Mankga Ledile T, Yessoufou Kowiyou, Moteetee Annah M, Daru Barnabas H, van der Bank Michelle
African Centre for DNA Barcoding, Department of Botany and Plant Biotechnology, University of Johannesburg, PO Box 524, Auckland Park 2006, Johannesburg, South Africa.
Zookeys. 2013 Dec 30(365):215-33. doi: 10.3897/zookeys.365.5730.
Medicinal plants cover a broad range of taxa, which may be phylogenetically less related but morphologically very similar. Such morphological similarity between species may lead to misidentification and inappropriate use. Also the substitution of a medicinal plant by a cheaper alternative (e.g. other non-medicinal plant species), either due to misidentification, or deliberately to cheat consumers, is an issue of growing concern. In this study, we used DNA barcoding to identify commonly used medicinal plants in South Africa. Using the core plant barcodes, matK and rbcLa, obtained from processed and poorly conserved materials sold at the muthi traditional medicine market, we tested efficacy of the barcodes in species discrimination. Based on genetic divergence, PCR amplification efficiency and BLAST algorithm, we revealed varied discriminatory potentials for the DNA barcodes. In general, the barcodes exhibited high discriminatory power, indicating their effectiveness in verifying the identity of the most common plant species traded in South African medicinal markets. BLAST algorithm successfully matched 61% of the queries against a reference database, suggesting that most of the information supplied by sellers at traditional medicinal markets in South Africa is correct. Our findings reinforce the utility of DNA barcoding technique in limiting false identification that can harm public health.
药用植物涵盖了广泛的分类群,这些分类群在系统发育上可能关系较远,但形态上却非常相似。物种之间的这种形态相似性可能导致误认和不当使用。此外,由于误认或故意欺骗消费者,用较便宜的替代品(如其他非药用植物物种)替代药用植物也是一个日益受到关注的问题。在本研究中,我们使用DNA条形码技术来鉴定南非常用的药用植物。利用从传统草药市场上出售的经过加工且保存不佳的材料中获得的核心植物条形码matK和rbcLa,我们测试了这些条形码在物种鉴别中的功效。基于遗传差异、PCR扩增效率和BLAST算法,我们揭示了DNA条形码不同的鉴别潜力。总体而言,这些条形码表现出较高的鉴别能力,表明它们在验证南非药用市场上交易的最常见植物物种的身份方面是有效的。BLAST算法成功地将61%的查询结果与参考数据库进行了匹配,这表明南非传统药用市场上卖家提供的大部分信息是正确的。我们的研究结果强化了DNA条形码技术在限制可能危害公众健康的错误识别方面的实用性。