Population resequencing reveals candidate genes associated with salinity adaptation of the Pacific oyster Crassostrea gigas

Population resequencing reveals candidate genes associated with salinity adaptation of the... www.nature.com/scientificreports OPEN Population resequencing reveals candidate genes associated with salinity adaptation of the Pacific Received: 29 December 2017 oyster Crassostrea gigas Accepted: 17 May 2018 Published: xx xx xxxx 3 1,2,5 1,2,5 3 1,5 1,4,5 Zhicai She , Li Li , Jie Meng , Zhen Jia , Huayong Que & Guofan Zhang The Pacific oyster Crassostrea gigas is an important cultivated shellfish. As a euryhaline species, it has evolved adaptive mechanisms responding to the complex and changeable intertidal environment that it inhabits. To investigate the genetic basis of this salinity adaptation mechanism, we conducted a genome-wide association study using phenotypically differentiated populations (hyposalinity and hypersalinity adaptation populations, and control population), and confirmed our results using an independent population, high-resolution melting, and mRNA expression analysis. For the hyposalinity adaptation, we determined 24 genes, including Cg_CLCN7 (chloride channel protein 7) and Cg_AP1 (apoptosis 1 inhibitor), involved in the ion/water channel and transporter mechanisms, free amino acid and reactive oxygen species metabolism, immune responses, and chemical defence. Three SNPs located on these two genes were significantly differentiated between groups, as was Cg_CLCN7. For the hypersalinity adaptation, the biological process for positive regulating the developmental process was enriched. Enriched gene functions were focused on transcriptional regulation, signal transduction, and cell growth and differentiation, including calmodulin (Cg_CaM) and ficolin-2 (Cg_FCN2). These genes and polymorphisms possibly play an important role in oyster hyposalinity and hypersalinity adaptation. They not only further our understanding of salinity adaptation mechanisms but also provide markers for highly adaptable oyster strains suitable for breeding. e Th complex and changeable environment of the intertidal zone is easily influenced by rainfall and land condi- tions, and shows a larger variation in temperature and salinity than other shore zones . Salinity is one of the most important environmental factors, and varies considerably in intertidal zones . Of the many species inhabiting the intertidal zone, those belonging to the phylum Mollusca are the most common . The Pacific oyster Crassostrea gigas is representative of these intertidal organisms. Although this species has evolved mechanisms as adaptations to the intertidal environment, it is unable to withstand the dramatic fluctuations in salinity caused by alternating rainfall and drought, and therefore suffers large-scale mortality, especially in summer , bringing losses to economy. The Pacific oyster is an important cultivated shellfish, and is distributed along the north and south coasts of China. It is cultured mainly in small farms, where it has the characteristics of low input cost and high yield, and has become the main income source of coastal fishermen. When large-scale oyster mortality occurs, coastal s fi hermen therefore suffer serious economic losses. This would be alleviated by improved clarity concerning the salinity adaptation mechanisms and improvements in the resistance of oysters to salinity changes. Considerable eo ff rts have been made in this respect to date; scientists have found two relatively independent systems of adapta - tion to different levels of environmental salinity changes. In molluscs, resistance is based mainly on an impeded water-salt exchange with the external medium, due to mantle cavity hermetization. Salinity tolerance in molluscs Key Laboratory of Experimental Marine Biology, Institute of Oceanology, Chinese Academy of Sciences, Qingdao, 266071, Shandong, China. Laboratory for Marine Fisheries and Aquaculture, Qingdao National Laboratory for Marine Science and Technology, Qingdao, 266071, Shandong, China. Guangxi Key Laboratory of Beibu Gulf Marine Biodiversity Conservation, Qinzhou University, Qinzhou, 535011, Guangxi, China. Laboratory for Marine Biology and Biotechnology, Qingdao National Laboratory for Marine Science and Technology, Qingdao, 266071, Shandong, China. National & Local Joint Engineering Laboratory of Ecological Mariculture, Qingdao, 266071, Shandong, China. Correspondence and requests for materials should be addressed to L.L. (email: lili@qdio.ac.cn) or G.Z. (email: gfzhang@qdio.ac.cn) SCIENtIFIC RePo R TS | (2018) 8:8683 | DOI:10.1038/s41598-018-26953-w 1 www.nature.com/scientificreports/ Figure 1. Comparative results of phenotypic traits between groups. (a) shows the phenotypic traits of the first set of experimental materials; (b) shows the phenotypic traits of the second set of experimental materials. “*” e diff Th erence reached a significant level, “**” The difference reached a highly significant level. 5 6 7 is determined by cellular adaptation mechanisms . Free amino acids , RNA and protein synthesis , and inorganic 8–10 ions such as sodium and chlorine have proven to be important factors in salinity adaptation. Molecular stud- 11,12 ies into cellular osmoregulatory related genes have been conducted . Whole transcriptome responses to low salinity stress have been examined for the Pacific oyster , and the functional mechanism of salt stress effectors has been analysed . However, despite these previous efforts, the molecular mechanisms involved in salinity adap- tation remain unknown. Association analysis is a powerful tool for the dissection of complex traits and identification of alleles that contribute to a target trait . Genome-wide association studies (GWAS) are able to use genome-wide scanning to identify either the genetic variation or gene that is associated with complex phenotypic traits, or traits that are economically valuable . The advantages of GWAS are its high throughput, information content, and efficiency, allowing tens of thousands or more single nucleotide polymorphisms (SNPs) to be analysed at one time. GWAS can be applied to both quantitative and qualitative trait studies; case–control design is usually adopted for the 16,17 latter. It requires a full genome draft and a sufficient number of molecular markers ; with the completion of genome sequencing in pattern and non-pattern organisms, and decreases in sequencing costs, it has been widely 18–21 used in human, animal, and plant studies . However, GWAS results often yield a high false positive, so the method is generally used in analysing the few associated SNPs present in another larger population, as well as 22,23 mRNA expression in associated genes . RNA sequencing is used for gene expression analysis in the whole genome, and for determining the genes associated with the target trait . Recently, the availability of an oyster 1 25 reference genome sequence and the development of high-throughput SNP assays have made it possible to use GWAS to explore the genetic basis of oyster salinity adaptation. u Th s, in the present study, we used GWAS with high density SNPs, genotyped by whole-genome resequencing in different salt stress populations, to identify genes and natural allelic variations that contribute to salt stress differentiation in oysters. Results Phenotypic differentiation. By constructing the experiments with animals and phenotypic differentiation assays at different larval stages, we obtained phenotypic data for different populations. Using a one-way ANOVA, we found that there was a significant dier ff ence between different populations (Fig.  1). In the first set of experi - mental materials, we found a significant difference for larvae production, growth, and adhesive rates between the hyposalinity adaptation and control population; the difference between the growth and adhesive rate reached a highly significant level. The survival and growth rates were also significantly different between the hypersalinity adaptation and control populations, both to a highly signic fi ant level. In the second set of experimental materials, the larvae production and growth rates in the hyposalinity adaptation population showed very significant differ - ences compared to the control population; the same was also true for the hypersalinity adaptation population. SCIENtIFIC RePo R TS | (2018) 8:8683 | DOI:10.1038/s41598-018-26953-w 2 www.nature.com/scientificreports/ Allele frequency Gene classification Gene ID Gene annotation SNPs Genotype difference Scaffold Position Area OYG_10012229 Chloride channel protein 7 Cg_SNP_S1 Y 0.6074 scao ff ld190 78335 CDS Potassium voltage-gated channel Ion/Water channel OYG_10015111 Cg_SNP_S2 W 0.6017 scao ff ld578 105942 Intergenic protein OYG_10024446 Aquaporin-2 Cg_SNP_S3 R 0.7609 scao ff ld784 833760 Intergenic OYG_10017584 Solute carrier family 22 member 13 Cg_SNP_S4 M 0.75 scao ff ld1402 278732 Intergenic Transporter OYG_10017841 Excitatory amino acid transporter 1 Cg_SNP_S5 W 0.6396 scao ff ld72 196168 Intron OYG_10028337 Taurine transporter Cg_SNP_S6 Y 0.6428 scao ff ld419 251428 Intron OYG_10011934 Ornithine aminotransferase Cg_SNP_S7 K 0.6025 scao ff ld1720 23103 Intron FAA metabolism OYG_10012828 Ornithine aminotransferase Cg_SNP_S8 Y 0.6071 scao ff ld1603 315998 Intergenic Extracellular superoxide dismutase OYG_10004092 Cg_SNP_S9 Y 0.8 scao ff ld1558 5417 Intergenic [Cu-Zn] ROS-related OYG_10004145 Lysozyme Cg_SNP_S10 S 0.6666 scao ff ld40440 26183 Intron OYG_10007023 Glutathione peroxidase Cg_SNP_S11 R 0.6795 scao ff ld42508 2001 Intergenic Baculoviral IAP repeat-containing OYG_10005131 Cg_SNP_S12 R 0.6019 scao ff ld41522 22983 Intron protein 3 OYG_10010262 Complement C1q-like protein 4 Cg_SNP_S13 R 0.6171 scao ff ld181 89888 Intron OYG_10011513 Apoptosis 1 inhibitor Cg_SNP_S14 M 0.6667 scao ff ld43624 286034 Intron Immune response Complement C1q tumor necrosis OYG_10013066 Cg_SNP_S15 K 0.6025 scao ff ld1000 4977 Intergenic factor-related protein 3 OYG_10018846 Complement C1q-like protein 3 Cg_SNP_S16 Y 0.619 scao ff ld547 209775 Intron OYG_10019629 Scavenger receptor class F member 2 Cg_SNP_S17 Y 0.6421 scao ff ld1889 583732 Intron OYG_10027112 Apoptosis inhibitor IAP Cg_SNP_S18 W 0.6111 scao ff ld433 1161722 Intron OYG_10003807 Heat shock 70 kDa Cg_SNP_S19 Y 0.6071 scao ff ld40050 7148 Intergenic OYG_10004164 Stress-induced protein 1 Cg_SNP_S20 S 0.6333 scao ff ld1501 25676 Intron OYG_10011490 Cytochrome P450 1A2 Cg_SNP_S21 Y 0.6025 scao ff ld724 82719 Intron Chemical defence OYG_10014250 Cytochrome P450 3A9 Cg_SNP_S22 R 0.6307 scao ff ld1838 159533 Intergenic OYG_10022751 Heat shock 70 kDa protein 12 A Cg_SNP_S23 S 0.6428 scao ff ld365 718925 Intron OYG_10023593 Cytochrome P450 2U1 Cg_SNP_S24 S 0.6019 scao ff ld20 650996 Intergenic Table 1. Differentiated genes and single nucleotide polymorphisms associated with hyposalinity adaptation. Read alignment and SNP calling. DNA resequencing resulted in 462 million reads, or 93 Gb of raw sequencing data in total, 100% of which had quality scores at the CycleQ. 20 (a base quality greater than 20 and an error probability of 0.01) level. Aer fi ft ltering raw data based on high standards, we obtained high quality rese- quencing data. On average, about 76% of the reads covering 97% of the genome could be aligned to the genome; the depth ranged from 10 to 40×, mainly concentrated at about 35×. A total of 5.81, 5.94, and 5.64 million SNPs were identified in the hyposalinity adaptation, hypersalinity adaptation, and control populations, respectively. DNA differentiation for hyposalinity adaptation. For the hyposalinity adaptation, we obtained 681 differentiated SNPs and 934 differentiated genes, based on the allele frequency differentiation and F analy- ST ses. We did not find any biological processes or KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways related to hyposalinity adaptation, based on the enrichment analysis for the candidate gene set or expression profile. From the results of the GO (Gene Ontology Consortium; Supplementary Fig. S1) and COG (Clusters of Orthologous Groups; Supplementary Fig. S2) enrichment analyses, we found that differentiated genes were mainly concentrated on channel regulator and antioxidant activity, and signal transduction mechanisms. In order to lock down the genes associated with hyposalinity adaptation, we drew a Venn diagram for three gene groups: the differentiated genes obtained by allele frequency differentiation and F analysis, and the differ - ST entially expressed genes obtained by expression profile analysis under conditions of hyposalinity stress according to Meng et al. . Finally, we screened a total of 157 differentiated genes using expression profiling under hyposalin- ity stress conditions (Supplementary Fig. S3). Among these 157 genes, we found several genes related to the ion/ water channel and transporter mechanisms, free amino acid (FAA) and reactive oxygen species (ROS) metabo- lism, immunity responses, and chemical defence; we mainly focused on these genes (Table 1). Among the genes mentioned above, Cg_CLCN7 contained an SNP locus Cg_SNP_S1, of which the allele frequency difference between the hyposalinity adaptation and control populations reached 0.6074; there was also another locus with an adjacent large allele frequency. In the populations used for validation, 40 SNPs (Supplementary Table S1) in total were successfully genotyped; one (Cg_SNP_SV34) was located downstream of chloride channel protein 7 Cg_CLCN7, 2231 bp from Cg_SNP_S1, of which the allele frequency difference reached 0.9317. Additionally, we undertook an expression analysis for Cg_CLCN7 via qPCR, and found that the differ - ence between populations reached a very significant level. Gene expression was upregulated by only 0.23 in the hyposalinity adaptation population, while in the control population it was upregulated by as much as 9.91 (Fig. 2). All of these results indicated that it was possible Cg_CLCN7 and the SNPs Cg_SNP_S1 and Cg_SNP_SV34 play an important role in the process of hyposalinity adaptation in oysters. The distribution of SNPs with a large allele frequency difference on and around Cg_CLCN7 is shown in Fig.  3. SCIENtIFIC RePo R TS | (2018) 8:8683 | DOI:10.1038/s41598-018-26953-w 3 www.nature.com/scientificreports/ Figure 2. e exp Th ression variation in Cg_CLCN7 between groups. CK3d, the expression variation in Cg_CLCN7 of the control population on the third day; L3d, the expression variation in Cg_CLCN7 of the hyposalinity adaptation population on the third day. “**” The difference reached a highly significant level. Figure 3. Distribution of single nucleotide polymorphisms on and around Cg_CLCN7: UTR 5, 5ʹ-untranslated regions; UTR 3, 3′-untranslated regions. Y, transition between C and T; R, transition between A and G. In addition to Cg_CLCN7, the gene Cg_AP1 also contained one SNP, Cg_SNP_S14, of which the allele fre- quency difference between the hyposalinity adaptation and control populations reached 0.6667; in the popu- lations used for validation, the allele frequency difference reached 0.538. This indicated that Cg_SNP_S14 and the Cg_AP1 on which it is located might also play an important role in the process of hyposalinity adaptation in oysters. The F analysis determined that calmodulin (Cg_CaM), protein IMPACT homolog, apoptosis-inducing factor 1 ST (Cg_AIF1), and b fi ropellin-1 (Cg_FP1) all had very high F and that there was high differentiation between popu- ST lations. We selected these and used Circus software to show the SNP allele frequency die ff rence and F (Fig. 4). ST DNA differentiation for hypersalinity adaptation. Based on the allele frequency differentiation anal- ysis, we detected 438 differentiated SNPs located on 231 genes. Additionally, we found 558 differentiated genes, based on the F analysis. Then, we merged the above two groups of genes into a single gene set containing 779 ST differentiated genes, on which we performed an enrichment analysis. As a result, we did not find any KEGG path- ways related to hypersalinity adaptation, but we detected a significant difference in the positive regulation of the developmental process (Fig. 5), which indicated it might be related to hypersalinity adaptation. The functional classification of genes enriched in the biological process is shown in Fig.  6. Genes related to transcription regula- tion and signal transduction formed the highest proportion, while genes related to cell growth and differentiation accounted for eight percent. Based on the expression profile analysis, we detected 515 differentially expressed genes related to hypersalinity adaptation, and GO enrichment analysis showed that some biological processes were significantly enriched (P value shown in Fig. 7). Of these processes, positive regulation of growth rate and embryo development ending in birth or egg hatching were related to positive regulation of the developmental process, therefore supporting the finding that the latter might be related to hypersalinity adaptation at the expression level. Based on F analysis, Cg_FCN2, Cg_CaM, Monocarboxylate transporter (Cg_MCT), and Cytochrome P450 ST (Cg_CYP450) revealed very high F and were highly differentiated between populations. We selected these and ST showed the SNP allele frequency difference and F using Circos (Supplementary Fig. S4). ST Discussion e m Th echanisms underlying the adaptation of populations to their current environment is central to predicting their potential adaptive responses to environmental changes . Comprehending these mechanisms is therefore helpful in improving the resistance of populations to such change. We conducted a genome-wide association study using phenotypic differentiation populations (hyposalinity adaptation population, hypersalinity adaptation population, and control population) to search for genes and their variants involved in the salinity adaptation of the Pacific oyster. SCIENtIFIC RePo R TS | (2018) 8:8683 | DOI:10.1038/s41598-018-26953-w 4 www.nature.com/scientificreports/ Figure 4. Circos analysis of important genes associated with hyposalinity adaptation: scao ff ld933, the scao ff ld that Cg_FP1 located; scao ff ld488, the scao ff ld that Cg_AIF1 located; scao ff ld1391, the scao ff ld that Cg_CaM located; scao ff ld1377, the scao ff ld that protein IMPACT homolog located. The first circle shows the length of the 4 scao ff lds that the target genes located; the second circle shows the F value; the third circle shows the allelic ST frequency difference of the differential SNPs; the fourth circle shows the location of target genes. Figure 5. Enrichment analysis of the candidate differential gene set for the hypersalinity adaptation. SCIENtIFIC RePo R TS | (2018) 8:8683 | DOI:10.1038/s41598-018-26953-w 5 www.nature.com/scientificreports/ Figure 6. Functional classification of significantly enriched genes. Figure 7. Distribution of P values for significantly enriched biological processes identified in the expression profiling of the hypersalinity group. e m Th ajor phenotypic differences between the hyposalinity adaptation, hypersalinity adaptation and control populations in the test group were basically consistent with those in the validation group, though there are cer- tain differences between the two groups. There are many uncertainties that could influence the process of oyster development, and it’s really hard to achieve the same results of two independent artificial reproduction, maybe this is the reason that caused low repeatability of the treatment. For the hyposalinity adaptation, we obtained the gene set that was potentially associated with responding to hyposalinity. There were 24 genes in total, and the related functions could be divided into the following six categories: ion/water channel and transporter mechanisms, FAA and ROS metabolism, immune response, and chemical defences. This is similar to previous studies, as follows. According to the research results of Meng et al . , salt stress effectors mainly include ion channels, aquaporins, and FAA metabolism. Zhao et al . focus on biological functions such as osmoregulation, immune response, cell adhesion and communication, the cytoskeleton, cell 2+ 13 cycle, and Ca -binding proteins . In our study, we did not find any biological processes or KEGG pathways related to hyposalinity adaptation, based on the enrichment analysis for the candidate gene set or expression profile; this may be because the strength of the artificial stimulation was insufficient . According to our data, the SNPs Cg_SNP_S1 and Cg_SNP_SV34 showed considerable differentiation between the hyposalinity adaptation and control populations, and might therefore play an important role in hyposalinity adaptation in oysters. The Cg_CLCN7 gene on which they are located is also very important to hyposalinity adaptation, a role supported by its significant differential expression between groups (P < 0.01). Cl , which is transported by Cg_CLCN, is one of the important ions involved in osmoregulation in oysters. Previous studies have shown that oysters regulate their 5,28 intracellular concentration of solutes in order to adapt to surrounding conditions , and that, under osmotic + − + 2+29 stress conditions, ion channels mediate an ionic steady state for Na , Cl , K , and Ca . Cg_AP1, which con- tains SNP Cg_SNP_S14, is also associated with hyposalinity adaptation. The allele frequency of Cg_SNP_S14 was dier ff entiated in both the populations used for resequencing and those used for validation; this improved the reliability of the inference that Cg_AP1 was differentiated during hyposalinity adaptation in oysters. Cg_AP1 plays an important role in the immune response, which is in turn an important aspect of salinity stress responses 4,13 in organisms . For the hypersalinity adaptation, we performed an enrichment analysis on the candidate gene set, and deter- mined that the biological process related to positive regulation of development was enriched. Gene functions enriched in this process were focused on transcriptional regulation and signal transduction, which includes cell interaction, growth, and dier ff entiation. Transcriptional regulation and signal transduction are found widely in 30,31 salinity stress responses in organisms . Through the expression profile analysis, we also determined biological processes that were related to larval growth and development, such as nematode larval development, embryo development ending in birth or egg hatching, and positive regulation of growth rate. We found enrichment of the positive regulation of developmental processes both at the DNA and RNA level; this might be because artificial SCIENtIFIC RePo R TS | (2018) 8:8683 | DOI:10.1038/s41598-018-26953-w 6 www.nature.com/scientificreports/ Salinity adaptation populations Groups Families Phenotypic assay DNA assay RNA assay Samples larval production, survival, expression hyposalinity 1 100 DNA resequencing 46 growth, and adhesive rates profile analysis the first set of larval production, survival, expression control 1 100 DNA resequencing 46 experimental materials growth, and adhesive rates profile analysis larval production, survival, expression hypersalinity 1 100 DNA resequencing 46 growth, and adhesive rates profile analysis larval production, survival, hyposalinity 3 300 HRM qPCR 180 growth, and adhesive rates the second set of larval production, survival, control 3 300 HRM qPCR 270 experimental materials growth, and adhesive rates larval production, survival, hypersalinity 3 300 HRM qPCR 180 growth, and adhesive rates Table 2. Experimental materials used in the whole experiment. stimulation was carried out during the eye-spot larval stage, which is very important to oyster development. The stimulation therefore had a significant effect on the development process, resulting in the related genes making corresponding regulations to help organisms adapt to adverse environments. The genetic die ff rentiations exist in the oyster population originally, and the selection makes the allele fre - quency and genotype frequency of the population change. Individuals with different genetic backgrounds have different adaptabilities to salinity stress; the artificial salinity stress gathered the similar ones together, as well as their similar genetic backgrounds, thus the genetic die ff rentiations between die ff rent populations could be shown. Overall, phenotypic and genetic die ff rentiations suggest that die ff rent populations may have die ff rent adaptive potential to environmental variations. The identification of these genes and polymorphisms not only furthers our understanding of salinity adaptation mechanisms in oysters, but also provides markers of highly adaptable oyster strains suitable for breeding, which would promote research related to oyster breeding, reduce economic losses, and increase the benefits of oyster culture. Materials and Methods Experimental animals and artificial stimulation. er Th e were two sets of experimental materials in the whole experiment. The first was used to perform the association analysis and screen genges associated with salin- ity adaptation; the second was used to conform the results. The first set of materials was composed of 100 full-sib families crossed by 10 male and 10 female oysters, collected from Jiaonan, Qingdao, China. After fertilization, they were incubated in the same natural environmental conditions. Aer r ft eaching the eye-spot stage, the larvae were divided into three equally sized groups, and cultured under 15 ppt, 45 ppt, and 30 ppt salinity, respectively. e l Th arvae were fed four times a day and water was changed twice a day during the period of artificial stimulation. Aer eig ft ht days, all the groups were rehabilitated to a natural salinity level (30 ppt) and cultured in the same natu- ral environmental conditions. Seven months later, we sampled 46 individuals from each group to do the DNA and RNA assay, these being the hyposalinity adaptation, hypersalinity adaptation, and control populations. Using the same method as mentioned above, we produced a second set of experimental hyposalinity and hypersalinity adaptation populations and a control population, which contains three replicates and 300 full-sib families in total, and 90 individuals were sampled from each group to do the DNA and RNA assay. All the mate- rials used in the whole experiment were shown in Table 2. Salinity adaptation differentiation assay. We tested four indexes to assay the salt resistance die ff renti - ation between the test and control populations, including larval production, survival, growth, and adhesive rates. Both the two sets of experimental materials were used to test the differentiation: Larvae production rate differentiation assay. Aer ft fertilization, eggs were removed from each group and divided equally into three subgroups for incubation at different salinities: 15 ppt, 30 ppt, and 45 ppt. After 24 hours, the number of D-stage larvae in each subgroup was recorded, and the larvae production rate was cal- culated. The other fertilized eggs were incubated under natural conditions until D-stage larvae were produced. Survival rate differentiation assay. Aer in ft cubation, D-stage larvae were removed from each group and divided equally into three subgroups, and then cultured in 15 ppt, 30 ppt, and 45 ppt salinity conditions, respec- tively. After 18 days of this treatment, the number of surviving larvae in each subgroup was recorded, and the survival rate was calculated. The remaining D-stage larvae were cultured to the eye-spot stage under natural conditions. Growth rate differentiation assay. Before the D-stage larvae were divided, we selected 30 randomly from each group and measured shell height. Aer t ft he treatment, we again measured the height of 30 shells randomly selected from each subgroup and the growth rate was calculated. Adhesive rate differentiation assay. Eye-spot larvae were removed from each group and equally divided into three subgroups, and then cultured in 15 ppt, 30 ppt, and 45 ppt salinity conditions, respectively. Aer ft eight days of this treatment, we recorded the number of adherent larvae in each subgroup and calculated the adhesive rate. SCIENtIFIC RePo R TS | (2018) 8:8683 | DOI:10.1038/s41598-018-26953-w 7 www.nature.com/scientificreports/ After obtaining the phenotypic data, we performed a one-way ANOVA to compare the phenotypic traits between different salinity adaptation populations. Whole genome resequencing. We extracted DNA using the E.Z.N.A SQ tissue DNA Kit (Omega Bio-tek, Inc., Norcross, GA, USA) from the mantle of the three populations: the hyposalinity and hypersalinity adaptation populations, and the control population. The DNA integrity was assessed by agarose gel electrophoresis; the DNA concentration was assessed using a spectrophotometer (Nanodrop 2000; er Th mo Fisher Scientific, Wilmington, MA, USA). We selected 46 samples randomly for pooling from each population. A 101-bp paired-end sequenc- ing library with a 300-bp insert size was constructed for each DNA pool by the Beijing Biomarker Technologies Corporation, according to standard Illumina protocols, and each DNA library was sequenced with the HiSeq. 2000 genome analyzer (Illumina, Inc., San Diego, CA, USA). Read alignment and SNP calling. e ra Th w data was filtered to a high standard aer e ft valuation, and were then aligned to the oyster genome with the Burrows-Wheeler Alignment Tool (BWA) software (http://bio-bwa. sourceforge.net/bwa.shtml) . SNP calling was performed using SAMtools (http://samtools.sourceforge.net/sam- tools.shtml) , according to the alignment results of the sequencing data and oyster genome. The filtering criteria was: a sequencing depth of no lower than two, and three for heterozygous SNP; a maximum depth of no more than three times the average depth; a base quality higher than 20; and located outside the simple sequence repeat region. SNP allele frequency differentiation analysis. We calculated the allele frequency for each SNP locus, and determined the difference in allele frequency between different populations. Then, we extracted SNPs that showed die ff rent allele frequencies during pairwise comparison in both the hypo- and the hypersalinity adapta - tion populations. SNPs with a difference above 0.6 were selected as differentiated SNPs for salinity adaptation, and the genes on which these SNPs were located were selected as differentiated genes. F analysis. We also calculated F for all the 1000-bp windows on the genome. Then, the windows were ST ST sorted from high to low according to F , and those in the highest 1% were selected. We then extracted genes that ST overlapped with these selected windows, together with 5 Kb of both upstream and downstream of the windows, for both the hypo- and the hypersalinity adaptations. Gene set enrichment analysis. Genes obtained from the SNP allele frequency differentiation and F ST analyses were merged into a gene set for the hypo- and the hypersalinity adaptation, respectively; these were then used to undertake GO, COG, and the KEGG enrichment analyses. From the enrichment analysis, we determined the candidate gene set for hypo- and hypersalinity adaptation. Expression profile analysis. Invitrogen TRIzol reagent (Thermo Fisher Scientific, Wilmington, MA, USA) was used to extract the total RNA from the gills of the oysters used for whole genome resequencing. For each of the three populations, the total RNA of all 46 individuals was mixed together in equal amounts, and used to construct a sequencing library, which was then sequenced using the HiSeq. 2000 genome analyzer (Illumina, Inc., San Diego, CA, USA). First, the sequencing data was aligned to the reference gene sequence ae ft r evaluation using Blast software (The BLAST-Like Alignment Tool, http://genome.ucsc.edu/cgi-bin/hgBlat) , and we carried out a quantitative analysis of gene expression according to the alignment results. Second, we used EBSeq software to detect differentially expressed genes according to the quantitative results. Third, we compared the sequences of the die ff rentially expressed genes with NR (non-redundant), Swiss-Prot, GO, COG, and KEGG databases, using BLAST software for gene annotation. Finally, a COG, GO, and KEGG enrichment analysis was performed for the differentially expressed genes. Validation of the candidate polymorphisms and genes. We constructed 300 full-sib families crossed by 30 male and 30 female individuals collected from Jiaonan, Qingdao, China, and performed artificial stimula- tion to obtain another three salinity adaptation differentiated populations: hyposalinity and hypersalinity adap- tation populations, and the control population, as well as the salinity adaptation differentiation assay. This was undertaken in the same way as previously described. Differentiated SNPs (Table  1), and those developed in the partial differentiated genes (Supplementary Table S2) that were obtained from the SNP allele frequency differentiation analysis, were selected for validation. We used high-resolution melting (HRM) analysis for SNP development and genotyping in the three salinity adaptation differentiated populations, and then carried out SNP allele frequency differentiation analysis to deter - mine whether it was the same as in the populations used for resequencing. e diff Th erentiated genes (Supplementary Table S2) were also validated at the RNA level. We carried out arti- ficial stimulation on the three populations: hyposalinity stimulation (15 ppt) on the hyposalinity adaptation and control populations, and hypersalinity stimulation (45 ppt) on the hypersalinity adaptation and control popula- tions. For hyposalinity stimulation, we sampled 90 individuals from each population at zero hours as a control group, and then sampled another 90 individuals after three days as the test group, from each treatment. For hypersalinity stimulation, we used the same method as for the hyposalinity stimulation. Then, a quantitative polymerase chain reaction (qPCR) was performed to assay the expression difference of the differentiated genes between the control and test groups. Data availability. The oyster genome is available at http://www.oysterdb.com/FrontHomeAction. do?method=home. SCIENtIFIC RePo R TS | (2018) 8:8683 | DOI:10.1038/s41598-018-26953-w 8 www.nature.com/scientificreports/ References 1. Zhang, G. et al. The oyster genome reveals stress adaptation and complexity of shell formation. Nature 490, 49–54 (2012). 2. Macinnes, J. R. & Calabrese, A. Combined effects of salinity, temperature, and copper on embryos and early larvae of the American Oyster. Crassostrea virginica. Arch. Environ. Contam. Toxicol. 8, 553–562 (1979). 3. Liang, C. Y., Zhang, H. H. & Wu, J. F. Study on species composition, quantity distribution and biodiversity of intertidal benthos in Daya Bay. South China Fisheries Sci. 1, 42–48 (2005). 4. Meng, J. et al. 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EBSeq: an empirical Bayes hierarchical model for inference in RNA-seq experiments. Bioinformatics. 29, 1035–1043 (2013). 36. Wang, J. et al. Discovery and validation of genic single nucleotide polymorphisms in the Pacific oyster Crassostrea gigas. Mol. Ecol. Resour. 15, 123–135 (2015). Acknowledgements We thank the staff of the National & Local Joint Engineering Laboratory of Ecological Mariculture for their assistance with sampling and RNA extraction. We also thank Xuedi Du, Kai Song, and Jinpeng Wang for their valuable suggestions concerning the data analysis. Funding for this project was provided by the National Natural Science Foundation of China (31572620, 31530079), the National Basic Research Program of China (973 Program, No. 2010CB126402) and the Earmarked Fund for Modern Agro-industry Technology Research System (CARS-49), the Education Department Program of Guangxi (No. KY2016YB490), the Key Laboratory of Beibu Gulf Marine Biodiversity Conservation Program of Guangxi (No. 2018ZB07). Author Contributions G.Z., L.L., and Z.S. conceived and designed the experiments. Z.S. and Z.J. performed the experiments. Z.S. and J.M. analyzed the data. G.Z., L.L., H.Q., Z.S., and J.M. contributed reagents/materials/analysis tools. Z.S. wrote the paper. SCIENtIFIC RePo R TS | (2018) 8:8683 | DOI:10.1038/s41598-018-26953-w 9 www.nature.com/scientificreports/ Additional Information Supplementary information accompanies this paper at https://doi.org/10.1038/s41598-018-26953-w. Competing Interests: The authors declare no competing interests. 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Population resequencing reveals candidate genes associated with salinity adaptation of the Pacific oyster Crassostrea gigas

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www.nature.com/scientificreports OPEN Population resequencing reveals candidate genes associated with salinity adaptation of the Pacific Received: 29 December 2017 oyster Crassostrea gigas Accepted: 17 May 2018 Published: xx xx xxxx 3 1,2,5 1,2,5 3 1,5 1,4,5 Zhicai She , Li Li , Jie Meng , Zhen Jia , Huayong Que & Guofan Zhang The Pacific oyster Crassostrea gigas is an important cultivated shellfish. As a euryhaline species, it has evolved adaptive mechanisms responding to the complex and changeable intertidal environment that it inhabits. To investigate the genetic basis of this salinity adaptation mechanism, we conducted a genome-wide association study using phenotypically differentiated populations (hyposalinity and hypersalinity adaptation populations, and control population), and confirmed our results using an independent population, high-resolution melting, and mRNA expression analysis. For the hyposalinity adaptation, we determined 24 genes, including Cg_CLCN7 (chloride channel protein 7) and Cg_AP1 (apoptosis 1 inhibitor), involved in the ion/water channel and transporter mechanisms, free amino acid and reactive oxygen species metabolism, immune responses, and chemical defence. Three SNPs located on these two genes were significantly differentiated between groups, as was Cg_CLCN7. For the hypersalinity adaptation, the biological process for positive regulating the developmental process was enriched. Enriched gene functions were focused on transcriptional regulation, signal transduction, and cell growth and differentiation, including calmodulin (Cg_CaM) and ficolin-2 (Cg_FCN2). These genes and polymorphisms possibly play an important role in oyster hyposalinity and hypersalinity adaptation. They not only further our understanding of salinity adaptation mechanisms but also provide markers for highly adaptable oyster strains suitable for breeding. e Th complex and changeable environment of the intertidal zone is easily influenced by rainfall and land condi- tions, and shows a larger variation in temperature and salinity than other shore zones . Salinity is one of the most important environmental factors, and varies considerably in intertidal zones . Of the many species inhabiting the intertidal zone, those belonging to the phylum Mollusca are the most common . The Pacific oyster Crassostrea gigas is representative of these intertidal organisms. Although this species has evolved mechanisms as adaptations to the intertidal environment, it is unable to withstand the dramatic fluctuations in salinity caused by alternating rainfall and drought, and therefore suffers large-scale mortality, especially in summer , bringing losses to economy. The Pacific oyster is an important cultivated shellfish, and is distributed along the north and south coasts of China. It is cultured mainly in small farms, where it has the characteristics of low input cost and high yield, and has become the main income source of coastal fishermen. When large-scale oyster mortality occurs, coastal s fi hermen therefore suffer serious economic losses. This would be alleviated by improved clarity concerning the salinity adaptation mechanisms and improvements in the resistance of oysters to salinity changes. Considerable eo ff rts have been made in this respect to date; scientists have found two relatively independent systems of adapta - tion to different levels of environmental salinity changes. In molluscs, resistance is based mainly on an impeded water-salt exchange with the external medium, due to mantle cavity hermetization. Salinity tolerance in molluscs Key Laboratory of Experimental Marine Biology, Institute of Oceanology, Chinese Academy of Sciences, Qingdao, 266071, Shandong, China. Laboratory for Marine Fisheries and Aquaculture, Qingdao National Laboratory for Marine Science and Technology, Qingdao, 266071, Shandong, China. Guangxi Key Laboratory of Beibu Gulf Marine Biodiversity Conservation, Qinzhou University, Qinzhou, 535011, Guangxi, China. Laboratory for Marine Biology and Biotechnology, Qingdao National Laboratory for Marine Science and Technology, Qingdao, 266071, Shandong, China. National & Local Joint Engineering Laboratory of Ecological Mariculture, Qingdao, 266071, Shandong, China. Correspondence and requests for materials should be addressed to L.L. (email: lili@qdio.ac.cn) or G.Z. (email: gfzhang@qdio.ac.cn) SCIENtIFIC RePo R TS | (2018) 8:8683 | DOI:10.1038/s41598-018-26953-w 1 www.nature.com/scientificreports/ Figure 1. Comparative results of phenotypic traits between groups. (a) shows the phenotypic traits of the first set of experimental materials; (b) shows the phenotypic traits of the second set of experimental materials. “*” e diff Th erence reached a significant level, “**” The difference reached a highly significant level. 5 6 7 is determined by cellular adaptation mechanisms . Free amino acids , RNA and protein synthesis , and inorganic 8–10 ions such as sodium and chlorine have proven to be important factors in salinity adaptation. Molecular stud- 11,12 ies into cellular osmoregulatory related genes have been conducted . Whole transcriptome responses to low salinity stress have been examined for the Pacific oyster , and the functional mechanism of salt stress effectors has been analysed . However, despite these previous efforts, the molecular mechanisms involved in salinity adap- tation remain unknown. Association analysis is a powerful tool for the dissection of complex traits and identification of alleles that contribute to a target trait . Genome-wide association studies (GWAS) are able to use genome-wide scanning to identify either the genetic variation or gene that is associated with complex phenotypic traits, or traits that are economically valuable . The advantages of GWAS are its high throughput, information content, and efficiency, allowing tens of thousands or more single nucleotide polymorphisms (SNPs) to be analysed at one time. GWAS can be applied to both quantitative and qualitative trait studies; case–control design is usually adopted for the 16,17 latter. It requires a full genome draft and a sufficient number of molecular markers ; with the completion of genome sequencing in pattern and non-pattern organisms, and decreases in sequencing costs, it has been widely 18–21 used in human, animal, and plant studies . However, GWAS results often yield a high false positive, so the method is generally used in analysing the few associated SNPs present in another larger population, as well as 22,23 mRNA expression in associated genes . RNA sequencing is used for gene expression analysis in the whole genome, and for determining the genes associated with the target trait . Recently, the availability of an oyster 1 25 reference genome sequence and the development of high-throughput SNP assays have made it possible to use GWAS to explore the genetic basis of oyster salinity adaptation. u Th s, in the present study, we used GWAS with high density SNPs, genotyped by whole-genome resequencing in different salt stress populations, to identify genes and natural allelic variations that contribute to salt stress differentiation in oysters. Results Phenotypic differentiation. By constructing the experiments with animals and phenotypic differentiation assays at different larval stages, we obtained phenotypic data for different populations. Using a one-way ANOVA, we found that there was a significant dier ff ence between different populations (Fig.  1). In the first set of experi - mental materials, we found a significant difference for larvae production, growth, and adhesive rates between the hyposalinity adaptation and control population; the difference between the growth and adhesive rate reached a highly significant level. The survival and growth rates were also significantly different between the hypersalinity adaptation and control populations, both to a highly signic fi ant level. In the second set of experimental materials, the larvae production and growth rates in the hyposalinity adaptation population showed very significant differ - ences compared to the control population; the same was also true for the hypersalinity adaptation population. SCIENtIFIC RePo R TS | (2018) 8:8683 | DOI:10.1038/s41598-018-26953-w 2 www.nature.com/scientificreports/ Allele frequency Gene classification Gene ID Gene annotation SNPs Genotype difference Scaffold Position Area OYG_10012229 Chloride channel protein 7 Cg_SNP_S1 Y 0.6074 scao ff ld190 78335 CDS Potassium voltage-gated channel Ion/Water channel OYG_10015111 Cg_SNP_S2 W 0.6017 scao ff ld578 105942 Intergenic protein OYG_10024446 Aquaporin-2 Cg_SNP_S3 R 0.7609 scao ff ld784 833760 Intergenic OYG_10017584 Solute carrier family 22 member 13 Cg_SNP_S4 M 0.75 scao ff ld1402 278732 Intergenic Transporter OYG_10017841 Excitatory amino acid transporter 1 Cg_SNP_S5 W 0.6396 scao ff ld72 196168 Intron OYG_10028337 Taurine transporter Cg_SNP_S6 Y 0.6428 scao ff ld419 251428 Intron OYG_10011934 Ornithine aminotransferase Cg_SNP_S7 K 0.6025 scao ff ld1720 23103 Intron FAA metabolism OYG_10012828 Ornithine aminotransferase Cg_SNP_S8 Y 0.6071 scao ff ld1603 315998 Intergenic Extracellular superoxide dismutase OYG_10004092 Cg_SNP_S9 Y 0.8 scao ff ld1558 5417 Intergenic [Cu-Zn] ROS-related OYG_10004145 Lysozyme Cg_SNP_S10 S 0.6666 scao ff ld40440 26183 Intron OYG_10007023 Glutathione peroxidase Cg_SNP_S11 R 0.6795 scao ff ld42508 2001 Intergenic Baculoviral IAP repeat-containing OYG_10005131 Cg_SNP_S12 R 0.6019 scao ff ld41522 22983 Intron protein 3 OYG_10010262 Complement C1q-like protein 4 Cg_SNP_S13 R 0.6171 scao ff ld181 89888 Intron OYG_10011513 Apoptosis 1 inhibitor Cg_SNP_S14 M 0.6667 scao ff ld43624 286034 Intron Immune response Complement C1q tumor necrosis OYG_10013066 Cg_SNP_S15 K 0.6025 scao ff ld1000 4977 Intergenic factor-related protein 3 OYG_10018846 Complement C1q-like protein 3 Cg_SNP_S16 Y 0.619 scao ff ld547 209775 Intron OYG_10019629 Scavenger receptor class F member 2 Cg_SNP_S17 Y 0.6421 scao ff ld1889 583732 Intron OYG_10027112 Apoptosis inhibitor IAP Cg_SNP_S18 W 0.6111 scao ff ld433 1161722 Intron OYG_10003807 Heat shock 70 kDa Cg_SNP_S19 Y 0.6071 scao ff ld40050 7148 Intergenic OYG_10004164 Stress-induced protein 1 Cg_SNP_S20 S 0.6333 scao ff ld1501 25676 Intron OYG_10011490 Cytochrome P450 1A2 Cg_SNP_S21 Y 0.6025 scao ff ld724 82719 Intron Chemical defence OYG_10014250 Cytochrome P450 3A9 Cg_SNP_S22 R 0.6307 scao ff ld1838 159533 Intergenic OYG_10022751 Heat shock 70 kDa protein 12 A Cg_SNP_S23 S 0.6428 scao ff ld365 718925 Intron OYG_10023593 Cytochrome P450 2U1 Cg_SNP_S24 S 0.6019 scao ff ld20 650996 Intergenic Table 1. Differentiated genes and single nucleotide polymorphisms associated with hyposalinity adaptation. Read alignment and SNP calling. DNA resequencing resulted in 462 million reads, or 93 Gb of raw sequencing data in total, 100% of which had quality scores at the CycleQ. 20 (a base quality greater than 20 and an error probability of 0.01) level. Aer fi ft ltering raw data based on high standards, we obtained high quality rese- quencing data. On average, about 76% of the reads covering 97% of the genome could be aligned to the genome; the depth ranged from 10 to 40×, mainly concentrated at about 35×. A total of 5.81, 5.94, and 5.64 million SNPs were identified in the hyposalinity adaptation, hypersalinity adaptation, and control populations, respectively. DNA differentiation for hyposalinity adaptation. For the hyposalinity adaptation, we obtained 681 differentiated SNPs and 934 differentiated genes, based on the allele frequency differentiation and F analy- ST ses. We did not find any biological processes or KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways related to hyposalinity adaptation, based on the enrichment analysis for the candidate gene set or expression profile. From the results of the GO (Gene Ontology Consortium; Supplementary Fig. S1) and COG (Clusters of Orthologous Groups; Supplementary Fig. S2) enrichment analyses, we found that differentiated genes were mainly concentrated on channel regulator and antioxidant activity, and signal transduction mechanisms. In order to lock down the genes associated with hyposalinity adaptation, we drew a Venn diagram for three gene groups: the differentiated genes obtained by allele frequency differentiation and F analysis, and the differ - ST entially expressed genes obtained by expression profile analysis under conditions of hyposalinity stress according to Meng et al. . Finally, we screened a total of 157 differentiated genes using expression profiling under hyposalin- ity stress conditions (Supplementary Fig. S3). Among these 157 genes, we found several genes related to the ion/ water channel and transporter mechanisms, free amino acid (FAA) and reactive oxygen species (ROS) metabo- lism, immunity responses, and chemical defence; we mainly focused on these genes (Table 1). Among the genes mentioned above, Cg_CLCN7 contained an SNP locus Cg_SNP_S1, of which the allele frequency difference between the hyposalinity adaptation and control populations reached 0.6074; there was also another locus with an adjacent large allele frequency. In the populations used for validation, 40 SNPs (Supplementary Table S1) in total were successfully genotyped; one (Cg_SNP_SV34) was located downstream of chloride channel protein 7 Cg_CLCN7, 2231 bp from Cg_SNP_S1, of which the allele frequency difference reached 0.9317. Additionally, we undertook an expression analysis for Cg_CLCN7 via qPCR, and found that the differ - ence between populations reached a very significant level. Gene expression was upregulated by only 0.23 in the hyposalinity adaptation population, while in the control population it was upregulated by as much as 9.91 (Fig. 2). All of these results indicated that it was possible Cg_CLCN7 and the SNPs Cg_SNP_S1 and Cg_SNP_SV34 play an important role in the process of hyposalinity adaptation in oysters. The distribution of SNPs with a large allele frequency difference on and around Cg_CLCN7 is shown in Fig.  3. SCIENtIFIC RePo R TS | (2018) 8:8683 | DOI:10.1038/s41598-018-26953-w 3 www.nature.com/scientificreports/ Figure 2. e exp Th ression variation in Cg_CLCN7 between groups. CK3d, the expression variation in Cg_CLCN7 of the control population on the third day; L3d, the expression variation in Cg_CLCN7 of the hyposalinity adaptation population on the third day. “**” The difference reached a highly significant level. Figure 3. Distribution of single nucleotide polymorphisms on and around Cg_CLCN7: UTR 5, 5ʹ-untranslated regions; UTR 3, 3′-untranslated regions. Y, transition between C and T; R, transition between A and G. In addition to Cg_CLCN7, the gene Cg_AP1 also contained one SNP, Cg_SNP_S14, of which the allele fre- quency difference between the hyposalinity adaptation and control populations reached 0.6667; in the popu- lations used for validation, the allele frequency difference reached 0.538. This indicated that Cg_SNP_S14 and the Cg_AP1 on which it is located might also play an important role in the process of hyposalinity adaptation in oysters. The F analysis determined that calmodulin (Cg_CaM), protein IMPACT homolog, apoptosis-inducing factor 1 ST (Cg_AIF1), and b fi ropellin-1 (Cg_FP1) all had very high F and that there was high differentiation between popu- ST lations. We selected these and used Circus software to show the SNP allele frequency die ff rence and F (Fig. 4). ST DNA differentiation for hypersalinity adaptation. Based on the allele frequency differentiation anal- ysis, we detected 438 differentiated SNPs located on 231 genes. Additionally, we found 558 differentiated genes, based on the F analysis. Then, we merged the above two groups of genes into a single gene set containing 779 ST differentiated genes, on which we performed an enrichment analysis. As a result, we did not find any KEGG path- ways related to hypersalinity adaptation, but we detected a significant difference in the positive regulation of the developmental process (Fig. 5), which indicated it might be related to hypersalinity adaptation. The functional classification of genes enriched in the biological process is shown in Fig.  6. Genes related to transcription regula- tion and signal transduction formed the highest proportion, while genes related to cell growth and differentiation accounted for eight percent. Based on the expression profile analysis, we detected 515 differentially expressed genes related to hypersalinity adaptation, and GO enrichment analysis showed that some biological processes were significantly enriched (P value shown in Fig. 7). Of these processes, positive regulation of growth rate and embryo development ending in birth or egg hatching were related to positive regulation of the developmental process, therefore supporting the finding that the latter might be related to hypersalinity adaptation at the expression level. Based on F analysis, Cg_FCN2, Cg_CaM, Monocarboxylate transporter (Cg_MCT), and Cytochrome P450 ST (Cg_CYP450) revealed very high F and were highly differentiated between populations. We selected these and ST showed the SNP allele frequency difference and F using Circos (Supplementary Fig. S4). ST Discussion e m Th echanisms underlying the adaptation of populations to their current environment is central to predicting their potential adaptive responses to environmental changes . Comprehending these mechanisms is therefore helpful in improving the resistance of populations to such change. We conducted a genome-wide association study using phenotypic differentiation populations (hyposalinity adaptation population, hypersalinity adaptation population, and control population) to search for genes and their variants involved in the salinity adaptation of the Pacific oyster. SCIENtIFIC RePo R TS | (2018) 8:8683 | DOI:10.1038/s41598-018-26953-w 4 www.nature.com/scientificreports/ Figure 4. Circos analysis of important genes associated with hyposalinity adaptation: scao ff ld933, the scao ff ld that Cg_FP1 located; scao ff ld488, the scao ff ld that Cg_AIF1 located; scao ff ld1391, the scao ff ld that Cg_CaM located; scao ff ld1377, the scao ff ld that protein IMPACT homolog located. The first circle shows the length of the 4 scao ff lds that the target genes located; the second circle shows the F value; the third circle shows the allelic ST frequency difference of the differential SNPs; the fourth circle shows the location of target genes. Figure 5. Enrichment analysis of the candidate differential gene set for the hypersalinity adaptation. SCIENtIFIC RePo R TS | (2018) 8:8683 | DOI:10.1038/s41598-018-26953-w 5 www.nature.com/scientificreports/ Figure 6. Functional classification of significantly enriched genes. Figure 7. Distribution of P values for significantly enriched biological processes identified in the expression profiling of the hypersalinity group. e m Th ajor phenotypic differences between the hyposalinity adaptation, hypersalinity adaptation and control populations in the test group were basically consistent with those in the validation group, though there are cer- tain differences between the two groups. There are many uncertainties that could influence the process of oyster development, and it’s really hard to achieve the same results of two independent artificial reproduction, maybe this is the reason that caused low repeatability of the treatment. For the hyposalinity adaptation, we obtained the gene set that was potentially associated with responding to hyposalinity. There were 24 genes in total, and the related functions could be divided into the following six categories: ion/water channel and transporter mechanisms, FAA and ROS metabolism, immune response, and chemical defences. This is similar to previous studies, as follows. According to the research results of Meng et al . , salt stress effectors mainly include ion channels, aquaporins, and FAA metabolism. Zhao et al . focus on biological functions such as osmoregulation, immune response, cell adhesion and communication, the cytoskeleton, cell 2+ 13 cycle, and Ca -binding proteins . In our study, we did not find any biological processes or KEGG pathways related to hyposalinity adaptation, based on the enrichment analysis for the candidate gene set or expression profile; this may be because the strength of the artificial stimulation was insufficient . According to our data, the SNPs Cg_SNP_S1 and Cg_SNP_SV34 showed considerable differentiation between the hyposalinity adaptation and control populations, and might therefore play an important role in hyposalinity adaptation in oysters. The Cg_CLCN7 gene on which they are located is also very important to hyposalinity adaptation, a role supported by its significant differential expression between groups (P < 0.01). Cl , which is transported by Cg_CLCN, is one of the important ions involved in osmoregulation in oysters. Previous studies have shown that oysters regulate their 5,28 intracellular concentration of solutes in order to adapt to surrounding conditions , and that, under osmotic + − + 2+29 stress conditions, ion channels mediate an ionic steady state for Na , Cl , K , and Ca . Cg_AP1, which con- tains SNP Cg_SNP_S14, is also associated with hyposalinity adaptation. The allele frequency of Cg_SNP_S14 was dier ff entiated in both the populations used for resequencing and those used for validation; this improved the reliability of the inference that Cg_AP1 was differentiated during hyposalinity adaptation in oysters. Cg_AP1 plays an important role in the immune response, which is in turn an important aspect of salinity stress responses 4,13 in organisms . For the hypersalinity adaptation, we performed an enrichment analysis on the candidate gene set, and deter- mined that the biological process related to positive regulation of development was enriched. Gene functions enriched in this process were focused on transcriptional regulation and signal transduction, which includes cell interaction, growth, and dier ff entiation. Transcriptional regulation and signal transduction are found widely in 30,31 salinity stress responses in organisms . Through the expression profile analysis, we also determined biological processes that were related to larval growth and development, such as nematode larval development, embryo development ending in birth or egg hatching, and positive regulation of growth rate. We found enrichment of the positive regulation of developmental processes both at the DNA and RNA level; this might be because artificial SCIENtIFIC RePo R TS | (2018) 8:8683 | DOI:10.1038/s41598-018-26953-w 6 www.nature.com/scientificreports/ Salinity adaptation populations Groups Families Phenotypic assay DNA assay RNA assay Samples larval production, survival, expression hyposalinity 1 100 DNA resequencing 46 growth, and adhesive rates profile analysis the first set of larval production, survival, expression control 1 100 DNA resequencing 46 experimental materials growth, and adhesive rates profile analysis larval production, survival, expression hypersalinity 1 100 DNA resequencing 46 growth, and adhesive rates profile analysis larval production, survival, hyposalinity 3 300 HRM qPCR 180 growth, and adhesive rates the second set of larval production, survival, control 3 300 HRM qPCR 270 experimental materials growth, and adhesive rates larval production, survival, hypersalinity 3 300 HRM qPCR 180 growth, and adhesive rates Table 2. Experimental materials used in the whole experiment. stimulation was carried out during the eye-spot larval stage, which is very important to oyster development. The stimulation therefore had a significant effect on the development process, resulting in the related genes making corresponding regulations to help organisms adapt to adverse environments. The genetic die ff rentiations exist in the oyster population originally, and the selection makes the allele fre - quency and genotype frequency of the population change. Individuals with different genetic backgrounds have different adaptabilities to salinity stress; the artificial salinity stress gathered the similar ones together, as well as their similar genetic backgrounds, thus the genetic die ff rentiations between die ff rent populations could be shown. Overall, phenotypic and genetic die ff rentiations suggest that die ff rent populations may have die ff rent adaptive potential to environmental variations. The identification of these genes and polymorphisms not only furthers our understanding of salinity adaptation mechanisms in oysters, but also provides markers of highly adaptable oyster strains suitable for breeding, which would promote research related to oyster breeding, reduce economic losses, and increase the benefits of oyster culture. Materials and Methods Experimental animals and artificial stimulation. er Th e were two sets of experimental materials in the whole experiment. The first was used to perform the association analysis and screen genges associated with salin- ity adaptation; the second was used to conform the results. The first set of materials was composed of 100 full-sib families crossed by 10 male and 10 female oysters, collected from Jiaonan, Qingdao, China. After fertilization, they were incubated in the same natural environmental conditions. Aer r ft eaching the eye-spot stage, the larvae were divided into three equally sized groups, and cultured under 15 ppt, 45 ppt, and 30 ppt salinity, respectively. e l Th arvae were fed four times a day and water was changed twice a day during the period of artificial stimulation. Aer eig ft ht days, all the groups were rehabilitated to a natural salinity level (30 ppt) and cultured in the same natu- ral environmental conditions. Seven months later, we sampled 46 individuals from each group to do the DNA and RNA assay, these being the hyposalinity adaptation, hypersalinity adaptation, and control populations. Using the same method as mentioned above, we produced a second set of experimental hyposalinity and hypersalinity adaptation populations and a control population, which contains three replicates and 300 full-sib families in total, and 90 individuals were sampled from each group to do the DNA and RNA assay. All the mate- rials used in the whole experiment were shown in Table 2. Salinity adaptation differentiation assay. We tested four indexes to assay the salt resistance die ff renti - ation between the test and control populations, including larval production, survival, growth, and adhesive rates. Both the two sets of experimental materials were used to test the differentiation: Larvae production rate differentiation assay. Aer ft fertilization, eggs were removed from each group and divided equally into three subgroups for incubation at different salinities: 15 ppt, 30 ppt, and 45 ppt. After 24 hours, the number of D-stage larvae in each subgroup was recorded, and the larvae production rate was cal- culated. The other fertilized eggs were incubated under natural conditions until D-stage larvae were produced. Survival rate differentiation assay. Aer in ft cubation, D-stage larvae were removed from each group and divided equally into three subgroups, and then cultured in 15 ppt, 30 ppt, and 45 ppt salinity conditions, respec- tively. After 18 days of this treatment, the number of surviving larvae in each subgroup was recorded, and the survival rate was calculated. The remaining D-stage larvae were cultured to the eye-spot stage under natural conditions. Growth rate differentiation assay. Before the D-stage larvae were divided, we selected 30 randomly from each group and measured shell height. Aer t ft he treatment, we again measured the height of 30 shells randomly selected from each subgroup and the growth rate was calculated. Adhesive rate differentiation assay. Eye-spot larvae were removed from each group and equally divided into three subgroups, and then cultured in 15 ppt, 30 ppt, and 45 ppt salinity conditions, respectively. Aer ft eight days of this treatment, we recorded the number of adherent larvae in each subgroup and calculated the adhesive rate. SCIENtIFIC RePo R TS | (2018) 8:8683 | DOI:10.1038/s41598-018-26953-w 7 www.nature.com/scientificreports/ After obtaining the phenotypic data, we performed a one-way ANOVA to compare the phenotypic traits between different salinity adaptation populations. Whole genome resequencing. We extracted DNA using the E.Z.N.A SQ tissue DNA Kit (Omega Bio-tek, Inc., Norcross, GA, USA) from the mantle of the three populations: the hyposalinity and hypersalinity adaptation populations, and the control population. The DNA integrity was assessed by agarose gel electrophoresis; the DNA concentration was assessed using a spectrophotometer (Nanodrop 2000; er Th mo Fisher Scientific, Wilmington, MA, USA). We selected 46 samples randomly for pooling from each population. A 101-bp paired-end sequenc- ing library with a 300-bp insert size was constructed for each DNA pool by the Beijing Biomarker Technologies Corporation, according to standard Illumina protocols, and each DNA library was sequenced with the HiSeq. 2000 genome analyzer (Illumina, Inc., San Diego, CA, USA). Read alignment and SNP calling. e ra Th w data was filtered to a high standard aer e ft valuation, and were then aligned to the oyster genome with the Burrows-Wheeler Alignment Tool (BWA) software (http://bio-bwa. sourceforge.net/bwa.shtml) . SNP calling was performed using SAMtools (http://samtools.sourceforge.net/sam- tools.shtml) , according to the alignment results of the sequencing data and oyster genome. The filtering criteria was: a sequencing depth of no lower than two, and three for heterozygous SNP; a maximum depth of no more than three times the average depth; a base quality higher than 20; and located outside the simple sequence repeat region. SNP allele frequency differentiation analysis. We calculated the allele frequency for each SNP locus, and determined the difference in allele frequency between different populations. Then, we extracted SNPs that showed die ff rent allele frequencies during pairwise comparison in both the hypo- and the hypersalinity adapta - tion populations. SNPs with a difference above 0.6 were selected as differentiated SNPs for salinity adaptation, and the genes on which these SNPs were located were selected as differentiated genes. F analysis. We also calculated F for all the 1000-bp windows on the genome. Then, the windows were ST ST sorted from high to low according to F , and those in the highest 1% were selected. We then extracted genes that ST overlapped with these selected windows, together with 5 Kb of both upstream and downstream of the windows, for both the hypo- and the hypersalinity adaptations. Gene set enrichment analysis. Genes obtained from the SNP allele frequency differentiation and F ST analyses were merged into a gene set for the hypo- and the hypersalinity adaptation, respectively; these were then used to undertake GO, COG, and the KEGG enrichment analyses. From the enrichment analysis, we determined the candidate gene set for hypo- and hypersalinity adaptation. Expression profile analysis. Invitrogen TRIzol reagent (Thermo Fisher Scientific, Wilmington, MA, USA) was used to extract the total RNA from the gills of the oysters used for whole genome resequencing. For each of the three populations, the total RNA of all 46 individuals was mixed together in equal amounts, and used to construct a sequencing library, which was then sequenced using the HiSeq. 2000 genome analyzer (Illumina, Inc., San Diego, CA, USA). First, the sequencing data was aligned to the reference gene sequence ae ft r evaluation using Blast software (The BLAST-Like Alignment Tool, http://genome.ucsc.edu/cgi-bin/hgBlat) , and we carried out a quantitative analysis of gene expression according to the alignment results. Second, we used EBSeq software to detect differentially expressed genes according to the quantitative results. Third, we compared the sequences of the die ff rentially expressed genes with NR (non-redundant), Swiss-Prot, GO, COG, and KEGG databases, using BLAST software for gene annotation. Finally, a COG, GO, and KEGG enrichment analysis was performed for the differentially expressed genes. Validation of the candidate polymorphisms and genes. We constructed 300 full-sib families crossed by 30 male and 30 female individuals collected from Jiaonan, Qingdao, China, and performed artificial stimula- tion to obtain another three salinity adaptation differentiated populations: hyposalinity and hypersalinity adap- tation populations, and the control population, as well as the salinity adaptation differentiation assay. This was undertaken in the same way as previously described. Differentiated SNPs (Table  1), and those developed in the partial differentiated genes (Supplementary Table S2) that were obtained from the SNP allele frequency differentiation analysis, were selected for validation. We used high-resolution melting (HRM) analysis for SNP development and genotyping in the three salinity adaptation differentiated populations, and then carried out SNP allele frequency differentiation analysis to deter - mine whether it was the same as in the populations used for resequencing. e diff Th erentiated genes (Supplementary Table S2) were also validated at the RNA level. We carried out arti- ficial stimulation on the three populations: hyposalinity stimulation (15 ppt) on the hyposalinity adaptation and control populations, and hypersalinity stimulation (45 ppt) on the hypersalinity adaptation and control popula- tions. For hyposalinity stimulation, we sampled 90 individuals from each population at zero hours as a control group, and then sampled another 90 individuals after three days as the test group, from each treatment. For hypersalinity stimulation, we used the same method as for the hyposalinity stimulation. Then, a quantitative polymerase chain reaction (qPCR) was performed to assay the expression difference of the differentiated genes between the control and test groups. Data availability. The oyster genome is available at http://www.oysterdb.com/FrontHomeAction. do?method=home. SCIENtIFIC RePo R TS | (2018) 8:8683 | DOI:10.1038/s41598-018-26953-w 8 www.nature.com/scientificreports/ References 1. Zhang, G. et al. The oyster genome reveals stress adaptation and complexity of shell formation. Nature 490, 49–54 (2012). 2. Macinnes, J. R. & Calabrese, A. Combined effects of salinity, temperature, and copper on embryos and early larvae of the American Oyster. Crassostrea virginica. Arch. Environ. Contam. Toxicol. 8, 553–562 (1979). 3. Liang, C. Y., Zhang, H. H. & Wu, J. F. Study on species composition, quantity distribution and biodiversity of intertidal benthos in Daya Bay. South China Fisheries Sci. 1, 42–48 (2005). 4. Meng, J. et al. 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EBSeq: an empirical Bayes hierarchical model for inference in RNA-seq experiments. Bioinformatics. 29, 1035–1043 (2013). 36. Wang, J. et al. Discovery and validation of genic single nucleotide polymorphisms in the Pacific oyster Crassostrea gigas. Mol. Ecol. Resour. 15, 123–135 (2015). Acknowledgements We thank the staff of the National & Local Joint Engineering Laboratory of Ecological Mariculture for their assistance with sampling and RNA extraction. We also thank Xuedi Du, Kai Song, and Jinpeng Wang for their valuable suggestions concerning the data analysis. Funding for this project was provided by the National Natural Science Foundation of China (31572620, 31530079), the National Basic Research Program of China (973 Program, No. 2010CB126402) and the Earmarked Fund for Modern Agro-industry Technology Research System (CARS-49), the Education Department Program of Guangxi (No. KY2016YB490), the Key Laboratory of Beibu Gulf Marine Biodiversity Conservation Program of Guangxi (No. 2018ZB07). Author Contributions G.Z., L.L., and Z.S. conceived and designed the experiments. Z.S. and Z.J. performed the experiments. Z.S. and J.M. analyzed the data. G.Z., L.L., H.Q., Z.S., and J.M. contributed reagents/materials/analysis tools. Z.S. wrote the paper. SCIENtIFIC RePo R TS | (2018) 8:8683 | DOI:10.1038/s41598-018-26953-w 9 www.nature.com/scientificreports/ Additional Information Supplementary information accompanies this paper at https://doi.org/10.1038/s41598-018-26953-w. Competing Interests: The authors declare no competing interests. Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Cre- ative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not per- mitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. © The Author(s) 2018 SCIENtIFIC RePo R TS | (2018) 8:8683 | DOI:10.1038/s41598-018-26953-w 10

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