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On‐line untargeted metabolomics monitoring of an Escherichia coli succinate fermentation process
The real‐time monitoring of metabolites (RTMet) is instrumental for the industrial production of biobased fermentation products. This study shows the first application of untargeted on‐line metabolomics for the monitoring of undiluted fermentation broth samples taken automatically from a 5 L bioreac...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9541951/ https://www.ncbi.nlm.nih.gov/pubmed/35798686 http://dx.doi.org/10.1002/bit.28173 |
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author | Cortada‐Garcia, Joan Haggarty, Jennifer Moses, Tessa Daly, Rónán Arnold, Susan Alison Burgess, Karl |
author_facet | Cortada‐Garcia, Joan Haggarty, Jennifer Moses, Tessa Daly, Rónán Arnold, Susan Alison Burgess, Karl |
author_sort | Cortada‐Garcia, Joan |
collection | PubMed |
description | The real‐time monitoring of metabolites (RTMet) is instrumental for the industrial production of biobased fermentation products. This study shows the first application of untargeted on‐line metabolomics for the monitoring of undiluted fermentation broth samples taken automatically from a 5 L bioreactor every 5 min via flow injection mass spectrometry. The travel time from the bioreactor to the mass spectrometer was 30 s. Using mass spectrometry allows, on the one hand, the direct monitoring of targeted key process compounds of interest and, on the other hand, provides information on hundreds of additional untargeted compounds without requiring previous calibration data. In this study, this technology was applied in an Escherichia coli succinate fermentation process and 886 different m/z signals were monitored, including key process compounds (glucose, succinate, and pyruvate), potential biomarkers of biomass formation such as (R)‐2,3‐dihydroxy‐isovalerate and (R)‐2,3‐dihydroxy‐3‐methylpentanoate and compounds from the pentose phosphate pathway and nucleotide metabolism, among others. The main advantage of the RTMet technology is that it allows the monitoring of hundreds of signals without the requirement of developing partial least squares regression models, making it a perfect tool for bioprocess monitoring and for testing many different strains and process conditions for bioprocess development. |
format | Online Article Text |
id | pubmed-9541951 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-95419512022-10-14 On‐line untargeted metabolomics monitoring of an Escherichia coli succinate fermentation process Cortada‐Garcia, Joan Haggarty, Jennifer Moses, Tessa Daly, Rónán Arnold, Susan Alison Burgess, Karl Biotechnol Bioeng ARTICLES The real‐time monitoring of metabolites (RTMet) is instrumental for the industrial production of biobased fermentation products. This study shows the first application of untargeted on‐line metabolomics for the monitoring of undiluted fermentation broth samples taken automatically from a 5 L bioreactor every 5 min via flow injection mass spectrometry. The travel time from the bioreactor to the mass spectrometer was 30 s. Using mass spectrometry allows, on the one hand, the direct monitoring of targeted key process compounds of interest and, on the other hand, provides information on hundreds of additional untargeted compounds without requiring previous calibration data. In this study, this technology was applied in an Escherichia coli succinate fermentation process and 886 different m/z signals were monitored, including key process compounds (glucose, succinate, and pyruvate), potential biomarkers of biomass formation such as (R)‐2,3‐dihydroxy‐isovalerate and (R)‐2,3‐dihydroxy‐3‐methylpentanoate and compounds from the pentose phosphate pathway and nucleotide metabolism, among others. The main advantage of the RTMet technology is that it allows the monitoring of hundreds of signals without the requirement of developing partial least squares regression models, making it a perfect tool for bioprocess monitoring and for testing many different strains and process conditions for bioprocess development. John Wiley and Sons Inc. 2022-07-15 2022-10 /pmc/articles/PMC9541951/ /pubmed/35798686 http://dx.doi.org/10.1002/bit.28173 Text en © 2022 The Authors. Biotechnology and Bioengineering published by Wiley Periodicals LLC. https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | ARTICLES Cortada‐Garcia, Joan Haggarty, Jennifer Moses, Tessa Daly, Rónán Arnold, Susan Alison Burgess, Karl On‐line untargeted metabolomics monitoring of an Escherichia coli succinate fermentation process |
title | On‐line untargeted metabolomics monitoring of an Escherichia coli succinate fermentation process |
title_full | On‐line untargeted metabolomics monitoring of an Escherichia coli succinate fermentation process |
title_fullStr | On‐line untargeted metabolomics monitoring of an Escherichia coli succinate fermentation process |
title_full_unstemmed | On‐line untargeted metabolomics monitoring of an Escherichia coli succinate fermentation process |
title_short | On‐line untargeted metabolomics monitoring of an Escherichia coli succinate fermentation process |
title_sort | on‐line untargeted metabolomics monitoring of an escherichia coli succinate fermentation process |
topic | ARTICLES |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9541951/ https://www.ncbi.nlm.nih.gov/pubmed/35798686 http://dx.doi.org/10.1002/bit.28173 |
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