1. Disentangling temporal associations in marine microbial networks
- Author
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Ina Maria Deutschmann, Anders K. Krabberød, Francisco Latorre, Erwan Delage, Cèlia Marrasé, Vanessa Balagué, Josep M. Gasol, Ramon Massana, Damien Eveillard, Samuel Chaffron, Ramiro Logares, Institute of Marine Sciences / Institut de Ciències del Mar [Barcelona] (ICM), Consejo Superior de Investigaciones Científicas [Madrid] (CSIC), University of Oslo (UiO), 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), 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, Instituto de Ciencias del Mar de Barcelona (ICM), European Commission, Ministerio de Economía y Competitividad (España), Agencia Estatal de Investigación (España), Ministerio de Ciencia, Innovación y Universidades (España), and Centre National de la Recherche Scientifique (France)
- Subjects
Ocean ,Microbiology (medical) ,Time series ,Temporal network ,Association network ,Microorganisms ,[SDV.BBM.MN]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Molecular Networks [q-bio.MN] ,Plankton ,Microbiology ,[SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM] ,[SDV.EE.ECO]Life Sciences [q-bio]/Ecology, environment/Ecosystems ,Microbial interactions ,[SDV.BBM.GTP]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Genomics [q-bio.GN] ,[SDE.BE]Environmental Sciences/Biodiversity and Ecology ,Conserve and sustainably use the oceans, seas and marine resources for sustainable development ,[SDV.EE.IEO]Life Sciences [q-bio]/Ecology, environment/Symbiosis - Abstract
16 pages, 4 figures, 2 tables, supplementary information https://doi.org/10.1186/s40168-023-01523-z.-- Availability of data and materials: The BBMO microbial sequence abundances (ASV tables), taxonomic classifications, environmental data including nutrients, networks, and R-Markdowns for the data analysis including commands to run eLSA and EnDED (environmentally driven-edge-detection and computing Jaccard index) are publicly available: https://github.com/InaMariaDeutschmann/TemporalNetworkBBMO. DNA sequences are publicly available at the European Nucleotide Archive (https://www.ebi.ac.uk/ena; accession numbers PRJEB23788 for 18S rRNA genes & PRJEB38773 for 16S rRNA genes), 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, Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This project and IMD received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement no. 675752 (ESR2, https://www.singek.eu) to RL. RL was supported by a Ramón y Cajal fellowship (RYC-2013–12554, MINECO, Spain). This work was also supported by the projects INTERACTOMICS (CTM2015-69936-P, MINECO, Spain), MicroEcoSystems (240904, RCN, Norway), and MINIME (PID2019-105775RB-I00, AEI, Spain) to RL. FL was supported by the Spanish National Program FPI 2016 (BES-2016–076317, MICINN, Spain). SC was supported by the CNRS MITI through the interdisciplinary program Modélisation du Vivant (GOBITMAP grant). DE and SC were supported by the H2020 project AtlantECO (award number 862923). A range of projects from the EU and the Spanish Ministry of Science funded the data collection and ancillary analyses at the BBMO. We acknowledge funding of the Spanish government through the “Severo Ochoa Centre of Excellence” accreditation (CEX2019-000,928-S)
- Published
- 2023