Department of Applied Chemistry and Life Science, Molecular Genetics Laboratory, Toyohashi University of Technology, Toyohashi, Aichi, Japan.
PLoS One. 2020 Oct 7;15(10):e0240336. doi: 10.1371/journal.pone.0240336. eCollection 2020.
Nematodes are representative soil metazoans with diverged species that play crucial roles in nutrient recycling in the pedosphere. Qualitative and quantitative information on nematode communities is useful for assessing soil quality, and DNA barcode-mediated taxonomic analysis is a powerful tool to investigate taxonomic compositions and changes in nematode communities. Here, we investigated four regions (regions 1-4) of the 18S small subunit ribosomal RNA (SSU) gene as PCR targets of deep amplicon sequencing for the taxonomic profiling of individual soil nematodes. We determined the sequence variants (SVs) of 4 SSU regions for 96 nematodes (total 384 amplicons) isolated from copse soils and assigned their taxonomy using the QIIME2 software with dada2 or deblur algorithm and the SILVA database. Dada2 detected approximately 2-fold more nematode-derived SVs than deblur, and a larger number of SVs were obtained in regions 1 and 4 than those in other regions. These results and sufficient reference sequence coverage in region 4 indicated that DNA barcoding using a primer set for region 4 followed by dada2-based analysis would be most suitable for soil nematode taxonomic analysis. Eighteen SSU-derived operational taxonomic units (rOTUs) were obtained from 68 isolates, and their orders were determined based on the phylogenetic trees built by four regional sequences of rOTUs and 116 nematode reference species as well as the BLASTN search. The majority of the isolates were derived from three major orders Dorylaimida (6 rOTUs, 51.5% in 68 isolates), Rhabditida (4 rOTUs, 29.4%), and Triplonchida (7 rOTUs, 17.6%). The predicted feeding types of the isolates were fungivores (38.2% in total nematodes), plant feeders (32.4%), and 14.7% for both bacterivores and omnivores/predators. Additionally, we attempted to improve the branch structure of phylogenetic trees by using long nucleotide sequences artificially prepared by connecting regional sequences, but the effect was limited.
线虫是具有多样化物种的代表性土壤后生动物,它们在土壤圈养分循环中起着至关重要的作用。对线虫群落的定性和定量信息的了解有助于评估土壤质量,而 DNA 条码介导的分类分析是研究线虫群落分类组成和变化的有力工具。在这里,我们以 18S 小亚基核糖体 RNA(SSU)基因的四个区域(区域 1-4)作为 PCR 靶标,对线虫个体进行了深扩增子测序的分类分析。我们从 copse 土壤中分离出 96 条线虫(共 384 个扩增子),确定了 4 个 SSU 区域的序列变异(SVs),并使用 QIIME2 软件和 dada2 或 deblur 算法以及 SILVA 数据库对线虫进行了分类。Dada2 检测到的线虫衍生 SVs 约为 deblur 的两倍,而在区域 1 和 4 中获得的 SVs 数量多于其他区域。这些结果以及区域 4 中足够的参考序列覆盖率表明,使用区域 4 的引物组进行 DNA 条码测序,然后使用 dada2 进行分析,对线虫分类分析最为合适。从 68 个分离株中获得了 18 个 SSU 衍生的操作分类单元(rOTUs),并根据基于 rOTUs 的四个区域序列以及 116 种线虫参考种和 BLASTN 搜索构建的系统发育树确定了它们的目。大多数分离株来自三个主要目:Dorylaimida(6 个 rOTUs,68 个分离株中的 51.5%)、Rhabditida(4 个 rOTUs,29.4%)和 Triplonchida(7 个 rOTUs,17.6%)。分离株的预测摄食类型为真菌食者(总线虫的 38.2%)、植物食者(32.4%)和细菌食者和杂食者/捕食者(14.7%)。此外,我们试图通过连接区域序列人工制备长核苷酸序列来改善系统发育树的分支结构,但效果有限。