Eosinophil-associated Ribonuclease 11 Is a Macrophage Chemoattractant
Bibliographic record
Abstract
RNase A is the prototype of an extensive family of divergent proteins whose members share a unique disulfide-bonded tertiary structure, conserved catalytic motifs, and the ability to hydrolyze polymeric RNA. Several members of this family maintain independent roles as ribonucleases and modulators of innate immunity. Here we characterize mouse eosinophil-associated RNase (Ear) 11, a divergent member of the eosinophil ribonuclease cluster, and the only known RNase A ribonuclease expressed specifically in response to Th2 cytokine stimulation. Mouse Ear 11 is differentially expressed in somatic tissues at baseline (brain ≪ liver < lung < spleen); systemic stimulation with IL-33 results in 10-5000-fold increased expression in lung and spleen, respectively. Ear 11 is also expressed in response to protective priming of the respiratory mucosa with Lactobacillus plantarum; transcripts are detected both locally in lung as well as systemically in bone marrow and spleen. Mouse Ear 11 is enzymatically active, although substantially less so than mEar 1 and mEar 2; the relative catalytic efficiency (kcat/Km) of mEar 11 is diminished ∼1000-1500-fold. However, in contrast to RNase 2/EDN and mEar 2, which have been characterized as selective chemoattractants for CD11c(+) dendritic cells, mEar 11 has prominent chemoattractant activity for F4/80(+)CD11c(-) tissue macrophages. Chemoattractant activity is not dependent on full enzymatic activity, and requires no interaction with the pattern recognition receptor, Toll-like receptor 2 (TLR2). Taken together, this work characterizes a divergent RNase A ribonuclease with a unique expression pattern and function, and highlights the versatility of this family in promoting innate immunity.
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 | 0.001 |
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".