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Does It Pay Off to Explicitly Link Functional Gene Expression to Denitrification Rates in Reaction Models?

Environmental omics and molecular-biological data have been proposed to yield improved quantitative predictions of biogeochemical processes. The abundances of functional genes and transcripts relate to the number of cells and activity of microorganisms. However, whether molecular-biological data can...

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Autores principales: Störiko, Anna, Pagel, Holger, Mellage, Adrian, Cirpka, Olaf A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8250433/
https://www.ncbi.nlm.nih.gov/pubmed/34220770
http://dx.doi.org/10.3389/fmicb.2021.684146
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author Störiko, Anna
Pagel, Holger
Mellage, Adrian
Cirpka, Olaf A.
author_facet Störiko, Anna
Pagel, Holger
Mellage, Adrian
Cirpka, Olaf A.
author_sort Störiko, Anna
collection PubMed
description Environmental omics and molecular-biological data have been proposed to yield improved quantitative predictions of biogeochemical processes. The abundances of functional genes and transcripts relate to the number of cells and activity of microorganisms. However, whether molecular-biological data can be quantitatively linked to reaction rates remains an open question. We present an enzyme-based denitrification model that simulates concentrations of transcription factors, functional-gene transcripts, enzymes, and solutes. We calibrated the model using experimental data from a well-controlled batch experiment with the denitrifier Paracoccous denitrificans. The model accurately predicts denitrification rates and measured transcript dynamics. The relationship between simulated transcript concentrations and reaction rates exhibits strong non-linearity and hysteresis related to the faster dynamics of gene transcription and substrate consumption, relative to enzyme production and decay. Hence, assuming a unique relationship between transcript-to-gene ratios and reaction rates, as frequently suggested, may be an erroneous simplification. Comparing model results of our enzyme-based model to those of a classical Monod-type model reveals that both formulations perform equally well with respect to nitrogen species, indicating only a low benefit of integrating molecular-biological data for estimating denitrification rates. Nonetheless, the enzyme-based model is a valuable tool to improve our mechanistic understanding of the relationship between biomolecular quantities and reaction rates. Furthermore, our results highlight that both enzyme kinetics (i.e., substrate limitation and inhibition) and gene expression or enzyme dynamics are important controls on denitrification rates.
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spelling pubmed-82504332021-07-03 Does It Pay Off to Explicitly Link Functional Gene Expression to Denitrification Rates in Reaction Models? Störiko, Anna Pagel, Holger Mellage, Adrian Cirpka, Olaf A. Front Microbiol Microbiology Environmental omics and molecular-biological data have been proposed to yield improved quantitative predictions of biogeochemical processes. The abundances of functional genes and transcripts relate to the number of cells and activity of microorganisms. However, whether molecular-biological data can be quantitatively linked to reaction rates remains an open question. We present an enzyme-based denitrification model that simulates concentrations of transcription factors, functional-gene transcripts, enzymes, and solutes. We calibrated the model using experimental data from a well-controlled batch experiment with the denitrifier Paracoccous denitrificans. The model accurately predicts denitrification rates and measured transcript dynamics. The relationship between simulated transcript concentrations and reaction rates exhibits strong non-linearity and hysteresis related to the faster dynamics of gene transcription and substrate consumption, relative to enzyme production and decay. Hence, assuming a unique relationship between transcript-to-gene ratios and reaction rates, as frequently suggested, may be an erroneous simplification. Comparing model results of our enzyme-based model to those of a classical Monod-type model reveals that both formulations perform equally well with respect to nitrogen species, indicating only a low benefit of integrating molecular-biological data for estimating denitrification rates. Nonetheless, the enzyme-based model is a valuable tool to improve our mechanistic understanding of the relationship between biomolecular quantities and reaction rates. Furthermore, our results highlight that both enzyme kinetics (i.e., substrate limitation and inhibition) and gene expression or enzyme dynamics are important controls on denitrification rates. Frontiers Media S.A. 2021-06-18 /pmc/articles/PMC8250433/ /pubmed/34220770 http://dx.doi.org/10.3389/fmicb.2021.684146 Text en Copyright © 2021 Störiko, Pagel, Mellage and Cirpka. 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.
spellingShingle Microbiology
Störiko, Anna
Pagel, Holger
Mellage, Adrian
Cirpka, Olaf A.
Does It Pay Off to Explicitly Link Functional Gene Expression to Denitrification Rates in Reaction Models?
title Does It Pay Off to Explicitly Link Functional Gene Expression to Denitrification Rates in Reaction Models?
title_full Does It Pay Off to Explicitly Link Functional Gene Expression to Denitrification Rates in Reaction Models?
title_fullStr Does It Pay Off to Explicitly Link Functional Gene Expression to Denitrification Rates in Reaction Models?
title_full_unstemmed Does It Pay Off to Explicitly Link Functional Gene Expression to Denitrification Rates in Reaction Models?
title_short Does It Pay Off to Explicitly Link Functional Gene Expression to Denitrification Rates in Reaction Models?
title_sort does it pay off to explicitly link functional gene expression to denitrification rates in reaction models?
topic Microbiology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8250433/
https://www.ncbi.nlm.nih.gov/pubmed/34220770
http://dx.doi.org/10.3389/fmicb.2021.684146
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