Metabolic Profiling-based Data-mining for an Effective Chemical Combination to Induce Apoptosis of Cancer Cells

Motofumi Kumazoe, Yoshinori Fujimura, Shiori Hidaka, Yoonhee Kim, Kanako Murayama, Mika Takai, Yuhui Huang, Shuya Yamashita, Motoki Murata, Daisuke Miura, Hiroyuki Wariishi, Mari Maeda-Yamamoto, Hirofumi Tachibana

Research output: Contribution to journalArticlepeer-review

15 Citations (Scopus)

Abstract

Green tea extract (GTE) induces apoptosis of cancer cells without adversely affecting normal cells. Several clinical trials reported that GTE was well tolerated and had potential anti-cancer efficacy. Epigallocatechin-3-O-gallate (EGCG) is the primary compound responsible for the anti-cancer effect of GTE; however, the effect of EGCG alone is limited. To identify GTE compounds capable of potentiating EGCG bioactivity, we performed metabolic profiling of 43 green tea cultivar panels by liquid chromatography-mass spectrometry (LC-MS). Here, we revealed the polyphenol eriodictyol significantly potentiated apoptosis induction by EGCG in vitro and in a mouse tumour model by amplifying EGCG-induced activation of the 67-kDa laminin receptor (67LR)/protein kinase B/endothelial nitric oxide synthase/protein kinase C delta/acid sphingomyelinase signalling pathway. Our results show that metabolic profiling is an effective chemical-mining approach for identifying botanical drugs with therapeutic potential against multiple myeloma. Metabolic profiling-based data mining could be an efficient strategy for screening additional bioactive compounds and identifying effective chemical combinations.

Original languageEnglish
Article number9474
JournalScientific reports
Volume5
DOIs
Publication statusPublished - Mar 2015

All Science Journal Classification (ASJC) codes

  • General

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