Basophil-derived IL-4 promotes cutaneous Staphylococcus aureus infection
Bibliographic record
Abstract
Superficial cutaneous Staphylococcus aureus (S. aureus) infection in humans can lead to soft tissue infection, an important cause of morbidity and mortality. IL-17A production by skin TCRγδ+ cells in response to IL-1 and IL-23 produced by epithelial and immune cells is important for restraining S. aureus skin infection. How S. aureus evades this cutaneous innate immune response to establish infection is not clear. Here we show that mechanical injury of mouse skin by tape stripping predisposed mice to superficial skin infection with S. aureus. Topical application of S. aureus to tape-stripped skin caused cutaneous influx of basophils and increased Il4 expression. This basophil-derived IL-4 inhibited cutaneous IL-17A production by TCRγδ+ cells and promoted S. aureus infection of tape-stripped skin. We demonstrate that IL-4 acted on multiple checkpoints that suppress the cutaneous IL-17A response. It reduced Il1 and Il23 expression by keratinocytes, inhibited IL-1+IL-23-driven IL-17A production by TCRγδ+ cells, and impaired IL-17A-driven induction of neutrophil-attracting chemokines by keratinocytes. IL-4 receptor blockade is shown to promote Il17a expression and enhance bacterial clearance in tape-stripped mouse skin exposed to S. aureus, suggesting that it could serve as a therapeutic approach to prevent skin and soft tissue infection.
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 | 0.001 |
| 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".