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Communication Biology Volume 4, Article Number: 1287 (2021) Cite this article

Understanding the role of genetic differences and phenotypic plasticity in adaptation is the core of evolutionary biology, and it is also important for assessing the adaptation potential of species under climate change. Analysis of the horizontal assembly and resequencing of individual chromosomes distributed across latitudes in the estuarine oyster (Crassostrea ariakensis) revealed unexpectedly low genome diversity and population structure shaped by historical glaciations, geological events, and oceanographic forces. Strong selection signals were detected in genes that respond to temperature and salinity stress, especially in the expanded solute carrier family, highlighting the importance of gene expansion in environmental adaptation. Genes that exhibit high plasticity show strong selection in the upstream regulatory region that regulates transcription, indicating that selection is beneficial to plasticity. Our results show that the genome variation and population structure of marine bivalves are severely affected by climate history and physical forces. Gene expansion and selection may enhance phenotypic plasticity, which is essential for adapting to a rapidly changing environment.

Climate change poses a serious threat to global biodiversity and ecosystem stability. The oceans bear the impact of climate change because they absorb most of the heat from the sun and about one-third of the carbon dioxide produced by human activities1. The world’s oceans have become warmer and more acidic. Sea levels are rising, and ocean circulation patterns are changing. As the global temperature rises, dry regions become drier and humid regions become wetter2, increasing the difference in salinity between estuaries3. These rapid changes in the world’s oceans have had a huge impact on marine ecosystems, most of which are still poorly understood. The impact of different organisms may be different, and changes in key species may have a great impact on the stability of the ecosystem. Understanding how organisms adapt to environmental changes is the basis of evolutionary biology, and is essential for assessing their adaptive potential under climate change.

The ability of organisms to survive and adapt to sudden environmental changes depends on available genetic variation and phenotypic plasticity. The long-term genetic variation of a species is the result of its evolutionary history, shaped by mutation, selection and genetic drift4,5,6. Past and ongoing climatic and geological events may leave selection or bottleneck features in the genome and affect the geographic distribution of genetic variation7,8,9. Many recent studies have revealed the important role of phenotypic plasticity in environmental adaptation10,11,12,13,14,15. Phenotypic plasticity can buffer sudden environmental changes and provide time for adaptation to occur 12, 13, 14, 16. Phenotypic plasticity is particularly significant and important for sessile organisms living in estuaries and intertidal zones. They cannot cope with wide fluctuations in environmental conditions by avoiding them11,12,17, although the genetic basis for enhanced plasticity is unclear18, 19.

Oysters are a key species in coastal and estuary ecology, and provide key ecological services as filter feeders and habitat engineers. As a sessile bivalve that thrives in coastal areas, oysters are well adapted to highly dynamic environmental conditions, which is reflected in their high genetic diversity and plasticity 12, 20, 21, 22, 23, making them An interesting model for studying the response to climate change. In addition to the threats posed by climate change, the global oyster population has also fallen sharply due to overfishing, habitat destruction and infectious diseases24,25. The estuary (Jinjiang or Zhujiang) oyster (Crassostrea ariakensis, Figure 1a) is widely distributed in the large latitudes of the East Asian estuary, with a wide range of temperature and salinity 24, 25, 26, 27, 28. Unlike the Pacific oyster (Crassostrea gigas), which is a co-local sister species that is highly abundant in high-latitude and high-salinity waters, estuarine oysters are only found in low-salinity estuaries within their wide range of distribution and have low abundance. The evolutionary pathways leading to significant differences in abundance, distribution, and salinity preferences are not yet clear, but they are important for our understanding of adaptive evolution.

An estuary oyster (photo by Qian Lumin). b The Hi-C interaction heat map shows 10 chromosomes of the estuarine oyster. c CIRCOS diagram shows 10 chromosomes (a), distribution of GC content (b), transposable factor (c), coding sequence (d), and repetitive gene clusters of the solute vector family showing selection signals (e, see also Supplement Fig. 13). d Summary statistics of genome assembly.

Although genetic changes can be inferred from genetic markers, whole-genome analysis is essential to explore all genetic variations and identify genes and selection events that are critical to adaptation. Using multi-omics analysis, we have previously shown that Pacific oysters have a highly polymorphic genome, extensive environmental response genes, and a positive correlation between plasticity and local adaptation12,23. In this study, we assembled the genome of estuarine oysters at the chromosome level, resequenced 264 wild oysters collected from 11 estuaries, and conducted transcriptomics studies on environmental responses to understand their genetic variation, Population structure, phenotypic plasticity and genome characteristics. Choices or bottlenecks that may be related to environmental changes. Our results show that the genetic diversity of estuarine oysters is significantly lower than that of its sister species, which may be due to the influence of glaciers on its unique estuary lifestyle in the past. Its population structure is greatly affected by geological events and ocean currents. The expansion and selection of regulatory regions of environmental response genes may enhance phenotypic plasticity, which is essential for adapting to a rapidly changing environment.

We used a combination of nanopore long read lengths generated by 9 flow cells to produce a chromosome-level assembly of the estuarine oyster genome with an estimated size of 614.05 Mb (19-mer analysis) (Supplementary Figure 1, Supplementary Table 1). PromethlON platform (184 Gb, 299.24×), Illumina double-ended short read length (64 Gb) and Hi-C (106 Gb) sequence and layered assembly method. The final assembly is composed of 630 contigs with an N50 of 6.97 Mb, spanning 613.89 Mb, of which 99.6% or 416 scaffolds with an N50 of 62.26 Mb are assembled into 10 chromosomes corresponding to the number of haploids (Figure 1b and c, supplement) Table 2). To the best of our knowledge, this is the most continuous assembly ( Via contig N50). The coverage of the assembled genome was evaluated by mapping RNA-seq reads and Illumina genome reads, and more than 97.9% of the genome short reads and 97.2% RNA-seq reads were mapped to the assembly (Supplementary Table 4). In addition, the genome assembly captured 92.24% of the benchmark Universal Single Copy Ortholog (BUSCO) dataset (Figure 1d, Supplementary Data 1), indicating that the assembly has a high degree of integrity. Compared with Sanger sequencing, the accuracy of genome sequence is 98.32% (Supplementary Table 5). REAPR analysis shows that the fragment coverage distribution error and the low fragment coverage on the gap are both zero, indicating that the genome assembly is accurate. These results indicate that the estuarine oyster genome assembly is of high quality and can be used for downstream analysis.

The estuarine oyster genome encodes 29,631 protein-coding genes, as shown by homology, de novo prediction, and mRNA transcripts, of which 96.13% are functionally annotated (Supplementary Table 6). Various non-coding RNA sequences were also identified and annotated in the genome, including 1,077 transfer RNAs, 20 microRNAs, and 131 ribosomal RNAs. A total of 332.40 Mb (54.14%) of repetitive elements were identified (Supplementary Table 7), which is higher than the 43% observed in Pacific oysters. Gene density was negatively correlated with the content of repetitive elements in all chromosomes (Supplementary Figure 2, ρ = -0.308, p <0.001). The genome-wide polymorphism is 0.58%, which is less than half of Pacific oysters23, which may be due to the population bottleneck caused by glaciers in the past that has more serious impacts on their estuary lifestyles (see below).

We analyzed the macro-synonymity between C. ariakensis and two other oyster species that inhabit low-salinity estuaries, C. virginica and C. hongkongensis. High collinearity was found between C. ariakensis and C. hongkongensis, covering 20,571 genes of the C. ariakensis genome (Supplementary Data 2), with a range of 205.48 Mb, while the commonality between C. ariakensis and C. virginica The linearity is low, covering the 194.17 Mb C. ariakensis genome of 18,692 genes (Supplementary Figure 3, Supplementary Data 3). Our findings are consistent with the fact that the two Asian species C. ariakensis and C. hongkongensis have a closer phylogenetic relationship. They diverged before 22.3 Myr, while the Atlantic C. virginica and the Asian species had a closer phylogenetic relationship before 82.7 Myr. Disagreement 39. The assembly error in the C. virginica genome indicated by the difference from the linkage map (X. Guo, personal communication) can also explain some of the differences.

We generated 3.81 Tb of clean whole-genome resequencing data from 264 wild oysters collected from 11 estuaries covering most of the distribution area (Figure 2a). The genome mapping rate averaged 95.3%, ranging from 86.5% to 96.7%, and the average mapping read depth was 19.89 times. The data generated 145,271,754 SNPs (from 487,881 to 640,962 per person) and 103,080,822 indels (from 342,486 to 443,381 per person). Overall, each individual has 0.47 heterozygous SNPs per Kb (Supplementary Data 4), which is about 35 times lower than that of Pacific oyster populations.

a Sampling sites of 264 resequenced wild oysters in 11 estuaries along the coast of China. The arrow curve represents ocean currents in summer. a: Kuroshio, b: Yellow Sea warm current, c: Yellow Sea cold water mass, d: Bohai Circulation, e: China offshore current, f: South China Sea warm current. SCS: South China Sea, ECS: East China Sea, YS: Yellow Sea, BS: Bohai. DD: Dandong, YK: Yingkou, BZ: Binzhou, DY: Dongying, QD: Qingdao, NT: Nantong, SH: Shanghai, JLJ: Jiulong River, ZhJ: Zhangjiang, TS: Taishan, QZh: Qinzhou. b Figures of principal components 1 and 2 of 264 resequenced oysters based on whole genome data. c A phylogenetic tree of estuarine oysters inferred from genome-wide SNPs using the maximum likelihood (ML) method. NC-a: Northern China, including BZ and DY; NC-b: Northern China, including DD and YK; MC: Central China, including NT and SH; SC-a: South China, including JLJ and ZhJ; SC-b: South China , Including TS and QZh. d Nucleotide diversity (pi, below or above the circle) and genetic differences (FST, between populations) between the four populations.

The structural analysis of genome-wide SNPs revealed significant differences between different geographic populations, consistent with previous analyses of health-related traits, neutral markers, and transcriptome data, but with higher resolution. The optimal number of population clusters was determined to be k = 3 (Supplementary Figure 4), representing the northern coast of China (NC, 5 sites), Central China (MC, 2 sites), and South China (SC, 4 sites) (Supplementary Figure 5). Principal component analysis (PCA) explained 16% of the genetic variation through two PCs and consistently revealed three different populations corresponding to NC, MC and SC. In addition, oysters from Qingdao (QD) and southern subgroups (SC-b) including Taishan (TS) and Qinzhou (QZh) were detected as fine subgroups separated from NC and SC, respectively (Figure 2b). In addition, phylogenetic analysis using the maximum likelihood (ML) method supports PCA clustering, first to distinguish the southern population from other populations, and then in the NC located in the southern estuary of the Bohai Sea, including Binzhou (BZ) and Dongying (BZ) Identify a subpopulation (NC-a). DY) (Figure 2c). A total of 6 subgroups were identified in 11 estuaries, which greatly improved the resolution of fine population structure. Paired FST using all polymorphic positions showed that the difference between SC and other groups was very large, ranging from 0.143 to 0.225 (Figure 2d), while the difference within the SC, MC, and NC groups was much lower (Supplementary Table 8) ). Oysters from MC and NC cluster together in phylogenetic trees and have low genetic differences (FST <0.05), which is comparable to the difference between Pacific oyster populations in northern China12. In the three populations, linkage disequilibrium (LD, measured as r2) decreased to half of its maximum value (approximately 2.54-3.00 kb) (Supplementary Figure 6), which decayed much slower than the LD in Pacific oysters (~0.1 kb) 12. These results provide unprecedented insights into the fine population structure of estuarine oysters. Compared with Pacific oysters, the discovery of unusually low sequence diversity, slow LD decay and large population differences in estuarine oysters indicates that these two species have experienced different evolutionary forces.

In summer, the population structure of estuarine oysters is basically the same as the direction of ocean currents. Except for the medium population near the Yangtze River estuary, the north-south coastal currents do not cross (Figure 2a and Supplementary Movie 1). Nucleotide diversity (pi) is the highest in the intermediate population (3.56 × 10−4) and QD oyster (3.59 × 10−4), which may be because the confluence of the Southern and Northern Oceans has brought increased genetic diversity to the intermediate population (Figure 3a). These findings indicate that ocean currents play an important role in shaping and maintaining the structure of estuarine oyster populations12,43. The unusually high difference between the SC and NC populations can be explained by the fact that the currents from the south and north do not cross and cannot promote gene flow. The discharge of freshwater from the Yangtze River is considered to be the main obstacle restricting the flow of genes between the north and south populations of marine organisms7,44,45,46,47. However, for estuarine oysters, populations on the north (NT) and south (SH) sides of the Yangtze River clustered together as MCs, indicating that the Yangtze River is not an obstacle to gene flow in estuarine oysters that breed in low-salinity water. estuary.

a Nucleotide diversity (pi) of 11 geographic populations along the coast of China. b The demographic history of the three populations of estuarine oyster (ar) (SC, MC, and NC) and Pacific oyster (C. gigas or gi) inferred by the paired sequence Markov aggregation method. The Mindel Glacier (MG, 0.68~0.80 mya), Rees Glacier (MG, 0.24~0.37 mya) and Wielm Glacier (WG, 10,000~120,000 years ago) are covered by yellow shades. c Nucleotide diversity (pi) of the three populations of estuarine oyster and Pacific oyster (C. gigas). The data is displayed as a box plot; the central rectangle spans the first to third quartiles of the distribution, and the "whiskers" above and below the box show the maximum and minimum estimates. The line inside the rectangle represents the median, and the circle represents the outlier. d The ratio of SNPs that are highly heterozygous for 10 chromosomes in the northern (N_het) or southern (S_het) population only. e The ratio of genes with non-synonymous SNPs in the three oyster populations. The data is represented as a circle graph, each circle represents an individual oyster, and the bar graph shows the mean ± SEM. Asterisks indicate significant differences (*** p <0.001). Error bars represent SEM values.

In order to reconstruct the demographic history, the pairwise sequential Markov merger (PSMC) method was used to assess the fluctuations in effective population size (Ne) in response to Quaternary climate changes, using two or three estuaries from each of the three populations Genomic data of oysters, as well as three previously sequenced Pacific oysters for comparison12. In the past million years, both species have been severely affected by glacial events, as their Ne reached ~0.90 mya (MG, 0.68~0.80 mya) before Mindel Glacier, and then dropped significantly during the three glacial periods : MG, Riss (RG, 0.24~0.37 mya) and Würm (WG, 10,000~120,000 years ago) glaciation. The Ne of both species reached the bottom during the WG during the last ice age, but the Pacific oyster population rebounded earlier and higher than the estuarine oyster, indicating that the last ice age had a greater impact on the estuary oyster. In addition, during the interglacial period before the last glacial period of ~0.2 mya, the SC and NC populations of estuarine oysters began to differentiate (Figure 3b). We also used the divergence time between C. gigas and C. angulata as a reference, and estimated the divergence time between SC and NC based on cytochrome oxidase I (COI) sequence data 39,46. The COI-based estimate puts the divergence time at 0.14~0.63 mya, which is close to the 0.2 mya estimated by PSMC using whole-genome data (Figure 3b). The differentiation time coincides with the formation of a land bridge between Taiwan and mainland China through tectonic movement from 0.2 mya to 25,000 years ago. This creates a physical barrier for the gene flow between the northern and southern populations that are now maintained by ocean currents.

The nucleotide diversity of all three populations of estuarine oysters is 25 times lower than that of Pacific oysters (pi_C. gigas = 9.27 × 10-3, Figure 3c). Similarly, sticklebacks adapted to freshwater areas exhibit lower pi and Ne values ​​of 49 than similar fish that live in salt water areas. These findings indicate that climate change may have different effects on species with different lifestyles, and estuarine oysters may be more sensitive to climate change than Pacific oysters that inhabit open waters with high salinity. In addition, the Ne curves of the central and northern populations split about 90,000 years ago, which corresponds to a sharp drop in the sea level of the WG subglacial 50 when the Bohai Sea dried up. The lower Ne and nucleotide diversity of the northern population (Figure 3a, b) supports a stronger bottleneck for the Bohai estuary oysters. Relatively low nucleotide diversity was also found in the Bohai Sea population of Pacific oysters, possibly due to sea level decline. The role of bottlenecks and geographic isolation caused by historical glaciations and tectonic events in shaping system geography has been demonstrated in other molluscs, such as the differences between the populations of the oyster Crassostrea virginica on the Atlantic coast and the eastern Gulf of Mexico. Our results provide the assumed timing of differentiation between estuarine oyster populations related to historical events and show that past climate and tectonic movements have played an important role in shaping the genetic diversity and population differentiation of estuarine oysters.

To explore the effects of selection and genetic drift, we compared highly heterozygous SNPs (heterozygosity> 0.5) in northern and southern populations. Among the 10 chromosomes, the number of SNPs that are highly heterozygous only in NC (14,373) is 1.89 times higher than the number of SNPs that are highly heterozygous only in SC (7,595) (Figure 3d). Both directed selection and genetic bottlenecks can reduce the number of heterozygous SNPs. It was found that the proportion of non-synonymous SNP genes in southern oysters (35.67% ± 0.35%) was significantly higher than that of central (33.33% ± 0.22%) and northern (31.82% ± 0.29%) oysters (p <0.01, Figure 1). 3e), indicating that southern oysters have experienced stronger selection. A stronger choice, possibly from strong environmental disturbance, reduced the number of heterozygous SNPs in southern oysters. Therefore, in addition to the isolation and bottlenecks of historical glaciations, geological events, and ocean power, selection or local adaptation may also play an important role in shaping the variation and systematic geography of estuarine oysters.

The oysters that inhabit the northern and southern estuaries experience very different environmental conditions. The southern habitat is characterized by high temperature and low salinity. Satellite remote sensing data from 2000 to 2017 show that the monthly average sea surface temperature (SST) of the southern habitat is 10.35°C higher than that of the northern habitat (Supplementary Figure 7). The salinity of the northern BZ site is 10.98 ‰3 higher than that of the southern TS site. Climate change may exacerbate the difference and cause the salinity of the northern habitat to increase, but the salinity of the southern habitat decreases 51,52. Therefore, temperature and salinity are the two most important environmental factors that contribute to the differences in the adaptability of populations between North and South. Due to discontinuous distribution and limited gene flow, southern populations have become locally adapted and have evolved higher tolerance to high temperature and low salinity3. We expect that some genomic regions will be selected and help adapt to the higher temperature and lower salinity conditions in the southern population.

In order to identify the characteristics of the selection scan, we calculated the fixation index (FST) and selection statistics (Tajima's D) between the two population pairs (SC vs. NC and SC vs. MC), and determined the FST outlier (formerly 1%, FST _north vs. MC). South >= 0.693, FST_middle and South >= 0.637). Only the genomic regions that overlap between the two population pairs and are located around the selectivity peak of the Tajima D value valley in one of the three populations are considered to be in selection. In the three oyster populations, a total of 24 selective regions were identified along chromosomes 2, 3, 4, 6, 8 and 9, spanning 51 candidate genes (44 annotations) (Figure 4a, b and Supplementary Figure 8- 13, Supplementary data 5)). Most of these candidate genes involve responses to environmental disturbances such as salinity and temperature 53,54,55,56,57,58,59,60.

a Global FST values ​​between two population pairs (top 1%, red line): north and south (up) and central and south (bottom). b Global Tajima D values ​​for northern (top), middle (middle), and south (bottom) populations. c Selected gene expression under the challenge of exposure to heat (6 hours at 37 °C) and high salinity (7 days at 60‰). Asterisks indicate significant differences (*p <0.05, **p <0.01). The blue to yellow color bars represent the relative expression levels of a given gene from low to high.

To evaluate the transcriptome response of estuarine oysters to temperature and salinity interference, we performed RNA-seq analysis on oysters exposed to high temperature (6 hours at 37°C) and high salinity (7 days at 60 ‰). Only genes with 10 or more aligned reads in >90% of the samples are used for subsequent analysis. Approximately 44.17% (10,279 out of 23,270) and 11.7% (2,757 out of 23,650) genes are differentially expressed under high salinity and temperature stress, respectively, of which 1,088 genes respond to both stressors (Supplementary Figure 14). For the 51 candidate genes from the selected region, a total of 29 genes were expressed, of which 75.9% (22 of 29) and 58.6% (17 of 29) were differentially expressed under high salinity and high temperature ( p <0.05, Figure 4c), indicating that most of the selected genes are involved in the response of these two factors to environmental interference. Thirteen genes respond to heat and salt stress, while only three genes are insensitive to these two stresses. Nine and four genes are highly responsive to high salinity and high temperature, respectively. The discovery that most genes from selected regions are involved in the response to temperature and salinity disturbances suggests that these two environmental factors are important driving factors for the adaptive evolution of estuarine oysters.

In the selected 24 regions, we found two tandem replication gene clusters belonging to the solute carrier family, 10 copies of Slc23a2, and 4 monocarboxylic acid transporter 12 (Mct12, also known as Slc16a12) (Supplementary Figure 13c), The regions located on the two chromosomes 9 have high differences among the three populations (FST = 0.81 and 0.76) (Supplementary Figure 13d). Genomic regions spanning the Slc23a2 gene family showed extremely low Tajima D values ​​in northern oysters, while these spanning Mct12 gene families had extremely low Tajima D values ​​in southern oysters (Supplementary Figure 13e). These findings indicate that the Slc23a2 and Mct12 genes are under directional selection in the northern and southern environments, respectively, highlighting the key role of the Slc family genes in the adaptation of marine species to marine species, such as porpoises and corals 60,61,62,63 against salt Degree and temperature change.

Ten copies of the Slc23a2 family belong to three orthogonal groups, two of which are annotated as purine permease (a: OG0011985 and c: OG0000489), and the other is annotated as uric acid transporter (OG0000633). The four copies of the Mct12 gene family belong to a positive group annotated as purine efflux pump (OG0000571). All these positive flocks are widely spread in estuarine oysters and two other types of oysters (Crassostrea virginica and Crassostrea hongkongensis) and high salinity waters (Supplementary Figure 15). In addition to their presence in the tandem array on chromosome 9, phylogenetic analysis further supports the expansion of solute carrier families, where repetitive genes are mainly clustered in orthogonal groups and lineage/species specific ways (Supplementary Figure 16). Gene copies that did not respond to heat and high salt stress were not expressed or expressed very low (FPKM <0.5) (Supplementary Table 9). In contrast, the three copies of Slc23a2 (Slc23a2_a1, Slc23a2_a2, and Slc23a2_c2) in the two extended original groups all responded to temperature and salinity challenges, while the three copies of Mct12 (Mct12_1, Mct12_2, and Mct12_3) only responded (Fig. Salinity Priority challenge) cluster together (Mct12_1/2/3 and Slc23a2_a1/2, Supplementary Figure 16). The amplification of these environmental response genes may enhance the complexity or plasticity of transcription and play an important role in adapting to different temperature and salinity conditions in estuarine oysters and two other species living in similar environments. This finding provides further evidence that gene duplication is essential for stress adaptation, as previously demonstrated by the amplification of the heat shock protein and apoptosis inhibitor gene families in Pacific oysters and other marine bivalves That's 23, 35, 64, 65.

To understand the interaction between differences and plasticity, we used FST to detect differences in different genomic regions (genes, upstream and downstream) and the transcriptional plasticity of 29 genes from selected regions in response to environmental translocations. Prior to transcription analysis, F1 offspring produced from northern and southern populations were acclimated in the northern and southern habitats for three months. 14 of 29 genes showed high plasticity (HP). When they translocate to the non-native environment of two oyster populations, they showed significantly different expression (p <0.05), while other genes Their plasticity (LP) is low, and their expression translocates/changes to the environment (Figure 5a). The transcriptional plasticity of HP gene is 19.15% higher than that of LP gene (p = 0.0255, Supplementary Figure 17).

a Selected gene expression levels in the northern (N) and southern (S) populations (capital letters) adapted to the northern (n) and southern (s) environments (lowercase letters), showing low plasticity (LP, orange) and high Plasticity (HP, purple) genes. The blue to red color bars represent the relative expression levels of a given gene from low to high. b Genetic differences (FST) between genes and intergenic regions (left) and selected 29 genes (middle) at the genomic level, and LP and HP genes (total, right) in the genes and intergenic regions. c The genetic difference (FST) between the LP and HP genes [top: upstream (left) and bottom: downstream (right)] and gene (middle) regions. Asterisks indicate significant differences (*p <0.05, ***p <0.001). The data is displayed as a box plot; the central rectangle spans the first to third quartiles of the distribution, and the "whiskers" above and below the box show the maximum and minimum estimates. The line inside the rectangle represents the median, and the circle represents the outlier.

For the entire genome, the gene region showed a significantly higher FST value (average FST_genic = 0.163) than the intergenic region (FST_intergenic = 0.138, p <0.001, Wilcoxon signed rank test, Figure 5b), indicating that the gene region-driven selection is stronger. The division of people and southerners. For the 29 genes from the selected region, compared with all genes in the genome, both the gene (FST = 0.7745) and the intergenic (FST = 0.7585) region showed strong selection signals or divergence, and the difference between the gene and the intergenic region The difference between them is not significant (p> 0.05). For genes and intergenic regions, genes with high plasticity showed significantly (p = 0.0230) higher differences (FST = 0.7816) than LP genes (FST = 0.7274) between northern and southern oysters (FST = 0.7816) (Figure 5b). Specifically, the upstream intergenic region of the HP gene showed a significantly higher (p = 0.01512) difference between the northern and southern populations (FST = 0.7958) than the LP gene (FST = 0.7402), while there was no difference between the two types Significantly different genes and genes in the region between downstream genes (p> 0.05, Figure 5c). In addition, the LP gene shows a higher difference in the gene region than the upstream and downstream regions. Although the selection is prioritized for the genomic level and the gene region of the LP gene, HP genes that exhibit high plasticity show stronger selection in the upstream intergenic region, in which key regulatory elements such as promoters and enhancers are present, which may regulate gene expression and Enhance transcriptional plasticity18. These results indicate that the selection and differentiation of upstream regulatory regions may enhance phenotypic plasticity, which helps the survival and adaptation of organisms facing environmental disturbances66. The mechanism by which selection is conducive to plasticity is not yet clear. Directional selection tends to reduce genetic variation and promote local adaptation. The choice of regulatory elements may produce differences and transcriptional complexity, thereby enhancing phenotypic plasticity at the species level18,19. Targeted selection acting on repeated genes may guide the expression of repeated member genes under different conditions, thereby enhancing overall phenotypic plasticity. Balanced selection may also preserve the diversity of regulatory elements and increase plasticity. Some people think that balanced selection is common in oysters and is important to their evolution67.

In summary, the analysis of the estuarine oyster genome reveals extremely low genetic diversity and fine population structure, which are shaped and maintained by climate history, geological events, and ocean forces. The integration of genome and transcriptome analysis revealed that genes in selected regions are mainly involved in environmental responses, including extended solute carrier gene families that are important for environmental adaptation. The expansion and selection of upstream regulatory regions of environmental response genes may be crucial for adapting to the rapidly changing environment of estuarine oysters.

A wild estuarine oyster Crasostrea ariakensis was obtained from the Bohai Sea prefecture in northern China. Collect four tissue samples (gills, mantle, adductor muscle, and lip touch) and quickly freeze in liquid nitrogen. The DNeasy Blood & Tissue Kit (Qiagen, Hilden, Germany) was used to extract genomic DNA from the adductor muscle and used to construct the Oxford Nanopore Technology (ONT) long-read sequencing library. Use NEB Next FFPE DNA Repair Mix Kit (M6630, USA) to repair gDNA (2 μg), and then use ONT template preparation kit (SQK-LSK109, UK) to process according to the manufacturer's instructions. The large fragment library is premixed with the loading beads and pipetted into the previously used and washed R9 flow cell. According to the manufacturer's instructions, the library was sequenced on the ONT PromethION platform with nine R9 cells and an ONT sequencing kit (EXP-FLP001.PRO.6, UK). In addition, a library with an insert size of approximately 350 bp was prepared and sequenced using the Illumina HiSeq 4000 platform.

We used k = 17, 19, and 21 to perform k-mer copy number (KCN) analysis to estimate genome size. K is the smallest odd number that meets the following formula: 4k/G> 200, where G represents the genome size (bp). The previous study estimated the genome size of C. gigas and C. ariakensis by flow cytometry, where the ratio of their respective C values ​​of 0.89 pg and 0.99 pg68 was 1.112. Assuming that the genome size of C. gigas is 586.8 Mb34, the genome size of C. ariakensis will be 652.7 Mb. Therefore, we chose 19-mer for genome size estimation.

The trimmed Illumina short reads are used to generate the KCN distribution. The 19-mer KCN distribution shows two different peaks (Supplementary Figure 1). The first peak (KCN = 44) represents heterozygous single copy k-mers, while the second peak (KCN = 89) represents homozygous single copy k-mers in the genome. The genome size is estimated by the formula G = K_num/peak depth.

The nanopore length reads with a read N50 of 33,230 and an average read length of 23,240 bp, which is used for initial genome assembly. The error correction of the clean data was carried out using Canu69 v1.5, and then assembled using Canu, WTDBD270 and SMARTdenovo tools. Use Quickmerge71 v0.2.2 to connect the three components, and use Racon72 to perform 3 cycles of long read calibration on the components, and use Pilon73 v1.22 to use the default parameters (Supplementary Tables 10 and 11) to calibrate the Illumina reading for 3 cycles. Then, use Purge Haplotigs74 software to remove redundant heterozygous contigs, and the parameter is "purge_haplotigs purge -a 55".

Use the same adductor muscle to crosslink with formaldehyde at room temperature and digest the DNA with HindIII. The 5'overhangs are adjusted with biotinylated nucleotides and connected to free blunt ends. Then, reverse cross-linking, purify the DNA and further cut into fragments of 300-700 bp. Streptavidin beads are used to separate biotin-labeled fragments for PCR enrichment. The Hi-C library was constructed using Illumina's paired-end kit according to the manufacturer's instructions, and then sequenced on the Illumina HiSeq 4000 platform. Contigs and scaffolds, then use LACHESIS75 to sort and orientate super scaffolds using the following parameters: cluster_min_re_sites = 47, cluster_max_link_density = 2, cluster_noninformative_ratio = 2, order_min_n_res_in_trun = 40, order_min_n_re_res = 40

The Hi-C contact heat map of the number of Hi-C links between 100 kb windows on the pseudochromosome was used to evaluate the accuracy of Hi-C assembly. A universal single-copy ortholog (BUSCO, obd10) with 954 conserved genes was used for benchmarking to evaluate the completeness and accuracy of gene coverage. The completeness and accuracy of the assembly were also evaluated by using Burrows-Wheeler Aligner76 v.0.7.8 RNA-seq reads (see Protein Coding Gene Notes) and the mapping rate of Illumina genome reads. In addition, the accuracy of the genome sequence is assessed by REAPR77 v. 1.0.18, and the default parameters are used to identify errors in genome assembly.

Use homology and de novo-based methods to identify and classify transposable elements (TE). RepeatScout and LTR_FINDER are used to build de novo repeat library. The library based on de novo was further classified with PASTEClassifier78 to obtain a consensus library and combined with the duplicate library of Repbase data. RepeatMasker79 v4.0.5 is used to identify TE in the genome of estuarine oyster by combinatorial library.

Three methods are used to identify protein-coding genes: ab initio prediction, homology-based prediction, and mRNA-based prediction. For de novo prediction, use five ab initio gene prediction programs Genscan80 v1.0, Augustus81 v2.4 (transcriptome data of estuarine oysters used for training), GlimmerHMM82 v3.0.4, GeneID83 v1.4 and SNAP84 for prediction duplication masking Genes in the genome (hard masking). For predictions based on homologs, the protein sequences of 10 well-annotated species annotated by NCBI eukaryotic genome annotation pipeline, Homo sapiens, Danio rerio, Aplysia californica, Strongylocentrotus purpuratus, C. gigas, C. virginica, Biomphalaria an glabrata, Octopus bimaculoides and Mizuhopecten yessoensis, downloaded from NCBI and used tblastn85 to align with the duplication masked estuarine oyster genome, E value ≤ 1E-05. We use GeMoMa86 v1.3.1 to predict gene models based on aligned sequences. For mRNA-based predictions, four tissues (gills, mantle, adductor muscle, and lip touch) were collected from the same oyster used for genome sequencing. According to the manufacturer's instructions, total RNA was extracted separately using TRIzol reagent (OMEGA, USA). The quality and quantity of RNA were evaluated using Agilent 2100 instrument and Qubit 2.0 fluorometer (Thermo Fisher, USA), respectively. RNA from the four tissues is combined into a pool in equimolar amounts. Use SMARTer PCR cDNA Synthesis Kit (Clontech, USA) to obtain full-length coding DNA (cDNA). The library was constructed using SMRTbell® Template Prep Kit 2.0 (PacBio, USA). cDNA was sequenced through PacBio Sequel II platform and SMRT cell 8 M (Biomarker, China). Filter RNA-seq data to remove adaptors, then trim to remove low-quality bases. Use TopHat287 to align the clean reads with the reference genome, and then use Trinity88 for assembly. Use PASA89 v2.2.2 to predict genome-wide annotation based on the transcriptome. Finally, by integrating all gene models predicted by the three methods, EVidenceModeler90 (EVM) v1.1.1 was used to generate weighted and non-redundant gene sets (Supplementary Table 12).

Using the integrated gene set, genBlastA91 v1.0.4 was used to identify homologous sequences in the genome, and GeneWise92 was used to identify pseudogenes. Use tRNAscan-SE93 v1.3.1 software with default eukaryotic parameters to define transfer RNA (tRNA). MicroRNA and rRNA were identified using Infernal BLASTN94 against Rfam95 database v12.0.

The functional annotation of protein-coding genes was performed by using BLAST94 v2.2.31 to align them with NCBI non-redundant protein 96 (NR), SwissProt97, KOG98 and TrEMBL97 databases, with a maximum e value of 1e-05. Identify the domain by searching the Pfam99 database using HMMER100 v3.0. Genes are mapped to gene ontology (GO) terms and KEGG pathways to determine their optimal functional classification.

We analyzed the macro-synonymity between C. ariakensis and the other two oyster species C. virginica and C. hongkongensis that live in low-salinity estuaries. First, we use Diamond101 v0.9.29.130 to compare the gene sequences between two oyster species to identify homologous gene pairs (e value <1e-5, C score> 0.5). MCScanX102 is used to analyze the chromosomal collinearity (significance of alignment = 1e-10) between C. ariakensis and other two oyster species on the same line, using Diamond result files and gff files. The maximum gap size is set to 25 genes, and the smallest synline block requires 5 genes.

We collected 264 wild estuarine oysters from 11 estuaries (Figure 3a), covering most of their distribution 26,28. Use standard phenol-chloroform extraction to isolate genomic DNA from gill tissues, and construct a double-ended library with an insert size of approximately 350 bp according to the manufacturer’s instructions (Illumina Inc., San Diego, CA, USA) for use on Illumina HiSeq X 10 Sequencer. We obtained approximately 14.42 Gb of clean data for each sample, with an average coverage depth of 19.9 times (15-28 times) (Supplementary Data 4 and 6). Use BWA76 and default parameters (bwa mem -M -t 10 -T 20) to map 150 bp paired-end reads to the estuarine oyster reference genome (PRJNA715058). Then the mapped data is converted to BAM format and sorted by SAMtools v.1.3.1103 to remove duplicate readings. If multiple pairs have the same external coordinates, the read pair with the highest mapping quality is retained.

Genome Analysis Toolkit (GATK) v.3.7104 module HaplotypeCaller is used to obtain high-quality mutation calls for each sample. SNP uses the parameter'QD <2.0 || to further filter FS> 60.0 || MQ <40.0'. Similarly, use the parameter'QD <2.0 || to call and filter insert and delete FS> 60.0'. The filtered SNP is annotated by SnpEff105, and then divided into exon regions, intron regions, splice site regions, upstream and downstream intergenic regions, and heterozygous or homozygous variants. In order to characterize the types of mutations in northern and southern oysters, Plink106 was used to filter the original SNPs using parameters with MAF> 0.05 and Int> 0.8. If more than half of the individuals are heterozygous in this group but not in other groups, the retained SNPs are individually classified as highly heterozygous in this group. Variations in exons are further classified as synonymous or non-synonymous SNPs. Using the function wilcoxsign-test in the R package "coin", a two-sided two-sample Wilcoxon signed-rank test was performed to test whether the proportion of genes with non-synonymous mutations in the northern and southern populations is different.

Use the default settings of ADMIXTURE v.1.23107 to infer the population structure. The number of assumed genetic clusters K ranged from 2 to 5, and the best K was evaluated by cross-validation errors. The individual-based maximum likelihood (ML) phylogenetic tree is constructed using MEGA108 under the Jukes-Cantor model, with 1000 bootstrap programs, and is visualized using FigTree. Use Eigensoft109 to perform PCA on the genome-wide SNPs of all 264 individuals. To evaluate LD attenuation, use Plink v.1.07106 and the command –ld-window-r2 0 –ld-window 99999 –ld-window-kb 500 to calculate the parameter r2 between any two loci in each chromosome. Calculate the average r2 value. For each distance length, the whole genome LD is averaged on all chromosomes. LD attenuation is plotted against distance length. Popgenome R package110 is used to calculate Tajima's D, global FST and nucleotide diversity (p) using a 100-kb sliding window with a step size of 10-kb.

We implemented PSMC111 to estimate the dynamics of effective population size (Ne) and possible divergence time over the past few million years. A total of eight estuarine oysters from northern (n = 3), central (n = 2), and southern (n = 3) populations and three Pacific oysters with high sequencing depth (20-28x) were used12. In order to minimize the probability of false alarms, the sequencing depth of SNPs is filtered by parameters: MinDepth = average depth/3, MaxDepth = average depth×2. The PSMC parameters are set to: -N25 -t15 -r5 -p '4 25 \(*\) 2 4 6'to estimate historical Ne. The estimated generation time (g) of the two species is set to 1, and the mutation rate (μ) is calculated at the same time, following the formula T_divergence = Ks/2μ, which is 0.3×10-8 and 0.2×10-8 for estuarine and Pacific oysters, respectively. respectively.

In order to identify selective scans that may help adapt to the southern environment, we calculated the fixed (FST) and selection statistics (Tajima's D) between two pairs of populations in a 100-kb sliding window, north to south and middle pair South. The step size is 10-kb. The genomic regions showing strong selection signals are defined as: (1) the region of the top 1% FST value that overlaps in the two pairwise comparisons; (2) the region of the Tajima D value distribution along the chromosome in one of the three populations area.

From four oyster species with high-quality genomes, C. ariakensis, C. hongkongensis33, C. virginica (NCBI assembly biology project PRJNA376014, GCA_002022765.4 C_virginica-3.0) and C. gigas34 genes are used for orthologs Orthofinder 3.12, determined to use the default parameters of the protein sequence (e value = 1e-5). For the selected extended Slc gene family, we constructed a phylogenetic tree of orthologs belonging to the Slc23a2 and Mct12 gene families in oysters, including C. gigas, C. ariakensis, C. hongkongensis, and C. virginica. Using the maximum likelihood (ML) method and the Whelan And Goldman (WAG) model in MEGA7 software 108, 1,000 guides were used to infer phylogenetic relationships from protein sequences.

In order to study the response of estuarine oysters to the challenge of high temperature and high salinity, we collected wild oysters and exposed them to different temperatures (20 °C and 37 °C for 6 hours) and salinity (20 ‰ and 60 ‰ for 7 days) ,respectively. The control oysters were exposed to seawater with a salinity of 20 ‰ at 20 °C. The gills from five oysters were sampled individually and quickly frozen in liquid nitrogen for subsequent transcriptomics analysis.

To explore the plasticity of candidate genes from selected regions, we measured the transcriptional changes of oysters affected by native and non-native environments after mutual transplantation. In short, wild oysters from the northern (Binzhou: BZ, Bohai) and southern (Taishan: TS, East China Sea) environments are collected for the production of F1 offspring. For each population, 40 males and 40 females are selected as parents. The eggs are pooled together​​and then divided into 40 beakers, each of which is fertilized with sperm from one of the 40 males to produce all possible crosses. The fertilized eggs from every 8 males were cultivated as a group, and the 5 groups were raised to the larval stage in separate water tanks in the hatchery and nursery. Two-month-old F1 juveniles from each of the two populations were explanted in two source habitats to assess their response to mutual transplantation or environmental changes. After three months of acclimatization in the north and south, the gills of 5 oysters were extracted from each population in the two habitats and snap-frozen in liquid nitrogen for subsequent transcriptomics analysis.

According to the manufacturer’s protocol, RNAprep Pure Tissue Kit (Tiangen) was used to isolate total RNA from gills sampled in laboratory challenge experiments (high temperature and high salinity) and field mutual transplantation experiments. RNA integrity and concentration were detected by 1.2% agarose gel electrophoresis and Nanodrop 2000 spectrophotometer. Treat with DNAse I to remove DNA contamination. The RNA integrity was further evaluated using Agilent Bioanalyzer 2100 and RNA Nano 6000 detection kits. Use NEBNext UltraTM RNA Library Prep Kit to perform 150-bp paired-end sequencing on the Illumina HiSeq 4000 platform, and use 1 μg RNA for each sample to construct a sequencing library. Obtain clean data by deleting reads containing adapters, reads containing poly-N, and low-quality reads. TopHat2 is used to map clean reads to the estuarine oyster genome. StringTie v2.0 is used to read assembly. Only further analyze and annotate readings that have a perfect match or a mismatch. Gene expression levels are estimated by transcripts per thousand bases per million mapped fragments (FPKM). We use DESeq2 to determine differentially expressed genes (DEG) between different populations or treatments. Genes with p-value <0.01 adjusted using Benjamin and Hochberg's method were accepted as DEG. For genes from selected regions, the genes that were significantly differentially expressed between the northern and southern habitats of the two oyster populations were defined as high plasticity genes (HP), while other genes were defined as low plasticity genes (LP). Use the pheatmap package in R software to perform hierarchical clustering analysis to show the differential expression of these genes.

DESeq2 in the R software was used to determine the difference in gene expression levels between the northern and southern oyster populations under two environmental habitats and under high temperature and high salt stress conditions. 264 wild oysters collected from 11 estuaries were used for whole-genome resequencing, and 40 individuals were used to compare gene expression differences in different habitats, high temperature and high salinity conditions. For all numbers, the criteria for statistical significance are set to *P <0.05, **P <0.01, and ***P <0.001.

For more information on the research design, please see the abstract of the nature research report linked to this article.

The genome, whole-genome resequencing and transcriptome data sets are stored in the Sequence Access Archive (SRA) database under the accession number PRJNA715058 or the accession number JAGFMG000000000.1. In addition, we uploaded the annotation files of the Bayou oyster genome, including gene location (.gff3), protein model (.pep), CDS (.cds) and exon (.exon) sequences to FigShare online (https://figshare) .com /articles/dataset/Genome_of_the_estuarine_oyster_provides_insights_into_climate_impact_and_adaptive_plasticity/16557390). The source data in the figure below. Figures 3 and 5b,c are provided in Supplementary Data 7.

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Thanks to X. Wang, Z. Jia, Z. She, Y. Zhang, Z. Yu, W. Quan, Z. Huo, X. Yan, ZZ and Y. Ning for sample collection, and B. Yin and J. Qi Provide information about ocean currents. LL is supported by the Strategic Key Research Program of the Chinese Academy of Sciences (No. XDA23050402) and the National Key Research and Development Program (No. 2018YFD0900304). AL is supported by the National Natural Science Foundation of China (No. 32101353) and the China Postdoctoral Science Foundation (No. 2019TQ0324). AL and LL are supported by the key deployment project (No. COMS2019Q06) of the Marine Science Research Center of the Chinese Academy of Sciences. AL is also supported by the Outstanding Young Scientist Research Fund of the Key Laboratory of Experimental Marine Biology, Chinese Academy of Sciences (No. KLEMB-DYS04). LL is also supported by the Technology and Modern Agricultural Technology Research System (No. CARS-49).

These authors made equal contributions: Ao Li, He Dai, Siming Guo.

Key Laboratory of Marine Experimental Biology of Shandong Province, Ocean Science Center, Institute of Oceanology, Chinese Academy of Sciences, Qingdao, China

Ao Li, Ziyan Zhang, Kexin Zhang, Chaogang Wang, Xinxing Wang, Wei Wang, Li Li & Guofan Zhang

Marine Biology and Biotechnology Laboratory, National Laboratory of Marine Science and Technology Pilot Test, Qingdao, China

Li Ao & Zhang Guofan

Biomarker Technologies Corporation, Beijing, China

He Dai, Chen Hongju, Li Xumin, Zheng Hongkun

Haskin Shellfish Research Laboratory, Department of Marine and Coastal Sciences, Rutgers University, Port Norris, USA

National Pilot Laboratory of Marine Science and Technology, Laboratory of Marine Fisheries Science and Food Production Process, Qingdao, China

Zhang Ziyan, Zhang Kexin, Wang Chaogang, Wang Xinxing, Wang Wei and Li Li

University of Chinese Academy of Sciences, Beijing, China

Zhang Ziyan, Zhang Kexin, Wang Chaogang, Wang Xinxing and Li Li

National and Local Joint Engineering Key Laboratory of Eco-Marine Aquaculture, Institute of Oceanology, Chinese Academy of Sciences, Qingdao, China

Wang Wei, Li Li, Zhang Guofan

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LL, GZ and XG conceived the research and participated in the final data analysis, interpretation and manuscript writing. AL conducted data analysis and drafted a manuscript. HD, AL, HC, XL and HZ facilitate selective scanning analysis. AL, ZZ, KZ, CW and XW collect and sample oyster specimens. AL and WW produced F1 descendants. AL, LL, XG and GZ revised the manuscript. All authors approved the publication of the manuscript.

Corresponding author Li Li or Zhang Guofan.

The author declares no competing interests.

Peer review information Communications Biology thanks the anonymous reviewers for their contributions to the peer review of this work. Main processing editor: George Inglis. Peer review reports are available.

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Li, A., Dai, H., Guo, X. etc. The genome of the estuarine oyster provides insights into climate impact and adaptive plasticity. Public Biology 4, 1287 (2021). https://doi.org/10.1038/s42003-021-02823-6

DOI: https://doi.org/10.1038/s42003-021-02823-6

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