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...
Published in: | Frontiers in Microbiology |
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2022
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Online Access: | https://doi.org/10.3389/fmicb.2022.804575 https://doaj.org/article/e318bba5b6f849dbb83a2368b33a27be |
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ftdoajarticles:oai:doaj.org/article:e318bba5b6f849dbb83a2368b33a27be 2023-05-15T14:59:59+02:00 Mapping Microbial Abundance and Prevalence to Changing Oxygen Concentration in Deep-Sea Sediments Using Machine Learning and Differential Abundance Tor Einar Møller Sven Le Moine Bauer Bjarte Hannisdal Rui Zhao Tamara Baumberger Desiree L. Roerdink Amandine Dupuis Ingunn H. Thorseth Rolf Birger Pedersen Steffen Leth Jørgensen 2022-05-01T00:00:00Z https://doi.org/10.3389/fmicb.2022.804575 https://doaj.org/article/e318bba5b6f849dbb83a2368b33a27be EN eng Frontiers Media S.A. https://www.frontiersin.org/articles/10.3389/fmicb.2022.804575/full https://doaj.org/toc/1664-302X 1664-302X doi:10.3389/fmicb.2022.804575 https://doaj.org/article/e318bba5b6f849dbb83a2368b33a27be Frontiers in Microbiology, Vol 13 (2022) Support Vector Machines Compositional Data Analysis Arctic Mid-Ocean Ridge Norwegian-Greenland Sea threshold response classification Microbiology QR1-502 article 2022 ftdoajarticles https://doi.org/10.3389/fmicb.2022.804575 2022-12-30T23:31:04Z 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. Article in Journal/Newspaper Arctic Greenland Greenland Sea Directory of Open Access Journals: DOAJ Articles Arctic Greenland Frontiers in Microbiology 13 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
Support Vector Machines Compositional Data Analysis Arctic Mid-Ocean Ridge Norwegian-Greenland Sea threshold response classification Microbiology QR1-502 |
spellingShingle |
Support Vector Machines Compositional Data Analysis Arctic Mid-Ocean Ridge Norwegian-Greenland Sea threshold response classification Microbiology QR1-502 Tor Einar Møller Sven Le Moine Bauer Bjarte Hannisdal Rui Zhao Tamara Baumberger Desiree L. Roerdink Amandine Dupuis Ingunn H. Thorseth Rolf Birger Pedersen Steffen Leth Jørgensen Mapping Microbial Abundance and Prevalence to Changing Oxygen Concentration in Deep-Sea Sediments Using Machine Learning and Differential Abundance |
topic_facet |
Support Vector Machines Compositional Data Analysis Arctic Mid-Ocean Ridge Norwegian-Greenland Sea threshold response classification Microbiology QR1-502 |
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 |
Article in Journal/Newspaper |
author |
Tor Einar Møller Sven Le Moine Bauer Bjarte Hannisdal Rui Zhao Tamara Baumberger Desiree L. Roerdink Amandine Dupuis Ingunn H. Thorseth Rolf Birger Pedersen Steffen Leth Jørgensen |
author_facet |
Tor Einar Møller Sven Le Moine Bauer Bjarte Hannisdal Rui Zhao Tamara Baumberger Desiree L. Roerdink Amandine Dupuis Ingunn H. Thorseth Rolf Birger Pedersen Steffen Leth Jørgensen |
author_sort |
Tor Einar Møller |
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 |
https://doi.org/10.3389/fmicb.2022.804575 https://doaj.org/article/e318bba5b6f849dbb83a2368b33a27be |
geographic |
Arctic Greenland |
geographic_facet |
Arctic Greenland |
genre |
Arctic Greenland Greenland Sea |
genre_facet |
Arctic Greenland Greenland Sea |
op_source |
Frontiers in Microbiology, Vol 13 (2022) |
op_relation |
https://www.frontiersin.org/articles/10.3389/fmicb.2022.804575/full https://doaj.org/toc/1664-302X 1664-302X doi:10.3389/fmicb.2022.804575 https://doaj.org/article/e318bba5b6f849dbb83a2368b33a27be |
op_doi |
https://doi.org/10.3389/fmicb.2022.804575 |
container_title |
Frontiers in Microbiology |
container_volume |
13 |
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1766332092244819968 |