Innate Response Activator B Cells Aggravate Atherosclerosis by Stimulating T Helper-1 Adaptive Immunity
Bibliographic record
Abstract
BACKGROUND: Atherosclerotic lesions grow via the accumulation of leukocytes and oxidized lipoproteins in the vessel wall. Leukocytes can attenuate or augment atherosclerosis through the release of cytokines, chemokines, and other mediators. Deciphering how leukocytes develop, oppose, and complement each other's function and shape the course of disease can illuminate our understanding of atherosclerosis. Innate response activator (IRA) B cells are a recently described population of granulocyte macrophage colony-stimulating factor-secreting cells of hitherto unknown function in atherosclerosis. METHODS AND RESULTS: Here, we show that IRA B cells arise during atherosclerosis in mice and humans. In response to a high-cholesterol diet, IRA B cell numbers increase preferentially in secondary lymphoid organs via Myd88-dependent signaling. Mixed chimeric mice lacking B cell-derived granulocyte macrophage colony-stimulating factor develop smaller lesions with fewer macrophages and effector T cells. Mechanistically, IRA B cells promote the expansion of classic dendritic cells, which then generate interferon γ-producing T helper-1 cells. This IRA B cell-dependent T helper-1 skewing manifests in an IgG1-to-IgG2c isotype switch in the immunoglobulin response against oxidized lipoproteins. CONCLUSIONS: Granulocyte macrophage colony-stimulating factor-producing IRA B cells alter adaptive immune processes and shift the leukocyte response toward a T helper-1-associated milieu that aggravates atherosclerosis.
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".