Bibliographic record
Abstract
Pulmonary fibrosis (PF) is a pathology featured in many diseases frequently classified as interstitial lung diseases. Deregulated extracellular matrix (ECM) remodeling disrupts lung architecture, causing decreased lung function and patient mortality. Current therapies are limited to slowing rather than halting disease progression. We previously identified fibroblast-derived ephrin-B2 as a key mediator of PF. This work seeks to further evaluate the role of ephrin-B2 and its potential receptor ephB4 in PF development. Ephrin-B2 is highly expressed in endothelial cells within lung tissue, and this work seeks to address whether endothelial expression of ephrin-B2 contributes to PF. We have previously identified ephB4 as a likely target for ephrin-B2 signaling in PF. I sought to evaluate whether ephB4 ablation on fibroblasts was sufficient in protecting against the development of PF using a conditional type I collagen-driven Ephb4 KO mouse line that I generated. Ephb4 CKO substantially protects against bleomycin-induced PF in mice. RNA sequencing was performed on lung fibroblasts isolated from ephB4 KO mice and control mice to assess its signaling mechanism. 11 ECM and 7 protein transport genes were downregulated with the KO of fibroblast ephB4; this is the first study to link ephB4 signaling to fibrotic extracellular matrix remodeling and secretion. To evaluate whether ephB4 pharmacological inhibition could also impact ECM and protein transport NanoString was performed on mouse and human fibroblasts treated with profibrotic cytokine TGF-β1 and ephB4 inhibitor NVP-BHG712. ECM and protein transport genes were downregulated in mouse and human fibroblasts. Human fibroblasts treated with NVP-BHG712 downregulate the expression of classic pro-fibrotic markers. Mice administered with ephB4 inhibitor 5 days post-bleomycin challenge developed markedly less PF compared to controls. RNA sequencing of human IPF fibroblasts indicate that a key potential downstream target of ephB4 signaling is ELN. ELN was shown through human protein-protein interactions to share TGF-β1 as an interaction partner with ephB4. Mice therapeutically treated with ephb4 inhibitor downregulate ELN in lung tissue after initiation of fibrosis. Ultimately, ephB4 signaling shows excellent potential in both understanding the mechanisms of fibrosis pathology, and in future translation towards addressing human disease, potentially by modulating key ECM proteins like ELN.
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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".