Transcriptional profiling of mast cell and basophil responses to differential stimuli identifies unique gene signatures.
Bibliographic record
Abstract
Abstract Mast cells (MC) and basophils (BΦ) are key effector cells in allergy that are developmentally related. Functionally, they both release mediators like histamine and granule proteases. However, studies also suggest that MC and BΦ have non-redundant roles in allergic diseases. Their ability to respond to IL-33—an epithelium-derived cytokine that helps initiate allergic inflammation—is not well understood. To fully characterize the transcriptional responses and heterogeneity of MC and BΦ, we performed large-scale comparative microarrays of bone marrow-derived mast cells (BMMCs) and basophils (BMBs) either at rest, upon IgE activation, or upon IL-33 activation. Hierarchical clustering demonstrated that BMMCs had activation-specific transcriptional signatures. IgE-crosslinking upregulated 1803 unique genes including Ccl7, Fxyd6, and Cd33; while IL-33 stimulation induced 1918 genes including Il1b, Cd72, and Tnfaip2. Focused bioinformatics pathway analysis demonstrated IgE-activation aligned with pathways relating to complement activation and CD137 signaling, while IL-33 initiated responses in the NF-κB and IL-10 signaling pathways. Furthermore, BMBs activated via IgE-crosslinking induced type 2 immune response genes like Il2, Il4, Ccl17 and Il13 which were surprisingly absent in IL-33 stimulated BMBs. Further analysis revealed cell-specific transcriptional signatures with 4360 genes that were significant in BMBs but not in BMMCs after IgE activation, and 1645 after IL-33 stimulation. Here we show that MC and BΦ have cell-specific and activation-specific transcriptional responses, and our data suggest novel gene networks and pathways that may shape how the immune system responds to allergens and innate cytokines.
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 | 0.001 | 0.001 |
| 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 | 0.001 |
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".