Novel insights into GCN2 and mTOR signaling cross talk during efferocytosis 2828
Bibliographic record
Abstract
Abstract Description The ability of cells to adapt to environmental changes is essential for their growth and survival. Eukaryotic cells, including macrophages (Mφ), utilize the GCN2 and mTOR pathways to regulate metabolism in response to microenvironmental cues. Efferocytosis (phagocytosis of apoptotic cells [AC]), plays a critical role in preventing autoimmunity and promoting immune tolerance. This process requires precise metabolic regulation, as Mφ must efficiently process AC-derived materials to control inflammation. While GCN2 and mTOR pathways are well-studied under amino acid deprivation, their roles in efferocytosis remain unclear. Here we show efferocytosis activates both mTORC1 and GCN2 in Mφ. Initial mTORC1 activation facilitates the recycling of AC-derived amino acids and cholesterol while down-regulating phagocytic receptors to mitigate efferocytosis-induced metabolic stress. Simultaneously, GCN2 activation promotes the production of anti-inflammatory cytokines and suppresses prolonged mTORC1 activity to prevent an inflammatory phenotype. Our findings reveal that GCN2-deficient Mφ displayed heightened mTORC1 activity, a pro-inflammatory phenotype, and impaired suppression of CD4 T cells after efferocytosis. These findings highlight the critical role of GCN2-mediated mTORC1 regulation in maintaining efferocytosis-driven immune tolerance in Mφ, with implications for cancer therapy and autoimmune disorders that require efficient efferocytosis. Funding Sources Supported by NIH/NCI 1R01CA255670; Medicine by design; the TFRI; and CIHR operating grants 406694, 436605, and 518004. Topic Categories Immune Response Regulation: Molecular Mechanisms (IRM)
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".