Evidence map›Paper›PMID 35798686›Full record

ArticleBiotechnology and bioengineering2022

On-line untargeted metabolomics monitoring of an Escherichia coli succinate fermentation process.

Joan Cortada-Garcia, Jennifer Haggarty, Tessa Moses, Rónán Daly, Susan Alison Arnold, Karl Burgess

Open access · hybridAbstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
1.1field-weighted citation impact, top 24% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed, 14 citations in OpenAlex.

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  5. 'Dark' COBioprocess and biosystems engineering · 2024
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 3 institutions in 1 country.

Joan Cortada-GarciaInstitute of Quantitative Biology, Biochemistry and Biotechnology, School of Biological Sciences, University of Edinburgh, Edinburgh, UK.ORCID 0000-0001-5989-9215
Jennifer HaggartyInstitute of Infection, Immunity and Inflammation, Glasgow Polyomics, University of Glasgow, Glasgow, UK.
Tessa MosesEdinOmics, SynthSys-Centre for Synthetic and Systems Biology, School of Biological Sciences, The University of Edinburgh, Edinburgh, UK.
Rónán DalyInstitute of Infection, Immunity and Inflammation, Glasgow Polyomics, University of Glasgow, Glasgow, UK.
Susan Alison ArnoldIngenza Ltd., Roslin Innovation Centre, Roslin, UK.
Karl BurgessInstitute of Quantitative Biology, Biochemistry and Biotechnology, School of Biological Sciences, University of Edinburgh, Edinburgh, UK.ORCID 0000-0002-0881-715X
University of Edinburgh · GBUniversity of Glasgow · GBRoslin Institute · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Escherichia coliSuccinic AcidFermentationMetabolomicsSuccinatesSuccinatesSuccinic Acidbioprocess monitoringfermentation monitoringon-line metabolomicsreal-time metabolomicssuccinate

Identifiers

PMID35798686
PMCPMC9541951
OpenAlexW4284890645

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.