Alternatives to Laboratory Animals 2014-09-01

Lung fibrosis: drug screening and disease biomarker identification with a lung slice culture model and subtracted cDNA Library.

Tong Guo, Ka Yee Lok, Changhe Yu, Zhuo Li

文献索引：Altern. Lab. Anim. 42(4) , 235-43, (2014)

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摘要

Pulmonary fibrosis is a progressive and irreversible disorder with no appropriate cure. A practical and effective experimental model that recapitulates the disease will greatly benefit the research community and, ultimately, patients. In this study, we tested the lung slice culture (LSC) system for its potential use in drug screening and disease biomarker identification. Fibrosis was induced by treating rat lung slices with 1ng/ml TGF-β1 and 2.5μM CdCl2, quantified by measuring the content of hydroxyproline, and confirmed by detecting the expression of collagen type III alpha 1 (Col3α1) and connective tissue growth factor (CTGF) genes. The anti-fibrotic effects of pirfenidone, spironolactone and eplerenone were assessed by their capability to reduce hydroxyproline content. A subtractive hybridisation technique was used to create two cDNA libraries (subtracted and unsubtracted) from lung slices. The housekeeping gene glyceraldehyde-3-phosphate dehydrogenase (GAPDH) was employed to assess the subtraction efficiency of the subtracted cDNA library. Clones from the two libraries were sequenced and the genes were identified by performing a BLAST search on the NCBI GenBank database. Furthermore, the relevance of the genes to fibrosis formation was verified. The results presented here show that fibrosis was effectively induced in cultured lung slices, which exhibited significantly elevated levels of hydroxyproline and Col3α1/CTGF gene expression. Several inhibitors have demonstrated their anti-fibrotic effects by significantly reducing hydroxyproline content. The subtracted cDNA library, which was enriched for differentially expressed genes, was used to successfully identify genes associated with fibrosis. Collectively, the results indicate that our LSC system is an effective model for the screening of drug candidates and for disease biomarker identification. 2014 FRAME.