Abstract Detail

Enriching basic and applied botany through multi-stakeholder collaborations

Chao , Krystal Shinhuei [1], Metzgar, Jordan [2].

Predicting medicinal activity in Chinese magnoliid dicot species using phylogenetic diversity.

Thousands of years of Chinese history and culture has generated a vast traditional knowledge of medicinal plants. China’s high botanical diversity, with over 30,000 native plant species, has aided the development of the Han Chinese pharmacopoeia. The well-known malarial drug artemisinin developed by Chinese scientists from Artemisia annua is one of many modern drugs that are directly or indirectly derived from plants. In this study, we documented traditional medicinal uses of magnoliid dicot species in China. The magnoliid dicots are a group of woody flowering plants that include magnolias, nutmeg, cinnamon, and black pepper. There are about 9,000 species of magnoliid dicots worldwide with 869 species in China, 72 of which have medicinal properties. We created a table of medicinal uses from an extensive literature search. Species were categorized into 19 medicinal uses that included: inflammation, lungs/cough, stomach, and analgesic uses, among others. We constructed a phylogeny of Chinese magnoliid dicot taxa using matK sequences downloaded from GenBank. We used Phylocom to identify “hot nodes,” clades that are over-represented with medicinal species. Our results predict which magnoliid dicot should be investigated further for potential drug development.

1 - Virginia Polytechnic Institute and State University, Biological Sciences, 926 West Campus Drive, Blacksburg, VA, 24061, USA
2 - Virginia Tech, Biological Sciences, 926 W. Campus Dr, MC 0406, Derring Hall 2119, Blacksburg, VA, 24061, United States

magnoliid dicot
phylogenetic diversity
traditional Chinese medicine.

Presentation Type: Colloquium Presentations
Session: COL03, Enriching basic and applied botany through multi-stakeholder collaborations
Location: Virtual/Virtual
Date: Thursday, July 30th, 2020
Time: 10:45 AM
Number: COL03003
Abstract ID:63
Candidate for Awards:None

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