The amino acid sensor GCN2 inhibits inflammatory responses to apoptotic cells promoting tolerance and suppressing systemic autoimmunity
Bibliographic record
Abstract
Efficient apoptotic cell clearance and induction of immunologic tolerance is a critical mechanism preventing autoimmunity and associated pathology. Our laboratory has reported that apoptotic cells induce tolerance by a mechanism dependent on the tryptophan catabolizing enzyme indoleamine 2,3 dioxygenase 1 (IDO1) in splenic macrophages (MΦ). The metabolic-stress sensing protein kinase GCN2 is a primary downstream effector of IDO1; thus, we tested its role in apoptotic cell-driven immune suppression. In vitro, expression of IDO1 in MΦs significantly enhanced apoptotic cell-driven IL-10 and suppressed IL-12 production in a GCN2-dependent mechanism. Suppression of IL-12 protein production was due to attenuation of IL-12 mRNA association with polyribosomes inhibiting translation while IL-10 mRNA association with polyribosomes was not affected. In vivo, apoptotic cell challenge drove a rapid, GCN2-dependent stress response in splenic MΦs with increased IL-10 and TGF-β production, whereas myeloid-specific deletion of GCN2 abrogated regulatory cytokine production with provocation of inflammatory T-cell responses to apoptotic cell antigens and failure of long-tolerance induction. Consistent with a role in prevention of apoptotic cell driven autoreactivity, myeloid deletion of GCN2 in lupus-prone mice resulted in increased immune cell activation, humoral autoimmunity, renal pathology, and mortality. In contrast, activation of GCN2 with an agonist significantly reduced anti-DNA autoantibodies and protected mice from disease. Thus, this study implicates a key role for GCN2 signals in regulating the tolerogenic response to apoptotic cells and limiting autoimmunity.
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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".