β-Cell Cre Expression and Reduced <i>Ins1</i> Gene Dosage Protect Mice From Type 1 Diabetes
Bibliographic record
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 preclinical models. In the type 1 diabetes (T1D) research field, multiple lines of nonobese diabetic (NOD) mice have been engineered to express Cre recombinase in pancreatic β cells using insulin promoter fragments, but tissue promiscuity remains a concern. Constitutive Ins1tm1.1(cre)Thor (Ins1Cre) mice on the C57/bl6-J background have high β-cell specificity with no reported off-target effects. We explored whether NOD:Ins1Cre mice could be used to investigate β-cell gene deletion in T1D disease modeling. We studied wild-type (Ins1WT/WT), Ins1 heterozygous (Ins1Cre/WT or Ins1Neo/WT), and Ins1 null (Ins1Cre/Neo) littermates on a NOD background. Female Ins1Neo/WT mice exhibited significant protection from diabetes, with further near-complete protection in Ins1Cre/WT mice. The effects of combined neomycin and Cre knockin in Ins1Neo/Cre mice were not additive to the Cre knockin alone. In Ins1Neo/Cre mice, protection from diabetes was associated with reduced insulitis at age 12 weeks. 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 important implications for the experimental design and interpretation of preclinical T1D studies using β-cell-selective 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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| 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".