Decomposition Profile Data Analysis for Deep Understanding of Multiple Effects of Natural Products

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.