Off-target genomic effects in MRP8-Cre driver mice complicate its use in weight gain and metabolic studies
Bibliographic record
Abstract
Abstract Background Thromboinflammation of adipose tissue involves accumulation of pro-fibrinogenic factors to adipose tissue in obesity, which promotes immune cell infiltration, affects weight gain and can lead to metabolic dysfunction. The role of neutrophil genes in inflammation are frequently investigated using MRP8-Cre mice to generate neutrophil-specific knockouts. Recent study demonstrated that MRP8-Cre mice have off-target deletions in Serpine1 and Ap1s1 genes. Serpine1 , encoding plasminogen activator inhibitor-1, is a key anti-fibrinolytic factor linked to thromboinflammation and metabolic dysfunctions in obesity. In this study, we provide evidence suggesting a critical limitation in using MRP8-Cre model to study adipose tissue, weight-related dysfunctions and/or metabolic disorders. Methods MRP8-Cre and F13a1 -/-MRP8 mice were placed on either control diet or high-fat diet for 16 weeks, with body weight monitored weekly. The expression of Serpine 1, Ap1s1 and Adgre1 (macrophage marker) genes in inguinal and epididymal adipose tissues were analyzed using qRT-PCR and compared to the wild-type mice. Results MRP8-Cre shows no Serpine1 or Ap1s1 expression in inguinal and epididymal adipose tissues. MRP8-Cre mouse is resistant to weight gain on obesogenic diet and does not show macrophage marker in adipose tissue compared to control obesity model. The resistance to weight gain translates to a neutrophil knockout model, F13a1 -/-MRP8 that was not expected to show resistance to weight gain as its global knockout does not exhibit this phenotype. Conclusion Our work suggests that MRP8-Cre model may not be suitable to investigate metabolic outcomes of neutrophil genes, or pathologies that have underlying etiology in thomboinflammation.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".