A <i>Drosophila</i> model targets Eiger/TNFα to alleviate obesity-related insulin resistance and macrophage infiltration
Bibliographic record
Abstract
Obesity is associated with various metabolic disorders, such as insulin resistance and adipose tissue inflammation (ATM), characterized by macrophage infiltration into adipose cells. This study presents a new Drosophila model to investigate the mechanisms underlying these obesity-related pathologies. We employed genetic manipulation to reduce ecdysone levels to prolong the larval stage. These animals are hyperphagic and exhibit features resembling obesity in mammals, including increased lipid storage, adipocyte hypertrophy and high circulating glucose levels. Moreover, we observed significant infiltration of immune cells (hemocytes) into the fat bodies, accompanied by insulin resistance. We found that attenuation of Eiger/TNFα signaling reduced ATM and improved insulin sensitivity. Furthermore, using metformin and the antioxidants anthocyanins, we ameliorated both phenotypes. Our data highlight evolutionarily conserved mechanisms allowing the development of Drosophila models for discovering therapeutic pathways in adipose tissue immune cell infiltration and insulin resistance. Our model can also provide a platform to perform genetic screens or test the efficacy of therapeutic interventions for diseases such as obesity, type 2 diabetes and non-alcoholic fatty liver disease.
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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) | 0.000 | 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".