Detection and quantification of hepatitis A virus and norovirus in Spanish authorized shellfish harvesting areas

International Journal of Food Microbiology 193 (2015) 43–50

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Detection and quantification of hepatitis A virus and norovirus in Spanish authorized shellfish harvesting areas David Polo, Miguel F. Varela, Jesús L. Romalde ⁎ Departamento de Microbiología y Parasitología, CIBUS-Facultad de Biología, Universidad de Santiago de Compostela, Santiago de Compostela 15782, Spain

a r t i c l e

i n f o

Article history: Received 15 July 2014 Received in revised form 17 September 2014 Accepted 4 October 2014 Available online 13 October 2014 Keywords: Shellfish Hepatitis A virus Norovirus RT-qPCR Survey

a b s t r a c t An 18-month survey was conducted in ten class “B” harvesting areas from two Galician Rias (NW of Spain), the most important bivalve production area in Europe, to determine the prevalence of hepatitis A virus (HAV) and human norovirus (NoV), including genogroups I (GI) and II (GII). Quantification was performed by reverse transcription real-time PCR (RT-qPCR), according to the recently developed standard method ISO/TS 15216-1:2013. Four bivalve species were studied, including wild and cultured mussels (Mytilus galloprovincialis), clams (Venerupis philippinarum and Venerupis decussata) and cockles (Cerastoderma edule). Overall, 55.4% of samples were contaminated by at least one of the studied viruses, being detected the simultaneous presence of two or three viruses in 11.3% of the cases. NoV GI was the most prevalent virus (32.1%), followed by NoV GII (25.6%) and HAV (10.1%). Cultured mussels showed the highest percentage of positive samples (61.4%), followed by cockles (59.4%), wild mussels (54.3%) and clams (38.7%). Viral contamination levels for most of the positive samples ranged from 102 to 103 RNA copies/g of digestive tissue (RNAc/g DT). The presence of viral contamination was statistically higher (P b 0.0001) in warm months (April to September) than in cold months (October to March). The data presented here may contribute to the development of more representative sampling strategies, in monitoring and management of shellfish growing areas as well as being useful in a future scenario in which viral critical values are adopted in legislation. © 2014 Elsevier B.V. All rights reserved.

1. Introduction Norovirus (NoV) (family Caliciviridae) is considered the major cause of acute sporadic and epidemic human gastroenteritis worldwide and has emerged until becoming the leading etiological agent of foodborne diseases (Atmar and Estes, 2006; Koopmans and Duizer, 2004; Rodriguez-Lazaro et al., 2012; Scallan et al., 2011). Hepatitis A virus (HAV) (family Picornaviridae) is less common in countries with a high standard of hygiene, although it possesses the risk of leading to more severe disease outcomes (Hollinger and Emerson, 2001). Both pathogens are responsible for most of the shellfish-borne viral outbreaks attending to the number of cases and the severity of the illness (Koopmans and Duizer, 2004). As pathogens with fecal-oral route of transmission, HAV, NoV and other enteric viruses are shed in high numbers in the feces of infected individuals (Atmar and Estes, 2006). Current wastewater treatments fail to ensure the complete removal of viral pathogens (Da Silva et al., 2007) that can be discharged into fresh, marine and estuarine waters through different sources and, therefore, contaminate shellfish growing waters (Iwai et al., 2009; Maalouf et al., 2010). The filter-feeding nature

⁎ Corresponding author. Tel.: +34 881816908; fax: +34 881816966. E-mail address: [email protected] (J.L. Romalde).

http://dx.doi.org/10.1016/j.ijfoodmicro.2014.10.007 0168-1605/© 2014 Elsevier B.V. All rights reserved.

of bivalves and their traditional way of consumption (often raw or slightly cooked) make the shellfish a high-risk food group and one of the most common vehicles of food-borne viral illnesses (Lees, 2000). Currently, shellfish risk assessment and the management of the harvesting areas continue to rely uniquely on bacteriological standards, like Escherichia coli (Anon, 2004), despite the proven fact of being unreliable tools to indicate the viral presence in harvesting areas or to control the efficacy of processes like shellfish depuration (Chalmers and McMillan, 1995; Richards et al., 2010; Romalde et al., 2002). In this sense, from a virological point of view, shellfish safety continues to be a sanitary challenge. Strains of human NoV and HAV do not grow, or grow poorly, in vitro, and their detection in food matrices relies on molecular techniques. The standardization of molecular methods is necessary before their adoption within regulatory frameworks and their routine implementation in food analysis. A standard method for virus detection and quantification in foodstuffs including shellfish (ISO/TS 15216-1:2013, 2013) is currently available and the adoption of viral standards into European Union legislation is being considered. Galicia (NW Spain) has over 1,660 km (1,030 mi) of coastline, including offshore islands, with a particular topography, characterized by the presence of many-like inlets, called rías. These estuaries have a high primary productivity and are specially suited to shellfish production. Spain is by far the greatest mussel producer in Europe and the

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D. Polo et al. / International Journal of Food Microbiology 193 (2015) 43–50

second in the world (annual production around 300,000 metric tons) (FAO, 2012), and Galicia (NW coast) represents 95% of the total Spanish production. Moreover, other captures from natural beds like clam, cockle and oyster species are also important. Viral surveillance of shellfish based on RT-qPCR data contributes to reflect the state of the circulating viruses in harvesting areas and population, as well as the associated health risk to the consumers. In addition, it provides valuable information for the establishment of cutoff values in a future viral legislation and their impact in the aquaculture sector. Notwithstanding, only few long-term surveys using RT-qPCR technology have been carried out in the EU (Croci et al., 2007; Lowther et al., 2012b; Rajko-Nenow et al., 2012; Suffredini et al., 2014; Vilariño et al., 2009) and other geographical areas (Benabbes et al., 2013; Seo et al., 2014). This study represents an 18-month systematic survey of HAV and NoV contamination in mussels, clams and cockles from two rías of Galicia following the principles outlined in the new ISO technical specification. 2. Materials and methods 2.1. Shellfish sampling A total of 168 samples from two different Galician rías, one in the North and the other in the South (Fig. 1), were analyzed. The samples included wild and raft cultured Mediterranean mussels (Mytilus galloprovincialis), Manila and carpet-shell clams (Venerupis philippinarum and Venerupis decussata, respectively) and cockles (Cerastoderma edule), collected from 10 harvesting areas (N1-N5 and S1-S5). EU regulations (Anon, 2004) classified shellfish harvesting areas into A, B or C category on the basis of E. coli levels as follows: A (≤ 230 cfu E. coli/100 g shellfish), B (230–4,600 cfu E. coli/100 g shellfish), C (4,600–46,000 cfu E. coli/100 g shellfish). All the areas studied here were classified as “B.” Shellfish samples were collected monthly during 18 months, from January 2011 to June 2012. Each sample was composed of 10 (mussels) or 20 (clams and cockles) individuals. Molluscs were kept at 4 °C during shipment and analyzed within the same day. 2.2. Viral recovery and RNA extraction Viral recovery from shellfish was carried out according to the ISO/TS 15216-1:2013 with minor modifications. Briefly, mussels were shucked, and the digestive tissues (DT) were removed by dissection and pooled to get a final weight between 2 and 3 g. Known amounts of mengovirus clone (vMC0), kindly provided by Dr. Albert Bosch (University of Barcelona, Spain), were spiked to each sample (10 μl of mengovirus stock, 105 plaque-forming units (pfu)/ml) as an independent nucleic acid extraction efficiency control (Costafreda et al., 2006). Tissues were homogenized with one volume (1:1 w/v) in peptone water (0.1%; pH 7.4). Then sample homogenates were strongly shaken for 1 h and centrifuged at 1,000 ×g for 5 min, recovering the supernatant. Viral RNA was extracted in duplicate from each homogenate using Nucleospin® RNA Virus Kit (Macherey-Nagel; Germany), from a sample volume of 150 μl according to the manufacturer's protocol. The RNA was eluted in RNAsa-free sterile water and stored at −80 °C.

was classified as valid (N 5%) or invalid (b5%). The presence of RT-PCR inhibitors and the determination of the RT-qPCR efficiency were tested by means of the external controls (EC). Briefly, 2.5 μl of EC, containing approximately 103 copies of the target sequence for HAV, NoV GI or NoV GII, was mixed with 2.5 μl of each extracted RNA and the Ct values of these reactions were compared with the Ct value of the EC mixed only with RNA-free sterile water. The efficiency was classified as valid (N25%) or invalid (b 25%). Following the CEN/ISO method, samples with a b5% extraction efficiency or a b25% RT-qPCR efficiency were re-extracted and tested again. Primer sets and probes used for Mengovirus and HAV were as follows: Mengo 110, Mengo 209 and Mengo 147 (probe) (Pintó et al., 2009) and HAV68, HAV240 and HAV150 (probe) (Costafreda et al., 2006), respectively. For NoV GI, QNIF4 (Da Silva et al., 2007), NV1LCR and NV1Lpr (probe) (Svraka et al., 2007) and for NoV GII, QNIF2d (Loisy et al., 2005), COG2R (Kageyama et al., 2003) and QNIFS (probe) (Loisy et al., 2005) were employed as previously described but with black hole quencher 1 (BHQ-1) for the probes. Amplification conditions were reverse transcription at 55 °C for 1 h, denaturation at 95 °C for 5 min, followed by 45 cycles of amplification with a denaturation at 95 °C for 15 s, annealing at 60 °C for 1 min and extension at 65 °C for 1 min. Viral RNA was tested undiluted and at ten-fold dilution to reduce the effect of potential RT-PCR inhibitors. Negative controls containing no nucleic acid as well as positive controls were introduced in each run. Quantification was estimated by standard curves constructed with serial dilutions of RNA in the case of HAV and RNA transcripts for NoV GI and GII, plotting the number of genome copies against the Ct. Results were expressed as number of RNA viral genome copies per gram of digestive tissue (RNA/g DT). Values given are the average of the results obtained for each pair of two subsamples. The limit of quantification was 102 RNA/g DT. The limit of detection was 101 RNA/g DT. 2.4. Statistics ANOVA analysis was performed to compare the results of the extraction and RT-qPCR efficiencies obtained among the different months and viral species of the study. Chi-square test was carried out between viral frequencies of detection for each sampling point. Fisher's exact test was performed to determine the existence of differences in the viral prevalence for the warm and cold months. Spearman's rank correlation analysis was employed to correlate the results of positive samples pooled by month and climatic data (rainfall and water temperature). Meteorological data were obtained from Meteogalicia (the regional meteorological agency for Galicia, Spain) and INTECMAR (Technological Institute for the Control of the Marine environment of Galicia). These correlations were performed with the total accumulated rain of the previous month, assuming that each molluscan harvesting area was mainly affected by the rains of the preceding month. All statistical analyses were performed using the statistical package IBM SPSS v20.0.0 software. 3. Results 3.1. Viral detection and quantification

2.3. RT-qPCR The RT-qPCR for HAV and NoV (GI and GII) was performed on an Mx3005p QPCR System (Stratagene; USA) thermocycler. Platinum® Quantitative RT-PCR ThermoscriptTM One-step System kit (Invitrogen; France) was used in a 25 μl total volume, using 5 μl of extracted RNA. Quantification was also carried out following the principles outlined in the CEN/ISO technical specification. The extraction efficiency was determined by comparing the Cycle threshold (Ct) value of the Mengovirus-positive control with the Ct value of each sample for Mengovirus (Costafreda et al., 2006) and

According to the classification mentioned above, all samples rendered valid extraction efficiencies (N 5%); 106 samples (63.1%) rendered N10% extraction efficiencies and 62 samples (36.9%) showed efficiencies between 5% and 10%. The average RT-qPCR efficiencies for each virus were 62.1% for HAV, 72.4% for NoV GI and 78.0% for NoV GII. No significant differences in extraction and/or RT-qPCR efficiencies between different viruses, molluscs or seasons of the year were observed. With regard to the overall viral detection, 55.4% of the samples (93 out of 168) showed contamination with at least one of the virus

D. Polo et al. / International Journal of Food Microbiology 193 (2015) 43–50

45

Fig. 1. Sampling points location. N, sampling points in the North area; S, sampling points on the South area. 1 and 2, raft cultured mussels (Mytilus galloprovincialis); 3, wild mussels (M. galloprovincialis), 4 clams (Venerupis philippinarum in the North and Venerupis decussata in the South); 5 cockles (Cerastoderma edule).

analyzed. All the viruses studied were detected in all the molluscan species, both in the North and South areas. NoV GI was the most prevalent virus (32.1% of the total samples analyzed) followed by NoV GII (25.6%) and HAV (10.1%). Mixed contaminations (contamination with more than one virus) appeared in 19 out of 168 samples (11.3%)

(Table 1), including 10 samples contaminated simultaneously with HAV and NoV GII, 7 samples with NoV GI and GII and 2 samples with HAV, NoV GI and GII (Table 2). Similar percentages of contamination were found in North and South, with 55.5% and 55.1% of contaminated samples, respectively.

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D. Polo et al. / International Journal of Food Microbiology 193 (2015) 43–50

Table 1 Number of positive samples detected for the total samples analyzed (Total), warm (April–September) and cold (October–March) months, sampling zone (north and south) and mollusc species (raft culture (C) and wild (W) mussels, clams and cockles). The correspondent percentages (with regard to the total samples analyzed in each group) are reported in brackets. N, number of samples analyzed; N+, number of positive samples; HAV+, NoV GI + and NoV GII+, number of positive samples for each virus; Co, number of simultaneous detection with more than one virus in the same sample. Positive results Total

N N+ HAV + NoV GI + NoV GII + Co

Season

168 93 (55.4) 17 (10.1) 54 (32.1) 43 (25.6) 19 (11.3)

Area

Molluscan species

Warm

Cold

North

South

Mussel (C)

Mussel (W)

Clam

Cockle

81 58 (71.6) 14 (17.3) 36 (44.4) 27 (33.3) 17 (21.0)

87 35 (40.2) 3 (3.4) 18 (20.7) 16 (18.4) 2 (2.3)

90 50 (55.5) 11 (12.2) 27 (30.0) 23 (25.5) 10 (11.1)

78 43 (55.1) 6 (7.7) 27 (34.6) 20 (25.6) 9 (11.5)

70 43 (61.4) 7 (10.0) 25 (35.7) 22 (31.4) 8 (11.4)

35 19 (54.3) 4 (11.4) 10 (28.6) 9 (25.7) 4 (11.4)

31 12 (38.7) 2 (6.5) 7 (22.6) 5 (16.1) 2 (6.5)

32 19 (59.4) 4 (12.5) 12 (37.5) 7 (21.9) 4 (12.5)

NoV GI was the more prevalent virus, detected in 30.0% (North) and 34.6% (South) of the samples analyzed, followed by NoV GII (25.5% and 25.6%, respectively) and HAV (12.2% and 7.7%). Mixed contaminations were found in 11.1% (North) and 11.5% (South) of the total samples analyzed (Tables 1 and 2). All sampling points on the North (N1–N5) and South (S1–S5) showed similar frequencies of viral detection without significant differences (Table 2). Cultured mussels showed the highest percentage of positive samples (61.4%), followed by cockles (59.4%), wild mussels (54.3%) and clams (38.7%) being NoV GI the most prevalent virus in all the molluscs (Table 1). With regard to mixed contaminations, cockles showed the highest percentage (12.5%) of samples contaminated with more than one virus, followed by cultured (11.4%) and wild mussels (11.4%) and clams (6.5) (Table 1). Overall quantification levels ranged from 102 to 106 copies/g DT; however, most of the positive samples ranged between 102 and 103 RNAc/g DT (Table 3). The distribution of positive results in quantity ranges for each virus is represented in Fig. 2. HAV yielded the highest average viral levels (9.3 × 103 RNAc/g DT), followed by NoV GII (2.5 × 103 RNAc/g DT) and NoV GI (2.3 × 103 RNAc/g DT). By molluscan species, wild mussels showed the highest average values (6.5 × 103 RNAc/g DT) followed by cultured mussels (4.2 × 103 RNAc/g DT), clams (3.5 × 103 RNAc/g DT) and cockles (2.1 × 103 RNAc/g DT).

HAV was detected only in 4 months, between March and June of 2012 (Fig. 3). Mixed contaminations were detected in 5 out of 18 months, but mainly between September and November 2011 (all of them with the presence of NoV GI and GII) and between May and June 2012 (most of them with the presence of HAV and GII) (Table 2). The average quantification levels and the monthly percentage of positive samples for each virus along the study period are shown in Fig. 3. Fisher's exact test showed highly significant differences for the virus prevalence between warm months (April to September) and the cold months (October to March), being more prevalent in warm months (P b 0.0001). A moderate, but not significant, positive correlation between the number of positive samples and the average rainfalls was observed. No correlation was found between the number of positive samples and water temperature or salinity.

4. Discussion This study includes a systematic surveillance of the viral prevalence in the most important commercial molluscs from ten commercial harvesting areas in Galicia (NW Spain), the first shellfish production area in Europe. To date, this is the widest study in Spain on viral contamination of bivalve shellfish attending to the duration of the study and the viral and molluscan species analyzed. It is important to take into account that RT-qPCR does not discriminate between infectious and noninfectious viral particles. However, nowadays, this technique is the most suitable to detect the viral presence in shellfish samples. Some approaches like long-range or binding RT-qPCR (Li et al., 2014; Wolf et al., 2009) or the use of propidium monoazide RT-qPCR (Sánchez et al., 2012) have been recently suggested as techniques to distinguish between infectious and non-infectious viral particles. However, such techniques need to be optimized and standardized before being implemented and used in systematic viral surveys.

3.2. Seasonality of viral detection Results obtained for the ten sampling points were grouped to evaluate their monthly prevalence. NoV GI was the most prevalent virus, detected in 13 out of 18 months, but principally between February and May 2011 and between August and September 2012 (these periods represents 46.3% and 31.5% of the total detections, respectively). NoV GII was detected in 8 months, in January and between September and December in 2011, and between April and June in 2012 (48.8% and 51.2% of the total detections respectively).

Table 2 Viral species detected along the study period in each harvesting area on the north (N1–N5) and south (S1–S5). The correspondent classification of each area according to EU regulations is reported in brackets. 1, 2, raft cultured mussels; 3, wild mussels; 4, clams; 5, cockles. ■, NoV GI; ❖, NoV GII; ⊙, HAV. *Fd, frequency of virus detection, defined as number of samples with viral presence/total number of samples. ns, not sampled. Harvest. area

N1 (B) N2 (B) N3 (B) N4 (B) N5 (B) S1 (B) S2 (B) S3 (B) S4 (B) S5 (B) Fd

2011

2012

Jan

Feb

Mar

Apr

❖ ❖

■

■ ■ ■ ■ ■ ■ ■ ■

■ ■ ■ ■ ■ ns ns ns ns ns 1

■ ■ ■ ■

❖ 0.5

■

0.4

■ 0.9

May

Jun

Jul

■ ■ ■ ■ ■ ■ ■ ■ 0.7

■

■ 0.2

0.1

Aug

Sep

Oct

■ ■ ■ ■ ■ ■ ■ ■ ■

■ ■ ■ ❖ ■❖ ■❖ ❖ ■❖ ■ ■ 1

❖ ❖

0.9

Nov

Dec

❖ ❖

❖ ❖

■❖ ❖ ■❖ ❖

❖ ❖

0.2

Feb

Mar

0.4

Apr

May

Jun

Fdb

❖ ❖ ⊙ ❖ ❖⊙ ⊙ ns ns 0.7

❖⊙ ❖⊙ ❖⊙ ❖⊙ ❖⊙ ❖ ■❖ ❖ ns ns 1

❖⊙ ■❖⊙ ❖⊙ ❖ ❖ ❖ ■❖⊙ ❖⊙ ❖⊙ ■❖ 1

0.55 0.61 0.61 0.38 0.61 0.64 0.64 0.47 0.38 0.57

⊙

⊙

ns ❖ 0.7

Jan

ns ns 0

■ 0.1

⊙ 0.3

Table 3 Viral quantification for each harvesting area in the North (N) and South (S). 1 and 2, raft cultured mussels; 3, wild mussels; 4, clams; 5, cockles. −, negative sample; +, positive sample but outside of the quantification range; Nt, not tested. Virus

NoV GI

NoV GII

N1 N2 N3 N4 N5 S1 S2 S3 S4 S5 N1 N2 N3 N4 N5 S1 S2 S3 S4 S5 N1 N2 N3 N4 N5 S1 S2 S3 S4 S5

2011

2012

Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

Jan

Feb

Mar

Apr

May

– – – – – – – – – – – – – – – 1.6 1.9 – – – 2.8 5.6 – – – – – – – +

– – – – – – – – – – 1.5 × 103 – 1.2 × 102 – 2.0 × 102 – + – – – – – – – – – – – – –

– – – – – – – – – – 9.1 1.4 1.1 3.0 1.2 9.3 2.5 4.6 – 3.7 – – – – – – – – – –

– – – – – Nt Nt Nt Nt Nt 1.3 8.2 2.5 7.7 9.1 NS NS NS NS NS – – – – – Nt Nt Nt Nt Nt

– – – – – – – – – – – – 1.0 – 1.1 + 4.5 3.5 1.3 5.3 – – – – – – – – – –

– – – – – – – – – – 6.6 × 102 – – – – – – – + – – – – – – – – – – –

– – – – – – – – – – – – – – – 9.1 × 102 – – – – – – – – – – – – – –

– – – – – – – – – – 1.8 3.0 6.2 5.0 6.7 1.2 1.1 2.0 5.0 – – – – – – – – – – –

– – – – – – – – – – 1.6 4.0 5.9 – 4.7 5.8 – + 2.6 1.9 – – – 1.4 2.8 + 8.0 9.8 – –

– – – – – – – – Nt – – – – – – – – – Nt – 3.9 × 104 1.7 × 102 – – – – – – Nt –

– – – – – – – – – – – – – – + – 8.1 – – – – 2.0 1.5 – 1.2 1.5 1.1 4.1 – 3.0

– – – – – – – – – – – – – – – – – – – – – 2.0 × 102 2.9 × 102 – – 1.1 × 103 + – – –

– – – – – – – – Nt Nt – – – – – – – – Nt Nt – – – – – – – – Nt Nt

– – – – – – – – – – – – – – – – – – – 2.4 × 104 – – – – – – – – – –

– 1.5 × 103 – – 2.1 × 104 – – – – 4.3 × 103 – – – – – – – – – – – – – – – – – – – –

– – – – 1.5 – 7.9 3.5 Nt Nt – – – – – – – – Nt Nt – – 4.2 5.5 – 8.8 5.9 – Nt Nt

1.2 1.4 2.5 5.9 8.5 – – – Nt Nt – – – – – – + – Nt Nt 3.2 3.6 4.6 2.9 3.0 1.8 2.8 2.7 Nt Nt

× 102 × 104

× 104 × 102

× × × × × × × ×

102 103 103 102 103 102 103 104

× 103

× × × × ×

105 104 104 104 104

× 104 × 103 × × × ×

104 103 102 102

× × × × × × × × ×

103 103 103 103 102 103 103 102 103

× 103 × 102 × 102 × 103 × 102

× 103 × 103

× 103 × 102 × 102 × 102

× 103

× 102 × 103 × × × ×

102 103 103 102

× 103

× 102 × 103 × 104

× 104 × 103 × 102 × 102

× × × × ×

× × × × × × × ×

Jun 105 104 103 102 103

103 104 103 103 104 104 103 103

5.5 1.4 3.7 – – – 1.2 4.3 7.3 – – 6.4 – – – – + – – 5.4 8.0 5.9 5.3 1.8 4.1 2.5 6.6 4.4 1.7 3.5

× 102 × 104 × 103

× 105 × 106 × 104

× 102

× × × × × × × × × × ×

102 104 102 103 104 103 104 102 104 103 103

D. Polo et al. / International Journal of Food Microbiology 193 (2015) 43–50

HAV

Area

47

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D. Polo et al. / International Journal of Food Microbiology 193 (2015) 43–50

Fig. 2. Overall distribution of positive results in quantity ranges for each virus. +, positive samples out of the quantification limit (b100 RNA copies/g DT).

Results obtained for extraction and RT-qPCR efficiencies showed no significant differences between virus, bivalve species or season of the year, which confer higher robustness to the data for it subsequent analysis and interpretation. A high viral prevalence was observed in the production areas studied, with 55.4% of the samples being contaminated with at least one virus. Other studies, using RT-qPCR, reported similar prevalence results (Lowther et al., 2012b; Suffredini et al., 2012, 2014; Vilariño et al., 2009). In the present study, NoV was significantly more frequently detected than HAV. These data are in accordance with other studies in the south of Europe (Croci et al., 2007; Mesquita et al., 2011; Suffredini et al., 2012, 2014) and other countries (Benabbes et al., 2013; Seo et al., 2014). However, the sudden appearance of HAV in the spring of 2012 could indicate an outbreak and/or the emergence

Fig. 3. Monthly geometric mean quantification numbers (bars) and percentage of positive samples (dotted lines) for HAV (A), NoV GI (B) and NoV GII (C) along the studied period.

of new strains of HAV, although further studies are needed to confirm this hypothesis. The improvement of sanitary conditions and hygiene practices in developed countries has changed the epidemiological patterns of hepatitis A, being registered an increased susceptibility to this pathogen in the adult population (Koopmans and Duizer, 2004). The circulation of newly emerging strains of HAV in Spain that likely escaped the protective effect of available vaccines and the critical role of shellfish global trade in their introduction was recently reported (Pérez-Sautu et al., 2011a,b; Polo et al., 2010). These facts, besides the capacity of shellfish to reflect the prevalence shift of viral strains circulating in human population (Manso and Romalde, 2013), could explain certain sudden prevalence peaks of HAV in non-endemic areas. The extended bibliography about the lack of correlation between the current classifications based on E. coli and the presence of viruses (Romalde et al., 2002; Svraka et al., 2007; Vilariño et al., 2009) indicates that the best approach is to detect the virus itself, mainly for HAV and NoV. However, the use of a particular mollusc species as indicator for the other species in close harvesting areas could be a reasonable and effective way to minimize costs and have a faster management of the contamination. Among molluscan species, mussels have a high filtration rate (Bayne et al., 1976), even higher than oysters at low temperatures (Comeau et al., 2008). In this work, mussels showed the highest detection frequency along the studied period and the highest quantification levels. Similar results were previously reported (Suffredini et al., 2012, 2014), showing the potential of mussels as indicator species for preharvest viral surveillance. Nevertheless, in a post-harvest surveillance scenario, like depuration process, other species more sensitive to environmental stress like clams (Bayne et al., 1976) could be more adequate as indicator of viral absence or to control the efficacy of the process (Suffredini et al., 2014; Polo et al., 2014a). We recently reported slower depuration rates and higher contamination levels after 7 days of commercial depuration in clams than in mussels for the elimination of F + RNA phages, HAV and NoV (Polo et al., 2014b). NoV GI was the most detected virus, despite the fact that NoV GII is the main genogroup circulating in human population (Lopman et al., 2004). These results contrasted to those obtained by Vilariño et al. (2009), who in a study circumscribed to the same South area, reported a higher prevalence of NoV GII over NoV GI and would indicate an epidemiological drift in the NW Spain. The higher occurrence of NoV GI in shellfish and shellfish related outbreaks was also reported in previous studies (Jothikumar et al., 2005; Le Guyader et al., 2006, 2009; Lowther et al., 2012b; Polo et al., 2010). The epidemiological data suggest that GI strains are more related to food/water-borne outbreaks, while GII strains are more frequently found in feces and healthcare settings with person-to-person transmission (Lopman et al., 2004; Maalouf et al., 2010; Verhoef et al., 2010). These differences in genogroup frequency distribution (genogroup profiles) could even be used to differentiate transmission modes and food-borne outbreaks related to early contamination in the food chain (i.e., primary production) from those related to later contamination caused by food handlers (Verhoef et al., 2010). Previous studies reported differences between genogroups in the bioaccumulation efficiencies of the molluscs, related to the expression in shellfish of specific glycan ligands similar to human histo-blood group antigens (HBGAs) (Le Guyader et al., 2006; Maalouf et al., 2010; Tian et al., 2007). NoV GI strains showed higher efficiency to be concentrated in oysters than GII strains (Maalouf et al., 2010). Moreover, plankton was recently found to be a source of high NoV concentrations, being NoV GI more frequently detected and at higher concentrations than GII (Gentry et al., 2009). This suggests the existence of a plankton-based reservoir for these viruses in polluted waters and could indicate a link, especially for GI, with their accumulation by shellfish. Shellfish can often contain a cocktail of viruses. The proportion of mixed contaminations reported here (11.3% of the total samples) may result from sewage influx containing multiple viruses. These simultaneous infections, besides being a way to facilitate the emergence of

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new recombinants strains, could lead to a higher severity of symptoms, two episodes of gastroenteritis or gastroenteritis followed by hepatitis in the affected people (Lees, 2000). In addition, mixed contamination in shellfish is an important issue to take into account for the application of a viral quantitative criterion since there are evidences of a correlation between higher viral levels in shellfish and the increase in the risk of infection by HAV and NoV (Lowther et al., 2012a; Pintó et al., 2009). In this sense, the criterion for NoV should be based on their total load (GI plus GII) (EFSA Panel on Biological Hazards, BIOHAZ, 2012). In the case of HAV, its relatively low prevalence and the high risk associated with the infection might justify the adoption of a presence/absence criterion (Suffredini et al., 2014). Different monthly prevalence was seen between viruses. Although many studies reported a seasonal distribution of NoV detections in shellfish, water and sewage samples mainly in the cold months (Iwai et al., 2009; Le Guyader et al., 2000; Lowther et al., 2012b; Suffredini et al., 2012), the results presented here showed important increases in NoV detection in spring were detected. Atypical spring and summer peaks of NoV outbreaks have been reported concurrently with the emergence of novel genetic variants (Lopman et al., 2004). Wang and Deng (2012) also reported that oyster NoV outbreaks displays strong seasonality with the outbreak peak commonly in December–March in the United States and April–May in Europe. Similar findings were also reported by Verhoef et al. (2008). In addition, several factors could influence or modulate the monthly prevalence reported from different countries because of the latitude (and their associated climatologic and oceanographic characteristic) to other more particular features of the area itself, such as increases in tourism and/or consuming rates, seasonality of the ingestion, etc. Maalouf et al. (2010) reported a seasonal effect on the expression of these specific ligands, most visibly for the GI.1 strain, with a peak in late winter and spring, a period when GI strains are regularly involved in oyster-related outbreaks. These observations may explain some of the distinct epidemiological features of strains from different genogroups and, at least in part, the results obtained in this work. Bivalve shellfish harvested in Galician rías are typically associated with streams and rivers draining large upland watersheds, which hydrodynamic levels are intimately linked to rainfalls. As it is widely recognized, rainfall is one of the most important environmental parameters that can influence the viral detection in estuaries and shellfish due to the runoffs or sewage treatment plant failures. Here, a correspondence between the number of detections and the rainfalls was observed but not statistically significant. Increases in rainfalls are more often associated to cold months; however, in Galicia, heavy rainfalls are typical in spring and autumn. In fact, the total of rainfalls for cold and warm months along the study period were 158.4 L/m2 and 153.1 L/m2, respectively. This could favor increases in viral detection in warm months. In addition, Galician rías also have particular oceanographic conditions with spring–summer upwelling events, which result in the high marine productivity of this region (Alvarez et al., 2012). These events produce plankton blooms that could act as a reservoir for the adsorbed viruses, besides maintaining the water temperature near the winter levels. The attachment of viruses to plankton particles by electrostatic interactions (similar to the viral adsorption to sediments) could favor the viral scape to the photo-inactivation and consequently their accumulation in shellfish (Gentry et al., 2009). Viral contamination in different molluscs and seasons must be taken into account for the classification of shellfish harvesting areas and can contribute to the development of more representative sampling strategies, in monitoring and managing the growing areas as well as being useful in a future scenario in which viral critical values are adopted in legislation. Acknowledgements This work was supported in part by grant 10MMA200010PR from Xunta de Galicia (Spain).

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