Mitochondrial metabolism in <i>Drosophila</i> macrophage-like cells regulates body growth via modulation of cytokine and insulin signaling
Bibliographic record
Abstract
Macrophages play critical roles in regulating and maintaining tissue and whole-body metabolism in normal and disease states. While the cell-cell signaling pathways that underlie these functions are becoming clear, less is known about how alterations in macrophage metabolism influence their roles as regulators of systemic physiology. Here, we investigate this by examining Drosophila macrophage-like cells called hemocytes. We used knockdown of TFAM, a mitochondrial genome transcription factor, to reduce mitochondrial OxPhos activity specifically in larval hemocytes. We find that this reduction in hemocyte OxPhos leads to a decrease in larval growth and body size. These effects are associated with a suppression of systemic insulin, the main endocrine stimulator of body growth. We also find that TFAM knockdown leads to decreased hemocyte JNK signaling and decreased expression of the TNF alpha homolog, Eiger in hemocytes. Furthermore, we show that genetic knockdown of hemocyte JNK signaling or Eiger expression mimics the effects of TFAM knockdown and leads to a non-autonomous suppression of body size without altering hemocyte numbers. Our data suggest that modulation of hemocyte mitochondrial metabolism can determine their non-autonomous effects on organismal growth by altering cytokine and systemic insulin signaling. Given that nutrient availability can control mitochondrial metabolism, our findings may explain how macrophages function as nutrient-responsive regulators of tissue and whole-body physiology and homeostasis.
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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".