1. Introduction
Marsupenaeus japonicus, also known as kuruma shrimp and Japanese tiger prawn, is widely distributed in the Indo-West Pacific waters, eastern Africa, the Red Sea, and the Mediterranean Sea [1]. In China, it is mainly found in the waters south of the mouth of the Yangtze River [2,3]. M. japonicus is one of the most important farmed shrimp species in the world. It has a high quality and a delicious taste, so it is very much loved. Compared with other prawns, M. japonicus has many advantages, such as a fast growth rate, high fecundity, and high efficiency of water-less transportation [4,5,6]. The global annual aquaculture production of M. japonicus increased from 10,000 tons in 1991 to 50,000 tons in 2004, but has remained near 50,000 tons in the past two decades [7]. Long-term stable production means that the farming industry of M. japonicus has encountered bottlenecks, among which slow growth rate and low farming yield are the key factors affecting its industrial development [8]. M. japonicus often inhabits sandy or sandy-muddy sea areas with water depth of 10–100 m and has a habit of burrowing in the sand [9]. After four to five molts, the shrimp larvae can transition to benthic life. This habit makes it impossible to significantly increase its breeding density. M. japonicus has a strong habit of cannibalism, in which larger individuals prey on smaller or weaker individuals who have just shed their exoskeleton [10].
During the culture of M. japonicus, under the same food, water quality, and environment, individual differences within the same family of shrimp seeds are obvious, and the growth rate of some shrimp seeds is slow [8]. The growth and development of animals is a process in which a large number of cells continuously grow, divide, and differentiate to make tissues, organs, and systems mature in structure and function. Although only limited genomic information is currently available for most crustacean species, many putative candidate genes have been identified that are involved in growth and muscle development in some species [11]. Zeng et al. Reference [12] conducted comparative transcriptome analysis on the muscle tissues of 3-month-old Chinese perch, Siniperca chuatsi, with significantly different body weights in the same family and obtained many differentially expressed genes related to protein synthesis, digestion, RNA transport, and other functions. Wang et al. [13] conducted a comparative transcriptome analysis of the muscle tissue of a 6-month-old common carp Cyprinus carpio in the same family with different growth rates and screened out 749 differentially expressed genes, such as myoglobin, myosin light chain 2b, and troponin type I, which are related to muscle growth. Wang et al. [14] conducted a comparative transcriptome analysis of the mantles of blacklip pearl oyster, Pinctada margaritifera of different sizes from the same family and found a total of 1921 differentially expressed genes, including cuticular growth factor receptor, cathepsin B, and insulin-like protein receptor. Huang et al. [15] conducted comparative miRNA and comparative proteomic analysis of the different sizes of disc abalone, Haliotis discus hannai, in one family and found many differentially expressed genes related to muscle growth between the two groups of samples, such as thyroid hormone signaling, bone morphogenetic protein 7, and actin cytoskeleton regulation. To screen the muscle growth-related genes regulated by the myostatin gene in Chinese shrimp, Fenneropenaeus chinensis, Yan et al. [16] performed comparative transcriptome analysis on individuals in the control group and the Mstn expression inhibition group and identified 29 Mstn-regulated genes relating to muscle growth. To reveal the molecular basis of the growth difference between fast-growing and slow-growing red swamp crayfish Procambarus clarkii, Guo et al. [17] identified 122 growth-related differentially expressed genes using RNA-Seq and Iso-seq strategies. Other studies using transcriptome techniques to study growth traits included Penaeus monodon [18], Acanthopagrus schlegelii [19], Paramisgurnus dabryanus [20], and Macrobrachium rosenbergii [21].
To further reveal the molecular mechanism of the obvious growth differences in the culture of M. japonicus, this study used comparative transcriptome sequencing technology to analyze the transcriptomes of individuals with different growth characteristics in the same family and identified potential growth trait–related genes. Functional research on the up- and downregulated genes lays the foundation for molecular marker-assisted breeding of M. japonicus.
2. Materials and Methods
2.1. Ethics Statement
This study was approved by the Animal Care and Use Committee of Jiangsu Ocean University (protocol no. 2020-37; approval date: 1 September 2019). All procedures involving animals were performed in accordance with guidelines for the Care and Use of Laboratory Animals in China.
2.2. Sample Collection
An experiment in the cultivation of M. japonicus was carried out at an aquaculture company. M. japonicus was derived from a laboratory-proposed full-sib family. About 4000 larvae were grown in conical-bottomed tanks (diameter 1.0 m, height 1.2 m) at a density of 200 individuals/m2 and fed twice a day with continuous aeration. The salinity was kept at 27–29 parts per thousand and the pH was kept at 8.1–8.3 throughout the breeding period. The filtered seawater was renewed every 2 days, and the entire cultivation lasted for 70 days.
Feeding was stopped 24 h before the formal experiment. We randomly selected 200 individuals from the same family, measured their body weight, and selected the top 30 individuals as the large individual group, and the last 30 individuals as the small individual group. Then, 18 individuals were selected from the large individual group and the small individual group as experimental samples. The average weight of individuals in the fast-growing group was 2.241 ± 0.54 g, and in the slow-growing group was 0.733 ± 0.32 g. There was a significant difference in body weight between the two groups. The experimental shrimp were euthanized with the anesthetic alcohol: eugenol = 10:1. The first abdominal muscle tissue of 18 shrimp from the two groups was snap-frozen in liquid nitrogen and then transferred to −80 °C for RNA extraction.
2.3. RNA Extraction and cDNA Synthesis
In the experiment, TRIzol reagent (TaKaRa, Dalian, China) was used to extract RNA from muscle tissue samples. One percent agarose gel electrophoresis was used to assess total RNA quantity and contamination, a spectrophotometer was used to determine RNA purity and concentration, and a bioanalyzer was used to determine total RNA integrity. The muscle RNA of nine shrimp in the fast-growing group and the slow-growing group, respectively, was randomly divided into three groups. Equal amounts of total RNA from each group (containing three individuals) were pooled. Finally, each group had three RNA samples for transcriptome and real-time quantitative PCR analysis.
2.4. Library Sequencing, Assembly, and Functional Annotation
Three micrograms of RNA per sample were taken to synthesize first- and second-strand cDNA. Six cDNA libraries were generated using Illumina kits and sequenced on the Illumina sequencing platform. High-quality data were obtained by removing linker sequences and low-quality data. High-quality data were reassembled using Trinity software (v1.0, Cambridge MA, USA) [22] with the recommended parameters. The resulting reference sequences were used for subsequent analysis. HISAT2 software (v2.0.4, Santa Cruz, CA, USA) [23] was used to quickly and accurately align the clean reads against the reference genome of M. japonicus to obtain the location information of the reads in the reference genome. StringTie software (v1.3.1, Baltimore, MD, USA) [24] was used to read the map data. Assembled transcripts were annotated against the National Center for Biotechnology Information (NCBI)-nr, NCBI-nt, Protein family (Pfam), EuKaryotic Orthologous Groups (KOG), KEGG Orthology (KO), Gene Ontology (GO), and Swiss-Prot databases.
2.5. Differentially Expressed Gene Analysis
The number of reads corresponding to each gene was determined using the featureCounts tool of the subread software (v1.5, Victoria, Australia) [25]. The transcript expression levels of the different groups were normalized to transcripts per kilobase per million fragments using RSEM software (v1.2.15, Heidelberg, Germany) [26]. Gene function annotation was done with reference to the NCBI and KEGG databases. Differentially expressed genes between the two groups (fast-growing and slow-growing groups) were screened using the DESeq2 method [27]. Genes were considered to be significantly differentially expressed when the padj < 0.05 and |log2(Fold change)| > 1. GO enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were performed using ClusterProfiler software (v4.0, Guangzhou, China) [28].
2.6. Homologous Cloning and Sequence Analysis
Total RNA was extracted using the TRIzol reagent (Takara, Dalian, China). The purity and integrity of the RNAs were tested. First-strand cDNA was synthesized using the TransScript kit (Takara, Dalian, China) as instructed. Primers were designed based on our transcripts and sequences from closely related species in NCBI. The PCR-amplified product was ligated into the pMD19-T vector (Takara, Dalian, China), transformed into Escherichia coli DH5α cells (Takara, Dalian, China) and sequenced by paired-end sequencing. Open reading frames of genes were predicted using the ORF Finder tool (
2.7. Real-Time PCR Verification
To verify the reliability of the transcriptome sequencing results, the expression levels of ten differentially expressed genes between the fast-growing group and the slow-growing group were detected by real-time PCR. The Primer 5 software [33] was used to design quantitative primers based on transcript sequences (Table S1). Quantitative PCRs were run in triplicate using the SYBR kit (Takara, Dalian, China), and three biological replicates were studied. The results were normalized using EF1α as a reference gene and the expression levels of DEGs was calculated using the 2−△△Ct method. The quantitative PCR results were compared with transcriptome data. Expression data were analyzed with SPSS software (v18.0, SPSS Inc., Chicago, IL, USA) and displayed as the mean and standard deviation.
2.8. Single Nucleotide Polymorphism (SNP) Detection
We used Picard tools (
3. Results
3.1. Transcriptome Sequencing and Assembly
Illumina sequencing yielded a total of 20.61 Gb of data, including 140.5 million raw data and 137.4 million clean reads for the fast-growing group (Table 1). There were a total of 20.74 Gb of data, including 141.2 million raw data and 138.2 million clean data for the slow-growing group. We obtained a total of 22,948 Unigene sequences with an average length of 1654.25 bp and predicted 1776 new genes. The mapping rates of the six cDNA libraries to the reference genome of M. japonicus ranged from 86.99% to 94.24%. Based on the FRKM (fragments per kilobase of transcript sequence per million base pairs sequenced) value, we calculated the Pearson correlation coefficient R2 value as a measure of the correlation of the samples (Figure 1). The R2 value of the sample was greater than 0.85.
3.2. Identification of Differentially Expressed Genes
By comparing the relative expression abundances of the two groups of samples, we identified differentially expressed genes. We chose padj < 0.05 and |log2(Fold change)| > 1 as the criterion for defining the differential expression of genes between the fast-growing group and the slow-growing group. A total of 1375 differentially expressed genes were identified in this study, of which 1109 genes were upregulated in the fast-growing group and 266 genes were downregulated. Among these differentially expressed genes, some may be related to muscle growth of M. japonicus, such as tubulin α-1 chain, ecdysteroid kinase, myosin heavy chain C, cuticular protein, sarcoplasmic calcium-binding protein beta chain, mitochondrial basic amino acid transporter, and actin (Table 2).
3.3. Enrichment Analysis of Differentially Expressed Genes
To understand which biological processes and pathways are involved in the growth regulation of M. japonicus, we performed GO and KEGG analyses on the differentially expressed genes. All differentially expressed genes were assigned to groups among the 30 functional groups in the GO annotation system (Figure 2). The significant GO molecular function entries mainly regarded the structural constituent of the cuticle. Among the genes of the biological process, most were involved in the chitin metabolic process. The extracellular region was the most significantly enriched among the GO entries for cellular components.
To identify biological pathways that were repressed or activated differentially during the growth of M. japonicus, these differentially expressed genes were annotated in the KEGG database. These differentially expressed genes were annotated to 72 KEGG pathways. The bottom panel of Figure 3 shows the top 20 significantly enriched pathways. Carbon metabolism (dpx01200), biosynthesis of cofactors (dpx01240), cysteine and methionine metabolism (dpx00270), and glycolysis/gluconeogenesis (dpx00010), and biosynthesis of amino acids (dpx01230) were enriched.
3.4. Quantitative PCR to Verify Differentially Expressed Genes
We screened 10 differentially expressed genes of interest from M. japonicus muscle tissue using real-time quantitative PCR analysis to validate the expression patterns in the transcriptome data. There was a good correlation between the RNA-seq data expression of candidate genes and the results of real-time PCR, and the expression trends of the same gene between the two groups were consistent (Figure 4). Therefore, the differential gene expression patterns in transcriptome sequencing were found to be reliable.
3.5. Single-Nucleotide Polymorphism (SNP) Analysis
We used SnpEff software v3.0 [35] to annotate the variant sites between the two growth groups and performed an analysis on each variant site based on the annotation information, including functional statistics of variant sites (synonymous mutations, missense mutations, and nonsense mutations), variant site region (exon, intron, or intergenic), and variation site impact (high, moderate, low, modifier). From the functional data of variant sites, missense mutation sites numbered 37,339, nonsense mutation sites 339, and synonymous mutation sites 176,640, accounting for 82.4% of the total (Figure S1). From the impact data of variant sites, the number of high-impact mutation sites was 1656, the number of low-impact mutation sites was 177,921, and the number of medium-impact mutation sites was 38,678 (Figure S2). From the regional data of variant sites, the number of intron regions was 47,095 and the number of exon regions was 216,251. They made up a proportion of approximately 31.0% (Figure S3).
A total of 233,263 SNP sites were identified in this study, of which 157,920 were transition sites and 75,343 were transversion sites, for the ratio of transition to transversion of 2.1 (Figure 5). According to Liew et al.’s method [36], the SNP loci were divided into four categories: there were 36,054 in class 1 (CA/AC/TG/GT), 157,920 in class 2 (CT/GA/TC/AG), 13,538 in class 3 (CG/GC), and 25,751 in class 4 (AT/TA).
3.6. Structure and Phylogenetic Analysis of Cuticle Protein Genes
The open reading frame of the cuticle protein (novel.358) gene was 441 bp, encoding a total of 146 amino acids (Figure 6). The theoretical molecular mass of the cuticular protein was 15729.27 u, and the isoelectric point was 4.26 through analysis with the program in ExPASy. The SOPMA server was used to predict the secondary structure of cuticular proteins, which yielded 43 α-helices, 11 β-sheets, and other structures. The results using the Novopro online tool (
The amino acid sequences of cuticular proteins of M. japonicus and a variety of closely related organisms all show a certain degree of similarity. The results of the sequence comparison showed that the sequence similarity of M. japonicus with L. vannamei was 88.5%, and the similarity with F. chinensis was 87.2%. Phylogenetic analysis of the amino acid sequence of the novel cuticle genes was carried out. As shown in Figure 7, M. japonicus first grouped with shrimp, such as L. vannamei, F. chinensis, and P. monodon and then grouped with other decapod crustaceans, Homarus americanus, Procambarus clarkii, and Cherax quadricarinatus.
4. Discussion
The growth and development of crustaceans are represented by a discontinuous process of molting [37]. Crustacean growth is mainly concentrated on the molting stage. During the cultivation of M. japonicus of the same batch, there are often obvious differences in the growth of individual larvae, but the underlying growth regulation mechanism is not yet clear. In this study, we screened for differentially expressed genes related to growth by the comparative transcriptome analysis of M. japonicus individuals with different growth rates from one full-sib family. This study identified 1375 differentially expressed genes between fast-growing and slow-growing M. japonicus. Transcriptome and quantitative PCR analysis identified several genes associated with molting or muscle growth.
Muscle growth in crustaceans is intermittent and closely associated with the molt cycle due to the presence of the rigid calcified exoskeleton. Increases in muscle mass are restricted to the ecdysial period when the old exoskeleton is shed and the new exoskeleton expands in size [38]. Tissue growth in crustacea occurs at specific stages of the molt cycle and is influenced by a number of physical, hormonal, and environmental factors [39]. Consequently, growth is closely associated with the stages surrounding ecdysis when there is a considerable increase in the rate of water uptake and a subsequent increase in hydrostatic pressure causing the new uncalcified exoskeleton to expand, providing space for tissue growth [40]. Typically, the larger individuals at the beginning have a stronger competitive advantage and will have access to more food. Traditional selective breeding methods are used to select growth-advantaged individuals within a family or population as parents of the next generation.
Troponin, which contains three subunits: inhibitory (TnI), tropomyosin binding (TnT), and Ca2+ binding (TnC), regulates muscle contraction and relaxation [41,42]. The troponin T subunit binds to tropomyosin, while the I subunit inhibits the interaction of myosin and actin and the C subunit triggers muscle contraction through dynamic structural changes upon binding of Ca2+ [43]. In this study, the expression of the TnC gene was significantly upregulated in the large-size group, while the TnI gene was significantly upregulated in the small-size group. Zhao et al. [8] found that the TnI gene of M. japonicus was significantly upregulated in early developmental stages. Wang et al. [44] found that the expression levels of the TnT and TnC genes of E. carinicauda were significantly upregulated in larger individuals compared with smaller individuals. In our previous study about E. carinicauda, we found that TnT and TnC were significantly upregulated in the fast-growing group [45]. These data also suggest that different subunits of troponin do regulate the growth of crustaceans, but the interactions between the different subunits need to be further explored.
Myosin is an important component of muscle cells, and plays an important role in muscle movement, cytoplasmic flow, and signal transduction [46]. Haezsch et al. [47] found that the expression levels of myosin light chain (MLC) and myosin heavy chain (MHC) affect muscle growth and muscle fiber composition. The results of this study showed that MHC genes were significantly upregulated in the fast-growing individual group. MHC has ATPase activity and can bind to actin, allowing MHC to play a leading role in muscle contraction [48]. Zhao et al. [8] showed that the MHC transcription level in the muscle of the large-size group of M. japonicus was significantly increased. The expression of the myosin gene was upregulated in Asian blue crab Portunus trituberculatus with large size [49]. The mutual regulatory relationship between actin and myosin in M. japonicus remains to be further explored.
Crustaceans have a hard outer cuticle whose organic matter is mainly chitin and cuticular proteins [50,51]. The muscles and exoskeletal cuticle form the arthropod musculoskeletal system and are essential for locomotion [52]. In crustaceans, reports on the microscopic architecture of cuticle–muscle connections refer to different body regions in adult specimens, and the integrity of the connections between tendon cells and chitinous matrix of the cuticle is also maintained in the premolt [52]. Cuticular proteins are important in the formation of new cuticles before and after molting [53,54]. Different types of cuticular proteins bind to long-chain chitin and affect the structure and function of the cuticle [55]. Research on cuticular protein genes has mainly focused on insects, such as Drosophila and silkworm [56,57,58]. Cesar et al. [59] established a cDNA library of pacific white shrimp L. vannamei abdominal muscle and identified multiple cuticle protein genes. It is also possible that cuticle proteins are not specific to epidermal tissue. Tissue distribution analysis revealed that a novel cuticle protein gene, LvCPAP1, was predominantly expressed in the epidermis, stomach, and muscle [60]. In L. vannamei, 13,000 DEGs were identified in families with high and low growth performance, including genes encoding cuticle, chitin, ecdysteroids, and muscle proteins [61]. The results of this study showed that the expression of cuticular proteins in large individual M. japonicus was 5 times higher than that in small individuals. The isoelectric point of the cuticle of M. japonicus is 4.26, which is typical of acidic molecules, which was similar to the result of the cuticular protein gene in the Chinese mitten crab Eriocheir sinensis [62]. Acidic macromolecules play a crucial role in cuticular hardening [63,64]. Different types of cuticular proteins bind to long-chain chitin and affect the structure and function of the cuticle [65]. The DD4 and DD5 acidic cuticular proteins have been cloned from the cuticular tissue of the tail fan in the late molting stage of M. japonicus, which have calcium-binding ability [66,67]. Two calcification-related peptides, CAP1 and CAP2, have been isolated and purified from the cuticular matrix of red swamp crayfish, and both are rich in acidic amino acids [68,69]. Future studies should explore the expression characteristics of this cuticular protein in the premolt, intermolt, and postmolt stages of M. japonicus, and develop more SNPs for marker-assisted selection of M. japonicus.
5. Conclusions
In this study, RNA-Seq and qRT-PCR were used to find gene expression differences between fast-growing and slow-growing M. japonicus individuals. We identified several key genes belonging to pathways related to the growth of M. japonicus. The dynamic expression characteristics of these genes in the molting process and different developmental stages of M. japonicus need to be further studied. In addition, our data can be used to screen for SNP loci of growth-related genes to provide data for marker-assisted breeding. Taken together, our findings help to elucidate the molecular regulatory mechanisms of crustacean growth.
Conceptualization, P.W., F.Y. and C.X; Methodology, X.L., S.X. and J.Z.; Validation, L.W., X.Z. (Xinlei Zhou) and X.Z. (Xinyi Zhou); Writing—original draft preparation, P.W. and F.Y.; Writing—review and editing, B.Y., H.G. and C.X.; Funding acquisition, C.X. and P.W. All authors have read and agreed to the published version of the manuscript.
This study was approved by the Animal Care and Use Committee of Jiangsu University (protocol no. 2020-37; approval date: 1 September 2019). All procedures involving animals were performed in accordance with guidelines for the Care and Use of Laboratory Animals in China.
The datasets presented in this study can be found in the online version. The Illumina sequence reads generated during the present study are available in the NCBI SRA database under the BioProject ID: PRJNA970064.
The authors declare no conflict of interest.
Footnotes
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Enlarge this image.Figure 1. Principal component analysis (A) and heatmap (B) of transcriptomes of six samples based on Pearson’s correlation coefficient.
Enlarge this image.Figure 2. GO functional annotation for differentially expressed genes. BP represents biological process, CC represents cellular components, and MF represents molecular function.
Enlarge this image.Figure 4. The fold−change of differentially expressed genes determined by RNA−Seq and qRT−PCR. ICP: novel 358, EK: Ecdysteroid kinase, SCBP: Sarcoplasmic calcium−binding protein, beta chain, MHC: Myosin heavy chain, TC: Troponin C, TI: Troponin I, CAS: Crustacyanin−A2 subunit, Hsp70: Heat shock protein 70, IAP: Inhibitor of apoptosis protein, TRY: Trypsin−1. Each bar represents the mean ± S.D (n = 3). A significant difference between groups at p < 0.05 (n = 3, ANOVA) is indicated by different letters above the bars.
Enlarge this image.Figure 6. Multiple alignments of the deduced AA sequences of the cuticle protein (novel 358) with other cuticle proteins (A), structural domains (B), and three-dimensional prediction (C). The dotted line represents the signal peptide, and the black box represents low complexity.
Enlarge this image.Figure 7. Phylogenetic analysis and protein domain analysis of cuticle protein, for Portunus trituberculatus (MPC28856.1), Eriocheir sinensis (XP_050700776.1), Cherax quadricarinatus (XP_053639595.1), Procambarus clarkii (BAM99303.1), Homarus americanus (XP_042218166.1), Fenneropenaeus chinensis (XP_047469304.1), Litopenaeus vannamei (XP_027239199.1), Penaeus monodon (XP_037780114.1), Aedes aegypti (EAT39943.1), Bombyx mori (NP_001166723.1), Eumeta japonica (GBP72130.1), Hyposmocoma kahamanoa (XP_026328697.1), Nilaparvata lugens (QCP68952.1), Homalodisca vitripennis (XP_046666636.1), Aphis craccivora (KAF0756676.1), Hirondellea gigas (LAC22872.1), and Hyalella azteca (XP_018010166.1).
Summary statistics for sequencing data.
| Sample | Raw Reads | Clean Reads | Clean Bases (G) | Q20 (%) | Q30 (%) | GC (%) |
|---|---|---|---|---|---|---|
| S1 | 46,763,772 | 45,912,332 | 6.89 | 98.03 | 94.4 | 50.12 |
| S2 | 48,942,932 | 47,858,272 | 7.18 | 98.26 | 94.85 | 50.04 |
| S3 | 45,487,750 | 44,475,408 | 6.67 | 98.03 | 94.52 | 51.44 |
| L1 | 48,220,794 | 47,140,442 | 7.07 | 98.48 | 95.41 | 51.04 |
| L2 | 47,360,730 | 46,243,766 | 6.94 | 98.28 | 94.82 | 48.69 |
| L3 | 44,954,984 | 43,991,266 | 6.6 | 98.46 | 95.5 | 52.72 |
Representative growth-related differentially expressed genes.
| Gene_id | Gene_Description | Log2 (Fold Change) | padj |
|---|---|---|---|
| Hic_asm_3.1371 | Cuticle protein AMP4 | 8.005 | 0.026 |
| Hic_asm_3.1791 | Chitin binding Peritrophin-A domain | 7.675 | 0.020 |
| Hic_asm_29.1989 | Insect cuticle protein | 5.620 | 0.001 |
| Hic_asm_3.3781 | Transmembrane protease serine 11D | 5.533 | 0.045 |
| Hic_asm_21.381 | Tubulin alpha-1 chain | 5.078 | 0.004 |
| Hic_asm_5.353 | Ecdysteroid kinase | 4.628 | 0.001 |
| Hic_asm_4.2410 | Myosin heavy chain C | 3.630 | 0.002 |
| Hic_asm_7.2362 | Sarcoplasmic calcium-binding protein | 4.349 | 0.005 |
| Hic_asm_28.2387 | Mitochondrial basic amino acids transporter | 3.939 | 0.006 |
| Hic_asm_37.790 | Troponin C | 2.759 | 0.002 |
| Hic_asm_16.1606 | Mitochondrial enolase superfamily member 1 | 2.656 | 0.024 |
| Hic_asm_27.1758 | phosphatidylinositol 4,5-bisphosphate phosphodiesterase | 2.618 | 0.020 |
| Hic_asm_13.2114 | Mitochondrial carnitine/acylcarnitine carrier protein | 2.082 | 0.000 |
| Hic_asm_12.2116 | Mitochondrial dicarboxylate carrier | 2.061 | 0.048 |
| Hic_asm_30.362 | Coactosin-like protein | 1.965 | 0.025 |
| Hic_asm_22.2436 | Carbonyl reductase (NADPH) 3 | −4.978 | 0.002 |
| Hic_asm_35.393 | Trypsin-1 | −3.509 | 0.047 |
| Hic_asm_12.273 | Inhibitor of apoptosis protein | −3.098 | 0.001 |
| Hic_asm_3.2630 | Methyl farnesoate epoxidase | −3.064 | 0.002 |
| Hic_asm_32.1003 | Alpha-amylase | −2.876 | 0.020 |
| Hic_asm_26.1575 | Superoxide dismutase (Cu-Zn) | −2.805 | 0.024 |
| Hic_asm_38.1026 | Actin-2, muscle-specific | −2.536 | 0.000 |
| Hic_asm_14.203 | zinc-RING finger domain | −2.520 | 0.017 |
| Hic_asm_8.3322 | Heat shock 70 kDa protein | −2.441 | 0.017 |
| Hic_asm_16.604 | Crustacyanin-A2 subunit | −2.414 | 0.005 |
| Hic_asm_1.2118 | Ubiquitin carboxyl-terminal hydrolase 22 | −2.334 | 0.020 |
| Hic_asm_25.2408 | Glutathione S-transferase D7 | −2.394 | 0.006 |
| Hic_asm_19.605 | Pyruvate kinase | −1.968 | 0.026 |
| Hic_asm_36.203 | Superoxide dismutase (Mn), mitochondrial | −1.656 | 0.019 |
| Hic_asm_17.2603 | Troponin I | −1.549 | 0.002 |
Supplementary Materials
The following supporting information can be downloaded at:
References
1. Tsoi, K.H.; Ma, K.Y.; Wu, T.H.; Fennessy, S.T.; Chu, K.H.; Chan, T.Y. Verification of the cryptic species Penaeus pulchricaudatus in the commercially important kuruma shrimp P. japonicus (Decapoda: Penaeidae) using molecular taxonomy. Invertebr. Syst.; 2014; 28, pp. 476-490. [DOI: https://dx.doi.org/10.1071/IS14001]
2. Wang, P.; Chen, B.; Zheng, J.; Cheng, W.; Zhang, H.; Wang, J.; Su, Y.; Xu, P.; Mao, Y. Fine-scale population genetic structure and parapatric cryptic species of kuruma shrimp (Marsupenaeus japonicus), along the northwestern Pacific coast of China. Front. Genet.; 2020; 11, 118. [DOI: https://dx.doi.org/10.3389/fgene.2020.00118] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/32161618]
3. He, Y.Q.; Su, Y.Q.; Mao, Y.; Wang, J. Genetic diversity analysis of microsatellite DNA in different varieties of Marsupenaneus japonicus. J. Fish. China; 2012; 36, pp. 1520-1528. [DOI: https://dx.doi.org/10.3724/SP.J.1231.2012.28018]
4. Wang, P.; Mao, Y.; Su, Y.; Wang, J. Comparative analysis of transcriptomic data shows the effects of multiple evolutionary selection processes on codon usage in Marsupenaeus japonicus and Marsupenaeus pulchricaudatus. BMC Genom.; 2021; 22, pp. 1-14. [DOI: https://dx.doi.org/10.1186/s12864-021-08106-y]
5. Duan, Y.; Zhang, J.; Dong, H.; Wang, Y.; Liu, Q.; Li, H. Effect of desiccation and resubmersion on the oxidative stress response of the kuruma shrimp Marsupenaeus japonicus. Fish Shellfish Immunol.; 2016; 49, pp. 91-99. [DOI: https://dx.doi.org/10.1016/j.fsi.2015.12.018] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/26700171]
6. Hewitt, D.; Duncan, P. Effect of high water temperature on the survival, moulting and food consumption of Penaeus (Marsupenaeus) japonicus (Bate, 1888). Aquac. Res.; 2001; 32, pp. 305-313. [DOI: https://dx.doi.org/10.1046/j.1365-2109.2001.00560.x]
7. FAO (Food and Agriculture Organization of the United Nations). Global Production Statistics. 2018; Available online: http://www.fao.org/fishery/statistics/global-aquaculture-production/query/en (accessed on 30 March 2018).
8. Zhao, J.; He, Z.; Chen, X.; Huang, Y.; Xie, J.; Qin, X.; Ni, Z.; Sun, C. Growth trait gene analysis of kuruma shrimp (Marsupenaeus japonicus) by transcriptome study. Comp. Biochem. Physiol. Part D Genom. Proteom.; 2021; 40, 100874. [DOI: https://dx.doi.org/10.1016/j.cbd.2021.100874]
9. Cheng, W.; Zhang, H.; Wang, P.; Wei, Y.; Chen, C.; Hou, Y.; Deng, X.; Li, S.; Sun, S.; Cai, Q. The multiple influences of natural farming environment on the cultured population behavior of kuruma prawn, Penaeus japonicus. Animals; 2022; 12, 3383. [DOI: https://dx.doi.org/10.3390/ani12233383]
10. Kim, S.-K.; Guo, Q.; Jang, I.-K. Effect of biofloc on the survival and growth of the postlarvae of three penaeids (Litopenaeus vannamei, Fenneropenaeus chinensis, and Marsupenaeus japonicus) and their biofloc feeding efficiencies, as related to the morphological structure of the third maxilliped. J. Crustac. Biol.; 2015; 35, pp. 41-50.
11. Jung, H.; Lyons, R.E.; Hurwood, D.A.; Mather, P.B. Genes and growth performance in crustacean species: A review of relevant genomic studies in crustaceans and other taxa. Rev. Aquac.; 2013; 5, pp. 77-110. [DOI: https://dx.doi.org/10.1111/raq.12005]
12. Shuang, Z.; Xuange, L.; Pengfei, W.; Peng, X.; Lei, Z.; Lei, Z.; Qindong, T.; Zhi, C.; Guifeng, L. Muscle transcriptome of Siniperca chuatsi with different weights from a full-sib family. J. Fish. Sci. China; 2020; 27, pp. 53-61.
13. Lanmei, W.; Wenbin, Z.; Jianjun, F.; Zaijie, D. De novo transcriptome analysis and comparison of the FFRC No. 2 strain common carp (Cyprinus carpio) associated with its muscle growth. J. Fish. China; 2021; 45, pp. 79-87.
14. Wang, Z.; Liang, F.; Huang, R.; Deng, Y.; Li, J. Identification of the differentially expressed genes of Pinctada maxima individuals with different sizes through transcriptome analysis. Reg. Stud. Mar. Sci.; 2019; 26, 100512. [DOI: https://dx.doi.org/10.1016/j.rsma.2019.100512]
15. Huang, J.; You, W.; Luo, X.; Ke, C. iTRAQ-based identification of proteins related to muscle growth in the Pacific abalone, Haliotis discus hannai. Int. J. Mol. Sci.; 2017; 18, 2237. [DOI: https://dx.doi.org/10.3390/ijms18112237] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/29068414]
16. Yunjie, Y.; Xia, L.; Xianhong, M.; Sheng, L.; Baolong, C.; Jie, K. Screening of genes related to muscle growth under the myostatin regulation by RNA-seq in Fenneropenaeus chinensis. Prog. Fish. Sci.; 2021; 42, pp. 55-63.
17. Guo, X.F.; Zhou, Y.L.; Liu, M.; Wang, Z.W.; Gui, J.F. Integrated application of Iso-seq and RNA-seq provides insights into unsynchronized growth in red swamp crayfish (Procambarus clarkii). Aquac. Rep.; 2022; 22, 101008. [DOI: https://dx.doi.org/10.1016/j.aqrep.2022.101008]
18. Uengwetwanit, T.; Uawisetwathana, U.; Arayamethakorn, S.; Khudet, J.; Chaiyapechara, S.; Karoonuthaisiri, N.; Rungrassamee, W. Multi-omics analysis to examine microbiota, host gene expression and metabolites in the intestine of black tiger shrimp (Penaeus monodon) with different growth performance. PeerJ; 2020; 8, e9646. [DOI: https://dx.doi.org/10.7717/peerj.9646]
19. Lin, Z.; Zhang, Z.; Solberg, M.F.; Chen, Z.; Wei, M.; Zhu, F.; Jia, C.; Meng, Q.; Zhang, Z. Comparative transcriptome analysis of mixed tissues of black porgy (Acanthopagrus schlegelii) with differing growth rates. Aquac. Res.; 2021; 52, pp. 5800-5813. [DOI: https://dx.doi.org/10.1111/are.15455]
20. Zhao, L.; He, K.; Xiao, Q.; Liu, Q.; Luo, W.; Luo, J.; Fu, H.; Li, J.; Wu, X.; Du, J. Comparative transcriptome profiles of large and small bodied large-scale loaches cultivated in paddy fields. Sci. Rep.; 2021; 11, 4936. [DOI: https://dx.doi.org/10.1038/s41598-021-84519-9]
21. Li, Y.; Liu, Z.; Dai, X. Transcriptome analysis of growth retardation and normal Macrobrachium rosenbergii. Genom. Appl. Biol.; 2021; 40, pp. 89-100. (In Chinese)
22. Grabherr, M.G.; Haas, B.J.; Yassour, M.; Levin, J.Z.; Amit, I. Full-length transcriptome assembly from RNA-Seq data without a reference genome. Nat. Biotechnol.; 2013; 29, pp. 644-652. [DOI: https://dx.doi.org/10.1038/nbt.1883]
23. Mortazavi, A.; Williams, B.A.; Mccue, K.; Schaeffer, L.; Wold, B. Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat. Methods; 2008; 5, pp. 621-628. [DOI: https://dx.doi.org/10.1038/nmeth.1226] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/18516045]
24. Pertea, M.; Pertea, G.M.; Antonescu, C.M.; Chang, T.C.; Mendell, J.T.; Salzberg, S.L. StringTie enables improved reconstruction of a transcriptome from RNA-seq reads. Nat. Biotechnol.; 2015; 33, pp. 290-295. [DOI: https://dx.doi.org/10.1038/nbt.3122] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/25690850]
25. Yang, L.; Smyth, G.K.; Wei, S. featureCounts: An efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics; 2014; 30, pp. 923-930.
26. Simon, A.; Wolfgang, H. Differential expression analysis for sequence count data. Genome Biol.; 2010; 11, 106.
27. Love, M.I.; Huber, W.; Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol.; 2014; 15, 550. [DOI: https://dx.doi.org/10.1186/s13059-014-0550-8] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/25516281]
28. Wu, T.; Hu, E.; Xu, S.; Chen, M.; Guo, P.; Dai, Z.; Feng, T.; Zhou, L.; Tang, W.; Zhan, L. et al. clusterProfiler 4.0: A universal enrichment tool for interpreting omics data. Innovation; 2021; 2, 100141. [DOI: https://dx.doi.org/10.1016/j.xinn.2021.100141]
29. Letunic, I.; Doerks, T.; Bork, P. SMART 7: Recent updates to the protein domain annotation resource. Nucleic Acids Res.; 2011; 40, pp. 302-305. [DOI: https://dx.doi.org/10.1093/nar/gkr931]
30. Geourjon, C.; Deleage, G. SOPMA: Significant improvements in protein secondary structure prediction by consensus prediction from multiple alignments. Bioinformatics; 1995; 11, pp. 681-684. [DOI: https://dx.doi.org/10.1093/bioinformatics/11.6.681]
31. Sigrist, C.J.A.; Lorenzo, C.; Edouard, D.C.; Langendijk-Genevaux, P.S.; Virginie, B.; Amos, B.; Nicolas, H. PROSITE, a protein domain database for functional characterization and annotation. Nucleic Acids Res.; 2010; 38, pp. 161-166. [DOI: https://dx.doi.org/10.1093/nar/gkp885]
32. Kumar, S.; Stecher, G.; Tamura, K. MEGA7: Molecular evolutionary genetics analysis version 7.0 for bigger datasets. Mol. Biol. Evol.; 2016; 33, pp. 1870-1874. [DOI: https://dx.doi.org/10.1093/molbev/msw054] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/27004904]
33. Lalitha, S.J. Primer Premier 5. Biotech Softw. Internet Rep. Comput. Softw. J. Sci.; 2000; 1, pp. 270-272. [DOI: https://dx.doi.org/10.1089/152791600459894]
34. McKenna, A.; Hanna, M.; Banks, E.; Sivachenko, A.; Cibulskis, K.; Kernytsky, A.; Garimella, K.; Altshuler, D.; Gabriel, S.; Daly, M. The Genome Analysis Toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res.; 2010; 20, pp. 1297-1303. [DOI: https://dx.doi.org/10.1101/gr.107524.110]
35. Cingolani, P. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3. Fly; 2012; 6, pp. 1-13. [DOI: https://dx.doi.org/10.4161/fly.19695]
36. Liew, M.; Pryor, R.; Palais, R.; Meadows, C.; Erali, M.; Lyon, E.; Wittwer, C. Genotyping of single-nucleotide polymorphisms by high-resolution melting of small amplicons. Clin. Chem.; 2004; 50, pp. 1156-1164. [DOI: https://dx.doi.org/10.1373/clinchem.2004.032136] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/15229148]
37. Skov, M.W.; Hartnoll, R.G. Comparative suitability of binocular observation, burrow counting and excavation for the quantification of the mangrove fiddler crab Uca annulipes (H. Milne Edwards). Adv. Decapod Crustac. Res.; 2001; 154, pp. 201-212.
38. El Haj, A.J.; Harrison, P.; Whiteley, N.M. Regulation of muscle gene expression in Crustacea over the moult cycle. Symp. Soc. Exp. Biol.; 1992; 46, pp. 151-165.
39. Haj, A.J.E.; Whiteley, N.M. Molecular regulation of muscle growth in Crustacea. J. Mar. Biol. Assoc. UK; 1997; 77, pp. 95-106. [DOI: https://dx.doi.org/10.1017/S0025315400033804]
40. Mykles, D.L. The mechanism of fluid absorption at ecdysis in the American lobster, Homarus americanus. J. Exp. Biol.; 2015; 84, pp. 89-102. [DOI: https://dx.doi.org/10.1242/jeb.84.1.89]
41. Tobacman, L.S. Troponin revealed: Uncovering the structure of the thin filament on-off switch in striated muscle. Biophys. J.; 2021; 120, pp. 1-9. [DOI: https://dx.doi.org/10.1016/j.bpj.2020.11.014]
42. Cao, T.X.; Thongam, U.; Jin, J.P. Invertebrate troponin: Insights into the evolution and regulation of striated muscle contraction. Arch. Biochem. Biophys.; 2019; 666, pp. 40-45. [DOI: https://dx.doi.org/10.1016/j.abb.2019.03.013] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/30928296]
43. Ochiai, Y.; Ozawa, H. Biochemical and physicochemical characteristics of the major muscle proteins from fish and shellfish. Fish. Sci.; 2020; 86, pp. 729-740. [DOI: https://dx.doi.org/10.1007/s12562-020-01444-y]
44. Wang, J.; Ge, Q.; Li, J.; Chen, Z.; Li, J. Isolation and characterization of three skeletal troponin genes and association with growth-related traits in Exopalaemon carinicauda. Mol. Biol. Rep.; 2019; 46, pp. 705-718. [DOI: https://dx.doi.org/10.1007/s11033-018-4526-8]
45. Xing, C.F.; Xiong, J.Y.; Xie, S.M.; Guo, H.X.; Hua, S.S.; Yao, Y.J.; Zhu, J.W.; Yan, B.L.; Shen, X.; Gao, H. et al. Comparative transcriptome and gut microbiota analysis of Exopalaemon carinicauda with different growth rates from a full-sib family. Aquac. Rep.; 2023; 30, 101580. [DOI: https://dx.doi.org/10.1016/j.aqrep.2023.101580]
46. Heissler, S.M.; Sellers, J.R. Kinetic adaptations of myosins for their diverse cellular functions. Traffic; 2016; 17, pp. 839-859. [DOI: https://dx.doi.org/10.1111/tra.12388]
47. Harzsch, S.; Kreissl, S. Myogenesis in the thoracic limbs of the American lobster. Arthropod Struct. Dev.; 2010; 39, pp. 423-435. [DOI: https://dx.doi.org/10.1016/j.asd.2010.06.001] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/20615480]
48. Andruchov, O.; Andruchova, O.; Wang, Y.; Galler, S. Dependence of cross-bridge kinetics on myosin light chain isoforms in rabbit and rat skeletal muscle fibres. J. Physiol.; 2006; 571, pp. 231-242. [DOI: https://dx.doi.org/10.1113/jphysiol.2005.099770]
49. Ren, X.; Yu, X.; Gao, B.; Li, J.; Liu, P. iTRAQ-based identification of differentially expressed proteins related to growth in the swimming crab, Portunus trituberculatus. Aquac. Res.; 2017; 48, pp. 3257-3267. [DOI: https://dx.doi.org/10.1111/are.13155]
50. Zhang, X.; Yuan, J.; Li, F.; Xiang, J. Chitin synthesis and degradation in crustaceans: A genomic view and application. Mar. Drugs; 2021; 19, 153. [DOI: https://dx.doi.org/10.3390/md19030153]
51. Abehsera, S.; Weil, S.; Manor, R.; Sagi, A. The search for proteins involved in the formation of crustacean cuticular structures. Hydrobiologia; 2018; 825, pp. 29-45. [DOI: https://dx.doi.org/10.1007/s10750-018-3684-y]
52. Mrak, P.; Bogataj, U.; Štrus, J.; Žnidaršič, N. Cuticle morphogenesis in crustacean embryonic and postembryonic stages. Arthropod Struct. Dev.; 2017; 46, pp. 77-95. [DOI: https://dx.doi.org/10.1016/j.asd.2016.11.001] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/27816526]
53. Nagasawa, H. The crustacean cuticle: Structure, composition and mineralization. Front. Biosci.; 2012; 4, pp. 711-720. [DOI: https://dx.doi.org/10.2741/e412]
54. Vincent, J.F. Arthropod cuticle: A natural composite shell system. Compos. Part A Appl. Sci. Manuf.; 2002; 33, pp. 1311-1315. [DOI: https://dx.doi.org/10.1016/S1359-835X(02)00167-7]
55. Willis, J.H.; Papandreou, N.C.; Iconomidou, V.A.; Hamodrakas, S.J. Cuticular proteins. Insect Molecular Biology and Biochemistry; Academic Press: San Diego, CA, USA, 2012; pp. 134-166.
56. Cornman, R.S. Molecular evolution of Drosophila cuticular protein genes. PLoS ONE; 2009; 4, e8345. [DOI: https://dx.doi.org/10.1371/journal.pone.0008345] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/20019874]
57. Wang, X.; Xie, X.; Xie, K.; Liu, Q.; Li, Y.; Tan, X.; Dong, H.; Li, X.; Dong, Z.; Xia, Q. Chitin and cuticle proteins form the cuticular layer in the spinning duct of silkworm. Acta Biomater.; 2022; 145, pp. 260-271. [DOI: https://dx.doi.org/10.1016/j.actbio.2022.03.043] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/35364319]
58. Balabanidou, V.; Grigoraki, L.; Vontas, J. Insect cuticle: A critical determinant of insecticide resistance. Curr. Opin. Insect Sci.; 2018; 27, pp. 68-74. [DOI: https://dx.doi.org/10.1016/j.cois.2018.03.001]
59. Cesar, J.R.; Zhao, B.; Yang, J. Analysis of expressed sequence tags from abdominal muscle cDNA library of the pacific white shrimp Litopenaeus vannamei. Animal; 2008; 2, pp. 1377-1383. [DOI: https://dx.doi.org/10.1017/S175173110800267X]
60. Yang, F.; Li, X.; Li, S.; Xiang, J.; Li, F. A novel cuticle protein involved in WSSV infection to the Pacific white shrimp Litopenaeus vannamei. Dev. Comp. Immunol.; 2019; 102, 103491. [DOI: https://dx.doi.org/10.1016/j.dci.2019.103491]
61. Santos, C.A.; Andrade, S.C.S.; Teixeira, A.K.; Farias, F.; Guerrelhas, A.C.; Rocha, J.L.; Freitas, P.D. Transcriptome differential expression analysis reveals the activated genes in Litopenaeus vannamei shrimp families of superior growth performance. Aquaculture; 2021; 531, 735871. [DOI: https://dx.doi.org/10.1016/j.aquaculture.2020.735871]
62. Jundong, C.; Pengyun, D.; Xuguang, L.; Keran, B. Cloning and expression analysis of epidermal protein gene EsCAP from Chinese Mitten crab. Jiangsu Agric. Sci.; 2021; 49, pp. 74-80.
63. Coblentz, F.E.; Shafer, T.H.; Roer, R.D. Cuticular proteins from the blue crab alter in vitro calcium carbonate mineralization. Comp. Biochem. Physiol. Part B Biochem. Mol. Biol.; 1998; 121, pp. 349-360. [DOI: https://dx.doi.org/10.1016/S0305-0491(98)10117-7]
64. Glazer, L.; Sagi, A. On the involvement of proteins in the assembly of the crayfish gastrolith extracellular matrix. Invertebr. Reprod. Dev.; 2012; 56, pp. 57-65. [DOI: https://dx.doi.org/10.1080/07924259.2011.588010]
65. Andersen, S.O. Insect cuticular sclerotization: A review. Insect Biochem. Mol. Biol.; 2010; 40, pp. 166-178. [DOI: https://dx.doi.org/10.1016/j.ibmb.2009.10.007] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/19932179]
66. Endo, H.; Persson, P.; Watanabe, T. Molecular cloning of the crustacean DD4 cDNA encoding a Ca2+-binding protein. Biochem. Biophys. Res. Commun.; 2000; 276, pp. 286-291. [DOI: https://dx.doi.org/10.1006/bbrc.2000.3446] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/11006119]
67. Endo, H.; Takagi, Y.; Ozaki, N.; Kogure, T.; Watanabe, T. A crustacean Ca2+-binding protein with a glutamate-rich sequence promotes CaCO3 crystallization. Biochem. J.; 2004; 384, pp. 159-167. [DOI: https://dx.doi.org/10.1042/BJ20041052]
68. Inoue, H.; Ohira, T.; Ozaki, N.; Nagasawa, H. Cloning and expression of a cDNA encoding a matrix peptide associated with calcification in the exoskeleton of the crayfish. Comp. Biochem. Physiol. Part B Biochem. Mol. Biol.; 2003; 136, pp. 755-765. [DOI: https://dx.doi.org/10.1016/S1096-4959(03)00210-0]
69. Inoue, H.; Ohira, T.; Ozaki, N.; Nagasawa, H. A novel calcium-binding peptide from the cuticle of the crayfish, Procambarus clarkii. Biochem. Biophys. Res. Commun.; 2004; 318, pp. 649-654. [DOI: https://dx.doi.org/10.1016/j.bbrc.2004.04.075]
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