Mapping Microbial Abundance and Prevalence to Changing Oxygen Concentration in Deep-Sea Sediments Using Machine Learning and Differential Abundance
Oxygen constitutes one of the strongest factors explaining microbial taxonomic variability in deep-sea sediments. However, deep-sea microbiome studies often lack the spatial resolution to study the oxygen gradient and transition zone beyond the oxic-anoxic dichotomy, thus leaving important questions...
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ftpubmed:oai:pubmedcentral.nih.gov:9158483 2023-05-15T15:08:12+02:00 Mapping Microbial Abundance and Prevalence to Changing Oxygen Concentration in Deep-Sea Sediments Using Machine Learning and Differential Abundance Møller, Tor Einar Le Moine Bauer, Sven Hannisdal, Bjarte Zhao, Rui Baumberger, Tamara Roerdink, Desiree L. Dupuis, Amandine Thorseth, Ingunn H. Pedersen, Rolf Birger Jørgensen, Steffen Leth 2022-05-18 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9158483/ https://doi.org/10.3389/fmicb.2022.804575 en eng Frontiers Media S.A. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9158483/ http://dx.doi.org/10.3389/fmicb.2022.804575 Copyright © 2022 Møller, Le Moine Bauer, Hannisdal, Zhao, Baumberger, Roerdink, Dupuis, Thorseth, Pedersen and Jørgensen. https://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. CC-BY Front Microbiol Microbiology Text 2022 ftpubmed https://doi.org/10.3389/fmicb.2022.804575 2022-06-05T01:18:21Z Oxygen constitutes one of the strongest factors explaining microbial taxonomic variability in deep-sea sediments. However, deep-sea microbiome studies often lack the spatial resolution to study the oxygen gradient and transition zone beyond the oxic-anoxic dichotomy, thus leaving important questions regarding the microbial response to changing conditions unanswered. Here, we use machine learning and differential abundance analysis on 184 samples from 11 sediment cores retrieved along the Arctic Mid-Ocean Ridge to study how changing oxygen concentrations (1) are predicted by the relative abundance of higher taxa and (2) influence the distribution of individual Operational Taxonomic Units. We find that some of the most abundant classes of microorganisms can be used to classify samples according to oxygen concentration. At the level of Operational Taxonomic Units, however, representatives of common classes are not differentially abundant from high-oxic to low-oxic conditions. This weakened response to changing oxygen concentration suggests that the abundance and prevalence of highly abundant OTUs may be better explained by other variables than oxygen. Our results suggest that a relatively homogeneous microbiome is recruited to the benthos, and that the microbiome then becomes more heterogeneous as oxygen drops below 25 μM. Our analytical approach takes into account the oft-ignored compositional nature of relative abundance data, and provides a framework for extracting biologically meaningful associations from datasets spanning multiple sedimentary cores. Text Arctic PubMed Central (PMC) Arctic Frontiers in Microbiology 13 |
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PubMed Central (PMC) |
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English |
topic |
Microbiology |
spellingShingle |
Microbiology Møller, Tor Einar Le Moine Bauer, Sven Hannisdal, Bjarte Zhao, Rui Baumberger, Tamara Roerdink, Desiree L. Dupuis, Amandine Thorseth, Ingunn H. Pedersen, Rolf Birger Jørgensen, Steffen Leth Mapping Microbial Abundance and Prevalence to Changing Oxygen Concentration in Deep-Sea Sediments Using Machine Learning and Differential Abundance |
topic_facet |
Microbiology |
description |
Oxygen constitutes one of the strongest factors explaining microbial taxonomic variability in deep-sea sediments. However, deep-sea microbiome studies often lack the spatial resolution to study the oxygen gradient and transition zone beyond the oxic-anoxic dichotomy, thus leaving important questions regarding the microbial response to changing conditions unanswered. Here, we use machine learning and differential abundance analysis on 184 samples from 11 sediment cores retrieved along the Arctic Mid-Ocean Ridge to study how changing oxygen concentrations (1) are predicted by the relative abundance of higher taxa and (2) influence the distribution of individual Operational Taxonomic Units. We find that some of the most abundant classes of microorganisms can be used to classify samples according to oxygen concentration. At the level of Operational Taxonomic Units, however, representatives of common classes are not differentially abundant from high-oxic to low-oxic conditions. This weakened response to changing oxygen concentration suggests that the abundance and prevalence of highly abundant OTUs may be better explained by other variables than oxygen. Our results suggest that a relatively homogeneous microbiome is recruited to the benthos, and that the microbiome then becomes more heterogeneous as oxygen drops below 25 μM. Our analytical approach takes into account the oft-ignored compositional nature of relative abundance data, and provides a framework for extracting biologically meaningful associations from datasets spanning multiple sedimentary cores. |
format |
Text |
author |
Møller, Tor Einar Le Moine Bauer, Sven Hannisdal, Bjarte Zhao, Rui Baumberger, Tamara Roerdink, Desiree L. Dupuis, Amandine Thorseth, Ingunn H. Pedersen, Rolf Birger Jørgensen, Steffen Leth |
author_facet |
Møller, Tor Einar Le Moine Bauer, Sven Hannisdal, Bjarte Zhao, Rui Baumberger, Tamara Roerdink, Desiree L. Dupuis, Amandine Thorseth, Ingunn H. Pedersen, Rolf Birger Jørgensen, Steffen Leth |
author_sort |
Møller, Tor Einar |
title |
Mapping Microbial Abundance and Prevalence to Changing Oxygen Concentration in Deep-Sea Sediments Using Machine Learning and Differential Abundance |
title_short |
Mapping Microbial Abundance and Prevalence to Changing Oxygen Concentration in Deep-Sea Sediments Using Machine Learning and Differential Abundance |
title_full |
Mapping Microbial Abundance and Prevalence to Changing Oxygen Concentration in Deep-Sea Sediments Using Machine Learning and Differential Abundance |
title_fullStr |
Mapping Microbial Abundance and Prevalence to Changing Oxygen Concentration in Deep-Sea Sediments Using Machine Learning and Differential Abundance |
title_full_unstemmed |
Mapping Microbial Abundance and Prevalence to Changing Oxygen Concentration in Deep-Sea Sediments Using Machine Learning and Differential Abundance |
title_sort |
mapping microbial abundance and prevalence to changing oxygen concentration in deep-sea sediments using machine learning and differential abundance |
publisher |
Frontiers Media S.A. |
publishDate |
2022 |
url |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9158483/ https://doi.org/10.3389/fmicb.2022.804575 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic |
genre_facet |
Arctic |
op_source |
Front Microbiol |
op_relation |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9158483/ http://dx.doi.org/10.3389/fmicb.2022.804575 |
op_rights |
Copyright © 2022 Møller, Le Moine Bauer, Hannisdal, Zhao, Baumberger, Roerdink, Dupuis, Thorseth, Pedersen and Jørgensen. https://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. |
op_rightsnorm |
CC-BY |
op_doi |
https://doi.org/10.3389/fmicb.2022.804575 |
container_title |
Frontiers in Microbiology |
container_volume |
13 |
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1766339602702925824 |