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Microbial dark matter could add uncertainties to metagenomic trait estimations

Nature Microbiology (2024)Cite this article

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The Original Article was published on 05 October 2023

arising from: G. Piton et al. Nat. Microbiol. https://doi.org/10.1038/s41564-023-01465-0 (2023).

Estimating life history traits of bacterial communities from metagenomes is an increasingly common practice in microbial ecology research1,2,3,4. Contributing to this area of inquiry, Piton et al.5 use a global dataset of soil metagenomes to describe life history trait dimensions of soil bacteria. Our analysis of the same dataset suggests that the genome size analysis of Piton et al. may underestimate potential biases introduced by varying proportions of non-bacterial DNA among different ecosystem types. We demonstrate how this potential bias could influence the relationships of this trait with environmental variables, thus altering the interpretation of the identified trait dimensions.

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Fig. 1: Evidence of varying proportions of non-bacterial DNA in soil metagenomes from different ecosystems.
Fig. 2: Evidence that varying proportions of non-bacterial DNA influences estimates of bacterial community life history and relationships with environmental variables.

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Author information

Authors and Affiliations

  1. Department of Plant and Soil Sciences, University of Kentucky, Lexington, KY, USA

    Ernest D. Osburn

  2. Department of Soil and Water Systems, University of Idaho, Moscow, ID, USA

    Ernest D. Osburn & Michael S. Strickland

  3. Department of Biology, Radford University, Radford, VA, USA

    Steven G. McBride

Authors
  1. Ernest D. Osburn
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  2. Steven G. McBride
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  3. Michael S. Strickland
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Contributions

E.D.O and S.G.M. conceived the study. E.D.O conducted the bioinformatic and statistical analyses. M.S.S. supervised the project. All authors contributed to the writing and editing of the manuscript.

Corresponding author

Correspondence to Ernest D. Osburn.

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The authors declare no competing interests.

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Nature Microbiology thanks Kate Buckeridge and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1

Percent of metagenomic reads not classified to any taxon (a), percent of reads classified as bacteria between forested and non-forested ecosystems (b), and percentage of genome equivalents recovered in the ‘bacterial’ metagenomes that were found in the full metagenomes (c). Classification was done using kraken2 with RefSeq genomes for bacteria, archaea, viruses, fungi, and protists as reference databases. Genomes within the metagenomes were quantified by determining mean coverage of 30 single-copy genes using MicrobeCensus. The R2 value and line of best fit in (a) and (c) are from linear regression. Asterisks in (b) indicate significantly higher percentage of reads classified as bacterial in non-forested environments (p < 0.001).

Extended Data Fig. 2

Average genome size in the full metagenomes as a function of the percentage of reads classified as bacteria (a) and the ratio of eukaryotic to bacterial reads (b). R2 values and lines of best fit are from linear regression (both p < 0.001). Average genome sizes were determined using MicrobeCensus.

Extended Data Fig. 3

Metagenome GC content in the full (a) and bacterial (b) metagenomes and average 16S rRNA gene copy number in the full (c) and bacterial (d) metagenomes as a function of soil pH. R2 values and best-fit lines on (a) and (b) are from linear regression (both p < 0.001). Regression models for 16S gene copy number in (c) and (d) were not significant (both p > 0.05).

Supplementary information

Supplementary Material

Supplementary methods, bioinformatic analyses and statistical analyses.

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Osburn, E.D., McBride, S.G. & Strickland, M.S. Microbial dark matter could add uncertainties to metagenomic trait estimations. Nat Microbiol (2024). https://doi.org/10.1038/s41564-024-01687-w

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