Adipose mitochondrial metabolism controls body growth by modulating systemic cytokine and insulin signaling
Bibliographic record
Abstract
Animals must adapt their growth to fluctuations in nutrient availability to ensure proper development. These adaptations often rely on specific nutrient-sensing tissues that control whole-body physiology through inter-organ communication. While the signaling mechanisms that underlie this communication are well studied, the contributions of metabolic alterations in nutrient-sensing tissues are less clear. Here, we show how the reprogramming of adipose mitochondria controls whole-body growth in Drosophila larvae. We find that dietary nutrients alter fat-body mitochondrial morphology to lower their bioenergetic activity, leading to rewiring of fat-body glucose metabolism. Strikingly, we find that genetic reduction of mitochondrial bioenergetics just in the fat body is sufficient to accelerate body growth and development. These growth effects are caused by inhibition of the fat-derived secreted peptides ImpL2 and tumor necrosis factor alpha (TNF-α)/Eiger, leading to enhanced systemic insulin signaling. Our work reveals how reprogramming of mitochondrial metabolism in one nutrient-sensing tissue can couple nutrient availability to whole-body growth.
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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 | 0.000 | 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 | 0.000 |
| 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".