AAV8 Ins1-Cre can produce efficient β-cell recombination but requires consideration of off-target effects
Bibliographic record
Abstract
Abstract In vivo genetic manipulation is used to study the impact of gene deletion or re-expression on β-cell function and organism physiology. Cre-LoxP is a system wherein LoxP sites flanking a gene are recognized by Cre recombinase. Cre transgenic mice are the most prevalent technology used to deliver Cre but many models have caveats of off-target recombination, impaired β-cell function, and high cost of animal production. Inducible estrogen receptor conjugated Cre models face leaky recombination and confounding effects of tamoxifen. As an alternative, we characterize an adeno associated virus (AAV) with a rat insulin 1 promoter driving Cre recombinase (AAV8 Ins1-Cre) that is economical and rapid to implement, and has limited caveats. Intraperitoneal AAV8 Ins1-Cre produced efficient β-cell recombination, alongside some hepatic, exocrine pancreas, α-cell, δ-cell, and hypothalamic recombination. Delivery of lower doses via the pancreatic duct retained good rates of β-cell recombination and limited rates of off-target recombination. Unlike inducible Cre in transgenic mice, AAV8 Ins1-Cre required no tamoxifen and premature recombination was avoided. We demonstrate the utility of this technology by inducing hyperglycemia in inducible insulin knockout mice (Ins1−/−;Ins2f/f). AAV-mediated expression of Cre in β-cells provides an effective alternative to transgenic approaches for inducible knockout studies.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".