Environ Sci Pollut Res DOI 10.1007/s11356-014-3651-8

RESEARCH ARTICLE

Seasonal distribution of potentially pathogenic Acanthamoeba species from drinking water reservoirs in Taiwan Po-Min Kao & Bing-Mu Hsu & Tsui-Kang Hsu & Jorn-Hon Liu & Hsiang-Yu Chang & Wen-Tsai Ji & Kai-Jiun Tzeng & Shih-Wei Huang & Yu-Li Huang

Received: 20 August 2014 / Accepted: 22 September 2014 # Springer-Verlag Berlin Heidelberg 2014

Abstract In order to detect the presence/absence of Acanthamoeba along with geographical variations, water quality variations and seasonal change of Acanthamoeba in Taiwan was investigated by 18S ribosomal RNA (rRNA) gene TaqMan quantitative real-time PCR. Samples were collected quarterly at 19 drinking water reservoir sites from November 2012 to August 2013. Acanthamoeba was detected in 39.5 % (30/76) of the water sample, and the detection rate was 63.2 % (12/19) from samples collected in autumn. The average concentration of Acanthamoeba was 3.59×104 copies/L. For geographic distribution, the detection rate for Acanthamoeba at the northern region was higher than the central and southern regions in all seasons. Results of Spearman rank test revealed that heterotrophic plate count (HPC) had a negative correlation (R=−0.502), while

Responsible editor: Philippe Garrigues Tsui-Kang Hsu and Po-Min Kao equally contributed to this work. P.
dissolved oxygen (DO) had a positive correlation (R=0.463) in summer. Significant differences were found only between the presence/absence of Acanthamoeba and HPC in summer (MannWhitney U test, P < 0.05). T2 and T4 genotypes of Acanthamoeba were identified, and T4 was the most commonly identified Acanthamoeba genotypes. The presence of Acanthamoeba in reservoirs presented a potential public health threat and should be further examined. Keywords Acanthamoeba . Reservoir . TaqMan . Quantitative real-time PCR . Heterotrophic plate count

Introduction Amoebida family belongs to Kingdom Protozoa, which can be categorized into intestinal parasitic protozoan and free-living species (Visvesvara and Schuster 2008a, b). Most amoebae are free-living and nonpathogenic to human (Schuster and Visvesvara 2004). Free-living amoebae (FLA) have been isolated from human bodies, swimming pools, bottled mineral water, contact lens solutions, and even dust (Nagington et al. 1974; Seal et al. 1999; da Rocha-Azevedo et al. 2009). Acanthamoeba, Naegleria, Balamuthia, and Hartmanella are free-living amoebae commonly found in a wide variety of natural habitats including water, soil, and air (Page 1980; Martínez 1985; Martinez and Visvesvara 1997). Pathogenic Acanthamoeba was first isolated from dust in 1913 by Puschkarew and named Amoeba polyphagus (Page 1967), and the genus Acanthamoeba was created in 1931 by Volkonsky (Visvesvara and Schuster 2008a, b). More than 24 species have been recognized in the genus Acanthamoeba, and they are classified into three different groups by cyst morphology and trophozoite size (Booton et al. 2005; Visvesvara and Schuster 2008a, b). By subgenus classification and taxonomy, Acanthamoeba is classified into 17 different genotypes, T1 to T17, according to 18S ribosomal RNA

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nucleotide sequence, with 5 % or more sequence divergence between different genotypes (Hewett et al. 2003; Corsaro and Venditti 2010; Nuprasert et al. 2010). The first cases which clearly established Acanthamoeba as causative agents of disease in humans were reported in the early 1970s. Subsequently, human infection by Acanthamoeba has been reported worldwide (Ringsted et al. 1976; Hirst et al. 1984). Part of Acanthamoeba species are pathogenic for animals and humans, among which Acanthamoeba polyphaga, Acanthamoeba castellanii, and Acanthamoeba culbertsoni are the three most common species to infect human (Kilvington and White 1994). The pathogenic Acanthamoeba in the human body mainly causes two diseases: granulomatous amoebic encephalitis (GAE) may occur through respiratory infection or skin invasion, and Acanthamoeba keratitis (AK) is a vision-threatening infection of the cornea (Martínez et al. 1977; Gianinazzi et al. 2010; Liang et al. 2010). The emergence of Acanthamoeba species had certain correlation with environmental factors. In an earlier study on FLA in warm monomictic lake in South Carolina, USA, Acanthamoeba was detected at a higher rate than in other seasons (Kyle and Noblet 1986). Similarly, Acanthamoeba species were isolated mainly in the summer from water bodies in Oklahoma, USA (John and Howard 1995). Seasonal distribution may be related to the occurrence of Acanthamoeba species, but the differences have not been well evaluated. Many human diseases are caused by pathogenic microorganisms, which are in turn influenced by a range of seasonal distribution. The influences of seasonal distribution on freeliving amoebae have been discussed in a number of reports (Rodriguez Zaragoza et al. 2005; Warner et al. 2007). Studies specific to Acanthamoeba in reservoirs in Taiwan are sparse. With subtropical climate pattern and seasonal rainfalls, the seasonal effects of Acanthamoeba in freshwater reservoirs in Taiwan warrant close examination. In this study, we present seasonal variation of Acanthamoeba species in freshwater reservoirs in Taiwan. Quantitative real-time PCR assay was used to determine the presence and concentration of Acanthamoeba in water samples. A confirmative analysis was designed to determine the phylogenic relationships of Acanthamoeba species in different seasons using 18S ribosomal RNA (rRNA) gene sequences. In addition, various physical and microbiological water quality parameters were compared with detection of Acanthamoeba species in each reservoir.

Materials and methods Sample collection area of study Nineteen freshwater reservoirs in Taiwan were included in this study. The reservoirs are located in the northern, central, and southern regions (see Fig. 1). Water samples were taken in autumn (November 2012), winter (February 2013), spring

(May 2013), and summer (August 2013). The raw water intake area at each reservoir was selected as sampling point. For each sample, 1 L raw water was collected in a sterile polypropylene bottle and stored at 4 °C for subsequent analyses within 24 h.

Sample pretreatment and DNA extraction Each 1 L water sample was filtered through GN-6 Metricel® MCE Membrane Disc Filters (pore size 0.45 μm, diameter 45 mm) (Pall, USA). After filtration, the membranes were scraped, and the collected material was washed with 100 mL eluting fluid consisted of phosphate-buffered saline (PBS; 7.5 mM Na2HPO4 , 3.3 mM NaH2PO4, 108 mM NaCl, pH 7.2). The resulting solution was then transferred into two 50 mL conical centrifuge tubes and centrifuged at 2600×g for 30 min. After removing the top 45 mL, the remaining pellet was resuspended with PBS at 4 °C for further DNA extraction and PCR. DNA extraction was obtained with the MagPurix Bacterial DNA Extraction Kit ZP02006 and automated DNA extraction by MagPurix 12 s Automated Nucleic Acid Purification System (Zinexts Life Science Corp., Taiwan) according to the manufacturer’s specifications. The suspension was analyzed for the presence of Acanthamoeba specific genes by PCR and quantitative real-time PCR.

PCR molecular identification of Acanthamoeba For Acanthamoeba typing, the ASA.S1 region of Acanthamoeba 18S rRNA gene was amplified with primers JDP1 and JDP2 (Schroeder et al. 2001). PCR products were electrophoresed on 2 % agarose gel (Biobasic Inc., Canada) stained with a solution of ethidium bromide and visualized under UV light. The sequence analysis was done using a Bio-Dye terminator cycle sequencing kit (Applied Biosystems, USA).

Quantitative real-time PCR detection of Acanthamoeba The TaqMan real-time PCR assay primers set AcantF900 and AcantR1100 were designed for Acanthamoeba gene sequence. The TaqMan probe used was the FAM-labeled AcantP1000 (Qvarnstrom et al. 2006). The TaqMan realtime PCR was performed using an ABI StepOneTM RealTime PCR Systems (Applied Biosystems, Singapore). For each assay, cycle threshold (Ct) value was determined in order to quantify each DNA product. A negative DNA control (using double-distilled water instead of DNA template), positive DNA control (Acanthamoeba lenticulata ATCC30841), and water sample DNA were included in each run.

Environ Sci Pollut Res Fig. 1 Locations of sampling sites in 19 Taiwan’s reservoirs

A

Hsinshan

Sishih

B Shimen

C

D

Baoshan Baoshan 2nd

E

Mingde

F

Liyutan

Deji

G H I Sun Moon Lake

Lantan

Toushe

K L

Renyitan

M

Paiho Wushantou

Wushe

J

N O P

Agongdian Chengching Lake Fongshan

Q R Mudan

S

Copy number standard curve of the 18S rRNA gene in Acanthamoeba The yT&A clone vector kit (Yeastern Biotech Corporation, Taiwan) was used to determine the Acanthamoeba 18S rRNA gene copy number. Recombinant plasmid DNA was purified by HiYield™ plasmid mini kit (Real Biotech Corporation, Taiwan). Following purification, the concentration of plasmid DNA was determined using a NanoDrop ND1000 spectrophotometer (NanoDrop Technologies, USA). The number of construct copies in the plasmid solution was calculated based on plasmid and insert sizes. A plasmid-based standard curve was generated with tenfold serial dilutions of plasmid containing the 18S rRNA gene fragment sequence of the target. Concentrations were verified by quantitative real-time PCR. This plasmid-based standard curve, with a concentration of 1.3×108 gene copies per liter for the dilution with the highest copy number, was used for determining the copy number of the 18S rRNA gene in Acanthamoeba. Analysis of water quality parameters Physical and microbiological parameters are based on water quality monitoring items of EPA, Taiwan, ROC. The physicochemical water quality parameters, including water

temperature, conductivity, concentrations of chlorophyll a (Chl-a), concentrations of dissolved oxygen (DO), and pH were recorded real-time by a portable multi-parameter meter (HI9828, Hanna Instruments Inc., USA). Turbidity was measured using a ratio turbidimeter (Waterproof Portable TN100, Eutech Instruments Pte Ltd, Singapore). Other parameters, such as NH4-N and total phosphorus (TP) were determined by analytical kits (Spectroquant®, 114752 and 100673, Merck Millipore, Germany), and total suspended solids (TSS) were analyzed according to standard method (Method 209-C) (APHA 2005). Carlson’s trophic state index (CTSI) is used for evaluating the trophic state condition of reservoirs (Carlson 1977). Additional water samples were taken for each sampling sites in 300 mL sterile sampling bags (Nasco WhirlPak, USA) for microbiological water quality parameters. The samples were kept in coolers during transportation to the laboratory for subsequent analyses within 24 h. Total coliforms were measured by membrane filtration and a differential medium described in the standard method for the examination of water and wastewater (Methods 9222 B) (APHA 2005). The total coliform culture was placed in m-Endo LES agar (Difco, USA) at 36 °C for 24 h before counting. Heterotrophic bacteria were cultured on the M-heterotrophic plate count (HPC) agar base and measured by the spread plate method (Methods 9215C) (APHA 2005). The Mann-Whitney U test

Environ Sci Pollut Res Autumn Winter Spring Summer

6 5

Number of isolates

was used to compare association between water quality parameters and presence/absence of Acanthamoeba. Spearman rank test was calculated between the concentrations of Acanthamoeba and the water quality parameters. Significance was set at a P level of <0.05. The statistical software used was STATISTICA® version 6.0 (StatSoft, Inc., USA).

4 3 2

Results and discussion

1

Detection and quantification of Acanthamoeba in reservoir water samples

0 Northern

Middle

Southern

Regions

The detection results of Acanthamoeba from reservoirs are shown in Table 1. Acanthamoeba was detected in 30 of the 76 samples (39.5 %). The result was in agreement with previous finding that Acanthamoeba were prevalent in reservoir water ecosystem (Hoffmann and Michel 2001; Garcia et al. 2013). Previous studies have proven the ubiquitous Acanthamoeba

Fig. 2 Geographical distribution of pathogenic Acanthamoeba by four seasons of isolation, northern (sampling sites A–G), middle (sampling sites H–M), southern (sampling sites N–S) regions of Taiwan

occurrence in different water types (Trabelsi et al. 2012). As far as we know, our study was the first in Taiwan on seasonal

Table 1 Detection results of Acanthamoeba-positive water samples at the 19 reservoir sites during four seasons Sampling Sample Number of season size detection for Acanthamoeba

Percent of Positive DNA sequencing result detection reservoir site of species name for Acanthamoeba (accession no.)

Identity (%)

Genotypes

Autumn

63.2

98 99

T4 T4

99 99, 99, 99, 99 99, 99 99

T4 T4 T4 T4

99 98 98, 99, 97, 98, 98 99, 99 100 96 99 95 98

T4 T4 T4

98 96, 98 98 99 98

T4 T4 T4 T4 T4

Winter

19

19

12

9

47.4

Spring

19

3

15.8

Summer

19

6

31.6

Total

76

30

39.5

A C

Acanthamoeba sp. RON2 (JQ418517) Acanthamoeba castellanii Ma ATCC 50370 (U07414) D Acanthamoeba triangularis (AF316547) E, H, N, R Acanthamoeba polyphaga (GU320583) F, Q Acanthamoeba genotype T4 (JX043490) G Acanthamoeba castellanii ATCC 50370 (AY690456) L Acanthamoeba culbertsoni (AY690459) O Acanthamoeba sp. CRIB45 (EU377586) A, G, J, K, P Acanthamoeba sp. UIC 1504 (EU168069) B, I D R H P O A B, I D F N

Acanthamoeba culbertsoni (AY690459) Acanthamoeba polyphaga (AF019051) Acanthamoeba palestinensis (AF260719) Acanthamoeba culbertsoni (KF881887) Acanthamoeba genotype T4 (JX043490) Acanthamoeba mauritaniensis (AY351647) Acanthamoeba culbertsoni (KF881887) Acanthamoeba castellanii (KF881889) Acanthamoeba hatchetti (AF260722) Acanthamoeba polyphaga (GU596994) Acanthamoeba genotype T4 (JX043490)

T4 T4 T2 T4 T4 T4

Cell density (copies/L)

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4

4.0x10

4

3.5x10

4

3.0x10

4

2.5x10

4

2.0x10

4

1.5x10

4

1.0x10

4

5.0x10

3

0.0 Autumn

Winter

Spring

Summer

Fig. 3 Seasons change of Acanthamoeba concentrations in positive water samples at the reservoir sampling sites, as measured by TaqMan quantitative real-time PCR

changes in the presence and pathogenic genera of Acanthamoeba in reservoirs. The results showed that Acanthamoeba does occur in Taiwan’s reservoirs in different seasons. The overall detection rate for Acanthamoeba is varied by season, which ranged from 15.8 in spring to 63.2 in autumn. The low Acanthamoeba detection rate in spring may be attributed to the community’s time lag for becoming stabilized after autumn and winter depending on water availability. The higher detection rate of Acanthamoeba in autumn coincided with lower seasonal rainfall and lower water level registered in the reservoirs during the years 2012 and 2013. The results are similar to previous reports of higher detection rates of Acanthamoeba in reservoirs in autumn (John and Howard 1995; Garcia et al. 2013). The geographic distribution and occurrence of Acanthamoeba in Taiwan’s reservoirs were further compared by sampling season, sampling region, and the results are

presented in Fig. 2. Acanthamoeba were detected in all seasons. The overall Acanthamoeba detection rate was 46.4 % (13/28) for the northern reservoirs, 33.3 % (8/24) for the central reservoirs, and 37.5 % (9/24) for the southern reservoirs. Again, the highest percentage of Acanthamoeba detection rate was found among autumn samples in the northern reservoirs (6/7, 85.7 %). In contrast, Acanthamoeba was not detected in samples from the northern reservoirs in the spring. Therefore, seasonal change and geographic distribution were associated with the occurrence of Acanthamoeba. The TaqMan quantitative real-time PCR data results of Acanthamoeba-positive samples are shown in Fig. 3. The concentrations of Acanthamoeba was lower in spring (1.1× 102-1.7×103 copies/L, with average at 6.4×102 copies/L). The higher concentrations of Acanthamoeba in autumn may have been a result of low water level registered in reservoirs, which was also reflected in increase of organic matter and low water flow. The presence of Acanthamoeba in reservoirs may be a potential health risk in Taiwan. The considerably high Acanthamoeba found in some of the reservoirs suggest that these reservoirs might be a source for contamination in water treatment plants. It may be necessary to assure the efficiency of water treatment plants when considering Acanthamoeba infection and hazard control.

Identification of Acanthamoeba According to the DNA sequencing and gene analysis, Acanthamoeba species differed by season at these sites. The DNA sequences of sample strains were compared with Acanthamoeba reference strains from the NCBI GenBank to determine the likelihood of specific strains, and the results are shown in Table 1. The identified Acanthamoeba species from

Table 2 Mean and ranges of water quality parameters at the 19 sites during four seasons Water quality parameters

Autumn

Winter

Spring

Summer

HPC (CFU/mL) Total coliforms (CFU/100 mL) Turbidity (NTU) Temperature (°C) pH CTSI Conductivity (μmho/cm) DO (mg/L)

4450 (100–27,500) 1992.3 (0–13,500) 5.5 (1–25) 25.6 (22.6–28.1) 8.2 (7.6–8.4) 44.3 (23.2–80.8) 365.7 (136–590) 7.5 (4.2–8.7)

4516.7 (5–14,150) 18.2 (0–204) 7.1 (0.9–45) 20.1 (17–23.7) 8.3 (7.8–8.5) 46 (34.2–78.6) 380 (171–651) 8.7 (3.5–12.2)

9315.4 (650–60,500) 292 (0–3500) 5 (2.1–11) 25.3 (11.4–29.5) 8.4 (7.7–9.1) 49.7 (41.6–75.9) 375.3 (172–595) 8.5 (3.8–10.6)

4239.2 (5–11,955) 256.3 (0–2112) 9.2 (1.2–60) 29.2 (18.7–31.8) 8.5 (7.5–9.4) 50.2 (42.3–74.1) 327.5 (126–461) 8.6 (7.2–11.8)

TSS (mg/L) Chl-a (μg/L) TP (mg/L) NH4-N (mg/L)

6.3 (1.3–28.5) 10 (0.1–96.9) 0.038 (0.002–0.372) 0.18 (0.01–2.05)

7.4 (1–21) 3.2 (0.8–11.6) 0.094 (0.006–0.994) 0.47 (0.01–5.41)

5.8 (2.3–14.8) 5.5 (0–29.2) 0.015 (0–0.027) 0.01 (0–0.05)

10.6 (1.3–49.2) 10.3 (1.8–59.5) 0.053 (0.008–0.429) 0.11 (0.01–1.2)

HPC heterotrophic plate count, CTSI Carlson’s trophic state index, DO dissolved oxygen, TSS total suspended solids, Chl-a concentration of chlorophyll a, TP total phosphorus

*P<0.05

M-W U test Mann-Whitney U test, Spearman test Spearman rank test, HPC heterotrophic plate count, CTSI Carlson’s trophic state index, DO dissolved oxygen, TSS total suspended solids, Chl-a concentration of chlorophyll a, TP total phosphorus

P=0.029*, R=−0.502 P=0.365, R=−0.224 P=0.278, R=−0.262 P=0.554, R=0.145 P=0.186, R=0.317 P=0.212, R=0.300 P=0.526, R=−0.155 P=0.046*, R=0.463 P=0.775, R=0.070 P=0.655, R=0.110 P=0.166, R=0.331 P=0.174, R=0.325 P=0.044* P=0.599 P=0.174 P=0.511 P=0.254 P=0.236 P=0.483 P=0.066 P=0.861 P=0.589 P=0.174 P=0.483 P=0.106, R=0.383 P=0.805, R=−0.061 P=0.478, R=0.173 P=0.906, R=−0.029 P=0.707, R=−0.093 P=0.404, R=−0.203 P=0.729, R=0.085 P=0.259, R=−0.272 P=0.441, R=0.188 P=0.306, R=−0.248 P=0.957, R=−0.013 P=0.533, R=−0.152 P=0.094 P=0.823 P=0.402 P=0.737 P=0.696 P=0.434 P=0.615 P=0.219 P=0.371 P=0.240 P=0.955 P=0.615 P=0.793, R=0.065 P=1.000, R=0.000 P=0.932, R=0.021 P=0.797, R=0.063 P=0.914, R=−0.027 P=0.411, R=0.200 P=0.665, R=−0.106 P=0.954, R=−0.014 P=0.536, R=−0.151 P=0.330, R=0.236 P=0.187, R=0.316 P=0.517, R=0.159 P=0.870 P=1.000 P=0.935 P=0.967 P=0.838 P=0.568 P=0.744 P=0.568 P=0.903 P=0.744 P=0.327 P=0.488 P=0.309, R=0.247 P=0.866, R=0.041 P=0.108, R=−0.381 P=0.750, R=0.078 P=0.982, R=−0.005 P=0.843, R=0.049 P=0.676, R=0.103 P=0.874, R=0.039 P=0.820, R=−0.056 P=0.232, R=0.288 P=0.643, R=0.114 P=0.287, R=−0.258 P=0.311 P=0.447 P=0.331 P=0.833 P=0.642 P=0.108 P=0.933 P=0.673 P=0.447 P=0.076 P=0.151 P=0.899 HPC (CFU/mL) Total coliforms (CFU/100 mL) Turbidity (NTU) Temperature (°C) pH CTSI Conductivity (μmho/cm) DO (mg/L) TSS (mg/L) Chl-a (μg/L) TP (mg/L) NH4-N (mg/L)

M-W U test M-W U test Spearman test M-W U test

Spearman test

M-W U test

Spearman test

Summer Spring Winter Autumn

Mean and ranges of the water quality parameters at the 19 sampling sites during four seasons are shown in Table 2. Results of nonparametric test are given in Table 3. Results of the Mann-Whitney U test and Spearman test showed that water quality variables did not significantly affect the presence/absence and concentration of Acanthamoeba. Significant difference (M-W U test, P<0.05) was observed between the presence/absence of Acanthamoeba and HPC from reservoir water samples collected in summer. Further, detection of Acanthamoeba in summer showed a negative correlation with HPC (R=−0.502) and a positive correlation with DO (R=0.463) through Spearman test. Based on the statistical results, HPC was an influential factor for Acanthamoeba in summer. Not only the presence/ absence but also concentration of Acanthamoeba was influenced by HPC. In previous studies, HPC from natural

Water quality parameters

Relationships between Acanthamoeba and water quality variables

Table 3 Nonparametric test results for differences and correlations for Acanthamoeba in term of water quality parameters

the studied reservoirs included Acanthamoeba sp. (JQ418517; EU377586; EU168069, n=7), A. polyphaga (GU320583; AF019051; GU596994, n=6), A. culbertsoni (AY690459; KF881887, n = 5), A. castellanii (U07414; AY690456; KF881889, n=4), Acanthamoeba genotype T4 (JX043490, n = 4), Acanthamoeba triangularis (AF316547, n = 1), Acanthamoeba mauritaniensis (AY351647, n = 1), Acanthamoeba palestinensis (AF260719, n = 1), and Acanthamoeba hatchetti (AF260722, n=1). A. castellanii, A. culbertsoni, A. hatchetti, A. palestinensis, and A. polyphaga are the most commonly pathogenic species to infect human (Visvesvara and Schuster 2008a, b). T4 genotype was the most prevalent in the reservoir samples (96.7 %, 29/30), and the finding was in agreement with earlier studies (Garcia et al. 2013). T2 and T4 genotypes have been described as a causative agent of pathogenicity in humans (Maghsood et al. 2005). These genotypes were associated with AK and GAE (Walochnik et al. 2008; Ledee et al. 2009). The highest proportion of AK cases (above 90 %) was associated with the T4 genotype, possibly due to its greater virulence and greater transmissibility with respect to other genotypes (Maghsood et al. 2005). Therefore, this study provides best evidence to support the proposition that reservoirs in Taiwan may pose a potential health risk and Acanthamoeba pollution of domestic drinking water supply in these areas. Health authorities and operators of water treatment plants need to be aware of these potential hazards and provide tactful measures and relevant guidelines to assure safety. For seasonal distribution, the isolated Acanthamoeba species from positive water samples in autumn (n=8) was higher than that in winter (n=4), spring (n=3), and summer (n=5). The results further suggested seasonal and geographic influence on the diversity of Acanthamoeba species in reservoir.

Spearman test

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inhabitants in various aquatic environments were shown to cause the predation of Acanthamoeba (Huang and Hsu 2010; Kao et al. 2013a, b). In addition, there was no significant difference between DO and presence of Acanthamoeba. The results support a previous report of no significant difference in DO for Acanthamoeba (Bui et al. 2012). However, DO was a major contributor to amoeba-mediated growth enhancement in A. castellanii (Bui et al. 2012). This may be why concentration of Acanthamoeba was significantly correlated with DO. The results also suggested the importance of seasonal characteristics over physicochemical and microbiological water quality parameters in reservoir, and the seasonal changes may play an important role in the presence/absence and concentration of Acanthamoeba.

Conclusions According to the results of PCR, the overall detection rate was 39.5 % for Acanthamoeba in all reservoir samples. For geographical distributions, the Acanthamoeba isolates at the northern region was higher than the central and southern regions in all seasons. The detection rate of Acanthamoeba in autumn and winter was higher than that in spring and summer. Acanthamoeba concentrations were higher in autumn. The most commonly identified Acanthamoeba genotypes were T4, which was mainly associated with Acanthamoeba keratitis. In summer, significant difference was found between the presence/absence of Acanthamoeba and HPC. Moreover, HPC was negatively correlated with the Acanthamoeba, while DO showed a positive correlation. Health authorities and operators of water treatment plants need to be aware of these potential hazards and provide tactful measures and relevant guidelines to assure safety. Acknowledgments This work was supported by a research grant from National Science Council of Taiwan, ROC (NSC 102-2116-M-194-006).

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