Dynamics and Determinants of Amoeba Community, Occurrence and Abundance in Subtropical Reservoirs and Rivers

Water Research 146 (2018) 177e186

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Dynamics and determinants of amoeba community, occurrence and abundance in subtropical reservoirs and rivers

Kexin Ren a, Yuanyuan Xue a, c, Regin Rønn d, e, f, Lemian Liu a, Huihuang Chen a, ** * Christopher Rensing b, f, , Jun Yang a, a Aquatic EcoHealth Group, Key Laboratory of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China b Fujian Provincial Key Laboratory of Soil Environmental Health and Regulation, College of Resources and the Environment, Fujian Agriculture & Forestry University, Fuzhou 350002, China c University of Chinese Academy of Sciences, Beijing 100049, China d Department of Biology, University of Copenhagen, Copenhagen, Denmark e Arctic Station, University of Copenhagen, Qeqertarsuaq, Greenland f Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China article info abstract

Article history: Free-living amoebae are widespread in freshwater ecosystems. Although many studies have investigated Received 27 April 2018 changes in their communities across space, the temporal variability and the drivers of community Received in revised form changes across different habitat types are poorly understood. A total of 108 surface water samples were 1 September 2018 collected on a seasonal basis from four reservoirs and two rivers in Xiamen city, subtropical China. We Accepted 4 September 2018 used high throughput sequencing and qPCR methods to explore the occurrence and abundance of free- Available online 5 September 2018 living amoebae. In total, 335 amoeba OTUs were detected, and only 32 OTUs were shared by reservoir and river habitats. The reservoirs and rivers harbored unique amoebae communities and exhibited Keywords: Protozoa distinct seasonal patterns in community composition. High abundance of the 18S rRNA gene of Acan- Plankton thamoeba was observed in spring and summer, whereas the abundance was low in autumn and winter. In High-throughput sequencing addition, the abundance of Hartmannella was significantly higher when isolated from reservoirs in Community composition summer/autumn and from river in spring/summer. Moreover, the temporal patterns of amoebae com- Seasonal dynamics munities were significantly associated with water temperature, indicating that temperature is an important variable controlling the ecological dynamics of amoebae populations. However, our compar- ative analysis indicated that both environmental selection, and neutral processes, significantly contrib- uted to amoeba community assembly. The genera detected here include pathogenic species and species that can act as vectors for microbial pathogens, which can cause human infections. © 2018 Elsevier Ltd. All rights reserved.

1. Introduction and fertility maintenance of aquatic and terrestrial ecosystems (Thomas et al., 2006; Falkowski et al., 2008; Vanessa et al., 2012; Free-living amoebae feed on various microorganisms including Bonilla-Lemus et al., 2014). Most amoebae are able to form cysts fungi and bacteria and constitute signiﬁcant links in microbial food allowing them to survive adverse conditions. When favorable webs in water and soil. Amoebae are widely distributed both in conditions resume, the amoebae can excyst and revert to the active natural (Hsu et al., 2011; Hsu, 2016) and man-made aquatic eco- feeding stage (Khan, 2006). The cysts can remain viable for years, systems (Thomas et al., 2008; Delafont et al., 2013, 2016), and they which allow amoebae to be very resilient to diverse and changing play key roles in water puriﬁcation and in biogeochemical cycling environmental conditions (Hauer et al., 2001). Many amoebae have a broad ecological niche amplitude and may thrive in different environments. Despite this general ecological versatility, the

* Corresponding author. abundance and distribution pattern of individual species, and ** Corresponding author. Fujian Agriculture & Forestry University, China. thereby the community composition, is strongly dependent on E-mail addresses: [email protected] (C. Rensing), [email protected] (J. Yang). https://doi.org/10.1016/j.watres.2018.09.011 0043-1354/© 2018 Elsevier Ltd. All rights reserved. 178 K. Ren et al. / Water Research 146 (2018) 177e186 environmental conditions such as temperature, moisture, pH and Shidou, Bantou, and Tingxi Reservoirs all have forested catchments food availability (Rodríguez-Zaragoza, 1994; Bass and Bischoff, with Bantou having the most human modification of these three 2001). The composition of amoeba communities varies between sites (having some buildings, small scale agriculture and plantation seasons in drinking water treatment system (Thomas et al., 2008; forestry scattered amongst the more semi-natural woodland). Ling et al., 2016), but mechanisms that mediate seasonal variation Houxi River is located on the west of Xiamen city with a total length in community structure have not been well investigated. Similarly, of approximately 23 km and a drainage area of 205 km2. The most studies of amoeba communities have focused on a single type landscape river is situated at the campus of the Institute of Urban of habitat (Bass and Bischoff, 2001; Ettinger et al., 2002; Ramirez Environment (Chinese Academy of Sciences) and is designed to et al., 2014; Delafont et al., 2016), while the variation in composi- collect rainfall from the roofs of the buildings and from lawns and tion of free-living amoeba communities between habitats remains squares of the campus area (Wang et al., 2013). poorly understood. The expanding use of high-throughput For each season, twenty-seven water samples (2.5 L) were sequencing provides new opportunities to investigate the rela- collected from autumn 2013 to summer 2014. Water samples were tionship between environmental variables and composition of divided into two subsamples: one for water chemistry analysis, and amoeba communities (Liu et al., 2017). the other for analysis of amoeba communities. The water samples The study of free-living amoebae is particularly relevant in (800 ml) for amoeba analysis were pre-filtered through 200 mm aquatic systems that serve as drinking water resources. Amoebae mesh to remove larger particles and metazoans, and subsequently are important predators on bacteria and contribute to the removal filtered through a 0.22 mm pore-size polycarbonate filters (47 mm of bacteria from water systems, but amoebae can also serve as diameter, Millipore, Billerica, MA, USA). The filters were stored vectors of pathogenic bacteria and some amoebae may act as at 80 C until DNA extraction (Liu et al., 2017). opportunistic pathogens (Khan, 2006). For example, it is well known that amoebae may serve as hosts of two common respira- 2.2. Environmental variables tory pathogens: Legionella and Mycobacterium (Guimaraes et al., 2016), and some strains of Acanthamoeba, Hartmannella and Nae- Water temperature (WT), dissolved oxygen (DO), pH, chloro- gleria can directly cause infections in humans (Abedkhojasteh et al., phyll a (Chl a), oxidation-reduction potential (ORP), electrical 2013; Geisen et al., 2014). Although the reported cases are conductivity (EC) and turbidity were measured in situ in reservoirs extremely rare, the diagnosis of free-living amoebae must never be and rivers with a multiparameter water quality analyzer (Hydrolab overlooked as these are able to cause serious and fatal diseases. To DS5, Hach Company, Loveland, CO, USA). The total carbon (TC), total date, we still know little about whether free-living amoeba com- organic carbon (TOC) and total nitrogen (TN) contents were munities exhibit a habitat-dependent pattern, and how the as- determined using an elemental analyzer (Vario MAX CNS, Ele- sembly proceeds that subsequently drives the amoeba community mentar, Hanau, Germany), while total phosphorus (TP), ammonium (Delafont et al., 2016). Studies on the dynamics of free-living nitrogen (NH4eN), nitrate nitrogen (NO3eN) and nitrite nitrogen amoeba communities can enhance our knowledge on the (NO2eN) concentrations were measured by a flow injection dispersal mechanism of amoeba across seasons and water ecosys- analyzer (QC8500, Lachat Instruments, Milwaukee, USA) following tems. It can also contribute to strategies for prevention and control standard methods (Greenberg et al., 1992). of the emergence and spread of free-living amoebae in both natural reservoir and river (Hoffmann and Michel, 2001). 2.3. DNA extraction and sequence-based-approaches The aim of this study was to evaluate the influence of habitat and seasonal variation on the composition of free-living amoeba Membranes with microbes were cut into small pieces with a communities in subtropical reservoirs and rivers. In particular, we sterilized cutter, and total DNA was extracted using FastDNA SPIN wished to identify the important environmental factors that affect Kit (MP Biomedicals, Santa Ana, CA, USA) following the manufac- the dynamics of amoeba communities. To achieve this, we per- turer's instructions. The DNA concentration was measured using formed a yearlong sampling from four drinking water reservoirs NanoDrop ND-1000 (Thermo Scientific, Wilmington, DE, USA). and two rivers located in Xiamen (southeastern China) to deter- High-throughput (next-generation) sequencing and quantitative/ mine the abundance and community structure of free-living conventional PCR methods were used to characterize the compo- amoebae in those habitats. We used Illumina high-throughput sition and dynamics of microorganisms, and different methods of sequencing to characterize the amoeba communities in the sam- all sample sites are shown in Table S1. Sites from Shidou Reservoir, ples, and we measured a range of environmental variables. Hubian Reservoir and landscape River were selected as represen- Furthermore, we used conventional and quantitative PCR to detect tatives for Illumina sequencing (60 samples). The V9 hypervariable presence/absence and 18S rRNA gene copy numbers of some region of eukaryotic 18S rRNA gene (130 bp) was amplified with common amoeba genera respectively. primers 1380F (50-CCCTGCCHTTTGTACACAC-30) and 1510R (50- CCTTCYGCAGGTTCACCTAC-30)(Amaral-Zettler et al., 2009; Liu 2. Materials and methods et al., 2017). Sequencing was performed on the Illumina Miseq platform (Illumina, San Diego, California, USA) using a paired-end 2.1. Study sites and sample collection approach (Liu et al., 2017). Further, quantitative PCR was used to estimate the concentration of 18S rRNA gene of two common The sampling sites include four reservoirs and two rivers of amoebae taxa: Acanthamoeba and Hartmannella (Kuiper et al., Xiamen city (Fig. 1). Xiamen has a monsoonal humid subtropical 2006; Le Calvez et al., 2012). Finally, conventional PCR was climate, characterized by annual mean temperature 21 C and applied to detect the presence/absence of eight amoeba genera (i.e. annual rainfall 1468 mm (Yang et al., 2016). The rainfall is Acanthamoeba, Balamuthia, Echinamoeba, Glaeseria, Hartmannella, concentrated from May to September (Liu et al., 2013). All four Naegleria, Vahlkampfia and Willaertia). The PCR primers are listed in reservoirs were described in our previous studies (Yang et al., 2012, Table S2. 2016), the main purposes of these reservoirs are flood control, irrigation, recreation, and water supply for the city of Xiamen. 2.4. Bioinformatics These four reservoirs are located along an urban-to-rural gradient, extending from urban sites to more rural sites in the nearby hills. Raw reads were merged, and low-quality reads were trimmed K. Ren et al. / Water Research 146 (2018) 177e186 179

Fig. 1. Map of Xiamen showing the sampling stations, including four reservoirs and two rivers. Black solid lines indicate the rivers (Houxi and Landscape rivers) and grey shadow indicate reservoirs. TX, SD, BT and HB represent Tingxi, Shidou, Bantou and Hubian reservoirs, respectively.

using the MOTHUR (Schloss et al., 2011) with default settings. The heterotrophic, naked, phagotrophic amoeboid organisms (Adl et al., clean sequences were assigned to operational taxonomic units 2012); Euglyphida which include Euglypha and Paulinella; Retaria (OTUs) by “unoise3” module of USEARCH, and the OTU table was (thin elliptical scales, presence of specialized scales around the generated with 97% similarity threshold using VSEARCH (Rognes pseudostome with typical indentation) which include Reticulomyxa et al., 2016). Finally, OTUs were matched against the trimmed V9 (freshwater foraminifers); Thecofilosea which include Rhogostoma region (V9_PR2) database (Guillou et al., 2013; de Vargas et al., and Pseudodifflugia (thecate amoebae with ventral cleft that emits 2015). After removing singletons and doubletons, mitochondrion, fine pseudopodia). From Excavata we included Discoba (Hetero- chloroplast sequences, bacteria, archaea and unknown organisms, lobosea) with the genera Naegleria and Vahlkampfia (Gruberellid we retained a total of 6,152,981 sequences for the 60 samples. Thus, amoebae); Stygamoebida with the genus Stygamoeba (Flattened, an OTU table of eukaryotic plankton sequences was retained. The elongate amoebae resembling tooth-pick or splinters); Long- raw sequence data have been deposited into the GenBank sequence amoebia with the genera Mayorella and Dermamoeba (Flattened, read archive (SRA) under the accession numbers SRP062446 for elongated cell with pointed subpseudopodia and centrosomes). reservoirs (24 samples) and SRP092216 for the river (36 samples). Other amoebae groups include Gymnophrys, Parvamoeba, and The cleaned and high-quality data of total sequences were used Pseudomastigamoeba (Adl et al., 2012). to characterize the amoeba communities. To generate the retained and conservative amoebal OTU table (1980 sequences with 335 2.5. Sloan neutral community model for amoeba community OTUs for the 60 samples), we first discarded Apusozoa, Arch- aeplastida, Hacrobia, Stramenopiles and Opisthokonta (including We evaluated the fit of the Sloan neutral community model Metazoa and fungi). We used overall taxonomy given in the data- (NCM) for amoeba community compositions to determine the po- bases (V9_PR2), and then selected the amoebal taxa from Amoe- tential importance of neutral processes on community assembly bozoa (excluding flagellates), Rhizaria and Excavata based on the and the least-squares method was employed to generate the best fit revised classification of eukaryotes in Adl et al. (2012). Recent distribution curve (Sloan et al., 2006). Nm is an estimate of molecular studies divide Amoebozoa into two major subclades, dispersal between communities and determines the correlation Lobosa and Conosa, with a possible third lineage, Breviatea, as sister between occurrence frequency and mean relative abundance, with to them both (Smirnov et al., 2011). Lobosa is further divided into N describing the metacommunity size and m being immigration two subdivisions: Discosea (e.g. Vanella) and Tubulinea (e.g. rate (Sloan et al., 2006). All computations were performed in R Arcella); while Conosa is subdivided into three subgroups: Variosea (version 3.4.3) (R Core Team, 2017). (e.g. Filamoeba), Archamoebea (e.g. Entamoeba), and Mycetozoa or slime molds (e.g. Dictyostelium)(Smirnov et al., 2011; Adl et al., 2.6. Real-time quantitative PCR 2012). Within Rhizaria we selected sequences belonging to amoebal organisms from the Cercozoa and Foraminifera. The selected Quantitative PCR (qPCR) was performed to quantify the number taxa include: Vampyrellida, with the major genera of Hyalodiscus, of amoebal gene copies in reservoir and river samples. The ampli- Vampyrella and Leptophrys, because they consist of exclusively fication was carried out in triplicate with a thermal profile of 180 K. Ren et al. / Water Research 146 (2018) 177e186

3 min at 95 C, followed by 40 cycles of 20 s at 95 C, 30 s at 56 C, We used the Kruskal-Wallis test to compare the difference in and 40 s at 72 C. The reaction mixture consisted of 20 ng template environmental variables between reservoir and river samples DNA, 0.4 mM each primer, 2 SYBR premix Ex Taq II. Thermal across the four seasons. cycling, and data analysis were carried out with a qPCR detection We created classification models using the environmental var- system on a Light Cycler 480 (Roche Scientific, Indianapolis, IN, iables to predict cluster membership of individual trees. We used USA) to assess the abundance of 18S rRNA gene. Standard curves recursive partitioning for classification with the “rpart” R-package were produced for the 18S rRNA gene using cloned fragments of the (version 4.1e12) (Therneau et al., 2018) to create these models. The plasmid for Acanthamoeba and Hartmannella with the concentra- models can result in a single classification tree for each site, a de- tions 2.34 1010 ng/ml and 2.67 1010 ng/ml, respectively. Gene cision tree with leaves representing the two growth signals, and fragments were diluted (107e103 gene copy/ml) to generate the branches representing environmental variables that most strongly standard curve. We have performed both positive and negative drive growth patterns (Loh, 2014). At the branches, the classifica- control during PCR and qPCR in triplicate. We amplified each of the tion trees display thresholds between the two clusters in terms of 40 reactions in triplicate for qPCR. The amplification efficiency (E) the importance of environmental variables. The advantage of of qPCR was estimated using the slope of the standard curve applying classification trees is the capability of identifying a broad through the following formula: E ¼ (10 1/slope)-1. After evaluation range of environmental factors which can significantly affect the of the analysis parameters, the relation efficiencies for the standard amoeba occurrence. Goodness of fit of classification tree was curves were: r2 ¼ 0.9920 for Acanthamoeba and 0.9901 for Hart- calculated using deviance defined by the multinomial log- mannella, respectively. The reasonable efficiency of the PCR likelihood (De'ath and Fabricius, 2000; Elith et al., 2008). To mini- amplification was 101.8% for Acanthamoeba and 99.8% for Hart- mize overfitting, trees were pruned to minimize cross-validated mannella. The gene copy number in different samples was error. For factor predictors, the data were ran based on conven- expressed as log copy numbers of gene per liter of water. Spear- tional PCR (categorized as either presence or absence) and the man's rank correlation coefficients between the abundance or di- environmental conditions. versity of amoeba 18S rRNA gene (Acanthamoeba and Hartmannella) and environmental factors were calculated using “psych” R-package (version 1.7.8) (Revelle, 2017). One-way analysis 3. Results of variance and Tukey's test were performed to determine the significance of differences between samples. 3.1. Strong habitat pattern but weak seasonal change of amoeba community based on Illumina sequencing 2.7. Conventional PCR amplification for detection of specific genera The distribution of all eukaryotic plankton sequences generated For PCR amplification, each 25 mL reaction contained 10 ng of by Illumina high-throughput sequencing is shown in Fig. 2. Pro- template DNA, 0.5 mM of each primer, 2.5U Taq DNA polymerase portion of relative abundance between 8.3% (minimum) and (Takara Bio Inc., Otsu, Shiga, Japan) and 2 mM MgCl2. The primers 22.7%(maximum) of these sequences belonged to clades mainly used in this study are listed in Table S2. Agarose gel electrophoresis consisting of protozoa from the lineages Alveolata, Amoebozoa, of the 50 mL PCR product was performed prior to purification (QIA Apusozoa, Archaeplastida, Excavata and Rhizaria (Fig. 2B). Tubuli- quick Gel Extraction Kit, Qiagen, Dusseldorf, Germany). Purified nea and Discosea together constitute the amoebozoan subphylum PCR products were ligated into the pMD 18T-vector (Takara Bio Inc., Lobosa, which never have cilia or flagella, whereas Variosea Otsu, Shiga, Japan) and transformed into Escherichia coli DH5a together with Mycetozoa and Archamoebea are now grouped as the competent cells. Positive clones were grown in Luria-Bertani (LB) subphylum Conosa, whose constituent lineages either have cilia or medium over night at 37 C. Sequences of the 18S rRNA gene were flagella or have lost them secondarily are the main focus of this manually checked and then compared with amoebae sequences paper (Fig. 2C). The relative abundance of protozoa and/or amoeba deposited in the GenBank database using the BLASTN. The repre- in reservoirs was higher than that in rivers over four seasons. The sentative sequences were deposited in the GenBank nucleotide relative abundance of free-living amoebae peaked during spring/ sequence database under the accession no. KJ000408 to KJ000410. summer in reservoirs and during summer in rivers (Fig. 2C). Overall, the most diverse and abundant OTUs in reservoirs were 2.8. Statistical analyses assigned to the groups of Mycetozoa, Discoba and Foraminifera. They exhibited significantly higher relative abundance and di- Prior to redundancy analysis (RDA), the environmental data versity in the reservoir compared with the river (Fig. 3A). However, were log (xþ1) transformed, except pH, to improve normality and some groups such as Variosea, Discosea, Tubulinea, Meso- homoscedasticity. The explanatory variables were sequentially mycetozoa, Endomyxa and Filosea exhibited an inverse trend and removed with the highest variance inflation factor (VIF), until all they were more diverse and abundant in the river (Fig. 3A). VIFs were less than 10, to eliminate collinearity among environ- We observed two distinct groups corresponding to the two mental variables (Nakazawa and Nakazawa, 2017). We used a types of habitats (reservoir and river) and four seasons (spring, forward-selection procedure with 999 Monte Carlo permutation summer, autumn and winter) (Fig. 3B). Further, amoeba commu- tests to select the environmental variables that significantly nity compositions were significantly different with regard to both explained the variation of amoeba communities (P < 0.05). In season and habitat, as tested with ANOSIM. However, the com- addition, to investigate the habitat effect, we analyzed the two munity differences between habitats were greater than the seasons amoeba communities from reservoir and river stations separately (Global R ¼ 0.545 vs Global R ¼ 0.244) (Table 1), indicating that the with the same environmental factors. Non-metric multidimen- amoeba community patterns were highly consistent within habitat sional scaling (NMDS) ordination and analysis of similarities types. Our Venn diagrams indicated that only 9.6% OTUs (32 OTUs) (ANOSIM) were used to investigate differences in community were shared by reservoir and river habitats (Fig. 3D). At OTU level, compositions. The community compositions between samples 14 OTUs (6.5% of reservoir taxa) and 4 OTUs (2.6% of river taxa) were analyzed based on the Bray-Curtis similarity of amoebal OTU were shared among four seasons in reservoir and river, respectively, read relative abundance. These analyses were performed by and most of the unique OTUs occurred in summer season in both “vegan” package in R software (version 3.4.3) (Oksanen et al., 2018). reservoir and river. K. Ren et al. / Water Research 146 (2018) 177e186 181

Fig. 2. Plankton community composition from reservoir and river based on high-throughput sequencing of 18S rRNA gene. (A) The top panel shows the distribution of overall eukaryotic plankton taxonomic groups. (B) The middle panel presents distribution of protozoan taxa including the supergroups Alveolata, Amoebozoa, Apusozoa, Archaeplastida, Excavata and Rhizaria. (C) The bottom panel shows the distribution of free-living amoebae from Amoebozoa, Excavata and Rhizaria.

3.2. Relationship between ecological variables and processes and reservoirs than river, except for summer (Fig. 4). However, there amoebae community was no significant (Mann-Whitney U test, P > 0.05) difference between reservoir sites and river sites for water temperature (WT), The impact of physicochemical parameters on the amoebae dissolved oxygen (DO), pH, nitrate nitrogen (NO3eN) and nitrite communities was investigated using redundancy analysis (RDA). nitrogen (NO2eN). The first and second RDA axes explained 9.7% and 5.9% of the community variance, respectively. Water temperature (WT), dis- 3.3. Detection of specific genera using conventional PCR, qPCR and solved oxygen (DO), oxidation-reduction potential (ORP), electrical high-throughput sequencing conductivity (EC), turbidity and total carbon (TC) were the significant variables affecting the amoeba community (P < 0.01) (Fig. 3C). Only three genera, namely Acanthamoeba, Hartmannella and The neutral community model explained 66.9% and 52.2% of the Naegleria, were detected by the conventional PCR method. In the variation in the relative abundances of amoebal OTUs for reservoirs river habitat, Acanthamoeba was detected in all four seasons with and rivers, respectively (Fig. S1). The Nm-value was a little higher moderate occurrence frequency, whereas it was not detected in the for reservoir (Nm ¼ 26.5) than for river (Nm ¼ 11.5). reservoirs’ samples by conventional PCR. Hartmannella was detec- Reservoir waterbodies and river waters exhibited distinctive ted in most sites in all seasons in the rivers and was also found in and contrasting environmental conditions (Fig. 4). In all cases, many sites in the reservoirs with highest incidence in summer chlorophyll a (Chl a), oxidation-reduction potential (ORP), electrical (Fig. S2A). Naegleria was detected only three times in summer conductivity (EC), turbidity, total carbon (TC), total organic carbon samples from the reservoirs. e (TOC), total nitrogen (TN), ammonium nitrogen (NH4 N), total The river samples had the highest copy number of the 18S rRNA e phosphorus (TP) and phosphate phosphorus (PO4 P) were signif- gene of Acanthamoeba (11087 ± 4745 copy/L) in spring and icantly (Mann-Whitney U test, P < 0.05) higher at river sites than numbers then gradually decreased in autumn (2646 ± 737 copy/L) reservoir sites (Fig. 4). The oxidation-reduction potential (ORP) and winter (3535 ± 1249 copy/L) (Fig. S2B). The abundance of 18S showed seasonal variation in reservoirs, and had higher levels in rRNA gene of Hartmannella was significantly higher in spring/ 182 K. Ren et al. / Water Research 146 (2018) 177e186

Fig. 3. The spatiotemporal distribution pattern of amoeba community and its relationship with ecological variables. (A) Relative abundance of free-living amoebae in rivers and reservoirs identified at the class level based on Illumina sequencing. The top panel refers to the richness between the taxonomic groups (number of OTUs), whereas the bottom panel indicates relative abundance between groups (number of sequences). (B) Non-metric multidimensional scaling (NMDS) ordination of amoeba communities based on the Bray- Curtis dissimilarity, indicating amoeba distribution varies strongly depending on habitat types. (C) Redundancy analysis (RDA) showing the amoeba communities in relation to significant environmental factors. Environmental variables were chosen based on significance calculated from individual RDA results and variance inflation factors (VIFs < 10). (D) Venn diagram showing the number of OTUs that are unique and shared between four different seasons of reservoir and river. WT, DO, ORP, EC and TC represent water temperature, dissolved oxygen, oxidation-reduction potential, electrical conductivity, and total carbon, respectively. **P < 0.01, *P < 0.05, n.s. P > 0.05.

Table 1 significant positive correlation with water temperature, turbidity Results of the ANOSIM test statistic for amoebae community across four seasons and and ammonium nitrogen (NH4eN) in rivers (Table S2). Similarly, two habitats. there was a positive correlation between the abundance of Hart- Group R P-value mannella and water temperature (WT) in both reservoirs and rivers. Seasons 0.244 0.001 A negative relationship was found between Hartmannella and total Habitats (reservoir vs. river) 0.545 0.001 carbon (TC) in reservoirs (Table S2). fi An ANOSIM R-value of 0 means that all communities would be identical, while R- The classi cation tree of Acanthamoeba presence in river value of 1 indicates that communities are highly distinct between groups. generated a pruned tree with three splits and four terminal nodes (Fig. 5A). The classification tree of Hartmannella presence in reservoirs generated a pruned tree with three splits and four terminal summer/autumn of reservoir samples and spring/summer of river nodes (Fig. 5B). This taxon showed a distinct pattern explained by samples than in other samples (Fig. S2B). Only one OTU of Acan- different environmental factors. Water temperature (WT), turbidity thamoeba and three OTUs of Hartmannella were identified from our and dissolved oxygen (DO) were the major factors affecting the Illumina sequencing data (Fig. S2C), and other amoeba genera from presence of Hartmannella in reservoirs. The determined node for fi Illumina sequencing are shown in Fig. S3. Hartmannella presence was classi ed by river environmental conditions with electrical conductivity (EC) values 428.4 (mS/cm) in which Hartmannella was likely to be present (Fig. 5C). The water 3.4. Relationship between amoebae abundance/occurrence and temperature and electrical conductivity were the key environ- environmental variables mental factors affecting the presence of Hartmannella in reservoirs and rivers, respectively. The abundance of Acanthamoeba 18S rRNA gene showed a K. Ren et al. / Water Research 146 (2018) 177e186 183

Fig. 4. Seasonal variation in environmental factors in reservoirs and rivers. WT, DO, Chl a, ORP, EC, TC, TOC, TN, NH4eN, NO3eN, NO2eN, TP, PO4eP represent water temperature, dissolved oxygen, chlorophyll a, oxidation-reduction potential, electrical conductivity, total carbon, total organic carbon, total nitrogen, ammonium nitrogen, nitrate nitrogen, nitrite nitrogen, total phosphorus and phosphate phosphorus, respectively. Error bars indicate standard errors of mean for each season (n ¼ 12 and 15 for reservoirs and rivers, respectively), statistical analysis is nonparametric Mann-Whitney U test, and P values are indicated.

4. Discussion In our study, both Acanthamoeba and Hartmannella were the common genera detected by conventional PCR. This is in accor- 4.1. The habitat pattern prevails over seasonal change of amoebae dance with previous studies where Acanthamoeba was the most community prevalent genus of free-living amoebae found in different aquatic environments (Magnet et al., 2013), and Hartmannella has been Here, we address the generalizability of free-living amoebae described as one of the most representative genera of amoebae in distribution patterns. We demonstrate that amoeba taxon distri- drinking water networks (Hoffmann and Michel, 2001; Thomas bution depends more on habitat type than on seasonal variation. It et al., 2008; Valster et al., 2009). Other genera identified in this could have been expected that the ecological properties in the study, such as Gymnamoeba, have been detected in several types of reservoirs were significantly different from those in rivers; rivers water such as ponds (Anderson, 1997), Naegleria and Vannella in are constantly influenced by runoff, drainage density and vegeta- rivers and streams (Ettinger et al., 2002; Bonilla-Lemus et al., 2014), tion form of upstream areas (Tornes et al., 2014). In this study, it is and Naegleria in water-hyacinth root (Ramirez et al., 2010). clear that rivers have much higher chlorophyll a (Chl a), electrical The impact of seasonality on free-living amoebae community conductivity (EC), turbidity and nutrients than reservoirs (Fig. 4; has already been studied, but without giving consistent results. Table S4). Generally, amoebae will be favored in habitats with a Previous studies demonstrated that Acanthamoeba or Hartmannella relatively high concentration of carbon and nitrogen and with high can thrive in water ecosystems all year round at a range of tem- turbidity (Anderson, 1997; Douglas-Helders et al., 2003). In this peratures (Thomas et al., 2008) and they have the ability to regard, amoebae inhabiting these aquatic ecosystems can be accommodate gradual water temperature changes to survive considered as part of a broader metacommunity that is expressed (Delafont et al., 2016). However, a previous study reported a differentially in response to local environmental and hydrological considerable number of Acanthamoeba and Hartmannella in a river conditions. Thus, some members of the amoeba communities are during the summer (Ettinger et al., 2002). The high abundance of always present but vary in population size, switching between Acanthamoeba detected in spring and summer may be caused by absence (or lower than detection limit) and presence, due to their favorable growth temperature, about 30 C(Geisen et al., environmental changes (Bass and Bischoff, 2001). 2014). 184 K. Ren et al. / Water Research 146 (2018) 177e186

Fig. 5. Classiﬁcation trees for Acanthamoeba and Hartmannella presence or absence based on conventional PCR. (A) Acanthamoeba from river; (B) Hartmannella from reservoirs; (C) Hartmannella from river. Presence was deﬁned as detectable by PCR (the number in the round frame represents estimated rate, a value closed to 2 suggests more possibility to be detected, while a value near to 1 indicates lower detected rate ).

4.2. Water temperature, turbidity and nutrients correlated with the 4.3. Amoebae community assembly exhibited stronger stochastic amoebae community dynamics processes in reservoir than river

Previous studies regarding the temperature tolerance of pro- Our results indicate that the importance of stochastic processes tozoa, especially amoebae, implied that temperature is an in shaping free-living amoebae communities is stronger in reser- important factor for the distribution of free-living amoebae voir than in river. The neutral community model is a valid approach (Thomas et al., 2006; Bonilla-Lemus et al., 2014; Delafont et al., for inferring stochastic processes on the community assembly, and 2016). Our results also suggest that water temperature and has been successfully applied to a wide range of ecological phe- turbidity directly or indirectly affected the amoebae communities nomena (Sloan et al., 2006; Zhou and Ning, 2017). Regarding and the presence of Acanthamoeba and Hartmannella (Fig. 3C; immigration rate, the m value was higher in reservoir (0.80) than in Fig. 5A and B). This is in accordance with a previous study that river (0.35), indicating that the dispersal ability was higher in Acanthamoeba in a river was richer during spring and early reservoir than river. The role of dispersal has been emphasized summer, and no Hartmannella could be isolated in late summer within the metacommunity concept, which concerns local com- (Ettinger et al., 2002). Higher environmental temperature favors munities that are linked to each other by the dispersal of multiple thermophilic amoebae, like Acanthamoeba and Hartmannella and potentially interacting species (Leibold et al., 2004). High (Delafont et al., 2016). Water temperature varies considerably dispersal ability allows amoebae to have a high probability of within aquatic ecosystems, generating both opportunities and colonizing suitable habitats from regional pools, thereby poten- constraints for their inhabitants (Schindler, 2017). In this study, tially reducing the variation of community composition (Yang et al., seasonal shifts in the free-living amoebae community covaried 2010). As river ecosystems are dynamic under normal conditions with water temperature (Fig. 4). From our results it becomes clear and inﬂuenced by drainage density and hydrological exchange, that when the temperature is lowered through a small interval, impact of migration and some of the key environmental variables especially when the change occurs near the physiological opti- can change by transport from upstream to downstream (Hullar mum, the growth rate of amoeba may be unaffected (Thomas et al., 2006). Our results suggest that both neutral and determin- et al., 2008). When the fall in temperature is greater, the growth istic processes interact during the amoeba community assembly, rate may be retarded suddenly or gradually, or, in rare cases, there and their importance may differ depending on the habitat type. may even be an acceleration for a brief time (Miller et al., 2018). High turbidity and nutrients indicated that the water contained 4.4. Methodological issues and potential limitations suspended and soluble material that can provide nutrition and encourage growth of the amoeba (Douglas-Helders et al., 2003). It The use of PCR-based techniques to estimate community may imply that nutrients (e.g., carbon, nitrogen, phosphorous) composition is complicated by the fact that rRNA gene copy num- concentrations can directly and indirectly inﬂuence free-living ber varies from one to thousands in single eukaryotic genomes amoebae community composition (Zahn et al., 2016). The deep (Prokopowich et al., 2003; Bik et al., 2012). Furthermore, in addition deterministic mechanisms underlying community assembly of to the variation from species to species it is also known that the free-living amoebae deserves further study. DNA content can vary substantially between growth phases. K. Ren et al. / Water Research 146 (2018) 177e186 185

Therefore, it has been suggested that the copy number of the 18S attention on sampling scale effects (spatial extent and time-scale) rRNA gene might vary during the growth cycle of protozoa (Bowers and species interactions, and consider more environmental fac- et al., 2000). These facts preclude a direct translation of rDNA read tors (like irradiance and suspended particulates). The advent of number into abundance of individual organisms. However, the molecular techniques has enabled the characterization of amoebae number of rDNA copies per genome correlates positively to the size communities without the need of culturing and provides easier, and particularly to the biovolume of the eukaryotic cell it repre- faster and better opportunities to understand their ecology and sents (de Vargas et al., 2015). Results from Tara Oceans stations their possible impact on the health of humans and on various further confirmed the correlation between biovolume and V9 rDNA ecosystems. A continual effort to monitor and establish the dy- abundance data, which was used for Illumina sequencing in this namics of microbial organisms in freshwater ecosystems, particu- study. The macroelement contents (including carbon and nitrogen larly with an emphasis on free-living amoebae, over the long term with ecological significance) exhibited a higher correlation with is necessary to determine the impact of rising surface water tem- plankton biomass or biovolume than plankton abundance (Lv et al., perature and increasing eutrophication on community structure 2014; Sandrini et al., 2014). Previous results with Hartmannella and function. vermiformis indicate that the copy number of the 18S rRNA gene in cysts did not differ significantly from that in trophozoites. The Acknowledgments estimated copy number for the 18S rRNA gene on the genome of H. vermiformis (1330 ± 127 [mean ± SE] copies/cell) exceeds the We thank Dr. Yongming Wang for sampling and some com- values reported for other unicellular eukaryotes (Le Blancq et al., ments on an earlier version of this paper. This study was supported 1997; Galluzzi et al., 2004). These reports and our observations by National Natural Science Foundation of China (31672312, indicate that the use of PCR-based techniques is a relevant 31370471 and 31770123), and the Xiamen Municipal Bureau of approach to rapidly and accurately estimate community composi- Science and Technology (3502Z20172024 and 3502Z20171003). tion of amoebae in natural freshwater environments. The research was also supported by the Wuhan Branch, Super- However, complex matrices such as surface waters may contain computing Center, Chinese Academy of Sciences, China. organic and inorganic compounds which interfere with several fi steps in the DNA isolation and ampli cation protocols during the Appendix A. Supplementary data qPCR amplification of target DNA. Difference of CT value by qPCR in environmental samples (2.89 for reservoir and 2.93 for river) is Supplementary data related to this article can be found at slightly less than 3.32 with 10-fold dilution, indicating there is https://doi.org/10.1016/j.watres.2018.09.011. minor inhibition in the environmental samples. Based on the in- ternal control experiments, however, no significant difference of References inhibition value was found between the reservoir samples spiked with undiluted/10-fold diluted cultured amoeba cells and river Abedkhojasteh, H., Niyyati, M., Rahimi, F., Heidari, M., Farnia, S., Rezaeian, M., 2013. samples (Kruskal-Wallis test, P > 0.05). These data suggest that the First report of Hartmannella keratitis in a cosmetic soft contact lens wearer in qPCR results were subject to somewhat inhibition effect, but the Iran. Iran. J. Parasitol. 8 (3), 481e485. Adl, S.M., Simpson, A.G., Lane, C.E., Lukes, J., Bass, D., Bowser, S.S., Brown, M.W., results from the reservoir and river samples from the same season Burki, F., Dunthorn, M., Hampl, V., Heiss, A., Hoppenrath, M., Lara, E., Gall, L.L., are comparable under same procedure or condition. 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