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Can We Use Functional Genetics to Predict the Fate of Nitrogen in Estuaries?

Increasing nitrogen (N) loads present a threat to estuaries, which are among the most heavily populated and perturbed parts of the world. N removal is largely mediated by the sediment microbial process of denitrification, in direct competition to dissimilatory nitrate reduction to ammonium (DNRA), w...

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Autores principales: Raes, Eric J., Karsh, Kristen, Kessler, Adam J., Cook, Perran L. M., Holmes, Bronwyn H., van de Kamp, Jodie, Bodrossy, Levente, Bissett, Andrew
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7325967/
https://www.ncbi.nlm.nih.gov/pubmed/32655525
http://dx.doi.org/10.3389/fmicb.2020.01261
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author Raes, Eric J.
Karsh, Kristen
Kessler, Adam J.
Cook, Perran L. M.
Holmes, Bronwyn H.
van de Kamp, Jodie
Bodrossy, Levente
Bissett, Andrew
author_facet Raes, Eric J.
Karsh, Kristen
Kessler, Adam J.
Cook, Perran L. M.
Holmes, Bronwyn H.
van de Kamp, Jodie
Bodrossy, Levente
Bissett, Andrew
author_sort Raes, Eric J.
collection PubMed
description Increasing nitrogen (N) loads present a threat to estuaries, which are among the most heavily populated and perturbed parts of the world. N removal is largely mediated by the sediment microbial process of denitrification, in direct competition to dissimilatory nitrate reduction to ammonium (DNRA), which recycles nitrate to ammonium. Molecular proxies for N pathways are increasingly measured and analyzed, a major question in microbial ecology, however, is whether these proxies can add predictive power around the fate of N. We analyzed the diversity and community composition of sediment nirS and nrfA genes in 11 temperate estuaries, covering four types of land use in Australia, and analyzed how these might be used to predict N removal. Our data suggest that sediment microbiomes play a central role in controlling the magnitude of the individual N removal rates in the 11 estuaries. Inclusion, however, of relative gene abundances of 16S, nirS, nrfA, including their ratios did not improve physicochemical measurement-based regression models to predict rates of denitrification or DNRA. Co-occurrence network analyses of nirS showed a greater modularity and a lower number of keystone OTUs in pristine sites compared to urban estuaries, suggesting a higher degree of niche partitioning in pristine estuaries. The distinctive differences between the urban and pristine network structures suggest that the nirS gene could be a likely gene candidate to understand the mechanisms by which these denitrifying communities form and respond to anthropogenic pressures.
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spelling pubmed-73259672020-07-09 Can We Use Functional Genetics to Predict the Fate of Nitrogen in Estuaries? Raes, Eric J. Karsh, Kristen Kessler, Adam J. Cook, Perran L. M. Holmes, Bronwyn H. van de Kamp, Jodie Bodrossy, Levente Bissett, Andrew Front Microbiol Microbiology Increasing nitrogen (N) loads present a threat to estuaries, which are among the most heavily populated and perturbed parts of the world. N removal is largely mediated by the sediment microbial process of denitrification, in direct competition to dissimilatory nitrate reduction to ammonium (DNRA), which recycles nitrate to ammonium. Molecular proxies for N pathways are increasingly measured and analyzed, a major question in microbial ecology, however, is whether these proxies can add predictive power around the fate of N. We analyzed the diversity and community composition of sediment nirS and nrfA genes in 11 temperate estuaries, covering four types of land use in Australia, and analyzed how these might be used to predict N removal. Our data suggest that sediment microbiomes play a central role in controlling the magnitude of the individual N removal rates in the 11 estuaries. Inclusion, however, of relative gene abundances of 16S, nirS, nrfA, including their ratios did not improve physicochemical measurement-based regression models to predict rates of denitrification or DNRA. Co-occurrence network analyses of nirS showed a greater modularity and a lower number of keystone OTUs in pristine sites compared to urban estuaries, suggesting a higher degree of niche partitioning in pristine estuaries. The distinctive differences between the urban and pristine network structures suggest that the nirS gene could be a likely gene candidate to understand the mechanisms by which these denitrifying communities form and respond to anthropogenic pressures. Frontiers Media S.A. 2020-06-11 /pmc/articles/PMC7325967/ /pubmed/32655525 http://dx.doi.org/10.3389/fmicb.2020.01261 Text en Copyright © 2020 Raes, Karsh, Kessler, Cook, Holmes, van de Kamp, Bodrossy and Bissett. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Microbiology
Raes, Eric J.
Karsh, Kristen
Kessler, Adam J.
Cook, Perran L. M.
Holmes, Bronwyn H.
van de Kamp, Jodie
Bodrossy, Levente
Bissett, Andrew
Can We Use Functional Genetics to Predict the Fate of Nitrogen in Estuaries?
title Can We Use Functional Genetics to Predict the Fate of Nitrogen in Estuaries?
title_full Can We Use Functional Genetics to Predict the Fate of Nitrogen in Estuaries?
title_fullStr Can We Use Functional Genetics to Predict the Fate of Nitrogen in Estuaries?
title_full_unstemmed Can We Use Functional Genetics to Predict the Fate of Nitrogen in Estuaries?
title_short Can We Use Functional Genetics to Predict the Fate of Nitrogen in Estuaries?
title_sort can we use functional genetics to predict the fate of nitrogen in estuaries?
topic Microbiology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7325967/
https://www.ncbi.nlm.nih.gov/pubmed/32655525
http://dx.doi.org/10.3389/fmicb.2020.01261
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