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The chromosome-level genome provides insights into the adaptive evolution of the visual system in Oratosquilla oratoria

BMC Biology volume 23, Article number: 38 (2025) Cite this article

Abstract

Background

The marine crustacean Oratosquilla oratoria is economically significant in seafood and aquaculture industries. However, the lack of high-quality genome assembly has hindered our understanding of O. oratoria, particularly the mechanisms underlying its developed visual system.

Results

We generated a chromosome-level genome assembly for O. oratoria (2.97 Gb, 44 pseudo-chromosomes) using combination sequencing strategies. Our analysis revealed that more than half of the genome was covered by repeat sequences, and LINE elements showed significant expansion. Furthermore, phylogenetic analysis revealed a close relationship of O. oratoria with Dendrobranchiata and Pleocyemata. In addition, the evolutionary rate of O. oratoria was slightly faster than that of Dendrobranchiata and slower than that of Pleocyemata. Interestingly, we observed the significant expansion of middle-wavelength-sensitive (MWS) opsins in the O. oratoria genome by tandem duplication, partially contributing to their unique visual capabilities. Compared with other crustaceans, O. oratoria has evolved a thicker cornea that was possibly driven by visual adaptations and ecological requirements. Employing comparative transcriptome analysis, we identified a tandemly duplicated cuticle protein (CP) cluster that was specifically expanded and expressed in the ocular tissues of O. oratoria, potentially contributing to the thick cornea of O. oratoria.

Conclusions

Our study established the first chromosome-level genome for Stomatopoda species, providing a valuable genomic resource for studying the molecular mechanisms underlying the developed visual system of O. oratoria.

Background

Oratosquilla oratoria, commonly known as the mantis shrimp or the Japanese mantis shrimp, is a species of marine crustacean belonging to the order Stomatopoda [1, 2]. It is widely distributed in the coastal waters of the southeastern and eastern regions, particularly in the seas bordering China, Japan, and Korea [3,4,5] (Fig. 1a). O. oratoria is economically significant in various aspects, such as culinary delicacy, commercial fishing, and aquaculture potential, as well as being a popular bait in the fishing industry. O. oratoria is characterized by its vibrant colors, unique body structure, and remarkable hunting abilities [6, 7]. One of the most distinctive features of O. oratoria is their remarkable visual system, which enables them to detect an extensive range of color vision and polarized light. O. oratoria has highly complicated compound eyes that are composed of thousands of discrete light-sensing units or ommatidia [8,9,10,11]. Noticeably, each ommatidium functions as an independent visual receptor, enabling them to perceive detailed environmental changes. O. oratoria is also characterized by its trinocular vision. Unlike other crustaceans, the compound eyes of O. oratoria are divided into three distinct regions, the dorsal and ventral hemispheres, separated by a specialized band of small eyes called the “midband” at the equatorial region [12]. Previous studies have provided a comprehensive overview of the intricate and sophisticated visual systems in mantis shrimps possibly due to the presence of the diverse opsins. For instance, previous researchers have delved into the molecular mechanisms and the diversity of opsins that enable stomatopods to perceive a wide spectrum of light, including ultraviolet and polarized light [8, 13]. The studies emphasize the evolutionary adaptations that have endowed mantis shrimps with their exceptional color vision, which surpasses that of many other species, including humans. Several studies have further explored the complexity of the visual system in mantis shrimp, discussing the variety of opsins as well as the ecological and behavioral implications of their unique vision. Their works highlighted how these crustaceans use their advanced vision for tasks such as foraging, predator avoidance, and social interactions [12, 14,15,16]. These studies contribute significantly to our understanding of the diversity and adaptability of visual systems in the animal kingdom and open avenues for further research into the genetic and neural basis of complex visual processing. In addition, O. oratoria is distinguished by its powerful and highly developed claws, allowing it to deliver powerful blows when hunting and defending against invasion [11, 17,18,19,20].

Fig. 1
figure 1

Distribution, genomic features, and transposon insertion times of the O. oratoria. a Main distribution areas of the O. oratoria; b circular representation of the O. oratoria genome, from outermost to innermost tracks depicting chromosome, GC content, gene distribution, and expression levels in the O. oratoria eyes during day and night; c Hi-C chromosomal interaction map; d analysis of transposon insertions and their burst times in different species

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Opsins, a class of G-protein-coupled receptors with a seven-(pass)-transmembrane domain, perform critical functions in vision by capturing light and initiating a cascade of biochemical reactions that eventually result in the perception of visual stimuli [21,22,23]. Opsins are classified into two types based on their properties and functions: rhodopsin, a type of photoreceptor cell found in the retina of vertebrate eyes responsible for vision under low-light conditions, and cone opsins, another type of photoreceptor cell found in cone cells responsible for color vision under bright light conditions [21, 24]. Cone opsins are further classified into three types based on their function in different color spectra: long-wavelength-sensitive (LWS) opsins, which are sensitive to red and orange colors; middle-wavelength-sensitive (MWS) opsins, which are sensitive to green and yellow colors; and short-wavelength-sensitive (SWS) opsins, which are sensitive to blue and violet colors [25, 26]. Tandem duplication is a common mechanism for opsins, especially in fishes, insects, and crustaceans [27,28,29,30]. Tandem duplication events frequently result in an expansion of the opsin gene repertoire. Tandemly duplicated opsins can accumulate mutations over time, resulting in diverse opsin variants with potentially distinct functions that enable organisms to perceive and distinguish a broader range of light wavelengths. Previous studies have demonstrated that LWS opsins expand through tandem duplication in the Litopenaeus vannamei genome, which facilitates their increased sensitivity to long-wavelength light and contributes to their better adaptation to the benthic environment [31].

The cornea is the transparent outermost layer of the eye, which serves as a protective barrier and assists in focusing incoming light onto the retina. Its thickness can influence various aspects of visual function, including the refraction of light and the transmission of visual signals to the underlying structures of the eye [32]. Previous studies using proteomic and gene expression analyses have revealed the vital roles of cuticle proteins (CPs) in the formation of the corneal lens cuticle in Drosophila melanogaster, Thermonectus marmoratus, and Tribolium castaneum [33,34,35,36]. However, systematic analysis of gene deployment and expression regulation of CPs in the compound eyes of crustaceans remains limited.

Currently, only one genome assembly is available for Stomatopoda, which is the draft genome of O. oratoria, as reported in our previous study [37]. However, the quality of this genome assembly is inadequate, affecting the precision of resolving repetitive regions and making it challenging to investigate structural variants and chromosomal evolution. In this study, we used long-read PacBio sequencing, short-read Illumina sequencing, and Hi-C technologies to generate high-quality chromosome-level genome assembly of O. oratoria. By integrating multi-omics data, we uncovered the molecular mechanisms underlying the developed visual system in O. oratoria.

Results

Genome sequencing and assembly of O. oratoria

Based on Illumina reads (~ 200 Gb, 70-fold), the estimated genome size of O. oratoria was approximately 2.9 Gb, with a heterozygosity of 1.8% (Additional file 1: Fig. S1). A draft genome assembly containing 4938 contigs was generated using 263 Gb (90-fold) PacBio long reads, with a total size of 2.97 Gb and a contig N50 of 1.66 Mb (Table 1). Using the Hi-C data (~ 280 Gb, 96.6-fold), a chromosome-level genome of O. oratoria was constructed with 44 pseudo-chromosomes and a scaffold N50 of 65,979,262 bp (Fig. 1b, c; Table 1). The overall anchoring rate was approximately 95.27%, and approximately 99.92% of the contigs longer than 100 kb were successfully anchored (Additional file 2: Table S2).

Table 1 Statistics of O. oratoria genome assembly
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Protein-coding genes in the O. oratoria genome were predicted using a combination of ab initio, homolog-based, and transcript-based predictions. A total of 30,831 gene models were identified. BUSCO analysis based on the protein mode showed 90.3% completeness (Table 1; Additional file 2: Table S3). More than 88% of the total genes were functionally annotated based on several gene function databases, including Nr, Swiss-Prot, COG, eggNOG-Mapper, Interpro, Pfam, GO, and KEGG (Additional file 2: Table S4, S5).

Repetitive sequences accounted for 60.46% of the O. oratoria genome. Notably, the proportions of long interspersed nuclear elements (LINE) (19.58%) and simple repeats (19.42%) were significantly higher than those of other types of repeats (Additional file 2: Table S1). Additionally, a recent (~ 0.17–0.27 Mya) large-scale amplification of LTR-retrotransposons was observed in the genomes of O. oratoria. This pattern was also detected in the genomes of Portunus trituberculatus, Macrobrachium nipponense, and Eriocheir japonica sinensis, suggesting that these marine crustaceans might have experienced a turbulent environment during that period of time (Fig. 1d).

Population history

We performed a Pairwise sequentially Markovian coalescent (PSMC) analysis to determine the demographic history of the O. oratoria population. The effective population size of O. oratoria gradually increased from approximately half a million years ago, when sea levels and global temperatures fluctuated frequently. The effective population size then decreased steadily between 100,000 and 17,000 years ago. Consistently, the Ice Age resulted in a slight reduction in both sea level and global temperature. Following the Ice Age, the global temperature of the earth increased significantly, resulting in a rise in sea level. Therefore, the effective population size increased significantly. These findings suggest that sea level and global temperature fluctuations may influence the effective population size of O. oratoria (Fig. 2a).

Fig. 2
figure 2

Population history of the O. oratoria, comparative genomic analysis of the genome, and species evolutionary rates. a Population history analysis of the O. oratoria using PSMC; b phylogenetic analysis, gene family expansions and contractions, and positive selection analysis in the O. oratoria among crustaceans; c evolutionary rates of the O. oratoria and other species, with C. rotundicauda as the outgroup. d GO and KEGG pathway analysis of expanded gene families in the O. oratoria; e functional enrichment of positively selected genes in the O. oratoria

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Genome evolution

To further understand the evolutionary history of O. oratoria, we performed a phylogenetic analysis by comparing it with 10 crustacean species, and Carcinoscorpius rotundicauda, which belongs to Merostomata, was selected as the outgroup (Additional file 2: Table S6). In total, we identified 1214 single-copy orthologous genes among these species, which were used to determine phylogenetic relationships. The phylogenetic tree revealed that Decapoda was divided into two distinct clades, corresponding to Dendrobranchiata and Pleocyemata, which is consistent with previous studies [38,39,40]. O. oratoria, which belongs to the Stomatopoda, was clearly separated from the Decapoda (Fig. 2b). Based on the estimated divergence time, the common ancestor of O. oratoria, Cambarus, and crab diverged approximately 592.2 Mya ago, whereas the Dendrobranchiata and the Pleocyemata separated about 378.51 Mya ago.

Furthermore, we estimated the evolutionary rates of O. oratoria by comparing them with species from Crustacea and Insecta (Additional file 2: Table S6). Our analysis revealed that the majority of Pleocyemata, Senticaudata, and Insecta species, such as E. j. sinensis, Hyalella azteca, and D. melanogaster, evolved at a significantly higher rate than O. oratoria, while most of the Dendrobranchiata species evolved at a slower rate than O. oratoria (Fig. 2c). These findings suggest that the evolutionary rates of O. oratoria may fall between those of the Dendrobranchiata and Pleocyemata species.

Comparative genomic analysis between O. oratoria and other species revealed significant expansion and contraction of 107 and three gene families, respectively, in O. oratoria (p < 0.01, Fig. 2b). Interestingly, the functions of the expanded gene families were associated with eye and visual development, such as the regulation of retinal cell programmed cell death, phototransduction, retinoid binding, phospholipase C-activating rhodopsin-mediated signaling pathway, and optomotor response (Fig. 2d; Additional file 2: Table S7, S8). In addition, 204 gene families were identified as being positively selected in O. oratoria, Functional enrichment analysis revealed that these genes were enriched in eye morphogenesis, photoreceptor cell differentiation, sensory organ morphogenesis, synapse and axon guidance, neurons, and others (Fig. 2e; Additional file 2: Table S9). Taken together, these results imply that the genes associated with the visual system and development of O. oratoria have undergone strong selection, facilitating the adaptation of O. oratoria to specific ecological niches, environmental changes, or physiological needs such as foraging and self-protection.

Expansion of MWS opsins in O. oratoria

Given the distinctive visual capabilities of O. oratoria, we performed genome-wide identification of genes or gene families associated with the visual system and development in the O. oratoria genome, including rhabdomeric opsins (r-opsins), neural retina-specific leucine zipper (NRL) genes, SOX2, SX3/6, PITX3, PAX2/5/6/8, crystallin, and BMP2/4. Notably, the number of opsin genes was significantly higher in crustaceans, including O. oratoria (Fig. 3a). The abundant repertoire of opsin genes that were possibly driven by the evolutionary pressures and adaptations to different visual requirements and light conditions can enable crustaceans to have more refined and specialized visual capabilities.

Fig. 3
figure 3

Comparative analysis of vision-related genes. a Comparison of the number of vision-related receptors and transcription factors in representative arthropod species, with the species phylogenetic tree on the left and the corresponding gene counts on the right. b Distribution of opsin genes identified in the O. oratoria genome on chromosomes. c Phylogenetic tree of opsin genes in the O. oratoria (left) and analysis of conserved domains (right). d Inter-species evolutionary analysis of opsin genes in different species, including long-wavelength sensitive (LWS), medium-wavelength sensitive (MWS), and short-wavelength sensitive (SWS) opsin genes

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In total, 34 r-opsins were identified in the O. oratoria genome, including 7 LWS opsins, 26 MWS opsins, and 1 SWS opsins (Fig. 3a, b, c; Additional file 1: Fig. S3; Additional file 2: Table S10). Notably, MWS opsins were significantly amplified through tandem duplication, reflecting the adaptations of O. oratoria to unique visual requirements. We further performed phylogenetic analysis to compare the opsins of O. oratoria with six other species, including L. vannamei, E. j. sinensis, M. nipponense, Procambarus clarkii, P. trituberculatus, and D. melanogaster (Fig. 3d; Additional file 1: Fig. S2; Additional file 2: Table S11). Based on the phylogenetic tree, MWS opsins were divided into three distinct clades, with MWS-I and MWS-II showing significant expansion and MWS-III were lost in the O. oratoria genome. Previous studies have revealed that the expansion of LWS opsins in the L. vannamei genome contributes to their sensitivity to light at 560 nm, thereby facilitating adaptive evolution to the benthic environment [31]. Considering that O. oratoria mainly reside in the shallow ocean at depths of 5–50 m where green light with a wavelength of 495–570 nm can effectively penetrate, we hypothesized that expansion of the MWS opsins could facilitate the sensitivity of O. oratoria to this specific light spectrum. To validate this hypothesis, we designed an experiment by exposing O. oratoria to green light (520–530 nm wavelength) and subsequently examined the expression of the MWS opsins. Our findings showed that nearly all MWS opsins were not expressed under the condition of visible light spectrum. In contrast, 5 out of the 10 MWS opsins were significantly activated under12 h of green light exposure (Additional file 1: Fig. S4), implying a specialized adaptation in the visual system of O. oratoria that had evolved to meet specific ecological demands, such as foraging, predator detection, or mate selection, where the ability to discriminate between different colors was crucial.

A cuticle gene cluster contributes to a thick cornea in O. oratoria

O. oratoria has highly developed compound eyes, consisting of three separate sections: the midband (MB), the dorsal hemisphere (DH), and the ventral hemisphere (VH). To physiologically investigate the eye structure of O. oratoria, we collected ocular tissues of O. oratoria and compared them with those of P. clarkii, E. j. sinensis, and L. vannamei (Fig. 4a). Notably, O. oratoria showed significantly thicker corneas than those of the other species (Fig. 4b). We hypothesized that O. oratoria had evolved thick corneas to meet visual and physical requirements such as self-defense, hunting, and environmental adaptation.

Fig. 4
figure 4

O. oratoria eye structure. a Structure of the O. oratoria eye, with DH representing the dorsal half, MB representing the midband, and VH representing the ventral half; b histological section structures of P. clarkii, O. oratoria, E. j. sinensis, and L. vannamei

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To decipher the molecular mechanism responsible for the thick cornea of O. oratoria, we conducted the comparative transcriptomic analysis by exploiting the bulk RNA-seqs derived from the ocular tissues of O. oratoria and five other crustaceans, including P. clarkii, E. j. sinensis, L. vannamei, M. nipponense, and P. trituberculatus (Additional file 1: Fig. S5, S6; Additional file 2: Table S12). Overall, 10,687 orthologous groups among the six species were extracted for the subsequent analyses. Weighted gene co-expression network analysis (WGCNA) using these orthologous groups was employed to compare gene expression patterns between O. oratoria and other species. A total of 16 gene expression modules were identified, and the MEbrown module showed a significant correlation with O. oratoria (correlation = 0.98, p-value = 3e−13, Fig. 5a, b). Then, we screened out 746 genes that were significantly correlated with the MEbrown module (correlation ≥ 0.8). Principle component analysis (PCA) using the expression levels of these genes revealed a clear separation of O. oratoria from the other species (Fig. 5c). Together, these genes represented the unique gene deployment and expression regulation in the corneal cells of O. oratoria compared to the other species. Moreover, GO enrichment analysis for these genes revealed a strong association with “structural constituent of cuticle,” and “structural constituent of chitin-based cuticle” (Fig. 5d).

Fig. 5
figure 5

Comparative transcriptomic analysis of eyes in different representative species. a Cluster dendrogram showing the expression profiles of homologous genes in 6 species related to eye phenotype. b Coefficient heatmap of modules associated with eye phenotype. c Principal component analysis of genes in module MEbrown associated with eye cornea. d GO functional enrichment of genes in the MEbrown module associated with O. oratoria vision. e Phylogenetic analysis of gene families associated with eyes and cornea in different species

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Interestingly, we identified a tandemly duplicated cuticle protein (CP) cluster among these genes that comprised 16 CPs and spanned a genomic region of 203 kb. Cuticle proteins in the cornea were implicated in the structural and optical properties of the lens, with potential roles in light refraction, transparency, and stability, as well as in the formation of a refractive gradient that reduces spherical aberration [34, 41]. To determine the phylogeny of the CP cluster, we performed a phylogenetic analysis of all CPs from the six species. Our analysis identified 185, 430, 140, 800, 228, and 53 putative CPs in O. oratoria, P. clarkii, E. j. sinensis, L. vannamei, M. nipponense, and P. trituberculatus, respectively. We found that the CP cluster of O. oratoria was phylogenetically close to two CPs of M. nipponense and one CP of L. vannamei, suggesting that this CP cluster was only amplified in the O. oratoria genome (Fig. 5e). Notably, the 16 CPs were only expressed in the ocular tissues of O. oratoria but were silent in M. nipponense and L. vannamei, supporting the exclusive expression of the CP cluster in O. oratoria. Taken together, our findings revealed that one tandemly duplicated CP cluster was specifically expanded and exclusively expressed in the ocular tissues of O. oratoria, potentially contributing to its thicker cornea than other crustaceans. We speculated the thick cornea of O. oratoria might be driven by visual requirements, physiological needs, and adaptation to environmental changes.

Discussion

To date, there are approximately 450 known species in the order Stomatopoda. However, our understanding of Stomatopoda is limited because of the lack of adequate genomic resources. In the previous study, we reported the draft genome of O. oratoria with a total size of 3.01 Gb, which is the first genome assembly in Stomatopoda [37]. However, this genome was assembled using only short-read technology, which resulted in numerous assembly errors, particularly in regions with high heterozygosity, complexity, and enriched repeat elements. In this study, we improved the genome quality of O. oratoria by combining long-read PacBio and Hi-C sequencing technologies and regenerated a high-quality chromosome-level genome for O. oratoria. This is the first study to report the chromosome-level genome in Stomatopoda. Additionally, the estimated genome size using the previous NGS data is only 2.56 Gb, which is significantly smaller than the final genome size, indicating that the NGS data may not be as good as expected. Therefore, we have re-sequenced the NGS data using a different individual in this study.

Approximately 60.46% of the O. oratoria genome was occupied by repeat sequences, similar to other phylogenetically close species such as Penaeus monodon (62.5%), M. nipponense (50.2%), and E. j. sinensis (61.42%). We observed that the proportion of LINE is more prevalent than other transposable elements (TEs), which is also observed in the genomes of other species, including P. clarkii, Paralithodes platypus, E. j. sinensis, P. monodon, and Callinectes sapidus [42]. Previous studies have revealed a strong association between genome size and the amplification of repeat elements [42, 43]. Consequently, we hypothesized that amplification of LINEs may partially contribute to the large genome size of O. oratoria. Phylogenetic analysis confirmed a close relationship between O. oratoria and Decapoda, supporting the taxonomy proposed in the previous studies [44]. Our results also indicate that the evolutionary rate of O. oratoria falls between that of Dendrobranchiata and Pleocyemata. Differences in ecological niches and environmental conditions may partially account for the relatively different evolutionary rates among these species.

Applying PSMC analysis, we were able to evaluate the dynamics of the effective population size of O. oratoria in the evolutionary process. The results revealed the possible correlation of O. oratoria population size with sea level and global temperature. Nonetheless, the estimation of ancestral population size using the PSMC method could be compromised if it fails to consider the structured characteristics of populations and the possibility of erroneous signals indicating changes in population size [45, 46]. Therefore, more evidence and analysis need to be conducted to support the conclusions and to control for potential biases.

In contrast to dipteran flies that have an extremely large number of crystallin to enhance their visual capability, we observed that the opsin genes were significantly expanded in crustaceans, especially in dendrobranchiata, such as L. vannamei, which showed expansion of LWS opsins, contributing to its adaptation to the benthic habitat [31, 47]. Previous research on stomatopod opsins has revealed a fascinating aspect of visual biology. Mantis shrimps have exhibited an unparalleled range of spectral sensitivity, which includes the ability to detect colors humans cannot see, as well as polarized light. This advanced visual capability is a result of their diverse array of opsins and is a remarkable example of how evolution can tailor sensory systems to suit the specific needs of an organism within its environment [8, 12,13,14,15,16]. Our study has revealed a remarkable number of r-opsins in the O. oratoria genome, potentially allowing for a broad range of color vision capabilities. Interestingly, we found that three genomic loci of MWS opsins underwent tandem duplication events, leading to the expansion of MWS opsin repertoire in the O. oratoria genome. The presence of gene clusters may result in different opsin variants by accumulating mutations over time, enhancing the sensitivity of O. oratoria to different spectra and allowing them to perceive a wider range of colors. The diversification of opsins through tandem duplication has been proposed to be driven by the unique visual demands to accommodate their ecological niche. To validate this hypothesis, we exposed O. oratoria to green light for 12 h and found that several MWS opsins were significantly activated. We noticed that these MWS opsins were almost not expressed under the condition of visible light, indicating the activation of MWS opsins under specific ecological requirements. We have also exposed O. oratoria to blue light while the MWS opsins remained silenced, implying that O. oratoria was not sensitive to blue light. Given that O. oratoria are highly visual predators, tandem duplication of MWS opsins may facilitate the detection of prey, identification of potential mates, and effective navigation of specific environments.

The cornea of O. oratoria is significantly thicker than that of other crustaceans, possibly driven by special visual and physical requirements. Our integrative analysis revealed that tandem duplication in the O. oratoria genome and the exclusive expression of the CP cluster could be key contributors to their thicker cornea than other crustaceans. The thick cornea can assist O. oratoria in optimizing light refraction, ensuring that light rays are properly focused onto the retina for clear and precise vision, contributing to their visual acuity. Additionally, the thick cornea enables O. oratoria to prey more effectively by allowing them to perceive finer details. Given that O. oratoria is a carnivorous predator, its thick cornea also serves as a physical barrier, providing increased protection against external mechanical trauma and potential injury, such as predator invasion, impact, abrasion, or foreign object penetration.

Conclusions

In this study, we assembled a high-quality chromosome-level genome for O. oratoria, which is the first chromosome-level genome for Stomatopoda. Phylogenetic analysis revealed the relationship of O. oratoria with the Decapoda. Comparative genomic analysis revealed a significant expansion of MWS opsins in the O. oratoria genome. Using comparative transcriptome analysis, we identified a tandemly-duplicated CP cluster that was specifically expanded in the O. oratoria genome and exclusively expressed in the ocular tissue of O. oratoria, which could be the key factor contributing to the thick cornea of O. oratoria. Our study provides valuable insights into the adaptive evolution of the visual system in O. oratoria.

Methods

Sample collection and DNA library preparation

The male O. oratoria sample was collected from Sheyang Port, Yancheng, Jiangsu Province, China (longitude: 120.478384, latitude: 33.817789). The sample was obtained from the intertidal areas and conserved at the Key Laboratory of Tidal Flat and Fisheries Resource of Jiangsu Province for breeding. One male specimen was selected for genome assessment and sequencing purposes. DNA was extracted from muscle tissue according to the manual of the DNeasy Blood & Tissue Kit (QIAGEN, Valencia, CA). Samples were then examined for degradation and DNA fragment size using 1% agarose gel electrophoresis, followed by DNA purity using Nanodrop and accurate quantification of DNA concentration using Qubit 2.0. After obtaining high-quality DNA samples, the libraries were constructed using Illumina HiSeq, PacBio, and Hi-C sequencing technologies, respectively. A total of 150 Gb sequencing data were obtained from the Illumina platform, 260 Gb from the PacBio platform, and 120 Gb from the Hi-C platform.

RNA-seq library preparation

RNA from ocular tissues from six species, including O. oratoria, M. nipponense, L. vannamei, P. clarkii, E. j. sinensis, and P. trituberculatus, were extracted using an RNA extraction kit and Trizol reagent (Invitrogen, CA, USA) following the manufacturer's instructions. The purity and integrity of the extracted RNA were assessed using a NanoDrop 2000 spectrophotometer (NanoDrop Technologies, Wilmington, DE, USA) and Bioanalyzer 2100 system (Agilent Technologies, CA, USA). The presence of RNA contamination was determined using a 1.5% agarose gel. The samples were then sequenced using the Illumina Hiseq 2500 transcriptome platform (the sequencing work was completed by Wuhan Fraser Gene Information Co., Ltd.), with a sequencing volume of 8 Gb for each sample.

Frozen slice and hematoxylin–eosin (H&E) staining observation

The eyes of laboratory-cultured O. oratoria, L. vannamei, P. clarkia, and E. j. sinensis were detached from their eyestalks and immediately immersed in a fixative containing 95% ethanol solution, 37–40% formaldehyde solution, glacial acetic acid, and distilled water at a volume ratio of 3:2:1:2. After 24 h, the eyes were removed from the fixative and immersed in 200 g·L−1 sucrose solution for 24 h. Next, the eyes were embedded in a mixture of 20 g·L−1 sucrose solution and Tissue-Tek O.C.T. compound at a volume ratio of 1:1 and placed for 2 h. Finally, the eyes were removed and placed in an embedding box with Tissue-Tek O.C.T. compound. A Leica CM1950 cryosectioner was used to cut the sections to a thickness of 7–10 μm. More complete sections were selected for H&E staining. Tissue sections were mounted with neutral balsam and photographed using an Olympus CX23 Microscope.

qRT-PCR for verification of MWS expression

To investigate the important roles of MWS opsins expansion in the adaptive evolution of O. oratoria, we exposed O. oratoria to green light for 12 h and examined the expression change of MWS opsins. Ten MWS opsins (Orat_gene23324, Orat_gene23325, Orat_gene23326, Orat_gene23337, Orat_gene23338, Orat_gene23339, Orat_gene27158, Orat_gene27159, Orat_gene27682, Orat_gene27683) were selected for quantitative real-time PCR (qRT-PCR) verification. Primer design was performed by Oligo7 software (Additional file 2: Table S13). The ChamQ SYBR qPCR Master Mix (Low ROX Premixed) was performed according to the instructions (Vazyme Biotech Co., Ltd Beijing, China). The qRT-PCR reaction was performed on a 96-well plate with a reaction volume of 10 μL. The reaction conditions were 94 °C for 2 min, followed by 40 cycles of 94 °C for 15 s, 60 °C for 20 s, and 72 °C for 30 s. The relative expression levels (REL) of MWS opsins in dark environment and light environment were calculated by the comparative threshold cycle method (2−ΔACt method).

Genome survey analysis and assembly

FastQC (v0.11.9) was used to estimate sequencing quality, and the raw sequencing data were subjected to removing adapter and low-quality sequences using fastp and Trimmomatic [48,49,50]. According to the k-mer frequency counted by jellyfish count, k-mer = 21 was selected for GenomeScope to further estimate the genome size, heterozygosity, and repeat content [51, 52]. The clean Pacbio reads were de novo assembled using NextDenovo with the parameters of “minimap2_options_raw = -x ava-pb -t 64, sort_options = -m 40g -t 64 -k 60, minimap2_options_cns = -x ava-pb -t 64 –mode 1 –kn 19 –wn 19 -k17 -w17, read_cutoff = 5k, seed_depth = 72.” purge_dups was used to remove redundancy. The draft genome was polished using Illumina reads by Nextpolish [53]. The completeness of the genome was evaluated using BUSCO [54]. The clean Hi-C sequencing data were filtered using hicpro to remove single-mapped, multiple-mapped, and duplicated reads [55]. Juicer and 3D-DNA were used for scaffolding, sorting, orienting and finally generating the chromosome-level genome assembly [56, 57]. Juice-box was used to manually correct the chromosome boundaries, misjoin, inversions, and translocations in genome assembly [58].

Genome annotation

Gene structure prediction was performed using GETA software (v2.5.7) (https://github.com/chenlianfu/GETA) that combined three gene prediction strategies, including de novo method, homolog-based method, and transcriptome-based method. Multiple tools were exploited, including PASA [59], genewise [60], AUGUSTUS [61], Hisat2 [62], hmmsearch [63], BGM2AT, exonerate, and so on. The gene functions were predicted using interpro-scan and eggNOG-Mapper, and multiple gene function databases were involved, including GO, KEGG, Pfam, COG, and Swiss-prot [64,65,66,67,68,69,70].

Phylogenetic analysis

To study phylogenetic relationship of O. oratoria, several closed species with published genome assembly were selected for phylogenetic analysis including the following 28 species: O. oratoria (this assembly), L. vannamei [31], P. clarkii [71], Penaeus chinensis [72], P. monodon [73], M. nipponense [74], Scylla paramamosain [75], E. j. sinensis [76], C. rotundicauda [77], P. platypus [42], C. sapidus [78], P. trituberculatus [79], Apis mellifera [80], Argiope bruennich [81], Musca domestica [82], D. melanogaster [83], Cherax quadricarinatus [84], Penaeus japonicus [85], Homarus americanus [86], H. azteca [87], Petrolisthes cinctipes [88], Halocaridina rubra [89], Penaeus indicus [90], Petrolisthes manimaculis [88], Chionoecetes opilio [91], Armadillidium nasatum [92], Trinorchestia longiramus [93], and Monomorium pharaonis [94] (Additional file 2: Table S6). OrthoFinder (v2.5.2) and OrthoMCL were used to identify the single-copy orthologous genes among the species [95, 96]. Muscle (v3.8.31) was used for multiple sequences alignment and Gblocks (v0.91b) was exploited to extract the conserved regions by using the parameters of “-b4 = 10 -b5 = h -t = p” [97, 98]. ProtTest3 was used to find the best substitution model (AIC: LG + I + G + F; BIC: LG + I + G + F), and RAxML (v8.2.12) was used for construction of phylogenetic tree using Maximum Likelihood algorithm and Bayesalgorithm [99, 100].

MCMCTree was exploited to estimate the divergence time among O. oratoria and the 11 selected species [101]. The estimated divergence time between L. vannamei and Penaeus chinensis (between 57.8 Mya to 108.3 Mya) as well as L. vannamei and P. platypus (between 161 to 447 Mya) that were derived from TimeTree5 were used as the reference [102]. Gene family expansion and contraction were analyzed by using CAFÉ (V5.5.1.0) [103].

Positive selection analysis

The single-copy orthologous groups generated by OrthoFinder and OrthoMCL were used for positive selection analysis [95, 96]. Muscle was used for pair-wise sequence alignment [97]. PAML branch-site model analysis was performed to examine the positively selective genes [101].

Identification and comparative analysis of opsins

The protein sequences of opsins in Daphnia pulex, Scophthalmus maximus, Neogonodactylus oerstedii genomes were used as the reference sequences and then blastp with parameter of “evalue e−9” was used to identify the putative opsins in the genome assemblies of A. mellifera, M. pharaonis, D. melanogaster, M. domestica, O. oratoria, M. nipponense, L. vannamei, P. chinensis, P. monodon, P. clarkii, P. platypus, E. j. sinensis, S. paramamosain, C. sapidus, P. trituberculatus, A. bruennichi, and C. rotundicauda. The opsin candidates were further filtered by scanning the conserved seven-(pass)-transmembrane domain.

The opsins of O. oratoria were compared with the six closest species, including L. vannamei, M. nipponense, P. clarkii, D. melanogaster, E. j. sinensis, and P. trituberculatus. MEGA7 was used to construct the phylogenetic tree based on the multiple alignment of the protein sequences of the opsins [104]. The opsins were classified as long-wavelength sensitive (LWS), middle-wavelength sensitive (MWS), and short-wavelength sensitive (SWS) based on the previous studies. TBtools was used for the localization and visualization of opsins [105]. In addition, other genes associated with the development of the visual system, including NRL, SOX2, SIX3/6, PITX3, PAX2/5/6/8, Crystallin, and BMP2/4, were identified using blastp with a parameter of “evalue e−9.”

Comparative transcriptome analysis

The raw RNA-seq data was trimmed by Trimmomatic (v0.38) to filter out low-quality sequencing reads [49]. The clean RNA-seq data was aligned to the reference genomes of O. oratoria, M. nipponense, L. vannamei, P. clarkii, E. j. sinensis, and P. trituberculatus separately (Additional file 2: Table S6) using the Hisat2 [62]. StringTie (v2.2.1) was used for quantification of gene expression [106]. Differential expression analysis was performed by using DESeq2 [107].

To compare gene expression patterns among the six species, we generated an inter-species expression matrix using orthologous groups. Only orthologous genes existing in all six species were included. For the calculation of expression of the orthologous group with multiple genes, normalized gene expression (transcripts per million, TPM) of all associated genes in the orthologous group was summed up per species. The expression matrix was then used for weighted gene co-expression network analysis (WGCNA) [108]. Six species were set as presumptive “phenotype” data, and then the expression of orthologs was correlated with the “phenotype.” P-value less than e−5 was considered as significant correlation.

Population history analysis

Pairwise sequentially Markovian coalescent (PSMC) with parameters of “-N 25 -r 5 -t 15 -p ‘4 + 25*2 + 4 + 6’” was used for population history analysis [109]. The clean reads were aligned to the reference genome using Bwa (v0.7.15) [110]. The SAMtools mpileup and BCFtoolscall [111] commands were used to obtain consensus sequences of the genomes. The fq2psmcfa software was used to convert the consensus sequences into a specific format. The psmc_plot.pl script with parameters of “-g 1 -u 4.59e−9” was used for visualization.

Estimation of evolutionary rates among crustaceans

We analyzed the relative evolutionary rates of O. oratoria and other species using two methods. (a) We conducted an analysis of connected protein sequences using Tajima’s relative rate test model in Mega7 [104]. (b) To employ the tpcv model from the literature [112] to examine molecular evolution. For both methods, we evaluated the evolutionary rate of the mantis shrimp relative to other species, with C. rotundicauda as the outgroup.

Data availability

Raw sequencing reads from RNA-seq of eyes, including O. oratoria, M. nipponense, L. vannamei, P. clarkii, E. j. sinensis and P. trituberculatus were deposited in the National Center for Biotechnology Information database under the accession number PRJNA1058173 [113]. The assembled genome of O. oratoria was deposited at GenBank under the accession JAYWIU000000000 [114].

Abbreviations

Oo:

O. oratoria

Mn:

M. nipponense

Lv:

L. vannamei

Pc:

P. clarkii

Es:

E. j. sinensis

Pt:

P. trituberculatus

References

  1. Van Der Wal C, Ahyong ST, Ho SYW, Lo N. The evolutionary history of Stomatopoda (Crustacea: Malacostraca) inferred from molecular data. PeerJ. 2017;5: e3844.

    Article  PubMed  Google Scholar 

  2. Manning RB. Keys to the species of Oratosquilla (Crustacea, Stomatopoda), with descriptions of two new species. Smithson Contrib Zool. 1971. https://doiorg.publicaciones.saludcastillayleon.es/10.5479/si.00810282.71.

    Article  Google Scholar 

  3. Komai T. Stomatopoda of Japan and adjacent localities, vol. 3. College of Science: Kyoto Imperial University; 1927.

    Google Scholar 

  4. Huang Z, Smithsonian Institution. Marine Species and Their Distribution in China’s Seas. Krieger Pub. Co.; 2001. p. 1–599.

  5. Cheng J, Sha ZL. Cryptic diversity in the Japanese mantis shrimp Oratosquilla oratoria (Crustacea: Squillidae): Allopatric diversification, secondary contact and hybridization. Sci Rep. 2017;7(1):1972.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Wang C, Xu S, Mei W, Zhang Y, Wang D. A biological basic character of Oratosquilla oratoria. Zhejiang Coll Fish (in Chinese). 1996;15(1):60–2.

    CAS  Google Scholar 

  7. Kodama K, Yamakawa T, Shimizu T, Aoki I. Age estimation of the wild population of Japanese mantis shrimp Oratosquilla oratoria (Crustacea: Stomatopoda) in Tokyo Bay, Japan, using lipofuscin as an age marker. Fish Sci. 2005;71:141–50.

    Article  CAS  Google Scholar 

  8. Thoen HH, How MJ, Chiou TH, Marshall J. A Different Form of Color Vision in Mantis Shrimp. Science. 2014;343(6169):411–3.

    Article  CAS  PubMed  Google Scholar 

  9. Cronin TW, Bok MJ, Marshall NJ, Caldwell RL. Filtering and polychromatic vision in mantis shrimps: themes in visible and ultraviolet vision. Philos Trans R Soc Lond B Biol Sci. 2014;369(1636):20130032.

  10. Daly IM, How MJ, Partridge JC, Roberts NW. Complex gaze stabilization in mantis shrimp. Proc Biol Sci. 1878;2018(285):20180594.

    Google Scholar 

  11. Cronin TW, Marshall NJ. The Unique Visual World of Mantis Shrimps. In: Prete F, editor. Complex Worlds From Simpler Nervous Systems. Cambridge: MIT Press; 2004. p. 239–68.

  12. Porter ML, Speiser DI, Zaharoff AK, Caldwell RL, Cronin TW, Oakley TH. The evolution of complexity in the visual systems of stomatopods: insights from transcriptomics. Integr Comp Biol. 2013;53(1):39–49.

    Article  CAS  PubMed  Google Scholar 

  13. Bok MJ, Porter ML, Place AR, Cronin TW. Biological sunscreens tune polychromatic ultraviolet vision in mantis shrimp. Curr Biol. 2014;24(14):1636–42.

    Article  CAS  PubMed  Google Scholar 

  14. Porter ML, Awata H, Bok MJ, Cronin TW. Exceptional diversity of opsin expression patterns in Neogonodactylus oerstedii (Stomatopoda) retinas. Proc Natl Acad Sci U S A. 2020;117(16):8948–57.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Cronin TW, Porter ML, Bok MJ, Wolf JB, Robinson PR. The molecular genetics and evolution of colour and polarization vision in stomatopod crustaceans. Ophthalmic Physiol Opt. 2010;30(5):460–9.

    Article  CAS  PubMed  Google Scholar 

  16. Porter ML, Bok MJ, Robinson PR, Cronin TW. Molecular diversity of visual pigments in Stomatopoda (Crustacea). Vis Neurosci. 2009;26(3):255–65.

    Article  PubMed  Google Scholar 

  17. Hamano T, Matsuura S. Optimal prey size for the Japanese mantis shrimp from structure of the raptorial claw. Nippon Suisan Gakkai Shi. 1986;52(1):1–10.

    Article  Google Scholar 

  18. Patek S. The most powerful movements in biology: from jellyfish stingers to mantis shrimp appendages, it takes more than muscle to move extremely fast. Am Sci. 2015;103(5):330–8.

    Article  Google Scholar 

  19. Patek S. The power of mantis shrimp strikes: interdisciplinary impacts of an extreme cascade of energy release. Integr Comp Biol. 2019;59(6):1573–85.

    Article  CAS  PubMed  Google Scholar 

  20. Patek SN, Korff W, Caldwell RL. Deadly strike mechanism of a mantis shrimp. Nature. 2004;428(6985):819–20.

    Article  CAS  PubMed  Google Scholar 

  21. Sharpe LT, Stockman A, Jagle H, Nathans J. Opsin genes, cone photopigments, color vision, and color blindness. Color vision: from genes to perception. Cambridge: Cambridge University Press; 1999. p. 3–52.

  22. Casey PJ, Gilman AG. G protein involvement in receptor-effector coupling. J Biol Chem. 1988;263(6):2577–80.

    Article  CAS  PubMed  Google Scholar 

  23. Attwood TK, Findlay JB. Fingerprinting G-protein-coupled receptors. Protein Eng. 1994;7(2):195–203.

    Article  CAS  PubMed  Google Scholar 

  24. Terakita A. The opsins. Genome Biol. 2005;6(3):213.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Kawamura S, Yokoyama S. Phylogenetic relationships among short wavelength-sensitive opsins of American chameleon (Anolis carolinensis) and other vertebrates. Vision Res. 1996;36(18):2797–804.

    Article  CAS  PubMed  Google Scholar 

  26. Bowmaker JK. Evolution of vertebrate visual pigments. Vision Res. 2008;48(20):2022–41.

    Article  CAS  PubMed  Google Scholar 

  27. Rennison DJ, Owens GL, Taylor JS. Opsin gene duplication and divergence in ray-finned fish. Mol Phylogenet Evol. 2012;62(3):986–1008.

    Article  PubMed  Google Scholar 

  28. Lin J, Wang F, Li W, Wang T. The rises and falls of opsin genes in 59 ray-finned fish genomes and their implications for environmental adaptation. Sci Rep. 2017;7(1):15568.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Xu P, Lu B, Chao J, Holdbrook R, Liang G, Lu Y. The evolution of opsin genes in five species of mirid bugs: duplication of long-wavelength opsins and loss of blue-sensitive opsins. BMC Ecol Evol. 2021;21(1):66.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Brandon C, Greenwold M, Dudycha J. Ancient and Recent Duplications Support Functional Diversity of Daphnia Opsins. J Mol Evol. 2017;84(1):12–28.

    Article  CAS  PubMed  Google Scholar 

  31. Zhang X, Yuan J, Sun Y, Li S, Gao Y, Yu Y, et al. Penaeid shrimp genome provides insights into benthic adaptation and frequent molting. Nat Commun. 2019;10(1):356.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Hughes WF. Dictionary of Eye Terminology. Arch Ophthalmol. 1984;102(7):964–1064.

    Article  Google Scholar 

  33. Chen Q, Sasikala-Appukuttan AK, Husain Z, Shrivastava A, Spain M, Sendler ED, et al. Global gene expression analysis reveals complex cuticle organization of the tribolium compound eye. Genome Biol Evol. 2023;15(1):evac181.

    Article  PubMed  Google Scholar 

  34. Stahl AL, Charlton-Perkins M, Buschbeck EK, Cook TA. The cuticular nature of corneal lenses in Drosophila melanogaster. Dev Genes Evol. 2017;227(4):271–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Shelby CE. The Biology of Tribolium with Special Emphasis on Genetic Aspects. Bull Entomol Soc Am. 1977;23:298–9.

    Google Scholar 

  36. Stahl AL, Baucom RS, Cook TA, Buschbeck EK. A Complex Lens for a Complex Eye. Integr Comp Biol. 2017;57(5):1071–81.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Sun X, Wang G, Yang J, Yu W, Xu J, Tang B, et al. Whole genome evaluation analysis and preliminary Assembly of Oratosquilla oratoria (Stomatopoda: Squillidae). Mol Biol Rep. 2023;50(5):4165–73.

    Article  CAS  PubMed  Google Scholar 

  38. Wolfe JM, Breinholt JW, Crandall KA, Lemmon AR, Lemmon EM, Timm LE, et al. A phylogenomic framework, evolutionary timeline and genomic resources for comparative studies of decapod crustaceans. Proc Biol Sci. 1901;2019(286):20190079.

    Google Scholar 

  39. Lin FJ, Liu Y, Sha Z, Tsang LM, Chu KH, Chan TY, et al. Evolution and phylogeny of the mud shrimps (Crustacea: Decapoda) revealed from complete mitochondrial genomes. BMC Genomics. 2012;13:631.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Shen H, Braband A, Scholtz G. Mitogenomic analysis of decapod crustacean phylogeny corroborates traditional views on their relationships. Mol Phylogenet Evol. 2013;66(3):776–89.

    Article  PubMed  Google Scholar 

  41. Kuballa AV, Merritt DJ, Elizur A. Gene expression profiling of cuticular proteins across the moult cycle of the crab Portunus pelagicus. BMC Biol. 2007;5:45.

    Article  PubMed  PubMed Central  Google Scholar 

  42. Tang B, Wang Z, Liu Q, Wang Z, Ren Y, Guo H, et al. Chromosome-level genome assembly of Paralithodes platypus provides insights into evolution and adaptation of king crabs. Mol Ecol Resour. 2021;21(2):511–25.

    Article  PubMed  Google Scholar 

  43. Rutz C, Bonassin L, Kress A, Francesconi C, Boštjančić LL, Merlat D, et al. Abundance and Diversification of Repetitive Elements in Decapoda Genomes. Genes (Basel). 2023;14(8):1627.

    Article  CAS  PubMed  Google Scholar 

  44. Liu Y, Cui Z. The complete mitochondrial genome of the mantid shrimp Oratosquilla oratoria (Crustacea: Malacostraca: Stomatopoda): Novel non-coding regions features and phylogenetic implications of the Stomatopoda. Comp Biochem Physiol Part D Genomics Proteomics. 2010;5(3):190–8.

    Article  CAS  PubMed  Google Scholar 

  45. Mazet O, Rodriguez W, Grusea S, Boitard S, Chikhi L. On the importance of being structured: instantaneous coalescence rates and human evolution–lessons for ancestral population size inference? Heredity (Edinb). 2016;116(4):362–71.

    Article  CAS  PubMed  Google Scholar 

  46. Mather N, Traves SM, Ho SYW. A practical introduction to sequentially Markovian coalescent methods for estimating demographic history from genomic data. Ecol Evol. 2020;10(1):579–89.

    Article  PubMed  Google Scholar 

  47. Palecanda S, Iwanicki T, Steck M, Porter ML. Crustacean conundrums: a review of opsin diversity and evolution. Philos Trans R Soc Lond B Biol Sci. 1862;2022(377):20210289.

    Google Scholar 

  48. Andrews S. FastQC: a quality control tool for high throughput sequence data. Barbraham Bioinformatics. 2010. https://www.bioinformatics.babraham.ac.uk/projects/fastqc/.

  49. Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30(15):2114–20.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Chen S, Zhou Y, Chen Y, Gu J. fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics. 2018;34(17):i884–90.

    Article  PubMed  PubMed Central  Google Scholar 

  51. Ranallo-Benavidez TR, Jaron KS, Schatz MC. GenomeScope 2.0 and Smudgeplot for reference-free profiling of polyploid genomes. Nat Commun. 2020;11(1):1432.

  52. Marcais G, Kingsford C. A fast, lock-free approach for efficient parallel counting of occurrences of k-mers. Bioinformatics. 2011;27(6):764–70.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Hu J, Fan J, Sun Z, Liu S. NextPolish: a fast and efficient genome polishing tool for long-read assembly. Bioinformatics. 2019;36(7):2253–5.

    Article  Google Scholar 

  54. Simao FA, Waterhouse RM, Ioannidis P, Kriventseva EV, Zdobnov EM. BUSCO: assessing genome assembly and annotation completeness with single-copy orthologs. Bioinformatics. 2015;31(19):3210–2.

    Article  CAS  PubMed  Google Scholar 

  55. Servant N, Varoquaux N, Lajoie BR, Viara E, Chen CJ, Vert JP, et al. HiC-Pro: an optimized and flexible pipeline for Hi-C data processing. Genome Biol. 2015;16:259.

    Article  PubMed  PubMed Central  Google Scholar 

  56. Durand NC, Shamim MS, Machol I, Rao SS, Huntley MH, Lander ES, et al. Juicer Provides a One-Click System for Analyzing Loop-Resolution Hi-C Experiments. Cell Syst. 2016;3(1):95–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Dudchenko O, Batra SS, Omer AD, Nyquist SK, Hoeger M, Durand NC, et al. De novo assembly of the Aedes aegypti genome using Hi-C yields chromosome-length scaffolds. Science. 2017;356(6333):92–5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Robinson JT, Turner D, Durand NC, Thorvaldsdottir H, Mesirov JP, Aiden EL. Juicebox.js Provides a Cloud-Based Visualization System for Hi-C Data. Cell Syst. 2018;6(2):256–58 e1.

  59. Haas BJ, Salzberg SL, Zhu W, Pertea M, Allen JE, Orvis J, et al. Automated eukaryotic gene structure annotation using EVidenceModeler and the Program to Assemble Spliced Alignments. Genome Biol. 2008;9(1):R7.

    Article  PubMed  PubMed Central  Google Scholar 

  60. Birney E, Clamp M, Durbin R. GeneWise and Genomewise. Genome Res. 2004;14(5):988–95.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Stanke M, Keller O, Gunduz I, Hayes A, Waack S, Morgenstern B. AUGUSTUS: ab initio prediction of alternative transcripts. Nucleic Acids Res. 2006;34:W435–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Kim D, Paggi JM, Park C, Bennett C, Salzberg SL. Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype. Nat Biotechnol. 2019;37(8):907–15.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Johnson LS, Eddy SR, Portugaly E. Hidden Markov model speed heuristic and iterative HMM search procedure. BMC Bioinformatics. 2010;11:431.

    Article  PubMed  PubMed Central  Google Scholar 

  64. Huerta-Cepas J, Forslund K, Coelho LP, Szklarczyk D, Jensen LJ, von Mering C, et al. Fast Genome-Wide Functional Annotation through Orthology Assignment by eggNOG-Mapper. Mol Biol Evol. 2017;34(8):2115–22.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Jones P, Binns D, Chang HY, Fraser M, Li W, McAnulla C, et al. InterProScan 5: genome-scale protein function classification. Bioinformatics. 2014;30(9):1236–40.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Kanehisa M, Goto S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000;28(1):27–30.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium Nature genetics. 2000;25(1):25–9.

    Article  CAS  PubMed  Google Scholar 

  68. Boeckmann B, Bairoch A, Apweiler R, Blatter M-C, Estreicher A, Gasteiger E, et al. The SWISS-PROT protein knowledgebase and its supplement TrEMBL in 2003. Nucleic Acids Res. 2003;31(1):365–70.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Punta M, Coggill PC, Eberhardt RY, Mistry J, Tate J, Boursnell C, et al. The Pfam protein families database. Nucleic Acids Res. 2012;40:D290-301.

    Article  CAS  PubMed  Google Scholar 

  70. Tatusov RL, Galperin MY, Natale DA, Koonin EV. The COG database: a tool for genome-scale analysis of protein functions and evolution. Nucleic Acids Res. 2000;28(1):33–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Xu Z, Gao T, Xu Y, Li X, Li J, Lin H, et al. A chromosome-level reference genome of red swamp crayfish Procambarus clarkii provides insights into the gene families regarding growth or development in crustaceans. Genomics. 2021;113(5):3274–84.

    Article  CAS  PubMed  Google Scholar 

  72. Wang Q, Ren X, Liu P, Li J, Lv J, Wang J, et al. Improved genome assembly of Chinese shrimp (Fenneropenaeus chinensis) suggests adaptation to the environment during evolution and domestication. Mol Ecol Resour. 2022;22(1):334–44.

    Article  CAS  PubMed  Google Scholar 

  73. Uengwetwanit T, Pootakham W, Nookaew I, Sonthirod C, Angthong P, Sittikankaew K, et al. A chromosome-level assembly of the black tiger shrimp (Penaeus monodon) genome facilitates the identification of growth-associated genes. Mol Ecol Resour. 2021;21(5):1620–40.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Jin S, Bian C, Jiang S, Han K, Xiong Y, Zhang W, et al. A chromosome-level genome assembly of the oriental river prawn, Macrobrachium nipponense. Gigascience. 2021;10(1):giaa160.

    Article  PubMed  PubMed Central  Google Scholar 

  75. Zhao M, Wang W, Zhang F, Ma C, Liu Z, Yang M-H, et al. A chromosome-level genome of the mud crab (Scylla paramamosain estampador) provides insights into the evolution of chemical and light perception in this crustacean. Mol Ecol Resour. 2021;21(4):1299–317.

    Article  CAS  PubMed  Google Scholar 

  76. Tang B, Wang Z, Liu Q, Zhang H, Li Y. High-Quality Genome Assembly of Eriocheir japonica sinensis Reveals Its Unique Genome Evolution. Front Genet. 2020;10:1340.

    Article  PubMed  PubMed Central  Google Scholar 

  77. Shingate P, Ravi V, Prasad A, Tay BH, Garg KM, Chattopadhyay B, et al. Chromosome-level assembly of the horseshoe crab genome provides insights into its genome evolution. Nat Commun. 2020;11(1):2322.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Bachvaroff TR, McDonald RC, Plough LV, Chung JS. Chromosome-level genome assembly of the blue crab, Callinectes sapidus. G3 (Bethesda). 2021;11(9):jkab212.

    Article  PubMed  Google Scholar 

  79. Tang B, Zhang D, Li H, Jiang S, Zhang H, Xuan F, et al. Chromosome-level genome assembly reveals the unique genome evolution of the swimming crab (Portunus trituberculatus). Gigascience. 2020;9(1):giz161.

    Article  PubMed  PubMed Central  Google Scholar 

  80. Wallberg A, Bunikis I, Pettersson OV, Mosbech MB, Childers AK, Evans JD, et al. A hybrid de novo genome assembly of the honeybee, Apis mellifera, with chromosome-length scaffolds. BMC Genomics. 2019;20(1):275.

    Article  PubMed  PubMed Central  Google Scholar 

  81. Sheffer MM, Hoppe A, Krehenwinkel H, Uhl G, Kuss AW, Jensen L, et al. Chromosome-level reference genome of the European wasp spider Argiope bruennichi: a resource for studies on range expansion and evolutionary adaptation. Gigascience. 2021;10(1):giaa148.

    Article  PubMed  PubMed Central  Google Scholar 

  82. Scott JG, Warren WC, Beukeboom LW, Bopp D, Clark AG, Giers SD, et al. Genome of the house fly, Musca domestica L., a global vector of diseases with adaptations to a septic environment. Genome Biol. 2014;15(10):466.

    Article  PubMed  PubMed Central  Google Scholar 

  83. Dos Santos G, Schroeder AJ, Goodman JL, Strelets VB, Crosby MA, Thurmond J, et al. FlyBase: introduction of the Drosophila melanogaster Release 6 reference genome assembly and large-scale migration of genome annotations. Nucleic Acids Res. 2014;43(D1):D690–7.

    Article  PubMed  PubMed Central  Google Scholar 

  84. Chen H, Zhang R, Liu F, Shao C, Liu F, Li W, et al. The chromosome-level genome of Cherax quadricarinatus. Sci Data. 2023;10(1):215.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Zhang Y, Zhang C, Yao N, Huang J, Sun X, Zhao B, et al. Construction of a high-density linkage map and detection of sex-specific markers in Penaeus japonicus. PeerJ. 2021;9: e12390.

    Article  PubMed  PubMed Central  Google Scholar 

  86. Polinski JM, Zimin AV, Clark KF, Kohn AB, Sadowski N, Timp W, et al. The American lobster genome reveals insights on longevity, neural, and immune adaptations. Sci Adv. 2021;7(26):eabe8290.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. Poynton HC, Hasenbein S, Benoit JB, Sepulveda MS, Poelchau MF, Hughes DST, et al. The Toxicogenome of Hyalella azteca: A Model for Sediment Ecotoxicology and Evolutionary Toxicology. Environ Sci Technol. 2018;52(10):6009–22.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Angst P, Dexter E, Stillman JH. Genome assemblies of two species of porcelain crab, Petrolisthes cinctipes and Petrolisthes manimaculis (Anomura: Porcellanidae). G3 (Bethesda). 2023;14(2):jkad281.

  89. Smith C. Halocaridina rubra isolate EP-1, whole genome shotgun sequencing project. GenBank https://www.ncbi.nlm.nih.gov/datasets/genome/GCA_037179515.1/. 2024.

  90. Katneni VK, Shekhar MS, Jangam AK, Krishnan K, Prabhudas SK, Kaikkolante N, et al. A Superior Contiguous Whole Genome Assembly for Shrimp (Penaeus indicus). Front Mar Sci. 2022;8:808354.

  91. Jeong JH, Ryu S. Chionoecetes opilio isolate MADBK_172401_WGS, whole genome shotgun sequencing project. GenBank https://www.ncbi.nlm.nih.gov/datasets/genome/GCA_016584305.1/. 2021.

  92. Becking T, Gilbert C, Cordaux R. Impact of transposable elements on genome size variation between two closely related crustacean species. Anal Biochem. 2020;600: 113770.

    Article  CAS  PubMed  Google Scholar 

  93. Patra AK, Chung O, Yoo JY, Kim MS, Yoon MG, Choi J-H, et al. First draft genome for the sand-hopper Trinorchestia longiramus. Sci Data. 2020;7(1):85.

    Article  PubMed  PubMed Central  Google Scholar 

  94. Gao Q, Xiong Z, Larsen RS, Zhou L, Zhao J, Ding G, et al. High-quality chromosome-level genome assembly and full-length transcriptome analysis of the pharaoh ant Monomorium pharaonis. Gigascience. 2020;9(12):giaa143.

    Article  PubMed  PubMed Central  Google Scholar 

  95. Emms DM, Kelly S. OrthoFinder: phylogenetic orthology inference for comparative genomics. Genome Biol. 2019;20(1):238.

    Article  PubMed  PubMed Central  Google Scholar 

  96. Li L, Stoeckert CJ Jr, Roos DS. OrthoMCL: identification of ortholog groups for eukaryotic genomes. Genome Res. 2003;13(9):2178–89.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  97. Edgar RC. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 2004;32(5):1792–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  98. Castresana J. Selection of conserved blocks from multiple alignments for their use in phylogenetic analysis. Mol Biol Evol. 2000;17(4):540–52.

    Article  CAS  PubMed  Google Scholar 

  99. Rokas A. Phylogenetic Analysis of Protein Sequence Data Using the Randomized Axelerated Maximum Likelihood (RAXML) Program. Curr Protoc Mol Biol. 2011;96(1):19.11.1–19.11.14.

  100. Darriba D, Taboada GL, Doallo R, Posada D. ProtTest 3: fast selection of best-fit models of protein evolution. Bioinformatics. 2011;27(8):1164–5.

    Article  CAS  PubMed  Google Scholar 

  101. Yang Z. PAML 4: phylogenetic analysis by maximum likelihood. Mol Biol Evol. 2007;24(8):1586–91.

    Article  CAS  PubMed  Google Scholar 

  102. Kumar S, Suleski M, Craig JM, Kasprowicz AE, Sanderford M, Li M, et al. TimeTree 5: an expanded resource for species divergence times. Mol Biol Evol. 2022;39(8):msac174.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  103. De Bie T, Cristianini N, Demuth JP, Hahn MW. CAFE: a computational tool for the study of gene family evolution. Bioinformatics. 2006;22(10):1269–71.

    Article  PubMed  Google Scholar 

  104. Kumar S, Stecher G, Tamura K. MEGA7: Molecular Evolutionary Genetics Analysis Version 7.0 for Bigger Datasets. Mol Biol Evol. 2016;33(7):1870–4.

  105. Chen C, Chen H, Zhang Y, Thomas HR, Frank MH, He Y, et al. TBtools: An Integrative Toolkit Developed for Interactive Analyses of Big Biological Data. Mol Plant. 2020;13(8):1194–202.

    Article  CAS  PubMed  Google Scholar 

  106. Pertea M, Pertea G, Antonescu C, Chang T, Mendell J, Salzberg S. StringTie enables improved reconstruction of a transcriptome from RNA-seq reads. Nat Biotechnol. 2015;33(3):290–5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  107. Love M, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15(12):550.

    Article  PubMed  PubMed Central  Google Scholar 

  108. Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics. 2008;9:559.

    Article  PubMed  PubMed Central  Google Scholar 

  109. Nadachowska-Brzyska K, Burri R, Smeds L, Ellegren H. PSMC analysis of effective population sizes in molecular ecology and its application to black-and-white Ficedula flycatchers. Mol Ecol. 2016;25(5):1058–72.

    Article  PubMed  PubMed Central  Google Scholar 

  110. Li H, Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics. 2009;25(14):1754–60.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  111. Danecek P, Bonfield JK, Liddle J, Marshall J, Ohan V, Pollard MO, et al. Twelve years of SAMtools and BCFtools. Gigascience. 2021;10(2):giab008.

    Article  PubMed  PubMed Central  Google Scholar 

  112. Takezaki N, Rzhetsky A, Nei M. Phylogenetic test of the molecular clock and linearized trees. Mol Biol Evol. 1995;12(5):823–33.

    CAS  PubMed  Google Scholar 

  113. Transcriptome raw data of eyes, including Oratosquilla oratoria, Macrobrachium nipponense, Litopenaeus vannamei, Procambarus clarkii, Eriocheir japonica sinensis and Portunus trituberculatus. GenBank https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1058173/. 2023.

  114. Zhang D. Oratosquilla oratoria isolate Oo_2021, whole genome shotgun sequencing project. GeneBank https://www.ncbi.nlm.nih.gov/nuccore/JAYWIU000000000.1/. 2025.

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Acknowledgements

The work was funded by the National Natural Science Foundation of China (32070526) and sponsored by “Qing Lan Project,” “333 Project,” and “Outstanding Young Talents of YCTU.”

Funding

The work was funded by the National Natural Science Foundation of China (32070526).

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Author notes

  1. Daizhen Zhang, Xiaoli Sun, Lianfu Chen and Lianyu Lin contributed equally to this work.

Authors and Affiliations

  1. Jiangsu Key Laboratory for Bioresources of Saline Soils, Jiangsu Provincial Key Laboratory of Coastal Wetland Bioresources and Environmental Protection, Jiangsu Synthetic Innovation Center for Coastal Bio-Agriculture, Yancheng Teachers University, Yancheng, 224007, China

    Daizhen Zhang, Xiaoli Sun, Chijie Yin, Wenqi Yang, Qiuning Liu, Huabin Zhang, Senhao Jiang, Boping Tang & Gang Wang

  2. College of Biotechnology and Pharmaceutical Engineering, Nanjing Tech University, Nanjing, 211816, China

    Xiaoli Sun

  3. Institute of Applied Mycology, Plant Science and Technology College, Huazhong Agricultural University, Wuhan, 430070, China

    Lianfu Chen

  4. State Key Laboratory of Ecological Pest Control for Fujian and Taiwan Crops, College of Life Science, Fujian Agriculture and Forestry University, Fuzhou, 350002, China

    Lianyu Lin

  5. College of Fisheries and Life Science, Shanghai Ocean University, Shanghai, 201306, China

    Chijie Yin & Wenqi Yang

  6. Lianyungang Key Laboratory of Biotechnology, Lianyungang Normal College, Lianyungang, China

    Jun Liu

  7. Center for Ecological and Environmental Sciences, Northwestern Polytechnical University, Xi’an, China

    Yongxin Li

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  1. Daizhen Zhang
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Contributions

All authors contributed to the study conception and design. Conceptualization and designed research were performed by DZ and GW; Methodology was performed by LC, YL, LL, and BT; Formal analysis was performed by JL, QL, HZ, LL, and SJ; Data curation and investigation were performed by CY, LL, and WY; Experimental verification was conducted by LL and GW; XS, DZ, and GW wrote the paper. All authors read and approved the final manuscript.

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Correspondence to Daizhen Zhang, Yongxin Li, Boping Tang or Gang Wang.

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Supplementary Information

12915_2025_2146_MOESM1_ESM.pdf

Additional file 1. Figures S1-S6. Fig. S1 - Research graph of the diploid O. oratoria, the horizontal axis represents sequencing depth, and the vertical axis represents the frequency of k-mer occurrence, with the main peak located around 30. Fig. S2 - The O. oratoria and representative species in the arthropod phylum have different numbers of photoreceptor genes for various light wavelengths. LWS represents long-wave-sensitive, MWS represents medium-wave-sensitive, and SWS represents short-wave-sensitive. Fig. S3 - Phylogenetic tree, motif, and gene structure analysis of the opsin protein family genes in O. oratoria. Fig. S4 - Relative expression of MWS opsins in green light stimulated O. oratoria eyes. Fig. S5 - The principal component analysis was conducted to compare the gene expression levels of eye tissues of 6 species, including O. oratoria, M. nipponense, L. vannamei, P. clarkii, E. j. sinensis, and P. trituberculatus. Fig. S6 - Correlation analysis of gene expression levels in the transcriptome analysis of 6 species. O. oratoria, M. nipponense, L. vannamei, P. clarkii, E. j. sinensis, and P. trituberculatus.

12915_2025_2146_MOESM2_ESM.xlsx

Additional file 2. Tables S1-S13. Table S1 - Classification of repeated sequences in O. oratoria.Table S2 - Statistics of chromosomal-level assembly of O. oratoria.Table S3 - BUSCO analysis of annotation completeness of O. oratoria genome. Table S4 - General statistics of gene function annotation. Table S5 - Gene function annotation using eggnog-mapper software. Table S6 - Genome information of related species. Table S7 - KEGG enrichment of the expanded gene families. Table S8 - GO enrichment of the expanded gene families. Table S9 - GO enrichment of the positive selection genes. Table S10 - Information of 34 opsins in the O. oratoria genome. Table S11 - Information of opsins in seven species. Table S12 - Gene expression matrix of six species used for WGCNA. Table S13 - Primers of 10 MWS opsins used for quantitative real-time PCR.

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Zhang, D., Sun, X., Chen, L. et al. The chromosome-level genome provides insights into the adaptive evolution of the visual system in Oratosquilla oratoria. BMC Biol 23, 38 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12915-025-02146-6

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BMC Biology

ISSN: 1741-7007

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