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4. Building a Corpus for Biomedical Relation Extraction of Species Mentions

Author

Khettari, Oumaima El, Quiniou, Solen, and Chaffron, Samuel

Subjects
Computer Science - Computation and Language
Abstract

We present a manually annotated corpus, Species-Species Interaction, for extracting meaningful binary relations between species, in biomedical texts, at sentence level, with a focus on the gut microbiota. The corpus leverages PubTator to annotate species in full-text articles after evaluating different Named Entity Recognition species taggers. Our first results are promising for extracting relations between species using BERT and its biomedical variants., Comment: Accepted in BioNLP@ACL 2023

Published
2023

6. Ocean-wide comparisons of mesopelagic planktonic community structures

Author

Rigonato, Janaina, Budinich, Marko, Murillo, Alejandro A., Brandão, Manoela C., Pierella Karlusich, Juan J., Soviadan, Yawouvi Dodji, Gregory, Ann C., Endo, Hisashi, Kokoszka, Florian, Vik, Dean, Henry, Nicolas, Frémont, Paul, Labadie, Karine, Zayed, Ahmed A., Dimier, Céline, Picheral, Marc, Searson, Sarah, Poulain, Julie, Kandels, Stefanie, Pesant, Stéphane, Karsenti, Eric, Bork, Peer, Bowler, Chris, de Vargas, Colomban, Eveillard, Damien, Gehlen, Marion, Iudicone, Daniele, Lombard, Fabien, Ogata, Hiroyuki, Stemmann, Lars, Sullivan, Matthew B., Sunagawa, Shinichi, Wincker, Patrick, Chaffron, Samuel, and Jaillon, Olivier

Published
2023
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10. Community‐Level Responses to Iron Availability in Open Ocean Plankton Ecosystems

Author

Caputi, Luigi, Carradec, Quentin, Eveillard, Damien, Kirilovsky, Amos, Pelletier, Eric, Pierella Karlusich, Juan J, Rocha Jimenez Vieira, Fabio, Villar, Emilie, Chaffron, Samuel, Malviya, Shruti, Scalco, Eleonora, Acinas, Silvia G, Alberti, Adriana, Aury, Jean‐Marc, Benoiston, Anne‐Sophie, Bertrand, Alexis, Biard, Tristan, Bittner, Lucie, Boccara, Martine, Brum, Jennifer R, Brunet, Christophe, Busseni, Greta, Carratalà, Anna, Claustre, Hervé, Coelho, Luis Pedro, Colin, Sébastien, D'Aniello, Salvatore, Da Silva, Corinne, Del Core, Marianna, Doré, Hugo, Gasparini, Stéphane, Kokoszka, Florian, Jamet, Jean‐Louis, Lejeusne, Christophe, Lepoivre, Cyrille, Lescot, Magali, Lima‐Mendez, Gipsi, Lombard, Fabien, Lukeš, Julius, Maillet, Nicolas, Madoui, Mohammed‐Amin, Martinez, Elodie, Mazzocchi, Maria Grazia, Néou, Mario B, Paz‐Yepes, Javier, Poulain, Julie, Ramondenc, Simon, Romagnan, Jean‐Baptiste, Roux, Simon, Salvagio Manta, Daniela, Sanges, Remo, Speich, Sabrina, Sprovieri, Mario, Sunagawa, Shinichi, Taillandier, Vincent, Tanaka, Atsuko, Tirichine, Leila, Trottier, Camille, Uitz, Julia, Veluchamy, Alaguraj, Veselá, Jana, Vincent, Flora, Yau, Sheree, Kandels‐Lewis, Stefanie, Searson, Sarah, Dimier, Céline, Picheral, Marc, Bork, Peer, Boss, Emmanuel, Vargas, Colomban, Follows, Michael J, Grimsley, Nigel, Guidi, Lionel, Hingamp, Pascal, Karsenti, Eric, Sordino, Paolo, Stemmann, Lars, Sullivan, Matthew B, Tagliabue, Alessandro, Zingone, Adriana, Garczarek, Laurence, d'Ortenzio, Fabrizio, Testor, Pierre, Not, Fabrice, d'Alcalà, Maurizio Ribera, Wincker, Patrick, Bowler, Chris, Iudicone, Daniele, Gorsky, Gabriel, and Jaillon, Olivier

Subjects
Genetics, Life Below Water, Atmospheric Sciences, Geochemistry, Oceanography, Meteorology & Atmospheric Sciences
Abstract

Predicting responses of plankton to variations in essential nutrients is hampered by limited in situ measurements, a poor understanding of community composition, and the lack of reference gene catalogs for key taxa. Iron is a key driver of plankton dynamics and, therefore, of global biogeochemical cycles and climate. To assess the impact of iron availability on plankton communities, we explored the comprehensive bio-oceanographic and bio-omics data sets from Tara Oceans in the context of the iron products from two state-of-the-art global scale biogeochemical models. We obtained novel information about adaptation and acclimation toward iron in a range of phytoplankton, including picocyanobacteria and diatoms, and identified whole subcommunities covarying with iron. Many of the observed global patterns were recapitulated in the Marquesas archipelago, where frequent plankton blooms are believed to be caused by natural iron fertilization, although they are not captured in large-scale biogeochemical models. This work provides a proof of concept that integrative analyses, spanning from genes to ecosystems and viruses to zooplankton, can disentangle the complexity of plankton communities and can lead to more accurate formulations of resource bioavailability in biogeochemical models, thus improving our understanding of plankton resilience in a changing environment.

Published
2019

12. Compendium of 530 metagenome-assembled bacterial and archaeal genomes from the polar Arctic Ocean

Author

Royo-Llonch, Marta, Sánchez, Pablo, Ruiz-González, Clara, Salazar, Guillem, Pedrós-Alió, Carlos, Sebastián, Marta, Labadie, Karine, Paoli, Lucas, M. Ibarbalz, Federico, Zinger, Lucie, Churcheward, Benjamin, Chaffron, Samuel, Eveillard, Damien, Karsenti, Eric, Sunagawa, Shinichi, Wincker, Patrick, Karp-Boss, Lee, Bowler, Chris, and Acinas, Silvia G.

Published
2021
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15. Ecological associations distribution modelling of marine plankton at a global scale.

Author

Gaudin, Marinna, Eveillard, Damien, and Chaffron, Samuel

Subjects
BIOGEOCHEMICAL cycles, MARINE plankton, CLIMATE & biogeography, STATISTICAL learning, FOOD chains
Abstract

Marine plankton communities form intricate networks of interacting organisms at the base of the food chain, and play a central role in regulating ocean biogeochemical cycles and climate. However, predicting plankton community shifts in response to climate change remains challenging. While species distribution models are valuable tools for predicting changes in species biogeography under climate change scenarios, they generally overlook the key role of biotic interactions, which can significantly shape ecological processes and ecosystem responses. Here, we introduce a novel statistical framework, association distribution modelling (ADM), designed to model and predict ecological associations distribution in space and time. Applied on a Tara Oceans genome-resolved metagenomics dataset, the present-day biogeography of ADM-inferred marine plankton associations revealed four major biogeographic biomes organized along a latitudinal gradient. We predicted the evolution of these biome-specific communities in response to a climate change scenario, highlighting differential responses to environmental change. Finally, we explored the functional potential of impacted plankton communities, focusing on carbon fixation, outlining the predicted evolution of its geographical distribution and implications for ecosystem function. This article is part of the theme issue 'Connected interactions: enriching food web research by spatial and social interactions'. [ABSTRACT FROM AUTHOR]

Published
2024
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16. Plankton networks driving carbon export in the oligotrophic ocean

Author

Guidi, Lionel, Chaffron, Samuel, Bittner, Lucie, Eveillard, Damien, Larhlimi, Abdelhalim, Roux, Simon, Darzi, Youssef, Audic, Stephane, Berline, Léo, Brum, Jennifer R, Coelho, Luis Pedro, Espinoza, Julio Cesar Ignacio, Malviya, Shruti, Sunagawa, Shinichi, Dimier, Céline, Kandels-Lewis, Stefanie, Picheral, Marc, Poulain, Julie, Searson, Sarah, Stemmann, Lars, Not, Fabrice, Hingamp, Pascal, Speich, Sabrina, Follows, Mick, Karp-Boss, Lee, Boss, Emmanuel, Ogata, Hiroyuki, Pesant, Stephane, Weissenbach, Jean, Wincker, Patrick, Acinas, Silvia G, Bork, Peer, de Vargas, Colomban, Iudicone, Daniele, Sullivan, Matthew B, Raes, Jeroen, Karsenti, Eric, Bowler, Chris, and Gorsky, Gabriel

Subjects
Aquatic Organisms, Carbon, Chlorophyll, Dinoflagellida, Ecosystem, Expeditions, Genes, Bacterial, Genes, Viral, Geography, Oceans and Seas, Photosynthesis, Plankton, Seawater, Synechococcus, Tara Oceans coordinators, General Science & Technology
Abstract

The biological carbon pump is the process by which CO2 is transformed to organic carbon via photosynthesis, exported through sinking particles, and finally sequestered in the deep ocean. While the intensity of the pump correlates with plankton community composition, the underlying ecosystem structure driving the process remains largely uncharacterized. Here we use environmental and metagenomic data gathered during the Tara Oceans expedition to improve our understanding of carbon export in the oligotrophic ocean. We show that specific plankton communities, from the surface and deep chlorophyll maximum, correlate with carbon export at 150 m and highlight unexpected taxa such as Radiolaria and alveolate parasites, as well as Synechococcus and their phages, as lineages most strongly associated with carbon export in the subtropical, nutrient-depleted, oligotrophic ocean. Additionally, we show that the relative abundance of a few bacterial and viral genes can predict a significant fraction of the variability in carbon export in these regions.

Published
2016

17. Disentangling microbial networks across pelagic zones in the tropical and subtropical global ocean

Author

Ministerio de Economía y Competitividad (España), Ministerio de Ciencia e Innovación (España), European Commission, Agencia Estatal de Investigación (España), Deutschmann, Ina, Delage, Erwan, Giner, Caterina R., Sebastián, Marta, Poulain, Julie, Arístegui, Javier, Duarte, Carlos M., Acinas, Silvia G., Massana, Ramon, Gasol, Josep M., Eveillard, Damien, Chaffron, Samuel, Logares, Ramiro, Ministerio de Economía y Competitividad (España), Ministerio de Ciencia e Innovación (España), European Commission, Agencia Estatal de Investigación (España), Deutschmann, Ina, Delage, Erwan, Giner, Caterina R., Sebastián, Marta, Poulain, Julie, Arístegui, Javier, Duarte, Carlos M., Acinas, Silvia G., Massana, Ramon, Gasol, Josep M., Eveillard, Damien, Chaffron, Samuel, and Logares, Ramiro

Abstract

Microbial interactions are vital in maintaining ocean ecosystem function, yet their dynamic nature and complexity remain largely unexplored. Here, we use association networks to investigate possible ecological interactions in the marine microbiome among archaea, bacteria, and picoeukaryotes throughout different depths and geographical regions of the tropical and subtropical global ocean. Our findings reveal that potential microbial interactions change with depth and geographical scale, exhibiting highly heterogeneous distributions. A few potential interactions were global, meaning they occurred across regions at the same depth, while 11-36% were regional within specific depths. The bathypelagic zone had the lowest proportion of global associations, and regional associations increased with depth. Moreover, we observed that most surface water associations do not persist in deeper ocean layers despite microbial vertical dispersal. Our work contributes to a deeper understanding of the tropical and subtropical global ocean interactome, which is essential for addressing the challenges posed by global change

Published
2024

21. Machine learning in marine ecology: an overview of techniques and applications

Author

Rubbens, Peter, primary, Brodie, Stephanie, additional, Cordier, Tristan, additional, Destro Barcellos, Diogo, additional, Devos, Paul, additional, Fernandes-Salvador, Jose A, additional, Fincham, Jennifer I, additional, Gomes, Alessandra, additional, Handegard, Nils Olav, additional, Howell, Kerry, additional, Jamet, Cédric, additional, Kartveit, Kyrre Heldal, additional, Moustahfid, Hassan, additional, Parcerisas, Clea, additional, Politikos, Dimitris, additional, Sauzède, Raphaëlle, additional, Sokolova, Maria, additional, Uusitalo, Laura, additional, Van den Bulcke, Laure, additional, van Helmond, Aloysius T M, additional, Watson, Jordan T, additional, Welch, Heather, additional, Beltran-Perez, Oscar, additional, Chaffron, Samuel, additional, Greenberg, David S, additional, Kühn, Bernhard, additional, Kiko, Rainer, additional, Lo, Madiop, additional, Lopes, Rubens M, additional, Möller, Klas Ove, additional, Michaels, William, additional, Pala, Ahmet, additional, Romagnan, Jean-Baptiste, additional, Schuchert, Pia, additional, Seydi, Vahid, additional, Villasante, Sebastian, additional, Malde, Ketil, additional, and Irisson, Jean-Olivier, additional

Published
2023
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25. Population-level analysis of gut microbiome variation

Author

Falony, Gwen, Joossens, Marie, Vieira-Silva, Sara, Wang, Jun, Darzi, Youssef, Faust, Karoline, Kurilshikov, Alexander, Bonder, Marc Jan, Valles-Colomer, Mireia, Vandeputte, Doris, Tito, Raul Y., Chaffron, Samuel, Rymenans, Leen, Verspecht, Chloë, De Sutter, Lise, Lima-Mendez, Gipsi, D'hoe, Kevin, Jonckheere, Karl, Homola, Daniel, Garcia, Roberto, Tigchelaar, Ettje F., Eeckhaudt, Linda, Fu, Jingyuan, Henckaerts, Liesbet, Zhernakova, Alexandra, Wijmenga, Cisca, and Raes, Jeroen

Published
2016

26. AtlantECO deliverable D5.2 Report on the All-Atlantic interactome

Author

Chaffron, Samuel, Eveillard, Damien, Gaudin, Marinna, and Regimbeau, Antoine

Abstract

The main goals of WP5 for Advances in Systems Ecology are to develop cutting-edge network analysis methods, to define an all-Atlantic interactome and ecological niches, and to provide indicators of ecosystem stability and sensitivity to environmental stressors and drivers. The main goal of D5.2, associated with Task 5.2, was to deliver a report on the all-Atlantic interactome by constructing cross-kingdom interaction networks for marine microbiome and plastisphere communities. In this task, we examined the (functional) community organisation of microbiomes and the plastisphere at the scale of the global ocean, using metabarcodes, metagenomes, and metatranscriptomes, and specific traits such as genome size and metabolic potential. In particular, we resolved the global-ocean cross-kingdom interactome of viruses, prokaryotes, and eukaryotes, that is, the viral-host interactome, a crucial regulator of carbon fluxes and plankton community dynamics. We also investigated the diversity and community structure in the North Pacific gyre and the Mediterranean Sea to reveal a niche partitioning of plastics-associated microbial communities. In addition, we leveraged metagenomes and metatranscriptomes into a computational workflow to reconstruct the community metabolic landscape and phenotypes of global ocean microbiomes and demonstrate how they can be used to estimate ecosystem-scale marine biogeochemistry. Finally, we also developed a computational framework, integrating metagenomic and metatranscriptomic information at the genome-scale through ecological and metabolic modelling to improve our functional and mechanistic understanding of microbial interactions driving ecosystem functionsin situ.

Published
2023
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27. AtlantECO Deliverable 2.1: AtlantECO-BASE1

Author

Vogt, Meike, Sarmento, Hugo, Benedetti, Fabio, Huber, Paula, Arboleda-Baena, Clara, Bader, Ruby Rose, Eriksson, Dominic, Knecht, Nielja, Chénier, Noémy, Jaillon, Olivier, Frémont, Paul, Lombard, Fabien, Guidi, Lionel, Ricour, Florian, Van Sebille, Erik, Schmiz, Sophie, Manral, Darshika, Clerc, Corentin, Santos, Gleice, Maiorano, Luigi, De Angelis, Daniele, Chaffron, Samuel, Eveillard, Damien, Amaral-Zettler, Linda, Gehlen, Marion, Benard, Germain, and Frölicher, Thomas

Subjects
marine plankton data, plastisphere observations, microplastics observations, optical imaging observations, omics observations, traditional microscopy observations, carbon flux data observations
Abstract

This deliverable reports on Task 2.2 ‘Assembly of observations about microbiomes, plastics, the plastisphere and carbon fluxes’. It used protocols established in task 2.1 ‘Definition of common standards for the assembly of spatially explicit data’ to compile, quality-control and grid existing high-quality observations into a knowledge base of observations (D2.1). Data included into AtlantECO-BASE1 consisted of contributions from the five following data sources and tasks: Task 2.2.1 ‘Microbiome data from traditional microscopy (presence-absence, abundance and biomass)’, Task 2.2.2 ‘Microbiome data from state-of-the-art optical/imaging analysis’, Task 2.2.3 ‘Microbiome and plastisphere data from state-of-the-art genetic analyses’, Task 2.2.4 ‘Nano-, micro and macroplastics data from state-of-the-art sampling methods’, and Task 2.2.5 ‘Carbon flux data from estimated from high resolution bio-optical sensors’. Additional data contributions and mapping efforts from other partners and work packages (Task 2.3) are also included. A comprehensive list and description of all data sets collected can be found in the Appendix Tables to this document.

Published
2023
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28. Predicting global distributions of eukaryotic plankton communities from satellite data

Author

80795055, 70291432, Kaneko, Hiroto, Endo, Hisashi, Henry, Nicolas, Berney, Cédric, Mahé, Frédéric, Poulain, Julie, Labadie, Karine, Beluche, Odette, El Hourany, Roy, Tara Oceans Coordinators, Chaffron, Samuel, Wincker, Patrick, Nakamura, Ryosuke, Karp-Boss, Lee, Boss, Emmanuel, Bowler, Chris, de Vargas, Colomban, Tomii, Kentaro, Ogata, Hiroyuki, 80795055, 70291432, Kaneko, Hiroto, Endo, Hisashi, Henry, Nicolas, Berney, Cédric, Mahé, Frédéric, Poulain, Julie, Labadie, Karine, Beluche, Odette, El Hourany, Roy, Tara Oceans Coordinators, Chaffron, Samuel, Wincker, Patrick, Nakamura, Ryosuke, Karp-Boss, Lee, Boss, Emmanuel, Bowler, Chris, de Vargas, Colomban, Tomii, Kentaro, and Ogata, Hiroyuki

Abstract

Satellite remote sensing is a powerful tool to monitor the global dynamics of marine plankton. Previous research has focused on developing models to predict the size or taxonomic groups of phytoplankton. Here, we present an approach to identify community types from a global plankton network that includes phytoplankton and heterotrophic protists and to predict their biogeography using global satellite observations. Six plankton community types were identified from a co-occurrence network inferred using a novel rDNA 18 S V4 planetary-scale eukaryotic metabarcoding dataset. Machine learning techniques were then applied to construct a model that predicted these community types from satellite data. The model showed an overall 67% accuracy in the prediction of the community types. The prediction using 17 satellite-derived parameters showed better performance than that using only temperature and/or the concentration of chlorophyll a. The constructed model predicted the global spatiotemporal distribution of community types over 19 years. The predicted distributions exhibited strong seasonal changes in community types in the subarctic–subtropical boundary regions, which were consistent with previous field observations. The model also identified the long-term trends in the distribution of community types, which suggested responses to ocean warming.

Published
2023

29. Genomic adaptation of giant viruses in polar oceans

Author

80795055, 70291432, Meng, Lingjie, Delmont, Tom O., Gaïa, Morgan, Pelletier, Eric, Fernàndez-Guerra, Antonio, Chaffron, Samuel, Neches, Russell Y., Wu, Junyi, Kaneko, Hiroto, Endo, Hisashi, Ogata, Hiroyuki, 80795055, 70291432, Meng, Lingjie, Delmont, Tom O., Gaïa, Morgan, Pelletier, Eric, Fernàndez-Guerra, Antonio, Chaffron, Samuel, Neches, Russell Y., Wu, Junyi, Kaneko, Hiroto, Endo, Hisashi, and Ogata, Hiroyuki

Abstract

Despite being perennially frigid, polar oceans form an ecosystem hosting high and unique biodiversity. Various organisms show different adaptive strategies in this habitat, but how viruses adapt to this environment is largely unknown. Viruses of phyla Nucleocytoviricota and Mirusviricota are groups of eukaryote-infecting large and giant DNA viruses with genomes encoding a variety of functions. Here, by leveraging the Global Ocean Eukaryotic Viral database, we investigate the biogeography and functional repertoire of these viruses at a global scale. We first confirm the existence of an ecological barrier that clearly separates polar and nonpolar viral communities, and then demonstrate that temperature drives dramatic changes in the virus–host network at the polar–nonpolar boundary. Ancestral niche reconstruction suggests that adaptation of these viruses to polar conditions has occurred repeatedly over the course of evolution, with polar-adapted viruses in the modern ocean being scattered across their phylogeny. Numerous viral genes are specifically associated with polar adaptation, although most of their homologues are not identified as polar-adaptive genes in eukaryotes. These results suggest that giant viruses adapt to cold environments by changing their functional repertoire, and this viral evolutionary strategy is distinct from the polar adaptation strategy of their hosts.

Published
2023

30. Machine learning in marine ecology: an overview of techniques and applications

Author

Rubbens, Peter, Brodie, Stephanie, Cordier, Tristan, Destro Barcellos, Diogo, Devos, Paul, Fernandes-Salvador, Jose A, Fincham, Jennifer I, Gomes, Alessandra, Handegard, Nils Olav, Howell, Kerry, Jamet, Cédric, Kartveit, Kyrre Heldal, Moustahfid, Hassan, Parcerisas, Clea, Politikos, Dimitris, Sauzède, Raphaëlle, Sokolova, Maria, Uusitalo, Laura, Van den Bulcke, Laure, van Helmond, Aloysius T M, Watson, Jordan T, Welch, Heather, Beltran-Perez, Oscar, Chaffron, Samuel, Greenberg, David S, Kühn, Bernhard, Kiko, Rainer, Lo, Madiop, Lopes, Rubens M, Möller, Klas Ove, Michaels, William, Pala, Ahmet, Romagnan, Jean-Baptiste, Schuchert, Pia, Seydi, Vahid, Villasante, Sebastian, Malde, Ketil, Irisson, Jean-Olivier, Whidden, Christopher, Rubbens, Peter, Brodie, Stephanie, Cordier, Tristan, Destro Barcellos, Diogo, Devos, Paul, Fernandes-Salvador, Jose A, Fincham, Jennifer I, Gomes, Alessandra, Handegard, Nils Olav, Howell, Kerry, Jamet, Cédric, Kartveit, Kyrre Heldal, Moustahfid, Hassan, Parcerisas, Clea, Politikos, Dimitris, Sauzède, Raphaëlle, Sokolova, Maria, Uusitalo, Laura, Van den Bulcke, Laure, van Helmond, Aloysius T M, Watson, Jordan T, Welch, Heather, Beltran-Perez, Oscar, Chaffron, Samuel, Greenberg, David S, Kühn, Bernhard, Kiko, Rainer, Lo, Madiop, Lopes, Rubens M, Möller, Klas Ove, Michaels, William, Pala, Ahmet, Romagnan, Jean-Baptiste, Schuchert, Pia, Seydi, Vahid, Villasante, Sebastian, Malde, Ketil, Irisson, Jean-Olivier, and Whidden, Christopher

Abstract

Machine learning covers a large set of algorithms that can be trained to identify patterns in data. Thanks to the increase in the amount of data and computing power available, it has become pervasive across scientific disciplines. We first highlight why machine learning is needed in marine ecology. Then we provide a quick primer on machine learning techniques and vocabulary. We built a database of & SIM;1000 publications that implement such techniques to analyse marine ecology data. For various data types (images, optical spectra, acoustics, omics, geolocations, biogeochemical profiles, and satellite imagery), we present a historical perspective on applications that proved influential, can serve as templates for new work, or represent the diversity of approaches. Then, we illustrate how machine learning can be used to better understand ecological systems, by combining various sources of marine data. Through this coverage of the literature, we demonstrate an increase in the proportion of marine ecology studies that use machine learning, the pervasiveness of images as a data source, the dominance of machine learning for classification-type problems, and a shift towards deep learning for all data types. This overview is meant to guide researchers who wish to apply machine learning methods to their marine datasets.

Published
2023
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31. Pan-Arctic plankton community structure and its global connectivity

Author

Ibarbalz, Federico M., Henry, Nicolas, Mahe, Frédéric, Ardyna, Mathieu, Zingone, Adriana, Scalco, Eleonora, Lovejoy, Thomas E., Lombard, Fabien, Jaillon, Olivier, Iudicone, Daniele, Malviya, Shruti, Sullivan, Matthew B., Chaffron, Samuel, Karsenti, Eric, Babin, Marcel, Boss, Emmanuel, Wincker, Patrick, Zinger, Lucie, de Vargas, Colomban, Bowler, Chris, Karp-Boss, Lee, Ibarbalz, Federico M., Henry, Nicolas, Mahe, Frédéric, Ardyna, Mathieu, Zingone, Adriana, Scalco, Eleonora, Lovejoy, Thomas E., Lombard, Fabien, Jaillon, Olivier, Iudicone, Daniele, Malviya, Shruti, Sullivan, Matthew B., Chaffron, Samuel, Karsenti, Eric, Babin, Marcel, Boss, Emmanuel, Wincker, Patrick, Zinger, Lucie, de Vargas, Colomban, Bowler, Chris, and Karp-Boss, Lee

Abstract

The Arctic Ocean (AO) is being rapidly transformed by global warming, but its biodiversity remains understudied for many planktonic organisms, in particular for unicellular eukaryotes that play pivotal roles in marine food webs and biogeochemical cycles. The aim of this study was to characterize the biogeographic ranges of species that comprise the contemporary pool of unicellular eukaryotes in the AO as a first step toward understanding mechanisms that structure these communities and identifying potential target species for monitoring. Leveraging the Tara Oceans DNA metabarcoding data, we mapped the global distributions of operational taxonomic units (OTUs) found on Arctic shelves into five biogeographic categories, identified biogeographic indicators, and inferred the degree to which AO communities of unicellular eukaryotes share members with assemblages from lower latitudes. Arctic/Polar indicator OTUs, as well as some globally ubiquitous OTUs, dominated the detection and abundance of DNA reads in the Arctic samples. OTUs detected only in Arctic samples (Arctic-exclusives) showed restricted distribution with relatively low abundances, accounting for 10–16% of the total Arctic OTU pool. OTUs with high abundances in tropical and/or temperate latitudes (non-Polar indicators) were also found in the AO but mainly at its periphery. We observed a large change in community taxonomic composition across the Atlantic-Arctic continuum, supporting the idea that advection and environmental filtering are important processes that shape plankton assemblages in the AO. Altogether, this study highlights the connectivity between the AO and other oceans, and provides a framework for monitoring and assessing future changes in this vulnerable ecosystem.

Published
2023

32. Disentangling temporal associations in marine microbial networks

Author

European Commission, Ministerio de Economía y Competitividad (España), Agencia Estatal de Investigación (España), Ministerio de Ciencia, Innovación y Universidades (España), Centre National de la Recherche Scientifique (France), Deutschmann, Ina, Krabberød, Anders K., Latorre, Fran, Delage, Erwan, Marrasé, Cèlia, Balagué, Vanessa, Gasol, Josep M., Massana, Ramon, Eveillard, Damien, Chaffron, Samuel, Logares, Ramiro, European Commission, Ministerio de Economía y Competitividad (España), Agencia Estatal de Investigación (España), Ministerio de Ciencia, Innovación y Universidades (España), Centre National de la Recherche Scientifique (France), Deutschmann, Ina, Krabberød, Anders K., Latorre, Fran, Delage, Erwan, Marrasé, Cèlia, Balagué, Vanessa, Gasol, Josep M., Massana, Ramon, Eveillard, Damien, Chaffron, Samuel, and Logares, Ramiro

Abstract

Background: Microbial interactions are fundamental for Earth’s ecosystem functioning and biogeochemical cycling. Nevertheless, they are challenging to identify and remain barely known. Omics-based censuses are helpful in predicting microbial interactions through the statistical inference of single (static) association networks. Yet, microbial interactions are dynamic and we have limited knowledge of how they change over time. Here, we investigate the dynamics of microbial associations in a 10-year marine time series in the Mediterranean Sea using an approach inferring a time-resolved (temporal) network from a single static network. Results: A single static network including microbial eukaryotes and bacteria was built using metabarcoding data derived from 120 monthly samples. For the decade, we aimed to identify persistent, seasonal, and temporary microbial associations by determining a temporal network that captures the interactome of each individual sample. We found that the temporal network appears to follow an annual cycle, collapsing, and reassembling when transiting between colder and warmer waters. We observed higher association repeatability in colder than in warmer months. Only 16 associations could be validated using observations reported in literature, underlining our knowledge gap in marine microbial ecological interactions. Conclusions: Our results indicate that marine microbial associations follow recurrent temporal dynamics in temperate zones, which need to be accounted for to better understand the functioning of the ocean microbiome. The constructed marine temporal network may serve as a resource for testing season-specific microbial interaction hypotheses. The applied approach can be transferred to microbiome studies in other ecosystems

Published
2023

33. Ocean-wide comparisons of mesopelagic planktonic community structures

Author

Fonds Français pour l'Environnement Mondial, Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (Brasil), European Commission, Centre National de la Recherche Scientifique (France), European Molecular Biology Laboratory, Ministère de la Recherche et des Technologies (France), Agencia Estatal de Investigación (España), Rigonato, Janaina, Budinich, Marko, Murillo, Alejandro A., Brandão, Manoela C., Pierella Karlusich, Juan J., Soviadan, Yawouvi Dodji, Gregory, Ann C., Endo, Hisashi, Kokoszka, Florian, Vik, Dean, Henry, Nicolas, Frémont, Paul, Labadie, Karine, Zayed, Ahmed A., Dimier, Céline, Picheral, Marc, Searson, Sarah, Poulain, Julie, Kandels‐Lewis, Stefanie, Pesant, Stéphane, Karsenti, Eric, Tara Oceans Coordinators, Acinas, Silvia G., Bork, Peer, Bowler, Chris, Vargas, Colomban de, Eveillard, Damien, Gehlen, Marion, Iudicone, Daniele, Lombard, Fabien, Ogata, Hiroyuki, Stemmann, Lars, Sullivan, Matthew B., Sunagawa, Shinichi, Wincker, Patrick, Chaffron, Samuel, Jaillon, Olivier, Fonds Français pour l'Environnement Mondial, Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (Brasil), European Commission, Centre National de la Recherche Scientifique (France), European Molecular Biology Laboratory, Ministère de la Recherche et des Technologies (France), Agencia Estatal de Investigación (España), Rigonato, Janaina, Budinich, Marko, Murillo, Alejandro A., Brandão, Manoela C., Pierella Karlusich, Juan J., Soviadan, Yawouvi Dodji, Gregory, Ann C., Endo, Hisashi, Kokoszka, Florian, Vik, Dean, Henry, Nicolas, Frémont, Paul, Labadie, Karine, Zayed, Ahmed A., Dimier, Céline, Picheral, Marc, Searson, Sarah, Poulain, Julie, Kandels‐Lewis, Stefanie, Pesant, Stéphane, Karsenti, Eric, Tara Oceans Coordinators, Acinas, Silvia G., Bork, Peer, Bowler, Chris, Vargas, Colomban de, Eveillard, Damien, Gehlen, Marion, Iudicone, Daniele, Lombard, Fabien, Ogata, Hiroyuki, Stemmann, Lars, Sullivan, Matthew B., Sunagawa, Shinichi, Wincker, Patrick, Chaffron, Samuel, and Jaillon, Olivier

Abstract

For decades, marine plankton have been investigated for their capacity to modulate biogeochemical cycles and provide fishery resources. Between the sunlit (epipelagic) layer and the deep dark waters, lies a vast and heterogeneous part of the ocean: the mesopelagic zone. How plankton composition is shaped by environment has been well-explored in the epipelagic but much less in the mesopelagic ocean. Here, we conducted comparative analyses of trans-kingdom community assemblages thriving in the mesopelagic oxygen minimum zone (OMZ), mesopelagic oxic, and their epipelagic counterparts. We identified nine distinct types of intermediate water masses that correlate with variation in mesopelagic community composition. Furthermore, oxygen, NO3− and particle flux together appeared as the main drivers governing these communities. Novel taxonomic signatures emerged from OMZ while a global co-occurrence network analysis showed that about 70% of the abundance of mesopelagic plankton groups is organized into three community modules. One module gathers prokaryotes, pico-eukaryotes and Nucleo-Cytoplasmic Large DNA Viruses (NCLDV) from oxic regions, and the two other modules are enriched in OMZ prokaryotes and OMZ pico-eukaryotes, respectively. We hypothesize that OMZ conditions led to a diversification of ecological niches, and thus communities, due to selective pressure from limited resources. Our study further clarifies the interplay between environmental factors in the mesopelagic oxic and OMZ, and the compositional features of communities

Published
2023

34. Predicting global distributions of eukaryotic plankton communities from satellite data

Author

Kyoto University, France Génomique, European Research Council, European Commission, Agencia Estatal de Investigación (España), Kaneko, Hiroto, Endo, Hisashi, Henry, Nicolas, Berney, Cédric, Mahé, Frédéric, Poulain, Julie, Labadie, Karine, Beluche, Odette, El Hourany, Roy, Tara Oceans Coordinators, Acinas, Silvia G., Chaffron, Samuel, Wincker, Patrick, Nakamura, Ryosuke, Karp-Boss, Lee, Boss, Emmanuel, Bowler, Chris, Vargas, Colomban de, Tomii, Kentaro, Ogata, Hiroyuki, Kyoto University, France Génomique, European Research Council, European Commission, Agencia Estatal de Investigación (España), Kaneko, Hiroto, Endo, Hisashi, Henry, Nicolas, Berney, Cédric, Mahé, Frédéric, Poulain, Julie, Labadie, Karine, Beluche, Odette, El Hourany, Roy, Tara Oceans Coordinators, Acinas, Silvia G., Chaffron, Samuel, Wincker, Patrick, Nakamura, Ryosuke, Karp-Boss, Lee, Boss, Emmanuel, Bowler, Chris, Vargas, Colomban de, Tomii, Kentaro, and Ogata, Hiroyuki

Abstract

Satellite remote sensing is a powerful tool to monitor the global dynamics of marine plankton. Previous research has focused on developing models to predict the size or taxonomic groups of phytoplankton. Here, we present an approach to identify community types from a global plankton network that includes phytoplankton and heterotrophic protists and to predict their biogeography using global satellite observations. Six plankton community types were identified from a co-occurrence network inferred using a novel rDNA 18 S V4 planetary-scale eukaryotic metabarcoding dataset. Machine learning techniques were then applied to construct a model that predicted these community types from satellite data. The model showed an overall 67% accuracy in the prediction of the community types. The prediction using 17 satellite-derived parameters showed better performance than that using only temperature and/or the concentration of chlorophyll a. The constructed model predicted the global spatiotemporal distribution of community types over 19 years. The predicted distributions exhibited strong seasonal changes in community types in the subarctic–subtropical boundary regions, which were consistent with previous field observations. The model also identified the long-term trends in the distribution of community types, which suggested responses to ocean warming

Published
2023

36. Genomic adaptation of giant viruses in polar oceans

Author

Meng, Lingjie, primary, Delmont, Tom O., additional, Gaïa, Morgan, additional, Pelletier, Eric, additional, Fernàndez-Guerra, Antonio, additional, Chaffron, Samuel, additional, Neches, Russell Y., additional, Wu, Junyi, additional, Kaneko, Hiroto, additional, Endo, Hisashi, additional, and Ogata, Hiroyuki, additional

Published
2023
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37. Faeces‐derived extracellular vesicles participate in the onset of barrier dysfunction leading to liver diseases

Author

Fizanne, Lionel, primary, Villard, Alexandre, additional, Benabbou, Nadia, additional, Recoquillon, Sylvain, additional, Soleti, Raffaella, additional, Delage, Erwan, additional, Wertheimer, Mireille, additional, Vidal‐Gómez, Xavier, additional, Oullier, Thibauld, additional, Chaffron, Samuel, additional, Martínez, M. Carmen, additional, Neunlist, Michel, additional, Boursier, Jérôme, additional, and Andriantsitohaina, Ramaroson, additional

Published
2023
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38. Could the Microbiota Be a Predictive Factor for the Clinical Response to Probiotic Supplementation in IBS-D? A Cohort Study

Author

Marchix, Justine, primary, Quénéhervé, Lucille, additional, Bordron, Philippe, additional, Aubert, Philippe, additional, Durand, Tony, additional, Oullier, Thibauld, additional, Blondeau, Claude, additional, Ait Abdellah, Samira, additional, Bruley des Varannes, Stanislas, additional, Chaffron, Samuel, additional, Coron, Emmanuel, additional, and Neunlist, Michel, additional

Published
2023
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39. Combined in vivo and in situ genome-resolved metagenomics reveals novel symbiotic nitrogen fixing interactions between non-cyanobacterial diazotrophs and microalgae

Author

CHANDOLA, UDITA, primary, TROTTIER, CAMILLE, additional, GAUDIN, MARINNA, additional, MANIRAKIZA, ERIC, additional, MENICOT, SAMUEL, additional, LOUVET, ISABELLE, additional, LACOUR, THOMAS, additional, CHAUMIER, TIMOTHEE, additional, TANAKA, ATSUKO, additional, Chaffron, Samuel, additional, and Tirichine, Leila, additional

Published
2023
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40. Maternal prebiotic supplementation impacts colitis development in offspring mice

Author

Lê, Amélie, primary, Selle, Amandine, additional, Aubert, Philippe, additional, Durand, Tony, additional, Brosseau, Carole, additional, Bordron, Philippe, additional, Delage, Erwan, additional, Chaffron, Samuel, additional, Petitfils, Camille, additional, Cenac, Nicolas, additional, Neunlist, Michel, additional, Bodinier, Marie, additional, and Rolli-Derkinderen, Malvyne, additional

Published
2023
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42. Global observation of plankton communities from space

Author

Kaneko, Hiroto, Endo, Hisashi, Henry, Nicolas, Berney, Cédric, Mahé, Frédéric, Poulain, Julie, Labadie, Karine, Beluche, Odette, El Hourany, Roy, Chaffron, Samuel, Wincker, Patrick, Nakamura, Ryosuke, Karp-Boss, Lee, Boss, Emmanuel, Bowler, Chris, de Vargas, Colomban, Tomii, Kentaro, Ogata, Hiroyuki, Institute for Chemical Research, Kyoto University, ABiMS - Informatique et bioinformatique = Analysis and Bioinformatics for Marine Science (ABIMS), Fédération de recherche de Roscoff (FR2424), Station biologique de Roscoff (SBR), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Station biologique de Roscoff (SBR), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Global Oceans Systems Ecology & Evolution - Tara Oceans (GOSEE), Université de Perpignan Via Domitia (UPVD)-École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Aix Marseille Université (AMU)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université d'Évry-Val-d'Essonne (UEVE)-Université de Toulon (UTLN)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut de Recherche pour le Développement (IRD [France-Nord])-Ecole Normale Supérieure Paris-Saclay (ENS Paris Saclay)-European Molecular Biology Laboratory (EMBL)-École Centrale de Nantes (Nantes Univ - ECN), Nantes Université (Nantes Univ)-Nantes Université (Nantes Univ)-Université australe du Chili, Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Plant Health Institute of Montpellier (UMR PHIM), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut de Recherche pour le Développement (IRD)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro Montpellier, 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 Montpellier (UM), Département Systèmes Biologiques (Cirad-BIOS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Genoscope - Centre national de séquençage [Evry] (GENOSCOPE), Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Institut de biologie de l'ENS Paris (IBENS), Département de Biologie - ENS Paris, École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Laboratoire des Sciences du Numérique de Nantes (LS2N), Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-École Centrale de Nantes (Nantes Univ - ECN), Nantes Université (Nantes Univ)-Nantes Université (Nantes Univ)-Nantes université - UFR des Sciences et des Techniques (Nantes univ - UFR ST), Nantes Université - pôle Sciences et technologie, Nantes Université (Nantes Univ)-Nantes Université (Nantes Univ)-Nantes Université - pôle Sciences et technologie, Nantes Université (Nantes Univ), National Institute of Advanced Industrial Science and Technology (AIST), University of Maine, School of Marine Sciences, ECOlogy of MArine Plankton (ECOMAP), Adaptation et diversité en milieu marin (ADMM), Institut national des sciences de l'Univers (INSU - CNRS)-Station biologique de Roscoff (SBR), and Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Station biologique de Roscoff (SBR)

Subjects
global plankton network, Surveillance communautés de planctons, Plankton communities monitoring, Satelites imagery, [SDV]Life Sciences [q-bio], Observations par satellite, Réseau mondial de plancton, Satellite remote sensing observations, Images satellites
Abstract

Satellite remote sensing from space is a powerful way to monitor the global dynamics of marine plankton. Previous research has focused on developing models to predict the size or taxonomic groups of phytoplankton. Here we present an approach to identify representative communities from a global plankton network that included both zooplankton and phytoplankton and using global satellite observations to predict their biogeography. Six representative plankton communities were identified from a global co-occurrence network inferred using a novel rDNA 18S V4 planetary-scale eukaryotic metabarcoding dataset. Machine learning techniques were then applied to train a model that predicted these representative communities from satellite data. The model showed an overall 67% accuracy in the prediction of the representative communities. The prediction based on 17 satellite-derived parameters showed better performance than based only on temperature and/or the concentration of chlorophyll a. The trained model allowed to predict the global spatiotemporal distribution of communities over 19-years. Our model exhibited strong seasonal changes in the community compositions in the subarctic-subtropical boundary regions, which were consistent with previous field observations. This network-oriented approach can easily be extended to more comprehensive models including prokaryotes as well as viruses.

Published
2022

44. Combinedin vivoandin situgenome-resolved metagenomics reveals novel symbiotic nitrogen fixing interactions between non-cyanobacterial diazotrophs and microalgae

Author

Chandola, Udita, primary, Trottier, Camille, additional, Gaudin, Marinna, additional, Manirakiza, Eric, additional, Menicot, Samuel, additional, Louvet, Isabelle, additional, Lacour, Thomas, additional, Chaumier, Timothée, additional, Tanaka, Atsuko, additional, Chaffron, Samuel, additional, and Tirichine, Leila, additional

Published
2022
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45. Campaign Summary Report - Mission Microbiomes from Salvador de Bahia (2021-10-15) to Rio de Janeiro (2021-11-03) on board AtlantECO flagship SV Tara

Author

Chaffron, Samuel, Linkowski, Thomas, Couet, Douglas, Becker, Erica, and Junger, Pedro

Abstract

Mission Microbiomes Leg 8 took place between Salvador de Bahia and Rio de Janeiro, Brazil. It corresponded to the first AtlantECO “Biodiscovery” leg, the first case Study on "Molecular Bioprospecting" of the mission. The two principal objectives of this leg were 1) to augment plankton biodiversity discovery through replicated omics samplings, and 2) to enable the potential discovery of new bioproducts & chemicals for industrial applications with high socio-economic value, such as new ways to produce pharmaceutical products or natural enzymes able to digest complex molecules such as pollutants or plastics. Given the bioprospecting focus of this leg, sampling stations were all located within international waters, along the Vitória-Trindade Chain (VTC) consisting of 11 heterogeneous seamounts. Due to this location, an additional objective of this leg was 3) to produce samples potentially also valuable for the H2020 EU-funded All-Atlantic sister project iAtlantic (Integrated Assessment of Atlantic Marine Ecosystems in Space and Time), which overall goal is to measure the impact of climate change on the Atlantic, and notably on deep-sea corals of the VTC seamounts. In line with these objectives, the main questions we wish to address are: a) Are we under-sampling the functional and taxonomic biodiversity of marine microbiomes? What are the “rare” species? (Is everything everywhere?); b) What is the nutrient/light dynamics of a DCM? Is the DCM a vertically homogeneous feature or is it made of vertically separated niches? and c) What are the pelagic microbiomes associated with deep coral reefs? Can we characterize the vertical connectivity between the pelagic microbiomes and deep-sea corals (e.g., downward export of organic matter, using a genomic signature)? What is the survival of coral larvae in the pelagic ecosystem?

Published
2022
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46. Diversity and ecological footprint of Global Ocean RNA viruses

Author

Dominguez-Huerta, Guillermo, Zayed, Ahmed, Wainaina, James, Guo, Jiarong, Tian, Funing, Pratama, Akbar Adjie, Bolduc, Benjamin, Mohssen, Mohamed, Zablocki, Olivier, Pelletier, Eric, Delage, Erwan, Alberti, Adriana, Aury, Jean-Marc, Carradec, Quentin, da Silva, Corinne, Labadie, Karine, Poulain, Julie, Bowler, Chris, Eveillard, Damien, Guidi, Lionel, Karsenti, Eric, Kuhn, Jens, Ogata, Hiroyuki, Wincker, Patrick, Culley, Alexander, Chaffron, Samuel, Sullivan, Matthew, Department of Microbiology [Columbus], Ohio State University [Columbus] (OSU), Global Oceans Systems Ecology & Evolution - Tara Oceans (GOSEE), Université de Perpignan Via Domitia (UPVD)-École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Aix Marseille Université (AMU)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université d'Évry-Val-d'Essonne (UEVE)-Université de Toulon (UTLN)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut de Recherche pour le Développement (IRD [France-Nord])-Ecole Normale Supérieure Paris-Saclay (ENS Paris Saclay)-European Molecular Biology Laboratory (EMBL)-École Centrale de Nantes (Nantes Univ - ECN), Nantes Université (Nantes Univ)-Nantes Université (Nantes Univ)-Université australe du Chili, Génomique métabolique (UMR 8030), Genoscope - Centre national de séquençage [Evry] (GENOSCOPE), Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université d'Évry-Val-d'Essonne (UEVE)-Centre National de la Recherche Scientifique (CNRS), Laboratoire des Sciences du Numérique de Nantes (LS2N), Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-École Centrale de Nantes (Nantes Univ - ECN), Nantes Université (Nantes Univ)-Nantes Université (Nantes Univ)-Nantes université - UFR des Sciences et des Techniques (Nantes univ - UFR ST), Nantes Université - pôle Sciences et technologie, Nantes Université (Nantes Univ)-Nantes Université (Nantes Univ)-Nantes Université - pôle Sciences et technologie, Nantes Université (Nantes Univ), Institut de biologie de l'ENS Paris (IBENS), Département de Biologie - ENS Paris, École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Combinatoire et Bioinformatique (LS2N - équipe COMBI), Nantes Université (Nantes Univ)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Laboratoire d'océanographie de Villefranche (LOV), Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut de la Mer de Villefranche (IMEV), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Integrated Research Facility at Fort Detrick (IRF-Frederick), National Institute of Allergy and Infectious Diseases [Bethesda] (NIAID-NIH), National Institutes of Health [Bethesda] (NIH)-National Institutes of Health [Bethesda] (NIH), Institute for Chemical Research, Kyoto University, Département de Biochimie, de Microbiologie et de Bio-informatique, Université Laval, Université Laval [Québec] (ULaval), and Department of Civil, Environmental and Geodetic Engineering [Columbus]

Subjects
Multidisciplinary, Virome, Oceans and Seas, [SDV.BBM.MN]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Molecular Networks [q-bio.MN], Plankton, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], Carbon Cycle, [SDV.EE.ECO]Life Sciences [q-bio]/Ecology, environment/Ecosystems, [SDV.BBM.GTP]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Genomics [q-bio.GN], RNA Viruses, Seawater, [SDE.BE]Environmental Sciences/Biodiversity and Ecology, Ecosystem, [SDV.EE.IEO]Life Sciences [q-bio]/Ecology, environment/Symbiosis
Abstract

International audience; DNA viruses are increasingly recognized as influencing marine microbes and microbe-mediated biogeochemical cycling. However, little is known about global marine RNA virus diversity, ecology, and ecosystem roles. In this study, we uncover patterns and predictors of marine RNA virus community- and “species”-level diversity and contextualize their ecological impacts from pole to pole. Our analyses revealed four ecological zones, latitudinal and depth diversity patterns, and environmental correlates for RNA viruses. Our findings only partially parallel those of cosampled plankton and show unexpectedly high polar ecological interactions. The influence of RNA viruses on ecosystems appears to be large, as predicted hosts are ecologically important. Moreover, the occurrence of auxiliary metabolic genes indicates that RNA viruses cause reprogramming of diverse host metabolisms, including photosynthesis and carbon cycling, and that RNA virus abundances predict ocean carbon export.

Published
2022
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47. Seasonal dynamics of marine protist communities in tidally mixed coastal waters

Author

Caracciolo, Mariarita, primary, Rigaut‐Jalabert, Fabienne, additional, Romac, Sarah, additional, Mahé, Frédéric, additional, Forsans, Samuel, additional, Gac, Jean‐Philippe, additional, Arsenieff, Laure, additional, Manno, Maxime, additional, Chaffron, Samuel, additional, Cariou, Thierry, additional, Hoebeke, Mark, additional, Bozec, Yann, additional, Goberville, Eric, additional, Le Gall, Florence, additional, Guilloux, Loïc, additional, Baudoux, Anne‐Claire, additional, de Vargas, Colomban, additional, Not, Fabrice, additional, Thiébaut, Eric, additional, Henry, Nicolas, additional, and Simon, Nathalie, additional

Published
2022
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48. AtlantECO deliverable 5.1- Reference catalogue of network reconstruction methods

Author

Budinich Marko, Eveillard Damien, and Chaffron Samuel

Abstract

The main computational goals of WP5 for Advances in Systems Ecology are to develop cutting edge network analysis methods, to define an all-Atlantic interactome and ecological niches, and to provide indicators of ecosystem stability and sensitivity to environmental stressors and drivers. Here, we reviewed the literature for ecological network reconstruction methods from several disciplines (microbiology, ecology, bioinformatics). We built a computational pipeline for the inference of species ecological networks from heterogeneous data types, combining statistical and ecological metrics, as well as probabilistic and machine learning algorithms. Reference databases of known ecological interactions obtained from the literature can be used to benchmark and validate inferred networks. This review of network inference algorithms is accompanied by a computational workflow:AtlantEcoNet(publicly available at:https://gitlab.univ-nantes.fr/mbudinich/atlanteconet), which builds upon existing software by integrating a selection of complementary methods for ecological network inference, analysis, and validation. This workflow will be used in Task 5.2 to build an all-Atlantic plankton ecological network from omics data compiled within WP2.

Published
2022
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50. Priorities for ocean microbiome research

Author

Tara Ocean Foundation, Abreu, Andre, Bourgois, Etienne, Gristwood, Adam, Troublé, Romain, Tara Oceans, Acinas, Silvia G., Bork, Peer, Boss, Emmanuel, Bowler, Chris, Budinich, Marko, Chaffron, Samuel, de Vargas, Colomban, Delmont, Tom O., Eveillard, Damien, Guidi, Lionel, Iudicone, Daniele, Kandels, Stephanie, Morlon, Hélène, Lombard, Fabien, Pepperkok, Rainer, Pierella Karlusich, Juan José, Piganeau, Gwenael, Régimbeau, Antoine, Sommeria-Klein, Guilhem, Stemmann, Lars, Sullivan, Matthew B., Sunagawa, Shinichi, Wincker, Patrick, Zablocki, Olivier, European Molecular Biology Laboratory (EMBL), Arendt, Detlev, Bilic, Josipa, Finn, Robert, Heard, Edith, Rouse, Brendan, Vamathevan, Jessica, European Marine Biological Resource Centre - European Research Infrastructure Consortium (EMBRC-ERIC), Casotti, Raffaella, Cancio, Ibon, Cunliffe, Michael, Kervella, Anne Emmanuelle, Kooistra, Wiebe H.C.F., Obst, Matthias, Pade, Nicolas, Power, Deborah M., Santi, Ioulia, Tsagaraki, Tatiana Margo, Vanaverbeke, Jan, European Commission, Agencia Estatal de Investigación (España), Tara Ocean Foundation, Tara Expéditions, Institute of Marine Sciences / Institut de Ciències del Mar [Barcelona] (ICM), Consejo Superior de Investigaciones Científicas [Madrid] (CSIC), European Molecular Biology Laboratory [Heidelberg] (EMBL), University of Maine, Institut de biologie de l'ENS Paris (IBENS), Département de Biologie - ENS Paris, École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Global Oceans Systems Ecology & Evolution - Tara Oceans (GOSEE), Université de Perpignan Via Domitia (UPVD)-École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Aix Marseille Université (AMU)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université d'Évry-Val-d'Essonne (UEVE)-Université de Toulon (UTLN)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut de Recherche pour le Développement (IRD [France-Nord])-Ecole Normale Supérieure Paris-Saclay (ENS Paris Saclay)-European Molecular Biology Laboratory (EMBL)-École Centrale de Nantes (Nantes Univ - ECN), Nantes Université (Nantes Univ)-Nantes Université (Nantes Univ)-Université australe du Chili, Combinatoire et Bioinformatique (LS2N - équipe COMBI), Laboratoire des Sciences du Numérique de Nantes (LS2N), Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-École Centrale de Nantes (Nantes Univ - ECN), Nantes Université (Nantes Univ)-Nantes Université (Nantes Univ)-Nantes université - UFR des Sciences et des Techniques (Nantes univ - UFR ST), Nantes Université - pôle Sciences et technologie, Nantes Université (Nantes Univ)-Nantes Université (Nantes Univ)-Nantes Université - pôle Sciences et technologie, Nantes Université (Nantes Univ)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Nantes Université (Nantes Univ), Station biologique de Roscoff (SBR), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Genoscope - Centre national de séquençage [Evry] (GENOSCOPE), Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Laboratoire d'océanographie de Villefranche (LOV), Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut de la Mer de Villefranche (IMEV), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), and Stazione Zoologica Anton Dohrn (SZN)

Subjects
Microbiology (medical), Genome, Marine, Bacteria, Ecology, Immunology, [SDV.BBM.MN]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Molecular Networks [q-bio.MN], Cell Biology, Terrestrial, Plankton, Applied Microbiology and Biotechnology, Microbiology, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], [SDV.EE.ECO]Life Sciences [q-bio]/Ecology, environment/Ecosystems, [SDV.BBM.GTP]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Genomics [q-bio.GN], Sequence, Viruses, Genetics, Phages, Biomass, [SDE.BE]Environmental Sciences/Biodiversity and Ecology, [SDV.EE.IEO]Life Sciences [q-bio]/Ecology, environment/Symbiosis
Abstract

This article is contribution number 131 of Tara Oceans.-- 11 pages, 5 figures, 1 table, 1 box, Microbial communities have essential roles in ocean ecology and planetary health. Microbes participate in nutrient cycles, remove huge quantities of carbon dioxide from the air and support ocean food webs. The taxonomic and functional diversity of the global ocean microbiome has been revealed by technological advances in sampling, DNA sequencing and bioinformatics. A better understanding of the ocean microbiome could underpin strategies to address environmental and societal challenges, including achievement of multiple Sustainable Development Goals way beyond SDG 14 ‘life below water’. We propose a set of priorities for understanding and protecting the ocean microbiome, which include delineating interactions between microbiota, sustainably applying resources from oceanic microorganisms and creating policy- and funder-friendly ocean education resources, and discuss how to achieve these ambitious goals, We thank R. Zaayman-Gallant, T. Rauscher and F. Ibarbalz for preparation of the figures, and the European Union’s Horizon 2020 research and innovation project AtlantECO, under grant agreement no. 862923, With the institutional support of the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S)

Published
2022
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