RELMα Is Induced in Airway Epithelial Cells by Oncostatin M without Requirement of STAT6 or IL-6 in Mouse Lungs In Vivo
Bibliographic record
Abstract
Resistin-like molecule alpha (RELMα) and YM-1 are secreted proteins implicated in murine models of alternatively activated macrophage (AA/M2) accumulation and Th2-skewed inflammation. Since the gp130 cytokine Oncostatin M (OSM) induces a Th2-like cytokine and AA/M2 skewed inflammation in mouse lung, we here investigated regulation of RELMα and YM-1. Transient pulmonary overexpression of OSM by Adenovirus vector (AdOSM) markedly induced RELMα and YM-1 protein expression in total lung. In situ hybridization showed that RELMα mRNA was highly induced in airway epithelial cells (AEC) and was co-expressed with CD68 mRNA in some but not all CD68+ cells in parenchyma. IL-6 overexpression (a comparator gp130 cytokine) induced RELMα, but at significantly lower levels. IL-6 (assessing IL-6−/− mice) was not required, nor was STAT6 (IL-4/13 canonical signalling) for AdOSM-induction of RELMα in AEC. AEC responded directly to OSM in vitro as assessed by pSTAT3 activation. RELMα-deficient mice showed similar inflammatory cell infiltration and cytokine responses to wt in response to AdOSM, but showed less accumulation of CD206+ AA/M2 macrophages, reduced induction of extracellular matrix gene mRNAs for COL1A1, COL3A1, MMP13, and TIMP1, and reduced parenchymal alpha smooth muscle actin. Thus, RELMα is regulated by OSM in AEC and contributes to extracellular matrix remodelling in mouse lung.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".