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      Bioinformatics Approach to Mitigate Mislabeling in EU Seafood Market and Protect Consumer Health

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          Abstract

          Fisheries products are some of the most traded commodities world-wide and the potential for fraud is a serious concern. Fish fraud represents a threat to human health and poses serious concerns due to the consumption of toxins, highly allergenic species, contaminates or zoonotic parasites, which may be present in substituted fish. The substitution of more expensive fish by cheaper species, with similar morphological characteristics but different origins, reflects the need for greater transparency and traceability upon which which the security of the entire seafood value-chain depends. Even though EU regulations have made significant progress in consumer information by stringent labelling requirements, fraud is still widespread. Many molecular techniques such as DNA barcoding provide valuable support to enhance the Common Fisheries Policy (CFP) in the protection of consumer interests by unequivocally detecting any kind of fraud. This paper aims to highlight both the engagement of EU fishery policy and the opportunity offered by new biotechnology instruments to mitigate the growing fraud in the globalized fish market and to enforce the food security system to protect consumers’ health. In this paper, after a presentation of EU rules on fish labeling and a general overview on the current state of the global fish market, we discuss the public health implications and the opportunities offered by several techniques based on genetics, reporting a case study to show the efficacy of the DNA barcoding methodology in assessing fish traceability and identification, comparing different species of the Epinephelus genus, Mottled Grouper ( Mycteroperca rubra) and Wreckfish ( Polyprion americanus), often improperly sold with the commercial name of “grouper”.

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          Most cited references52

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          MEGA7: Molecular Evolutionary Genetics Analysis Version 7.0 for Bigger Datasets.

          We present the latest version of the Molecular Evolutionary Genetics Analysis (Mega) software, which contains many sophisticated methods and tools for phylogenomics and phylomedicine. In this major upgrade, Mega has been optimized for use on 64-bit computing systems for analyzing larger datasets. Researchers can now explore and analyze tens of thousands of sequences in Mega The new version also provides an advanced wizard for building timetrees and includes a new functionality to automatically predict gene duplication events in gene family trees. The 64-bit Mega is made available in two interfaces: graphical and command line. The graphical user interface (GUI) is a native Microsoft Windows application that can also be used on Mac OS X. The command line Mega is available as native applications for Windows, Linux, and Mac OS X. They are intended for use in high-throughput and scripted analysis. Both versions are available from www.megasoftware.net free of charge.
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            The neighbor-joining method: a new method for reconstructing phylogenetic trees.

            N Saitou, M Nei (1987)
            A new method called the neighbor-joining method is proposed for reconstructing phylogenetic trees from evolutionary distance data. The principle of this method is to find pairs of operational taxonomic units (OTUs [= neighbors]) that minimize the total branch length at each stage of clustering of OTUs starting with a starlike tree. The branch lengths as well as the topology of a parsimonious tree can quickly be obtained by using this method. Using computer simulation, we studied the efficiency of this method in obtaining the correct unrooted tree in comparison with that of five other tree-making methods: the unweighted pair group method of analysis, Farris's method, Sattath and Tversky's method, Li's method, and Tateno et al.'s modified Farris method. The new, neighbor-joining method and Sattath and Tversky's method are shown to be generally better than the other methods.
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              A simple method for estimating evolutionary rates of base substitutions through comparative studies of nucleotide sequences.

              Some simple formulae were obtained which enable us to estimate evolutionary distances in terms of the number of nucleotide substitutions (and, also, the evolutionary rates when the divergence times are known). In comparing a pair of nucleotide sequences, we distinguish two types of differences; if homologous sites are occupied by different nucleotide bases but both are purines or both pyrimidines, the difference is called type I (or "transition" type), while, if one of the two is a purine and the other is a pyrimidine, the difference is called type II (or "transversion" type). Letting P and Q be respectively the fractions of nucleotide sites showing type I and type II differences between two sequences compared, then the evolutionary distance per site is K = -(1/2) ln [(1-2P-Q) square root of 1-2Q]. The evolutionary rate per year is then given by k = K/(2T), where T is the time since the divergence of the two sequences. If only the third codon positions are compared, the synonymous component of the evolutionary base substitutions per site is estimated by K'S = -(1/2) ln (1-2P-Q). Also, formulae for standard errors were obtained. Some examples were worked out using reported globin sequences to show that synonymous substitutions occur at much higher rates than amino acid-altering substitutions in evolution.
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                Author and article information

                Contributors
                Role: Academic Editor
                Role: Academic Editor
                Role: Academic Editor
                Journal
                Int J Environ Res Public Health
                Int J Environ Res Public Health
                ijerph
                International Journal of Environmental Research and Public Health
                MDPI
                1661-7827
                1660-4601
                14 July 2021
                July 2021
                : 18
                : 14
                : 7497
                Affiliations
                [1 ]Department of Agriculture, Food and Environment (Di3A), University of Catania, via S. Sofia 100, 95123 Catania, Italy; clamonaco@ 123456unict.it (C.M.); daniela.spina@ 123456unict.it (D.S.); peri@ 123456unict.it (I.P.)
                [2 ]Bioinformatics Unit, Department of Clinical and Experimental Medicine, University of Catania, via S. Sofia 64, 95123 Catania, Italy; alfredo.pulvirenti@ 123456dmi.unict.it (A.P.); alaimos@ 123456dmi.unict.it (S.A.)
                Author notes
                [* ]Correspondence: vindigni@ 123456unict.it
                Author information
                https://orcid.org/0000-0003-4825-7617
                https://orcid.org/0000-0001-6352-9575
                Article
                ijerph-18-07497
                10.3390/ijerph18147497
                8305968
                34299949
                6c96fdc4-741d-4209-abd7-27145a41ac3b
                © 2021 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( https://creativecommons.org/licenses/by/4.0/).

                History
                : 05 June 2021
                : 09 July 2021
                Categories
                Article

                Public health
                food safety,common fishery policy,dna barcoding,fish mislabeling,consumer information

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