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Microbial turnover times in the deep seabed studied by amino acid racemization modelling

The study of active microbial populations in deep, energy-limited marine sediments has extended our knowledge of the limits of life on Earth. Typically, microbial activity in the deep biosphere is calculated by transport-reaction modelling of pore water solutes or from experimental measurements invo...

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Autores principales: Braun, Stefan, Mhatre, Snehit S., Jaussi, Marion, Røy, Hans, Kjeldsen, Kasper U., Pearce, Christof, Seidenkrantz, Marit-Solveig, Jørgensen, Bo Barker, Lomstein, Bente Aa.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5516024/
https://www.ncbi.nlm.nih.gov/pubmed/28720809
http://dx.doi.org/10.1038/s41598-017-05972-z
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author Braun, Stefan
Mhatre, Snehit S.
Jaussi, Marion
Røy, Hans
Kjeldsen, Kasper U.
Pearce, Christof
Seidenkrantz, Marit-Solveig
Jørgensen, Bo Barker
Lomstein, Bente Aa.
author_facet Braun, Stefan
Mhatre, Snehit S.
Jaussi, Marion
Røy, Hans
Kjeldsen, Kasper U.
Pearce, Christof
Seidenkrantz, Marit-Solveig
Jørgensen, Bo Barker
Lomstein, Bente Aa.
author_sort Braun, Stefan
collection PubMed
description The study of active microbial populations in deep, energy-limited marine sediments has extended our knowledge of the limits of life on Earth. Typically, microbial activity in the deep biosphere is calculated by transport-reaction modelling of pore water solutes or from experimental measurements involving radiotracers. Here we modelled microbial activity from the degree of D:L-aspartic acid racemization in microbial necromass (remains of dead microbial biomass) in sediments up to ten million years old. This recently developed approach (D:L-amino acid modelling) does not require incubation experiments and is highly sensitive in stable, low-activity environments. We applied for the first time newly established constraints on several important input parameters of the D:L-amino acid model, such as a higher aspartic acid racemization rate constant and a lower cell-specific carbon content of sub-seafloor microorganisms. Our model results show that the pool of necromass amino acids is turned over by microbial activity every few thousand years, while the turnover times of vegetative cells are in the order of years to decades. Notably, microbial turnover times in million-year-old sediment from the Peru Margin are up to 100-fold shorter than previous estimates, highlighting the influence of microbial activities on element cycling over geologic time scales.
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spelling pubmed-55160242017-07-19 Microbial turnover times in the deep seabed studied by amino acid racemization modelling Braun, Stefan Mhatre, Snehit S. Jaussi, Marion Røy, Hans Kjeldsen, Kasper U. Pearce, Christof Seidenkrantz, Marit-Solveig Jørgensen, Bo Barker Lomstein, Bente Aa. Sci Rep Article The study of active microbial populations in deep, energy-limited marine sediments has extended our knowledge of the limits of life on Earth. Typically, microbial activity in the deep biosphere is calculated by transport-reaction modelling of pore water solutes or from experimental measurements involving radiotracers. Here we modelled microbial activity from the degree of D:L-aspartic acid racemization in microbial necromass (remains of dead microbial biomass) in sediments up to ten million years old. This recently developed approach (D:L-amino acid modelling) does not require incubation experiments and is highly sensitive in stable, low-activity environments. We applied for the first time newly established constraints on several important input parameters of the D:L-amino acid model, such as a higher aspartic acid racemization rate constant and a lower cell-specific carbon content of sub-seafloor microorganisms. Our model results show that the pool of necromass amino acids is turned over by microbial activity every few thousand years, while the turnover times of vegetative cells are in the order of years to decades. Notably, microbial turnover times in million-year-old sediment from the Peru Margin are up to 100-fold shorter than previous estimates, highlighting the influence of microbial activities on element cycling over geologic time scales. Nature Publishing Group UK 2017-07-18 /pmc/articles/PMC5516024/ /pubmed/28720809 http://dx.doi.org/10.1038/s41598-017-05972-z Text en © The Author(s) 2017 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
Braun, Stefan
Mhatre, Snehit S.
Jaussi, Marion
Røy, Hans
Kjeldsen, Kasper U.
Pearce, Christof
Seidenkrantz, Marit-Solveig
Jørgensen, Bo Barker
Lomstein, Bente Aa.
Microbial turnover times in the deep seabed studied by amino acid racemization modelling
title Microbial turnover times in the deep seabed studied by amino acid racemization modelling
title_full Microbial turnover times in the deep seabed studied by amino acid racemization modelling
title_fullStr Microbial turnover times in the deep seabed studied by amino acid racemization modelling
title_full_unstemmed Microbial turnover times in the deep seabed studied by amino acid racemization modelling
title_short Microbial turnover times in the deep seabed studied by amino acid racemization modelling
title_sort microbial turnover times in the deep seabed studied by amino acid racemization modelling
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5516024/
https://www.ncbi.nlm.nih.gov/pubmed/28720809
http://dx.doi.org/10.1038/s41598-017-05972-z
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