Identification of Ephrin type-B receptor 4 as a critical mediator of tissue fibrosis
Bibliographic record
Abstract
Pulmonary fibrosis (PF) is a pathology associated with interstitial lung diseases (ILDs), including idiopathic pulmonary fibrosis (IPF). Fibrosis promotes continual secretion of extracellular matrix (ECM), producing nonfunctional scar tissue and causing organ failure. This study investigated the tyrosine kinase receptor Ephrin type-B receptor 4 (EphB4) as a mediator of PF. To this end, we generated mice with conditional Col1a2-driven deletion of Ephb4 and used a preclinical mouse model of PF, total and single nuclei RNA (snRNA) sequencing, NanoString, previously published single-cell data, computational analysis, and functional assays of mouse and human healthy control and IPF lung fibroblasts. Col1a2-CreERT-driven Ephb4 deletion, or EphB4 inhibition via NVP-BHG712, markedly protected against bleomycin-induced PF. Total RNA-Seq of fibroblasts isolated from Ephb4-deficient fibrotic mouse lungs exhibited reduced expression of ECM, ER Cargo, and protein trafficking-related genes. NVP-BHG712 reduced expression of these identified genes in mouse lung fibroblasts under fibrotic conditions in vitro. snRNA-Seq of mouse lungs treated with NVP-BHG712 identified transcriptomic changes of ECM genes in specific fibroblast subpopulations. RNA-Seq, computational, and functional assays using mouse and human IPF fibroblasts identified elastin as a key mediator involved in EphB4 signaling. Combined, our data show that EphB4 is a crucial mediator of PF.
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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) | 0.000 | 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".