Development of primer set for the identification of fish species in surimi products using denaturing gradient gel electrophoresis

Accepted Manuscript Development of primer set for the identification of fish species in surimi products using denaturing gradient gel electrophoresis Eun Soo Noh, Yeon Jung Park, Eun Mi Kim, Cheul Min An, Jung Youn Park, KyoungHo Kim, Jung-Hun Song, Jung-Ha Kang PII:

S0956-7135(17)30145-7

DOI:

10.1016/j.foodcont.2017.03.024

Reference:

JFCO 5525

To appear in:

Food Control

Received Date: 27 July 2016 Revised Date:

8 March 2017

Accepted Date: 19 March 2017

Please cite this article as: Noh E.S., Park Y.J., Kim E.M., An C.M., Park J.Y., Kim K.-H., Song J.-H. & Kang J.-H., Development of primer set for the identification of fish species in surimi products using denaturing gradient gel electrophoresis, Food Control (2017), doi: 10.1016/j.foodcont.2017.03.024. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

ACCEPTED MANUSCRIPT 1

Short communication

2

Development of primer set for the identification of fish species in surimi products using

3

denaturing gradient gel electrophoresis

RI PT

4 5

Eun Soo Noh a, Yeon Jung Park a, Eun Mi Kim a, Cheul Min An a, Jung Youn Park a,

6

Kyoung-Ho Kim b, Jung-Hun Song c, Jung-Ha Kang a,*

SC

7

8

a

9

Gijanghaean-ro, Gijang-eup, Gijang-gun, Busan, 46083, Korea

M AN U

Biotechnology Research Division, National Institute of Fisheries Science, 216,

10

b

11

Yongso-ro, Nam-gu, Busan, 48513, Korea

12

c

13

University, 45 Yongso-ro, Nam-gu, Busan, 48513, Korea

Department of Microbiology, College of Natural Science, Pukyong National University, 45

TE D

Faculty of Marine Business and Economics, College of Natural Science, Pukyong National

14

*Corresponding author: Jung-Ha Kang

16

Tel.: +82 51 720 2460

17

Fax.: +82 51 720 2456

18

E-mail address: [email protected] (J.H. Kang).

AC C

EP

15

19 20 21 1

ACCEPTED MANUSCRIPT 1

Abstract The purpose of this study was to develop a DNA marker for the identification of fish

3

species in processed surimi products. A DNA marker was designed based on the

4

mitochondrial cytochrome c oxidase subunit I gene, and fish species in surimi products were

5

identified using a molecular fingerprinting technique, denaturing gradient gel electrophoresis

6

(DGGE); the results were subjected to sequence-based analysis. The DGGE profiles indicated

7

the presence in surimi products of a greater diversity of fish species than reported previously:

8

20 species belonging to 16 genera were identified. Therefore, our method facilitates the

9

simple and rapid detection and identification of fish species in seafood products produced

SC

M AN U

10

RI PT

2

from minced fish.

11

EP

TE D

Keyword: Surimi; denaturing gradient gel electrophoresis; identification; universal primer

AC C

12

2

ACCEPTED MANUSCRIPT 13

1. Introduction Surimi is a processed seafood that is simple to prepare and low cost (Yin & Park, 2014;

15

Yoon, Kim, Kim, Jo, & Cho, 2014). Imitation crab meat, which is made from white fish, such

16

as pollock and cod, is an example of a surimi product. Alaska pollock (Theragra

17

chalcogramma) is a major raw material for surimi (Poowakanjana & Park, 2013). Because of

18

the decline in the Alaska pollock catch rate from 250,000 MT in 2003 to about 125,000 MT

19

in 2010, other fish species have been considered for surimi production (Poowakanjana &

20

Park, 2013). Consequently, Pacific whiting (Merluccius productus), northern blue whiting

21

(Micromesistius poutassou), southern Blue whiting (Micromesistius autralis), atka mackerel

22

(Pleurogrammus azonus), threadfin bream (Nemipterus sp.) and jack mackerel (Trachurus

23

murphy) are now in use as raw materials for production of surimi (Park, 2005).

SC

M AN U

24

RI PT

14

Food companies and consumers are focused on the safety and quality of food, and surimi quality is influenced by the type of fish included (Shiku, Hamaguchi, Benjakul, Visessanguan,

26

& Tanaka, 2004). In general, whiteness and texture of white flesh fish result in a high-quality

27

product with high-protein and low-fat (Martin-Sanchez, Navarro, Perez-Alvarez, & Kuri,

28

2009). Therefore, identifying the fish species in surimi is important for quality assurance.

29

Some companies seek to make a profit by replacing higher-priced with lower-priced fish

30

(Keskin & Atar, 2012). Indeed, substituting expensive fish species with those of lower cost is

31

easy to use for unfair profits and illegal sales such as fraudulent labeling because consumers

32

are unable to identify the fish species in surimi products (Huxley-Jones, Shaw, Fletcher, &

33

Parnell, 2012). A study of fish fillets reported that lower-cost fish species were used in place

34

of those specified on the label (Pinto, et al., 2015). According to the U.S. Food and Drug

35

Administration and the European Union guidelines, the most important factor for seafood

AC C

EP

TE D

25

1

ACCEPTED MANUSCRIPT 36

quality control is identification of the fish species therein (Galal-Khallaf, Ardura, Borrell, &

37

Garcia-Vazquez, 2016; Keskin & Atar, 2012). Rapid and accurate identification of fish

38

species is, therefore, essential. Additionally, the profit motive and rapid increases in demand

39

have resulted in overfishing and in various fish

40

Khallaf, et al., 2016). Therefore, the identification of the fish species in surimi is required for

41

the management of overfishing and the conservation of endangered species.

RI PT

species becoming endangered (Galal-

DNA-based analysis is required for the identification of fish species in surimi, as it is

43

virtually impossible to distinguish fish species based on their morphology (Huxley-Jones, et

44

al., 2012). Several recent studies have employed molecular techniques to identify the fish

45

species in processed seafood products (Galal-Khallaf, et al., 2016; Keskin & Atar, 2012;

46

Pinto, et al., 2015; Zhao, et al., 2013). However, because those studies were focused on

47

identifying only a single species, the methods are unsuitable for use with surimi.

M AN U

SC

42

Denaturing gradient gel electrophoresis (DGGE) uses the reduction in electrophoretic

49

mobility according to the denaturing characteristics of PCR products to facilitate their in-gel

50

separation (G. Muyzer & Smalla, 1998). Therefore, DGGE enables the identification of the

51

various fish species in a minced fish product (Boon, Windt, Verstraet, & Top, 2002; Muyzer,

52

Waal, & Uitterlinden, 1993). DGGE analysis has been adapted by environmental

53

microbiologists for bacterial community characterization (Griffiths, Whiteley, O'Donnell, &

54

Bailey, 2000; G. Muyzer, 1999). This method has also been widely used to characterize

55

microbial community diversity and composition as well as structural changes in various

56

environments, including foodstuffs (Arcuri, Sheikha, Rychlik, Piro-Metayer, & Montet,

57

2013; Ercolini, 2004; Hong, Pruden, & Reardon, 2007). However, to our knowledge, no

58

study has applied DGGE to identify the fish species in a minced foodstuff.

AC C

EP

TE D

48

2

ACCEPTED MANUSCRIPT The aims of the present study were to design DGGE primers targeting the cytochrome c

60

oxidase subunit I (COI) gene to identify the fish species in surimi products. To our

61

knowledge, this study is the first attempt to analyze fish species using a DGGE method.

AC C

EP

TE D

M AN U

SC

RI PT

59

3

ACCEPTED MANUSCRIPT 62

2. Materials and methods

63

2.1. Sample preparation Twenty surimi products were purchased from markets in Busan, South Korea in February

65

2015 and transported to the laboratory under refrigerated conditions. Sample information is

66

provided in Supplementary table 1.

RI PT

64

67

2.2. DNA extraction

SC

68

Dried surimi product (200 mg) was subjected to DNA extraction in triplicate using a

70

Qiagen DNeasy Blood & Tissue Kit (Qiagen, Valencia, California) according to the

71

manufacturer’s instructions. DNA was quantified using the NanoVue system (GE Healthcare

72

Europe, Munich, Germany), and triplicate samples were pooled into a single sample.

73 74

2.3. Primer design and optimization

M AN U

69

Whole-length mitochondrial sequences from five fish species used in surimi products—

76

Nemipterus virgatus (KR701906), Theragra chalcogramma (AB094061), Micromesistius

77

poutassou (FR751401), Pleurogrammus azonus (AB744047), and Trachurus japonicus

78

(AP003092)—were

79

(http://www.ncbi.nlm.gov) and were aligned using the ClustalW software in the BioEdit

80

platform version 7.0.9.0 (http://www.mbio.ncsu.edu/BioEdit/bioedit.html, Hall 1999) (Table

81

1). The COI gene was used as the target for identification of fish species. Design of the

82

primers took into consideration the GC content, nucleotide composition at the 3’ end, melting

83

temperature, secondary structure, product size, and coverage of fish species. A primer pair

84

targeting a 213-bp region of the COI gene was designed (Table 2). Gradient PCR was

EP

TE D

75

from

the

GenBank

database

at

the

NCBI

AC C

obtained

4

ACCEPTED MANUSCRIPT 85

performed at 46, 48, 50, 52, 54, and 56°C to identify the optimum amplification temperature.

86

The PCR products were separated in a 1% agarose gel and visualized using 1× Redsafe

87

Nucleic Acid Staining Solution (iNtRON, South Korea).

89

RI PT

88

2.4. Primer test

To evaluate the coverage of the primer pair, 152 genomic DNA samples from 76 fish

91

species of 28 families were obtained from the Fisheries’ Genetic Resources Management

92

Center (National Institute of Fisheries Science, South Korea); these are presented in the

93

Supplementary table 2. The reference sequences were aligned and compared with those of the

94

ShortFish-F and ShortFish-R primers. Reference DNA was amplified by PCR with the

95

designed primers and a 46 to 56°C temperature gradient. A neighbor-joining tree was

96

constructed using the MEGA 5.0 software to evaluate the resolution of the 213-bp amplicons.

M AN U

SC

90

98

TE D

97

2.5. DGGE-PCR amplification

To increase primer specificity, the touchdown PCR method was used. A GC-clamp (CGC

100

CCG CCG CGC GCG GCG GGC GGG GCG GGG GCA CGG GGG G) was attached to the

101

5’ end of the forward primer to stabilize the PCR product during DGGE analysis (Ferris,

102

Muyzer, & Ward, 1996). The 20 µl PCR reaction volume contained 1× Ex Taq buffer with

103

1.5 mM MgCl2, 0.2 mM dNTP mixture, 0.5 µM each primer, 0.5 units TaKaRa Ex Taq

104

polymerase (TaKaRa Shuzo, Shiga, Japan), and 1 µl of template DNA (50 ng/µl). The

105

touchdown thermocycling conditions were: initial denaturation at 94°C for 7 min using 35

106

cycles of amplification comprising denaturation at 94°C for 1 min, annealing at 52°C to 48°C

107

for 1 min (the annealing temperature decreased by 1°C every five cycles and was then

AC C

EP

99

5

ACCEPTED MANUSCRIPT maintained at 48°C), and extension at 72°C for 1 min; this was followed by a final extension

109

at 72°C for 7 min. Negative controls without a DNA template were included in each analysis.

110

PCR products were visualized by electrophoresis in a 1% agarose gel, followed by

111

visualization with 1× Redsafe Nucleic Acid Staining Solution (iNtRON, South Korea).

RI PT

108

112 113

2.6. DGGE analysis

PCR products were purified using a GeneAll Expin PCR SV Kit (GeneAll Biotechnology

115

Co., South Korea). Purified DNA was analyzed using the DCode Mutation Detection System

116

(Bio-Rad, Hercules, USA) in 8% (w/v) polyacrylamide gels with denaturing gradients of 20%

117

to 50% and 30% to 60% (100% denaturing solution contained 7 M urea and 40% formamide).

118

Electrophoresis was performed at 20 V for 10 min and then at 80 V and 60°C for 14 h in 1×

119

TAE buffer. After washing with distilled water, the gel was stained with 2× SYBR gold

120

(Invitrogen, USA) for 30 min and imaged using the Molecular Imager Gel Doc System

121

(ATTO E-graph, TaKaRa, Japan).

M AN U

TE D

123

2.7. Identification of DGGE bands

EP

122

SC

114

For taxonomic classification, 30 DGGE bands were isolated from the gel and identified by

125

re-amplification, sequencing, and sequence comparison (band numbers and their taxonomic

126

positions are shown in Fig. 1 and Table 3, respectively). The gel segments were placed into

127

1.5-ml tubes, resuspended in 100 µl of distilled water, and then stored at 4°C overnight.

128

Extracted DNA was used as a template for re-amplification with the ShortFish-F (lacking the

129

GC clamp) and ShortFish-R primers in a PCR reaction volume of 20 µl. The reaction

130

temperature profile consisted of denaturation at 96°C for 2 min, followed by 25 cycles of

AC C

124

6

ACCEPTED MANUSCRIPT denaturation at 96°C for 15 s, annealing at 50°C at 5 s, and extension at 60°C for 2 min. The

132

PCR fragments were next separated on an ABI 3500 Genetic Analyzer (Applied Biosystems).

133

The PCR products were subjected to Sanger sequencing using the BigDye Terminator v. 1.1

134

chemistry (Applied Biosystems). Sequencing reactions were carried out using 1 µl of purified

135

PCR product, 2 µl of 5× BigDye Terminator v. 1.1 sequencing buffer, 0.8 µl of BigDye

136

terminator reaction mix v. 1.1, and 1 µl of 0.1 pM ShortFish-F forward primer in a total

137

volume of 10 µl.

SC

RI PT

131

AC C

EP

TE D

M AN U

138

7

ACCEPTED MANUSCRIPT 139

3. Results and discussion Food-processing conditions, such as temperature and pH, may result in the degradation of

141

DNA (Gryson, 2010). Amplifying >200-bp DNA fragments from surimi products is

142

problematic because of DNA degradation during storage and processing (e.g., frying, boiling,

143

and broiling) (Galal-Khallaf, et al., 2016). Although shorter DNA fragments include less

144

information, analysis of degraded DNA is needed to identify the raw materials in processed

145

food and develop novel markers for DNA amplification. The evolutionary relationships of

146

five major monophyletic clades of vertebrates and extinct Pleistocene fauna were analyzed

147

using the Uni-Minibar primers, which amplify ~130-bp sequences (Ficetola, et al., 2010;

148

Meusnier, et al., 2008; Taylor, 1996). In general, ~100-bp DNA sequences yield only 90%

149

species resolution (Meusnier, et al., 2008). Consequently, a novel, higher-resolution DNA

150

marker is required. The mitochondrial COI gene has been shown to be a robust genetic

151

marker in systematic phylogenetic research (Hebert, Cywinska, Ball, & deWaard, 2003;

152

Hebert, Ratnasingham, & deWaard, 2003; Smith, Mcveach, & Steinke, 2008). Therefore, the

153

conserved regions of various COI sequences were compared to develop the forward primer

154

ShortFish-F (5’- CAC AAA GAC ATT GGC ACC C -3’) and reverse primer ShortFish-R

155

(5’- AGT CAG TTT CCG AAC CCT CC -3’) (Table 2).

EP

TE D

M AN U

SC

RI PT

140

The coverage of the ShortFish-F/ShortFish-R primer pair was tested using sequence

157

matching. Some species showed mismatched sequences comprising 0–3 bases compared with

158

the corresponding positions of the COI region of the total of 76 species. However, as the

159

mismatched bases were not located at the 3’ end, the primer was tolerant of mismatched

160

sequences (Neff, Turk, & Kalishman, 2002). The sequence of the reverse primer ShortFish-R

161

was also compared with reference sequences. Zero to four mismatched bases were identified

AC C

156

8

ACCEPTED MANUSCRIPT in the reverse primer, but the 3’ end sequences matched perfectly; therefore, this primer was

163

also tolerant of mismatched sequences. Gradient PCR using reference DNA indicated that the

164

original 213-bp DNA fragments could be amplified from all samples at annealing

165

temperatures of 48–52°C. The phylogenetic analysis showed that most species could be

166

distinguished using short sequences, with the exception of five species in two families—

167

Carangidae (Trachurus sp. and Trachiotus sp.) and Tetraodontidae (Takifugu sp.). Each

168

family constitutes a clade with 100% similarity (Fig. 2). This finding supports the results of a

169

previous study that made use of short sequences to examine the genetic relationships among

170

five major monophyletic clades. The whole-length COI sequence enabled identification of

171

97% of species, and 95% and 90% resolutions were reported for 250-bp and 100-bp

172

sequences, respectively (Meusnier, et al., 2008). In this study, the 213-bp sequence yielded a

173

93% identification rate. Therefore, this novel biomarker is suitable for the identification of

174

fish species.

TE D

M AN U

SC

RI PT

162

Genomic DNA from the surimi products was used as a template for DGGE-PCR. PCR

176

amplification using the primers GC-ShortFish-F (5’- CGC CCG CCG CGC GCG GCG GGC

177

GGG GCG GGG GCA CGG GGG GCA CAA AGA CAT TGG CAC CC -3’)/ShortFish-R

178

was performed using a touchdown cycling protocol. Twenty PCR products were separated by

179

DGGE in 8% polyacrylamide gels. The gel with a 20% to 50% denaturing gradient showed a

180

resolution superior to that of the gel with a 30% to 60% denaturing gradient (data not shown).

181

Twenty samples showed diverse DGGE fingerprints; however, several showed similar or

182

identical patterns (A-H, J, O, P, R and S in Fig. 1) and were clustered in groups, the members

183

of which shared raw materials. Therefore, this analysis could be used to differentiate raw

AC C

EP

175

9

ACCEPTED MANUSCRIPT 184

materials. The average number of DGGE bands was 10. Based on the position of each band, a

185

total of 30 bands (6–15 per lane) were identified (Fig. 1). The DGGE bands facilitated species identification using reference sequences in the

187

GenBank database (Table 3). The results revealed that surimi products comprise a

188

considerable variety of fish species (e.g., Nemipterus virgatus, Theragra chalcogramma,

189

Micromesistius poutassou, Pleurogrammus azonus, and Trachurus japonicas). The sequences

190

of 30 separate bands corresponded to 20 fish species. Seven species were affiliated with two

191

or three sequences (17 in total): Nemipterus japonicas (DGGE band nos. 14 and 15),

192

Trachurus japonicus (nos. 2, 3, and 4), Gadus chalcogrammus (nos. 12 and 13), Trichiurus

193

lepturus (nos. 17 and 18), Selar crumenophthalmus (nos. 19, 20, and 21), Megalaspis cordyla

194

(nos. 23 and 24), and Saurida tumbil (nos. 27, 28, and 29). Therefore, 20 fish species could

195

be detected by DGGE using the primer set designed in this study. The results indicate the

196

presence of a variety of fish species in surimi products.

M AN U

SC

RI PT

186

Band 14, which was Nemipterus japonicus, Japanese threadfin bream, was present in all

198

surimi samples. This species is a common ingredient in surimi products sold in Korea, but its

199

proportion differed among the samples, as indicated by the variation in band intensity.

200

Because DGGE is a semi-quantitative method, the abundance of specific fish species in

201

surimi products can be estimated (Cani, 2013; G. Muyzer & Waal, 1994).

AC C

EP

TE D

197

202

Band 1 (Nemipterus randalli) was present in all surimi samples, with the exception of B

203

and S. Nemipterus spp. are important in the fishery industry; their catches ranked 20th in

204

2011 and 2012 (Moffitt & Cajas-Cano, 2014). Another Nemipterus sp., N. bipunctatus, was

205

also detected, but its band position in other samples was not clear.

10

ACCEPTED MANUSCRIPT Most bands were present in only one sample. For example, bands 2, 3, 4, and 5 were

207

present only in sample J, and they were affiliated with the genus Trachurus. Bands 12 and 13

208

were present in samples O, Q, R, and T, and these formed a distinct pattern including 30

209

unique ingredients.

RI PT

206

In some cases, the same species occupied different band positions, as has been reported

211

previously (Gonzalez-Arenzana, Lopez, Santamaria, & Lopez-Alfaro, 2013; Hu, Zhou, Xu,

212

Li, & Han, 2009). This was likely due to intraspecies heterogeneity, such as with regard to

213

haplotype (Case, et al., 2007). In this study, all bands were identified using PCR-cloning, and

214

the major disadvantage of DGGE (Van-Moreira, Conceicao, Nunes, & Manaia, 2013) (i.e., a

215

single band for multiple organisms) was not encountered. This was probably because of the

216

presence of few DNA sequences in a single sample, which differs from other studies of

217

microbial diversity. The detection sensitivity of DGGE has been reported to be 1–2% of the

218

total population (Kan, Wang, & Chen, 2006); however, this is unimportant for identifying the

219

major fish species used in surimi.

TE D

M AN U

SC

210

Due to the increasing demand for fish products, determination of the species composition

221

has become an important issue for both consumers and food regulatory authorities. The novel

222

primer set and DGGE method developed in this study enabled the semi-quantitative

223

identification of fish species (Andorra, Landi, Mas, Esteve-Zarzoso, & Guillamon, 2010).

224

Therefore, this DGGE method is suitable for a variety of applications, such as detection of

225

various ingredients for make fish feed. In conclusion, we developed a method for the rapid

226

identification of fish species that will enhance the ability to control the quality of minced

227

foodstuffs.

AC C

EP

220

228

11

ACCEPTED MANUSCRIPT 229

Acknowledgments This work was supported by a grant from the National Institute of Fisheries Science

231

(R2016037) and this research was a part of project titled “Development of Practical

232

Techniques to Establish Fisheries Forensic Center, funded by the Ministry of Oceans and

233

Fisheries, Korea.

RI PT

230

AC C

EP

TE D

M AN U

SC

234

12

ACCEPTED MANUSCRIPT References

236

Andorra, I., Landi, S., Mas, A., Esteve-Zarzoso, B., & Guillamon, J. M. (2010). Effect of

237

fermentation temperature on microbial population evolution using culture-

238

independent and dependent techniques. Food Research International, 43(3), 773-779.

RI PT

235

Arcuri, E. F., Sheikha, A. F. E., Rychlik, T., Piro-Metayer, I., & Montet, D. (2013).

240

Determination of cheese origin by using 16S rDNA fingerprinting of bacteria

241

communities by PCR-DGGE: Preliminary application to traditional Minas cheese.

242

Food Control, 30(1), 1-6.

SC

239

Boon, N., Windt, W., Verstraet, W., & Top, E. (2002). Evaluation of nested PCR-DGGE with

244

group-specific 16S rRNA primers for the analysis of bacterial communities from

245

different wastewater treatment plants. FEMS Microbiol Ecol, 39, 101-112.

246 247

M AN U

243

Cani, P. D. (2013). Gut microbiota and obesity: lessons from the microbiome. Briefings in Functional Genomics, 12, 381-387.

Case, R. J., Boucher, Y., Dahllof, I., Holmstorm, C., Doolittle, W. F., & Kjelleberg, S.

249

(2007). Use of 16S rRNA and rpoB genes as molecular markers for microbial ecology

250

studies. Applied and Environmental Microbiology, 73(1), 278-188.

EP

252

Ercolini, D. (2004). PCR-DGGE fingerprinting: novel strategies for detection of microbes in food. Journal of Microbiological Methods, 56(3), 297-314.

AC C

251

TE D

248

253

Ferris, M. J., Muyzer, G., & Ward, D. M. (1996). Denaturing gradient gel electrophoresis

254

profiles of 16S rRNA-defined populations inhabiting a hot spring microbial mat

255 256 257

community. A new molecular approach to analyze the genetic diversity of mixed microbial communities. Appl Environ Microbiol, 62(2), 340-346.

Ficetola, G. F., Coissac, E., Zundel, S., Riaz, T., Shehzad, W., Bessiere, J., Taberlet, P., &

13

ACCEPTED MANUSCRIPT 258

Pompanon, F. (2010). An in silico approach for the evaluation of DNA barcodes.

259

BMC Genomics, 11, 434. Galal-Khallaf, A., Ardura, A., Borrell, Y. J., & Garcia-Vazquez, E. (2016). Towards more

261

sustainable surimi? PCR-cloning approach for DNA barcoding reveals the use of

262

species of low trophic level and aquaculture in Asian surimi. Food Control, 61, 62-69.

263

Gonzalez-Arenzana, L., Lopez, R., Santamaria, P., & Lopez-Alfaro, I. (2013). Dynamics of

264

lactic acid bacteria populations in Rioja wines by PCR-DGGE, comparison with

265

culture-dependent methods. Applied Microbial and Cell Physiology, 97, 6931-6941.

SC

RI PT

260

Griffiths, R. I., Whiteley, A. S., O'Donnell, A. G., & Bailey, M. J. (2000). Rapid method for

267

coextraction of DNA and RNA from Natural Environments for Analysis of

268

Ribosomal DNA- and rRNA-Based Microbial Community. Appl. Environ. Microbiol.,

269

66(12), 5488-5491.

271

Gryson, N. (2010). Effect of food processing on plant DNA degradation and PCR-based

TE D

270

M AN U

266

GMO analysis: a review. Anal Bioanal Chem, 396(6), 2003-2022. Hebert, P. D. N., Cywinska, A., Ball, S. L., & deWaard, J. R. (2003). Biological

273

identifications through DNA barcodes. Proceedings of the Royal Society of London B,

274

270, 313-322.

276 277

Hebert, P. D. N., Ratnasingham, S., & deWaard, J. R. (2003). Barcoding animal life:

AC C

275

EP

272

cytochrome c oxidase subunit 1 divergences among closely related species. Proceedings of the Royal Society of London B, 270, S96-S99.

278

Hong, H., Pruden, A., & Reardon, K. F. (2007). Comparison of CE-SSCP and DGGE for

279

monitoring a complex microbial community remediationg mine drainage. Journal of

280

Microbiological Methods, 69, 52-64.

14

ACCEPTED MANUSCRIPT 281

Hu, P., Zhou, G., Xu, X., Li, C., & Han, Y. (2009). Characterization of the predominant

282

spoilage bacteria in sliced vacuum-packed cooked ham based on 16S rDNA-DGGE.

283

Food Control, 20(2), 99-104. Huxley-Jones, E., Shaw, J. L. A., Fletcher, C., & Parnell, J. (2012). Use of DNA barcoding to

285

reveal species composition of convenience seafood. Conservation Biology, 26(2),

286

367-371.

RI PT

284

Kan, J., Wang, K., & Chen, F. (2006). Temporal variation and detection limit of an estuarine

288

bacterioplonkton community analyzed by denaturing gradient gel electrophoresis

289

(DGGE). Aquatic Microbial Ecology, 42, 7-18.

291

M AN U

290

SC

287

Keskin, E., & Atar, H. H. (2012). Molecular identification of fish species from surimi-based products labeled as Alaskan pollock. Journal of Applied Ichthyology, 28, 811-814. Martin-Sanchez, A. M., Navarro, C., Perez-Alvarez, J. A., & Kuri, V. (2009). Alternatives for

293

efficient and sustainable production of surimi: a review. Comprehensive Reviews in

294

Food Science and Food Safety, 8(4), 359-374.

TE D

292

Meusnier, I., Singer, G. A., Landry, J. F., Hickey, D. A., Hebert, P. D., & Hajibabaei, M.

296

(2008). A universal DNA mini-barcode for biodiversity analysis. BMC Genomics, 9,

297

214.

299 300 301

Moffitt, C. M., & Cajas-Cano, L. (2014). Blue Growth: The 2014 FAO State of World

AC C

298

EP

295

Fisheries and Aqaculture. American Fisheries Society, 39(11), 552-553.

Muyzer, G. (1999). DGGE/TGGE a method for identifying genes from natural ecosystem. Current Opinion in Microbiology, 2(3), 317-322.

302

Muyzer, G., & Smalla, K. (1998). Application of Denaturing gradient gel electrophoresis

303

(DGGE) and temperature gradient gel electrophoresis (TGGE) in microbial ecology.

15

ACCEPTED MANUSCRIPT 304

Antonie van leeuwenhoek, 73(1), 127-141. Muyzer, G., Waal, E. C. de., & Uitterlinden, A. G. (1993). Profiling of complex microbial

306

population by DGGE analysis of polymerase chain reaction amplified genes encoding

307

for 16S rRNA. Applied Environmental Microbiology, 59, 695-700.

RI PT

305

Muyzer, G., & Waal, E. C. de. (1994). Determination of the genetic diversity of microbial

309

communities using DGGE analysis of PCR-amplified 16S rDNA. Microbial Mats, 35,

310

207-214.

312 313 314

Neff, M. M., Turk, E., & Kalishman, M. (2002). Web-based primer design for single nucleotide polymorphism analysis. Trends in Genetics, 18(12), 613-615.

M AN U

311

SC

308

Park, J. W. (2005). Surimi and surimi seafood (2nd ed.). Boca Raton, FL: Taylor & Francis/CRC Press.

Pinto, A. D., Marchetti, P., Mottola, A., Bozzo, G., bonerba, E., Ceci, E., Bottaro, M., &

316

Tantillo, G. (2015). Species identification in fish fillet products using DNA

317

barcoding. Fisheries Research, 170, 9-13.

TE D

315

Poowakanjana, S., & Park, J. W. (2013). Biochemical characterisation of Alaska pollock,

319

Pacific whiting, and threadfin bream surimi as affected by comminution conditions.

320

Food Chem, 138(1), 200-207.

322 323 324 325 326

Shiku, Y., Hamaguchi, P. Y., Benjakul, S., Visessanguan, W., & Tanaka, M. (2004). Effect of

AC C

321

EP

318

surimi quality on properties of edible films based on Alaska pollack. Food Chem, 86(4), 493-499.

Smith, P. J., Mcveach, S. M., & Steinke, D. (2008). DNA barcoding for the identification of smoked fish products. Journal of Fish Biology, 72(2), 464-471. Taylor, P. G. (1996). Reproducibility of ancient DNA sequences from extinct pleistocene

16

ACCEPTED MANUSCRIPT 327

fauna. Mol Biol Evol, 13, 283-285. Van-Moreira, I., Conceicao, E., Nunes, O. C., & Manaia, C. M. (2013). Bacterial diversity

329

from the source to the tap: a comparative study based on 16S rRNA gene-DGGE and

330

culture-dependent methods. FEMS Microbiol Ecol, 83, 361-374.

331 332

RI PT

328

Yin, T., & Park, J. W. (2014). Effects of nano-scaled fish bone on the gelation properties of Alaska pollock surimi. Food Chem, 150, 463-468.

Yoon, M., Kim, J.-S., Kim, D., Jo, J., & Cho, S. (2014). Optimization of the processing

334

conditions for the preparation of surimi products containing rice flour. Fisheries and

335

Aquatic Sciences, 17(2), 167-173.

M AN U

SC

333

336

Zhao, W., Zhao, Y., Pan, Y., Wang, X., Wang, Z., & Xie, J. (2013). Authentication and

337

traceability of Nibea albiflora from surimi products by species-specific polymerase

338

chain reaction. Food Control, 31, 97-101.

EP AC C

340

TE D

339

17

ACCEPTED MANUSCRIPT

5 6 4 2 A A A A A

5 6 4 3 C C C C C

5 6 4 4 A A A A A

5 6 4 5 A A A A A

5 6 4 6 A A A A A

5 6 4 7 G G G G G

5 6 4 8 A A A A A

5 6 4 9 C C C C C

5 6 5 0 A A A A A

5 6 5 1 T T T T T

5 6 5 2 C T T T C

5 8 3 5 G G G G G

5 8 3 6 C A C C A

5 8 3 7 G G G G G

5 8 3 8 G G G G G

5 8 3 9 G C C T C

5 8 4 0 T T T T T

5 8 4 1 T T T T T

5 8 4 2 C T C C T

5 8 4 3 G G G G G

5 8 4 4 G G G G G

5 8 4 5 A A G G A

AC C

EP

TE D

343 344

18

5 6 5 3 G G G G G

5 6 5 4 G G G G G

5 6 5 5 C C C C C

5 6 5 6 A A A A A

5 6 5 7 C C C C C

5 6 5 8 C C C C C

5 6 5 9 C C C C C

5 8 4 6 A A A A A

5 8 4 7 A A A A A

5 8 4 8 C C C C C

5 8 4 9 T T T T T

5 8 5 0 G G G G G

5 8 5 1 A A A A A

5 8 5 2 C C C C C

RI PT

5 8 3 4 N. virgatus G T. chalcogramma G M. poutassou G P. azonus G T. japonicus G

primer regions with complete mitochondrial sequences of reference

SC

Table 1. Alignment of species 5 6 4 1 N. virgatus C T. chalcogramma C M. poutassou C P. azonus C T. japonicus C

M AN U

341 342

5 8 5 3 T T T T T

ACCEPTED MANUSCRIPT 345

Table 2. Universal primers designed from conserved region of COI gene Primer name Sequence Fragment size ShortFish-F 5'- CACAAAGACATTGGCACCC -3' 213 bp ShortFish-R 5'- AGTCAGTTTCCGAACCCTCC -3'

Developed for Fish

AC C

EP

TE D

M AN U

SC

RI PT

346 347

19

ACCEPTED MANUSCRIPT Table 3. Sequence analysis of bands excised from DGGE gels

349 350

SC

RI PT

Size Accession no. 172 KM538438 174 KC970408 174 KP267655 174 KP267655 162 KM006769 165 KT307783 165 KP267650 174 FJ583314 164 KF809419 166 EU600136 165 KP722759 174 KT321137 174 KT321137 174 KF009634 172 KF009634 165 JQ350137 168 JQ681420 174 JN242479 164 JF494494 173 JF494494 164 JF494494 174 KJ202178 164 KR011052 174 KR011052 174 JX261479 166 KP266852 172 KP267628 174 KM459006 170 KM459006 160 JQ738596

M AN U

Homology 100% 99% 98% 98% 95% 100% 100% 99% 100% 96% 99% 99% 99% 99% 97% 97% 99% 95% 97% 99% 97% 95% 98% 98% 96% 99% 99% 97% 98% 96%

TE D

Nearest match Nemipterus randalli Trachurus japonicus Trachurus japonicus Trachurus japonicus Trachurus novaezelandiae Oreochromis niloticus Branchiostegus argentatus Dactyloptena orientalis Scolopsis taenioptera Lutjanus bengalensis Pennahia macrocephalus Gadus chalcogrammus Gadus chalcogrammus Nemipterus japonicus Nemipterus japonicus Nemipterus bipunctatus Trichiurus lepturus Trichiurus lepturus Selar crumenophthalmus Selar crumenophthalmus Selar crumenophthalmus Mene maculata Megalaspis cordyla Megalaspis cordyla Decapterus maruadisi Saurida undosquamis Saurida tumbil Saurida tumbil Saurida tumbil Larimichthys polyactis

EP

Band 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

AC C

348

20

351

AC C

EP

TE D

M AN U

SC

RI PT

ACCEPTED MANUSCRIPT

352

Figure 1. DGGE profiles of the mitochondrial COI region in 20 surimi products. Sequenced

353

bands are numbered.

354

21

AC C

355

EP

TE D

M AN U

SC

RI PT

ACCEPTED MANUSCRIPT

356

Figure 2. Phylogenetic tree showing relationships among the 76 reference species. The

357

neighbor-joining tree is based on a 213-bp region of the COI gene. Scale bar represents 0.02

358

substitutions per nucleotide position.

359 360

22

ACCEPTED MANUSCRIPT Supplementary table 1. Characteristics of surimi products used in this study Production country Fish species labeled in Number Proportion of fish meat product A imported 80.96% – B Pakistan 64.81% – C China 54.84% Hairtail D imported 65.34% – E imported 66.51% – F imported 66.84% – G imported 80.69% White flesh fish H imported – I imported 66.51% – J imported 80.30% Horse mackerel 21.41% K imported 72.90% Croaker L imported 64.65% Hairtail and golden-thread M imported 64.65% Hairtail and golden-thread N imported 64.65% Hairtail and golden-thread O imported 62.00% – P imported 64.36% – Q imported 78.40% – R imported 81.05% – S imported 50.04% – T imported 54.16% – “–” indicates that the information was not labeled

EP AC C

362 363

TE D

M AN U

SC

RI PT

361

23

ACCEPTED MANUSCRIPT

EP

TE D

M AN U

SC

RI PT

Supplementary table 2. List of 76 species obtained from the NIFS No Scientific name Region Accession No 1 Eptatretus stoutii COI GU440317.1 2 Himantura sp. COI JX263423.1 3 Engraulis japonicus COI JF952723.1 4 Ilisha elongata COI HM030780.1 5 Sardinops melanostictus COI JQ266230.1 6 Gadus macrocephalus COI JQ354101.1 7 Gadus chalcogrammus COI JF952737.1 8 Lophius litulon COI EU660706.1 9 Cololabis saira COI JQ354059.1 10 Sebastes fasciatus COI KC015912.1 11 Sebastes alutus COI HQ712757.1 12 Pleurogrammus monopterygius COI JQ354278.1 13 Anthias nicholsi COI JQ774959.1 14 Acanthistius patachonicus COI EU074304.1 15 Branchiostegus albus COI EU861053.1 16 Carangoides equula COI AY541645.1 17 Trachurus japonicus COI JF952880.1 18 Pagrus major COI GU207340.1 19 Chrysophrys auratus COI DQ107829.1 20 Pagrus caeruleostictus COI JN868714.1 21 Larimichthys polyactis COI HQ385794.1 22 Larimichthys croces COI FJ595214.1 23 Pseudotolithus elongatus COI KF965495.1 24 Pseudotolithus typus COI KF965520.1 25 Miichthys miiuy COI JQ738461.1 26 Micropogonias undulatus COI JQ841936.1 27 Micropogonias furnieri COI GU225148.1 28 Atrobucca sp. COI JF492920.1 29 Atractoscion sp. COI DQ107824.1 30 Trichiurus japonicus COI JN990871.1 31 Trichiurus sp. COI JX124916.1 32 Scomber scombrus COI AB120717.1 33 Paralichthys isosceles COI JQ365476.1 34 Limanda aspera COI JX183913.1 35 Cynoglossus senegalensis COI EU513631.1 36 Cynoglossus lingua COI KF965355.1 37 Cynoglossus arel COI KF965470.1 38 Cynoglossus macrolepidotus COI KF965350.1 39 Cynoglossus bilineatus COI KF965375.1 40 Mustelus mosis COI HQ149887.1 41 Mustelus asterias COI KJ205083.1 42 Okamejei acutispina COI EU334812.1 43 Himantura gerrardi COI JF493648.1 44 Himantura astra COI DQ108157.1

AC C

364

24

ACCEPTED MANUSCRIPT

366 367

RI PT

JX263423.1 JN021234.1 JQ266230.1 JQ266230.1 DQ108056.1 KF930415.1 HM180794.1 JQ343911.1 FJ594966.1 HQ174861.1 KP641366.1 KJ642220.1 EF609485.1 EU074367.1 JQ350137.1 JX260908.1 JF494251.1 DQ885031.1 JF494030.1 KF461230.1 FJ265828.1 JQ738502.1 HQ712518.1 EU513629.1 KJ093731.1 JQ681796.1 KF442241.1 EU595161.1 AP009534.1 AP009534.1 HM102315.1 JQ681824.1

SC

COI COI COI COI COI COI COI COI COI COI COI COI COI COI COI COI COI COI COI COI COI COI COI COI COI COI COI COI COI COI COI COI

M AN U

TE D

EP

365

Himantura undulata Muraenesox bagio Sardinella aurita Sardinella maderensis Helicolenus barathri Sebastes ciliatus Platycephalus indicus Lateolabrax maculatus Epinephelus septemfasciatus Ephinehelus fuscoguttatus Chloroscombrus chrysurus Trachinotus anak Trachurus novaezelandiae Brama brama Nemipterus bipunctatus Macrospinosa cuja Pseudotolithus brachygnathus Pseudotolithus sp. Otolithes ruber Sciaenops ocellatus Trichiurus gangeticus Scomber japonicus Lepidopsetta polyxystrapolyxystra Cynoglossus monodi Ephippion guttifer Lagocephalus gloveri Lagocephalus guentheri Lagocephalus wheeleri Takifugu chinensis Takifugu pseudommus Takifugu rubripes Takifugu xanthopterus

AC C

45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76

25

ACCEPTED MANUSCRIPT Highlights

369

-

Efficient marker was developed to identify the fish species from processed food.

370

-

A method for the identification of fish species using DGGE has been developed.

371

-

DGGE profiles revealed the presence of a variety of fish species in surimi products.

RI PT

368

372

AC C

EP

TE D

M AN U

SC

373

26