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Genome assembly of redclaw crayfish (Cherax quadricarinatus) provides insights into its immune adaptation and hypoxia tolerance

BMC Genomics volume 25, Article number: 746 (2024) Cite this article

Abstract

Background

The introduction of non-native species is a primary driver of biodiversity loss in freshwater ecosystems. The redclaw crayfish (Cherax quadricarinatus) is a freshwater species that exhibits tolerance to hypoxic stresses, fluctuating temperatures, high ammonia concentration. These hardy physiological characteristics make C. quadricarinatus a popular aquaculture species and a potential invasive species that can negatively impact tropical and subtropical ecosystems. Investigating the genomic basis of environmental tolerances and immune adaptation in C. quadricarinatus will facilitate the development of management strategies of this potential invasive species.

Results

We constructed a chromosome-level genome of C. quadricarinatus by integrating Nanopore and PacBio techniques. Comparative genomic analysis suggested that transposable elements and tandem repeats drove genome size evolution in decapod crustaceans. The expansion of nine immune-related gene families contributed to the disease resistance of C. quadricarinatus. Three hypoxia-related genes (KDM3A, KDM5A, HMOX2) were identified as being subjected to positive selection in C. quadricarinatus. Additionally, in vivo analysis revealed that upregulating KDM5A was crucial for hypoxic response in C. quadricarinatus. Knockdown of KDM5A impaired hypoxia tolerance in this species.

Conclusions

Our results provide the genomic basis for hypoxic tolerance and immune adaptation in C. quadricarinatus, facilitating the management of this potential invasive species. Additionally, in vivo analysis in C. quadricarinatus suggests that the role of KDM5A in the hypoxic response of animals is complex.

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Background

Freshwater species face a consistently higher risk of extinction compared to their terrestrial and marine counterparts [1]. The introduction of non-native species affects ecosystem functioning and is a major driver of biodiversity loss in freshwater ecosystems [2]. Crayfish (Decapoda: Astacidea) are a diverse taxonomic group of freshwater crustaceans that play a key role in freshwater ecosystems. Crayfish have been consumed as food by human since prehistoric times, and have recreational, cultural, and scientific value [3, 4]. Therefore, they have been transported by human across the globe. They are relatively large-bodied, long-lived, densely populated, and feed at multiple trophic levels, thus affecting entire trophic webs [5]. When introduced outside their native range, crayfish species are likely to establish self-reproducing populations, spread from the point of introduction and become invasive. However, our understanding of the mechanisms driving crayfish invasions remains limited, hindering the development of effective preventative strategies.

Redclaw crayfish (Cherax quadricarinatus), which is native to northern Australia and southern New Guinea, is the second most cultured and caught crayfish species [6, 7]. Interest in redclaw crayfish for aquaculture has resulted in worldwide translocation of this species. The redclaw crayfish is resilient to hypoxic stress, tolerates a broad pH range, adapts to fluctuating temperatures, and withstands high levels of ammonia concentration [8]. The hardy physiological characteristics of the redclaw crayfish allow it to establish self-sustaining populations in the wild and negatively impact tropical and subtropical ecosystems [9, 10]. Therefore, C. quadricarinatus is considered as a potential invasive species. Additionally, C. quadricarinatus exhibits resistance to certain pathogens. It is reported that C. quadricarinatus is resistant to the acute hepatopancreatic necrosis disease (AHPND), which is caused by specific Vibro spp infections [11]. Infections by the bacilliform virus have been found in both wild and cultured C. quadricarinatus, but these do not cause disease or mortalities [12]. Experimental infection of C. quadricarinatus with Macrobrachium rosenbergii nodavirus showed that C. quadricarinatus has low susceptibility to the virus [13]. The genome sequence of C. quadricarinatus has been reported in previous studies [14, 15], while the genomic basis of its adaptation remains largely unknown. Investigating the genomic basis of its environmental tolerances and disease resistance will facilitate the development of management strategies for C. quadricarinatus.

It has been demonstrated that the changes of gene expression related to hypoxia is largely mediated by the activation of hypoxia-inducible factors (HIFs) [16]. Under normoxia, HIFα subunits are polyubiquitylated by the von Hippel–Lindau tumor suppressor protein (pVHL) complex and targeted for proteasomal degradation. Hypoxia inhibits the proteasomal degradation of HIFα, allowing it to bind with HIF1β and transcriptionally activate genes that promote adaptation to inadequate oxygen [17]. 2-oxoglutarate-dependent dioxygenases (2-OGDDs) are a family of over 60 enzymes that rely on oxygen for their activities [18]. 2-OGDDs with low oxygen affinity are major regulators of HIFs, thereby contributing to hypoxia-related transcriptional regulation [19]. Recent studies found that Jumonji C (JmJc) domain-containing histone lysine demethylases (KDM3A, KDM5A, KDM6A, KDM6B), members of the 2-OGDD family, act as direct sensors of hypoxia. The activities of these JmJc demethylases were altered under hypoxic conditions in human and mouse, leading to an increase to histone methylation and triggering the hypoxia-induced transcriptional changes [20,21,22]. However, the roles of KDMs in hypoxia-induced responses in invertebrates remain largely elusive.

Here, we assembled a chromosome-level genome of C. quadricarinatus by integrating Nanopore and PacBio techniques. Factors contributing to the evolution of genome sizes in decapod crustaceans were determined through comparative genomic analysis. The robust disease resistance of C. quadricarinatus was found to attributed to the expansion of immune-related gene families. Three hypoxia-related genes (KDM3A, KDM5A, HMOX2) were identified as being subjected to positive selection in C. quadricarinatus. Furthermore, we investigated the role of KDM5A in the hypoxic response of C. quadricarinatus. Our results provided insights into the genomic basis of disease resistance and hypoxia tolerance in C. quadricarinatus.

Results

Chromosome-level genome assembly of C. quadricarinatus

The genome of C. quadricarinatus were sequenced using a combination of Nanopore, PacBio, and Illumina shotgun sequencing. A total of 358 Gb of Nanopore reads and 159 Gb of Illumina reads were generated (Supplementary Table 1 and 2). In addition, 280 Gb of PacBio HiFi reads were generated for error correction (Supplementary Table 3). Based on the k-mer distribution of Illumina reads, the genome sizes of C. quadricarinatus were estimated to be 6.02 Gb (Supplementary Fig. 1). The C. quadricarinatus genome was assembled into contigs with Nanopore reads using Shasta and WTDBG2, respectively [23, 24]. The resulted contigs were assembled into longer sequences using quickmerge [25], and scaffolded using proximity ligation data from the Hi-C libraries to yield genome assembly (Supplementary Fig. 2; Supplementary Table 4). The final genome assembly of C. quadricarinatus consisted of 7,344 scaffolds (contig N50: 739.45 kb, scaffold N50: 33.93 Mb) assembled into 100 pseudomolecules, resulting in a total assembly size of 3.954 Gb (Fig. 1andTable 1). We compared the sequence consistency and integrity of our assembly with the previously published genome assembly of C. quadricarinatus (GCF_026875155.1) [14]. First, syntenic analysis showed high collinearity between the chromosomes of the previously published assembly and our assembly (Supplementary Fig. 3). Benchmarking Universal Single-Copy Orthologs (BUSCO) analysis indicated that 88.5% of conserved single-copy arthropod (Arthropoda) genes (odb10) were captured in our assembly, compared to 81.2% were captured in previously published assembly (Supplementary Table 5) [26]. Additionally, Merqury evaluation indicated that the consensus quality value (QV) of our assembly was 32.81, compared to 18.75 of previously published assembly, suggesting our assembly is of better quality (Supplementary Table 6) [27].

Fig. 1
figure 1

Genome assembly of C. quadricarinatus. Circos plot of the distribution of genomic elements in C. quadricarinatus. a GC content; b Density of tandem repeats; c Density of transposable elements; d Gene density

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Table 1 Genome assembly statistics of C. quadricarinatus
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Repetitive DNA in the C. quadricarinatus genome is exceptionally abundant, represented 93.36% (3.69 Gb) of the assembly (Supplementary Table 7). Transposable elements (TEs) account for 67.03% (2.65 Gb) of the C. quadricarinatus genome assembly. Long interspersed nuclear elements (LINEs) were the largest class of annotated TEs, making up 30.42% of the genome. Long terminal repeat (LTR) retrotransposons, which were the second largest class of TEs, represented 659.41 Mb (16.68%) of the genome. The proportion of LINEs and LTR retrotransposons in the C. quadricarinatus genome was relatively higher than that in the genomes of other crustaceans, which is derived from a recent expansion of retrotransposons (Supplementary Fig. 4 and 5; Supplementary Table 8). Protein-coding genes in the genomes were identified through a combination of ab initio, homology-based, and RNA-seq-based prediction approaches. In total, 17,698 protein-coding genes were identified in the C. quadricarinatus genome. BUSCO analysis identified 894 (88.3%) complete conserved single-copy arthropod (Arthropoda) genes (odb10) in the predicted gene models of C. quadricarinatus (Supplementary Table 9). In total, 16,509 (93.28%) gene models in the C. quadricarinatus genome can be annotated in at least one database (NCBI non-redudant, InterPro, KEGG, and eggNOG) (Supplementary Table 10).

Transposable elements and tandem repeats drive the expansion of decapod crustacean genomes

Crustaceans are characterized by having some of the most variable genome sizes among animals. The diverse genome sizes are associated with the physiological and life-history traits of these species, contributing to their adaptation [28]. Increases in genome sizes can be driven by several processes, including whole-genome duplication (WGD), TE expansion, intron expansion, and tandem gene duplication [29]. Of these, TE expansion and WGD are considered to be the most important factors [30]. To investigate the genome size evolution of decapod crustaceans, we compared the TE content among 10 decapod species with chromosome-level genome assemblies (Procambarus virginalis, Procambarus clarkii, Homarus americanus, Scylla paramamosain, Eriocheir sinensis, Macrobrachium nipponense, Penaeus monodon, Fenneropenaeus chinensis, Litopenaeus vannamei, C. quadricarinatus). A positive correlation was identified between genome size and TE content in these species (Fig. 2A and B). In addition to TE, the genome size of decapods is positively correlated with the content of tandem repeats (TRs) (Fig. 2A and C). The content of minisatellite is significantly higher in P. clarkii, C. quadricarinatus, and H. americanus compared to other decapods (P < 0.01), while M. nipponense, P. monodon, F. chinensis, and L. vannamei have higher content of simple sequence repeats (SSRs) than satellites and minisatellites (Fig. 2A). A previous study found the content of SSRs was significantly higher in penaeid shrimp species than in other decapods [31]. Our results suggested that TE and TR expansions drove the expansion of the genomes of decapod species.

Fig. 2
figure 2

The evolution of genome sizes in decapod crustaceans. A The comparison of genome sizes, transposable element (TE) content, and tandem repeat (TR) content among decapod species. B The correlation between TE content and genome sizes in decapod species. C The correlation between TR content and genome sizes in decapod species. D Frequency distribution of the synonymous substitution rates (Ks) among syntenic gene pairs of decapod species

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To investigate the contribution of WGD to the genome size evolution of decapods, the synonymous substitution rates (Ks) of syntenic gene pairs were estimated in C. quadricarinatus and 8 other decapod crustaceans (Fig. 2D). No obvious peak was identified in the Ks distributions of all 9 species. Additionally, limited WGD-derived duplicated gene pairs were identified in the genomes of these 9 species using DupGen_finder [32] (Supplementary Fig. 6; Supplementary Table 11). These results indicated that these 9 decapod crustaceans have not undergone WGD during evolution.

Gene families related to immunity expanded in the genome of C. quadricarinatus

Gene family expansion and contraction analyses were performed to dissect the genetic basis of adaptation in C. quadricarinatus. A maximum-likelihood (ML) phylogenetic tree of C. quadricarinatus and 12 arthropods was reconstructed with Drosophila melanogaster as the outgroup (Fig. 3A). Cherax quadricarinatus formed a clade with P. clarkii and P. virginalis. And H. americanus appeared sister to this clade. The ancestor of C. quadricarinatus diverged from the ancestors of P. clarkii and P. virginalis approximately 176 million years (Ma) ago (CI: 149.89–202.20 Ma). The divergence time of H. americanus and other three crayfish species was estimated to be approximately 214 Ma (CI: 183.85–245.06 Ma), consistent with the results of previous studies [33, 34].

Fig. 3
figure 3

The evolution of gene families in decapod crustaceans. A A species tree of 13 arthropod species with D. melanogaster as outgroup. B Gene family expansion and contraction analysis of decapod crustaceans. Numbers of expanded gene families are marked in green, and numbers of contracted gene families are marked in red. The number below the MRCA (most recent common ancestor) represents the total number of orthologs from OrthoMCL analysis used as input for CAFE expansion/contraction analysis. C The comparison of gene numbers within the nine expanded immune-related gene families among arthropod species. D A phylogenetic tree of IgLectin Proteins from C. quadricarinatus, P. clarkii, S. paramamosain, E. sinensis. E Genomic distribution of IgLectin genes in C. quadricarinatus

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Gene family analysis was performed based on the phylogenetic tree (Fig. 3B). Compared with other arthropods, 146 gene families were expanded, and 147 gene families were contracted in C. quadricarinatus (P < 0.01). Interestingly, nine gene families related to immunity were significantly expanded in the genome of C. quadricarinatus (Fig. 3C). The invertebrate immune system primarily relies on specific pattern recognition receptors (PRRs) to recognize the pathogen-associated molecular patterns (PAMPs) of invading pathogens [35]. PPR-PAMP interaction initiate a series of immune responses, including prophenoloxidase (proPO) activated system, antimicrobial peptides (AMP) synthesis, phagocytosis, encapsulation, blood clotting, and reactive oxygen species production [36]. Within the nine expanded gene families related to immunity in C. quadricarinatus, four encode PPRs (Ig domain-containing C-type lectin, ficolin, scavenger receptor cysteine-rich domain containing protein, Down syndrome cell adhesion molecule), two encode AMPs (anti-lipopolysaccharide factor, c-type lysozyme), and three encode other immune-related proteins (Laccase-1, Peritrophin-1, Phenoloxidase-activating factor 2) [37]. The expansion of these immune-related gene families in C. quadricarinatus confers a strong ability of pathogen recognition and clearance, thereby contributing to the strong disease resistance in this species.

The C-type lectin family, characterized by its signature C-type lectin-like domain, promotes antibacterial host defense across numerous animal species [38]. While most invertebrate C-type lectins contain a single carbohydrate-recognition domain, certain arthropods were found to have C-type lectins possessing additional functional domains [39, 40]. Vertebrate adaptive immunity primarily relies on immunoglobulins (Igs) belonging to the Ig superfamily [41]. Several proteins containing Ig-like domains play a crucial role in the innate immune response of invertebrates [42]. Recent studies identified a new C-type lectin protein possessing an Ig-like domain and a C-type lectin domain in E. sinensis and P. clarkii [43, 44]. It triggers strong antibacterial activities through regulating phagocytosis in hemocytes and maintaining microbiota homeostasis in the intestine. To study the evolution of the Ig domain-containing C-type lectin (IgLectin), we identified the genes encoding this special C-type lectin in the major groups of vertebrates and invertebrates. IgLectin genes were only identified in four decapod species (C. quadricarinatus, P. clarkii, S. paramamosain, E. sinensis) (Fig. 3D). While other decapods possess only one or two IgLectin genes, seven IgLectin genes formed a gene cluster on chromosome 10 of C. quadricarinatus (Fig. 3E). This result indicated that the family of IgLectin genes was expanded in the genome of C. quadricarinatus. The expansion of IgLectin genes, which are exclusively found in decapods, may played a crucial role in disease resistance in C. quadricarinatus.

KDM5A is crucial for the hypoxia tolerance of C. quadricarinatus

Positively selected genes (PSGs) were identified in the genome of C. quadricarinatus to investigate the genetic basis of stress resistance within this species. In total, 27 PSGs were identified in the C. quadricarinatus genome compared to twelve arthropod species (P. virginalis, P. clarkii, H. americanus, S. paramamosain, E. sinensis, M. nipponense, P. monodon, F. chinensis, L. vannamei, H. azteca, D. magna, D. melanogaster) (Supplementary Table 12). Intriguingly, three genes (KDM3A; KDM5A; Heme oxygenases-2, HMOX2) related to hypoxic response were positively selected in C. quadricarinatus.

KDM5A, a member of the JmjC domain-containing histone demethylase family, serves as a direct sensor of hypoxia. Hypoxia inactivates KDM5A in cancer cells, inhibiting the removal of a methyl group from H3K4me3 in the promoters of hypoxia-inducible genes. This leads to the activation of these genes and the induction of a hypoxic response [21]. As CqKDM5A has the highest omega score among the three identified positively selected hypoxia-related genes, we examined its function in the hypoxia response of C. quadricarinatus. Tissue distribution of CqKDM5A expression in C. quadricarinatus was examined using quantitative PCR (qPCR). The expression of CqKDM5A was detected in the eyestalk, intestine, muscle, hemocyte, hepatopancreas, stomach, and heart of C. quadricarinatus, but not in the gill and epidermis (Supplementary Fig. 7). To study whether CqKDM5A involved in the hypoxic response of C. quadricarinatus, we examined CqKDM5A expression in the hemocyte of C. quadricarinatus reared under hypoxic and normoxic conditions using quantitative PCR (qPCR) (Fig. 4A). The expression of CqKDM5A was significantly upregulated in the hemocyte of C. quadricarinatus after 12 h of hypoxia exposure. To further investigate the role of CqKDM5A in the hypoxic response, we used RNA interference (RNAi) to suppress CqKDM5A expression in C. quadricarinatus (Fig. 4A). RNAi-treated and control groups of C. quadricarinatus were maintained at hypoxic and normoxic conditions. After 120 h of hypoxic exposure, the survival rate in the RNAi-treated group was significantly lower compared to the control groups (χ2: 16.37, P < 0.0001) (Fig. 4B). This result indicated that suppressing CqKDM5A impaired the hypoxia tolerance of C. quadricarinatus. Furthermore, hypoxic tolerance was assessed in both adult and juvenile C. quadricarinatus. The expression of CqKDM5A was significantly higher in adult C. quadricarinatus than that in juveniles (Fig. 4C). The survival rate of adult crayfishes was significantly higher than that of juveniles after 120 h of hypoxic exposure (χ2: 21.20, P < 0.0001) (Fig. 4D). Taken together, these results suggested that upregulating CqKDM5A plays a critical role in the hypoxic tolerance of C. quadricarinatus.

Fig. 4
figure 4

The role of KDM5A in the hypoxic tolerance of C. quadricarinatus and L. vannamei. A RNA interference (RNAi) of the CqKDM5A gene in C. quadricarinatus. B Survival of wild-type, KDM5A-silenced, and GFP dsRNA treated C. quadricarinatus reared under normoxic and hypoxic conditions. C Quantification of the expression levels of CqKDM5A in adult and juvenile C. quadricarinatus. D Survival of adult and juvenile C. quadricarinatus when exposed to hypoxic stresses

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Discussion

In this study, we generated a chromosome-level genome sequence of C. quadricarinatus, which has a large genome size of over 6 Gb. Our final assembly is 3.95 Gb, approximately 2.07 Gb smaller than the estimated genome size. The previously published assembly (GCF_026875155.1) is 5.26 Gb [14], about 0.76 Gb smaller than the estimated genome size and 1.31 Gb larger than our assembly. However, BUSCO evaluation suggested that the completeness of our assembly is higher than the previously published assembly, and the quality value of our assembly assessed by Mercury software is much higher. This suggests that future studies are needed to generate a C. quadricarinatus genome assembly that includes the sequences that are missing from both current assemblies. In this study, we investigated the genome size evolution of 10 decapod crustaceans. We found that the genome size of these decapods is positively correlated with the content of both TEs and TRs, suggesting that the expansion of TEs and TRs contributes to the genome size expansion of decapod species. A previous study found the content of SSRs was significantly higher in penaeid shrimp species than in other decapods [31]. Consistent with this result, we found that three penaeid shrimp species (P. monodon, F. chinensis, and L. vannamei) and M. nipponense have a higher content of SSRs than satellites and minisatellites. In addition, we found that the content of minisatellite is significantly higher in P. clarkii, C. quadricarinatus, and H. americanus compared to other decapods. This suggests that minisatellites contribute to the genome evolution of certain decapod crustaceans.

Freshwater systems cover approximately 0.8% of the Earth’s surface but host almost 6% of the Earth’s described species. Despite this richness, freshwater ecosystems are currently the most endangered ecosystems. The decline in biodiversity within freshwater ecosystems surpasses that of other ecosystems [45]. The recent oxygen depletion in water resulted from rising global temperatures and anthropogenic eutrophication severely impacts the functioning and services of freshwater ecosystems [46]. Additionally, the increased connectivity of the global human population has amplified the frequency of biological invasion and pathogen transmission, potentially leading to the extinctions of freshwater species [47]. Crayfish are a diverse taxonomic group of freshwater crustaceans that includes both critically endangered endemic species and highly successful invasive species [48]. Due to their importance in aquaculture and popularity in the aquarium pet trade, numerous translocations of the Redclaw crayfish have occurred. The strong environmental tolerance and resistance to diseases of C. quadricarinatus allow it to establish self-sustaining populations in wild, negatively impacting the ecosystems it invades [8]. In this study, we investigated the genetic basis of disease resistance and hypoxia tolerance in C. quadricarinatus by generating a chromosome-level reference sequence. The strong disease resistance of C. quadricarinatus was found to be attributed to the expansion of nine immune-related gene families. Additionally, three genes critical for hypoxic response were identified as being subjected to positive selection in C. quadricarinatus, contributing to the hypoxia resistance in this species. These results elucidated the genetic basis underlying the invasive potential of C. quadricarinatus, thereby facilitating the development of preventative strategies to control its spread and mitigate its ecological impacts. Additionally, our results shed light on the protection of freshwater crayfish species that are susceptible to challenging environments.

Crayfish exhibit remarkable adaption to low dissolved oxygen conditions in water. Specifically, C. quadricarinatus have evolved adaptive molecular response to hypoxia, capable of surviving in low dissolved oxygen concentrations below 1 mg/L [8]. Hypoxia influences the activity of histone-modifying enzymes, which modulate the posttranslational modification of histones as well as nonhistone proteins [49]. A recent study shows that chromatin can sense oxygen independently of HIF through KDM5A [21]. The demethylase activity of KDM5A is suppressed in human cultured cells exposed to hypoxic conditions, leading to a global increase in the levels of H3K4me3 and elevated expression levels of hypoxia-inducible genes. Knockdown of KDM5A results in increased H3K4me3 and expression levels of the STAG2 and LOX genes, mirroring the hypoxia-induced cellular responses. Here, we performed in vivo analysis to investigate the role of KDM5A in the hypoxic response of C. quadricarinatus. Hypoxia upregulated KDM5A expression in C. quadricarinatus. And siRNA mediated silencing of KDM5A impaired the hypoxic response of this decapod species. These results suggested that while suppressing KDM5A expression triggers hypoxic response in human cultured cells, increasing KDM5A expression is essential for hypoxia tolerance in C. quadricarinatus. Due to their aquatic habitats, crustaceans encounter hypoxic conditions more frequently than mammals. Consequently, the mechanisms of hypoxic response may be different between these two animal groups. Our results elucidated the role of KDM5A in the hypoxia tolerance of C. quadricarinatus, highlighting the complexity of this HIF-independent hypoxic response mechanism.

Conclusions

In conclusion, this study found that the expansion of nine immune-related gene families contributed to the strong disease resistance of C. quadricarinatus. Furthermore, three genes crucial for hypoxic response (KDM3A, KDM5A, HMOX2) were found to be subjected to positive selection in C. quadricarinatus. In vivo analyses revealed that upregulating KDM5A expression plays a crucial role in the hypoxic response of C. quadricarinatus. Our results provided the genetic basis of developing management strategies for C. quadricarinatus, a species with invasive potential.

Methods

Sampling and genome sequencing

One female C. quadricarinatus individual collected from the experimental pond of Zhejiang Institute of Freshwater Fisheries in Zhejiang Province, China was used for genome sequencing. High-quality DNA was extracted from muscle cells of C. quadricarinatus using DNeasy Blood & Tissue Kits (Qiagen) in accordance with the manufacturer’s protocol. DNA Quality and quantity were measured via standard agarose-gel electrophoresis and a Qubit 3.0 Fluorometer (Invitrogen), respectively. Nanopore sequencing libraries of C. quadricarinatus were constructed and sequenced using the Nanopore PromethION platform (Oxford Nanopore Technologies) (90X raw-read coverage). For Illumina sequencing, short-insert paired-end (PE) (150 bp) DNA libraries of C. quadricarinatus were constructed in accordance with the manufacturer’s instruction. Sequencing of PE libraries were performed (2 \(\times\) 150 bp) on the Illumina NovaSeq 6000 platform (Illumina).

PacBio HiFi reads were generated to correct errors in the draft genome assembly. To construct PacBio HiFi sequencing libraries, high-quality DNA was extracted from muscle cells of C. quadricarinatus using DNeasy Blood & Tissue Kits (Qiagen) in accordance with the manufacturer’s protocol. The extracted genomic DNA was sheared by g-TUBEs (Covaris) according to the expected size of the fragments for library construction. Sheared DNA fragments were damage repaired, end repaired, and ligated with the hairpin adaptors for PacBio sequencing. The sequencing libraries were size selected by the BluePippin system (Sage Science) and purified by AMPure PB beads (Pacific Bioscience). Quality of the sequencing libraries were measured by the Agilent 2100 Bioanalyzer (Agilent technologies). Sequencing was performed on a PacBio Sequel II instrument (Pacific Bioscience).

Hi-C library was constructed based on a previously published procedure [50]. In brief, muscle sample of C. quadricarinatus were cut into 2 cm pieces and vacuum infiltrated in nuclei isolation buffer supplemented with 2% formaldehyde. Crosslinking was stopped by adding glycine and additional vacuum infiltration. Fixed tissue was frozen in liquid nitrogen and grounded to powder before re-suspending in nuclei isolation buffer to obtain a suspension of nuclei. The purified nuclei were digested with HindIII and marked by incubating with biotin-14-dCTP. The ligated DNA was sheared into 300 − 600 bp fragments, blunt-end repaired, A-tailed, and purified through biotin-streptavidin-mediated pull-down. Hi-C library was sequenced (2 \(\times\) 150 bp) on the Illumina NovaSeq 6000 platform (Illumina).

Transcriptome sequencing

Eye, gill, heart, intestine, hepatopancreas, and muscle samples were collected from the C. quadricarinatus specimen to construct sequencing libraries for strand-specific RNA-sequencing (RNA-seq). Total RNA was extracted with TRIzol reagent (Invitrogen). Purity and integrity of RNA were determined using a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific) and Bioanalyzer 2100 system (Agilent), respectively. The mRNA was enriched from total RNA using poly-T oligo-attached magnetic beads. rRNA was removed using a TruSeq Stranded Total RNA Library Prep kit (Illumina). PE libraries were constructed using a VAHTSTM mRNA-seq V2 Library Prep Kit for Illumina (Vazyme) and sequenced (2 \(\times\) 150 bp) using the Illumina NovaSeq 6000 platform (Illumina).

Genome assembly

Low-quality (≥ 10% unidentified nucleotide and/or ≥ 50% nucleotides with a Phred score < 5) and sequencing adapter-contaminated Illumina reads were filtered and trimmed with Fastp (v0.21.0) [51]. The resulting high-quality Illumina reads were used in the following analyses. The sizes and heterozygosity of C. quadricarinatus genomes were estimated using high-quality Illumina reads based on k-mer frequency-distribution. The number of k-mers and the peak depth of k-mer sizes at 17 was obtained using Jellyfish (v2.3.0) [52] with the -C setting. Genome size was estimated based on a previously described k-mer analysis [53]. The heterozygosity of C. quadricarinatus genome was determined by fitting the k-mer distribution of Arabidopsis thaliana using Kmerfreq implemented in SOAPdenovo2 (r242) [54].

Low-quality Nanopore reads were filtered using custom Python script. The filtered reads were then corrected using NextDenovo (v1.0) (https://github.com/Nextomics/NextDenovo). Two draft genome assemblies were generated using filtered and corrected Nanopore reads with Shasta (v0.8.0) [23] and WTDBG2 (v2.5) [24], respectively. The contigs of the two draft assemblies were subject to error correction using PacBio HiFi reads with Racon (v1.4.16) three times [55]. The corrected contigs were then polished with high-quality Illumina reads with Nextpolish (v1.2.4) three times [56]. Haplotypic duplications in the error-corrected contigs were identified and removed using purge_dups (v1.2.3) [57]. The resulted contigs were assembled into longer sequences using quickmerge (v0.3) [25].

We used Hi-C to correct misjoins, to order and orient contigs, and to merge overlaps. Low-quality Hi-C reads were filtered using fastp (v0.21.0) with default parameters [51]. Filtered Illumina reads were aligned to the assembled contigs using Bowtie2 (v2.3.2) [58]. Scaffolding was accomplished using LACHESIS with parameters “CLUSTER MIN RE SITES = 100, CLUSTER MAX LINK DENSITY = 2.5, CLUSTER NONINFORMATIVE RATIO = 1.4, ORDER MIN N RES IN TRUNK = 60” [59].

The completeness and quality of the assembly was evaluated using QUAST (v5.0.2), and Benchmarking Universal Single Copy Orthologs (BUSCO, v4.0.5) against the conserved Arthropoda dataset (obd10) [26, 60]. Additionally, Merqury (v1.3) [27] was used to assess the completeness and quality of the four assemblies with k-mer set to 17.

Genome annotation

Repetitive elements in the assembly were identified by de novo predictions using RepeatMasker (v4.1.0) (https://www.repeatmasker.org/). RepeatModeler (v2.0.1) was used to build the de novo repeat libraries of C. quadricarinatus [61]. To identify repetitive elements, sequences from the assembly were aligned to the de novo repeat library using RepeatMasker (v4.1.0). Additionally, repetitive elements in the C. quadricarinatus genome assembly were identified by homology searches against known repeat databases using RepeatMasker (v4.1.0). A repeat landscape of C. quadricarinatus genome was obtained using an R script that was modified from https://github.com/ValentinaBoP/TransposableElements.

Protein-coding genes in the C. quadricarinatus genome were predicted using RNA-seq-based approach. Short RNA-seq reads were aligned to genome assembly using HISAT2 (v2-2.1). Gene models were predicted based on the alignment results of HISAT2 using StringTie (v2.1.4) [62], and coding regions were identified using TransDecoder (v5.5.0) [63]. The completeness of predicted gene models was evaluated using BUSCO (v4.0.5) against the conserved Arthropoda dataset (odb10) [26]. To assign functions to the predicted proteins, we aligned the C. quadricarinatus protein models against NCBI nonredundant (NR) amino acid sequences and UniProt databases using BLASTP with an E-value cutoff of 10−5. Protein models were also aligned against the eggNOG database using eggNOG-Mapper [64], and against the InterPro database using InterProScan [65]. Finally, Kyoto Encyclopedia of Genes and Genomes (KEGG) annotation of the protein models was performed using BlastKOALA [66].

Tandem repeats identification

Tandem repeats in the genomes of D. melanogaster, D. magna, H. azteca, L. vannamei, F. chinensis, P. monodon, M. nipponense, E. sinensis, S. paramamosain, H. americanus, P. clarkii, P. virginalis, and C. quadricarinatus were identified using tandem repeat finder (TRF) (v4.09.1) [67]. In addition to TRF, we used GMATA (v2.3) and SciRoKo (v3.3) to identify SSRs in genomes of these arthropod species [68, 69]. TRF-, GMATA- and SciRoKo-identified SSRs were integrated into a nonredundant set using a custom Python script.

Whole genome duplication and duplicated gene analysis

To determine whether recent WGD occurred in C. quadricarinatus as well as 8 decapods with chromosome-level genome assemblies (P. clarkii, H. americanus, S. paramamosain, E. sinensis, M. nipponense, P. monodon, F. chinensis, and L. vannamei), we estimated the distribution of Ks for each paralog in using wgd (v1.1.2) [70].

All-versus-all protein blast among 9 decapod species was performed using MCScanX [71]. Results of all-versus-all protein blast were inputted into DupGen_finder to determine modes of duplicated gene pairs [32].

Phylogenetic reconstruction

Protein sequences of 12 arthropod species (Drosophila melanogaster, Daphnia magna, Hyalella azteca, Litopenaeus vannamei, Fenneropenaeus chinensis, Penaeus monodon, Macrobrachium nipponense, Eriocheir sinensis, Scylla paramamosain, Homarus americanus, Procambarus clarkii, Procambarus virginalis) were downloaded from NCBI. Protein sequences shorter than 50 amino acids were removed. OrthoFinder (v 2.5.4) [72] was applied to determine and cluster gene families among these 12 species and C. quadricarinatus. Gene clusters with > 100 gene copies in one or more species were removed. Single-copy orthologs in each gene cluster were aligned using MAFFT (v7.490) [73]. Alignments were trimmed using ClipKit (v1.2.0) with “gappy” mode [74]. The phylogenetic tree was reconstructed with the trimmed alignments using a maximum-likelihood method implemented in IQ-TREE2 (v2.1.2) with D. melanogaster as outgroup [75]. The best-fit substitution model was selected using ModelFinder algorithm [76]. Branch supports were assessed using the ultrafast bootstrap (UFBoot) approach with 1,000 replicates [77].

To estimate the divergence time between species or clade, the trimmed alignments of single-copy orthologs were concatenated using PhyloSuite (v1.2.2) [78]. Concatenated alignment was used to estimated divergent times among species using the MCMCtree module of the PAML package (v4.9) [79]. MCMCtree analysis was performed using the maximum-likelihood tree reconstructed by IQ-TREE2 as a guide tree and calibrated with the divergent time obtained from the TimeTree database (minimum = 58 million years and soft maximum = 108 million years between L. vannamei and P. monodon; minimum = 154 million years and soft maximum = 242 million years between C. quadricarinatus and P. clarkii; minimum = 526 million years and soft maximum = 578 million years between H. azteca and D. magna) [80].

Gene family expansion and contraction analysis

CAFÉ (v5) was applied to determine the significance of gene-family expansion and contraction among the 13 arthropod species based on the MCMCtree-generated ultrametric tree and OrthoMCL-determined gene clusters used for species tree reconstruction [81].

The IgLectin protein family was significantly expanded in the C. quadricarinatus genome compared with other decapod species. Phylogenetic tree reconstruction was performed to investigate the evolutionary relationships of IgLectin proteins from C. quadricarinatus and other decapod species. The IgLectin protein sequences from P. clarkii (AGL46986.1) and E. sinensis (UIS31342.1) were downloaded from NCBI [43, 44]. And the Ig-Lectin protein sequences from S. paramamosain were extracted from published genome sequence [82]. Protein sequences were aligned using MAFFT (v7.490) [83]. The phylogenetic tree was reconstructed using maximum-likelihood alignment implemented in IQ-TREE2 (v2.2.0), with a C-type Lectin protein (XP_045031378.1) from D. magna as the outgroup. The best-fit substitution model was selected using the ModelFinder algorithm [76]. Branch supports were assessed using UFBoot with 1000 replicates [77].

Identification of positively selected genes

PSGs in the C. quadricarinatus genome were identified using PosiGene (v0.1) [84] with parameters “-as = D.melanogaster, -ts = C. quadricarinatus -rs = D.melanogaster, -nhsbr”. Genes with a P-value < 0.05 were identified to have been subject to positive selection.

Tissue distribution and pattern during development of CqKDM5A expression

Juvenile crayfish (1.5 ± 0.3 g) and adult crayfish (250 ± 3 g) were obtained from Zhongshan, Guangdong Province, China., and cultivated in aerated tanks at 26 °C for a minimum of two weeks before being used in the experiments.

To investigate the tissue distribution of CqKDM5A expression, samples of gill, epidermis, heart, stomach, hepatopancreases, hemocyte, muscle, intestine, and eyestalk were collected from juvenile crayfish. In addition, samples of hemocyte were collected from adult crayfish and juvenile crayfish to investigate the expression patten of CqKDM5A during growth. The expression levels of CqKDM5A were determined using qPCR assay, with Cqβ-actin (GenBank No. AY430093.1) used as the internal control for normalization (Supplementary Table 13) [85]. The primer pair efficiency was evaluated following the MIQE method, with a tenfold logarithmic dilution of a cDNA mixture used to generate a linear standard curve [86].

RNAi knockdown of CqKDM5A

Specific dsRNA targeting the CqKDM5A and CqGFP genes were synthesized through in vitro transcription using the T7 RiboMAX Express RNAi System kit (Promega, cat. no. P1700, USA) (Supplementary Table 13). The experimental groups were injected with CqKDM5A dsRNA (2 μg/g per individual), and the control groups were injected with GFP dsRNA.

Crayfish without dsRNA injection, crayfish injected with GFP dsRNA, and crayfish injected with CqKDM5A dsRNA were maintained in both normoxic and hypoxic conditions. For hypoxia experiments, crayfish were reared in sealed aquariums covered with plastic films and filled with cooled boiling water (1 ± 0.1 mg/L dissolved oxygen, 0‰ salinity, 25 °C). The dissolved oxygen levels were measured using a dissolved oxygen analyzer (Yieryi, DO9100, China). Every twelve hours, crayfish were transferred to new tanks with freshly treated water to maintain the hypoxic environment. Expression levels of CqKDM5A were determined using qPCR assay, with Cqβ-actin (GenBank No. AY430093.1) used as the internal control for normalization (Supplementary Table 13).

Availability of data and materials

Raw reads and genome assemblies are accessible in NCBI under BioProject number PRJNA904538. Raw reads and genome assemblies are also available at the CNGB Sequence Archive (CNSA) of China National GeneBank DataBase (CNGBdb) with accession number CNP0005505.

Abbreviations

KDM3A:

Lysine demethylase 3A

KDM5A:

Lysine demethylase 5A

HMOX2:

Heme oxygenases-2

HIF:

Hypoxia-inducible factor

PHD:

Oxygen-dependent prolyl hydroxylase

FIH:

Factor-inhibiting HIF

ARNT:

Aryl hydrocarbon nuclear translocator

2-OGDD:

2-Oxoglutarate-dependent dioxygenase

JmJc:

Jumonji C

KDM6A:

Lysine demethylase 6A

KDM6B:

Lysine demethylase 6B

PSG:

Positively selected gene

BUSCO:

Benchmarking Universal Single-Copy Orthologs

QV:

Quality value

TE:

Transposable element

LINE:

Long interspersed nuclear element

LTR:

Long terminal repeat

WGD:

Whole-genome duplication

TR:

Tandem repeat

SSR:

Simple sequence repeat

Ks:

Synonymous nucleotide substitutions

ML:

Maximum-likelihood

Ma:

Million years ago

PRR:

Pattern recognition receptor

PAMP:

Pathogen-associated molecular pattern

proPO:

Prophenoloxidase

AMP:

Antimicrobial peptide

Ig:

Immunoglobulin

qPCR:

Quantitative PCR

RNAi:

RNA interference

PE:

Paired-end

NR:

Nonredundant

KEGG:

Kyoto Encyclopedia of Genes and Genomes

TRF:

Tandem repeat finder

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Acknowledgements

We gratefully acknowledge the National Supercomputing Center in Guangzhou for provision of computational resources.

Funding

This study was supported by the earmarked fund of China Agriculture Research System for CARS-48, Key Scientific and Technological Grant of Zhejiang for Breeding New Agricultural Varieties (2021C02069-4–5), Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) (SML2023SP234), Fundamental Research Funds for the Central Universities, Sun Yat-sen University (23ptpy23).

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Author notes

  1. Ziwei Liu, Jianbo Zheng and Haoyang Li contributed equally to this work.

Authors and Affiliations

  1. State Key Laboratory for Biocontrol, School of Marine Sciences, Sun Yat-Sen University, Zhuhai, 519000, China

    Ziwei Liu, Ke Fang, Jian He, Dandan Zhou, Jianguo He & Muhua Wang

  2. Key Laboratory of Genetics and Breeding, Zhejiang Institute of Freshwater Fisheries, Huzhou, 313001, China

    Jianbo Zheng, Meili Chi, Zhimin Gu & Fei Li

  3. China-ASEAN Belt and Road Joint Laboratory On Mariculture Technology, Guangdong Provincial Key Laboratory of Aquatic Economic Animals, School of Life Sciences, Sun Yat-Sen University, Guangzhou, 510275, China

    Haoyang Li, Sheng Wang, Shaoping Weng, Jianguo He & Muhua Wang

  4. Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, 519000, China

    Haoyang Li, Jian He, Shaoping Weng, Jianguo He & Muhua Wang

  5. Zhejiang Academy of Agricultural Sciences, Hangzhou, 310021, China

    Zhimin Gu

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  1. Ziwei Liu
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Contributions

J.G.H., F.L., M.W. conceived of the project and designed research; J.Z., D.Z., S.P.W. collected the sample; Z.L., K.F. assembled and annotated the genomes; Z.L., J.Z., M.C., Z.G. conducted the evolutionary analyses; H.L., S.W., J.H. conducted the experiments of hypoxia tolerance; J.G.H., F.L., M.W. wrote the paper with contribution from all authors.

Corresponding authors

Correspondence to Jianguo He, Fei Li or Muhua Wang.

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Ethics approval and consent to participate

All animal experiments were approved by Institutional Animal Care and Use Committee of Sun Yat-Sen University (Approval No. SYSU-IACUC-2023-B0005). Crayfishes and shrimps were anaesthetized on ice prior to all experiments. All efforts were made to minimize animal suffering.

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The authors declare no competing interests.

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Liu, Z., Zheng, J., Li, H. et al. Genome assembly of redclaw crayfish (Cherax quadricarinatus) provides insights into its immune adaptation and hypoxia tolerance. BMC Genomics 25, 746 (2024). https://doi.org/10.1186/s12864-024-10673-9

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  • DOI: https://doi.org/10.1186/s12864-024-10673-9

Keywords

BMC Genomics

ISSN: 1471-2164

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