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A Complement to DNA Barcoding Reference Library for Identification of Fish from the Northeast Pacific

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A Complement to DNA Barcoding Reference Library for Identification of Fish from the Northeast Pacific Genome A complement to DNA barcoding reference library for identification of fish from the Northeast Pacific Journal: Genome Manuscript ID gen-2020-0192.R1 Manuscript Type: Article Date Submitted by the 18-Mar-2021 Author: Complete List of Authors: Turanov, Sergei; A.V. Zhirmunsky National Scientific Center of Marine Biology, Laboratory of Molecular Systematic; Far Eastern State Technical Fisheries University, Chair of Water Biological Resources and Aquaculture Kartavtsev, Yuri; A.V. Zhirmunsky Institute of Marine Biology FEB RAS, Lab of MolecularDraft Systematics Keyword: barcoding gap, Enophrys, Albatrossia, Coryphaenoides, deep sea fish Is the invited manuscript for consideration in a Special Not applicable (regular submission) Issue? : © The Author(s) or their Institution(s) Page 1 of 23 Genome 1 A complement to DNA barcoding reference library for identification of fish from the 2 Northeast Pacific 3 4 Sergei V. Turanov1,2,*, Yuri Ph. Kartavtsev1 5 6 1Laboratory of Molecular Systematic, A.V. Zhirmunsky National Scientific Center of Marine 7 Biology, Far Eastern Branch, Russian Academy of Sciences, 690041 Vladivostok, Russia 8 2Chair of Water Biological Resources and Aquaculture, Far Eastern State Technical Fisheries 9 University, 690087 Vladivostok, Russia 10 *Corresponding author; [email protected]; 17, Palchevsky St., Vladivostok 690041, Russia Draft 1 © The Author(s) or their Institution(s) Genome Page 2 of 23 12 Abstract 13 The seas of the North Pacific Ocean are characterized by a large variety of fish fauna, 14 including endemic species. Molecular genetic methods, often based on DNA barcoding approaches, 15 have been recently used to determine species boundaries and identify cryptic diversity within these 16 species. This study complements the DNA barcode library of fish from the Northeast Pacific area. 17 A library based on 154 sequences of the mitochondrial COI gene from 44 species was assembled 18 and analyzed. It was found that 39 species (89%) can be unambiguously identified by the clear 19 thresholds forming a barcoding gap. Deviations from the standard 2% threshold value resulted in 20 detection of the species Enophrys lucasi in the sample, which is not typical for the eastern part of 21 the Bering Sea. This barcoding gap also made it possible to identify naturally occurring low values 22 of interspecific divergence of eulittoral taxa Aspidophoroides and the deep-sea genus 23 Coryphaenoides. Synonymy of the genusDraft Albatrossia in favor of the genus Coryphaenoides is 24 suggested based on both the original and previously published data. 25 Keywords: COI; barcoding gap; Enophrys; Albatrossia; Coryphaenoides; deep sea fish. 26 27 1. Introduction 28 The seas of the North Pacific Ocean are characterized by rich biotopes, and contain a wide 29 endemic variety of hydrobionts that have attracted the attention of taxonomists of different 30 specializations. Data from recent studies applying integrated approaches (Turanov and Kartavtsev 31 2014; Turanov et al. 2016; Moreva et al. 2017; Skurikhina et al. 2018; Smé et al. 2019; Turanov 32 2019; Chernyshev 2020; Jung et al. 2020; Stonik and Efimova 2020; Skriptsova and Kalita 2020) 33 show that the species diversity of fish and other aquatic organisms in this region is undervalued. 34 Molecular genetic approaches can be an undeniable leader among the supportive tools in 35 biodiversity studies. 36 Molecular genetic techniques not only help to document existing diversity (Hebert et al. 37 2003a, 2003b) and discover cryptic species (Bickford et al. 2007; Hubert and Hanner 2015), but 2 © The Author(s) or their Institution(s) Page 3 of 23 Genome 38 they can also be used to identify taxonomic discrepancies or cases of intentional substitution in the 39 commercial distribution of fish and shellfish biota (Galimberti et al. 2013; Khaksar et al. 2015; 40 Nedunoori et al. 2017). The comprehensive nature of molecular genetic methods enable 41 development of solutions to identify single species (Pfleger et al. 2016; Schenekar et al. 2020b; 42 Yusishen et al. 2020), as well as the use of rapid analysis technologies to monitor species diversity 43 (Lecaudey et al. 2019; Belevich et al. 2020; Schenekar et al. 2020a). However, the development of 44 unified methods has been hindered by the lack of a verified DNA barcode database of living 45 organisms in the region of the world where such methods could be implemented (McGee et al. 2019; 46 Weigand et al. 2019; Schenekar et al. 2020a). 47 DNA barcoding was originally developed to facilitate taking inventory of the entire species 48 diversity (Hebert et al. 2003a, 2003b), but has now evolved into a global initiative to ensure the 49 speed and quality of monitoring and conservationDraft measures (DeSalle and Goldstein 2019). There 50 are still limitations related to both the methodology and conceptual issues of evolutionary biology 51 and species definition (Meyer and Paulay 2005; Krishnamurthy and Francis 2012; Collins and 52 Cruickshank 2013; DeSalle and Goldstein 2019), however DNA barcoding is nevertheless 53 extremely useful. Further development is still required, especially for studying the biotic diversity 54 of the Northeast Pacific Ocean. Previously, this approach has been shown to be reliable for 55 detecting cryptic species diversity, and limitations have been identified regarding the applicability 56 of a strict threshold for many perch-like fish species (Turanov et al. 2016) from this region. This 57 paper provides an update to the reference barcode database of fish from the Far Eastern seas of 58 Russia, with taxonomic comments. 59 2. Material and methods 60 Fish specimens were collected using gillnets (Sea of Japan) and bottom trawls (Sea of 61 Okhotsk and Bering Sea) during the period from 2007 to 2011 (Fig. 1). Species identification was 62 conducted according to the most commonly used identification keys for the area (Lindberg and 63 Krasyukova 1987; Nakabo 2002) and subsequently adjusted to the current nomenclature (Parin et al. 3 © The Author(s) or their Institution(s) Genome Page 4 of 23 64 2014; Fricke et al. 2019). The sampling consisted of 154 specimens representing 44 species from 33 65 genera, 15 families and 6 orders. Each species had between one and five specimens. Voucher 66 specimens of the fish investigated are kept under corresponding numbers in the museum of the 67 NSCMB FEB RAS (Supplement S11). A piece of skeletal muscle tissue was taken from each 68 specimen and stored in 95% ethanol. Total DNA was isolated from this tissue using a K-Sorb 69 commercial kit (Syntol, Moscow). 70 The samples were then genotyped by a fragment of the mitochondrial COI gene using a 71 cocktail of universal C_FishF1t1–C_FishR1t1 primers (Ivanova et al. 2007). The PCR reaction 72 mixture (total volume 25 µl) included 1 µl of total DNA solution (20–150 ng), 5 µl of ready-made 73 PCR mixture ScreenMix (Eurogen, Moscow), 0.4 mM of primer solution, and deionized water up to 74 the final volume. The thermal cycling conditions consisted of preheating at 94ºC for 2 min, and 30 75 cycles according to the following scheme:Draft denaturation at 94ºC for 40 sec., annealing at 52ºC for 40 76 sec., and 1 min. elongation at 72ºC with final elongation for 10 min. To evaluate the PCR results, 77 electrophoresis of amplicons was performed in 1% agarose gel stained with ethidium bromide, 78 visualized under UV light. The amplified COI fragments were purified by alcohol precipitation and 79 then sequenced with appropriate primers (Ivanova et al., 2007) using the BrightDye™ Terminator 80 Cycle Sequencing Kit v3.1 (NimaGen). Capillary electrophoresis of the fragments was performed 81 on an ABI Prism 3130 DNA Genetic Analyzer sequencer (Applied Biosystems, USA). The 82 consensus sequences from the obtained chromatograms were assembled using Geneious software 83 (Kearse et al. 2012). Sequence alignment and subsequent correction of the reading frame (if 84 necessary) were performed in MEGA 7 (Kumar et al. 2016) using the MUSCLE algorithm (Edgar 85 2004). During the alignment, the closest matches from the output data of BLAST (Altschul et al. 86 1990) in GenBank (Benson et al. 2018) were used as reference sequences. The sequences with all 87 the necessary information and pictures with lifetime coloration were placed in BOLD 88 (Ratnasingham and Hebert 2007, 2013) in a project called FFES and uploaded to GenBank 1 gen-2020-0192.R1suppla 4 © The Author(s) or their Institution(s) Page 5 of 23 Genome 89 (Supplement S12). The genetic distances (p-distances) as well as their corrected values according to 90 the two-parameter Kimura model (Kimura 1980) were calculated using the BOLD workbench. The 91 upper conditional threshold value of intraspecific genetic distances was assumed to be the minimum 92 value of distances within a genus between different species. The BarcodingR package (Zhang et al. 93 2017) was used to calculate and plot a graph reflecting the Barcoding gap or the presence of a 94 threshold between intraspecific and interspecific genetic distances (Meyer and Paulay 2005; Meier 95 et al. 2006, 2008). We also used the BIN (Barcode Index Number, (Ratnasingham and Hebert 2013)) 96 discordance report information provided by the BOLD workbench. To test the assumption of 97 genetic differentiation between species with extraordinarily low interspecific genetic distances, we 98 used the geneflow Fst indices with a permutation test based on 10,000 replicates in DnaSP 5 99 (Librado and Rozas 2009). In addition to the distance-based criteria for species delimitation, we 100 used a topological approach (i.e., constructionDraft of phylogenetic trees and identification of 101 monophyletic clusters corresponding to species groups pre-defined by morphological features). For 102 this purpose, a neighbor joining (NJ) tree was constructed in the program MEGA 7 using the K2P 103 model, based on available sequences. The robustness of the tree topology was estimated based on 104 the results of 1,000 pseudo-replicas of the non-parametric bootstrap test.
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