Evidence map›Paper›PMID 28486471›Full record

ArticlePloS one2017

Model-based analysis of N-glycosylation in Chinese hamster ovary cells.

Frederick J Krambeck, Sandra V Bennun, Mikael R Andersen, Michael J Betenbaugh

Abstract read
In one paragraph

Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.

0numbers the graph read from it
0cells of the map it votes in
24citing 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

24 citing papers in PubMed.

  1. Article
  2. Review
  3. In silico simulation of glycosylation and related pathways.Analytical and bioanalytical chemistry · 2024
    Review
  4. Article
  5. Article
  6. Review
  7. Article
  8. Article
  9. Review
  10. Article
  11. Hidden Relationships betweenInternational journal of molecular sciences · 2022
    Review
  12. Computational Modeling ofMolecules (Basel, Switzerland) · 2022
    Article
  13. An Interactive View of Glycosylation.Methods in molecular biology (Clifton, N.J.) · 2022
    Article
  14. Article
  15. Review
  16. Simulating the enzymes of ganglioside biosynthesis with Glycologue.Beilstein journal of organic chemistry · 2021
    Article
  17. Article
  18. Article
  19. Article
  20. Review
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

4 authors.

Frederick J KrambeckDepartment of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, United States of America.
Sandra V BennunDepartment of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, United States of America.
Mikael R AndersenDepartment of Systems Biology, Technical University of Denmark, Lyngby, Denmark.ORCID http://orcid.org/0000-0003-4794-6808
Michael J BetenbaughDepartment of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, United States of America.

Funding

Structural Basis of Immune Recognition of Glycosylated AntigensU54GM062116 · NIGMS · SCRIPPS RESEARCH INSTITUTE, THE · PI ALVAREZ, RICHARD · 2001 to 2010
$80.3M
Mathematical model relating gene expression to glycoform structureR41CA127885 · NCI · REACTECH, INC. · PI KRAMBECK, FREDERICK J · 2007 to 2008
$296k
NCI NIH HHS R41 CA127885NIGMS NIH HHS U54 GM062116
6 · The paper itself

Abstract

The Chinese hamster ovary (CHO) cell is the gold standard for manufacturing of glycosylated recombinant proteins for production of biotherapeutics. The similarity of its glycosylation patterns to the human versions enable the products of this cell line favorable pharmacokinetic properties and lower likelihood of causing immunogenic responses. Because glycan structures are the product of the concerted action of intracellular enzymes, it is difficult to predict a priori how the effects of genetic manipulations alter glycan structures of cells and therapeutic properties. For that reason, quantitative models able to predict glycosylation have emerged as promising tools to deal with the complexity of glycosylation processing. For example, an earlier version of the same model used in this study was used by others to successfully predict changes in enzyme activities that could produce a desired change in glycan structure. In this study we utilize an updated version of this model to provide a comprehensive analysis of N-glycosylation in ten Chinese hamster ovary (CHO) cell lines that include a wild type parent and nine mutants of CHO, through interpretation of previously published mass spectrometry data. The updated N-glycosylation mathematical model contains up to 50,605 glycan structures. Adjusting the enzyme activities in this model to match N-glycan mass spectra produces detailed predictions of the glycosylation process, enzyme activity profiles and complete glycosylation profiles of each of the cell lines. These profiles are consistent with biochemical and genetic data reported previously. The model-based results also predict glycosylation features of the cell lines not previously published, indicating more complex changes in glycosylation enzyme activities than just those resulting directly from gene mutations. The model predicts that the CHO cell lines possess regulatory mechanisms that allow them to adjust glycosylation enzyme activities to mitigate side effects of the primary loss or gain of glycosylation function known to exist in these mutant cell lines. Quantitative models of CHO cell glycosylation have the potential for predicting how glycoengineering manipulations might affect glycoform distributions to improve the therapeutic performance of glycoprotein products.

Indexed as

Models, BiologicalAnimalsCHO CellsCricetinaeCricetulusEnzymesGlycosylationSpectrometry, Mass, Matrix-Assisted Laser Desorption-IonizationEnzymes

Identifiers

PMID28486471
PMCPMC5423595

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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.