Lack of the E3 Ubiquitin Ligase March1 Affects CD8 T Cell Fate and Exacerbates Insulin Resistance in Obese Mice
Bibliographic record
Abstract
Obesity is a major risk factor for the development of insulin resistance and type 2 diabetes. However, the mechanisms that trigger the underlying adipose tissues inflammation are not completely understood. Here, we show that the E3 ubiquitin ligase March1 controls the phenotypic and functional properties of CD8+ T cells in mice white adipose tissue. In a diet-induced obesity model, mice lacking March1 (March1 KO) show increased insulin resistance compared to their WT counterparts. Also, in obese March1 KO mice, the proportions of effector/memory (Tem) and resident/memory (Trm) CD8+ T cells were higher in the visceral adipose tissue, but not in the spleen. The effect of March1 on insulin resistance and on the phenotype of adipose tissue CD8+ T cells was independent of MHC class II ubiquitination. Interestingly, we adoptively transferred either WT or MARCH1 KO splenic CD8+ T cells into obese WT chimeras that had been reconstituted with Rag1-deficient bone marrow. We observed an enrichment of Tem and Trm cells and exacerbated insulin resistance in mice that received March1 KO CD8 T cells. Mechanistically, we found that March1 deficiency alters the metabolic activity of CD8+ T cells. Our results provide additional evidence of the involvement of CD8+ T cells in adipose tissue inflammation and suggest that March1 controls the metabolic reprogramming of these lymphocytes.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".