datasets, we identified footprints predictive of their environment that can potentially be used as biosensors. For example, we show a strong multivariate correlation between the energy-conversion strategies of a community and multiple environmental gradients (e.g., temperature). Moreover, we identified covariation in amino acid transport and cofactor synthesis, suggesting that limiting amounts of cofactor can (partially) explain increased import of amino acids in nutrient-limited conditions.

Published
2009

62. Computational approaches to predict bacteriophage-host relationships

Author

Edwards, R.A., McNair, K., Faust, K., Raes, J., Dutilh, B.E., Edwards, R.A., McNair, K., Faust, K., Raes, J., and Dutilh, B.E.

Abstract

Contains fulltext : 172194.pdf (publisher's version ) (Open Access), Metagenomics has changed the face of virus discovery by enabling the accurate identification of viral genome sequences without requiring isolation of the viruses. As a result, metagenomic virus discovery leaves the first and most fundamental question about any novel virus unanswered: What host does the virus infect? The diversity of the global virosphere and the volumes of data obtained in metagenomic sequencing projects demand computational tools for virus-host prediction. We focus on bacteriophages (phages, viruses that infect bacteria), the most abundant and diverse group of viruses found in environmental metagenomes. By analyzing 820 phages with annotated hosts, we review and assess the predictive power of in silico phage-host signals. Sequence homology approaches are the most effective at identifying known phage-host pairs. Compositional and abundance-based methods contain significant signal for phage-host classification, providing opportunities for analyzing the unknowns in viral metagenomes. Together, these computational approaches further our knowledge of the interactions between phages and their hosts. Importantly, we find that all reviewed signals significantly link phages to their hosts, illustrating how current knowledge and insights about the interaction mechanisms and ecology of coevolving phages and bacteria can be exploited to predict phage-host relationships, with potential relevance for medical and industrial applications.

Published
2016

63. Population-based metagenomics analysis reveals markers for gut microbiome composition and diversity

Author

Zhernakova, A., Kurilshikov, A., Bonder, M.J., Tigchelaar, E.F., Schirmer, M., Vatanen, T., Mujagic, Z., Vila, A.V., Falony, G., Vieira-Silva, S., Wang, J, Imhann, F., Brandsma, E., Jankipersadsing, S.A., Joossens, M., Cenit, M.C., Deelen, P., Swertz, M.A., Weersma, R.K., Feskens, E.J.M., Netea, M.G., Gevers, D., Jonkers, D., Franke, L., Aulchenko, Y.S., Huttenhower, C., Raes, J., Hofker, M.H., Xavier, R.J., Wijmenga, C., Fu, J., Zhernakova, A., Kurilshikov, A., Bonder, M.J., Tigchelaar, E.F., Schirmer, M., Vatanen, T., Mujagic, Z., Vila, A.V., Falony, G., Vieira-Silva, S., Wang, J, Imhann, F., Brandsma, E., Jankipersadsing, S.A., Joossens, M., Cenit, M.C., Deelen, P., Swertz, M.A., Weersma, R.K., Feskens, E.J.M., Netea, M.G., Gevers, D., Jonkers, D., Franke, L., Aulchenko, Y.S., Huttenhower, C., Raes, J., Hofker, M.H., Xavier, R.J., Wijmenga, C., and Fu, J.

Abstract

Contains fulltext : 171213.pdf (publisher's version ) (Closed access), Deep sequencing of the gut microbiomes of 1135 participants from a Dutch population-based cohort shows relations between the microbiome and 126 exogenous and intrinsic host factors, including 31 intrinsic factors, 12 diseases, 19 drug groups, 4 smoking categories, and 60 dietary factors. These factors collectively explain 18.7% of the variation seen in the interindividual distance of microbial composition. We could associate 110 factors to 125 species and observed that fecal chromogranin A (CgA), a protein secreted by enteroendocrine cells, was exclusively associated with 61 microbial species whose abundance collectively accounted for 53% of microbial composition. Low CgA concentrations were seen in individuals with a more diverse microbiome. These results are an important step toward a better understanding of environment-diet-microbe-host interactions.

Published
2016

64. Enterotypes of the human gut microbiome

Author

Arumugam M, Raes J, Pelletier E, Le Paslier D, Yamada T, Mende DR, Fernandes GR, Tap J, Bruls T, Batto JM, Bertalan M, Borruel N, Casellas F, Fernandez L, Gautier L, Hansen T, Hattori M, Hayashi T, Kleerebezem M, Kurokawa K, Leclerc M, Levenez F, Manichanh C, Nielsen HB, Nielsen T, Pons N, Poulain J, Qin J, Sicheritz-Ponten T, Tims S, Torrents D, Ugarte E, Zoetendal EG, Wang J, Guarner F, Pedersen O, de Vos WM, Brunak S, Dorxe9 J, MetaHIT Consortium, Antolxedn M, Artiguenave F, Blottiere HM, Almeida M, Brechot, Arumugam M, Raes J, Pelletier E, Le Paslier D, Yamada T, Mende DR, Fernandes GR, Tap J, Bruls T, Batto JM, Bertalan M, Borruel N, Casellas F, Fernandez L, Gautier L, Hansen T, Hattori M, Hayashi T, Kleerebezem M, Kurokawa K, Leclerc M, Levenez F, Manichanh C, Nielsen HB, Nielsen T, Pons N, Poulain J, Qin J, Sicheritz-Ponten T, Tims S, Torrents D, Ugarte E, Zoetendal EG, Wang J, Guarner F, Pedersen O, de Vos WM, Brunak S, Dorxe9 J, MetaHIT Consortium, Antolxedn M, Artiguenave F, Blottiere HM, Almeida M, and Brechot

Published
2011

65. Op safari in het Lichaam

Author

Raes, J., Kleerebezem, M., and de Vos, W.M.

Subjects
Microbiologie, Microbiology, VLAG
Published
2014

67. Commensal microbiota influence systemic autoimmune responses

Author

Praet, J.T. Van, Donovan, E., Vanassche, I., Drennan, M.B., Windels, F., Dendooven, A., Allais, L., Cuvelier, C.A., Loo, F.A.J. van de, Norris, P.S., Kruglov, A.A., Nedospasov, S.A., Rabot, S., Tito, R., Raes, J., Gaboriau-Routhiau, V., Cerf-Bensussan, N., Wiele, T. Van de, Eberl, G., Ware, C.F., Elewaut, D., Praet, J.T. Van, Donovan, E., Vanassche, I., Drennan, M.B., Windels, F., Dendooven, A., Allais, L., Cuvelier, C.A., Loo, F.A.J. van de, Norris, P.S., Kruglov, A.A., Nedospasov, S.A., Rabot, S., Tito, R., Raes, J., Gaboriau-Routhiau, V., Cerf-Bensussan, N., Wiele, T. Van de, Eberl, G., Ware, C.F., and Elewaut, D.

Abstract

Contains fulltext : 154818.pdf (publisher's version ) (Closed access), Antinuclear antibodies are a hallmark feature of generalized autoimmune diseases, including systemic lupus erythematosus and systemic sclerosis. However, the processes underlying the loss of tolerance against nuclear self-constituents remain largely unresolved. Using mice deficient in lymphotoxin and Hox11, we report that approximately 25% of mice lacking secondary lymphoid organs spontaneously develop specific antinuclear antibodies. Interestingly, we find this phenotype is not caused by a defect in central tolerance. Rather, cell-specific deletion and in vivo lymphotoxin blockade link these systemic autoimmune responses to the formation of gut-associated lymphoid tissue in the neonatal period of life. We further demonstrate antinuclear antibody production is influenced by the presence of commensal gut flora, in particular increased colonization with segmented filamentous bacteria, and IL-17 receptor signaling. Together, these data indicate that neonatal colonization of gut microbiota influences generalized autoimmunity in adult life.

Published
2015

68. Intestinal Microbiota And Diet in IBS: Causes, Consequences, or Epiphenomena?

Author

Rajilic-Stojanovic, M., Rajilic-Stojanovic, M., Jonkers, D.M., Salonen, A., Hanevik, K., Raes, J., Jalanka, J., de Vos, W.M., Manichanh, C., Golic, N., Enck, P., Philippou, E., Iraqi, F.A., Clarke, G., Spiller, R.C., Penders, J., Rajilic-Stojanovic, M., Rajilic-Stojanovic, M., Jonkers, D.M., Salonen, A., Hanevik, K., Raes, J., Jalanka, J., de Vos, W.M., Manichanh, C., Golic, N., Enck, P., Philippou, E., Iraqi, F.A., Clarke, G., Spiller, R.C., and Penders, J.

Abstract

Irritable bowel syndrome (IBS) is a heterogeneous functional disorder with a multifactorial etiology that involves the interplay of both host and environmental factors. Among environmental factors relevant for IBS etiology, the diet stands out given that the majority of IBS patients report their symptoms to be triggered by meals or specific foods. The diet provides substrates for microbial fermentation, and, as the composition of the intestinal microbiota is disturbed in IBS patients, the link between diet, microbiota composition, and microbial fermentation products might have an essential role in IBS etiology. In this review, we summarize current evidence regarding the impact of diet and the intestinal microbiota on IBS symptoms, as well as the reported interactions between diet and the microbiota composition. On the basis of the existing data, we suggest pathways (mechanisms) by which diet components, via the microbial fermentation, could trigger IBS symptoms. Finally, this review provides recommendations for future studies that would enable elucidation of the role of diet and microbiota and how these factors may be (inter)related in the pathophysiology of IBS.

Published
2015

69. Metagenomics meets time series analysis: unraveling microbial community dynamics

Author

Faust, K., Lahti, L.M., Gonze, D., de Vos, W.M., Raes, J., Faust, K., Lahti, L.M., Gonze, D., de Vos, W.M., and Raes, J.

Abstract

The recent increase in the number of microbial time series studies offers new insights into the stability and dynamics of microbial communities, from the world's oceans to human microbiota. Dedicated time series analysis tools allow taking full advantage of these data. Such tools can reveal periodic patterns, help to build predictive models or, on the contrary, quantify irregularities that make community behavior unpredictable. Microbial communities can change abruptly in response to small perturbations, linked to changing conditions or the presence of multiple stable states. With sufficient samples or time points, such alternative states can be detected. In addition, temporal variation of microbial interactions can be captured with time-varying networks. Here, we apply these techniques on multiple longitudinal datasets to illustrate their potential for microbiome research.

Published
2015

70. Towards microbial fermentation metabolites as markers for health benefits of prebiotics

Author

Verbeke, K., Boobis, A., Chiodini, A., Edwards, Christine, Franck, A., Kleerebezem, M., Nauta, A., Raes, J., Van Tol, E., Tuohy, K., Verbeke, K., Boobis, A., Chiodini, A., Edwards, Christine, Franck, A., Kleerebezem, M., Nauta, A., Raes, J., Van Tol, E., and Tuohy, K.

Abstract

Copyright © The ILSI Europe a.i.s.b.l. 2015. Available evidence on the bioactive, nutritional and putative detrimental properties of gut microbial metabolites has been evaluated to support a more integrated view of how prebiotics might affect host health throughout life. The present literature inventory targeted evidence for the physiological and nutritional effects of metabolites, for example, SCFA, the potential toxicity of other metabolites and attempted to determine normal concentration ranges. Furthermore, the biological relevance of more holistic approaches like faecal water toxicity assays and metabolomics and the limitations of faecal measurements were addressed. Existing literature indicates that protein fermentation metabolites (phenol, p-cresol, indole, ammonia), typically considered as potentially harmful, occur at concentration ranges in the colon such that no toxic effects are expected either locally or following systemic absorption. The endproducts of saccharolytic fermentation, SCFA, may have effects on colonic health, host physiology, immunity, lipid and protein metabolism and appetite control. However, measuring SCFA concentrations in faeces is insufficient to assess the dynamic processes of their nutrikinetics. Existing literature on the usefulness of faecal water toxicity measures as indicators of cancer risk seems limited. In conclusion, at present there is insufficient evidence to use changes in faecal bacterial metabolite concentrations as markers of prebiotic effectiveness. Integration of results from metabolomics and metagenomics holds promise for understanding the health implications of prebiotic microbiome modulation but adequate tools for data integration and interpretation are currently lacking. Similarly, studies measuring metabolite fluxes in different body compartments to provide a more accurate picture of their nutrikinetics are needed.

Published
2015

71. Prediction and identification of sequences coding for orphan enzymes using genomic and metagenomic neighbours

Author

Yamada, Takuji, Waller, A. S., Raes, J., Zelezniak, A., Perchat, N., Perret, A., Salanoubat, M., Patil, K. R., Weissenbach, J., Bork, P., European Molecular Biology Laboratory, European Molecular Biology Laboratory [Heidelberg] (EMBL), Génomique métabolique (UMR 8030), Genoscope - Centre national de séquençage [Evry] (GENOSCOPE), Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université d'Évry-Val-d'Essonne (UEVE)-Centre National de la Recherche Scientifique (CNRS), and Department of Bio-engineering Sciences

Subjects
Protein family, Systems biology, neighbourhood information, [SDV]Life Sciences [q-bio], Metabolic network, Genomics, Biology, Genome, Models, Biological, General Biochemistry, Genetics and Molecular Biology, Article, 03 medical and health sciences, Protein sequencing, [SDV.BBM.GTP]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Genomics [q-bio.GN], Databases, Genetic, genomics, metabolic pathways, Humans, [SDV.BBM]Life Sciences [q-bio]/Biochemistry, Molecular Biology, Gene, ComputingMilieux_MISCELLANEOUS, 030304 developmental biology, Genetics, 0303 health sciences, General Immunology and Microbiology, Applied Mathematics, Systems Biology, 030302 biochemistry & molecular biology, Chromosome Mapping, Sequence Analysis, DNA, Enzymes, Computational Theory and Mathematics, Metagenomics, Cardiovascular and Metabolic Diseases, orphan enzymes, Metagenome, General Agricultural and Biological Sciences, Metabolic Networks and Pathways, Information Systems
Abstract

Many characterized metabolic enzymes currently lack associated gene and protein sequences. Here, pathway and genomic neighbour data are used to assign genes to these ‘orphan enzymes,' and the predictions are validated with experimental assays and genome-scale metabolic modelling., A computational method is developed for assigning candidate sequences to orphan enzymes. The method uses metabolic pathway, genomic neighbourhood, genomic co-occurrence, and protein domain information to predict genes that are likely to perform a particular enzymatic function. Benchmarking of the scoring scheme based on the 4 features above revealed that some combinations of parameters yielded greater than 70% accuracy, and that high-confidence predictions could be generated for 131 orphan enzymes. Enzyme assay experiments confirmed the predicted enzymatic activity for two of the high-confidence candidate sequences. Predicted functions can improve the annotation of genomic and metagenomic data, and can reveal putative genes for enzymes with potential biotechnological applications. Incorporating the predicted enzymatic reactions into genome-scale metabolic models changed the flux connectivity and improved their ability to correctly predict gene essentiality, supporting the biological relevance of these predictions., Despite the current wealth of sequencing data, one-third of all biochemically characterized metabolic enzymes lack a corresponding gene or protein sequence, and as such can be considered orphan enzymes. They represent a major gap between our molecular and biochemical knowledge, and consequently are not amenable to modern systemic analyses. As 555 of these orphan enzymes have metabolic pathway neighbours, we developed a global framework that utilizes the pathway and (meta)genomic neighbour information to assign candidate sequences to orphan enzymes. For 131 orphan enzymes (37% of those for which (meta)genomic neighbours are available), we associate sequences to them using scoring parameters with an estimated accuracy of 70%, implying functional annotation of 16 345 gene sequences in numerous (meta)genomes. As a case in point, two of these candidate sequences were experimentally validated to encode the predicted activity. In addition, we augmented the currently available genome-scale metabolic models with these new sequence–function associations and were able to expand the models by on average 8%, with a considerable change in the flux connectivity patterns and improved essentiality prediction.

Published
2012

72. Assessment of metagenomic assembly using simulated next generation sequencing data

Author

Mende, D.R., Waller, A.S., Sunagawa, S., Jaervelin, A.I., Chan, M.M., Arumugam, M., Raes, J., and Bork, P.

Subjects
Cardiovascular and Metabolic Diseases, food and beverages
Abstract

Due to the complexity of the protocols and a limited knowledge of the nature of microbial communities, simulating metagenomic sequences plays an important role in testing the performance of existing tools and data analysis methods with metagenomic data. We developed metagenomic read simulators with platform-specific (Sanger, pyrosequencing, Illumina) base-error models, and simulated metagenomes of differing community complexities. We first evaluated the effect of rigorous quality control on Illumina data. Although quality filtering removed a large proportion of the data, it greatly improved the accuracy and contig lengths of resulting assemblies. We then compared the quality-trimmed Illumina assemblies to those from Sanger and pyrosequencing. For the simple community (10 genomes) all sequencing technologies assembled a similar amount and accurately represented the expected functional composition. For the more complex community (100 genomes) Illumina produced the best assemblies and more correctly resembled the expected functional composition. For the most complex community (400 genomes) there was very little assembly of reads from any sequencing technology. However, due to the longer read length the Sanger reads still represented the overall functional composition reasonably well. We further examined the effect of scaffolding of contigs using paired-end Illumina reads. It dramatically increased contig lengths of the simple community and yielded minor improvements to the more complex communities. Although the increase in contig length was accompanied by increased chimericity, it resulted in more complete genes and a better characterization of the functional repertoire. The metagenomic simulators developed for this research are freely available.

Published
2012

74. Enterotypes of the human gut microbiome

Author

Arumugam, M., Raes, J., Pelletier, E., Le Paslier, D., Yamada, Takuji, Mende, D. R., Fernandes, G. R., Tap, J., Bruls, T., Batto, J. M., Bertalan, M., Borruel, N., Casellas, F., Fernandez, L., Gautier, L., Hansen, T., Hattori, M., Hayashi, T., Kleerebezem, M., Kurokawa, K., Leclerc, M., Levenez, F., Manichanh, C., Nielsen, H. B., Nielsen, T., Pons, N., Poulain, J., Qin, J., Sicheritz-Ponten, T., Tims, S., Torrents, D., Ugarte, E., Zoetendal, E. G., Wang, J., Guarner, F., Pedersen, O., de Vos, W. M., Brunak, S., Dor�, J., Antol匤, M., Artiguenave, F., Blottiere, H. M., Almeida, M., Brechot, C., Cara, C., Chervaux, C., Cultrone, A., Delorme, C., Denariaz, G., Dervyn, R., Foerstner, K. U., Friss, C., van de Guchte, M., Guedon, E., Haimet, F., Huber, W., van Hylckama-Vlieg, J., Jamet, A., Juste, C., Kaci, G., Knol, J., Lakhdari, O., Layec, S., Le Roux, K., Maguin, E., M駻ieux, A., Melo Minardi, R., M'rini, C., Muller, J., Oozeer, R., Parkhill, J., Renault, P., Rescigno, M., Sanchez, N., Sunagawa, S., Torrejon, A., Turner, K., Vandemeulebrouck, G., Varela, E., Winogradsky, Y., Zeller, G., Weissenbach, J., Ehrlich, S. D., Bork, P., Consortium, MetaHIT, Microbiota Interaction with Human and Animal (MIHA), MICrobiologie de l'ALImentation au Service de la Santé (MICALIS), Institut National de la Recherche Agronomique (INRA)-AgroParisTech-Institut National de la Recherche Agronomique (INRA)-AgroParisTech, AgroParisTech-Université Paris-Saclay-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), European Molecular Biology Laboratory [Heidelberg] (EMBL), Genoscope - Centre national de séquençage [Evry] (GENOSCOPE), Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Center for Biological Sequence Analysis [Lyngby], Technical University of Denmark [Lyngby] (DTU), Digestive System Research Unit, Vall d'Hebron University Hospital [Barcelona], Barcelona Supercomputing Center - Centro Nacional de Supercomputacion (BSC - CNS), Centre Interlangues - Texte, Image, Langage (TIL), Université de Bourgogne (UB), Hagedorn Research Institute, Faculty of Health Sciences, University of Southern Denmark (SDU), NIZO [Ede, Netherlands], Institut National de la Recherche Agronomique (INRA), Génomique métabolique (UMR 8030), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université d'Évry-Val-d'Essonne (UEVE)-Centre National de la Recherche Scientifique (CNRS), Beijing Genomics Institute [Shenzhen] (BGI), Laboratory of Microbiology, Wageningen University and Research [Wageningen] (WUR), Institut National de la Recherche Agronomique (INRA)-AgroParisTech, Lundbeck Foundation Centre for Applied Medical Genomics in Personalized Disease Prediction, Prevention and Care (LuCAMP), Novo Nordisk Foundation, International Science and Technology Cooperation Project in China [0806], Agence Nationale de la Recherche (ANR), Institute for the encouragement of Scientific Research and Innovation of Brussels (ISRIB), Fund for Scientific Research Flanders (FWO), European Project: 201052,EC:FP7:HEALTH,FP7-HEALTH-2007-A,METAHIT(2008), Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, NIZO FOOD RESEARCH (NIZO), Nizo food research, Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Université d'Évry-Val-d'Essonne (UEVE), Wageningen University and Research Centre [Wageningen] (WUR), Danmarks Tekniske Universitet = Technical University of Denmark (DTU), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), and Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Université d'Évry-Val-d'Essonne (UEVE)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Male, intestinal microbiota, catalog, obesity, [SDV]Life Sciences [q-bio], pathways, Biodiversity, Biology, Microbiology, Article, diversity, 03 medical and health sciences, Feces, Human gut, mucin, Phylogenetics, Microbiologie, Humans, bacterial, Microbiome, genes, Phylogeny, 030304 developmental biology, VLAG, 2. Zero hunger, 0303 health sciences, metagenomics, Multidisciplinary, Bacteria, colon, 030306 microbiology, Host (biology), Ecology, [SDV.MHEP.HEG]Life Sciences [q-bio]/Human health and pathology/Hépatology and Gastroenterology, Bacterial Typing Techniques, Europe, Intestines, [SDV.MP]Life Sciences [q-bio]/Microbiology and Parasitology, Evolutionary biology, Metagenomics, Metagenome, Enterotype, Biological Markers, Female, Biomarkers, Human Microbiome Project
Abstract

International audience; Our knowledge of species and functional composition of the human gut microbiome is rapidly increasing, but it is still based on very few cohorts and little is known about variation across the world. By combining 22 newly sequenced faecal metagenomes of individuals from four countries with previously published data sets, here we identify three robust clusters (referred to as enterotypes hereafter) that are not nation or continent specific. We also confirmed the enterotypes in two published, larger cohorts, indicating that intestinal microbiota variation is generally stratified, not continuous. This indicates further the existence of a limited number of well-balanced host-microbial symbiotic states that might respond differently to diet and drug intake. The enterotypes are mostly driven by species composition, but abundant molecular functions are not necessarily provided by abundant species, highlighting the importance of a functional analysis to understand microbial communities. Although individual host properties such as body mass index, age, or gender cannot explain the observed enterotypes, data-driven marker genes or functional modules can be identified for each of these host properties. For example, twelve genes significantly correlate with age and three functional modules with the body mass index, hinting at a diagnostic potential of microbial markers.

Published
2011

75. Toward molecular trait-based ecology through integration of biogeochemical, geographical and metagenomic data

Author

Raes, J., Letunic, I., Yamada, Takuji, Jensen, L. J., Bork, P., and Department of Bio-engineering Sciences

Subjects
Ecology (disciplines), Climate, Oceans and Seas, Biodiversity, Biology, General Biochemistry, Genetics and Molecular Biology, Article, 03 medical and health sciences, Microbial ecology, Species Specificity, Seawater, 14. Life underwater, Ecosystem, 030304 developmental biology, 0303 health sciences, environmental genomics, General Immunology and Microbiology, ecosystems biology, Ecology, Geography, 030306 microbiology, Applied Mathematics, microbiology, Molecular Sequence Annotation, 15. Life on land, Adaptation, Physiological, Ecological indicator, Computational Theory and Mathematics, Cardiovascular and Metabolic Diseases, 13. Climate action, Metagenomics, Genetic Loci, Data Interpretation, Statistical, molecular trait-based ecology, Trait, Biological dispersal, Regression Analysis, Species richness, General Agricultural and Biological Sciences, Algorithms, Metabolic Networks and Pathways, Information Systems
Abstract

Using metagenomic ‘parts lists' to study microbial ecology remains a significant challenge. This work proposes a molecular trait-based approach to biogeography by integrating metagenomic data with external metadata and using functional community composition as readout., Climatic factors drive functional and phylogenetic composition of ocean microbial communities. Function dispersal is controlled by environmental conditions. Functional richness has a clear latitudinal gradient and correlates with primary production. Metagenomic data can be used as a predictor for ecosystem processes. To understand the relationship between community composition and environment, functional readouts are the most direct. Metagenomic data enable such trait-based ecology at the molecular level., Metagenomics (shotgun sequencing of pooled DNA of complete microbial communities) is widely used to investigate ecosystem functioning of environmental and clinical samples. However, the nature of this data (usually a gigantic collection of gene fragments of 1000s of organisms) makes it very hard to infer global patterns on microbial ecology of the environment at hand. To address important ecological questions such as ‘How do microbial communities adapt to the environmental conditions?', ‘What drives the functional variation across the globe and to what extent do genes disperse?' and ‘What drives variation of CO2 uptake across different locations and communities?', we integrated 25 ocean metagenomes from the Global Ocean Sampling project with geographical, meteorological and geophysicochemical data. We find that climatic factors (temperature, sunlight) are the major determinants of the functional and phylogenetic composition of an environment and the main limiting factor on whether functions dispersal across the planet. We find a distinct latitudinal gradient in the size and diversity of the functional repertoire of ocean microbial communities, peaking at 20°N, and which correlates with oceanic CO2 uptake. The latter can also be predicted from the molecular functional composition of an environmental sample. Together, our results show that the functional community composition derived from metagenomes can be used as quantitative predictor for molecular trait-based biogeography and ecology., Using metagenomic ‘parts lists' to infer global patterns on microbial ecology remains a significant challenge. To deduce important ecological indicators such as environmental adaptation, molecular trait dispersal, diversity variation and primary production from the gene pool of an ecosystem, we integrated 25 ocean metagenomes with geographical, meteorological and geophysicochemical data. We find that climatic factors (temperature, sunlight) are the major determinants of the biomolecular repertoire of each sample and the main limiting factor on functional trait dispersal (absence of biogeographic provincialism). Molecular functional richness and diversity show a distinct latitudinal gradient peaking at 20°N and correlate with primary production. The latter can also be predicted from the molecular functional composition of an environmental sample. Together, our results show that the functional community composition derived from metagenomes is an important quantitative readout for molecular trait-based biogeography and ecology.

Published
2011

76. Relationship between genome and epigenome - challenges and requirements for future research

Author

Almouzni, G, Altucci, L, Amati, B, Ashley, N, Baulcombe, D, Beaujean, N, Bock, C, Bongcam-Rudloff, E, Bousquet, J, Braun, S, Paillerets Brigitte, B, Bussemakers, M, Clarke, L, Conesa, A, Estivill, X, Fazeli, A, Grgurevic, N, Gut, I, Heijmans Bastiaan, T, Hermouet, S, Houwing-Duistermaat, J, Iacobucci, I, Ilas, J, Kandimalla, R, Krauss-Etschmann, S, Lasko, P, Lehmann, S, Lindroth, A, Majdic, G, Marcotte, E, Martinelli, G, Martinet, N, Meyer, E, Miceli, C, Mills, K, Moreno-Villanueva, M, Morvan, G, Nickel, D, Niesler, B, Nowacki, M, Nowak, J, Ossowski, S, Pelizzola, M, Pochet, R, Potocnik, U, Radwanska, M, Raes, J, Rattray, M, Robinson Mark, D, Roelen, B, Sauer, S, Schinzer, D, Slagboom, E, Spector, T, Stunnenberg Hendrik, G, Tiligada, E, Torres-Padilla, M, Tsonaka, R, Soom Ann, V, Vidakovic, M, Widschwendter, M, Almouzni Genevieve, Altucci Lucia, Amati Bruno, Ashley Neil, Baulcombe David, Beaujean Nathalie, Bock Christoph, Bongcam-Rudloff Erik, Bousquet Jean, Braun Sigurd, Paillerets Brigitte Bressac-de, Bussemakers Marion, Clarke Laura, Conesa Ana, Estivill Xavier, Fazeli Alireza, Grgurevic NeZa, Gut Ivo, Heijmans Bastiaan T, Hermouet Sylvie, Houwing-Duistermaat Jeanine, Iacobucci Ilaria, Ilas Janez, Kandimalla Raju, Krauss-Etschmann Susanne, Lasko Paul, Lehmann Soren, Lindroth Anders, Majdic Gregor, Marcotte Eric, Martinelli Giovanni, Martinet Nadine, Meyer Eric, Miceli Cristina, Mills Ken, Moreno-Villanueva Maria, Morvan Ghislaine, Nickel Dorthe, Niesler Beate, Nowacki Mariusz, Nowak Jacek, Ossowski Stephan, Pelizzola M, Pochet Roland, Potocnik Uros, Radwanska Magdalena, Raes Jeroen, Rattray Magnus, Robinson Mark D, Roelen Bernard, Sauer Sascha, Schinzer Dieter, Slagboom Eline, Spector Tim, Stunnenberg Hendrik G, Tiligada Ekaterini, Torres-Padilla Maria-Elena, Tsonaka Roula, Soom Ann Van, Vidakovic Melita, Widschwendter Martin, Almouzni, G, Altucci, L, Amati, B, Ashley, N, Baulcombe, D, Beaujean, N, Bock, C, Bongcam-Rudloff, E, Bousquet, J, Braun, S, Paillerets Brigitte, B, Bussemakers, M, Clarke, L, Conesa, A, Estivill, X, Fazeli, A, Grgurevic, N, Gut, I, Heijmans Bastiaan, T, Hermouet, S, Houwing-Duistermaat, J, Iacobucci, I, Ilas, J, Kandimalla, R, Krauss-Etschmann, S, Lasko, P, Lehmann, S, Lindroth, A, Majdic, G, Marcotte, E, Martinelli, G, Martinet, N, Meyer, E, Miceli, C, Mills, K, Moreno-Villanueva, M, Morvan, G, Nickel, D, Niesler, B, Nowacki, M, Nowak, J, Ossowski, S, Pelizzola, M, Pochet, R, Potocnik, U, Radwanska, M, Raes, J, Rattray, M, Robinson Mark, D, Roelen, B, Sauer, S, Schinzer, D, Slagboom, E, Spector, T, Stunnenberg Hendrik, G, Tiligada, E, Torres-Padilla, M, Tsonaka, R, Soom Ann, V, Vidakovic, M, Widschwendter, M, Almouzni Genevieve, Altucci Lucia, Amati Bruno, Ashley Neil, Baulcombe David, Beaujean Nathalie, Bock Christoph, Bongcam-Rudloff Erik, Bousquet Jean, Braun Sigurd, Paillerets Brigitte Bressac-de, Bussemakers Marion, Clarke Laura, Conesa Ana, Estivill Xavier, Fazeli Alireza, Grgurevic NeZa, Gut Ivo, Heijmans Bastiaan T, Hermouet Sylvie, Houwing-Duistermaat Jeanine, Iacobucci Ilaria, Ilas Janez, Kandimalla Raju, Krauss-Etschmann Susanne, Lasko Paul, Lehmann Soren, Lindroth Anders, Majdic Gregor, Marcotte Eric, Martinelli Giovanni, Martinet Nadine, Meyer Eric, Miceli Cristina, Mills Ken, Moreno-Villanueva Maria, Morvan Ghislaine, Nickel Dorthe, Niesler Beate, Nowacki Mariusz, Nowak Jacek, Ossowski Stephan, Pelizzola M, Pochet Roland, Potocnik Uros, Radwanska Magdalena, Raes Jeroen, Rattray Magnus, Robinson Mark D, Roelen Bernard, Sauer Sascha, Schinzer Dieter, Slagboom Eline, Spector Tim, Stunnenberg Hendrik G, Tiligada Ekaterini, Torres-Padilla Maria-Elena, Tsonaka Roula, Soom Ann Van, Vidakovic Melita, and Widschwendter Martin

Abstract

Understanding the links between genetic, epigenetic and non-genetic factors throughout the lifespan and across generations and their role in disease susceptibility and disease progression offer entirely new avenues and solutions to major problems in our society. To overcome the numerous challenges, we have come up with nine major conclusions to set the vision for future policies and research agendas at the European level.

Published
2014

77. Molecular eco-systems biology: towards an understanding of community function

Author

Raes, J. and Bork, P.

Subjects
Cardiovascular and Metabolic Diseases
Abstract

Systems-biology approaches, which are driven by genome sequencing and high-throughput functional genomics data, are revolutionizing single-cell-organism biology. With the advent of various high-throughput techniques that aim to characterize complete microbial ecosystems (metagenomics, meta-transcriptomics and meta-metabolomics), we propose that the time is ripe to consider molecular systems biology at the ecosystem level (eco-systems biology). Here, we discuss the necessary data types that are required to unite molecular microbiology and ecology to develop an understanding of community function and discuss the potential shortcomings of these approaches.

Published
2008

78. A molecular study of microbe transfer between distant environments

Author

Hooper, S.D., Raes, J., Foerstner, K.U., Harrington, E.D., Dalevi, D., and Bork, P.

Subjects
Cardiovascular and Metabolic Diseases, complex mixtures
Abstract

BACKGROUND: Environments and their organic content are generally not static and isolated, but in a constant state of exchange and interaction with each other. Through physical or biological processes, organisms, especially microbes, may be transferred between environments whose characteristics may be quite different. The transferred microbes may not survive in their new environment, but their DNA will be deposited. In this study, we compare two environmental sequencing projects to find molecular evidence of transfer of microbes over vast geographical distances. METHODOLOGY: By studying synonymous nucleotide composition, oligomer frequency and orthology between predicted genes in metagenomics data from two environments, terrestrial and aquatic, and by correlating with phylogenetic mappings, we find that both environments are likely to contain trace amounts of microbes which have been far removed from their original habitat. We also suggest a bias in direction from soil to sea, which is consistent with the cycles of planetary wind and water. CONCLUSIONS: Our findings support the Baas-Becking hypothesis formulated in 1934, which states that due to dispersion and population sizes, microbes are likely to be found in widely disparate environments. Furthermore, the availability of genetic material from distant environments is a possible font of novel gene functions for lateral gene transfer.

Published
2008

79. Millimeter-scale genetic gradients and community-level molecular convergence in a hypersaline microbial mat

Author

Kunin, V, Raes, J, Harris, J K, Spear, J R, Walker, J J, Ivanova, N, von Mering, C, Bebout, B M, Pace, N R, Bork, P, Hugenholtz, P, University of Zurich, and Hugenholtz, P

Subjects
2604 Applied Mathematics, 1300 General Biochemistry, Genetics and Molecular Biology, 2400 General Immunology and Microbiology, 570 Life sciences, biology, 1100 General Agricultural and Biological Sciences, U7 Systems Biology / Functional Genomics, 10124 Institute of Molecular Life Sciences
Published
2008
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80. Protein function space: viewing the limits or limited by our view?

Author

Raes, J., Harrington, E.D., Singh, A.H., and Bork, P.

Subjects
Cardiovascular and Metabolic Diseases
Abstract

Given that the number of protein functions on earth is finite, the rapid expansion of biological knowledge and the concomitant exponential increase in the number of protein sequences should, at some point, enable the estimation of the limits of protein function space. The functional coverage of protein sequences can be investigated using computational methods, especially given the massive amount of data being generated by large-scale environmental sequencing (metagenomics). In completely sequenced genomes, the fraction of proteins to which at least some functional features can be assigned has recently risen to as much as approximately 85%. Although this fraction is more uncertain in metagenomics surveys, because of environmental complexities and differences in analysis protocols, our global knowledge of protein functions still appears to be considerable. However, when we consider protein families, continued sequencing seems to yield an ever-increasing number of novel families. Until we reconcile these two views, the limits of protein space will remain obscured.

Published
2007

81. Prediction of effective genome size in metagenomic samples

Author

Raes, J, Korbel, J O, Lercher, M J, von Mering, C, Bork, P, University of Zurich, and Bork, P

Subjects
1307 Cell Biology, 1105 Ecology, Evolution, Behavior and Systematics, 1311 Genetics, Cardiovascular and Metabolic Diseases, 570 Life sciences, biology, U7 Systems Biology / Functional Genomics, 10124 Institute of Molecular Life Sciences
Abstract

We introduce a novel computational approach to predict effective genome size (EGS - a measure that includes multiple plasmid copies, inserted sequences and associated phages and viruses) from short sequencing reads of environmental genomics (or metagenomics) projects. We observe considerable EGS differences between environments and link this with ecological complexity as well as species composition (i.e. eukaryotes). For example, we estimate EGS in a complex, organism-dense farm soil sample at about 6.3 Mb whereas that of the bacteria therein is only 4.7 Mb; for bacteria in a nutrient-poor, organism-sparse ocean surface water sample, EGS is as low as 1.6 Mb. The method also permits evaluation of completion status and assembly bias in single-genome sequencing projects.

Published
2007
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82. The TORNADO1 and TORNADO2 genes function in several patterning processes during early leaf development in Arabidopsis thaliana

Author

Cnops G., Neyt P., Raes J., Petrarulo M., Nelissen H., Malenica Nenad, Luschnig C., Tietz O., Ditengou F., Palme K., Azmi A., Prinsen E., and Van Lijsebettens M.

Subjects
leaf development, leaf patterning, fungi, food and beverages
Abstract

In multicellular organisms, patterning is a process that generates axes in the primary body plan, creates domains upon organ formation, and finally leads to differentiation into tissues and cell types. We identified the Arabidopsis thaliana TORNADO1 (TRN1) and TRN2 genes and their role in leaf patterning processes such as lamina venation, symmetry, and lateral growth. In trn mutants, the leaf venation network had a severely reduced complexity: incomplete loops, no tertiary or quaternary veins, and vascular islands. The leaf laminas were asymmetric and narrow because of a severely reduced cell number. We postulate that the imbalance between cell proliferation and cell differentiation and the altered auxin distribution in both trn mutants cause asymmetric leaf growth and aberrant venation patterning. TRN1 and TRN2 were epistatic to ASYMMETRIC LEAVES1 with respect to leaf asymmetry, consistent with their expression in the shoot apical meristem and leaf primordia. TRN1 codes for a large plant-specific protein with conserved domains also found in a variety of signaling proteins, whereas TRN2 encodes a transmembrane protein of the tetraspanin family whose phylogenetic tree is presented. Double mutant analysis showed that TRN1 and TRN2 act in the same pathway.

Published
2006

84. Preface

Author

Bijleveld, H., Mariën, P., Paquier, Philippe, Raes, J., and van Borsel, J.

Published
2001

85. The human small intestinal microbiota is driven by rapid uptake and conversion of simple carbohydrates.

Author

Zoetendal, E.G., Raes, J., Bogert, B. van den, Arumugam, M., Booijink, C.C., Troost, F.J., Bork, P., Wels, M.W., Vos, W.M. de, Kleerebezem, M., Zoetendal, E.G., Raes, J., Bogert, B. van den, Arumugam, M., Booijink, C.C., Troost, F.J., Bork, P., Wels, M.W., Vos, W.M. de, and Kleerebezem, M.

Abstract

01 juli 2012, Item does not contain fulltext, The human gastrointestinal tract (GI tract) harbors a complex community of microbes. The microbiota composition varies between different locations in the GI tract, but most studies focus on the fecal microbiota, and that inhabiting the colonic mucosa. Consequently, little is known about the microbiota at other parts of the GI tract, which is especially true for the small intestine because of its limited accessibility. Here we deduce an ecological model of the microbiota composition and function in the small intestine, using complementing culture-independent approaches. Phylogenetic microarray analyses demonstrated that microbiota compositions that are typically found in effluent samples from ileostomists (subjects without a colon) can also be encountered in the small intestine of healthy individuals. Phylogenetic mapping of small intestinal metagenome of three different ileostomy effluent samples from a single individual indicated that Streptococcus sp., Escherichia coli, Clostridium sp. and high G+C organisms are most abundant in the small intestine. The compositions of these populations fluctuated in time and correlated to the short-chain fatty acids profiles that were determined in parallel. Comparative functional analysis with fecal metagenomes identified functions that are overrepresented in the small intestine, including simple carbohydrate transport phosphotransferase systems (PTS), central metabolism and biotin production. Moreover, metatranscriptome analysis supported high level in-situ expression of PTS and carbohydrate metabolic genes, especially those belonging to Streptococcus sp. Overall, our findings suggest that rapid uptake and fermentation of available carbohydrates contribute to maintaining the microbiota in the human small intestine.

Published
2012

87. A Holistic Approach to Marine Eco-Systems Biology

Author

Karsenti, E, Acinas, S, Bork, P, Bowler, C, De Vargas, C, Raes, J, Sullivan, M, Arendt, D, Benzoni, F, Claverie, J, Follows, M, Gorsky, G, Hingamp, P, Iudicone, D, Jaillon, O, Kandels-Lewis, S, Krzic, U, Not, F, Ogata, H, Pesant, S, Reynaud, E, Sardet, C, Sieracki, M, Speich, S, Velayoudon, D, Weissenbach, J, Wincker, P, Acinas, SG, Claverie, J-M, Reynaud, EG, Sieracki, ME, Karsenti, E, Acinas, S, Bork, P, Bowler, C, De Vargas, C, Raes, J, Sullivan, M, Arendt, D, Benzoni, F, Claverie, J, Follows, M, Gorsky, G, Hingamp, P, Iudicone, D, Jaillon, O, Kandels-Lewis, S, Krzic, U, Not, F, Ogata, H, Pesant, S, Reynaud, E, Sardet, C, Sieracki, M, Speich, S, Velayoudon, D, Weissenbach, J, Wincker, P, Acinas, SG, Claverie, J-M, Reynaud, EG, and Sieracki, ME

Published
2011

89. Research work of the Belgian Study Group on Voice Disorders 1996

Author

Van de Heyning, Paul, Remacle, M., van Cauwenberge, P., De Bodt, Marc, Wuyts, Floris, Moerman, M., van Lierde, K., Raes, J., Heylen, Louis, Sasserath, M., de Hassonville, A. Henri, Clement, P.A.R., Qiu, J., Molenberghs, Geert, Bruckers, L., Mertens, F., Pattyn, J., and Millet, B.

Published
1996

95. Preface

Author

Bijleveld, Henny, Mariën, Peter, Paquier, Philippe, Raes, J, van Borsel, J., Bijleveld, Henny, Mariën, Peter, Paquier, Philippe, Raes, J, and van Borsel, J.

Abstract

info:eu-repo/semantics/published

Published
2001

96. An analysis of spontaneous conversational speech fluency in children with acquired aphasia.

Author

Van Dongen, Hugo R, Paquier, Philippe, Raes, J, Creten, W L, Van Dongen, Hugo R, Paquier, Philippe, Raes, J, and Creten, W L

Abstract

We report on an instrumental analysis of spontaneous conversational speech (SCS) fluency in acquired childhood aphasia (ACA). Tape-recorded SCS samples of 25 children with ACA (clinical judgment: 12 nonfluent and 13 fluent), and of 12 dysarthric and 12 nonaphasic and nondysarthric right hemisphere injured children were analysed in order to: (1) investigate whether a more refined analysis can objectively contribute to the differentiation of patients who were labelled as fluent or nonfluent on the basis of a clinical judgment: (2) verify whether an instrumental analysis of phonation duration does confirm the subjective estimation of verbal rate (i.e. the number of words produced in a unit of time) in groups of children with acquired neurogenic speech/language disorders frequently met in clinical practice. The results are: (1) phonation rate (i.e. the vocalization percentage) seems to represent an adequate variable to distinguish clinically diagnosed nonfluent aphasic children from speech/language impaired children belonging to other clinical groups of acquired neurogenic speech/language disorders; (2) the verbal rate is highly correlated to the phonation rate in all investigated groups except the dysarthric one. We suggest the instrumental method discussed here might contribute to the differential diagnosis between dysarthric and aphasic disturbances in the acute stage of the disease. Concerning the study of ACA, the main issue of the present investigation is that an objective fluency measurement has succeeded in identifying aphasic children who obviously do not fit in with the standard doctrine on ACA, which claims that ACA is invariably nonfluent irrespective of lesion location., Journal Article, Research Support, Non-U.S. Gov't, info:eu-repo/semantics/published

Published
1994

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