| Literature DB >> 27052453 |
Yun Pyo Kang1, Sae Bom Lee1, Ji-Min Lee2, Hyung Min Kim1, Ji Yeon Hong1, Won Jun Lee1, Chang Woo Choi3, Hwa Kyun Shin3, Do-Jin Kim2, Eun Suk Koh4, Choon-Sik Park2, Sung Won Kwon1, Sung-Woo Park2.
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 (R(2)X = 0.37, R(2)Y = 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.Entities:
Keywords: gas chromatography−mass spectrometry (GC−MS); idiopathic pulmonary fibrosis; lung tissue; metabolic profiling
Mesh:
Year: 2016 PMID: 27052453 DOI: 10.1021/acs.jproteome.6b00156
Source DB: PubMed Journal: J Proteome Res ISSN: 1535-3893 Impact factor: 4.466