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Oxygen isotope effects during microbial sulfate reduction: applications to sediment cell abundances

The majority of anaerobic biogeochemical cycling occurs within marine sediments. To understand these processes, quantifying the distribution of active cells and gross metabolic activity is essential. We present an isotope model rooted in thermodynamics to draw quantitative links between cell-specifi...

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Autores principales: Bertran, E., Waldeck, A., Wing, B. A., Halevy, I., Leavitt, W. D., Bradley, A. S., Johnston, D. T.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7242377/
https://www.ncbi.nlm.nih.gov/pubmed/32152390
http://dx.doi.org/10.1038/s41396-020-0618-2
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author Bertran, E.
Waldeck, A.
Wing, B. A.
Halevy, I.
Leavitt, W. D.
Bradley, A. S.
Johnston, D. T.
author_facet Bertran, E.
Waldeck, A.
Wing, B. A.
Halevy, I.
Leavitt, W. D.
Bradley, A. S.
Johnston, D. T.
author_sort Bertran, E.
collection PubMed
description The majority of anaerobic biogeochemical cycling occurs within marine sediments. To understand these processes, quantifying the distribution of active cells and gross metabolic activity is essential. We present an isotope model rooted in thermodynamics to draw quantitative links between cell-specific sulfate reduction rates and active sedimentary cell abundances. This model is calibrated using data from a series of continuous culture experiments with two strains of sulfate reducing bacteria (freshwater bacterium Desulfovibrio vulgaris strain Hildenborough, and marine bacterium Desulfovibrio alaskensis strain G-20) grown on lactate across a range of metabolic rates and ambient sulfate concentrations. We use a combination of experimental sulfate oxygen isotope data and nonlinear regression fitting tools to solve for unknown kinetic, step-specific oxygen isotope effects. This approach enables identification of key isotopic reactions within the metabolic pathway, and defines a new, calibrated framework for understanding oxygen isotope variability in sulfate. This approach is then combined with porewater sulfate/sulfide concentration data and diagenetic modeling to reproduce measured (18)O/(16)O in porewater sulfate. From here, we infer cell-specific sulfate reduction rates and predict abundance of active cells of sulfate reducing bacteria, the result of which is consistent with direct biological measurements.
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spelling pubmed-72423772020-05-29 Oxygen isotope effects during microbial sulfate reduction: applications to sediment cell abundances Bertran, E. Waldeck, A. Wing, B. A. Halevy, I. Leavitt, W. D. Bradley, A. S. Johnston, D. T. ISME J Article The majority of anaerobic biogeochemical cycling occurs within marine sediments. To understand these processes, quantifying the distribution of active cells and gross metabolic activity is essential. We present an isotope model rooted in thermodynamics to draw quantitative links between cell-specific sulfate reduction rates and active sedimentary cell abundances. This model is calibrated using data from a series of continuous culture experiments with two strains of sulfate reducing bacteria (freshwater bacterium Desulfovibrio vulgaris strain Hildenborough, and marine bacterium Desulfovibrio alaskensis strain G-20) grown on lactate across a range of metabolic rates and ambient sulfate concentrations. We use a combination of experimental sulfate oxygen isotope data and nonlinear regression fitting tools to solve for unknown kinetic, step-specific oxygen isotope effects. This approach enables identification of key isotopic reactions within the metabolic pathway, and defines a new, calibrated framework for understanding oxygen isotope variability in sulfate. This approach is then combined with porewater sulfate/sulfide concentration data and diagenetic modeling to reproduce measured (18)O/(16)O in porewater sulfate. From here, we infer cell-specific sulfate reduction rates and predict abundance of active cells of sulfate reducing bacteria, the result of which is consistent with direct biological measurements. Nature Publishing Group UK 2020-03-09 2020-06 /pmc/articles/PMC7242377/ /pubmed/32152390 http://dx.doi.org/10.1038/s41396-020-0618-2 Text en © The Author(s) 2020 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
Bertran, E.
Waldeck, A.
Wing, B. A.
Halevy, I.
Leavitt, W. D.
Bradley, A. S.
Johnston, D. T.
Oxygen isotope effects during microbial sulfate reduction: applications to sediment cell abundances
title Oxygen isotope effects during microbial sulfate reduction: applications to sediment cell abundances
title_full Oxygen isotope effects during microbial sulfate reduction: applications to sediment cell abundances
title_fullStr Oxygen isotope effects during microbial sulfate reduction: applications to sediment cell abundances
title_full_unstemmed Oxygen isotope effects during microbial sulfate reduction: applications to sediment cell abundances
title_short Oxygen isotope effects during microbial sulfate reduction: applications to sediment cell abundances
title_sort oxygen isotope effects during microbial sulfate reduction: applications to sediment cell abundances
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7242377/
https://www.ncbi.nlm.nih.gov/pubmed/32152390
http://dx.doi.org/10.1038/s41396-020-0618-2
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