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Profiling of DIILD in a Bleomycin Rat Model

Gene expression and cellular profiling in a rat bleomycin model of drug-induced interstitial lung disease (DIILD)

Irma Mahmutovic Persson, Nina Fransén Pettersson, Jian Liu, Hanna Falk-Håkansson, Lars E. Olsson and Karin von Wachenfeldt - on behalf of the TRISTAN Consortium


ESR Conference 2020

Abstract

A large number of frequently prescribed drugs have the potential to cause DIILD. We have characterized a rat model of bleomycin-triggered DIILD, by gene profiling combined with flow cytometric characterization of immune cell populations in lungs over 28 days.


Methods & Results: Sprague-Dawley rats received a single dose of intratracheal bleomycin. Longitudinal imaging was performed (MRI and 18F-FDG-PET/CT) and BAL fluid, blood, lungs and spleen collected. Lung homogenates were used for analysis of gene expression (RT-qPCR), assessment of hydroxyproline content and for flow cytometric analysis of immune cell populations in lung. Early time points were dominated by pro-inflammatory gene expression. Interestingly, fibrosis related genes, such as Gremlin1, CTGF and TGFβs, were also up-regulated (p<0.001) during the inflammatory phase (d3-7). In addition, at later time points during the fibrosis phase (d14-28) inflammatory related genes such as CCL3 (p<0.01) and TNFα stayed up-regulated. Some genes, such as IL-4 and IL-5, revealed dual peaks at d7 and at d28. Animals identified by MRI to have more severe disease demonstrated a different gene profile compared to those with less disease.  Analysis of immune cell populations during the different stages of the disease showed increased numbers of eosinophils, neutrophils and NK cells at the early stages. Neutrophils and macrophages also showed up in a second cell-peak at d28.


Conclusion: Linking the pathological changes observed by imaging to gene expression patterns and immune cell profiles in the lung, has provided an increasing understanding of how biomarkers can be implemented to develop improved DIILD- and lung injury models.