Kerr Michael, Leavitt Steven D
Department of Biology, Brigham Young University, Provo, UT 84602, USA.
M.L. Bean Life Science Museum and Department of Biology, Brigham Young University, Provo, UT 84602, USA.
J Fungi (Basel). 2023 Jul 12;9(7):741. doi: 10.3390/jof9070741.
DNA barcoding approaches provide powerful tools for characterizing fungal diversity. However, DNA barcoding is limited by poor representation of species-level diversity in fungal sequence databases. Can the development of custom, regionally focused DNA reference libraries improve species-level identification rates for lichen-forming fungi? To explore this question, we created a regional ITS database for lichen-forming fungi (LFF) in the Intermountain West of the United States. The custom database comprised over 4800 sequences and represented over 600 formally described and provisional species. Lichen communities were sampled at 11 sites throughout the Intermountain West, and LFF diversity was characterized using high-throughput ITS2 amplicon sequencing. We compared the species-level identification success rates from our bulk community samples using our regional ITS database and the widely used UNITE database. The custom regional database resulted in significantly higher species-level assignments (72.3%) of candidate species than the UNITE database (28.3-34.2%). Within each site, identification of candidate species ranged from 72.3-82.1% using the custom database; and 31.5-55.4% using the UNITE database. These results highlight that developing regional databases may accelerate a wide range of LFF research by improving our ability to characterize species-level diversity using DNA barcoding.
DNA条形码技术为表征真菌多样性提供了强大的工具。然而，DNA条形码技术受到真菌序列数据库中物种水平多样性代表性不足的限制。开发定制的、区域聚焦的DNA参考文库能否提高地衣形成真菌的物种水平鉴定率？为了探究这个问题，我们为美国西部山间地区的地衣形成真菌（LFF）创建了一个区域内转录间隔区（ITS）数据库。这个定制数据库包含了超过4800个序列，代表了600多个正式描述和暂定的物种。我们在整个美国西部山间地区的11个地点采集了地衣群落样本，并使用高通量ITS2扩增子测序来表征LFF的多样性。我们使用我们的区域ITS数据库和广泛使用的UNITE数据库比较了大量群落样本的物种水平鉴定成功率。与UNITE数据库（28.3%-34.2%）相比，定制的区域数据库在候选物种的物种水平归类上显著更高（72.3%）。在每个地点，使用定制数据库对候选物种的鉴定率在72.3%-82.1%之间；使用UNITE数据库的鉴定率在31.5%-55.4%之间。这些结果表明，通过提高我们使用DNA条形码技术表征物种水平多样性的能力，开发区域数据库可能会加速广泛的LFF研究。
