Precise tuning of gene expression output levels in mammalian cells
Bibliographic record
Abstract
ABSTRACT Precise, analogue regulation of gene expression is critical for development, homeostasis and regeneration in mammals. In contrast, widely employed experimental and therapeutic approaches such as knock-in/out strategies are more suitable for binary control of gene activity, while RNA interference (RNAi) can lead to pervasive off-target effects and unpredictable levels of repression. Here we report on a method for the precise control of gene expression levels in mammalian cells based on engineered, synthetic microRNA response elements (MREs). To develop this system, we established a high-throughput sequencing approach for measuring the efficacy of thousands of miR-17 MRE variants. This allowed us to create a library of mi croRNA s ilencing-mediated f ine- t uners (miSFITs) of varying strength that can be employed to control the expression of user specified genes. To demonstrate the value of this technology, we used a panel of miSFITs to tune the expression of a peptide antigen in a mouse melanoma model. This analysis revealed that antigen expression level is a key determinant of the anti-tumour immune response in vitro and in vivo . miSFITs are a powerful tool for modulating gene expression output levels with applications in research and cellular engineering.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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".