| 环境DNA宏条形码技术在蓝藻群落监测中的应用 |
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| 引用本文： | 李小闯,霍守亮,张含笑,金小伟,李文攀,张军毅,张靖天.环境DNA宏条形码技术在蓝藻群落监测中的应用[J].环境科学研究,2021,34(2):372-381. |
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| 作者姓名： | 李小闯 霍守亮 张含笑 金小伟 李文攀 张军毅 张靖天 |
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| 作者单位： | 1.中国环境科学研究院, 环境基准与风险评估国家重点实验室, 北京 100012 |
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| 基金项目： | 国家重点研发计划项目（No.2017YFA0605003）；国家自然科学基金项目（No.51922010） |
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| 摘 要： | 全球气候变化下的蓝藻水华大规模暴发成为重要的环境问题，对蓝藻准确、高效和实时的监测是蓝藻水华防控的关键.近年来，环境DNA（environmental DNA，eDNA）宏条形码技术开始应用于蓝藻群落监测，弥补了显微镜镜检法物种鉴定难和受主观经验影响大的缺点.eDNA宏条形码为快速和大规模蓝藻群落监测提供了可能，其应用尚处于初步探索阶段，数据处理方法尚不成熟，因此有必要从引物选择、序列聚类、注释方法和绝对定量4个层面系统综述eDNA宏条形码技术在蓝藻群落监测中的研究进展，以推进eDNA宏条形码在蓝藻群落监测中的应用.引物选择应针对目标蓝藻类群，16S rRNA基因数据库涵盖蓝藻物种范围广，是最常用的eDNA宏条形码，但16S-23S rRNA基因间隔区域（internal transcribed spacer，ITS）和功能基因在属内物种间具有较好的区分效果，为eDNA宏条形码在蓝藻物种水平注释提供可能.序列聚类一般通过设定物种间分类阈值进行OTUs聚类，但可能丢失序列相似度高于分类阈值的不同物种；序列差异低至单核苷酸变异方法的应用进一步区分了隐蔽的物种和生态型，产生的序列具有生物学意义，可以实现跨数据集间的比较分析.在注释方法上，基于数据库参考序列距离相似度的注释方法，注释结果的分辨率和准确度不高；基于系统进化位置的注释方法，提升了注释结果的准确度，反映了物种间的进化关系.加入外源DNA的内标法，以及基于细胞体积和基因拷贝数目关系的细胞体积校正系数法为eDNA宏条形码物种丰度的绝对定量提供了可能.
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| 关 键 词： | 蓝藻 环境DNA 宏条形码 监测 方法优化 |
| 收稿时间： | 2020/5/22 0:00:00 |
| 修稿时间： | 2020/9/17 0:00:00 |
Application of Environmental DNA Metabarcoding in Monitoring Cyanobacterial Community |
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| LI Xiaochuang,HUO Shouliang,ZHANG Hanxiao,JIN Xiaowei,LI Wenpan,ZHANG Junyi,ZHANG Jingtian.Application of Environmental DNA Metabarcoding in Monitoring Cyanobacterial Community[J].Research of Environmental Sciences,2021,34(2):372-381. |
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| Authors: | LI Xiaochuang HUO Shouliang ZHANG Hanxiao JIN Xiaowei LI Wenpan ZHANG Junyi ZHANG Jingtian |
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| Affiliation: | 1.State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China2.China National Environmental Monitoring Centre, Beijing 100012, China3.Wuxi Environmental Monitoring Centre Station, Wuxi 214023, China |
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| Abstract: | Large-scale cyanobacterial blooms under global warming has become a serious environmental issue, thus accurate, efficient, and real-time monitoring of cyanobacteria is the key to the control of cyanobacterial blooms. In recent years, environmental DNA (eDNA) metabarcoding technique has been used to monitor cyanobacterial communities, and can well overcome the difficulties of species identification by microscopic inspection and the limitations of subjective experiences. The eDNA metabarcoding provides an opportunity for fast and large-scale monitoring of the cyanobacterial communities, while its application is still in the exploratory stage, and there are flaws when processing metabarcoding datasets. Thus, it is urgent to summarize the progress of eDNA metabarcoding in cyanobacterial community monitoring from four aspects:primer selection, sequences clustering, taxonomic annotation, and absolute quantification to prompt the application of eDNA metabarcoding. Primers should be targeted to the studied cyanobacterial populations, and the 16S rRNA genes are the common choice due to the database contains a wide range of cyanobacterial species, and the ITS region between 16S and 23S rRNA genes and functional genes can well discriminate among the species within the genus, providing the possibility for the annotation at species level for eDNA metabarcoding dataset. The OTUs generated by sequences clustering through the setting classification threshold values for species may lose some species when sequences share similarities higher than the thresholds. However, the methods distinguished sequence variants differing by as little as one nucleotide could reveal the hidden species and ecotypes. The generated sequences had biological meaning that could realize the comparisons among different datasets. On the annotation, the method that based on the similarities shared with the reference sequences within database, led to low resolution and accuracy of taxonomic annotation results. Phylogenetic placement method could improve the accuracy of annotation results, and the taxonomy reflected the evolutionary relationships among species. The addition of internal standard DNAs and the correction factor based on the correllations between cell biovolume and the number of gene copies in single cells allowed the absolute quantification of species anundance in metabarcoding dataset. |
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| Keywords: | Cyanobacteria environmental DNA metabarcoding monitoring method optimization |
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