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Metabolic Profiling Regarding Pathogenesis of Idiopathic Pulmonary Fibrosis

Authors
Kang, Yun PyoLee, Sae BomLee, Ji-minKim, Hyung MinHong, Ji YeonLee, Won JunChoi, Chang WooShin, Hwa KyunKim, Do-JinKoh, Eun SukPark, Choon-SikKwon, Sung WonPark, Sung-Woo
Issue Date
May-2016
Publisher
American Chemical Society
Keywords
idiopathic pulmonary fibrosis; lung tissue; gas chromatography-mass spectrometry (GC-MS); metabolic profiling
Citation
Journal of Proteome Research, v.15, no.5, pp 1717 - 1724
Pages
8
Journal Title
Journal of Proteome Research
Volume
15
Number
5
Start Page
1717
End Page
1724
URI
https://scholarworks.bwise.kr/sch/handle/2021.sw.sch/9175
DOI
10.1021/acs.jproteome.6b00156
ISSN
1535-3893
1535-3907
Abstract
Idiopathic pulmonary fibrosis (IPF) is a progressive, eventually fatal disease characterized by fibrosis of the lung parenchyma and loss of lung function. IPF is believed to be caused by repetitive alveolar epithelial cell injury and dysregulated repair process including uncontrolled proliferation of lung (myo) fibroblasts and excessive deposition of extracellular matrix proteins in the interstitial space; however, the pathogenic pathways involved in IPF have not been fully elucidated. In this study, we attempted to characterize metabolic changes of lung tissues involved in the pathogenesis of IPF using gas chromatography mass spectrometry-based metabolic profiling. Partial least-squares discriminant analysis (PLS-DA) model generated from metabolite data was able to discriminate between the control subjects and IPF patients ((RX)-X-2 = 0.37, (RY)-Y-2 = 0.613 and Q(2) (cumulative) = 0.54, receiver operator characteristic AUC > 0.9). We discovered 25 metabolite signatures of IPF using both univariate and multivariate statistical analyses (FDR < 0.05 and VIP score of PLS-DA > 1). These metabolite signatures indicated alteration in metabolic pathways: adenosine triphosphate degradation pathway, glycolysis pathway, glutathione biosynthesis pathway, and ornithine aminotransferase pathway. The results could provide additional insight into understanding the disease and potential for developing biomarkers.
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