Extraction of viral DNA/RNA from environmental samples as part of the analytical procedure in quantifying waterborne viruses, is of great importance. In this study, two commercially available kits were compared to assess their performance, the MO BIO PowerViral Environmental DNA/RNA Isolation kit and the Qiagen QIAamp Viral RNA Mini kit. A performance assessment of extraction kits for detecting and quantifying six human enteric viruses as the commonest waterborne pathogens and one plant virus as an alternative fecal indicator has been carried out using quantitative PCR (qPCR). Water samples were collected from seven sites in Singapore during March and April 2015. In general, a strong association was observed between two different viral DNA/RNA extraction kits and detection frequency of targets (P = 0.017). The Qiagen kit showed higher extraction efficiency than the MoBio kit. However, in terms of quantification, a significant difference was only observed in the occurrences of NoV GI and PMMoV between two different kits (P < 0.05), although the kits showed similar efficiency removing qPCR inhibitors. The Qiagen kit was preferred for routine water quality monitoring.

INTRODUCTION

The rapid urbanization of Singapore has increased the pressure to meet the water demands of an expanding population for whom drinking water, drainage, wastewater and sanitation services have to be provided. To safeguard public health, an understanding of the occurrence, distribution and diversity of waterborne pathogens in aquatic environments is critical.

Molecular detection methods have been considered an effective means for the rapid and sensitive detection of microbial pollution in environmental samples (Wong et al. 2012; Rezaeinejad et al. 2014; Liang et al. 2015). Among the viral pathogens, human enteric viruses have been suggested as a key marker in the routine monitoring of water quality for protecting public health. Enteric viruses are widely recognized as the most hazardous and commonest waterborne pathogens, causing both sporadic and outbreak-related illnesses (La Rosa et al. 2012). However, there are major challenges for virus detection in environmental samples, especially at low concentrations. The virus particles need to be concentrated prior to viral nucleic acid extraction (Ikner et al. 2012; Cashdollar & Wymer 2013). The efficiency of viral DNA/RNA extraction from environmental samples is very important in the detection of viral pathogens in aquatic environments. In this study two commercially available extraction kits: the MO BIO PowerViral Environmental DNA/RNA Isolation kit and the Qiagen QIAamp Viral RNA Mini kit were compared in terms of removing environmental inhibitors, and detection and quantification of six different enteric viruses – aichi virus (AiV), astrovirus (AstV), human adenoviruses (HAdV), norovirus GI (NoV GI), norovirus GII (NoV GII) and rotavirus (RotV) – and one plant virus as an alternative human fecal indicator (pepper mild mottle virus (PMMoV)).

MATERIAL AND METHODS

A total of 37 samples was collected from 7 sites in Singapore. The sampling sites receive water from different land uses classified into three main categories; (I) populated areas, (II) farming areas, and (III) low human impact areas. Samples were collected during March and April, the pre-southwest monsoon period, in 2015.

The first step in analyzing the water samples was to concentrate microorganisms using the disposable hollow-fiber membrane filtration method (Fresenius Hemoflow HF80S) (Liu et al. 2012). Tests showed that the recovery efficiency of hollow-fiber filtration using the bacteriophage MS2 was 70 to 80%. Secondary concentration was done using polyethylene glycol and Amicon ultrafiltration (Arnone & Walling 2007; Gibson 2014). Viral nucleic acids (RNA and DNA) were extracted with QIAamp Viral RNA Mini Kit (Qiagen) and MO BIO PowerViral Environmental DNA/RNA Isolation kit (Mobio). Complementary DNA was synthesized using ImProm-II Reverse Transcription System (Promega), and quantitative PCR (qPCR) was performed to detect the occurrence of the six human enteric viruses and one plant virus cited above (Kageyama et al. 2003; Le Cann et al. 2004; Jothikumar et al. 2005; Pang et al. 2012; Haramoto et al. 2013; Kitajima et al. 2013). Information related to the primer and probe sequences, reaction mixture and thermal conditions for qPCR reactions is given in Table 1. TaqMan® Exogenous Internal Positive Control Reagents were used to find the inhibition level in the environmental samples extracted with the different kits (Hartman et al. 2005).

Table 1

Oligonucleotide primers and probes used for qPCR measurements of different viral targets together with the qPCR reaction mixture and thermal condition

Target Primer/probe Sequence (5′-3′) Concentration Thermal cycle Reference 
AiV F primer GTCTCCACHGACACYAAYTGGAC 400 nM 95 °C 10 min; 45 cycles (95 °C 15 s; 60 °C 1 min) Kitajima et al. (2013
R primer GTTGTACATRGCAGCCCAGG 400 nM 
Probe FAM-TTYTCCTTYGTGCGTGC-MGB-NFQ 300 nM 
AstV F primer CCGAGTAGGATCGAGGGT 400 nm 95 °C 15 min; 45 cycles (95 °C 10 s; 55 °C 1 min;72 °C 10 s) Le Cann et al. (2004
R primer GCTTCTGATTAAATCAATTTTAA 400 nm 
Probe FAM-CTTTTCTGTCTCTGTTTAGATTATTTTAATCACC-BHQ1 100 nm 
HAdV F primer GGACGCCTCGGAGTACCTGAG 250 nM 95 °C 15 min; 45 cycles (95 °C 10 s; 55 °C 30 s; 72 °C 15 s) Jothikumar et al. (2005
R primer ACIGTGGGGTTTCTGAACTTGTT 250 nM 
Probe FAM-CTGGTGCAGTTCGCCCGTGCCA-BHQ1 150 nM 
NoV GI F primer CGYTGGATGCGNTTYCATGA 400 nM 95 °C 15 min; 45 cycles (95 °C 10 s; 55 °C 30 s; 72 °C 15 s) Kageyama et al. (2003
R primer CTTAGACGCCATCATCATTYAC 400 nM 
Probe FAM-TGTGGACAGGAGATCGCAATCTC-BHQ1 100 nM 
NoV GII F primer CAAGAGTCAATGTTTAGGTGGATGAG 400 nM 95 °C 15 min; 45 cycles (95 °C 10 s; 55 °C 30 s; 72 °C 15 s) Kageyama et al. (2003) 
R primer TCGACGCCATCTTCATTCACA 400 nM 
Probe FAM-TGGGAGGGCGATCGCAATCT-BHQ1 100 nM 
PMMoV F primer GAGTGGTTTGACCTTAACGTTTGA 900 nM 95 °C 10 min; 45 cycles (95 °C 5 s; 60 °C 1 min) Haramoto et al. (2013
R primer TTGTCGGTTGCAATGCAAGT 900 nM 
Probe FAM-CCTACCGAAGCAAATG-BHQ1 200 nM 
RotV F primer ACCATCTACACATGACCCTC 400 nM 95 °C 15 min; 45 cycles (95 °C 20 s; 60 °C 1 min; 72 °C 15 s) Pang et al. (2012
F primer ACCATCTTCACGTAACCCTC 400 nM 
R primer GGTCACATAACGCCCC 400 nM 
Probe FAM-ATGAGCACAATAGTTAAAAGCTAACACTGTCAA-TAMRA 200 nM 
Target Primer/probe Sequence (5′-3′) Concentration Thermal cycle Reference 
AiV F primer GTCTCCACHGACACYAAYTGGAC 400 nM 95 °C 10 min; 45 cycles (95 °C 15 s; 60 °C 1 min) Kitajima et al. (2013
R primer GTTGTACATRGCAGCCCAGG 400 nM 
Probe FAM-TTYTCCTTYGTGCGTGC-MGB-NFQ 300 nM 
AstV F primer CCGAGTAGGATCGAGGGT 400 nm 95 °C 15 min; 45 cycles (95 °C 10 s; 55 °C 1 min;72 °C 10 s) Le Cann et al. (2004
R primer GCTTCTGATTAAATCAATTTTAA 400 nm 
Probe FAM-CTTTTCTGTCTCTGTTTAGATTATTTTAATCACC-BHQ1 100 nm 
HAdV F primer GGACGCCTCGGAGTACCTGAG 250 nM 95 °C 15 min; 45 cycles (95 °C 10 s; 55 °C 30 s; 72 °C 15 s) Jothikumar et al. (2005
R primer ACIGTGGGGTTTCTGAACTTGTT 250 nM 
Probe FAM-CTGGTGCAGTTCGCCCGTGCCA-BHQ1 150 nM 
NoV GI F primer CGYTGGATGCGNTTYCATGA 400 nM 95 °C 15 min; 45 cycles (95 °C 10 s; 55 °C 30 s; 72 °C 15 s) Kageyama et al. (2003
R primer CTTAGACGCCATCATCATTYAC 400 nM 
Probe FAM-TGTGGACAGGAGATCGCAATCTC-BHQ1 100 nM 
NoV GII F primer CAAGAGTCAATGTTTAGGTGGATGAG 400 nM 95 °C 15 min; 45 cycles (95 °C 10 s; 55 °C 30 s; 72 °C 15 s) Kageyama et al. (2003) 
R primer TCGACGCCATCTTCATTCACA 400 nM 
Probe FAM-TGGGAGGGCGATCGCAATCT-BHQ1 100 nM 
PMMoV F primer GAGTGGTTTGACCTTAACGTTTGA 900 nM 95 °C 10 min; 45 cycles (95 °C 5 s; 60 °C 1 min) Haramoto et al. (2013
R primer TTGTCGGTTGCAATGCAAGT 900 nM 
Probe FAM-CCTACCGAAGCAAATG-BHQ1 200 nM 
RotV F primer ACCATCTACACATGACCCTC 400 nM 95 °C 15 min; 45 cycles (95 °C 20 s; 60 °C 1 min; 72 °C 15 s) Pang et al. (2012
F primer ACCATCTTCACGTAACCCTC 400 nM 
R primer GGTCACATAACGCCCC 400 nM 
Probe FAM-ATGAGCACAATAGTTAAAAGCTAACACTGTCAA-TAMRA 200 nM 

All statistical analyses were performed using SPSS version 22 (SPSS Inc., Chicago, IL, USA). A Kruskal-Wallis test was performed to determine whether the distribution of inhibition level differs significantly across the three main land use types. A Mann-Whitney U test was performed to determine whether the two extraction kits differed significantly in quantifying the targets or inhibitor removal efficiency, and which had the higher mean rank. A Pearson chi-squared test of independence was done to find the significance of association between kits and the detection frequency of different targets.

RESULTS AND CONCLUSIONS

The TaqMan® reagents were used to find the inhibition level of samples extracted with the Qiagen and Mobio kits. As shown in Table 2, the Kruskal-Wallis test showed no significant difference in inhibition level distribution across land use type categories (P = 0.797). The results illustrated in Figure 1 and Table 2 showed that the inhibition level of environmental samples extracted by the Qiagen and Mobio kits did not differ significantly (P = 0.209).
Table 2

Kruskal-Wallis and Mann-Whitney U inhibition level test results

  Kruskal–Wallis test for different land use types
 
Mann-Whitney U test for different kits
 
Land use type (N) Mean Rank Sig. Kits (N) Mean Rank Sig. 
Abs-Diff in inhibition level Populated (36) 37.32 0.797 Qiagen (37) 40.43 576 0.209 
Farming (14) 34.61 
Low human impact (24) 39.46 Mobio (37) 34.57 
  Kruskal–Wallis test for different land use types
 
Mann-Whitney U test for different kits
 
Land use type (N) Mean Rank Sig. Kits (N) Mean Rank Sig. 
Abs-Diff in inhibition level Populated (36) 37.32 0.797 Qiagen (37) 40.43 576 0.209 
Farming (14) 34.61 
Low human impact (24) 39.46 Mobio (37) 34.57 
Figure 1

Assessment of qPCR inhibitor presence in nucleic acid extracted using TaqMan® internal positive control; y axis shows the difference between Ct value of environmental samples and no template control (NTC).

Figure 1

Assessment of qPCR inhibitor presence in nucleic acid extracted using TaqMan® internal positive control; y axis shows the difference between Ct value of environmental samples and no template control (NTC).

Figure 2 shows the box plot of individual viral target concentrations extracted using the Qiagen and Mobio kits. In general, the geometric mean concentration of different viral targets is higher in samples extracted using the Qiagen kit (218.7 GC/L) than those from the Mobio kit (158.1 GC/L) –Table 3. All targets extracted using the Qiagen kit had higher geometric mean concentrations than those extracted using the Mobio kit, apart from AstV, for which the Mobio kit showed a higher geometric mean (276.7 GC/L) than the Qiagen kit (232.3 GC/L). The kits' quantification efficiency was compared using the Mann-Whitney U test – see Table 4. A significant difference was found for quantification of NoV GI and PMMoV, with a higher mean rank for the Qiagen kit (P< 0.05). Referring again to Table 3, the mean detection percentage of the Qiagen kit (38.5%) was higher than that of the Mobio kit (29.4%) for all the targets except NoV GII, for which the performance of both kits was the same (37.8%).
Table 3

Comparison of detection frequency and geometric mean concentrations of different targets extracted using the Qiagen and Mobio kits

  Detection percentage (%)
 
Geometric mean concentration (GC/L)
 
Qiagen Mobio Qiagen Mobio 
AiV 21.6 16.2 107.6 90.1 
AstV 67.6 56.8 232.3 276.7 
HAdV 10.8 8.1 25.5 24.3 
NoV GI 62.2 29.7 249.5 134.3 
NoV GII 37.8 37.8 394.9 395.6 
PMMoV 70.3 59.5 4,097.9 890.9 
RotV 5.4 2.7 93.1 85.9 
General 38.5 29.4 218.7 158.1 
  Detection percentage (%)
 
Geometric mean concentration (GC/L)
 
Qiagen Mobio Qiagen Mobio 
AiV 21.6 16.2 107.6 90.1 
AstV 67.6 56.8 232.3 276.7 
HAdV 10.8 8.1 25.5 24.3 
NoV GI 62.2 29.7 249.5 134.3 
NoV GII 37.8 37.8 394.9 395.6 
PMMoV 70.3 59.5 4,097.9 890.9 
RotV 5.4 2.7 93.1 85.9 
General 38.5 29.4 218.7 158.1 
Table 4

Mann-Whitney U test on the concentration of different targets in the environmental samples extracted with Qiagen and Mobio kits

Targets (GC/L) Kits Mean Rank Sig. 
AiV Qiagen 37 38.77 637.50 0.209 
Mobio 37 36.23 
Total 74  
AstV Qiagen 37 35.78 666.00 0.243 
Mobio 37 39.22 
Total 74  
HAdV Qiagen 37 37.95 668.00 0.443 
Mobio 37 37.05 
Total 74  
NoV GI Qiagen 37 43.78 452.00 0.003 
Mobio 37 31.22 
Total 74  
NoV GII Qiagen 37 37.31 677.50 0.467 
Mobio 37 37.69 
Total 74  
PMMoV Qiagen 37 41.91 521.50 0.036 
Mobio 37 33.09 
Total 74  
RotV Qiagen 37 38.00 666.00 0.373 
Mobio 37 37.00 
Total 74  
General Qiagen 259 268.89 31108.00 0.061 
Mobio 259 250.11 
Total 518  
Targets (GC/L) Kits Mean Rank Sig. 
AiV Qiagen 37 38.77 637.50 0.209 
Mobio 37 36.23 
Total 74  
AstV Qiagen 37 35.78 666.00 0.243 
Mobio 37 39.22 
Total 74  
HAdV Qiagen 37 37.95 668.00 0.443 
Mobio 37 37.05 
Total 74  
NoV GI Qiagen 37 43.78 452.00 0.003 
Mobio 37 31.22 
Total 74  
NoV GII Qiagen 37 37.31 677.50 0.467 
Mobio 37 37.69 
Total 74  
PMMoV Qiagen 37 41.91 521.50 0.036 
Mobio 37 33.09 
Total 74  
RotV Qiagen 37 38.00 666.00 0.373 
Mobio 37 37.00 
Total 74  
General Qiagen 259 268.89 31108.00 0.061 
Mobio 259 250.11 
Total 518  
Figure 2

Concentration of viral targets in plasmid copy number (PCN)/L by qPCR after DNA/RNA extraction using the Qiagen and Mobio kits.

Figure 2

Concentration of viral targets in plasmid copy number (PCN)/L by qPCR after DNA/RNA extraction using the Qiagen and Mobio kits.

The dependence of the kits on the detection frequency of different viral targets was assessed using Pearson chi-squared test of independence. Generally, there was a strong association between the extraction kit and detection frequency for all targets (χ2(1) = 4.85, P = 0.017) – Table 5. The Qiagen kit was the more likely to have a higher positive detection for all targets. The association between the detection of NoV GI and extraction was statistically significant (χ2(1) = 7.84, P = 0.005).

Table 5

Pearson chi-squared test of independence to assess the association between extraction kit and detection frequency of different targets

  Pearson Chi-squared df Sig. 
AiV 0.35 0.384 
AstV 0.92 0.236 
HAdV 0.16 0.500 
NoV GI 7.84 0.005 
NoV GII 0.00 0.595 
PMMoV 0.95 0.233 
RotV 0.345 0.500 
General 4.85 0.017 
  Pearson Chi-squared df Sig. 
AiV 0.35 0.384 
AstV 0.92 0.236 
HAdV 0.16 0.500 
NoV GI 7.84 0.005 
NoV GII 0.00 0.595 
PMMoV 0.95 0.233 
RotV 0.345 0.500 
General 4.85 0.017 

The results suggest that the Qiagen kit might be a more effective means of providing concentrated viral RNA/DNA for molecular analysis of environmental samples in tropical climate zones. However, the removal efficiency of inhibitors in the Qiagen kit was comparable to that of the Mobio kit.

CONCLUSIONS

Rapid and accurate detection and quantification of emerging microbial contaminants in environmental water samples is a critical step for routine water quality monitoring. The finding is crucial for future quantitative microbial risk assessment analysis of surface water for recreational activities. A good extraction kit should provide users with consistent DNA/RNA recovery for a variety of viral targets in different environmental matrices, while being efficient in removing the inhibitory substances present in environmental samples. In this study, Qiagen and Mobio kits with their integrated inhibitor removal technologies were shown to have similar efficiency in removing inhibitors from environmental matrices (Table 2 and Figure 1). Although the kits showed similar results in inhibition removal, the Qiagen kit showed higher performance in quantification of AiV, HAdV, NoV GI, NoV GII, PMMoV and RotV (Figure 2, Tables 3 and 4), while the Mobio kit performed better quantifying AstV but not at a statistically significant level (P = 0.243) –Table 4.

The Qiagen kit also performed better than the Mobio kit in detecting AiV, AstV, HAdV, NoV GI, PMMoV and RotV, while having a similar detection frequency for NoV GIITable 3. On the basis of the study's findings, use of the Qiagen kit is recommended for molecular analysis of environmental samples in routine water quality monitoring in tropical climate zones.

ACKNOWLEDGEMENTS

This research was funded by Singapore National Research Foundation (NRF) under its Research Innovation and Enterprise (RIE) plan for water domain (Ref: 1301-IRIS-37 [IDD 90301/1/65]). We would like to thank National University of Singapore for supporting this research.

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