Loss of Macrophage mTORC2 Drives Atherosclerosis via FoxO1 and IL-1β Signaling
Bibliographic record
Abstract
BACKGROUND: The mTOR (mechanistic target of rapamycin) pathway is a complex signaling cascade that regulates cellular growth, proliferation, metabolism, and survival. Although activation of mTOR signaling has been linked to atherosclerosis, its direct role in lesion progression and in plaque macrophages remains poorly understood. We previously demonstrated that mTORC1 (mTOR complex 1) activation promotes atherogenesis through inhibition of autophagy and increased apoptosis in macrophages. METHODS: Using macrophage-specific Rictor- and mTOR-deficient mice, we now dissect the distinct functions of mTORC2 pathways in atherogenesis. RESULTS: In contrast to the atheroprotective effect seen with blockade of macrophage mTORC1, macrophage-specific mTORC2-deficient mice exhibit an atherogenic phenotype, with larger, more complex lesions and increased cell death. In cultured macrophages, we show that mTORC2 signaling inhibits the FoxO1 (forkhead box protein O1) transcription factor, leading to suppression of proinflammatory pathways, especially the inflammasome/IL (interleukin)-1β response, a key mediator of vascular inflammation and atherosclerosis. In addition, administration of FoxO1 inhibitors efficiently rescued the proinflammatory response caused by mTORC2 deficiency both in vitro and in vivo. Interestingly, collective deletion of macrophage mTOR, which ablates mTORC1- and mTORC2-dependent pathways, leads to minimal change in plaque size or complexity, reflecting the balanced yet opposing roles of these signaling arms. CONCLUSIONS: Our data provide the first mechanistic details of macrophage mTOR signaling in atherosclerosis and suggest that therapeutic measures aimed at modulating mTOR need to account for its dichotomous functions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".