A Case Study on Microbial Diversity Impacts of a Wastewater Treatment Plant to the Receiving River

The discharge from wastewater treatment plants (WWTPs) into the receiving river is of concern for the health of the ecosystem. In this study, the microbial diversity impact of Fuxin Meng (FM) WWTP was completed with three pairs of primers. At the time of impact assessment, biochemical tests were carried out as well to investigate the water quality downstream and upstream FMWWTP. The general results showed this plant increased downstream biochemical oxygen demand (BOD) and chemical oxygen demand (COD) by 4.3- and 8.1-fold respectively, in comparison with the upstream. The microbial diversity impact from the plant was also investigated with three pairs of universal primers targeting rRNA genes and the internal transcribed spacer (ITS) by high-throughput sequencing. The downstream diversity of the bacterial and fungal communities was generally higher than upstream, with the exception of the eukaryotic community. Both biochemical and microbial as-sessments suggested that FMWWTP deteriorate Xi River water quality, potentially acting as a pollutive source. Of the receiving water. Accordingly, it was necessary to improve the operation of WWTP to live up to the environmental quality standards.

Just because of this, close attention had been paid to the river ecosystem by the locals. The urban environmental protection administrations microbiologically surveilled the river water to assess the extent of pollution for management guidance (Frigon et al., 2013). Studying the microbial diversity of river-water may help to improve the wastewater treatment facilities or to identify any healththreatening factors.
Wastewater effluents may contribute to water source pollution by loading nutrients into the receiving water. In addition, the microbes residing in the WWTP may flow out with the effluent current, which may disturb the microbial community of the river. Therefore, inadequate wastewater treatment may endanger the health of the aquatic ecosystem and cause diseases. Although chemical and biochemical testing, such as of total nitrogen (TN), total phosphorus (TP), biochemical oxygen demand (BOD), and chemical oxygen demand (COD), is conventionally available, few analyses had been performed on the microbial community using sequencing method for Xi. High-throughput sequencing has been irreplaceable as an emerging technology and greatly exceeds the reach of the regular methods used for water quality control. DNA sequencing makes it possible to reimagine water quality monitoring as it can provide more detailed and important information for Xi River management.
Microbial community composition partially reflects chemical parameters such as pH and pollutant concentrations. Therefore, it is necessary to promptly investigate the microbial diversity of the Xi River water subjected to the FMWWTP discharge (Yotova et al., 2019). In this study, river water samples were taken from sites both upstream and downstream of the outlet. The prokaryotic and eukaryotic microbial community diversities of the samples were surveyed by sequencing the 16S and 18S rRNA genes, respectively. This sequencing-based method of conserved genes is independent of culturing or microscopy. It is convincible that multiple primer sets provide a good solution for determining microbial community structure (Blackwood et al., 2005;Wang et al., 2018). In order to obtain an enhanced description of the microbial community structure and diversity of Xi, three universal pairs of primers were adopted to target different kingdoms for the assessment. Meanwhile, the other purpose of the design was to determine if the primer-specific microbial assessment corresponded with the biochemical assessments. The present analysis of the microbial communities was conducted with the aim of assessing the impact caused by the plant.
To the best of our knowledge, this is the first report to profile the Xi River microbial community using high-throughput sequencing technology. The results of the study may be conducive to upgrading the wastewater treatment processes of

Sampling and Water Quality Testing
The water samples were obtained from Xi River, Fuxin, Liaoning, China in the first week of May, 2018. The two sampling sites were 500 m upstream and 500 m downstream of the water outlet of FMWTP. The water samples were taken from approximately 30 -50 cm below the water surface using 12 sterilized glass bottles with 1 L capacity. The samples were immediately transported to the laboratory in an icebox with a constant temperature of approximately 4˚C.
The water quality testing complied with the GB3838-2002 regulations (environmental quality standards for surface water). Temperature and pH were measured at the two sampling sites. The BOD 5 , COD, TP, TN, and ammonia nitrogen (AN) were measured immediately after sample collection in the laboratory using the methods recommended in GB3838-2002(Shi et al., 2020Ye & Kameyama, 2020;Yotova et al., 2019).

Total DNA Extraction of Water Samples for Community Analysis
Approximately 10 L of fresh water sample from the Xi River was filtered through membranes with pore sizes of 0.22 µm to collect the bacterial and eukaryotic microorganisms until visible pellets were observed on the upper side of the membrane. Total bacterial DNA was extracted from the SB samples, using Bacteria Genomic DNA Extraction Kits (Tiangen Biotech Co., Ltd., Beijing). The genomic DNA for eukaryotic and fungal samples (SF and SFF) was extracted using Fungal Genomic DNA Extraction Kits (Tiangen). The extraction operation was performed following the manufacturer's protocol. The bacterial DNA samples from the upstream (SB1) and downstream (SB2) water sampling locations were amplified using a primer pair targeted to the 16S rRNA gene (27F 5'-AGAGTTTGATCCTGGCTCAG-3' and 338R 5'-TGCTGCCTCCCGTAGGAGT-3') (Ravel et al., 2011). The 27F/338R primer set was used to garner common bacteria residing in the water. The eukaryotic samples from the upstream (SF1) and downstream (SF2) water sampling locations were amplified using a primer pair targeted to the 18S rRNA gene (Euk1391F 5'-GTACACACCGCCCGTC-3' and EukBR 5'-TGATCCTTCTGCAGGTTCACCTAC-3') (Adams et al., 2013;Amaral-Zettler et al., 2009;Begerow et al., 2010). The forward primer, Euk1391F, is a universal primer, while EukBR is a reverse primer that favors eukaryotes; these primers were used to form a combination strategy for amplification. Finally, the fungal samples from the upstream (SFF1) and downstream (SFF2) water sampling locations were amplified using a primer pair targeting the ITS region (ITS1F 5'-CTTGGTCATTTAGAGGAAGTAA-3' and ITS2R 5'-GCTGCGTTCTTCATCGATGC-3'). The primer set ITS1F/ITS2R has already been shown to provide good resolution with regard to fungal identification (Gu- conduct the sequencing using the Illumina Hiseq × Ten Platform. Paired-end reads from the Miseq system were assembled using overlapping. OTU clustering analysis was carried out, which paved the way for in-depth data mining.

Data Processing and Bioinformatic Analysis
The bacteria, eukaryotes, and fungi present in the samples were allocated to phyla, orders, families, genera, and species by aligning the obtained sequences with public databases (Yin et al., 2014). The raw data were processed using Mothur and QIIME (Quantitative Insights Into Microbial Ecology) software to filter out sequences that displayed 5' mismatch, ambiguous bases, or more than eight homologous bases, or that were shorter than 150 bp. Chimeras were removed using the uchime method of Mothur. Sequences were clustered into OTUs using USEARCH (version 7.0; http://drive5.com/uparse/) using an identity threshold of 97%. "p < 0.01" was set as the default and used for all of the analyses.
Software and algorithms were derived from the databases of Ribosomal data-

Community Diversity Analysis
Alpha diversity index analysis was performed on the basis of the sequencing data and largely reflected the richness and abundance of the species within the community. The Shannon, Simpson, and Chao indexes and coverage values were used to describe the communities and their diversity (Zhao et al., 2014).

Biochemical Assessment of Water Quality
The overall quality of the upstream sampling site was superior to that of the downstream site according to GB3838-2002. FMWWTP was thus preliminarily predicted to impact its receiving water. All water quality data that were collected are shown in set by the authorities. The temperature (10.9˚C -11.4˚C) and pH (6.8 -6.9) were similar for both sampling sites. The TP concentration was 0.079 mg/L upstream

Genus-Level Bacterial and Eukaryotic Diversity
At the genus level, the bacterial community of SB1 was less complicated than that of SB2 (Figure 1). The genus "norank_c_Cyanobacteria" (3402 OTUs in SB1 and 3032 OTUs in SB2) was a rich component of both water samples, while the other dominant genera varied between samples. Cyanobacteria, which are the most common phytoplanktonic organisms associated with eutrophication of fresh water systems, can reduce water transparency, and cause oxygen shortages and cyanotoxin production (Rapala et al., 2006). Close attention should be paid to the potential threats of a Cyanobacteria bloom. In accordance with these data,    (Pjevac et al., 2017). The core bacterial genera in SB2 mainly function in the process of nitrification, denitrification, and organic substance metabolism. In the process of water treatment, ammonia-oxidizing, nitrifying, and other heterotrophic or autotrophic denitrifying bacteria remain in the treated water (Huang et al., 2010). With respect to the eukaryotic genera present in the water samples, SF2 was more diverse, with 87 genera, than SF1 with 76 genera. Overlapping genera were rare, which was an impact of the discharge from FMWWTP between the downstream and upstream samples. In SF1, "Unclassified_f_Euglenaceae" comprised 29.32% of the eukaryotic genera, 24.62% belonged to "Unclassified_g_norank_ o_Haplotaxida", 13.47% to "Unclassified_f_Euglenaceae" 9.61% to Heteronematina, and 6.86% to "norank_f_Bacillariophyceae" (Figure 2). Meanwhile, in SF2, 42.42% were "Unclassified_f_Bdelloidea", 18.78% were Petalomonas, 13.24% were "Unclassified_d_Eukaryota", and 9.87% were Euplotes. It is well-known that these protozoa play active roles in nutrient removal in aquatic systems, by means of mineralization or by feeding off dispersed bacteria to eliminate the pollution (Pernthaler, 2005). Protists (including flagellates, ciliates, and amoebae) are able to control bacterial growth and alter their diversity, architecture and function (Besemer, 2015).

Phylum-Level Bacterial, Eukaryotic, and Fungal Communities and Their Distributions
A total of 26 bacterial phyla were identified from the water samples. Phylogenetically close phyla were observed to cluster in the SB communities ( Figure 1).
Among these, the phylum Proteobacteria was evidently abundant in both SB1 (44.18%) and SB2 (52.11%). Proteobacteria are widely distributed and often dominate microbial communities within waterbodies. This phylum has a wide variety of members that are associated with nutrient metabolism, C and N cycling, and water purification. Therefore, it can be reasonably concluded that the water downstream of the discharge point was more likely to contain more members of the Proteobacteria phylum, including potentially health-threatening members.
The discharge from FMWWTP is the connection of sewage to water. Close attention should be paid to the disturbance of the microbial community structure and diversity in the natural waterbody. On the basis of these results and their implications, measures should be taken to improve the wastewater treatment processes to reduce the risks of reusing the water and to uphold responsibility H. F. Zhu et al.
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The phylum Bacteroidetes, which consists of Cytophagia, Flavobacteriia, and Sphingobacteriia, was the second most abundant phylum in both SB1 (31.31%) and SB2 (22.72%). Bacteroidetes are responsible for the degradation of suspended particles consisting of refractory organic material. The infectivity of this phylum alone is distinctly weaker than that of Proteobacteria. The abundances of Nitrospirae, Actinobacteria, Cyanobacteria, Acidobacteria, Chloroflexi, Parcubacteria, Chlorobi, and Planctomycetes in SB2 were greater than in SB1.
Among them, the Nitrospirae abundance increased from 0% (SB1) to 9.76% downstream water. Until now, the FMWWTP has strongly impacted the Xi River microbial community diversity via the heterogeneity of its effluent. Proteobacteria, along with the Cyanobacteria and Actinobacteria found, contained multiple genera that are considered to be available nitrogen and CO 2 fixers (Wakelin et al., 2008). Considering this, the wastewater must be treated with the aim of eliminating pollution from nutrient loading.
The eukaryotic microbial community, which is an important and abundant functional element of every aquatic ecosystem, was also influenced by FMWWTP ( Figure 2). The eukaryotic microbial diversity of SF2 was lower than that of SF1. This explained the reduction in BOD 5 in the downstream sample. Euglenozoa, Annelida, Ochrophyta, Rotifera, and Ciliophora are frequently identified from freshwater sediments and are also used as indicators for biomonitoring (Matsunaga et al., 2014). Rotifera species are regarded as links between the microbial loop and the aquatic food chain (Besemer, 2015). Several studies have shown that protozoa release excretions to bulk solutions and that the nutrients in the excretion may be available for bacteria to utilize for degrading wastes (Matsunaga et al., 2014). Thus, protozoans and metazoans contribute to the mineralization of organic pollutants (More et al., 2010).
The abundance of fungal phyla in the SFF samples was lower than the abundances of phyla in the SB or SF samples. This was due to the fact that the unclassified phyla occupied 87.5% (SFF1) and 70.8% (SFF2) of the total phyla in terms of relative abundance ( Figure 3). Rozellomycota, the most abundant fungal phylum, had a relative abundance of 6.52% in SFF1 and of 27.44% in SFF2. The second dominant phylum was Chytridiomycota, which had a relative abundance of 4.58% in SFF1 and no identification in SFF2. Chytridiomycota possess active motility via a single flagellum, and can degrade chitin and keratin. Species within this phylum are generally present in ponds, streams, and moist terrestrial habitats. In light of these relative changes in abundance, it was tentatively speculated that water mixing caused by discharge from FMWWTP was of little conse  Fungi, which play an irreplaceable role in water treatment, appear to show a greater ability to degrade organic matter than bacteria, which may be due to their stronger resistance to environmental stress (Prigione et al., 2008). Fungi are more tolerant of pollutants than bacteria, due to their large chromosomes. Furthermore, the fungal cell wall, which consists of an extra-polysaccharide matrix, provides protection against inhibitory compounds via adsorption. A variety of bacteria and fungi may co-metabolize substances simultaneously to achieve a better degradation in the wastewater.

The Community Assessment Was Primer-Preferential
The preferences of the primers were suggested in the process of community component identification. The primer sets, Euk1391F/EukBR and ITS1F/ITS2R, both target eukaryotes from the same sample. According to the data obtained, the primer pair, Euk1391F/EukBR, seems to preferably recognize eukaryotes, such as the protozoans or metazoans of the SF communities. Concomitantly, the ITS1F/ITS2R primer set was effective at characterizing fungi in the SFF samples, which was in accordance with the observations of other previous studies (Guarro et al., 1999;Xu, 2016). Even though the overlapping taxa identified using the two primer pairs were phylogenetically predictable, no crossover taxa were found upon analysis at the various taxon levels. Overall, the choice of primer set directly impacted the accuracy of the community analysis. Therefore, the understanding of the community structures was only primer-specific, which is true for all community assessments. It is necessary to obtain a complete description of the community structure and diversity by combining these results with the results obtained using different universal primer pairs.

Community Structures Were Sample-Sensitive
Fortunately, the same results of the diversity from primer Pairs 27F/338R and ITS1F/ITS2R offset the sensitivity originating from the samples. With regards to the community structures, it is easy to conclude that microbial communities can be regarded as the sentinels and integrators of environmental changes (Ramette, 2007). The insights gained from this work only represented the community structure at the specific sampling sites in the beginning of May and were limited to the current understanding of the evolution of microbial life.
Even the gene library size contributed to the community descriptions. The observed community shifts suggest that the community complexity might be much greater than previously thought between the upstream and downstream samples (Niu et al., 2016). Community analysis data are randomly influenced by the sampling method, sample processing, and environmental factors. The microbial community is well known to be shaped by the interplay of biotic and abiotic interactions (Besemer, 2015;Kumari et al., 2015). The interactions among H. F. Zhu et al.
microbes, mycotoxins, and pollutants remain complicated or unclear. In response to environmental factors, the water ecosystem may function as a buffering system for the shift between the rare and abundant strains in the community.
Further investigation is required to reveal these interactions. It is believed that a large proportion of microorganisms remain unidentified in the communities when 16S and 18S rRNA sequencing analysis is used.
This study is helpful for evaluating the safety of reusing water from rivers subjected to treated wastewater discharge, in terms of the prokaryotic and eukaryotic microorganisms present. Microbial community analysis is a prerequisite to the adoption of certain strategies to meet the stipulated sanitary standard. The identification of community members at the phylum and genus levels potentially serves as an indicator of the biochemical properties of the wastewater. Understanding the indigenous microbial community may help to promote river conservation and remediation through chemical, biological, or physical measures.

Conclusion
In this study, the results of the primer-preferential microbial assessment were generally in line with the biochemical analyses of Xi River. The alpha diversity was higher downstream than the upstream, except for the eukaryotic (SF) samples. Overall, the finding of microbial community assessment of FMWWTP gave side effect to its receiving water at the time. Thus it was necessary to improve the operation of WWTP to live up to the environmental quality standards.

Data Availability
All of the datasets support the findings of this study are available from the corresponding author H. Zhu upon reasonable request.