한빛사 논문
강원대학교
Abstract
Jiho Leea,1, Ricardo R. da Silvab,c,1, Hyeon Seok Janga, Hyun Woo Kimd, Yong Soo Kwona, Jung-Hwan Kime, Heejung Yanga,*
a College of Pharmacy, Kangwon National University, Chuncheon 24341, Republic of Korea
b Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, 92093, United States of America
c NPPNS, Department of Physics and Chemistry, School of Pharmaceutical Sciences of Ribeirão Preto, University of São Paulo, Avenida do Café s/n, 14040-903 Ribeirão Preto, SP, Brazil
d College of Pharmacy and Research Institute of Pharmaceutical Science, Seoul National University, Seoul 08826, Republic of Korea
e Department of Pharmacology, School of Medicine, Institute of Health Sciences, Gyeongsang National University, Jinju 52727, Republic of Korea
*Corresponding author : Heejung Yang
1Authors who contributed equally
Abstract
In liquid chromatography-mass spectrometry (LC-MS) metabolomics, data matrices with up to thousands of variables for each ion peak are subjected to multivariate analysis (MVA) to assess the homogeneity between samples. The large dimensions of LC/MS datasets hinder the identification of the discriminant or the metabolic markers. In the present study, the molecular network (MN) approach and two in silico annotation tools, network annotation propagation (NAP) and the hierarchical chemical classification method, ClassyFire, were used to annotate the metabolites of three Zanthoxylum species, Z. bungeanum, Z. schinifolium and Z. piperitum. The in silico annotation results of the MN nodes and the MVA variables were combined and visualized in loading plots. This approach helped intuitive detection of the variables that greatly contributed to the separation of the samples in the score plot as discriminant or metabolic markers, thereby allowing rapid annotation of two flavanone derivatives.
Keywords : LC/MS; multivariate analysis; molecular network; Zanthoxylum species
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