Tunable, proteolytic dosage control of CRISPR-Cas systems enables precise gene therapy for dosage sensitive disorders
Bibliographic record
Abstract
Summary The ability to modulate gene expression through modular and universal genetic tools like CRISPR-Cas has greatly advanced gene therapy for therapeutics and basic science. Yet, the inherent stochasticity of delivery methods cause variation in target gene expression at the single-cell level, limiting their applicability in systems that require more precise expression. Thus, we implement a modular incoherent feedforward loop based on proteolytic cleavage of Cas to reduce gene expression variability against the variability of vector delivery. We target a genome-integrated marker and demonstrate dosage control of gene activation and repression, post-delivery tuning, and RNA-based compatibility of the system. To illustrate therapeutic relevance, we target the gene RAI1 , the haploinsufficiency and triplosensitivity of which cause two autism-related syndromes. We demonstrate dosage-controlled gene activation for both human and mouse Rai1 via viral delivery to patient-derived cell lines and mouse cortical neurons. Overall, we established a robust dosage control circuit for uniform gene expression, beneficial for basic and translational research.
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 | 0.000 | 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 | 0.001 | 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".