Differential regulation of Type 1 and Type 2 mouse eosinophil activation by apoptotic cells
Bibliographic record
Abstract
Eosinophils are multifunctional, evolutionary conserved leukocytes that are involved in a plethora of responses ranging from regulation of tissue homeostasis, host defense and cancer. Although eosinophils have been studied mostly in the context of Type 2 inflammatory responses, it is now evident that they participate in Type 1 inflammatory responses and can respond to Type 1 cytokines such as IFN-γ. Notably, both Type 1- and Type 2 inflammatory environments are characterized by tissue damage and cell death. Collectively, this raises the possibility that eosinophils can interact with apoptotic cells, which can alter eosinophil activation in the inflammatory milieu. Herein, we demonstrate that eosinophils can bind and engulf apoptotic cells. We further show that exposure of eosinophils to apoptotic cells induces marked transcriptional changes in eosinophils, which polarize eosinophils towards an anti-inflammatory phenotype that is associated with wound healing and cell migration. Using an unbiased RNA sequencing approach, we demonstrate that apoptotic cells suppress the inflammatory responses of eosinophils that were activated with IFN-γ + E. coli (e.g., Type 1 eosinophils) and augment IL-4-induced eosinophil activation (e.g., Type 2 eosinophils). These data contribute to the growing understanding regarding the heterogeneity of eosinophil activation patterns and highlight apoptotic cells as potential regulators of eosinophil polarization.
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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 | 0.000 | 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) | 0.000 | 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".