Towards omics-based predictions of planktonic functional composition from environmental data

• Published in: Nature communications 2021-07, Vol.12 (1), p.4361-4361, Article 4361
• Main Authors: Faure, Emile, Ayata, Sakina-Dorothée, Bittner, Lucie
• Format: Article
• Language: English
• Subjects: 45; 45/23; 631/114/1305; 631/114/2404; 631/158/47; 631/158/670; Abundance; Archaea - genetics; Bacteria - genetics; Biodiversity and Ecology; Biogeochemical cycles; Biogeography; Classification; Cluster analysis; Composition; Context; Data analysis; Earth Sciences; Ecosystem; Environmental Sciences; Genomes; Geochemistry; Humanities and Social Sciences; Learning algorithms; Life Sciences; Machine Learning; Marine ecosystems; Mediterranean Sea; Metagenome; Microbiology and Parasitology; Microorganisms; multidisciplinary; Oceanography; Oceans; Outliers (statistics); Phylogeny; Phylogeography; Plankton - genetics; Predictions; Protein composition; Protein Interaction Maps; Proteins; Quantitative Methods; Science; Science (multidisciplinary); Sciences of the Universe; Seawater - microbiology
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-021-24547-1

Marine microbes play a crucial role in climate regulation, biogeochemical cycles, and trophic networks. Unprecedented amounts of data on planktonic communities were recently collected, sparking a need for innovative data-driven methodologies to quantify and predict their ecosystemic functions. We reanalyze 885 marine metagenome-assembled genomes through a network-based approach and detect 233,756 protein functional clusters, from which 15% are functionally unannotated. We investigate all clusters’ distributions across the global ocean through machine learning, identifying biogeographical provinces as the best predictors of protein functional clusters’ abundance. The abundances of 14,585 clusters are predictable from the environmental context, including 1347 functionally unannotated clusters. We analyze the biogeography of these 14,585 clusters, identifying the Mediterranean Sea as an outlier in terms of protein functional clusters composition. Applicable to any set of sequences, our approach constitutes a step towards quantitative predictions of functional composition from the environmental context. Advances in omics approaches could enable quantitative predictions of microbial functional composition. Here the authors re-analyze 885 metagenome-assembled genomes from Tara Oceans, and use a network approach to quantify protein functional clusters and explore their biogeography.