Open AccessDecomposition Profile Data Analysis for Deep Understanding of Multiple Effects of Natural Products
Author(s) -
Shumpei Nemoto,
Katsuaki Morita,
Tadahaya Mizuno,
Hiroyuki Kusuhara
Publication year - 2021
Publication title -
journal of natural products
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.976
H-Index - 139
eISSN - 1520-6025
pISSN - 0163-3864
DOI - 10.1021/acs.jnatprod.0c01381
Subject(s) - outlier , transcriptome , similarity (geometry) , natural product , set (abstract data type) , data set , decomposition , natural (archaeology) , computational biology , biological system , data mining , computer science , chemistry , biology , artificial intelligence , stereochemistry , ecology , biochemistry , paleontology , gene expression , image (mathematics) , gene , programming language
It is difficult to understand the entire effect of a natural product because such products generally have multiple effects. We propose a strategy to understand these effects effectively by decomposing them with a profile data analysis method we developed. A transcriptome profile data set was obtained from a public database and analyzed. Considering their high similarity in structure and transcriptome profile, we focused on rescinnamine and syrosingopine. Decomposed effects predicted clear differences between the compounds. Two of the decomposed effects, SREBF1 activation and HDAC inhibition, were investigated experimentally because the relationship between these effects and the compounds had not yet been reported. Analyses in vitro validated these effects, and their strength was consistent with predicted scores. Moreover, the number of outliers in decomposed effects per compound was higher in natural products than in drugs in the data set, which is consistent with the nature of the effects of natural products.