Different Lymphocyte Populations Direct Dichotomous Eosinophil or Neutrophil Responses to Pulmonary <i>Cryptococcus</i> Infection
Bibliographic record
Abstract
Many pulmonary infections elicit lymphocyte responses that lead to an accumulation of granulocytes in the lungs. A variety of lymphocytes are capable of directing eosinophils or neutrophils to the lungs, but the contribution of each subset remains enigmatic. In this study, we used a murine model to examine lymphocyte subsets that ultimately drive the eosinophil or neutrophil response to infection with the fungal pathogen Cryptococcus neoformans. We show that granulocytes are produced in the bone marrow, released into the blood stream, and accumulate in the lungs under the instruction of lung parenchymal lymphocytes. The eosinophils that populated the lungs of wild-type animals were highly dependent on Th cells or IL-5. Surprisingly, infected mice with Th cell impairment experienced a compensatory neutrophil response that required IL-17A. This unexpected swing in the response prompted us to investigate the ability of different lymphocyte subsets to produce this dichotomous eosinophilia or neutrophilia. We used mice with lymphocyte deficiencies to determine which of the remaining IL-5- or IL-17A-producing lymphocyte subsets dominated the neutrophil or eosinophil response. Finally, skewing the response toward neutrophil-inducing lymphocytes correlated with accelerated disease. Our data collectively demonstrate that the predominance of a lymphocyte subset determines the functional consequences of an immune response to pulmonary fungal infection that can ultimately affect disease.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".