Related Experiment Video
Updated: Oct 27, 2025
Oropharyngeal Administration of Bleomycin in the Murine Model of Pulmonary Fibrosis
Published on: May 9, 2025
Transcriptome analysis reveals key genes modulated by ALK5 inhibition in a bleomycin model of systemic sclerosis
Benjamin E Decato1, Ron Ammar1, Lauren Reinke-Breen1
1Research & Early Development, Bristol-Myers Squibb Company, Lawrenceville, NJ, USA.
Objective:
SSc is a rheumatic autoimmune disease affecting roughly 20 000 people worldwide and characterized by excessive collagen accumulation in the skin and internal organs. Despite the high morbidity and mortality associated with SSc, there are no approved disease-modifying agents. Our objective in this study was to explore transcriptomic and model-based drug discovery approaches for SSc.
Methods:
In this study, we explored the molecular basis for SSc pathogenesis in a well-studied mouse model of scleroderma. We profiled the skin and lung transcriptomes of mice at multiple timepoints, analysing the differential gene expression that underscores the development and resolution of bleomycin-induced fibrosis.
Results:
We observed shared expression signatures of upregulation and downregulation in fibrotic skin and lung tissue, and observed significant upregulation of key pro-fibrotic genes including GDF15, Saa3, Cxcl10, Spp1 and Timp1. To identify changes in gene expression in responses to anti-fibrotic therapy, we assessed the effect of TGF-β pathway inhibition via oral ALK5 (TGF-β receptor I) inhibitor SB525334 and observed a time-lagged response in the lung relative to skin. We also implemented a machine learning algorithm that showed promise at predicting lung function using transcriptome data from both skin and lung biopsies.
Conclusion:
This study provides the most comprehensive look at the gene expression dynamics of an animal model of SSc to date, provides a rich dataset for future comparative fibrotic disease research, and helps refine our understanding of pathways at work during SSc pathogenesis and intervention.
Insights
This study reveals shared gene expression patterns in fibrotic skin and lung tissue in a scleroderma mouse model. It identifies key pro-fibrotic genes and shows potential for machine learning in predicting lung function.
Area of Science:
- Rheumatic autoimmune diseases
- Fibrotic disease research
- Transcriptomics
Background:
- Systemic sclerosis (SSc) is a rheumatic autoimmune disease affecting approximately 20,000 people globally.
- SSc is characterized by excessive collagen accumulation in the skin and internal organs.
- No approved disease-modifying agents currently exist for SSc, despite high morbidity and mortality.
Purpose of the Study:
- To explore transcriptomic and model-based drug discovery approaches for SSc.
- To investigate the molecular basis of SSc pathogenesis using a mouse model.
- To analyze differential gene expression during fibrosis development and resolution.
Main Methods:
- Profiling of skin and lung transcriptomes in a mouse model of bleomycin-induced fibrosis.
- Analysis of differential gene expression at multiple timepoints.
- Assessment of TGF-β pathway inhibition using an ALK5 inhibitor (SB525334).
- Implementation of a machine learning algorithm for predicting lung function from transcriptome data.
Main Results:
- Shared gene expression signatures (upregulation and downregulation) were observed in fibrotic skin and lung tissue.
- Key pro-fibrotic genes (GDF15, Saa3, Cxcl10, Spp1, Timp1) were significantly upregulated.
- A time-lagged response to TGF-β pathway inhibition was noted in the lung compared to the skin.
- A machine learning algorithm showed promise in predicting lung function using skin and lung transcriptome data.
Conclusions:
- This study offers a comprehensive analysis of gene expression dynamics in an SSc animal model.
- The findings provide a valuable dataset for future comparative fibrotic disease research.
- The research refines understanding of SSc pathogenesis pathways and potential therapeutic interventions.
