15 results on '"Ripoll-Cladellas, Aida"'
Search Results
2. Antibody signatures against viruses and microbiome reflect past and chronic exposures and associate with aging and inflammation
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Andreu-Sánchez, Sergio, Ripoll-Cladellas, Aida, Culinscaia, Anna, Bulut, Ozlem, Bourgonje, Arno R., Netea, Mihai G., Lansdorp, Peter, Aubert, Geraldine, Bonder, Marc Jan, Franke, Lude, Vogl, Thomas, van der Wijst, Monique G.P., Melé, Marta, Van Baarle, Debbie, Fu, Jingyuan, and Zhernakova, Alexandra
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- 2024
- Full Text
- View/download PDF
3. Correction to: The genome sequence of the grape phylloxera provides insights into the evolution, adaptation, and invasion routes of an iconic pest
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Rispe, Claude, Legeai, Fabrice, Nabity, Paul D, Fernández, Rosa, Arora, Arinder K, Baa-Puyoulet, Patrice, Banfill, Celeste R, Bao, Leticia, Barberà, Miquel, Bouallègue, Maryem, Bretaudeau, Anthony, Brisson, Jennifer A, Calevro, Federica, Capy, Pierre, Catrice, Olivier, Chertemps, Thomas, Couture, Carole, Delière, Laurent, Douglas, Angela E, Dufault-Thompson, Keith, Escuer, Paula, Feng, Honglin, Forneck, Astrid, Gabaldón, Toni, Guigó, Roderic, Hilliou, Frédérique, Hinojosa-Alvarez, Silvia, Hsiao, Yi-min, Hudaverdian, Sylvie, Jacquin-Joly, Emmanuelle, James, Edward B, Johnston, Spencer, Joubard, Benjamin, Le Goff, Gaëlle, Le Trionnaire, Gaël, Librado, Pablo, Liu, Shanlin, Lombaert, Eric, Lu, Hsiao-ling, Maïbèche, Martine, Makni, Mohamed, Marcet-Houben, Marina, Martínez-Torres, David, Meslin, Camille, Montagné, Nicolas, Moran, Nancy A, Papura, Daciana, Parisot, Nicolas, Rahbé, Yvan, Lopes, Mélanie Ribeiro, Ripoll-Cladellas, Aida, Robin, Stéphanie, Roques, Céline, Roux, Pascale, Rozas, Julio, Sánchez-Gracia, Alejandro, Sánchez-Herrero, Jose F, Santesmasses, Didac, Scatoni, Iris, Serre, Rémy-Félix, Tang, Ming, Tian, Wenhua, Umina, Paul A, van Munster, Manuella, Vincent-Monégat, Carole, Wemmer, Joshua, Wilson, Alex CC, Zhang, Ying, Zhao, Chaoyang, Zhao, Jing, Zhao, Serena, Zhou, Xin, Delmotte, François, and Tagu, Denis
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Developmental Biology - Abstract
An amendment to this paper has been published and can be accessed via the original article.
- Published
- 2020
4. The genome sequence of the grape phylloxera provides insights into the evolution, adaptation, and invasion routes of an iconic pest
- Author
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Rispe, Claude, Legeai, Fabrice, Nabity, Paul D, Fernández, Rosa, Arora, Arinder K, Baa-Puyoulet, Patrice, Banfill, Celeste R, Bao, Leticia, Barberà, Miquel, Bouallègue, Maryem, Bretaudeau, Anthony, Brisson, Jennifer A, Calevro, Federica, Capy, Pierre, Catrice, Olivier, Chertemps, Thomas, Couture, Carole, Delière, Laurent, Douglas, Angela E, Dufault-Thompson, Keith, Escuer, Paula, Feng, Honglin, Forneck, Astrid, Gabaldón, Toni, Guigó, Roderic, Hilliou, Frédérique, Hinojosa-Alvarez, Silvia, Hsiao, Yi-min, Hudaverdian, Sylvie, Jacquin-Joly, Emmanuelle, James, Edward B, Johnston, Spencer, Joubard, Benjamin, Le Goff, Gaëlle, Le Trionnaire, Gaël, Librado, Pablo, Liu, Shanlin, Lombaert, Eric, Lu, Hsiao-ling, Maïbèche, Martine, Makni, Mohamed, Marcet-Houben, Marina, Martínez-Torres, David, Meslin, Camille, Montagné, Nicolas, Moran, Nancy A, Papura, Daciana, Parisot, Nicolas, Rahbé, Yvan, Lopes, Mélanie Ribeiro, Ripoll-Cladellas, Aida, Robin, Stéphanie, Roques, Céline, Roux, Pascale, Rozas, Julio, Sánchez-Gracia, Alejandro, Sánchez-Herrero, Jose F, Santesmasses, Didac, Scatoni, Iris, Serre, Rémy-Félix, Tang, Ming, Tian, Wenhua, Umina, Paul A, van Munster, Manuella, Vincent-Monégat, Carole, Wemmer, Joshua, Wilson, Alex CC, Zhang, Ying, Zhao, Chaoyang, Zhao, Jing, Zhao, Serena, Zhou, Xin, Delmotte, François, and Tagu, Denis
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Biological Sciences ,Genetics ,Human Genome ,Infection ,Climate Action ,Adaptation ,Biological ,Animal Distribution ,Animals ,Biological Evolution ,Genome ,Insect ,Hemiptera ,Introduced Species ,Vitis ,Arthropod genomes ,Daktulosphaira vitifoliae ,Gene duplications ,Host plant interactions ,Effectors ,Biological invasions ,Developmental Biology ,Biological sciences - Abstract
BackgroundAlthough native to North America, the invasion of the aphid-like grape phylloxera Daktulosphaira vitifoliae across the globe altered the course of grape cultivation. For the past 150 years, viticulture relied on grafting-resistant North American Vitis species as rootstocks, thereby limiting genetic stocks tolerant to other stressors such as pathogens and climate change. Limited understanding of the insect genetics resulted in successive outbreaks across the globe when rootstocks failed. Here we report the 294-Mb genome of D. vitifoliae as a basic tool to understand host plant manipulation, nutritional endosymbiosis, and enhance global viticulture.ResultsUsing a combination of genome, RNA, and population resequencing, we found grape phylloxera showed high duplication rates since its common ancestor with aphids, but similarity in most metabolic genes, despite lacking obligate nutritional symbioses and feeding from parenchyma. Similarly, no enrichment occurred in development genes in relation to viviparity. However, phylloxera evolved > 2700 unique genes that resemble putative effectors and are active during feeding. Population sequencing revealed the global invasion began from the upper Mississippi River in North America, spread to Europe and from there to the rest of the world.ConclusionsThe grape phylloxera genome reveals genetic architecture relative to the evolution of nutritional endosymbiosis, viviparity, and herbivory. The extraordinary expansion in effector genes also suggests novel adaptations to plant feeding and how insects induce complex plant phenotypes, for instance galls. Finally, our understanding of the origin of this invasive species and its genome provide genetics resources to alleviate rootstock bottlenecks restricting the advancement of viticulture.
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- 2020
5. The landscape of expression and alternative splicing variation across human traits
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García-Pérez, Raquel, Ramirez, Jose Miguel, Ripoll-Cladellas, Aida, Chazarra-Gil, Ruben, Oliveros, Winona, Soldatkina, Oleksandra, Bosio, Mattia, Rognon, Paul Joris, Capella-Gutierrez, Salvador, Calvo, Miquel, Reverter, Ferran, Guigó, Roderic, Aguet, François, Ferreira, Pedro G., Ardlie, Kristin G., and Melé, Marta
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- 2023
- Full Text
- View/download PDF
6. Genetic, parental and lifestyle factors influence telomere length
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Andreu-Sánchez, Sergio, Aubert, Geraldine, Ripoll-Cladellas, Aida, Henkelman, Sandra, Zhernakova, Daria V., Sinha, Trishla, Kurilshikov, Alexander, Cenit, Maria Carmen, Jan Bonder, Marc, Franke, Lude, Wijmenga, Cisca, Fu, Jingyuan, van der Wijst, Monique G. P., Melé, Marta, Lansdorp, Peter, and Zhernakova, Alexandra
- Published
- 2022
- Full Text
- View/download PDF
7. Antibody signatures against viruses and microbiome reflect past and chronic exposures and associate with aging and inflammation
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Barcelona Supercomputing Center, Andreu Sanchez, Sergio, Ripoll Cladellas, Aida, Culinscaia, Anna, Bulut, Ozlem, Bourgonje, Arno R., Melé, Marta, Barcelona Supercomputing Center, Andreu Sanchez, Sergio, Ripoll Cladellas, Aida, Culinscaia, Anna, Bulut, Ozlem, Bourgonje, Arno R., and Melé, Marta
- Abstract
Encounters with pathogens and other molecules can imprint long-lasting effects on our immune system, influencing future physiological outcomes. Given the wide range of microbes to which humans are exposed, their collective impact on health is not fully understood. To explore relations between exposures and biological aging and inflammation, we profiled an antibody-binding repertoire against 2,815 microbial, viral, and environmental peptides in a population cohort of 1,443 participants. Utilizing antibody-binding as a proxy for past exposures, we investigated their impact on biological aging, cell composition, and inflammation. Immune response against cytomegalovirus (CMV), rhinovirus, and gut bacteria relates with telomere length. Single-cell expression measurements identified an effect of CMV infection on the transcriptional landscape of subpopulations of CD8 and CD4 T-cells. This examination of the relationship between microbial exposures and biological aging and inflammation highlights a role for chronic infections (CMV and Epstein-Barr virus) and common pathogens (rhinoviruses and adenovirus C)., Peer Reviewed, "Article signat per 16 autors/es: Sergio Andreu-Sánchez, Aida Ripoll-Cladellas, Anna Culinscaia, Ozlem Bulut, Arno R. Bourgonje, Mihai G. Netea, Peter Lansdorp,Geraldine Aubert, Marc Jan Bonder, Lude Franke, Thomas Vogl, Monique G.P. van der Wijst, Marta Melé, Debbie Van Baarle, Jingyuan Fu , Alexandra Zhernakova", Postprint (published version)
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- 2024
8. Demuxafy: improvement in droplet assignment by integrating multiple single-cell demultiplexing and doublet detection methods
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Barcelona Supercomputing Center, Neavin, Drew, Senabouth, Anne, Arora, Himanshi, Lee, Jimmy Tsz Hang, Ripoll Cladellas, Aida, Melé, Marta, Barcelona Supercomputing Center, Neavin, Drew, Senabouth, Anne, Arora, Himanshi, Lee, Jimmy Tsz Hang, Ripoll Cladellas, Aida, and Melé, Marta
- Abstract
Recent innovations in single-cell RNA-sequencing (scRNA-seq) provide the technology to investigate biological questions at cellular resolution. Pooling cells from multiple individuals has become a common strategy, and droplets can subsequently be assigned to a specific individual by leveraging their inherent genetic differences. An implicit challenge with scRNA-seq is the occurrence of doublets—droplets containing two or more cells. We develop Demuxafy, a framework to enhance donor assignment and doublet removal through the consensus intersection of multiple demultiplexing and doublet detecting methods. Demuxafy significantly improves droplet assignment by separating singlets from doublets and classifying the correct individual., This work was funded by the National Health and Medical Research Council (NHMRC) Investigator grant (1175781), and funding from the Goodridge foundation. J.E.P is also supported by a fellowship from the Fok Foundation., Peer Reviewed, "Article signat per 13 autors/es: Drew Neavin, Anne Senabouth, Himanshi Arora, Jimmy Tsz Hang Lee, Aida Ripoll-Cladellas, sc-eQTLGen Consortium, Lude Franke, Shyam Prabhakar, Chun Jimmie Ye, Davis J. McCarthy, Marta Melé, Martin Hemberg & Joseph E. Powel", Postprint (published version)
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- 2024
9. The landscape of expression and alternative splicing variation across human traits
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Universitat Politècnica de Catalunya. Doctorat en Estadística i Investigació Operativa, García Pérez, Raquel, Ramírez Cardeñosa, José Miguel, Ripoll Cladellas, Aida, Chazarra Gil, Rubén, Oliveros Díez, Winona, Soldatkina, Oleksandra, Bosio, Mattia, Rognon, Paul Joris Denis, Capella Gutiérrez, Salvador, Calvo Llorca, Miguel, Reverter Comes, Ferran, Guigo Serra, Roderic, Aguet, François, Ferreira, Pedro G., Ardlie, Kristin G., Mele Messeguer, Marta, Universitat Politècnica de Catalunya. Doctorat en Estadística i Investigació Operativa, García Pérez, Raquel, Ramírez Cardeñosa, José Miguel, Ripoll Cladellas, Aida, Chazarra Gil, Rubén, Oliveros Díez, Winona, Soldatkina, Oleksandra, Bosio, Mattia, Rognon, Paul Joris Denis, Capella Gutiérrez, Salvador, Calvo Llorca, Miguel, Reverter Comes, Ferran, Guigo Serra, Roderic, Aguet, François, Ferreira, Pedro G., Ardlie, Kristin G., and Mele Messeguer, Marta
- Abstract
Understanding the consequences of individual transcriptome variation is fundamental to deciphering human biology and disease. We implement a statistical framework to quantify the contributions of 21 individual traits as drivers of gene expression and alternative splicing variation across 46 human tissues and 781 individuals from the Genotype-Tissue Expression project. We demonstrate that ancestry, sex, age, and BMI make additive and tissue-specific contributions to expression variability, whereas interactions are rare. Variation in splicing is dominated by ancestry and is under genetic control in most tissues, with ribosomal proteins showing a strong enrichment of tissue-shared splicing events. Our analyses reveal a systemic contribution of types 1 and 2 diabetes to tissue transcriptome variation with the strongest signal in the nerve, where histopathology image analysis identifies novel genes related to diabetic neuropathy. Our multi-tissue and multi-trait approach provides an extensive characterization of the main drivers of human transcriptome variation in health and disease., This study was funded by the HumTranscriptom project with reference PID2019-107937GA-I00. R.G.-P. was supported by a Juan de la Cierva fellowship (FJC2020-044119-I) funded by MCIN/AEI/10.13039/501100011033 and ‘‘European Union NextGenerationEU/PRTR.’’ J.M.R. was supported by a predoctoral fellowship from ‘‘la Caixa’’ Foundation (ID 100010434) with code LCF/BQ/DR22/11950022. A.R.-C. was supported by a Formación Personal Investigador (FPI) fellowship (PRE2019-090193) funded by MCIN/AEI. R.C.-G. was supported by an FPI fellowship (PRE2020-092510) funded by MCIN/AEI. M.M. was supported by a Ramon y Cajal fellowship (RYC-2017-22249)., Peer Reviewed, Postprint (published version)
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- 2023
10. Genetic, parental and lifestyle factors influence telomere length
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Andreu Sánchez, Sergio, Aubert, Geraldine, Ripoll Cladellas, Aida, Henkelman, Sandra, Zhernakova, Daria V, Sinha, Trishla, Kurilshikov, Alexander, Cénit, M. Carmen, Jan Bonder, Marc, Franke, Lude, Wijmenga, Cisca, Fu, Jingyuan, van der Wijst, Monique G P, Melé, Marta, Lansdorp, Peter, Zhernakova, Alexandra, Andreu Sánchez, Sergio, Aubert, Geraldine, Ripoll Cladellas, Aida, Henkelman, Sandra, Zhernakova, Daria V, Sinha, Trishla, Kurilshikov, Alexander, Cénit, M. Carmen, Jan Bonder, Marc, Franke, Lude, Wijmenga, Cisca, Fu, Jingyuan, van der Wijst, Monique G P, Melé, Marta, Lansdorp, Peter, and Zhernakova, Alexandra
- Abstract
The average length of telomere repeats (TL) declines with age and is considered to be a marker of biological ageing. Here, we measured TL in six blood cell types from 1046 individuals using the clinically validated Flow-FISH method. We identified remarkable cell-type-specific variations in TL. Host genetics, environmental, parental and intrinsic factors such as sex, parental age, and smoking are associated to variations in TL. By analysing the genome-wide methylation patterns, we identified that the association of maternal, but not paternal, age to TL is mediated by epigenetics. Single-cell RNA-sequencing data for 62 participants revealed differential gene expression in T-cells. Genes negatively associated with TL were enriched for pathways related to translation and nonsense-mediated decay. Altogether, this study addresses cell-type-specific differences in telomere biology and its relation to cell-type-specific gene expression and highlights how perinatal factors play a role in determining TL, on top of genetics and lifestyle.
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- 2022
11. Genetic, parental and lifestyle factors influence telomere length
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Barcelona Supercomputing Center, Andreu Sánchez, Sergio, Aubert, Geraldine, Ripoll Cladellas, Aida, Henkelman, Sandra, Zhernakova, Daria V., Melé, Marta, Barcelona Supercomputing Center, Andreu Sánchez, Sergio, Aubert, Geraldine, Ripoll Cladellas, Aida, Henkelman, Sandra, Zhernakova, Daria V., and Melé, Marta
- Abstract
The average length of telomere repeats (TL) declines with age and is considered to be a marker of biological ageing. Here, we measured TL in six blood cell types from 1046 individuals using the clinically validated Flow-FISH method. We identified remarkable cell-type-specific variations in TL. Host genetics, environmental, parental and intrinsic factors such as sex, parental age, and smoking are associated to variations in TL. By analysing the genome-wide methylation patterns, we identified that the association of maternal, but not paternal, age to TL is mediated by epigenetics. Single-cell RNA-sequencing data for 62 participants revealed differential gene expression in T-cells. Genes negatively associated with TL were enriched for pathways related to translation and nonsense-mediated decay. Altogether, this study addresses cell-type-specific differences in telomere biology and its relation to cell-type-specific gene expression and highlights how perinatal factors play a role in determining TL, on top of genetics and lifestyle., We thank J. Dekens for management, A. Maatman and M. Platteel for technical support and K. Mc Intyre for English editing. This project was funded by the BBMRI grant for measuring telomere length and by a Rosalind Franklin Fellowship to A.Z. The researchers participated in this project are supported by Netherlands Heart Foundation (IN-CONTROL CVON grants 2012-03 and 2018-27); the Netherlands Organization for Scientific Research (NWO) Gravitation Netherlands Organ-on-Chip Initiative to J.F. and C.W.; NWO Gravitation Exposome-NL (024.004.017) to J.F., A.K. and A.Z.; NWO-VIDI (864.13.013) and NWO-VICI (VI.C.202.022) to J.F.; NWO-VIDI (016.178.056) to A.Z.; NWO-VIDI (917.14.374) to L.F.; NWO-VENI (194.006) to D.V.Z.; NWO-VENI (192.029) to M.W.; NWO Spinoza Prize SPI 92–266 to C.W.; the European Research Council (ERC) (FP7/2007–2013/ERC Advanced Grant 2012-322698) to C.W.; ERC Starting grant 637640 to L.F.; ERC Starting Grant 715772 to A.Z.; ERC Consolidator Grant (grant agreement No. 101001678) to J.F.; and RuG Investment Agenda Grant Personalized Health to C.W. MM work is supported by RYC- 2017-22249 and PID2019-107937GA-I00 grants. T.S. holds scholarships from the Junior Scientific Masterclass, University of Groningen[grant no. 17–34]. AR is funded by a Formación Personal Investigador (FPI) grant [grant no. PRE2019-090193]. The Lifelines Biobank initiative has been made possible by a subsidy from the Dutch Ministry of Health, Welfare and Sport; the Dutch Ministry of Economic Affairs; the University Medical Centre Groningen (UMCG, the Netherlands); the University of Groningen and the Northern Provinces of the Netherlands. The authors wish to acknowledge the services of the Lifelines Cohort Study, the contributing research centres delivering data to Lifelines and all the study participants. Finally, we would like to acknowledge the Genomics Coordination Centre (GCC) at the University Medical College Groningen for the HPC support and the MOLGENIS team for the cloud storag, Peer Reviewed, "Article signat per 16 autors/es: Sergio Andreu-Sánchez, Geraldine Aubert, Aida Ripoll-Cladellas, Sandra Henkelman, Daria V. Zhernakova, Trishla Sinha, Alexander Kurilshikov, Maria Carmen Cenit, Marc Jan Bonder, Lude Franke, Cisca Wijmenga, Jingyuan Fu, Monique G. P. van der Wijst, Marta Melé, Peter Lansdorp & Alexandra Zhernakova", Postprint (published version)
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- 2022
12. Unveiling the transcriptional and cellular landscape of age across human tissues
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Ripoll-Cladellas, Aida, G.P. van der Wijst, Monique, and Melé, Marta|||0000-0001-8874-6453
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Aging ,Aging, Cell type deconvolution, Single-cell transcriptomics ,Cell type deconvolution ,High performance computing ,Informàtica::Arquitectura de computadors [Àrees temàtiques de la UPC] ,Càlcul intensiu (Informàtica) ,Single-cell transcriptomics - Abstract
As the aging population grows progressively around the globe, the need to research and develop strategies to healthy aging is ever more critical and takes on new urgency1. Primary hallmarks of aging include cell autonomous changes linked to epigenetic alterations, genomic instability, telomere attrition and loss of proteostasis (protein homeostasis), which are followed by antagonistic responses such as deregulated nutrient sensing, altered mitochondrial function and cellular senescence. In addition, many functions of the immune system show a progressive decline with age, referred as immunosenescence, leading to a higher risk of infection, cancer, and autoimmune diseases2. Although chronological age is the most powerful risk factor for most chronic diseases, the underlying molecular mechanisms that lead to generalized disease susceptibility are largely unknown. In recent years, rapidly developing high-throughput omics have provided a broader insight, with the identification of a number of longevity-relevant loci based on genome-wide association studies (GWAS) and epigenome analyses. Despite this success, APOE, FOXO3 and 5q33.3 are the only identified loci consistently associated with longevity3. Hence, the complexity of the aging phenomenon, influenced by genetic and epigenetic regulation, post-translational regulation, metabolic regulation, host–microbiome interactions, lifestyle, and many other elements, primarily explains the poor understanding of many of the molecular and cellular processes that underlie the progressive loss of healthy physiology.
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- 2021
13. The genome sequence of the grape phylloxera provides insights into the evolution, adaptation, and invasion routes of an iconic pest
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European Commission, Ministerio de Economía y Competitividad (España), National Institute of Food and Agriculture (US), Miami University, Rispe, Claude, Legeai, Fabrice, Nabity, Paul D., Fernández, Rosa, Arora, Arinder K., Baa-Puyoulet, Patrice, Banfill, Celeste R., Bao, Leticia, Barberà, Miquel, Bouallègue, Maryem, Bretaudeau, Anthony, Brisson, Jennifer A., Calevro, Federica, Capy, Pierre, Catrice, Olivier, Chertemps, Thomas, Couture, Carole, Delière, Laurent, Douglas, Angela E., Dufault-Thompson, Keith, Escuer, Paula, Feng, Honglin, Forneck, Astrid, Gabaldón, Toni, Guigó, Roderic, Hilliou, Fréderique, Hinojosa-Alvarez, Silvia, Hsiao, Yi-min, Hudaverdian, Sylvie, Jacquin-Joly, Emmanuelle, James, Edward B., Johnston, Spencer, Joubard, Benjamin, Le Goff, Gaëlle, Le Trionnaire, Gaël, Librado, Pablo, Liu, Shanlin, Lombaert, Eric, Lu, Hsiao-ling, Maïbèche-Coisne, Martine, Makni, Mohamed, Marcet-Houben, Marina, Martínez-Torres, David, Meslin, Camille, Montagné, Nicolas, Moran, Nancy A., Papura, Daciana, Parisot, Nicolas, Rahbé, Yvan, Ribeiro Lopes, Mélanie, Ripoll-Cladellas, Aida, Robin, Stéphanie, Roques, Céline, Roux, Pascale, Rozas, Julio, Sánchez-Gracia, Alejandro, Sánchez-Herrero, José F., Santesmasses, Didac, Scatoni, Iris, Serre, Rémy-Félix, Tang, Ming, Tian, Wenhua, Umina, Paul A., Munster, Manuella van, Vincent-Monégat, Carole, Wemmer, Joshua, Wilson, Alex C. C., Zhang, Ying, Zhao, Chaoyang, Zhao, Jing, Zhao, Serena, Zhou, Xin, Delmotte, François, Tagu, Denis, European Commission, Ministerio de Economía y Competitividad (España), National Institute of Food and Agriculture (US), Miami University, Rispe, Claude, Legeai, Fabrice, Nabity, Paul D., Fernández, Rosa, Arora, Arinder K., Baa-Puyoulet, Patrice, Banfill, Celeste R., Bao, Leticia, Barberà, Miquel, Bouallègue, Maryem, Bretaudeau, Anthony, Brisson, Jennifer A., Calevro, Federica, Capy, Pierre, Catrice, Olivier, Chertemps, Thomas, Couture, Carole, Delière, Laurent, Douglas, Angela E., Dufault-Thompson, Keith, Escuer, Paula, Feng, Honglin, Forneck, Astrid, Gabaldón, Toni, Guigó, Roderic, Hilliou, Fréderique, Hinojosa-Alvarez, Silvia, Hsiao, Yi-min, Hudaverdian, Sylvie, Jacquin-Joly, Emmanuelle, James, Edward B., Johnston, Spencer, Joubard, Benjamin, Le Goff, Gaëlle, Le Trionnaire, Gaël, Librado, Pablo, Liu, Shanlin, Lombaert, Eric, Lu, Hsiao-ling, Maïbèche-Coisne, Martine, Makni, Mohamed, Marcet-Houben, Marina, Martínez-Torres, David, Meslin, Camille, Montagné, Nicolas, Moran, Nancy A., Papura, Daciana, Parisot, Nicolas, Rahbé, Yvan, Ribeiro Lopes, Mélanie, Ripoll-Cladellas, Aida, Robin, Stéphanie, Roques, Céline, Roux, Pascale, Rozas, Julio, Sánchez-Gracia, Alejandro, Sánchez-Herrero, José F., Santesmasses, Didac, Scatoni, Iris, Serre, Rémy-Félix, Tang, Ming, Tian, Wenhua, Umina, Paul A., Munster, Manuella van, Vincent-Monégat, Carole, Wemmer, Joshua, Wilson, Alex C. C., Zhang, Ying, Zhao, Chaoyang, Zhao, Jing, Zhao, Serena, Zhou, Xin, Delmotte, François, and Tagu, Denis
- Abstract
Background: Although native to North America, the invasion of the aphid-like grape phylloxera Daktulosphaira vitifoliae across the globe altered the course of grape cultivation. For the past 150 years, viticulture relied on grafting-resistant North American Vitis species as rootstocks, thereby limiting genetic stocks tolerant to other stressors such as pathogens and climate change. Limited understanding of the insect genetics resulted in successive outbreaks across the globe when rootstocks failed. Here we report the 294-Mb genome of D. vitifoliae as a basic tool to understand host plant manipulation, nutritional endosymbiosis, and enhance global viticulture. Results: Using a combination of genome, RNA, and population resequencing, we found grape phylloxera showed high duplication rates since its common ancestor with aphids, but similarity in most metabolic genes, despite lacking obligate nutritional symbioses and feeding from parenchyma. Similarly, no enrichment occurred in development genes in relation to viviparity. However, phylloxera evolved > 2700 unique genes that resemble putative effectors and are active during feeding. Population sequencing revealed the global invasion began from the upper Mississippi River in North America, spread to Europe and from there to the rest of the world. Conclusions: The grape phylloxera genome reveals genetic architecture relative to the evolution of nutritional endosymbiosis, viviparity, and herbivory. The extraordinary expansion in effector genes also suggests novel adaptations to plant feeding and how insects induce complex plant phenotypes, for instance galls. Finally, our understanding of the origin of this invasive species and its genome provide genetics resources to alleviate rootstock bottlenecks restricting the advancement of viticulture.
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- 2020
14. Additional file 1 of The genome sequence of the grape phylloxera provides insights into the evolution, adaptation, and invasion routes of an iconic pest
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Rispe, Claude, Legeai, Fabrice, Nabity, Paul D., Fernández, Rosa, Arinder K. Arora, Baa-Puyoulet, Patrice, Banfill, Celeste R., Bao, Leticia, Barberà, Miquel, Maryem Bouallègue, Bretaudeau, Anthony, Brisson, Jennifer A., Calevro, Federica, Capy, Pierre, Catrice, Olivier, Chertemps, Thomas, Couture, Carole, Delière, Laurent, Douglas, Angela E., Dufault-Thompson, Keith, Escuer, Paula, Honglin Feng, Forneck, Astrid, Gabaldón, Toni, Guigó, Roderic, Hilliou, Frédérique, Hinojosa-Alvarez, Silvia, Hsiao, Yi-Min, Hudaverdian, Sylvie, Jacquin-Joly, Emmanuelle, James, Edward B., Johnston, Spencer, Joubard, Benjamin, Goff, Gaëlle Le, Trionnaire, Gaël Le, Librado, Pablo, Shanlin Liu, Lombaert, Eric, Hsiao-Ling Lu, Maïbèche, Martine, Makni, Mohamed, Marcet-Houben, Marina, Martínez-Torres, David, Meslin, Camille, Montagné, Nicolas, Moran, Nancy A., Papura, Daciana, Parisot, Nicolas, Rahbé, Yvan, Lopes, Mélanie Ribeiro, Ripoll-Cladellas, Aida, Robin, Stéphanie, Roques, Céline, Roux, Pascale, Rozas, Julio, Sánchez-Gracia, Alejandro, Sánchez-Herrero, Jose F., Didac Santesmasses, Scatoni, Iris, Rémy-Félix Serre, Tang, Ming, Wenhua Tian, Umina, Paul A., Munster, Manuella Van, Vincent-Monégat, Carole, Wemmer, Joshua, Wilson, Alex C. C., Zhang, Ying, Chaoyang Zhao, Zhao, Jing, Zhao, Serena, Zhou, Xin, Delmotte, François, and Tagu, Denis
- Subjects
2. Zero hunger - Abstract
Additional file 1: Figures. S1-S22, Table S1-S20, Methods and Results. Figure S1. Mitochondrial genome view of grape phylloxera. Figure S2. Proportion of transposable elements (TE) in the genome. Figure S3. GO terms of phylloxera-specific genes. Figure S4. Enriched GO terms in the phylloxera genome with and without TEs. Figure S5. Gene gain/loss at different nodes or branches. Figure S6. Species phylogenetic tree based on insect genomes and the transcriptomes of Planoccoccus citri and Adelges tsugae. Figure S7. Diagram of the gap-filling and annotation process. Figure S8. Urea cycle in D. vitifoliae and A. pisum. Figure S9. IMD immune pathway in D. vitifoliae.Figure S10. Phylogenetic tree of RR-1 cuticular proteins.Figure S11. Phylogenetic tree of RR-2 cuticular proteins.Figure S12. Comparison of miRNAs in D. vitifoliae and other insect genomes. Figure S13. Phylogenetic tree of aquaporin protein sequences. Figure S14. Comparison of the phylloxera PER protein with other insects. Figure S15. Amino acid alignment of PTTH amino acid sequences. Figure S16. Phylogeny of hemipteran ORs. Figure S17. Phylogeny of hemipteran GRs. Figure S18. Phylogenetic analysis of OBPs. Figure S19. Phylogenetic analysis of CSPs. Figure S20. Phylogenetic analysis of NPC2s. Figure S21. Distribution of cluster sizes of putative effectors. Figure S22. Physical distribution of the three largest clusters of effectors. Table S1. Genes of bacterial and fungal origin. Table S2. Statistics on TEs. Table S3. GO enrichment of genes duplicated at different ancestral nodes. Table S4. Metabolic gaps in the D. vitifoliae reaction network. Table S5. Functional annotation of metabolic genes. Table S6. Genes of the TOLL pathway. Table S7. Genes of the IMD pathway. Table S8. Statistics on cuticular proteins. Table S9. Developmental genes in D. vitifoliae and A. pisum. Table S10. miRNAs. Table S11. Clock-related genes. Table S12. List of ORs and GRs. Table S13. Number of OBPs, CSPs and NPC2s. Table S14. List of Cytochromes P450. Table S15. List of genes involved in detoxification. Table S16. Effector genes with predicted domains and their corresponding functions. Table S17. Statistics on sequence reads and SRA accessions used for the reference genome. Table S18. List of species used to study gene expansions. Table S19. Sampling sites and SRA used for population genomics analyses. Table S20. Prior distribution of parameters used for ABC modeling of invasion routes.
15. Additional file 1 of The genome sequence of the grape phylloxera provides insights into the evolution, adaptation, and invasion routes of an iconic pest
- Author
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Rispe, Claude, Legeai, Fabrice, Nabity, Paul D., Fernández, Rosa, Arinder K. Arora, Baa-Puyoulet, Patrice, Banfill, Celeste R., Bao, Leticia, Barberà, Miquel, Maryem Bouallègue, Bretaudeau, Anthony, Brisson, Jennifer A., Calevro, Federica, Capy, Pierre, Catrice, Olivier, Chertemps, Thomas, Couture, Carole, Delière, Laurent, Douglas, Angela E., Dufault-Thompson, Keith, Escuer, Paula, Honglin Feng, Forneck, Astrid, Gabaldón, Toni, Guigó, Roderic, Hilliou, Frédérique, Hinojosa-Alvarez, Silvia, Hsiao, Yi-Min, Hudaverdian, Sylvie, Jacquin-Joly, Emmanuelle, James, Edward B., Johnston, Spencer, Joubard, Benjamin, Goff, Gaëlle Le, Trionnaire, Gaël Le, Librado, Pablo, Shanlin Liu, Lombaert, Eric, Hsiao-Ling Lu, Maïbèche, Martine, Makni, Mohamed, Marcet-Houben, Marina, Martínez-Torres, David, Meslin, Camille, Montagné, Nicolas, Moran, Nancy A., Papura, Daciana, Parisot, Nicolas, Rahbé, Yvan, Lopes, Mélanie Ribeiro, Ripoll-Cladellas, Aida, Robin, Stéphanie, Roques, Céline, Roux, Pascale, Rozas, Julio, Sánchez-Gracia, Alejandro, Sánchez-Herrero, Jose F., Didac Santesmasses, Scatoni, Iris, Rémy-Félix Serre, Tang, Ming, Wenhua Tian, Umina, Paul A., Munster, Manuella Van, Vincent-Monégat, Carole, Wemmer, Joshua, Wilson, Alex C. C., Zhang, Ying, Chaoyang Zhao, Zhao, Jing, Zhao, Serena, Zhou, Xin, Delmotte, François, and Tagu, Denis
- Subjects
2. Zero hunger - Abstract
Additional file 1: Figures. S1-S22, Table S1-S20, Methods and Results. Figure S1. Mitochondrial genome view of grape phylloxera. Figure S2. Proportion of transposable elements (TE) in the genome. Figure S3. GO terms of phylloxera-specific genes. Figure S4. Enriched GO terms in the phylloxera genome with and without TEs. Figure S5. Gene gain/loss at different nodes or branches. Figure S6. Species phylogenetic tree based on insect genomes and the transcriptomes of Planoccoccus citri and Adelges tsugae. Figure S7. Diagram of the gap-filling and annotation process. Figure S8. Urea cycle in D. vitifoliae and A. pisum. Figure S9. IMD immune pathway in D. vitifoliae.Figure S10. Phylogenetic tree of RR-1 cuticular proteins.Figure S11. Phylogenetic tree of RR-2 cuticular proteins.Figure S12. Comparison of miRNAs in D. vitifoliae and other insect genomes. Figure S13. Phylogenetic tree of aquaporin protein sequences. Figure S14. Comparison of the phylloxera PER protein with other insects. Figure S15. Amino acid alignment of PTTH amino acid sequences. Figure S16. Phylogeny of hemipteran ORs. Figure S17. Phylogeny of hemipteran GRs. Figure S18. Phylogenetic analysis of OBPs. Figure S19. Phylogenetic analysis of CSPs. Figure S20. Phylogenetic analysis of NPC2s. Figure S21. Distribution of cluster sizes of putative effectors. Figure S22. Physical distribution of the three largest clusters of effectors. Table S1. Genes of bacterial and fungal origin. Table S2. Statistics on TEs. Table S3. GO enrichment of genes duplicated at different ancestral nodes. Table S4. Metabolic gaps in the D. vitifoliae reaction network. Table S5. Functional annotation of metabolic genes. Table S6. Genes of the TOLL pathway. Table S7. Genes of the IMD pathway. Table S8. Statistics on cuticular proteins. Table S9. Developmental genes in D. vitifoliae and A. pisum. Table S10. miRNAs. Table S11. Clock-related genes. Table S12. List of ORs and GRs. Table S13. Number of OBPs, CSPs and NPC2s. Table S14. List of Cytochromes P450. Table S15. List of genes involved in detoxification. Table S16. Effector genes with predicted domains and their corresponding functions. Table S17. Statistics on sequence reads and SRA accessions used for the reference genome. Table S18. List of species used to study gene expansions. Table S19. Sampling sites and SRA used for population genomics analyses. Table S20. Prior distribution of parameters used for ABC modeling of invasion routes.
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