Matrix attachment regions enhance transgene expression by manipulating position-dependent effects in stably transfected CHO-K1 cells
Bibliographic record
Abstract
We previously found that the position of matrix attachment regions (MARs) within the vector significantly affects its ability to enhance transgenic expression in the recombinant protein production. This study aims to systematically investigate the position-dependent impacts of MAR on transgene expression. We observed a significant increase in enhanced green fluorescent protein (eGFP) expression levels in stably transfected CHO-K1 cells with either MAR 1-68 or MAR X-29 when MARs located upstream of the promoter. This increase was especially evident with MAR flanked the expression cassette. Concurrently, a substantial increase was observed in the percentage of eGFP-expressing cells, with 97.8% and 96.0% in MAR-containing constructs versus 73.7% in MAR-absent constructs. Further analysis of erythropoietin (EPO) expression revealed that constructs with flanking MARs induced the highest EPO productivity. Bioinformatics analysis revealed that certain specific transcription factors are important in modulating the transcription process. In conclusion, vectors harboring both MARs around the expression cassette constitute an optimal construct for enhanced recombinant protein production in CHO-K1 cells. This insight underscores the importance of strategic MAR incorporation in vector design for optimized recombinant protein expression.
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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 | 0.000 |
| 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 | 0.001 |
| 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".