Evidence map›Paper›PMID 40219633›Full record

ArticleBiotechnology and bioengineering2025

Flux Sampling Suggests Metabolic Signatures of High Antibody-Producing CHO Cells.

Kate E Meeson, Joanne Watson, Susan Rosser, Ellie Hawke, Andrew Pitt, Tessa Moses, Leon Pybus, Magnus Rattray, Alan J Dickson, Jean-Marc Schwartz

Abstract read
In one paragraph

Article in Biotechnology and bioengineering, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
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

1 citing paper in PubMed.

  1. Article
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

10 authors.

Kate E MeesonFaculty of Biology, Medicine and Health, University of Manchester, Manchester, UK.ORCID https://orcid.org/0009-0009-1439-6302
Joanne WatsonFaculty of Biology, Medicine and Health, University of Manchester, Manchester, UK.ORCID https://orcid.org/0000-0002-7754-276X
Susan RosserEdinOmics, RRID:SCR_021838, Centre for Engineering Biology, School of Biological Sciences, University of Edinburgh, Edinburgh, UK.ORCID https://orcid.org/0000-0002-2560-6485
Ellie HawkeManchester Institute of Biotechnology, University of Manchester, Manchester, UK.
Andrew PittManchester Institute of Biotechnology, University of Manchester, Manchester, UK.ORCID https://orcid.org/0000-0003-3619-6503
Tessa MosesEdinOmics, RRID:SCR_021838, Centre for Engineering Biology, School of Biological Sciences, University of Edinburgh, Edinburgh, UK.ORCID https://orcid.org/0000-0001-9366-4727
Leon PybusFUJIFILM Diosynth Biotechnologies, Billingham, UK.ORCID https://orcid.org/0000-0001-5614-8758
Magnus RattrayFaculty of Biology, Medicine and Health, University of Manchester, Manchester, UK.ORCID https://orcid.org/0000-0001-8196-5565
Alan J DicksonManchester Institute of Biotechnology, University of Manchester, Manchester, UK.ORCID https://orcid.org/0000-0001-9490-645X
Jean-Marc SchwartzFaculty of Biology, Medicine and Health, University of Manchester, Manchester, UK.ORCID http://orcid.org/0000-0002-6472-0184

Funding

The authors acknowledge financial support from a Prosperity Partnership grant (EP/V038095/1) funded by EPSRC, BBSRC, and FUJIFILM Diosynth Biotechnologies. This grant involved the University of Manchester, University of Edinburgh, University of York, and staff at FUJIFILM Diosynth Biotechnologies.
6 · The paper itself

Abstract

Chinese hamster ovary (CHO) cells remain the industry standard for producing numerous therapeutic proteins, particularly monoclonal antibodies (mAbs). However, achieving higher recombinant protein titers remains an ongoing challenge and a fundamental understanding of the cellular mechanism driving improved bioprocess performance remains elusive. To directly address these challenges and achieve substantial improvements, a more in-depth understanding of cellular function within a bioprocess environment may be required. Over the past decade, significant advancements have been made in the building of genome-scale metabolic models (GEMs) for CHO cells, bridging the gap between high information content 'omics data and the ability to perform in silico phenotypic predictions. Here, time-course transcriptomics has been employed to constrain culture phase-specific GEMs, representing the early exponential, late exponential, and stationary/death phases of CHO cell fed-batch bioreactor culture. Temporal bioprocess data, including metabolite uptake and secretion rates, as well as growth and productivity, has been used to validate flux sampling results. Additionally, high mAb-producing solutions have been identified and the metabolic signatures associated with improved mAb production have been hypothesized. Finally, constraint-based modeling has been utilized to infer specific amino acids, cysteine, histidine, leucine, isoleucine, asparagine, and serine, which could drive increased mAb production and guide optimal media and feed formulations.

Indexed as

Antibodies, MonoclonalMetabolic Flux AnalysisMetabolomeModels, BiologicalRecombinant ProteinsAnimalsBioreactorsCHO CellsCricetinaeCricetulusAntibodies, MonoclonalRecombinant Proteins

Identifiers

PMID40219633
PMCPMC12152534

What OpenQuestion holds

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Registered trials

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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 OpenQuestion graph.