Production of α2,6-sialylated IgG1 in CHO cells
Bibliographic record
Abstract
The presence of α2,6-sialic acids on the Fc N-glycan provides anti-inflammatory properties to the IgGs through a mechanism that remains unclear. Fc-sialylated IgGs are rare in humans as well as in industrial host cell lines such as Chinese hamster ovary (CHO) cells. Facilitated access to well-characterized α2,6-sialylated IgGs would help elucidate the mechanism of this intriguing IgG's effector function. This study presents a method for the efficient Fc glycan α2,6-sialylation of a wild-type and a F243A IgG1 mutant by transient co-expression with the human α2,6-sialyltransferase 1 (ST6) and β1,4-galactosyltransferase 1 (GT) in CHO cells. Overexpression of ST6 alone only had a moderate effect on the glycoprofiles, whereas GT alone greatly enhanced Fc-galactosylation, but not sialylation. Overexpression of both GT and ST6 was necessary to obtain a glycoprofile dominated by α2,6-sialylated glycans in both antibodies. The wild-type was composed of the G2FS(6)1 glycan (38%) with remaining unsialylated glycans, while the mutant glycoprofile was essentially composed of G2FS(6)1 (25%), G2FS(3,6)2 (16%) and G2FS(6,6)2 (37%). The α2,6-linked sialic acids represented over 85% of all sialic acids in both antibodies. We discuss how the limited sialylation level in the wild-type IgG1 expressed alone or with GT results from the glycan interaction with Fc's amino acid residues or from intrinsic galactosyl- and sialyl-transferases substrate specificities.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".