Cargando…
Benchmarking microbial growth rate predictions from metagenomes
Growth rates are central to understanding microbial interactions and community dynamics. Metagenomic growth estimators have been developed, specifically codon usage bias (CUB) for maximum growth rates and “peak-to-trough ratio” (PTR) for in situ rates. Both were originally tested with pure cultures,...
Autores principales: | , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7852909/ https://www.ncbi.nlm.nih.gov/pubmed/32939027 http://dx.doi.org/10.1038/s41396-020-00773-1 |
_version_ | 1783645886470946816 |
---|---|
author | Long, Andrew M. Hou, Shengwei Ignacio-Espinoza, J. Cesar Fuhrman, Jed A. |
author_facet | Long, Andrew M. Hou, Shengwei Ignacio-Espinoza, J. Cesar Fuhrman, Jed A. |
author_sort | Long, Andrew M. |
collection | PubMed |
description | Growth rates are central to understanding microbial interactions and community dynamics. Metagenomic growth estimators have been developed, specifically codon usage bias (CUB) for maximum growth rates and “peak-to-trough ratio” (PTR) for in situ rates. Both were originally tested with pure cultures, but natural populations are more heterogeneous, especially in individual cell histories pertinent to PTR. To test these methods, we compared predictors with observed growth rates of freshly collected marine prokaryotes in unamended seawater. We prefiltered and diluted samples to remove grazers and greatly reduce virus infection, so net growth approximated gross growth. We sampled over 44 h for abundances and metagenomes, generating 101 metagenome-assembled genomes (MAGs), including Actinobacteria, Verrucomicrobia, SAR406, MGII archaea, etc. We tracked each MAG population by cell-abundance-normalized read recruitment, finding growth rates of 0 to 5.99 per day, the first reported rates for several groups, and used these rates as benchmarks. PTR, calculated by three methods, rarely correlated to growth (r ~−0.26–0.08), except for rapidly growing γ-Proteobacteria (r ~0.63–0.92), while CUB correlated moderately well to observed maximum growth rates (r = 0.57). This suggests that current PTR approaches poorly predict actual growth of most marine bacterial populations, but maximum growth rates can be approximated from genomic characteristics. |
format | Online Article Text |
id | pubmed-7852909 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-78529092021-02-08 Benchmarking microbial growth rate predictions from metagenomes Long, Andrew M. Hou, Shengwei Ignacio-Espinoza, J. Cesar Fuhrman, Jed A. ISME J Article Growth rates are central to understanding microbial interactions and community dynamics. Metagenomic growth estimators have been developed, specifically codon usage bias (CUB) for maximum growth rates and “peak-to-trough ratio” (PTR) for in situ rates. Both were originally tested with pure cultures, but natural populations are more heterogeneous, especially in individual cell histories pertinent to PTR. To test these methods, we compared predictors with observed growth rates of freshly collected marine prokaryotes in unamended seawater. We prefiltered and diluted samples to remove grazers and greatly reduce virus infection, so net growth approximated gross growth. We sampled over 44 h for abundances and metagenomes, generating 101 metagenome-assembled genomes (MAGs), including Actinobacteria, Verrucomicrobia, SAR406, MGII archaea, etc. We tracked each MAG population by cell-abundance-normalized read recruitment, finding growth rates of 0 to 5.99 per day, the first reported rates for several groups, and used these rates as benchmarks. PTR, calculated by three methods, rarely correlated to growth (r ~−0.26–0.08), except for rapidly growing γ-Proteobacteria (r ~0.63–0.92), while CUB correlated moderately well to observed maximum growth rates (r = 0.57). This suggests that current PTR approaches poorly predict actual growth of most marine bacterial populations, but maximum growth rates can be approximated from genomic characteristics. Nature Publishing Group UK 2020-09-16 2021-01 /pmc/articles/PMC7852909/ /pubmed/32939027 http://dx.doi.org/10.1038/s41396-020-00773-1 Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Long, Andrew M. Hou, Shengwei Ignacio-Espinoza, J. Cesar Fuhrman, Jed A. Benchmarking microbial growth rate predictions from metagenomes |
title | Benchmarking microbial growth rate predictions from metagenomes |
title_full | Benchmarking microbial growth rate predictions from metagenomes |
title_fullStr | Benchmarking microbial growth rate predictions from metagenomes |
title_full_unstemmed | Benchmarking microbial growth rate predictions from metagenomes |
title_short | Benchmarking microbial growth rate predictions from metagenomes |
title_sort | benchmarking microbial growth rate predictions from metagenomes |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7852909/ https://www.ncbi.nlm.nih.gov/pubmed/32939027 http://dx.doi.org/10.1038/s41396-020-00773-1 |
work_keys_str_mv | AT longandrewm benchmarkingmicrobialgrowthratepredictionsfrommetagenomes AT houshengwei benchmarkingmicrobialgrowthratepredictionsfrommetagenomes AT ignacioespinozajcesar benchmarkingmicrobialgrowthratepredictionsfrommetagenomes AT fuhrmanjeda benchmarkingmicrobialgrowthratepredictionsfrommetagenomes |