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Integrated Application of Transcriptomics and Metabolomics Provides Insights Into Unsynchronized Growth in Pearl Oyster Pinctada Fucata Martensii

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Integrated Application of Transcriptomics and Metabolomics Provides Insights Into Unsynchronized Growth in Pearl Oyster Pinctada Fucata Martensii Science of the Total Environment 666 (2019) 46–56 Contents lists available at ScienceDirect Science of the Total Environment journal homepage: www.elsevier.com/locate/scitotenv Integrated application of transcriptomics and metabolomics provides insights into unsynchronized growth in pearl oyster Pinctada fucata martensii Ruijuan Hao a, Xiaodong Du a,b, Chuangye Yang a,YuewenDenga,b,⁎, Zhe Zheng a,⁎, Qingheng Wang a,b a Fisheries College, Guangdong Ocean University, Zhanjiang 524088, China b Pearl Breeding and Processing Engineering Technology Research Centre of Guangdong Province, Zhanjiang 524088, China HIGHLIGHTS GRAPHICAL ABSTRACT • Metabolomics and transcriptomic were applied to analyze the bivalve unsyn- chronized growth. • Fast-growing group hold high biominer- alization activity. • Slow-growing group consumed more energy in response to environment stress. • Fast-growing group exhibited high di- gestion and anabolic ability. • Fast-growing group possessed high os- motic regulation ability. article info abstract Article history: Similar to other marine bivalves, Pinctada fucata martensii presents unsynchronized growth, which is one of the Received 5 October 2018 problems farmers currently face. However, the underlying mechanisms have not been studied. In the present Received in revised form 13 February 2019 study, pearl oyster P. f. martensii from cultured stocks were selected to produce a progeny stock. At 180 days, Accepted 14 February 2019 the stock was sorted by size, and fast- and slow-growing individuals were separately sampled. Then, Available online 15 February 2019 metabolomic and transcriptomic approaches were applied to assess the metabolic and transcript changes be- Editor: Julian Blasco tween the fast- and slow-growing P. f. martensii groups and understand the mechanism underlying their unsyn- chronized growth. In the metabolomics assay, 30 metabolites were considered significantly different metabolites Keywords: (SDMs) between the fast- and slow-growing groups and pathway analysis indicated that these SDMs were in- Unsynchronized growth volved in 20 pathways, including glutathione metabolism; sulfur metabolism; valine, leucine, and isoleucine bio- Metabolomics synthesis; and tryptophan metabolism. The transcriptome analysis of different growth groups showed 168 Transcriptomic differentially expressed genes (DEGs) and pathway enrichment analysis indicated that DEGs were involved in ex- Pinctada fucata martensii tracellular matrix-receptor interaction, pentose phosphate pathway, aromatic compound degradation. Inte- grated transcriptome and metabolome analyses showed that fast-growing individuals exhibited higher biomineralization activity than the slow-growing group, which consumed more energy than the fast-growing group in response to environmental stress. Fast-growing group also exhibited higher digestion, anabolic ability, and osmotic regulation ability than the slow-growing group. This study is the first work involving the integrated metabolomic and transcriptomic analyses to identify the key pathways to understand the molecular and meta- bolic mechanisms underlying unsynchronized bivalve growth. © 2019 Elsevier B.V. All rights reserved. ⁎ Corresponding authors at: Guangdong Ocean University, China. E-mail addresses: [email protected] (Y. Deng), [email protected] (Z. Zheng). https://doi.org/10.1016/j.scitotenv.2019.02.221 0048-9697/© 2019 Elsevier B.V. All rights reserved. R. Hao et al. / Science of the Total Environment 666 (2019) 46–56 47 1. Introduction worldwide. However, the growth of this species is also unsynchronized. It is reasonable to hypothesize that the unsynchronized growth of P. f. Growth performance is a major problem for aquaculture to increase martensii might result from the endogenic metabolic level differences. the production of shellfish farms. Researchers explored the factors Therefore, we utilized gas chromatography time-of-flight mass spec- influencing the growth performance of bivalves, such as genotype trometry (GC-TOF/MS)-based metabolomics approach, together with (Wada and Komaru, 1994; J.M. Yang et al., 2018), stocking density transcript analysis, to investigate the difference in metabolite profiles (Taylor et al., 1997), mesh covers (Taylor et al., 1998), salinity (Taylor between the fast- and slow-growing P. f. martensii. The connection net- et al., 2004), temperature (Wang et al., 2012), and food (Mills, 2000). works were mapped based on correlations between metabolites and However, for many marine invertebrates, bivalve growth is unsynchro- regulatory genes associated with growth performance difference. The nized and subject to considerable variability. Namely, differences in findings will provide new insights into the study of the molecular mech- growth rate are exhibited by different individuals under similar envi- anisms associated with growth traits and highlight the significance of an ronmental conditions (Brown, 1988; Tamayo et al., 2011; Hao et al., integrated approach for this research. 2018; Venter et al., 2018; Wang et al., 2019), which are one of the prob- lems farmers currently face. These differences have often been attrib- 2. Materials and methods uted to unexplained variance and given comparatively less research attention (Tamayo et al., 2011). However, a comprehensive overview 2.1. Experimental animals between the genes and the metabolites controlling pearl oyster growth performance is unclear. And this require an in-depth study to shorten The fifth-generation selected line of P. f. martensii for rapid growth the growth duration and eliminate the variability which would indeed was used in the experiment. The selected line was designated as reduce production costs and increase turnover in the long run (Ten “Haixuan NO 1”, and the development of the line was described in detail Doeschate and Coyne, 2008). by Du et al. (2015). A total of 48 mature animals (female: male = 28:20) Rapidadvancesintechnologyhavemadenext-generationsequenc- were used to produce the progeny stock. In October 2017, breeders ing suitable to conduct large-scale studies to identify candidate genes were mass spawned in the hatchery. The procedures for larval, juvenile, related to different phenotypes (Rondon et al., 2016; Wang et al., and adult rearing were detailed by Deng et al. (2009).Briefly, fertilized 2018a). However, phenotype is not only regulated by genes but also eggs were incubated in 200 L polyethylene tanks until the D stage, regulated at multiple other levels, including at the metabolite level. which occurred 24 h after fertilization. Then, larvae were transferred Transcriptome sequencing can provide important information on the to 1000 L polyethylene tanks. The density was maintained at 1 individ- gene expression level, which facilitates functional genomic studies, in- ual/mL. The water temperature was set at 28 ± 1 °C, and salinity was set cluding global gene expression, novel gene discovery, and full-length at 30.0 ± 0.5‰. Daily feeding consisted of Isochrysis galbana from day 2 gene assembly (Vera et al., 2008; Salem et al., 2010; Huang et al., to day 7 and a mixture of I. galbana and I. zhanjiangensis from day 7 to 2011; Qiao et al., 2018; Zhao et al., 2012). However, transcriptome se- day 60. At day 25, plastic plates were provided as the substrate for meta- quencing fails to study the real metabolite levels in organisms, thereby morphosis. At day 60, large individuals that are 3–5 mm in size were re- causing difficulty in confirming the critical pathways responsible for moved from the plates, transferred into net cages, and suspended in the regulating specific traits. Meanwhile, metabolomics is an analytical ap- sea of Liushagang, Zhanjiang. The shells were cleaned and placed in new proach used to study metabolites and understand the physiological nets at appropriate intervals. and biochemical statuses of a biosystem relative to phenotype (Cappello et al., 2017a; Hao et al., 2018; C.Y. Yang et al., 2018; Yang et al., 2019; Hayden et al., 2019; Jiang et al., 2019). For example, Hao 2.2. Growth measurement and sampling et al. (2018) reported that pearl oyster Pinctada maxima changes its metabolic status with different growth performances and showed that At 180 days, the stock was sorted by size, and fast- and slow- glycine, serine, and threonine metabolism and glutathione metabolism growing individuals were separately sampled. The shell length, shell made their contribution for the growth of oysters. This approach has width, and shell height of each sample were measured using a digital been used to study the global metabolites in cells, tissues, and biofluids caliper (0.02 mm accuracy). The shell weight and total weight of each of living systems and understand the physiological and biochemical sta- sample were measured using an electronic balance (0.01 g accuracy). tuses of biosystems with further biological principle interpretation (Cappello et al., 2017a; Hao et al., 2018; C.Y. Yang et al., 2018). There- 2.3. Metabolite extraction, detection, and analysis fore, using metabolomics together with transcriptomics to study the variation in growth is an effective method to provide insight into the Adductor muscles from each sample were dissected from fast- and unsynchronized growth of pearl oyster. The integration of these large- slow-growing individuals and stored in liquid N. A total of 10 adductor scale datasets, including transcriptome and metabolome, has been ap- muscles were collected from each group. Approximately 50 ± 1 mg plied successfully in other aquatic animals
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