Randomized Lasso Links Microbial Taxa with Aquatic Functional Groups Inferred from Flow Cytometry

ABSTRACT High-nucleic-acid (HNA) and low-nucleic-acid (LNA) bacteria are two operational groups identified by flow cytometry (FCM) in aquatic systems. A number of reports have shown that HNA cell density correlates strongly with heterotrophic production, while LNA cell density does not. However, whi...

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Autores principales:	Peter Rubbens, Marian L. Schmidt, Ruben Props, Bopaiah A. Biddanda, Nico Boon, Willem Waegeman, Vincent J. Denef
Formato:	article
Lenguaje:	EN
Publicado:	American Society for Microbiology 2019
Materias:	16S rRNA aquatic microbiology bacterioplankton flow cytometry heterotrophic productivity machine learning Microbiology QR1-502
Acceso en línea:	https://doaj.org/article/0f723d3972104145bbf0064049c0cfdd
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spelling	oai:doaj.org-article:0f723d3972104145bbf0064049c0cfdd2021-12-02T19:46:17ZRandomized Lasso Links Microbial Taxa with Aquatic Functional Groups Inferred from Flow Cytometry10.1128/mSystems.00093-192379-5077https://doaj.org/article/0f723d3972104145bbf0064049c0cfdd2019-10-01T00:00:00Zhttps://journals.asm.org/doi/10.1128/mSystems.00093-19https://doaj.org/toc/2379-5077ABSTRACT High-nucleic-acid (HNA) and low-nucleic-acid (LNA) bacteria are two operational groups identified by flow cytometry (FCM) in aquatic systems. A number of reports have shown that HNA cell density correlates strongly with heterotrophic production, while LNA cell density does not. However, which taxa are specifically associated with these groups, and by extension, productivity has remained elusive. Here, we addressed this knowledge gap by using a machine learning-based variable selection approach that integrated FCM and 16S rRNA gene sequencing data collected from 14 freshwater lakes spanning a broad range in physicochemical conditions. There was a strong association between bacterial heterotrophic production and HNA absolute cell abundances (R2 = 0.65), but not with the more abundant LNA cells. This solidifies findings, mainly from marine systems, that HNA and LNA bacteria could be considered separate functional groups, the former contributing a disproportionately large share of carbon cycling. Taxa selected by the models could predict HNA and LNA absolute cell abundances at all taxonomic levels. Selected operational taxonomic units (OTUs) ranged from low to high relative abundance and were mostly lake system specific (89.5% to 99.2%). A subset of selected OTUs was associated with both LNA and HNA groups (12.5% to 33.3%), suggesting either phenotypic plasticity or within-OTU genetic and physiological heterogeneity. These findings may lead to the identification of system-specific putative ecological indicators for heterotrophic productivity. Generally, our approach allows for the association of OTUs with specific functional groups in diverse ecosystems in order to improve our understanding of (microbial) biodiversity-ecosystem functioning relationships. IMPORTANCE A major goal in microbial ecology is to understand how microbial community structure influences ecosystem functioning. Various methods to directly associate bacterial taxa to functional groups in the environment are being developed. In this study, we applied machine learning methods to relate taxonomic data obtained from marker gene surveys to functional groups identified by flow cytometry. This allowed us to identify the taxa that are associated with heterotrophic productivity in freshwater lakes and indicated that the key contributors were highly system specific, regularly rare members of the community, and that some could possibly switch between being low and high contributors. Our approach provides a promising framework to identify taxa that contribute to ecosystem functioning and can be further developed to explore microbial contributions beyond heterotrophic production.Peter RubbensMarian L. SchmidtRuben PropsBopaiah A. BiddandaNico BoonWillem WaegemanVincent J. DenefAmerican Society for Microbiologyarticle16S rRNAaquatic microbiologybacterioplanktonflow cytometryheterotrophic productivitymachine learningMicrobiologyQR1-502ENmSystems, Vol 4, Iss 5 (2019)
institution	DOAJ
collection	DOAJ
language	EN
topic	16S rRNA aquatic microbiology bacterioplankton flow cytometry heterotrophic productivity machine learning Microbiology QR1-502
spellingShingle	16S rRNA aquatic microbiology bacterioplankton flow cytometry heterotrophic productivity machine learning Microbiology QR1-502 Peter Rubbens Marian L. Schmidt Ruben Props Bopaiah A. Biddanda Nico Boon Willem Waegeman Vincent J. Denef Randomized Lasso Links Microbial Taxa with Aquatic Functional Groups Inferred from Flow Cytometry
description	ABSTRACT High-nucleic-acid (HNA) and low-nucleic-acid (LNA) bacteria are two operational groups identified by flow cytometry (FCM) in aquatic systems. A number of reports have shown that HNA cell density correlates strongly with heterotrophic production, while LNA cell density does not. However, which taxa are specifically associated with these groups, and by extension, productivity has remained elusive. Here, we addressed this knowledge gap by using a machine learning-based variable selection approach that integrated FCM and 16S rRNA gene sequencing data collected from 14 freshwater lakes spanning a broad range in physicochemical conditions. There was a strong association between bacterial heterotrophic production and HNA absolute cell abundances (R2 = 0.65), but not with the more abundant LNA cells. This solidifies findings, mainly from marine systems, that HNA and LNA bacteria could be considered separate functional groups, the former contributing a disproportionately large share of carbon cycling. Taxa selected by the models could predict HNA and LNA absolute cell abundances at all taxonomic levels. Selected operational taxonomic units (OTUs) ranged from low to high relative abundance and were mostly lake system specific (89.5% to 99.2%). A subset of selected OTUs was associated with both LNA and HNA groups (12.5% to 33.3%), suggesting either phenotypic plasticity or within-OTU genetic and physiological heterogeneity. These findings may lead to the identification of system-specific putative ecological indicators for heterotrophic productivity. Generally, our approach allows for the association of OTUs with specific functional groups in diverse ecosystems in order to improve our understanding of (microbial) biodiversity-ecosystem functioning relationships. IMPORTANCE A major goal in microbial ecology is to understand how microbial community structure influences ecosystem functioning. Various methods to directly associate bacterial taxa to functional groups in the environment are being developed. In this study, we applied machine learning methods to relate taxonomic data obtained from marker gene surveys to functional groups identified by flow cytometry. This allowed us to identify the taxa that are associated with heterotrophic productivity in freshwater lakes and indicated that the key contributors were highly system specific, regularly rare members of the community, and that some could possibly switch between being low and high contributors. Our approach provides a promising framework to identify taxa that contribute to ecosystem functioning and can be further developed to explore microbial contributions beyond heterotrophic production.
format	article
author	Peter Rubbens Marian L. Schmidt Ruben Props Bopaiah A. Biddanda Nico Boon Willem Waegeman Vincent J. Denef
author_facet	Peter Rubbens Marian L. Schmidt Ruben Props Bopaiah A. Biddanda Nico Boon Willem Waegeman Vincent J. Denef
author_sort	Peter Rubbens
title	Randomized Lasso Links Microbial Taxa with Aquatic Functional Groups Inferred from Flow Cytometry
title_short	Randomized Lasso Links Microbial Taxa with Aquatic Functional Groups Inferred from Flow Cytometry
title_full	Randomized Lasso Links Microbial Taxa with Aquatic Functional Groups Inferred from Flow Cytometry
title_fullStr	Randomized Lasso Links Microbial Taxa with Aquatic Functional Groups Inferred from Flow Cytometry
title_full_unstemmed	Randomized Lasso Links Microbial Taxa with Aquatic Functional Groups Inferred from Flow Cytometry
title_sort	randomized lasso links microbial taxa with aquatic functional groups inferred from flow cytometry
publisher	American Society for Microbiology
publishDate	2019
url	https://doaj.org/article/0f723d3972104145bbf0064049c0cfdd
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Randomized Lasso Links Microbial Taxa with Aquatic Functional Groups Inferred from Flow Cytometry

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