Planta Med 2018; 84(06/07): 434-441
DOI: 10.1055/s-0043-121708
Natural Product Chemistry and Analytical Studies
Original Papers
Georg Thieme Verlag KG Stuttgart · New York

Identification of Potential Biomarkers from Aconitum carmichaelii, a Traditional Chinese Medicine, Using a Metabolomic Approach

Dake Zhao*
1   Laboratory of Ecology and Evolutionary Biology, State Key Laboratory for Conservation and Utilization of Bio-Resources in Yunnan University, Kunming, Peopleʼs Republic of China
2   Yunan Institue of Materia Medica, Yunnan Baiyao Group Company Limited, Kunming, Peopleʼs Republic of China
3   Key Laboratory of Tropical Plant Resources and Sustainable Use, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Kunming, Peopleʼs Republic of China
,
Yana Shi*
4   Institute of Medicine Plants, Yunnan Academy of Agricultural Sciences, Kunming, Peopleʼs Republic of China
,
Xinyan Zhu
4   Institute of Medicine Plants, Yunnan Academy of Agricultural Sciences, Kunming, Peopleʼs Republic of China
,
Li Liu
4   Institute of Medicine Plants, Yunnan Academy of Agricultural Sciences, Kunming, Peopleʼs Republic of China
,
Pengzhang Ji
4   Institute of Medicine Plants, Yunnan Academy of Agricultural Sciences, Kunming, Peopleʼs Republic of China
,
Chunlin Long
5   College of Life and Environmental Sciences, Minzu University of China, Beijing, Peopleʼs Republic of China
6   Kunming Institute of Botany, Chinese Academy of Sciences, Kunming, Peopleʼs Republic of China
,
Yong Shen
7   College of Agriculture and Biotechnology, Yunnan Agricultural University, Kunming, Peopleʼs Republic of China
,
Edward J. Kennelly
5   College of Life and Environmental Sciences, Minzu University of China, Beijing, Peopleʼs Republic of China
8   Department of Biological Sciences, Lehman College, City University of New York, Bronx, New York, USA
9   Ph.D. Programs in Biochemistry, Biology, and Chemistry, The Graduate Center, City University of New York, New York, USA
› Author Affiliations
Further Information

Publication History

received 23 August 2017
revised 27 September 2017

accepted 17 October 2017

Publication Date:
26 October 2017 (online)

Abstract

Despite their well-known toxicity, Aconitum species are important traditional medicines worldwide. Aconitum carmichaelii, known in Chinese as 附子 (fuzi), is an officially recognized traditional Chinese medicine with characteristic analgesic and anti-inflammatory activities, whose principal pharmacological ingredients are considered as aconitine-type diterpene alkaloids. Notwithstanding the long-recorded use of A. carmichaelii in traditional Chinese medicine, no single-entity aconitum alkaloid drug has been developed for clinical use. UPLC-Q-TOF-MS was used to investigate the marker compounds that can be used to differentiate A. carmichaelii from seven other Aconitum species collected in Yunnan Province. Nontargeted principle component analysis scores plots found that all the tested Aconitum species clustered into three distinct groups, and A. carmichaelii was significantly different chemically than the other seven species. Furthermore, the primary and lateral roots of A. carmichaelii also showed significant differences. Using orthogonal partial least squares discriminate analysis analysis, eight marker compounds were identified, including 14-acetylkarakoline, aconitine, carmichaeline, fuziline, hypaconitine, mesaconitine, neoline, and talatisamine. Four of these aconitum alkaloids, fuziline, hypaconitine, mesaconitine, and neoline, showed significant analgesic activity in a dose-dependent manner compared to the negative and positive controls. However, hypaconitine, mesaconitine, and neoline exhibited significant acute toxicity activity, while fuziline showed no acute toxicity in mice, suggesting the relative safety of this alkaloid. This study provides a good example of how to differentiate an authentic medicinal plant from common adulterants using a metabolomics approach, and to identify compounds that may be developed into new drugs.

* These authors contributed equally to this work.


Supporting Information

 
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