Community Characteristics Analysis of Eukaryotic Microplankton via ITS Gene Metabarcoding Based on Environmental DNA in Lower Reaches of Qiantang River, China

Eukaryotic microplankton plays an important role in water biotic community and in maintaining the stability of water ecosystems. Environmental DNA metabarcoding provides the opportunity to integrate traditional and emerging approaches to discover more new species, and develop molecular biotic indices that can be more rapidly, frequently, and robustly used in water quality assessments. In order to examine assemblages of eukaryotic microplankton in lower reaches of Qiantang River, ITS gene metabarcoding technology based on environmental DNA was carried out. As a result, various species of phytoplankton, fungi and zooplankton were annotated on. More phylum, classes and specieses of eukaryotic phytoplankton and zooplankton were found after compared communities taxa based on metabarcoding with that obtained from morphological examination. Nevertheless, Chlorophyceae was the most common assemblage both identified by using these two methods, also Mesocyclops leuckarti and Acanthocyclops bicuspidatus were both found to be the dominant species of Cyclopoida in the river. Additionally, the reads proportions of phytoplankton and zooplankton at the three freshwater sampling sites (Tonglu, Fuyang and Wenyan) decreased as temperature drop. Meanwhile, twenty classes of fungi were annotated on, of which the community characteristic was first researched in the river. There were significant spatial differences in values of Chao1 index for eukaryotic microplankton. Cluster analysis and Non-metric multidimensional scaling ordination further How to cite this paper: Zhang, A.J., Wang, J., Hao, Y.B., Xiao, S.S., Luo, W., Wang, G.X. and Zhou, Z.M. (2021) Community Characteristics Analysis of Eukaryotic Microplankton via ITS Gene Metabarcoding Based on Environmental DNA in Lower Reaches of Qiantang River, China. Open Journal of Animal Sciences, 11, 105-124. https://doi.org/10.4236/ojas.2021.112009 Received: January 13, 2021 Accepted: March 30, 2021 Published: April 2, 2021 Copyright © 2021 by author(s) and Scientific Research Publishing Inc. This work is licensed under the Creative Commons Attribution International License (CC BY 4.0). http://creativecommons.org/licenses/by/4.0/ Open Access


Introduction
Eukaryotic microplankton is a group of plankton with particle sizes less than 20-µm [1]. It plays an important role in water biotic community and in maintaining the stability of water ecosystems due to its important link in energy flow and material circulation [1]. As the basis of the food chain, analyzing its metacommunity structure is very important to assess the status and development tendency of an ecosystem [2].
Traditional practices for biological surveys of inland waters usually center on a common set of ecological indicators or indices/measurements of biodiversity [3], requiring many preparations, such as morphological taxonomic expertise, intact specimens and adequate time [4] [5]. Environmental DNA (eDNA) metabarcoding provides the opportunity to integrate traditional and emerging approaches to discover more new species [6] [7], and develop molecular biotic indices that can be more rapidly, frequently, and robustly used in water quality assessments [6] [8] [9]. So far, metabarcoding technology has been widely used for biodiversity monitoring in biological and environmental samples [10] [11] [12] [13]. Despite some limitations (each marker region might import some biases and the blast sequence database is incomplete), the practices of metabarcoding-based analysis for estimating diversity and relative abundance of taxonomic groups in aquatic systems will likely increase as technology improved [14]. Now, marker genes used for the analysis of plankton communities via eDNA metabarcoding often focuses on ribosomal DNA (rDNA), ribulose bisphosphate carboxylase large subunit gene (rbc L) and cytochrome C oxidase subunit gene 1 (cox1) [8] [15] [16] [17] [18]. Among these, the internal transcribed spacer (ITS), as one gene fragment of rDNA, has been widely used due to its fast evolution and high specificity [17], and its relevant metabarcoding practices for the purpose of monitoring diversity of eukaryon communities mainly focused on soil, plant and marine systems [19] [20] [21] [22].
Here, an ITS metabarcoding assessing assemblages of eukaryotic microplankton were conducted. Specifically, ITS gene sequence analysis was performed on surface water samples collected from four sites in Qiantang River lower reaches (Zhejiang, China), an important freshwater fishing water used for drinking, electricity generation, flood control and recreation, in order to analyze the community diversity of eukaryotic microplankton in the section, to assess the

Water Sample Collection and Physic Chemical Analysis
A total of eight water samples were collected in September and November 2019, at four separate sampling sites in Qiantang River lower reaches, including Tonglu, Fuyang, Wenyan and Jiashao sites, hereafter referred to as TL, FY, WY and JS separately ( Figure 1). All sampling, filtering, and other equipments were sterilized before use. the recording of which were following the scheme of [23]. All environmental variables were measured in triplicate.

DNA Sample Processing and High-Throughput Sequencing (HTS)
For each water sample, a residual 500-mL water subsample was then filtered through a 0.22-μm cellulose acetate filter paper using a peristaltic pump in the field. Then, each paper was placed inside a commercial sterile centrifuge tube and stored in a container filled with liquid nitrogen until subsequent manipulations were performed. DNA was extracted from filters using EZNA water DNA kit (Omega, USA) following the manufacturer's protocol. The concentration and purity of DNA were determined using NanoDrop 2000c spectrophotometer (Thermo, USA), then followed by multiplex PCR using the universal primers for ITS belonging to eukaryotic mitochondrial DNA fragments, ITS-F (5'-GTGA ATCATCGARTC-3), ITS-R (5'-TCCTCCGCTTATTGAT-3') [22].  Figure S1), the quantified, size-selected libraries were constructed and continuously diluted to a concentration suitable for sequencing. The libraries were finally sequenced on the Illumina MiSeq 2000 platform by following the manufacturer's protocols step by step.

Phytoplankton Samples Collection and Treatment
Phytoplankton samples were also simultaneously collected at the four sites. For phytoplankton counts, 1.0 L of water samples were sampled each time and preserved with 1% Lugol's iodine solution. Phytoplankton samples were concentrated to a final volume of 30 ml after sedimentation for 48 h. Thereafter, the taxa were verified and counted under 200× and 400× magnifications for at least 500 specimens [23]. The data were made to compare with that collected from ITS gene metabarcoding method.

Bioinformatics and Sequencing Data Upload
The raw sequencing FASTQ file was transformed to a FASTA file by the Fastx toolkit V0.0.1 [24]. Clean reads were gained after trimming the low quality se-

Date Analysis
Three α-diversity indices, including Chao1 estimators, Simpson index, and Shannon index, were calculated based on data obtained by metabarcoding monitoring. Additionally, Cluster analysis taken by group average method and Non-metric multidimensional scaling (NMDS) was employed to cluster samples in Primer 5.0 environment [26], of which species data were first transformed according to [27]. Additionally, basic data processing, drawing and statistical analyses (e.g. one-way ANOVA) were conducted using Excel 2007 and SPSS 16.0 software.

Environmental Characterization
The results of environmental variables are showed in Table 1. WT showed significant differences between months. TN showed significant negative association with transparency at p < 0.05, with COD at p < 0.01, and significant positive association with TP at p < 0.01. In addition, NH4-N showed a significant positive association with TP and WT at p < 0.05. The values of TN: TP mass ratios were all higher than 7 in all sampling sites, indicating the research area was generally P-limited at the experimental period.

Sequencing Analysis
ITS gene metabarcoding yielded 67,469 -129,150 raw reads, of which 55,687 -112,832 clean reads were obtained after optimization, resulting in effective data rates varying from 64.1% to 88.8% ( Table 2). The sequences clustered into a total of 5795 OTUs, varying from 706 to 1911 at an average of 1245 (Table 2).
Meanwhile, the rarefaction curves of each sample all showed the observed species number flatted out as sequence increasing, indicating the amount of sequencing data at the 97% similarity threshold was sufficient to satisfy the assessment of species diversity.

Community Structure Composition
In total, Phytoplankton, fungi, zooplankton and other eukaryotes were annotated on after Blast. Five classes of eukaryotic phytoplankton were annotated on,  in which Chlorophyceae, Trebouxiophyceae and Cryptophyceae had more reads abundance (Figure 2(a)). Genera Chlamydomonas, Micractinium, Chlorella, Crucigenia, Cryptomonas, Actinastrum, Gonium, Dictyosphaerium and Compactochlorella were the common phytoplankton, most of which belong to phylum Chlorophyta, except Cryptomonas which belongs to phylum Cryptophyta.

Seasonal Dynamics of Communities
Overall, the total reads of ITS annotated on eukaryotic microplankton in September was higher than that in November. The dominant assemblage in TL, FY and WY sites in September was phytoplankton, contributing 21.8%, 40.9% and 45.7% of the total reads respectively, followed by fungi (9.7%, 4.70% and 3.1%, respectively) and zooplankton (0.3%, 0.3% and 0.1%, respectively), however, the result in JS site was different, in which fungi was dominant (Table 2; Figure 3).

Diversity Analysis of Eukaryotic Microplankton
Significant spatial differences in values of Chao1 index were deduced (p < 0.05).
However, Shannon and Simpson indexes showed no significant spatio-temporal differences. Generally, the three indexes in September at the freshwater sampling sites were all higher than that in November, which was a little different from that in JS sites (Table 2).
Eight samples were divided into two clusters at the 20% level, cluster for JS-A and cluster for the other seven samples, indicating that the microplankton class composition of JS-A had the least similar with that of the other samples ( Figure   4(a)), which was also verified by using NMDS ordination method (Figure 4(b)).

Data of High-Throughput Sequencing (HTS) and Morphology Comparison
We compared the communities of phytoplankton and zooplankton taxa with results obtained from morphological analysis in order to determine potential biases of the primer set used in our study. Species of three phylum and five classes of eukaryotic phytoplankton were annotated by metabarcoding method, which was different from that identified by microscopic examination. Also, more than 104 phytoplankton species were identified by metabarcoding (193 species vs. 89 species) ( Table 3). Chlorophyceae was the most common assemblage, which was consistent with the finding via morphology, accounting for 12.93% -79.45% of the total eukaryotic phytoplankton reads, among which Chlamydomonas reinhardtii was dominant, contributing 1.22% of the total reads. Meanwhile, genera with higher reads proportion, such as Chlamydomonas, Chlorella, Crucigenia, Cryptomonas, Actinastrum, Gonium and Dictyosphaerium, were widespread in freshwater of Zhejiang province [27] [28] and were also dominant genera that identified via morphology (Table S1). Open Journal of Animal Sciences For the zooplankton, the comparisons were made with data reported by [29] and [30]. As a widely distributed taxa in Qiantang River, Cyclopoida was identified in this study, and the dominant species of Cyclopoida annotated on were Mesocyclops leuckarti and Acanthocyclops bicuspidatus, which was consistent with the discovery of [29]. However, as another dominant assemblage [30], rotifers were not annotated here.

The Feasibility of Microplankton Community Analysis Based on ITS Gene Metabarcoding
In this study, we selected ITS with fast evolution and high specificity as the amplicon to describe the community structure and its dynamics of eukaryotic microplankton in Qiantang River lower reaches. Here, the community characteristics of fungi in the Qiantang River were first researched, it's found that Dothideomycetes, Sordariomycetes, Eurotiomycetes and Tremellomycetes were the most common groups, which were different from that in the Yellow Sea concluded by using the same amplicon [22].
After compared the communities of phytoplankton and zooplankton taxa with results obtained from metabarcoding and morphological analysis, we revealed the same most common assemblage, and discovered 104 more species via metabarcoding. Compared with previous studies [28], it's speculated that the phytoplankton community had changed somewhat since then, but some dominant specieses maintained unchanged, genera Chlamydomonas and Cryptophyllum were still common dominant groups. Meanwhlie, Anthoathecata, a zooplankton taxa that hasn't been identified by microscopic examination, was predatory relationship between species may be a considerable reason. Generally, the DNA of the prey does not get separated out during the blast, especially if the prey items belong to the same genus as the predator. In addition, the preference of primer and differences in rRNA gene copy numbers may also explain some of these differences.
Overall, these findings demonstrated that metabarcoding could yield comparable results to conventional methods for several abundant eukaryotic taxa, but that each method has different limitations as far as accurately describing the eukaryotic composition in this river.

Community Diversity Characteristic of Eukaryotic Microplankton
Here that of the other samples, the content of salinity might be one of the reasons [31]. Studies have shown that some spatial differences in eukaryotic plankton α-diversity is more the result of selection by local environmental conditions than dispersal [32], the feasibility of α-diversity based on ITS rRNA gene metabarcoding might be a useful indicator for discriminating ecological condition.

Conclusion
Our data were generated using a primer set that targets the ITS region of ribosomal RNA gene, a region that has been widely used in biodiversity assessments in phytoplankton, fungi, zooplankton, etc. As a result, various species of phytoplankton, fungi and zooplankton were annotated. We identified several groups of eukaryotic phytoplankton and zooplankton that were not described by morphological analysis, and increased research on fungi in Qiantang River that never had been studied before. Chlorophyceae was the most common assemblage both identified by using ITS gene metabarcoding and morphological examination methods, also Mesocyclops leuckarti and Acanthocyclops bicuspidatus were Additional file 1: Figure S1. Results of eight water DNA samples amplified by the ITS primer. Table S1. Species of eukaryotic phytoplankton indentified by using ITS gene metabarcoding technology and morphological examination.