Identification of novel miRNA targets in CHO cell lines and characterization of their impact on protein N-glycosylation
Bibliographic record
Abstract
CHO cell lines are a workhorse for the production of pharmaceutical proteins, but show some limitations in the variability and stability of N-glycosylation profiles. One promising approach to addressing this at the required systems-level is miRNA, which can regulate a large number of genes and have predictable targets. Herein, we first identified de novo 656 potential miRNAs in the CHO genome based on a combination of literature, database searching, and miRNA sequencing. We further sequenced mRNA from the same cultures, and used a combination of mRNA-miRNA correlation analysis, target prediction and literature searches to find miRNAs potentially targeting N-glycosylation. Our ten best miRNA candidates were subjected to miRNA overexpression, knockdown, or knock-out in CHO cell lines. Out of the ten candidates, four (miR-128, miR-34c, miR-30b, and miR-449a) showed positive effects on N-glycosylation and could be applied directly for CHO cell engineering. The fact that 40% of the screened targets had a desired effect, and the prediction of 656 miRNAs illustrates the massive potential of miRNA engineering in CHO.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".