Ecosystem-wide metagenomic binning enables prediction of ecological niches from genomes

Alneberg, Johannes, Bennke, Christin, Beier, Sara, Bunse, Carina, Quince, Christopher, Ininbergs, Karolina, Riemann, Lasse, Ekman, Martin, Jürgens, Klaus, Labrenz, Matthias, Pinhassi, Jarone and Andersson, Anders F. (2020) Ecosystem-wide metagenomic binning enables prediction of ecological niches from genomes. Communications Biology, 3 (1). ISSN 2399-3642

Full text not available from this repository. (Request a copy)

Abstract

The genome encodes the metabolic and functional capabilities of an organism and should be a major determinant of its ecological niche. Yet, it is unknown if the niche can be predicted directly from the genome. Here, we conduct metagenomic binning on 123 water samples spanning major environmental gradients of the Baltic Sea. The resulting 1961 metagenome-assembled genomes represent 352 species-level clusters that correspond to 1/3 of the metagenome sequences of the prokaryotic size-fraction. By using machine-learning, the placement of a genome cluster along various niche gradients (salinity level, depth, size-fraction) could be predicted based solely on its functional genes. The same approach predicted the genomes’ placement in a virtual niche-space that captures the highest variation in distribution patterns. The predictions generally outperformed those inferred from phylogenetic information. Our study demonstrates a strong link between genome and ecological niche and provides a conceptual framework for predictive ecology based on genomic data.

Item Type: Article
Additional Information: Funding Information: This work resulted from the BONUS Blueprint project supported by BONUS (Art 185), funded jointly by the EU and the Swedish Research Council FORMAS, the Federal Ministry of Education and Research (BMBF) and the Danish Council for Independent Research. Funding was also provided through the Swedish governmental strong research programme EcoChange and the Swedish Research Council VR. Computations were performed on resources provided by the Swedish National Infrastructure for Computing (SNIC) through the Uppsala Multidisciplinary Center for Advanced Computational Science (UPPMAX). DNA sequencing was conducted at the Swedish National Genomics Infrastructure (NGI) at Science for Life Laboratory (SciLifeLab) in Stockholm. We are grateful to Warren Kretzschmar for providing advice on machine learning approaches. Open access funding provided by Royal Institute of Technology. Publisher Copyright: © 2020, The Author(s).
Uncontrolled Keywords: medicine (miscellaneous),biochemistry, genetics and molecular biology(all),agricultural and biological sciences(all) ,/dk/atira/pure/subjectarea/asjc/2700/2701
Faculty \ School: Faculty of Science > School of Biological Sciences
Related URLs:
Depositing User: LivePure Connector
Date Deposited: 08 Sep 2022 13:30
Last Modified: 30 Sep 2022 02:26
URI: https://ueaeprints.uea.ac.uk/id/eprint/87967
DOI: 10.1038/s42003-020-0856-x

Actions (login required)

View Item View Item