Dd Form 3035 2 Cold Chain Management Shipping Label Hybrid Items
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Estimating the rate of gamete and larval dispersal of coral species in the ocean is difficult but important for their conservation. Spatial autocorrelation analysis is useful for estimating the spatial extent of coral dispersal; However, the use of genome-wide single nucleotide polymorphisms (SNPs) has not been done in deep sea coral species. In this study, we investigate the spatial genetic structure of Japanese red coral, Corallium japonicum, sampled off the coast of Kochi, southwest of Shikoku Island in Japan. The Kochi region suffers from overharvesting due to its high commercial value. We assessed the power to detect significant spatial genetic structure by varying the number of loci and the amount of missing data using both de novo analysis and mapping analysis. Similar results were obtained for both de novo and mapping analysis, although a higher number of loci was detected by the mapping method. In addition, “more SNPs with more missing data” is generally more useful for determining superior spatial genetic structure than “smaller number of SNPs with less missing data”. Our data indicate that more than 700 independent SNPs are required to identify high-resolution spatial genetic structure. For the C. japonicum population in Kochi, the maximum initial distance segment that could detect significant spatial genetic structure was less than 11 km, which is approximately 11 km wide in the Kochi area. , because it could cause the extinction of this species in the area.
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Deep-sea corals play an important role in increasing deep-sea biodiversity by providing complex habitat structures for other species (Roberts et al., 2006). Some of them are one of the most important marine resources because despite the limited supply, they are used as raw materials for decoration, especially in China and Taiwan (Tsounis et al., 2010). In Japan, three deep-sea coral species (Corallium japonicum, Pleurocorallium elatius, and Pleurocorallium konojoj) are collected and used to make jewelry in Kochi, Kagoshima, Okinawa, and Ogasawara Islands (CITES, 2007, 2009). Precious corals have low fertility rates (Nonaka et al., 2015), slow growth rates (Luan et al., 2013), and are highly threatened by hunting and overexploitation (Iwasaki, 2019). The Japanese Ministry of the Environment has listed three coral species as endangered species on the Red List (Ministry of the Environment, 2017).
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The diametric and linear growth rates of the Japanese red coral C. japonicum are only 0.2 mm and 2–6 mm per year, respectively (Luan et al., 2013), with a maximum growth of up to 30 cm ( Iwasaki et al. , 2009).This indicates that he has a long life. Corallium japonicum is a reproductive species of gonochoristic distribution (Kishinouye, 1904); Thus, population size and the relationship between different populations can be described by the larval dispersal rate. Although this is particularly problematic for marine species (Berry et al., 2012), especially long-lived sessile species (Ayre and Hughes, 2000), defining population boundaries is important for preservation of a species. Furthermore, pelagic larval dispersal duration (PLD) is unknown for most marine coral species, including C. japonicum (Waller, 2005), further challenging estimates of spatial larval dispersal rates.
Spatial genetic structure (SGS) based on a sufficient number of loci can be used to evaluate the degree of genetic interaction between successively distributed populations and to estimate the spatial extent of a population, that is , to preserve a small and critical part. species (Epperson, 2005; Schwartz and McKelvey, 2009). SGS analyzes of the red coral Corallium rurum found at depths shallower than 50 m in the Mediterranean Sea suggest significant genetic structure within a few meters (Costantini et al., 2007); Another analysis showed an estimated patch size of less than 40 cm for this species (Ledoux et al., 2010; Costantini et al., 2018), suggesting that self-recruitment is critical for conservation. of red coral species. (Ledoux et al., 2010) However, to the best of our knowledge, there is no previous study on SGS analysis of deep-sea species because the Global Positioning System (GPS) is difficult to acquire data on marine species, especially deep -sea types, and the lack of ocean types. Adequate and appropriate indicators for SGS.
Multiplexed inter simple sequence repeat (ISSR) genotype sequencing (MIG-seq) using polymerase chain reaction (PCR) and advanced technology can identify a small number of independent single nucleotide polymorphisms (SNPs) located near microsatellite region. This method is relatively simple, cost-effective and can be performed even without a reference genome. Since MIG-seq is a PCR-based method, DNA of low concentration and/or quality, often obtained from deep-sea coral species (Eguchi et al., 2020), can be used. In addition, previous studies on octocoral species have shown that MIG-seq analysis of microsatellite loci and mitochondrial DNA (Richard et al., 2018; Takata) is useful for determining genetic structures and lines that cannot be identified using traditional genetic markers. et al., 2019).
When using high-throughput analysis, there is always a trade-off between the number of SNPs detected and the number of missing data per locus, such as restriction site-associated DNA sequencing (RAD-seq) (Miller et al. , 2007 ). Therefore, maintaining a balance between the number of SNPs and the ratios of missing data per locus is important to perform an accurate genetic analysis, even if less data is available for SGS analysis (Shafer et al ., 2017); Therefore, the robustness of using a reference genome to detect SGS using MIG-seq analysis has not been empirically investigated.
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In this study, we investigated the detection power of SGS by (1) varying the number of loci and amount of missing data using de novo analysis and mapping methods, and (2) determined the extent of C. japonicum (Japanese red). Coral) population (in terms of larval dispersal) in one of the most important fishing grounds, Kochi.
Using a traditional coral net, 88 of the C. japonicum colonies were completely sampled, and GPS data was recorded for each coral based on the location of the vessel from which the nets began to be released. fishermen from 2011 to 2014 off the coast of Kochi, Japan (Figure 1). The horizontal distance over which each coral was sampled was 18 km × 18 km. The maximum distance between colonies is 18.6 km. Each coral was sampled at the same depth (100-140 m), and therefore, we only considered the horizontal distance between samples. An approximately 1 cm piece of coral was collected from each sample and preserved in 99.5% ethanol until genomic DNA extraction.
Figure 1. Sample location from Kochi (A, B) and distribution of 88 C. japonicum colonies collected in this study area (C). Japanese red coral C. japonicum (D) Fig.
Genomic DNA was extracted by a modified thermal alkaline method (Nakabayashi et al., 2019; Takata et al., 2019). We used the MIG-seq method (Suyama and Matsuki, 2015) to obtain genome-wide SNP data. MIG-seq amplifies independent, uncharacterized genome-wide ISSR regions (Gupta et al., 1994; Zietkiewicz et al., 1994) using a simple 2-step PCR procedure. We used eight pairs of multiplex ISSR primers (MIG-seq primer set 1, Suyama and Matsuki, 2015) with the Multiplex PCR Assay Kit Ver. 2 (TaKaRa Bio Inc., Otsu, Shiga, Japan) for the first PCR; We used a total reaction volume of 7 μL at T100.
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Thermal Cycler (Bio-Rad) with the following conditions: initial denaturation at 94°C for 1 min, followed by 27 cycles of 94°C for 30 s, 38°C for 1 min, 72°C for 1 min, and final . Incubate at 72°C for 10 minutes. Samples were amplified in a second PCR, using PrimeSTAR GXL DNA polymerase (TaKaRa Bio Inc., Otsu, Shiga, Japan) in a total reaction volume of 12 μL under the following thermal cycling conditions: 21 cycles of 98 ° C for 10 s , 54 °C for 15 s and 68 °C for 1 min. PCR products are mixed and hybridized and run on agarose gel; Gel extraction of PCR products with a length of 350–800 bp was performed. Gel-extracted DNA samples were sequenced using a MiSeq (Sequencing Control Software v2.0.12, Illumina) with the MiSeq Reagent v3 150 cycle kit (Illumina). Image analysis and base calling were performed using real-time analysis software v1.17.21 (Illumina).
Samples were collected under Kochi Prefecture sampling permits (Sa 401, Sa 412, and Sa 423) and Okinawa Prefecture sampling permits (Oki 21-131).
To remove low-quality reads and primer sequences from the raw data, we used the FASTX-Toolkit version 0.0.14 (fastaq_quality_filter).
With a fast quality filter setting of -q 30 -p 40. For Illumina MiSeq runs, we removed adapter sequences from both the 5′ end (GTCAGATCGGAAGAGCACCACGTCTGAACTCCAGTCAC) and 3′ end (CAGAGATCGGAAGAGCGTCGTGTAGGG AAAGAC, Cutadapt3 version 1). 2011), and we excluded smaller reads.
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