Overexpression of Il-6 in Bleomycin-Treated Balb/C Mice Provides a New Model of Lung Fibrosis: Transcriptomic Comparison to Human Ipf
Bibliographic record
Abstract
Idiopathic pulmonary fibrosis (IPF) is a difficult to treat condition to treat with a high mortality rate. While preclinical models such as Bleomycin (Bleo)-induced fibrosis recapitulate some aspects of IPF, new models that more closely mimic the disease are warranted. We have developed and characterized a novel model in BALB/c mice using a transient overexpression of IL-6 combined with Bleo. This model’s transcriptomic signature was compared to published human IPF datasets. BALB/c females were treated with AdIL-6 in combination with Bleo (AdIL-6+Bleo) or control comparators, and underwent assessments of histopathology (ECM, α-SMA accumulation), collagen content and lung physiology (Elastance, EST). Flow cytometry was used to characterize T cell and Macrophage subsets, and T cell depletion studies (anti-CD4/CD8) were completed to assess T cells role in fibrosis. Bulk RNA sequencing generated gene signatures were compared to that of other rodent fibrosis models and published human datasets of IPF datasets. The AdIL-6+Bleo treatment resulted in robust fibrosis measures (day21), sustained fibrosis until at least day 50, a unique cytokine profile at day 7, and increased activated CD4+Tcells, CD8+Tcells, and CD16+MHChi/Clec7a/iNOS-/Arg1-/CD206- macrophages. T cell depletion markedly decreased fi-brotic responses and this population of macrophages. The Day 21 AdIL-6+Bleo transcriptome shared more genes with each of two human IPF datasets than Bleo alone in C57Bl/6 mice or other rodent models. We conclude that AdIL-6+Bleo in BALB/c mice offers a superior preclinical model for studying IPF mechanisms and potential therapeutic intervention.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".