ArticleBiotechnology and bioengineering2022
On-line untargeted metabolomics monitoring of an Escherichia coli succinate fermentation process.
Article in Biotechnology and bioengineering, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 14 citations in OpenAlex.
- Bisphenol AF Induces Hepatic Steatosis via Succinate-SUCNR1-Mediated Macrophage-Hepatocyte Interactions: An Adverse Outcome Pathway Study in Male C57BL/6 Mice.International journal of molecular sciences · 2025Article
- From Discovery to Delivery: A Rapid and Targeted Proteomics Workflow for Monitoring Chinese Hamster Ovary Biomanufacturing.Molecular & cellular proteomics : MCP · 2025Article
- Learning metabolic dynamics from irregular observations by Bidirectional Time-Series State Transfer Network.mSystems · 2024Article
- On-line targeted metabolomics for real-time monitoring of relevant compounds in fermentation processes.Biotechnology and bioengineering · 2024Article
- 'Dark' COBioprocess and biosystems engineering · 2024Article
- Streamlined identification of strain engineering targets for bioprocess improvement using metabolic pathway enrichment analysis.Scientific reports · 2023Article
- On-line untargeted metabolomics monitoring of an Escherichia coli succinate fermentation process.Biotechnology and bioengineering · 2022Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.