Human Bronchial Epithelial Cell–derived Factors from Severe Asthmatic Subjects Stimulate Eosinophil Differentiation
Bibliographic record
Abstract
Abstract Activated bronchial epithelial cells (BEC) release various alarmins, including thymic stromal lymphopoietin (TSLP), that drive type 2 inflammation. We hypothesize that BEC-derived factors promote in situ eosinophil differentiation and maturation, a process that is driven by an IL-5–rich microenvironment in asthmatic airways. To assess the eosinophilopoietic potential of epithelial-derived factors, eosinophil/basophil colony forming units (Eo/B-CFU) were enumerated in 14-day methylcellulose cultures of blood-derived nonadherent mononuclear cells incubated with BEC supernatants (BECSN) from healthy nonatopic controls (n = 8), mild atopic asthmatics (n = 9), and severe asthmatics (n = 5). Receptor-blocking antibodies were used to evaluate the contribution of alarmins. Modulation of the mRNA expression of transcription factors that are crucial for eosinophil differentiation was evaluated. BECSN stimulated the clonogenic expansion of eosinophil progenitors in vitro. In the presence of IL-5, Eo/B-CFU numbers were significantly greater in cocultures of BESCN from severe asthmatics compared with other groups. This was attenuated in the presence of a TSLP (but not an IL-33) receptor–blocking antibody. Recombinant human TSLP (optimal at 100 pg/ml) stimulated Eo/B-CFU growth, which was significantly enhanced in the presence of IL-5 (1 ng/ml). Overnight culture of CD34+ cells with IL-5 and TSLP synergistically increased GATA-binding factor 2 and CCAAT/enhancer-binding protein α mRNA expression. The eosinophilopoietic potential of factors derived from BEC is increased in severe asthma. Our data suggest that TSLP is a key alarmin that is produced by BECs and promotes in situ eosinophilopoiesis in a type 2–rich microenvironment.
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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) | 0.004 | 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".