On‐line untargeted metabolomics monitoring of an Escherichia coli succinate fermentation process. Issue 10 (15th July 2022)

• Record Type: Journal Article
• Authors: Cortada‐Garcia, Joan; Haggarty, Jennifer; Moses, Tessa; Daly, Rónán; Arnold, Susan Alison; Burgess, Karl
• Is Part Of: Biotechnology and bioengineering. Volume 119:Issue 10(2022)
• Page Start: 2757
• Page End: 2769
• Publication Date: 2022-07-15
• Subjects: bioprocess monitoring -- fermentation monitoring -- on‐line metabolomics -- real‐time metabolomics -- succinate; Biotechnology -- Periodicals; Bioengineering -- Periodicals; 660.6
• DOI: 10.1002/bit.28173
• Languages: English
• ISSNs: 0006-3592

Abstract

Abstract: 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. Abstract : Untargeted metabolomics was used in this study to monitor an Escherichia coli fermentation process of succinate production, achieving the automatic analysis of 886 m / z signals every 5 min from a bench‐top 5 L bioreactor. Cortada‐Garcia and co‐workers describe how this technology can be used to find key bioprocess compounds and monitoring biomarkers, as well as help identify relevant pathways for the bioprocess without the requirement of developing partial least squares regression models, making it a perfect tool for bioprocess development.