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A genome catalogue of lake bacterial diversity and its drivers at continental scale

Nature Microbiology volume 8, pages 1920–1934 (2023)Cite this article

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A Publisher Correction to this article was published on 11 October 2023

This article has been updated

Abstract

Lakes are heterogeneous ecosystems inhabited by a rich microbiome whose genomic diversity is poorly defined. We present a continental-scale study of metagenomes representing 6.5 million km2 of the most lake-rich landscape on Earth. Analysis of 308 Canadian lakes resulted in a metagenome-assembled genome (MAG) catalogue of 1,008 mostly novel bacterial genomospecies. Lake trophic state was a leading driver of taxonomic and functional diversity among MAG assemblages, reflecting the responses of communities profiled by 16S rRNA amplicons and gene-centric metagenomics. Coupling the MAG catalogue with watershed geomatics revealed terrestrial influences of soils and land use on assemblages. Agriculture and human population density were drivers of turnover, indicating detectable anthropogenic imprints on lake bacteria at the continental scale. The sensitivity of bacterial assemblages to human impact reinforces lakes as sentinels of environmental change. Overall, the LakePulse MAG catalogue greatly expands the freshwater genomic landscape, advancing an integrative view of diversity across Earth’s microbiomes.

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Fig. 1: The LakePulse MAG catalogue: geographical and environmental scope, quality and patterns of local-scale diversity.
Fig. 2: Taxonomic, phylogenetic and functional diversity of the LakePulse MAG catalogue.
Fig. 3: Niche breadth and environmental filtering of MAGs based on taxonomic and functional biogeography.
Fig. 4: Diversity of specific bacterial functions among lake assemblages and individual MAGs.
Fig. 5: Influences of watershed soil and land use on lake bacterial assemblages.

Data availability

Raw metagenome reads were archived in the European Nucleotide Archive under study accession PRJEB29238. Metagenome co-assemblies were deposited and annotated at the Joint Genome Institute (JGI) Genomes OnLine Database (GOLD) under study accession Gs0136026 and analysis projects Ga0495746 (Boreal/Taiga Cordilleras), Ga0495744 (Montane Cordillera), Ga0495745 (Pacific Maritime), Ga0495743 (Taiga Plains), Ga0485099 (Semi-Arid Plateaux), Ga0485102 (Boreal Plains), Ga0485100 (Prairies), Ga0364548 (Mixedwood Plains), Ga0373103 (Boreal Shield), Ga0372599 (Atlantic Highlands) and Ga0372598 (Atlantic Maritime). MAGs from co-assemblies and associated annotations were deposited in Dryad112 and at the European Nucleotide Archive under study accession PRJEB62834.

Publicly available datasets used in this study include HydroLakes v.1.0 (https://www.hydrosheds.org/products/hydrolakes), SoilGrids250m (https://www.soilgrids.org/), ERA5-Land (https://doi.org/10.24381/cds.e2161bac), Canada Ecozones v.5b (https://ccea-ccae.org/ecozones-downloads/), Rfam v.14.2 (https://ftp.ebi.ac.uk/pub/databases/Rfam/14.2/), Transporter Classification Database downloaded on 27 January 2021 (https://tcdb.org/public/tcdb), dbCAN2 HMMdb v.9 (https://bcb.unl.edu/dbCAN2/download/dbCAN-HMMdb-V9.txt), GTDB r95 (https://data.gtdb.ecogenomic.org/releases/release95/) and DADA2-formatted 16S rRNA gene sequences (GTDB_bac120_arc122_ssu_r95.fa.gz; https://doi.org/10.5281/zenodo.3951383). Source data are provided with this paper.

Code availability

Scripts associated with this study are available at https://github.com/rebeccagarner/lakepulse_mags.

Change history

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Acknowledgements

This study was funded by the NSERC Canadian LakePulse Network (Strategic Network Grant NETGP-479720) and Canada Research Chairs held by D.A.W. and Y.H. R.E.G. and V.E.O. received scholarships from the NSERC CREATE ÉcoLac Training Program in Lake and Fluvial Ecology. R.E.G. also received support from a Fonds de recherche du Québec – Nature et technologies Doctoral Research Scholarship and the Stephen Bronfman Graduate Scholarship in Environmental Studies. The pan-Canadian field campaigns were made possible through the immense commitment and efforts of the LakePulse sampling crews and support teams, and the cooperation and assistance of Indigenous groups, municipal and park employees, lake associations and landowners. We thank the LakePulse researchers involved in the generation, management and quality control of the dataset, and acknowledge contributions from J. Juric, P. MacKeigan, C. Paquette, M. Beaulieu, K. Griffiths, G. Potvin, B. Cremella, G. Diab, V. Fugère, J. Kim, A. Oliva and K. Velghe. We also thank W. Brookes, S. Simpson and Compute Canada for bioinformatics technical support.

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Authors and Affiliations

  1. Department of Biology, Concordia University, Montreal, Quebec, Canada

    Rebecca E. Garner, Vera E. Onana & David A. Walsh

  2. Groupe de recherche interuniversitaire en limnologie, Montreal, Quebec, Canada

    Rebecca E. Garner, Vera E. Onana, Yannick Huot & David A. Walsh

  3. Environment and Climate Change Canada, Montreal, Quebec, Canada

    Susanne A. Kraemer

  4. Département de géomatique appliquée, Université de Sherbrooke, Sherbrooke, Quebec, Canada

    Maxime Fradette, Marie-Pierre Varin & Yannick Huot

Authors
  1. Rebecca E. Garner
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  2. Susanne A. Kraemer
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  3. Vera E. Onana
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Contributions

D.A.W. conceived the study and designed the analyses with R.E.G. R.E.G., S.A.K. and V.E.O. performed the bioinformatic analyses. M.F. performed the geomatic analyses. M.-P.V. coordinated the LakePulse field campaigns and data quality control. Y.H. conceived and directed the LakePulse Network. R.E.G. and D.A.W. wrote the manuscript. All authors read and improved the final manuscript.

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Correspondence to David A. Walsh.

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Nature Microbiology thanks Luis Rodriguez-R, Helmut Bürgmann and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1

Project workflow illustrating the generation and analysis of the LakePulse MAG catalogue.

Extended Data Fig. 2

Distributions of (a) geography, (b) lake morphometry, (c) watershed soil, (d) land use, (e) climate, and (f) surface water physicochemistry variables. Colours represent ecozones and ecozone medians are indicated by dashed lines.

Extended Data Fig. 3

Number of MAGs generated within each ecozone co-assembly.

Extended Data Fig. 4

Comparison of taxonomic diversity in MAG and 16S rRNA gene ASV datasets: percentages of MAGs and ASVs assigned to (a) phyla and (b) orders.

Extended Data Fig. 5

Phylum-level biogeographic distributions of MAGs.

Extended Data Fig. 6 Hierarchical clustering (Ward’s linkage) of MAG phylum-level taxonomic assemblages.

Letter symbols above each MAG assemblage (scaled to relative TAD80) represent ecozones and coloured squares represent lake chlorophyll-a concentration.

Extended Data Fig. 7

PCoAs of (a) the taxonomic variation among ASV assemblages based on Jaccard dissimilarities and (b) the variation in metabolic gene content among metagenomes based on Bray-Curtis dissimilarities.

Extended Data Fig. 8 Significant predictors of (a) taxonomic and functional turnover across MAG assemblages and community profiles and (b) turnover in specific metabolic functions across MAG assemblages.

The height of bars represents the relative importance of predictors within the GDM. MAG assemblage turnover is shown above zero and community (ASV or metagenome) turnover is shown below zero.

Extended Data Fig. 9 Gene maps of polysaccharide utilization loci (PULs) identified across Bacteroidota MAGs.

Arrows indicate gene directions. Colours represent gene types (SusCD, CAZymes, tRNA genes, other KOs).

Extended Data Fig. 10

PCoA showing the variation in xenobiotics biodegradation and metabolism among MAG assemblages.

Supplementary information

Reporting Summary

Supplementary Tables 1–4

Supplementary Table 1: Summary of metagenome information: lake names, accession information, sampling coordinates and assembly characteristics. Table 2: Summary of MAG quality, characteristics, taxonomy, associated file names, marker gene content and TAD80 across 300 freshwater to oligosaline lakes. Table 3: Number of novel MAGs in each phylum. Table 4: Generalized dissimilarity modelling results for MAG assemblages and community (ASV and metagenome) profiles across lakes based on taxonomic and functional composition.

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Source data

Source Data Fig. 1

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Source Data Fig. 2

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Source Data Fig. 3

Statistical source data.

Source Data Fig. 4

Statistical source data.

Source Data Fig. 5

Statistical source data.

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Garner, R.E., Kraemer, S.A., Onana, V.E. et al. A genome catalogue of lake bacterial diversity and its drivers at continental scale. Nat Microbiol 8, 1920–1934 (2023). https://doi.org/10.1038/s41564-023-01435-6

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