Inhibition of microRNA-let-7a Increases the Specific Productivity of Antibody-Producing CHO Cell Lines
Bibliographic record
Abstract
Chinese hamster ovary cells (CHO) are the preferred cell line for the production of recombinant biopharmaceuticals, which constitutes a multi-billion dollar global market. Major challenges to improving protein productivity of CHO in large-scale production cultures include growth level, cellular stress, and translation rate. Because microRNA (miR, miRNA) can simultaneously perturb multiple pathways by inhibiting translation or destabilizing different mRNAs, we explored their utility to extend the cell growth phase or alter protein production per cell (specific productivity) with the goal of enhancing current optimization techniques to increase the production capacity of CHO cell cultures. To investigate the effect of altered microRNA expression on CHO cell viability and specific productivity, two clinically relevant antibody-producing CHO cell lines were stably transduced with lentiviral vectors encoding nine different miRNAs or anti-miRNAs based on their potential involvement in pathways critical for recombinant protein production. Inhibition of miR-let-7a led to a 50%~68% increase in specific productivity in two recombinant antibody-producing cell lines. Furthermore, following miR-let-7a inhibition, we identified increased expression of its targets HMGA2, MYC, NF2, NIRF, RAB40C, and eIF4a which are important mediators of apoptosis, protein translation, and cellular metabolism. Overall, this work provides proof of concept that exogenous microRNA modifications can positively affect specific productivity of CHO cell cultures and highlights the potential of miR-let-7a to have a broad impact on the complex biological functions necessary for improving the capabilities of CHO cell lines.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".