Beta-cell Cre expression and reduced <i>Ins1</i> gene dosage protect mice from type 1 diabetes
Bibliographic record
Abstract
Abstract A central goal of physiological research is the understanding of cell-specific roles of disease-associated genes. Cre-mediated recombineering is the tool of choice for cell type-specific analysis of gene function in pre-clinical models. In the type 1 diabetes research field, multiple lines of NOD mice have been engineered to express Cre recombinase in pancreatic β-cells using insulin promoter fragments, but tissue promiscuity remains a concern. Constitutive Ins1 tm1.1(cre)Thor ( Ins1 Cre ) mice on the C57/bl6-J background has high β-cell specificity and with no reported off-target effects. We explored if NOD: Ins1 Cre mice could be used to investigate β-cell gene deletion in type 1 diabetes disease modeling. We studied wildtype ( Ins1 WT/WT ), Ins1 heterozygous ( Ins1 Cre/WT or Ins1 Neo/WT ), and Ins1 null ( Ins1 Cre/Neo ) littermates on a NOD background. Female Ins1 Neo/WT mice exhibited significant protection from diabetes, with further near-complete protection in Ins1 Cre/WT mice. The effects of combined neomycin and Cre knock-in in Ins1 Neo/Cre mice were not additive to the Cre knock-in alone. In Ins1 Neo/Cre mice, protection from diabetes was associated with reduced insulitis at 12 weeks of age. Collectively, these data confirm previous reports that loss of Ins1 alleles protects NOD mice from diabetes development and demonstrates, for the first time, that Cre itself may have additional protective effects. This has significant implications for the experimental design and interpretation of pre-clinical type 1 diabetes studies using β-cell-specific Cre in NOD mice.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".