Cytokine trafficking of IL-9 and IL-13 through TfnRc+ vesicles in activated human eosinophils
Bibliographic record
Abstract
Abstract Eosinophils are granulocytes that are elevated in lung mucosa in approximately half of patients with allergic asthma. These highly granulated cells can synthesize and secrete many cytokines, including IL-9 and IL-13. We hypothesized that IL-9 and IL-13 are found as preformed mediators in crystalloid granules and secreted using distinct trafficking pathways. Human eosinophils were purified from peripheral venous blood, adhered to coverslips, and stimulated with platelet activating factor (PAF). Cells were immunolabeled with antibodies to IL-9 or IL-13 and colocalized with markers for secretory organelles, using CD63 for crystalloid granules and transferrin receptor (TfnRc) for vesicles. Fixed cells were imaged using super-resolution microscopy and quantified by colocalization using Pearson’s correlation coefficient. IL-9 immunofluorescence increased in a time-dependent manner to PAF, whereas colocalization of IL-9 and CD63 significantly increased from 0.52 to 0.67 after 5 min PAF. Colocalization of IL-9 with TfnRc significantly increased at 60 min of stimulation with PAF (0.54 at 0 min to 0.60 at 60 min). IL-13 showed lower colocalization with CD63 (0.55) than TfnRc (0.63) in unstimulated cells. Upon PAF stimulation, IL-13 intensity transiently decreased at 5 and 60 min, whereas colocalization of IL-13 with CD63 decreased throughout stimulation to 0.43. While colocalization of IL-13 with TfnRc transiently increased to 0.66 at 5 min PAF, it returned to near baseline levels (0.64) after 15 min PAF. Our results suggest that IL-9 and IL-13 are stored in crystalloid granules as well as endosomal structures, and that IL-9 is primarily trafficked to the cell surface via TfnRc+ endosome-like vesicles.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 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".