A Novel Drosophila Model to Investigate Adipose Tissue Macrophage Infiltration (ATM) and Obesity highlights the Therapeutic Potential of Attenuating Eiger/TNFα Signaling to Ameliorate Insulin Resistance and ATM
Bibliographic record
Abstract
Abstract Obesity is a global health concern associated with various metabolic disorders including insulin resistance and adipose tissue inflammation characterized by adipose tissue macrophage (ATM) infiltration. In this study, we present a novel Drosophila model to investigate the mechanisms underlying ATM infiltration and its association with obesity-related pathologies. Furthermore, we demonstrate the therapeutic potential of attenuating Eiger/TNFα signaling to ameliorate insulin resistance and ATM. To study ATM infiltration and its consequences, we established a novel Drosophila model (OBL) that mimics key aspects of human adipose tissue and allows for investigating ATM infiltration and other related metabolic disorders in a controlled experimental system. 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 levels of circulating glucose. Moreover, we observed a significant infiltration of immune cells (hemocytes) in the fat bodies accompanied by insulin resistance and systemic metabolic dysregulation. Furthermore, we found that attenuation of Eiger/TNFα signaling and using metformin and anti-oxidant bio-products like anthocyanins led to a reduction in ATM infiltration and improved insulin sensitivity. Our data suggest that the key mechanisms that trigger immune cell infiltration into adipose tissue are evolutionarily conserved and may provide the opportunity to develop Drosophila models to better understand pathways critical for immune cell recruitment into adipose tissue, in relation to the development of insulin resistance in metabolic diseases such as obesity and type 2 diabetes, and non-alcoholic fatty liver disease (NAFLD). We believe that our OBL model can also be a valuable tool and provide a platform either to perform genetic screens or to test the efficacy and safety of novel therapeutic interventions for these diseases.
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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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".