Low glucose depletes glycan precursors, reduces site occupancy and galactosylation of a monoclonal antibody in CHO cell culture
Bibliographic record
Abstract
Controlled feeding of glucose has been employed previously to enhance the productivity of recombinant glycoproteins but there is a concern that low concentrations of glucose could limit the synthesis of precursors of glycosylation. Here we investigate the effect of glucose depletion on the metabolism, productivity and glycosylation of a chimeric human-llama monoclonal antibody secreted by CHO cells. The cells were inoculated into media containing varying concentrations of glucose. Glucose depletion occurred in cultures with an initial glucose ≤5.5 mM and seeded at low density (2.5 × 10(5) cells/mL) or at high cell inoculum (≥2.5 × 10(6) cells/mL) at higher glucose concentration (up to 25 mM). Glucose-depleted cultures produced non-glycosylated Mabs (up to 51%), lower galactosylation index (GI <0.43) and decreased sialylation (by 85%) as measured by mass spectrometry and HPLC. At low glucose a reduced intracellular pool of nucleotides (0.03-0.23 fmoles/cell) was measured as well as a low adenylate energy charge (<0.57). Low glucose also reduced GDP-sugars (by 77%) and UDP-hexosamines (by 90%). The data indicate that under glucose deprivation, low levels of intracellular nucleotides and nucleotide sugars reduced the availability of the immediate precursors of glycosylation. These results are important when applied to the design of fed-batch cultures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".