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2. Antibody signatures against viruses and microbiome reflect past and chronic exposures and associate with aging and inflammation

Author

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, and Alexandra Zhernakova

Subjects
Immunology, Proteomics, Genomics, Science
Abstract

Summary: 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).

Published
2024
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3. Correction to: The genome sequence of the grape phylloxera provides insights into the evolution, adaptation, and invasion routes of an iconic pest

Author

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

Subjects
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

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

Subjects
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.

Published
2020

6. Genetic, parental and lifestyle factors influence telomere length

Author

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, and Alexandra Zhernakova

Subjects
Biology (General), QH301-705.5
Abstract

Deep molecular and phenotypic data highlights the links of human telomere lengths from six different blood cells with genetics, parental phenotypes mediated by epigenetic signals and expression changes at the single cell level.

Published
2022
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7. The genome sequence of the grape phylloxera provides insights into the evolution, adaptation, and invasion routes of an iconic pest

Author

Claude Rispe, Fabrice Legeai, Paul D. Nabity, Rosa Fernández, Arinder K. Arora, Patrice Baa-Puyoulet, Celeste R. Banfill, Leticia Bao, Miquel Barberà, Maryem Bouallègue, Anthony Bretaudeau, Jennifer A. Brisson, Federica Calevro, Pierre Capy, Olivier Catrice, Thomas Chertemps, Carole Couture, Laurent Delière, Angela E. Douglas, Keith Dufault-Thompson, Paula Escuer, Honglin Feng, Astrid Forneck, Toni Gabaldón, Roderic Guigó, Frédérique Hilliou, Silvia Hinojosa-Alvarez, Yi-min Hsiao, Sylvie Hudaverdian, Emmanuelle Jacquin-Joly, Edward B. James, Spencer Johnston, Benjamin Joubard, Gaëlle Le Goff, Gaël Le Trionnaire, Pablo Librado, Shanlin Liu, Eric Lombaert, Hsiao-ling Lu, Martine Maïbèche, Mohamed Makni, Marina Marcet-Houben, David Martínez-Torres, Camille Meslin, Nicolas Montagné, Nancy A. Moran, Daciana Papura, Nicolas Parisot, Yvan Rahbé, Mélanie Ribeiro Lopes, Aida Ripoll-Cladellas, Stéphanie Robin, Céline Roques, Pascale Roux, Julio Rozas, Alejandro Sánchez-Gracia, Jose F. Sánchez-Herrero, Didac Santesmasses, Iris Scatoni, Rémy-Félix Serre, Ming Tang, Wenhua Tian, Paul A. Umina, Manuella van Munster, Carole Vincent-Monégat, Joshua Wemmer, Alex C. C. Wilson, Ying Zhang, Chaoyang Zhao, Jing Zhao, Serena Zhao, Xin Zhou, François Delmotte, and Denis Tagu

Subjects
Arthropod genomes, Daktulosphaira vitifoliae, Gene duplications, Host plant interactions, Effectors, Biological invasions, Biology (General), QH301-705.5
Abstract

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.

Published
2020
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8. Antibody signatures against viruses and microbiome reflect past and chronic exposures and associate with aging and inflammation

Author

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)

Published
2024

9. Demuxafy: improvement in droplet assignment by integrating multiple single-cell demultiplexing and doublet detection methods

Author

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)

Published
2024

10. Correction to: The genome sequence of the grape phylloxera provides insights into the evolution, adaptation, and invasion routes of an iconic pest

Author

Claude Rispe, Fabrice Legeai, Paul D. Nabity, Rosa Fernández, Arinder K. Arora, Patrice Baa-Puyoulet, Celeste R. Banfill, Leticia Bao, Miquel Barberà, Maryem Bouallègue, Anthony Bretaudeau, Jennifer A. Brisson, Federica Calevro, Pierre Capy, Olivier Catrice, Thomas Chertemps, Carole Couture, Laurent Delière, Angela E. Douglas, Keith Dufault-Thompson, Paula Escuer, Honglin Feng, Astrid Forneck, Toni Gabaldón, Roderic Guigó, Frédérique Hilliou, Silvia Hinojosa-Alvarez, Yi-min Hsiao, Sylvie Hudaverdian, Emmanuelle Jacquin-Joly, Edward B. James, Spencer Johnston, Benjamin Joubard, Gaëlle Le Goff, Gaël Le Trionnaire, Pablo Librado, Shanlin Liu, Eric Lombaert, Hsiao-ling Lu, Martine Maïbèche, Mohamed Makni, Marina Marcet-Houben, David Martínez-Torres, Camille Meslin, Nicolas Montagné, Nancy A. Moran, Daciana Papura, Nicolas Parisot, Yvan Rahbé, Mélanie Ribeiro Lopes, Aida Ripoll-Cladellas, Stéphanie Robin, Céline Roques, Pascale Roux, Julio Rozas, Alejandro Sánchez-Gracia, Jose F. Sánchez-Herrero, Didac Santesmasses, Iris Scatoni, Rémy-Félix Serre, Ming Tang, Wenhua Tian, Paul A. Umina, Manuella van Munster, Carole Vincent-Monégat, Joshua Wemmer, Alex C. C. Wilson, Ying Zhang, Chaoyang Zhao, Jing Zhao, Serena Zhao, Xin Zhou, François Delmotte, and Denis Tagu

Subjects
Biology (General), QH301-705.5
Abstract

An amendment to this paper has been published and can be accessed via the original article.

Published
2020
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11. The landscape of expression and alternative splicing variation across human traits

Author

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)

Published
2023

12. Genetic, parental and lifestyle factors influence telomere length

Author

Andreu-Sánchez, Sergio, primary, Aubert, Geraldine, additional, Ripoll-Cladellas, Aida, additional, Henkelman, Sandra, additional, Zhernakova, Daria V., additional, Sinha, Trishla, additional, Kurilshikov, Alexander, additional, Cenit, Maria Carmen, additional, Jan Bonder, Marc, additional, Franke, Lude, additional, Wijmenga, Cisca, additional, Fu, Jingyuan, additional, van der Wijst, Monique G. P., additional, Melé, Marta, additional, Lansdorp, Peter, additional, and Zhernakova, Alexandra, additional

Published
2022
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13. Genetic, parental and lifestyle factors influence telomere length

Author

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.

Published
2022

14. Genetic, parental and lifestyle factors influence telomere length

Author

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)

Published
2022

15. Genetic, parental and lifestyle factors influence telomere length

Author

Aida Ripoll-Cladellas, Marc Jan Bonder, Daria V. Zhernakova, Lude Franke, and Sergio Andreu-Sánchez

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 1,046 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. Coupling these measurements to 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.

Published
2021
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16. Unveiling the transcriptional and cellular landscape of age across human tissues

Author

Ripoll-Cladellas, Aida, G.P. van der Wijst, Monique, and Melé, Marta|||0000-0001-8874-6453

Subjects
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.

Published
2021

17. Correction to: The genome sequence of the grape phylloxera provides insights into the evolution, adaptation, and invasion routes of an iconic pest

Author

Marina Marcet-Houben, Spencer Johnston, Roderic Guigó, Claude Rispe, François Delmotte, Yvan Rahbé, Edward B. James, Carole Couture, Keith Dufault-Thompson, Laurent Delière, Fabrice Legeai, Patrice Baa-Puyoulet, Hsiao-ling Lu, Julio Rozas, Gaël Le Trionnaire, Sylvie Hudaverdian, Rosa Fernández, Chaoyang Zhao, Olivier Catrice, Manuella van Munster, Federica Calevro, Honglin Feng, Alex C.C. Wilson, Arinder K. Arora, Anthony Bretaudeau, Martine Maïbèche, Yi Min Hsiao, Thomas Chertemps, Maryem Bouallègue, Paula Escuer, Jing Zhao, Céline Roques, Aida Ripoll-Cladellas, Pierre Capy, Alejandro Sánchez-Gracia, Wenhua Tian, Paul D. Nabity, Pablo Librado, David Martínez-Torres, Paul A Umina, Joshua Wemmer, Stéphanie Robin, Rémy Félix Serre, Frédérique Hilliou, Nancy A. Moran, Iris Scatoni, Jennifer A. Brisson, Shanlin Liu, Mélanie Ribeiro Lopes, Gaëlle Le Goff, Pascale Roux, Nicolas Montagné, Nicolas Parisot, Jose Francisco Sánchez-Herrero, Silvia Hinojosa-Alvarez, Daciana Papura, Emmanuelle Jacquin-Joly, Ming Tang, Mohamed Makni, Astrid Forneck, Eric Lombaert, Xin Zhou, Ying Zhang, Carole Vincent-Monégat, Leticia Bao, Celeste R. Banfill, Miquel Barberà, Didac Santesmasses, Angela E. Douglas, Benjamin Joubard, Camille Meslin, Denis Tagu, Toni Gabaldón, and Serena Zhao

Subjects
Physiology, Genome, Insect, Adaptation, Biological, Plant Science, General Biochemistry, Genetics and Molecular Biology, Hemiptera, Structural Biology, Animals, Vitis, Phylloxera, lcsh:QH301-705.5, Ecology, Evolution, Behavior and Systematics, Whole genome sequencing, biology, Correction, Cell Biology, Biological Sciences, biology.organism_classification, Biological Evolution, lcsh:Biology (General), Evolutionary biology, PEST analysis, Adaptation, General Agricultural and Biological Sciences, Introduced Species, Animal Distribution, Biotechnology, Developmental Biology
Abstract

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.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.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.

Published
2020

18. The genome sequence of the grape phylloxera provides insights into the evolution, adaptation, and invasion routes of an iconic pest

Author

Ying Zhang, Pascale Roux, Alex C.C. Wilson, Celeste R. Banfill, Roderic Guigó, Ming Tang, Carole Vincent-Monégat, Denis Tagu, Claude Rispe, Yi Min Hsiao, Angela E. Douglas, Daciana Papura, Keith Dufault-Thompson, Frédérique Hilliou, Shanlin Liu, Astrid Forneck, Nicolas Montagné, Eric Lombaert, Fabrice Legeai, Julio Rozas, Gaël Le Trionnaire, Yvan Rahbé, Anthony Bretaudeau, Jing Zhao, Silvia Hinojosa-Alvarez, Maryem Bouallègue, Joshua Wemmer, Stéphanie Robin, Jose Francisco Sánchez-Herrero, Pierre Capy, Federica Calevro, Xin Zhou, David Martínez-Torres, Martine Maïbèche, Patrice Baa-Puyoulet, Marina Marcet-Houben, Gaëlle Le Goff, Aida Ripoll-Cladellas, Mélanie Ribeiro Lopes, Wenhua Tian, Hsiao-ling Lu, François Delmotte, Toni Gabaldón, Arinder K. Arora, Paul A Umina, Rémy Félix Serre, Spencer Johnston, Olivier Catrice, Céline Roques, Paul D. Nabity, Serena Zhao, Pablo Librado, Miquel Barberà, Thomas Chertemps, Emmanuelle Jacquin-Joly, Benjamin Joubard, Leticia Bao, Jennifer A. Brisson, Camille Meslin, Honglin Feng, Manuella van Munster, Paula Escuer, Edward B. James, Rosa Fernández, Chaoyang Zhao, Mohamed Makni, Sylvie Hudaverdian, Nancy A. Moran, Iris Scatoni, Nicolas Parisot, Carole Couture, Didac Santesmasses, Laurent Delière, Alejandro Sánchez-Gracia, Biologie, Epidémiologie et analyse de risque en Santé Animale (BIOEPAR), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Institut de Génétique, Environnement et Protection des Plantes (IGEPP), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-AGROCAMPUS OUEST, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Scalable, Optimized and Parallel Algorithms for Genomics (GenScale), Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-GESTION DES DONNÉES ET DE LA CONNAISSANCE (IRISA-D7), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-CentraleSupélec-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Department of Botany and Plant Sciences [Riverside], University of California [Riverside] (UCR), University of California-University of California, Centre for Genomic Regulation [Barcelona] (CRG), Universitat Pompeu Fabra [Barcelona] (UPF)-Centro Nacional de Analisis Genomico [Barcelona] (CNAG), Institut de Biologia Evolutiva [Barcelona] (IBE / UPF - CSIC), Universitat Pompeu Fabra [Barcelona] (UPF), Cornell University [New York], Biologie Fonctionnelle, Insectes et Interactions (BF2I), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), University of Miami [Coral Gables], Universidad de la República (UDELAR), Universitat de València (UV), Faculté des Sciences Mathématiques, Physiques et Naturelles de Tunis (FST), Université de Tunis El Manar (UTM), University of Rochester [USA], Evolution, génomes, comportement et écologie (EGCE), Institut de Recherche pour le Développement (IRD)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Laboratoire des Interactions Plantes Microbes Environnement (LIPME), Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Institut d'écologie et des sciences de l'environnement de Paris (iEES Paris ), Institut de Recherche pour le Développement (IRD)-Sorbonne Université (SU)-Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12)-Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Santé et agroécologie du vignoble (UMR SAVE), Université de Bordeaux (UB)-Institut des Sciences de la Vigne et du Vin (ISVV)-Ecole Nationale Supérieure des Sciences Agronomiques de Bordeaux-Aquitaine (Bordeaux Sciences Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), University of Rhode Island (URI), Université de Barcelonne, Boyce Thompson Institute [Ithaca], Universität für Bodenkultur Wien [Vienne, Autriche] (BOKU), Center for Genomic Regulation (CRG-UPF), CIBER de Epidemiología y Salud Pública (CIBERESP), Institució Catalana de Recerca i Estudis Avançats (ICREA), Institut Sophia Agrobiotech (ISA), Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Côte d'Azur (UCA), National Taiwan University [Taiwan] (NTU), Chang Gung Memorial Hospital [Taipei] (CGMH), Texas A&M University [College Station], Anthropologie Moléculaire et Imagerie de Synthèse (AMIS), Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS), Beijing Genomics Institute [Shenzhen] (BGI), China Agricultural University (CAU), MingDao University (MDU), University of Texas at Austin [Austin], Trafic et signalisation membranaires chez les bactéries (MTSB), Microbiologie, adaptation et pathogénie (MAP), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS), Biologie et Génétique des Interactions Plante-Parasite (UMR BGPI), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Sciences pour l'environnement (SPE), Centre National de la Recherche Scientifique (CNRS)-Université Pascal Paoli (UPP), Génome et Transcriptome - Plateforme Génomique ( GeT-PlaGe), Plateforme Génome & Transcriptome (GET), Génopole Toulouse Midi-Pyrénées [Auzeville] (GENOTOUL), Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Ecole Nationale Vétérinaire de Toulouse (ENVT), Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Génopole Toulouse Midi-Pyrénées [Auzeville] (GENOTOUL), Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Brigham & Women’s Hospital [Boston] (BWH), Harvard Medical School [Boston] (HMS), University of California, University of Melbourne, INRAE (France), Juan de la Cierva-Incorporacion Fellowship (Government of Spain) : IJCI-2015-26627, Marie Sklodowska-Curie Fellowship : 747607, US National Institute of Food and Agriculture : 12216941, University of Miami Maytag Fellowship from the Department of Biology, William H. Evoy Graduate Research Support Fund from the Department of Biology, Molecular Biosciences Graduate Research, Award from the Department of Biology, Springer Nature, European Project: 764840,IGNITE, École nationale vétérinaire, agroalimentaire et de l'alimentation Nantes-Atlantique (ONIRIS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Université de Rennes (UR)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-INSTITUT AGRO Agrocampus Ouest, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), University of California [Riverside] (UC Riverside), University of California (UC)-University of California (UC), Department of Entomology [CALS], College of Agriculture and Life Sciences [Cornell University] (CALS), Cornell University [New York]-Cornell University [New York], Universidad de la República [Montevideo] (UDELAR), Institut de Recherche pour le Développement (IRD)-Sorbonne Université (SU)-Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Universitat de Barcelona (UB), Universität für Bodenkultur Wien = University of Natural Resources and Life [Vienne, Autriche] (BOKU), Université Nice Sophia Antipolis (1965 - 2019) (UNS), Université de Toulouse (UT)-Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro - Montpellier SupAgro, Université Pascal Paoli (UPP)-Centre National de la Recherche Scientifique (CNRS), Université de Toulouse (UT)-Université de Toulouse (UT)-Ecole Nationale Vétérinaire de Toulouse (ENVT), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Génopole Toulouse Midi-Pyrénées [Auzeville] (GENOTOUL), Université de Toulouse (UT)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), University of California (UC), Centre National de la Recherche Scientifique (CNRS)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon, European Commission, Ministerio de Economía y Competitividad (España), National Institute of Food and Agriculture (US), Miami University, Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-INSTITUT AGRO Agrocampus Ouest, Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes 1 (UR1), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut de Recherche pour le Développement (IRD)-Sorbonne Université (SU)-Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPC)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3), Institut National de la Recherche Agronomique (INRA)-École nationale vétérinaire, agroalimentaire et de l'alimentation Nantes-Atlantique (ONIRIS), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), and Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique)

Subjects
0106 biological sciences, Fil·loxera, Physiology, [SDV]Life Sciences [q-bio], Introduced species, Plant Science, 01 natural sciences, Genome, Gene duplications, Structural Biology, Vitis, lcsh:QH301-705.5, ComputingMilieux_MISCELLANEOUS, 2. Zero hunger, 0303 health sciences, education.field_of_study, Host plant interactions, Endosymbiosis, biology, food and beverages, Biological Sciences, Biological Evolution, General Agricultural and Biological Sciences, Rootstock, Infection, Daktulosphaira vitifoliae, Biotechnology, Research Article, Population, 010603 evolutionary biology, General Biochemistry, Genetics and Molecular Biology, Hemiptera, 03 medical and health sciences, Genetics, Insect pests, Animals, Plagues d'insectes, Adaptation, Biological invasions, Genomes, education, Phylloxera, Ecology, Evolution, Behavior and Systematics, 030304 developmental biology, Obligate, Human Genome, Viticultura, Cell Biology, 15. Life on land, biology.organism_classification, Biological, Effectors, Climate Action, lcsh:Biology (General), 13. Climate action, Evolutionary biology, Arthropod genomes, Introduced Species, Insect, Animal Distribution, Developmental Biology
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., This work has been funded by INRAE (France) and by the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement no. 764840 for the ITN IGNITE project. Rosa Fernandez was funded by a Juan de la Cierva-Incorporación Fellowship (Government of Spain, IJCI-2015-26627) and a Marie Skłodowska-Curie Fellowship (747607). Angela Douglas was supported by the US National Institute of Food and Agriculture Grant 12216941. Honglin Feng was supported by a University of Miami Maytag Fellowship, William H. Evoy Graduate Research Support Fund, and a Molecular Biosciences Graduate Research Award from the Department of Biology.

Published
2020
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19. The genome sequence of the grape phylloxera provides insights into the evolution, adaptation, and invasion routes of an iconic pest

Author

Rispe, C, Legeai, F, Nabity, PD, Fernandez, R, Arora, AK, Baa-Puyoulet, P, Banfill, CR, Bao, L, Barbera, M, Bouallegue, M, Bretaudeau, A, Brisson, JA, Calevro, F, Capy, P, Catrice, O, Chertemps, T, Couture, C, Deliere, L, Douglas, AE, Dufault-Thompson, K, Escuer, P, Feng, H, Forneck, A, Gabaldon, T, Guigo, R, Hilliou, F, Hinojosa-Alvarez, S, Hsiao, Y-M, Hudaverdian, S, Jacquin-Joly, E, James, EB, Johnston, S, Joubard, B, Le Goff, G, Le Trionnaire, G, Librado, P, Liu, S, Lombaert, E, Lu, H-L, Maibeche, M, Makni, M, Marcet-Houben, M, Martinez-Torres, D, Meslin, C, Montagne, N, Moran, NA, Papura, D, Parisot, N, Rahbe, Y, Lopes, MR, Ripoll-Cladellas, A, Robin, S, Roques, C, Roux, P, Rozas, J, Sanchez-Gracia, A, Sanchez-Herrero, JF, Santesmasses, D, Scatoni, I, Serre, R-F, Tang, M, Tian, W, Umina, PA, van Munster, M, Vincent-Monegat, C, Wemmer, J, Wilson, ACC, Zhang, Y, Zhao, C, Zhao, J, Zhao, S, Zhou, X, Delmotte, F, Tagu, D, Rispe, C, Legeai, F, Nabity, PD, Fernandez, R, Arora, AK, Baa-Puyoulet, P, Banfill, CR, Bao, L, Barbera, M, Bouallegue, M, Bretaudeau, A, Brisson, JA, Calevro, F, Capy, P, Catrice, O, Chertemps, T, Couture, C, Deliere, L, Douglas, AE, Dufault-Thompson, K, Escuer, P, Feng, H, Forneck, A, Gabaldon, T, Guigo, R, Hilliou, F, Hinojosa-Alvarez, S, Hsiao, Y-M, Hudaverdian, S, Jacquin-Joly, E, James, EB, Johnston, S, Joubard, B, Le Goff, G, Le Trionnaire, G, Librado, P, Liu, S, Lombaert, E, Lu, H-L, Maibeche, M, Makni, M, Marcet-Houben, M, Martinez-Torres, D, Meslin, C, Montagne, N, Moran, NA, Papura, D, Parisot, N, Rahbe, Y, Lopes, MR, Ripoll-Cladellas, A, Robin, S, Roques, C, Roux, P, Rozas, J, Sanchez-Gracia, A, Sanchez-Herrero, JF, Santesmasses, D, Scatoni, I, Serre, R-F, Tang, M, Tian, W, Umina, PA, van Munster, M, Vincent-Monegat, C, Wemmer, J, Wilson, ACC, Zhang, Y, Zhao, C, Zhao, J, Zhao, S, Zhou, X, Delmotte, F, and Tagu, D

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.

Published
2020

20. The genome sequence of the grape phylloxera provides insights into the evolution, adaptation, and invasion routes of an iconic pest (vol 18, 90, 2020)

Author

Rispe, C, Legeai, F, Nabity, PD, Fernandez, R, Arora, AK, Baa-Puyoulet, P, Banfill, CR, Bao, L, Barbera, M, Bouallegue, M, Bretaudeau, A, Brisson, JA, Calevro, F, Capy, P, Catrice, O, Chertemps, T, Couture, C, Deliere, L, Douglas, AE, Dufault-Thompson, K, Escuer, P, Feng, H, Forneck, A, Gabaldon, T, Guigo, R, Hilliou, F, Hinojosa-Alvarez, S, Hsiao, Y-M, Hudaverdian, S, Jacquin-Joly, E, James, EB, Johnston, S, Joubard, B, Le Goff, G, Le Trionnaire, G, Librado, P, Liu, S, Lombaert, E, Lu, H-L, Maibeche, M, Makni, M, Marcet-Houben, M, Martinez-Torres, D, Meslin, C, Montagne, N, Moran, NA, Papura, D, Parisot, N, Rahbe, Y, Lopes, MR, Ripoll-Cladellas, A, Robin, S, Roques, C, Roux, P, Rozas, J, Sanchez-Gracia, A, Sanchez-Herrero, JF, Santesmasses, D, Scatoni, I, Serre, R-F, Tang, M, Tian, W, Umina, PA, van Munster, M, Vincent-Monegat, C, Wemmer, J, Wilson, ACC, Zhang, Y, Zhao, C, Zhao, J, Zhao, S, Zhou, X, Delmotte, F, Tagu, D, Rispe, C, Legeai, F, Nabity, PD, Fernandez, R, Arora, AK, Baa-Puyoulet, P, Banfill, CR, Bao, L, Barbera, M, Bouallegue, M, Bretaudeau, A, Brisson, JA, Calevro, F, Capy, P, Catrice, O, Chertemps, T, Couture, C, Deliere, L, Douglas, AE, Dufault-Thompson, K, Escuer, P, Feng, H, Forneck, A, Gabaldon, T, Guigo, R, Hilliou, F, Hinojosa-Alvarez, S, Hsiao, Y-M, Hudaverdian, S, Jacquin-Joly, E, James, EB, Johnston, S, Joubard, B, Le Goff, G, Le Trionnaire, G, Librado, P, Liu, S, Lombaert, E, Lu, H-L, Maibeche, M, Makni, M, Marcet-Houben, M, Martinez-Torres, D, Meslin, C, Montagne, N, Moran, NA, Papura, D, Parisot, N, Rahbe, Y, Lopes, MR, Ripoll-Cladellas, A, Robin, S, Roques, C, Roux, P, Rozas, J, Sanchez-Gracia, A, Sanchez-Herrero, JF, Santesmasses, D, Scatoni, I, Serre, R-F, Tang, M, Tian, W, Umina, PA, van Munster, M, Vincent-Monegat, C, Wemmer, J, Wilson, ACC, Zhang, Y, Zhao, C, Zhao, J, Zhao, S, Zhou, X, Delmotte, F, and Tagu, D

Abstract

An amendment to this paper has been published and can be accessed via the original article.

Published
2020

21. The genome sequence of the grape phylloxera provides insights into the evolution, adaptation, and invasion routes of an iconic pest

Author

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.

Published
2020

22. 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

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.

23. 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

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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