De novo whole transcriptome sequencing reveals differentially expressed messenger RNA and MicroRNA in hepatopancreas of Pandalus hypsinotus

ABSTRACT

Pandalus hypsinotus (P. hypsinotus), also known as coonstripe shrimp, has high economic value and is widely distributed in Alaska, the Bering Sea and the East Sea of Korea. However, owing to the low catch rates and patchy spatial distribution, transcriptome-based studies on this species remain limited. Transcriptome analyses using PacBio Iso-Seq and Illumina RNA sequencing techniques were performed to establish a de novo unigene set of P. hypsinotus and identify differentially expressed genes (DEGs) in the hepatopancreas compared to the tail muscle. A search of all the unigenes against Gene Ontology (GO), KEGG, Pfam, BLASTN, and UniProt resulted in 46,703 annotated unigenes. A comparison of the sequences in the two tissue-derived libraries revealed 9,429 DEGs in the hepatopancreas. We also investigated the small RNAomes of P. hypsinotus using the Illumina small RNA sequencing technology. Because miRNAs are highly sensitive to stress responses and pathogen infections, they serve as effective indicators of the immune status, thereby acting as crucial tools for enhancing productivity and disease control in the aquaculture industry. The 130 identified microRNA (miRNA) candidates were classified into 32 orthologous and 98 novel miRNAs using BLASTN and miRBase. Through GO analysis and direct interactions between miRNAs and genes, the differentially expressed miRNAs (DEMs) and DEGs related to immune responses showed regulatory relationships based on their sequences. These findings provide a valuable resource for future studies on novel gene detection, gene mapping and biological pathways that may be regulated by miRNAs unique to P. hypsinotus.

Introduction

Pandalus hypsinotus (P. hypsinotus), which belongs to the family Pandalid, inhabits the coasts of Japan, Russia and Korea at depths of 200–350 m (Okumura et al. 2004). As their industry has gradually expanded worldwide over the past two decades, it has become an economically important marine shrimp species, accounting for 15% of the world's annual trade (FAO 2020; Abdel-Latif et al. 2022). Owing to its industrial advantages, the demand for P. hypsinotus has gradually increased in countries on the North Pacific rim (Haynes 1976). Despite the economic and industrial significance of P. hypsinotus, only a few genomic and biological studies have been conducted on the species.

Climate change affects the survival of many crustacean species and can have a particularly dramatic impact on their ecology. In particular, the decline in krill (Euphausia superba) populations, along with changes in the adaptability of Arctic fairy shrimp (Branchinecta paludosa) and northern shrimp (Pandalus borealis), indicate that crustaceans are sensitive to ocean warming and raise concerns regarding the stability of fishery supplies (Lindholm et al. 2015; Ouellet et al. 2017; Kawaguchi et al. 2024). Therefore, it is essential to focus on economically valuable shrimp fisheries to ensure a stable supply through aquaculture. However, aquaculture can be negatively affected by infectious diseases and stress caused by overcrowding (Sung et al. 2011; Stentiford et al. 2017; Ciji and Akhtar 2021; Robinson et al. 2023). This necessitates research on the genes and regulatory factors that influence shrimp immunity against infectious diseases. Research on the unidentified functional genes and regulatory factors of P. hypsinotus may contribute positively to the development of disease-resistant shrimp varieties or infection biomarkers for future P. hypsinotus aquaculture.

Transcriptome research is indispensable for a better understanding of the genetics of target species, and with the progress in high-throughput next-generation sequencing (NGS), the massive genomic sequences have become more accessible (Yang et al. 2009). NGS techniques have provided data on sequence polymorphisms and expression levels of genes involved in cellular development, immune responses and reproduction (Gao et al. 2014; Xie et al. 2014; Zhenzhen et al. 2014). However, the widely used second-generation sequencing generates short reads, limiting its ability to analyse co/post-transcriptional processing events. To overcome this limitation, a combination of the Illumina and Pacific BioSciences isoform sequencing (PacBio Iso-Seq) technologies has been used to obtain high accuracy data with long reading lengths at lower costs (Rhoads and Au 2015; Kingan et al. 2019). PacBio Iso-Seq can identify transcripts more accurately than conventional sequencing because it utilises a long-read sequencing system with an average read length of 1,000 bp (Gonzalez-Garay 2016). Hybridised analyses using the two sequencing techniques have been widely used in various species, including Larix kaempferi (Li et al. 2021), Populus ussuriensis (Li et al. 2022), Bradysia odoriphaga (Chen et al. 2022), Cotesia vestalis (Zhou et al. 2021) and Dugesia japonica (Tian et al. 2022). The present study further established the full-length transcriptome of P. hypsinotus using the PacBio Iso-Seq technique to provide a basis for genomic research. Simultaneously, Illumina RNA sequencing was conducted for comparative transcriptome profiling of the hepatopancreas and tail muscle tissues of P. hypsinotus.

Advances in sequencing technologies have led to major advances in shrimp immunity research. In particular, by identifying and analysing genes whose expression changed in response to pathogen infection and stress, we were able to elucidate shrimp immunity-related genes and their mechanisms (Tassanakajon et al. 2013). The hepatopancreas is the central metabolic organ of shrimps and plays a key role in immune response and detoxification (Vogt 2020). Functional analyses of genes with altered expression in the hepatopancreas following pathogen infection or stress have identified several genes that may influence shrimp immunity (Gross et al. 2001; Yohana et al. 2024). Representative genes such as C-type lectin, phenoloxidase and lysozyme have been found to be strongly associated with the immune response in shrimp infection and stress environments (Mai and Wang 2010; Amparyup et al. 2013; Travina-Arenas et al. 2013; Bi et al. 2020). This transcriptome-based identification of immune genes has been highly valuable in identifying novel immune genes, despite the lack of whole-genome data for crustaceans (Zhong et al. 2017).

Several studies have revealed that repeat sequences, such as transposable elements (TEs), constitute a large fraction of most eukaryotic genomes (Turcotte et al. 2001; Keane et al. 2013; Jin et al. 2015). TEs are mobile DNA fragments that often self-replicate and translocate to new chromosomal locations, leading to chromosomal rearrangements and dramatic changes in genome size. Eukaryotic TEs are divided into two classes, based on whether their transposition intermediates are RNA (class 1) or DNA (class 2) (Zhang and Wessler 2004; Wessler 2006). Both classes participate in genome reorganisation, recombination and chromosomal alterations such as deletions, inversions, duplications and translocations (Sudmant et al. 2015). These modifications may disrupt gene regulation and cause diverse biological dysfunctions related to crucial biological pathways by interacting with various genetic factors, including transcription factors, epigenetic components and microRNAs (miRNAs) (Hancks and Kazazian 2016; Rey et al. 2016; Lynch-Sutherland et al. 2020; Lee et al. 2023).

We analysed the TEs based on the results of PacBio Iso-Seq and Illumina sequencing. Moreover, differentially expressed novel and orthologous miRNAs were identified to reveal the association between the constructed unigenes and miRNAs by analysing Illumina small RNA sequencing data. The miRNAs are regulatory RNAs that bind to the 3’ untranslated region (UTR) of target mRNA to suppress translation or promote mRNA degradation (Zhu et al. 2016; Wang and Zhu 2017; Kim et al. 2025). Recently, various studies have revealed that some miRNAs regulate immune responses in several shrimp species (González-Duarte and García-Carreño 2020). For instance, a computational prediction study revealed that 25 miRNAs were differentially expressed in Fenneropenaeus chinensis (F. chinensis) infected with white spot syndrome virus (WSSV) (Li et al. 2017). Additionally, a previous study has shown that miR-589-5p regulates the immune response by targeting the hemocyanin gene, which is involved in the immunity of white-leg shrimp against WSSV (Bao et al. 2020). However, the biological relevance of miRNAs in P. hypsinotus has not been investigated.

In this study, we identified and listed miRNAs that potentially play a significant role in the metabolism of P. hypsinotus, along with their target genes. We investigated the potential regulatory functions of the miRNAs by analysing the interactions between these two factors. In particular, the hepatopancreas is closely related to the immunity of shrimp; therefore, the identification of potential immune-related genes and miRNAs with specific expression patterns in the hepatopancreas may reveal various factors involved in the metabolism of P. hypsinotus and provide basic information for further research on P. hypsinotus. To confirm this, we identified de novo unigenes and their associated regulatory miRNAs in P. hypsinotus using an integrated analysis of various NGS datasets. The identified mRNAs and miRNAs were subjected to further functional analyses to determine the potential biological associations within the mRNA-miRNA regulatory axis of P. hypsinotus. This study identified numerous mRNA transcripts and their associated regulatory miRNAs, thereby providing a strong basis for future genomic studies on P. hypsinotus.

Material and methods

Ethical statement

All the experiments were performed in accordance with the guidelines and regulations approved by the Pusan National University Institutional Animal Care and Use Committee (PNU-IACUC).

Sample preparation

Nine wild P. hypsinotus (16.48 ± 2.00 cm) were obtained from a local market located on the coast of Ulleung Island, East Sea of Korea, in June 2022. Live shrimps were transported to the laboratory within 24 h in aerated plastic bags containing seawater. P. hypsinotus is a deep-sea species that is difficult to acclimate in captivity. Upon arrival at the lab, the tissue extraction was performed immediately following the procedure used for other deep-sea organisms (Stefanni et al. 2014; Lan et al. 2018). All shrimp samples were ovigerous females aged four years or older. To maintain consistency, three individuals that exhibited significant size deviations were excluded from the study (Roa and Ernst 1996; Yamaguchi et al. 2014). All shrimp were anaesthetised by placing them in an ice bath for 20 min and euthanised by freshwater treatment. Four tissues, the hepatopancreas, gills, heart and tail muscle, were dissected from three individuals. All collected tissues were quickly placed in RNAlater reagent (Invitrogen) and stored at −80°C for further use.

K-mer analysis

Genomic DNA was extracted from tail muscle samples according to the CTAB manual. Raw sequencing data were preprocessed to obtain clean reads that were subjected to k-mer counting using Jellyfish v2.3.0 (Marçais and Kingsford 2011). The resulting k-mer frequency distribution was analysed using GenomeScope2 (Ranallo-Benavidez et al. 2020) to estimate genome properties, including size, heterozygosity and repeat content, based on statistical modelling. Sensitivity analysis was performed by varying the k-mer lengths.

De novo reference transcript assembly and gene annotation

RNA samples used for PacBio Iso-Seq were isolated from shrimp hepatopancreas and tail muscle samples using the GeneAll® Hybrid-R™ kit (GeneAll, Republic of Korea). To minimise potential errors arising from inter-individual variation, total RNA from the tail muscle, hepatopancreas, gill, and heart of three female shrimps was pooled for library construction (Hoang et al. 2017). Total RNA samples that met the quality control criteria concentration ≥ 65 ng/µL, Bioanalyser RNA integrity number (RIN) value ≥ 6, and 260/280 ratio between 1.5 and 2.2 were used for library preparation. HiFi reads were generated from PacBio Iso-Seq libraries with single-molecule resolution using the circular consensus sequencing mode. FLNC reads were identified using the PacBio Iso-Seq analysis in SMRT® Link v10.1.0 (https://www.pacb.com/support/software-downloads/). The N50 of the high-fidelity (HiFi) reads was 1,568 bp, exceeding the reference value of 1,500 bp, and was confirmed to be of high quality. To improve data quality, reads with a subread length of 300 bp or less were excluded from the generated reads. Representative unigenes (the final transcripts) were generated by clustering all high-quality (HQ) FLNC reads using CD-HIT-EST v4.6.6 (Fu et al. 2012) with a global sequence identity of over 90%. TransDecoder v3.0.0 was used to predict open reading frames (ORFs). For functional annotation, the final transcripts were searched using UniProt (Consortium 2015) and NCBI non-redundant databases (Pruitt et al. 2005) using BLASTX v2.2.29+ (Camacho et al. 2009) with an E-value cut-off of 1e-5. Protein domains were searched against InterProScan (Jones et al. 2014) using HMMER v3.1b1 (Meng and Ji 2013; Meng et al. 2018).

Species identification by PCR amplification

Among the Decapod species whose mitochondrial genomes were identified, five species were selected for species identification based on their close evolutionary distance to P. hypsinotus. The mitochondrial genomic sequences of five species were obtained from NCBI (Portunus trituberculatus, P. trituberculatus, NC_005037; Litopenaeus vannamei, L. vannamei, NC_009626; Pandalopsis japonica, MK043326; P. hypsinotus, MH920259; Lebbeus groenlandicus, NC_045223) and P. hypsinotus-specific PCR primers were designed through sequence alignment (Forward: 5’-AGA ACA ACC AAC CAA GAC ACT-3’; Reverse: 5’-CCC AGA TCA GGC TCC AAA GA-3’). DNA samples from the five species were used for PCR amplification with 2 × TOP simple DyeMix (aliquot)-HOT premix (Enzynomics, Republic of Korea). The PCR mixture contained 10 µL of 2 × TOPsimple DyeMix, 7 µL of DNase free water, 1 µL of gDNA template and 1 µL of each primer (10 pmol/µL). The condition of PCR was as follows: an initialisation at 95°C for 5 min, 40 thermal cycles of 94°C for 40 s, primer-specific annealing temperatures for 40 s, 72°C for 40 s, and a final elongation step at 72°C for 5 min. The PCR products were separated by electrophoresis on a 1.5% agarose gel.

Determination of repetitive sequences in unigenes and phylogenetic analysis

For the TE annotation of P. hypsinotus, both RepeatModeler and RepeatMasker v4.1.2 (http://www.repeatmasker.org) were used for de novo identification. The total transcriptome sequences of D. pulex and other decapod organisms (F. chinensis, ASM1920278v2; L. vannamei, ASM378908v1; Penaeus monodon, P. monodon, NSTDA_Pmon_1; Marsupenaeus japonicus, M. japonicus, Mj_TUMSAT_v1.0; Eriocheir sinensis, E. sinensis, ASM2467909v1; P. trituberculatus, ASM1759143v1; Homarus americanus, H. americanus, GMGI_Hamer_2.0; Daphnia pulex, D. pulex, ASM2113471v1). The predicted repeat sequences of each organism were analysed against the reference sequences of repeat elements using RepeatMasker v4.1.2, with RMBLAST v2.14.0 as the search engine. To identify the evolutionary relationships with other marine organisms, total mitochondrial genomic sequences were collected from the NCBI database, the accession numbers of which are listed in Supplementary Table 1. Fifteen mitochondrial genomes were aligned using MAFFT version 7 (https://mafft.cbrc.jp/alignment/server/) with the default parameters. A phylogenetic tree was constructed using the neighbour-joining (NJ) method and visualised using Phylo.io v1.0.0 with 1000 bootstrap replicates using the Jukes-Cantor substitution model.

Illumina RNA sequencing

Total RNA was extracted from shrimp’s hepatopancreas and tail muscle tissues using a GeneAll® Hybrid-R™ kit. Total RNA samples that met the quality control criteria of concentration ≥ 65 ng/µL, Bioanalyser RIN value ≥ 6, and 260/280 ratio between 1.5 and 2.2 were used for library preparation. To ensure minimum statistical significance, total RNA was extracted from three individual shrimps of similar sizes and subsequently used for library construction (Horn et al. 2017; González-Castellano et al. 2019). Libraries were prepared for 150 bp paired-end sequencing using the TruSeq Stranded mRNA Library Prep Kit (Illumina, CA, USA). A total of 500 ng of RNA was fragmented and reverse transcribed into single-stranded cDNAs using random hexamer primers, which were further used as a template for second-strand synthesis. After sequential end repair, A-tailing and adapter ligation, the cDNA libraries were amplified. These libraries were analysed qualitatively using a TapeStation 4200 instrument and a High-Sensitivity D1000 ScreenTape System (Agilent, CA, USA) and quantitatively using a KAPA library quantification kit (Kapa Biosystems, MA, USA), according to the manufacturer’s protocol. Following cluster amplification of the denatured templates, paired-end sequencing (2 × 150 bp) was performed using an Illumina NovaSeq 6000 (Illumina, CA, USA). The adapter sequences and ends of the RNA-Seq short reads with a Phred quality score of less than 20 were trimmed and reads shorter than 50 bp were simultaneously removed using Cutadapt v.2.8 (Martin 2011). The filtered reads were mapped to the reference genome using the BWA-MEM algorithm (Li 2013). Gene expression was estimated using RSEM v.1.3.1 (Li and Dewey 2011) with default settings. FPKM and TPM values were calculated to normalise the sequencing depth among the samples. Low-quality reads, determined as those having >10% of skipped bases (marked as ‘N’s), >40% of bases with quality scores of < 20, and average quality scores of <20, were filtered using in-house scripts. The filtered reads were mapped to the reference genome using the aligner TopHat v2.1.1 (Trapnell et al. 2009).

Analysis of differentially expressed unigenes

Gene expression levels were measured using Cufflinks v2.1.1 (Trapnell et al. 2010) using the gene annotation database of the species. To improve the measurement accuracy, multi-read-correction and frag-bias-correct options were applied. All other options were set to default values. Differential expression analysis was performed using Cuffdiff (Trapnell et al. 2012). Multiread and frag-bias correction options were applied to enhance the accuracy of the analysis. All other options were set to default values. The Q-value was calculated based on the p-value using the p.adjust function of the R package with default parameter settings. DEGs were identified based on a log2FC absolute value > 1, p-value < 0.05 and adjusted p-value cut-off = 0.05 to correct errors due to multiple testing (Benjamini and Hochberg 1995). While a log2FC absolute value greater than 0.5 is generally considered to indicate differential expression, we applied a more stringent threshold of 1.0 to ensure the robustness of our results (Santos et al. 2021).

Illumina small RNA sequencing

Specifically, sRNA molecules from 8–10 ng of total RNA were subjected to adapter ligation. Total RNA was extracted from the FFPE samples using the ReliaPrep FFPE Total RNA MiniPrep Kit. Total RNA samples that met the quality control criteria concentration ≥ 65 ng/µL, Bioanalyser RIN value ≥ 6, and 260/280 ratio between 1.5 and 2.2 were used for library preparation. All RNA samples used in this study passed the quality control and were used for library preparation. Libraries for 50 bp single-end sequencing were prepared using the NEXTflex Small RNA-Seq Kit (PerkinElmer Inc., USA). Small RNAs (sRNAs) were synthesised as single-stranded cDNAs via reverse transcription priming. Single-stranded cDNA was used as a template for second-strand synthesis of double-stranded cDNA through PCR (Polymerase Chain Reaction). Fragments of approximately 150 bp were isolated by size selection using gel electrophoresis. cDNA libraries were assessed for quality using a TapeStation 4200 (Agilent, CA, USA) and quantified using the KAPA library quantification kit (Kapa Biosystems, MA, USA) in accordance with the manufacturer’s instructions. Single-end sequencing (50 bp) was performed using an Illumina NovaSeq6000 (Illumina, CA, USA). The sequencing output was approximately 3 Gb per sample, and low-quality bases or reads, bases with a quality score of less than 20 and read lengths below 17 bp were trimmed or filtered using the Cutadapt tool ¹⁰¹. Filtered reads were mapped to the reference miRNAs of related species, such as Homo sapiens, Mus musculus, Gallus gallus, Danio rerio, Triops cancriformis (T. cancriformis), and M. japonicus, using the Bowtie2 aligner v2.4.5 (Langmead et al. 2009), followed by variant calling in the seed region of the miRNA. The mirDeep2 tool v0.1.3 (Friedländer et al. 2012) was used to estimate the expression. Variant calling was performed using GATK v4.2.4.0 (McKenna et al. 2010) to search for variants in the miRNA seed regions. The dcov option was set to 1,000 as the maximum depth threshold for the region. The miRNA expression levels were estimated with mirDeep2 (Friedländer et al. 2012) using the gene annotation database of the species, along with the stem-loop and mature miRNA sequence information obtained from miRBase v22 (Kozomara and Griffiths-Jones 2014). The potential for the secondary structure formation of novel miRNAs was thermodynamically evaluated. The minimum free energy (MFE) values, representing the likelihood of stem-loop structure assembly for each RNA molecule, were calculated using the RNAfold WebServer (http://rna.tbi.univie.ac.at/cgi-bin/RNAWebSuite/RNAfold.cgi).

Analysis of differentially expressed miRNA (DEM)

For DEM analysis, miRNA count data were generated using mirDeep2 (Friedländer et al. 2012). Based on the calculated read count data, DEMs were identified using the R package DESeq2 (Love et al. 2014), which performs robust normalisation to compare the tag count data. Normalisation factors were calculated using the iterative DEGES/edgeR method in the TCC package. The Q-value was calculated based on the p-value using the p.adjust function of the R package with default parameter settings. DEMs were identified based on a log2FC absolute value > 1, a p-value < 0.05, and an adjusted p-value cut-off = 0.05 to correct errors due to multiple testing (Benjamini and Hochberg 1995).

Total RNA extraction and cDNA synthesis

Total RNA was extracted from the four tissue samples of P. hypsinotus using Hybrid-R (GeneAll, Republic of Korea) according to the manufacturer’s instructions. The RNA quality and integrity were measured using an ND-1000 UV-Vis spectrophotometer (NanoDrop Technologies, Wilmington, DE, USA) and 1.5% (w/v) agarose gel electrophoresis, respectively. Total RNA was quantified as 200 ng/µL and mRNA was reverse transcribed using PrimeScript RT Reagent Kit with gDNA Eraser (TaKaRa, Japan) at 37°C for 1 h, followed by 85°C for 5 s. The cDNA for miRNA expression analysis was synthesised by HB miR Multi Assay Kit system I (HeimBiotek, Republic of Korea) at 37°C for 60 min, followed by incubation at 95°C for 5 min and then held at 4°C.

Quantitative real-time PCR (qPCR) amplification

The relative expression levels of differentially expressed genes in the hepatopancreas, tail muscle, gills and heart were evaluated by qPCR using a Quantstudio1 system (Applied Biosystems, Foster City, CA, USA) with 10 ng of each cDNA sample. To detect the eight differentially expressed genes, SYBR Green Q-PCR Master Mix with Low Rox (SmartGene, Daejeon, Republic of Korea) was used for qPCR according to the manufacturer’s instructions. EF1A gene was used as the reference gene for RNA expression analysis (Priyadarshi et al. 2015 Fang et al. 2018;). The conditions were as follows: hold at 95°C for 15 min for initial activation, followed by 40 thermal cycles of 95°C for 10 s and 60°C for 40 s; standard melting conditions for 90 s at 55°C, and then for 5 s each at 1°C increments between 55°C and 99°C (Table 4). The relative expression levels of orthologous miRNAs in the hepatopancreas, tail muscle, gills and heart were evaluated by qPCR using a Quantstudio1 system (Applied Biosystems, Foster City, CA, USA) with 10 ng of each cDNA sample. To express the eight miRNA the HB_I Real-Time PCR Master Mix Kit (HeimBiotek, Republic of Korea) was used for qPCR, according to the manufacturer’s instructions. A small nuclear RNA (snRNA), U6, was used as the reference gene for miRNA expression analysis. The conditions were as follows: hold at 95°C for 15 min for initial activation, followed by 40 thermal cycles of 95°C for 10 s and 60°C for 40 s; standard melting conditions for 90 s at 55°C, and then for 5 s each at 1°C increments between 55°C and 99°C. All samples were amplified in triplicates. To evaluate tissue-specific expression differences, the 2^-ΔΔCt method was employed. The ΔΔCt values were calculated by subtracting the average ΔCt of the control group from the ΔCt values of the hepatopancreas and tail muscle of each individual. Statistical significance was determined via one-way ANOVA conducted in R; notably, the analysis was conducted using ΔCt values rather than 2^-ΔΔCt to minimise potential errors associated with exponential data transformation. (* p < 0.05, ** p < 0.01, *** p < 0.001).

Table 4.

List of PCR primer sets.

Primer name		Primer sequences
Pandalus_Iso0021418	Forward	5′- AAT CAC AGT GCG AAA ACC CC −3′
	Reverse	5′- CCG GCA CTA GAT ACT CTG GG −3′
Pandalus_Iso0022168	Forward	5′- TGC TGA AAAGGG AGG AGG AG −3′
	Reverse	5′- GGC GATCTC TGT CTC TTC CA −3′
Pandalus_Iso0024546	Forward	5′- CAA CCT CGG CCA AAT CCT TC −3′
	Reverse	5′- CCC ATACAT GTC CCA CCG AT −3′
Pandalus_Iso0021869	Forward	5′- GGT TGT GAA TTC CCT GCT CG −3′
	Reverse	5′- ATG CGG CTT TCA GTG ATG AC −3′
Pandalus_Iso0011835	Forward	5′- GCA ACG CCG TCT ACC TTT TC −3′
	Reverse	5′- AGG GCA AGC TTC CTT CCA TC −3′
Pandalus_Iso0010486	Forward	5′- GCA ACG CCG TCT ACC TTT TC −3′
	Reverse	5′- AGG GCA AGC TTC CTT CCA TC −3′
Pandalus_Iso0000969	Forward	5′- ACA GGC ACG ACA CTC TCT TG −3′
	Reverse	5′- CTT CCA GGG TCT TCA GGC AG −3′
Pandalus_Iso0010252	Forward	5′- CCA CCC TCC TCC ACA TGT TC −3′
	Reverse	5′- CTT GCC AGC TTC CTT CTC GA −3′
EF1a	Forward	5′- TGC CCT GGA CAA CAT CGA GC −3′
	Reverse	5′- CGG GCA CTG TTC CAA TAC CT −3′
CLEC-miR-1000 bs	Forward	5′- GTG AGT TAC TGG GTG GGC AG −3′
	Reverse	5′- GCA GAA GGA CCC CAC AGT TC −3′
CASP-miR-315 bs	Forward	5′- AGG AAG CTG ACC AAG ATG CA −3′
	Reverse	5′- TTG GAA TGC TTG ATT ATC GTG GA −3′
LYZ-miR-281 bs	Forward	5′- GCT GTG TTG GGT CCT TAT TTT CA −3′
	Reverse	5′- GGA AAC CCA CCT GTC ATT GG −3′

Bioinformatic analyses of DEGs and miRNAs

Gene Ontology (GO) enrichment analysis for DEGs in the hepatopancreas compared to the tail muscle was conducted for gene functional classification using the Database for Annotation Visualization and Integrated Discovery (DAVID) website v6.8. All GO terms and pathways were screened at p-value < 0.05. By cross-analysing these DEGs with previously studied immune-related genes based on their functional annotation, predicted candidates as regulators of immune mechanisms were identified. BioEdit v7.2 and Cytoscape v3.2.0 were used to determine the interactions of candidate genes and DEMs. Potential interactions between the target genes and miRNAs were calculated thermodynamically using BiBiServ2-RNAhybrid v22.

Plasmid construct and luciferase reporter assay

cDNA samples were quantified at 500 ng/µL using the ND-1000 UV-Vis spectrophotometer (NanoDrop, USA). The cDNA was used for PCR amplification with the 2× TOP simple DyeMix (aliquot)-HOT premix (Enzynomics, Republic of Korea). Each of the three constructs, including the CLEC-miR-1000 bs, LYZ-miR-281 bs, and CASP-miR-315 bs, was generated by PCR amplification using one sense (S) primer and three anti-sense (AS) primers (Table 4). The PCR conditions were as follows: initialisation at 95°C for 5 min, 40 thermal cycles of 94°C for 40 s, primer-specific annealing at 60°C for 40 s, 72°C for 1 min, and a final elongation step at 72°C for 5 min. All PCR products were purified using the Exprep Plasmid SV Mini Kit (GeneAll, Republic of Korea). The plasmids were transfected into HEK293 cells seeded in 24-well plates at a density of 1 × 10⁵ cells/well, and grown to approximately 80% confluence. The cells in each lane of the plates were co-transfected with negative control, miRNA mimic or miRNA inhibitor, according to the experimental design. A total of 125 ng of plasmid and 2.75 pM of mimics, inhibitors, or negative control were used per well for cell transfection by JetPRIME® (Polyplus, France). After 24–28 h of incubation at 37°C in a 5% (v/v) CO₂ incubator, the cells were lysed using 1× Passive Buffer (Promega, USA). At 10 min post-lysis, the luciferase activities of the firefly and Renilla were measured by a Glomax® 20/20 Luminometer (Promega, USA) using a Dual Luciferase Assay Kit (Promega, USA) following the manufacturer's instructions.

Results

Pipeline for sequencing analyses of P. hypsinotus

First, we extracted DNA from the tail muscle of P. hypsinotus and conducted Illumina short-read sequencing to estimate the genome size. Second, to analyse the total transcriptome of P. hypsinotus, total RNA isolated from four different tissues (tail muscle, hepatopancreas, gill, and heart) was subjected to PacBio Iso-Seq, and the transcriptome of P. hypsinotus was assembled de novo. The resulting unigenes were functionally annotated using BLASTX and InterProScan. To determine the differential expression of each unigene in the hepatopancreas compared with that in the tail muscle, total RNA was isolated from the two types of tissues from three individuals and subjected to Illumina sequencing using a NovaSeq6000 machine. Illumina small RNA sequencing was performed to identify the miRNAs that could potentially interact with the differentially expressed unigenes. Reads were generated from an RNA library with approximate sizes of 17–25 nt, and the assembled potential miRNA candidates were selected by stem-loop structure prediction using miRDeep2 v0.1.3. The expression of DEGs and miRNAs was experimentally verified and unigenes with potential immune-related functions were determined by functional analysis. Finally, we investigated the potential complementary binding between the differentially expressed unigenes and miRNAs and constructed a network map of the differentially expressed unigenes and miRNAs (Figure 1).

Figure 1.

Overview of analysis strategy for de novo assembly of P. hypsinotus.

The size prediction of the P. hypsinotus genome

As a preliminary confirmation process for sequencing analyses, PCR was performed to identify the species. P. hypsinotus-specific primers were designed by sequence comparison with five decapod species. The identification of specific PCR bands suggested that the samples used for sequencing were P. hypsinotus (Supplementary Figure 1). To estimate the genome size, Illumina short-read sequencing was performed on DNA samples extracted from the tail muscle of P. hypsinotus. In total, 2,686,742,270 raw reads (405,698,082,770 bp) were generated. By performing K-mer analysis using Jellyfish v2.3.0, the estimated size of the whole genome was 8.1–8.5 Gb.

De novo reconstruction of transcriptomes and functional annotation of unigenes

To determine the accurate sequences of the P. hypsinotus transcriptome, all expressed mRNAs with poly(A) tails were isolated from each of the four tissues of three female shrimps, and gene candidates were identified using PacBio Iso-Seq SMRT technology. A total of 3,836,457 raw reads were generated and 5,618,120,705 bp of sequences were obtained from three P. hypsinotus RNA samples using PacBio Iso-Seq SMRT, with a median read length of highly accurate long reads (HiFi) of 1,464 bp (Supplementary Table 9). We identified 52,543 high- and 2,106 low-quality isoforms in the tail muscle; 52,352 high- and 1,563 low-quality isoforms in the hepatopancreas; and 61,032 high- and 1,017 low-quality isoforms in the gills. In total, 46,703 unigenes were estimated and identified using CD-HIT-EST v4.6.6 based on a high-quality full-length non-concatemer (FLNC). The mean length of the identified unigenes was 1,385 bp; the longest unigene was 7,729 bp; and the N50 was 1,961 bp (Supplementary Table 10).

Analyses of transposable elements

Transposable elements (TEs) induce various biological changes in the host genome. To evaluate the genetic influence of transposable elements on the genome of P. hypsinotus, the distribution of inserted transposable elements within the entire transcriptome was examined using the RepeatMasker programme. Approximately 34.16% of the entire transcriptome of P. hypsinotus consisted of repetitive sequences. Excluding unclassified repetitive sequences, the most abundant TEs were retrotransposons, including short interspersed elements (SINEs), long interspersed elements (LINEs) and long terminal repeat elements (LTRs), which account for 24% of all repetitive sequences (Figure 2). To compare with other species, we used RepeatMasker to analyse the ratio of transposable elements in transcriptome sequences of four species of penaeid shrimps (F. chinensis, ASM1920278v2; L. vannamei, ASM378908v1; P. monodon, NSTDA_Pmon_1; M. japonicus, Mj_TUMSAT_v1.0), two species of crab (E. sinensis, ASM2467909v1; P. trituberculatus, ASM1759143v1), one species of lobster (H. americanus, GMGI_Hamer_2.0), and D. pulex (ASM2113471v1). Among the analysed species, L. vannamei, belonging to the suborder Dendrobranchiata, exhibited the lowest TE content (13.14%). While most species maintained a TE proportion of approximately 20%, E. sinensis showed the highest proportion (44.92%), indicating that nearly half of its genomic sequence is composed of repetitive sequences. This result confirmed that P. hypsinotus has a significantly higher proportion of TE than most marine organisms, especially decapods, except for E. sinensis (Supplementary Figure 2).

Figure 2.

Analysis for the distribution of repeat sequences in unigenes. SINE: short interspersed nuclear element; LINE: long interspersed nuclear element; LTR: long terminal repeat.

Annotation and identification of evolutionary position of P. hypsinotus

To reveal sequence homology with reference genes, the open reading frames of unigenes were compared with those of the genes registered in the NCBI non-redundant (NCBI-nr) and UniProt databases using BLASTN with a cut-off E-value of 1e-5. In total, 24,200 unigenes significantly matched genes from other species in the NCBI non-redundancy database. H. americanus had the highest number of genetic sequences that matched the unigenes of P. hypsinotus, with 4,168 genetic sequences overlapping 19.08% of the total unigenes. In addition, genetic matches with unigenes were shown in the following order: Procambarus clarkia (P. clarkia) (3,861 unigenes, 15.95%), M. japonicus (3,388 unigenes, 14%), Cherax quadricarinatus (C. quadricarinatus) (2,396 unigenes, 9.9%), and F. chinensis (2,232 unigenes, 9.22%) (Figure 3(A)). UniProt data analysis revealed that 17,535 unigenes matched genes from other species. Drosophila melanogaster had the highest number of genetic sequences that matched the unigenes of P. hypsinotus, with 2,919 genetic sequences overlapping 16.65% of the total unigenes (Figure 3(B)). Additionally, HMMER v3.1b1 was used to search for protein domain homologies in the InterProScan database. By mapping the InterProScan database, we identified 21,521 protein domains from 46,703 unigenes. Integrated analysis of the results using the three databases revealed 26,462 functionally annotated unigenes, accounting for 56.66% of all the assembled unigenes (Supplementary Tables 2, 3 and 10).

Figure 3.

Species distribution of annotated unigenes using the NCBI-nr (A) and Uniprot (B) database.

To assess the similarity between the unigene sequences of P. hypsinotus and those of its relatives in the annotation results, we re-evaluated the phylogenetic position of P. hypsinotus. The phylogenetic tree comprised 15 marine species, including D. pulex as a non-decapod outgroup and 14 decapod species belonging to suborders of decapods (Supplementary Table 1). The phylogenetic tree revealed that P. hypsinotus is nested inside the caridean shrimps and is sister to Macrobrachium rosenbergii (M. rosenbergii) of the family Pandalidae. In contrast, P. hypsinotus has a relatively distant evolutionary position from penaeid shrimps (Dendrobranchiata), crabs (Brachyura) and lobsters (Pleocyemata) (Supplementary Figure 3). H. americanus was phylogenetically closer to P. hypsinotus than to penaeid shrimps or crabs, sharing a more recent common ancestor. Because of the limited genetic resources available for carideans, this result may explain why the BLAST alignment showed that the genes of P. hypsinotus had the most matches with those of H. americanus.

Differential expression analysis for hepatopancreas-specific genes

To examine the relative expression levels of annotated P. hypsinotus unigenes in the hepatopancreas compared to the tail muscle, Illumina Nova6000 RNA sequencing was conducted on the total RNAs from the hepatopancreas and tail muscle tissues of the three individual shrimps. The Illumina raw reads were produced with an average length of 98.1 M ± 27.5 M (mean ± standard deviation (SD)) and approximately 90% of reads were mapped to the assembled unigene sequences (Supplementary Table 11). Differential expression analysis was performed to identify DEGs in the hepatopancreas compared to the tail muscle of shrimp using TCC and DESeq2 based on the count values mapped to the unigenes. A total of 5,359 unigenes were upregulated and 4,071 were significantly downregulated in the hepatopancreas compared to those in the tail muscle with p-value < 0.05 and absolute Log2 fold changes (log2FC) > 1 (Figure 4(A)).

Figure 4.

Identification of DEGs in the hepatopancreas compared to the tail muscle, gill, and heart of three P. hypsinotus (A) The volcano plot shows genes with differential expression in the hepatopancreas and tail muscle tissues of three P. hypsinotus. The screening conditions were p < 0.05 and | log2FC | > 1. Red, black, and blue dots represent up-, non-, and down-regulated DEGs, respectively. Quantitative PCR validation of the Illumina RNA-seq data. (B) Up-regulated four genes and (C) down-regulated four genes were selected for qPCR analysis to confirm expression patterns in three shrimps in each tissue.

Relative expression analyses were performed for the four unigenes with the highest log2FC values: mediator of RNA polymerase II transcription subunit 16-like (Pandalus_Iso0021418), LOC108681169 isoform X2 (Pandalus_Iso0022168), legumain-like isoform X1 (Pandalus_Iso0024546), and HCYB (Pandalus_Iso0021869), and the four unigenes with the lowest log2FC values: SCPA (Pandalus_Iso0011835), SCP (Pandalus_Iso0010486), Msp300 (Pandalus_Iso0000969), and MYL2 (Pandalus_Iso0010252) using qPCR. qPCR was conducted on unsequenced gill and heart tissues to verify the sequencing results and confirm tissue-specific expression. The stomach tissue was excluded from the study. The three unigenes with the largest expression differences were identified as potential protein sequences, whereas the remaining unigenes were annotated as haemocyanin B. Meanwhile, the tail muscle-specific unigenes Pandalus_Iso0011835 and Pandalus_Iso0010486 were annotated as sarcoplasmic calcium-binding proteins, Pandalus_Iso0000969 as a 300 kDa muscle-specific protein, and Pandalus_Iso0010252 as myosin regulatory light chain 2. The analysed genes exhibited high sequence identity with homologous genes in related species (Supplementary Tables 4 and 5). The qPCR results showed significantly higher expression of mediator of RNA polymerase II transcription subunit 16-like, LOC108681169 isoform X2, legumain-like isoform X1, and HCYB in the hepatopancreas than in the tail muscle of all three individuals (Figure 4(B)). The expression of the four downregulated unigenes was also lower in the hepatopancreas than in the tail muscle of the three individuals. (Figure 4(C))

De novo reconstruction and evaluation of miRNAs

In shrimp, miRNAs are crucial genetic regulators of various biological processes, such as homeostasis, immunological defense and physiological adaptation. To identify potential miRNAs in P. hypsinotus, total RNA from the hepatopancreas and tail muscle tissues of the three shrimp samples was subjected to Illumina sRNA sequencing. A library for sequencing was prepared by selecting RNA sequences of 17–25 nucleotides in length. An average of 12.7 M ± 1.5 M (mean ± SD) raw reads was generated for each tissue, and an average of 11.1 M ± 1.9 M (mean ± SD) reads remained after trimming (Supplementary Table 12).

Known miRNA profiling and novel miRNA prediction

Based on the detection of stem-and-loop structures that characterise precursor miRNAs, potential miRNA candidates were identified from the assembled small RNA sequences using miRDeep2. When the predicted miRNA candidates in P. hypsinotus were compared with those of five species (Homo sapiens, Mus musculus, Gallus gallus, Danio rerio and D. pulex) registered in the miRBase v22 database, ten miRNAs that were similar to known miRNA sequences were identified. Sequence-based comparative analysis using BLASTN was also performed for the predicted miRNA candidates, with significance indicated by an E-value < 1e-5. Based on BLASTN alignment, we identified 42 orthologous miRNAs that were similar to the sequences of miRNAs from other species registered in the miRBase database (Supplementary Table 6). The 98 predicted miRNA candidates with no sequence similarity to known miRNAs were identified as novel (Supplementary Table 7). To function, miRNAs must fold into a stable stem-loop configuration. Our MFE analysis suggests that the novel miRNAs possess the thermodynamic stability necessary for spontaneous secondary structure formation, supporting their classification as novel miRNAs. Of the 42 identified orthologous miRNAs, 10 had identical sequences. In other words, among the structurally predicted miRNA candidates, 32 miRNAs (24.62%) were identified as orthologous miRNAs and 98 miRNAs (75.38%) were identified as novel miRNAs that are specifically expressed in P. hypsinotus (Figure 5(A)). Among the 32 orthologous miRNAs, 17 miRNAs were homologous to the miRNA sequences identified in other crustaceans (Supplementary Table 8).

Figure 5.

Identification of DEMs in the hepatopancreas compared to the tail muscle, gill, and heart of three P. hypsinotus. (A) The proportion of novel and orthologous miRNAs in small RNA sequencing data. (B) DEMs in the hepatopancreas compared to the tail muscle, gill, and heart in three P. hypsinotus. Quantitative PCR validation of Illumina small RNA-seq data. (C) Up-regulated four miRNAs and (D) down-regulated four miRNAs were selected for qPCR analysis to confirm expression patterns from three shrimps for each tissue.

Differential expression analysis of hepatopancreas-specific miRNAs

Based on the read count values, differential expression analysis was conducted to examine the relative expression levels of miRNAs in the hepatopancreas compared with those in the tail muscle of P. hypsinotus. Seventeen upregulated and twelve downregulated miRNAs were identified in the hepatopancreas compared with those in the tail muscle using DESeq2 with a p-value < 0.05 and |log2FC| > 1 (Figure 5(B)). Five downregulated and seven upregulated miRNAs were predicted to be specifically expressed in P. hypsinotus (Tables 1 and 2). Among the DEMs, eight orthologous miRNAs with the lowest or highest log2FC values were selected: phy-miR-1000, phy-miR-750, phy-miR-7, phy-miR-12, phy-miR-133, phy-miR-1, phy-miR-252 and phy-miR-10. Their relative expression in the hepatopancreas, tail muscle, gill and heart tissues of the three shrimps was experimentally verified using qPCR. The expression of four miRNAs (phy-miR-1000, phy-miR-750, phy-miR-7, and phy-miR-12) was upregulated in the hepatopancreas compared with that in the tail muscle in almost all shrimps. However, for miR-7, no significant expression differences were observed in the unsequenced gill and heart tissues (Figure 5(C)). Three miRNAs, phy-miR-133, phy-miR-1, and phy-miR-10, were downregulated in the hepatopancreas compared with that in the tail muscle of almost all shrimps. In contrast, phy-miR-252 was found to have decreased expression in the hepatopancreas compared to the tail muscle, but was highly expressed in the heart (Figure 5(D)).

Table 1.

The list of all down-regulated miRNAs.

GeneID	Ortholog		Log2FC	P-value
NW_025029662.1	phy-miR-133	Drosophila yakuba	−8.87334	6.40E-82
NW_025030139.1	phy-miR-1	Daphnia pulex	−6.48592	7.78E-44
NW_025030863.1	Novel		−6.85397	7.20E-24
NW_025038172.1	phy-miR-252	Heliconius melpomene	−6.38061	1.55E-09
NW_025033280.1	Novel		−20.9591	8.34E-08
NW_025032241.1	phy-miR-10	Apis mellifera	−5.8909	4.74E-05
NW_025030060.1	Novel		−4.01772	0.000795
NW_025034282.1	phy-miR-263a	Heliconius melpomene	−2.65654	0.020283
NW_025033643.1	Novel		−1.28581	0.00023
NW_025032188.1	Novel		−1.32715	0.001906
NW_025032505.1	phy-let-7	Saccoglossus kowalevskii	−1.18188	0.005443
NW_025031641.1	phy-miR-34b	Python bivittatus	−1.63479	0.005704

Table 2.

The list of all up-regulated miRNAs.

GeneID	Ortholog		Log2FC	P-value
NW_025031912.1	Novel		15.6994	5.97E-36
NW_025029678.1	Novel		5.18856	4.02E-30
NW_025031986.1	phy-miR-1000	Tribolium castaneum	10.5385	1.25E-17
NW_025032122.1	phy-miR-750	Polistes canadensis	9.08753	7.89E-17
NW_025033320.1	phy-miR-7	Spodoptera frugiperda	7.08775	2.66E-09
NW_025033119.1	phy-miR-315	Parasteatoda tepidariorum	3.73532	1.15E-07
NW_025029525.1	Novel		7.06792	1.82E-07
NW_025034256.1	phy-miR-281	Heliconius melpomene	4.51168	7.02E-07
NW_025029894.1	Novel		4.1992	5.35E-06
NW_025029975.1	Novel		2.35296	1.50E-05
NW_025030213.1	phy-miR-29b	Branchiostoma floridae	3.77089	2.90E-05
NW_025031861.1	Novel		2.53972	0.000469
NW_025029653.1	phy-miR-8	Apis mellifera	2.18833	0.009279
NW_025035658.1	phy-miR-12	Ixodes scapularis	7.61236	0.045014
NW_025035250.1	phy-bantam	Polistes canadensis	1.14491	0.015044
NW_025034598.1	phy-miR-184a	Capitella teleta	1.29418	0.023985
NW_025030441.1	Novel		1.17998	0.025472

Potential biological functions of hepatopancreas-specific genes

GO analysis was conducted to identify the biological functions of differentially expressed unigenes. The biological process associated with the most abundant 106 unigenes was collagen catabolism (GO:0030574). In addition, many unigenes were closely related to protein folding (GO:0006457), positive regulation of gene expression (GO:0010628), phosphorylation (GO: 00106310), digestion (GO:0007586), negative regulation of gene expression (GO:00100629), and chitin catabolism (GO:006032) (Figure 6(A)). According to a previous differential expression analysis, the gene that showed the greatest difference in expression between the hepatopancreas and tail muscle was haemocyanin B, which is closely related to immunity. We hypothesised that hepatopancreas-specific genes are closely associated with immune responses depending on the metabolic characteristics of the hepatopancreas. To test this hypothesis, a list of previously studied immune genes, such as lysozyme (LYZ), C-type lectin (CLEC) and Alpha-2-macroglobulin (A2M), was cross-analysed with the functionally annotated unigenes of P. hypsinotus. Among the differentially expressed unigenes in the hepatopancreas, 16 unigenes related to immune response were identified (Table 3).

Figure 6.

GO analysis for differentially expressed unigenes and interaction analysis with DEMs. (A) Top 7 functional annotations of differentially expressed P. hypsinotus unigenes through Gene Ontology analysis. (B) Interaction network between differentially expressed unigenes and miRNAs showing mutually contrasting expression. Red represents up-regulated immune-related genes and miRNAs, and blue represents down-regulated immune-related genes and miRNAs.

Table 3.

List of previously studied immune-related genes in Decapoda.

ID	Gene Symbol	Reference
Pandalus_Iso0036077	Anti-lipopolysaccharide factor	(Chen and Wang 2019; Soo et al. 2019; Yang et al. 2022)
Pandalus_Iso0035432	Lysozyme	(Fagutao et al. 2008; Yue et al. 2010; Zeng et al. 2013; Soo et al. 2019; Xu et al. 2020)
Pandalus_Iso0020781	Caspase	(Zeng et al. 2013; Soo et al. 2019; Wang et al. 2021c)
Pandalus_Iso0035429	Eukaryotic translation initiation factor	(Pan et al. 2005; Wang et al. 2021c)
Pandalus_Iso0022589	Chitinase	(Soo et al. 2019; Wang et al. 2021c)
Pandalus_Iso0001842	C-type lectin	(Pan et al. 2005; Zeng et al. 2013; Soo et al. 2019; Xu et al. 2020; Wang et al. 2021c)
Pandalus_Iso0000618	Alpha-2-Macroglobulin	(Fagutao et al. 2008; Pongsomboon et al. 2008; Yue et al. 2010; Xu et al. 2020)
Pandalus_Iso0035069	Serpin	(Pongsomboon et al. 2008; Soo et al. 2019)
Pandalus_Iso0003317	Cathepsin L	(Zeng et al. 2013; Xu et al. 2020)
Pandalus_Iso0002416	Catalase	(Chen and Wang 2019; Xu et al. 2020)
Pandalus_Iso0010619	Superoxide dismutase	(Yue et al. 2010; Chen and Wang 2019; Soo et al. 2019)
Pandalus_Iso0003014	Thioredoxin-1	(Chen and Wang 2019; Xu et al. 2020; Yang et al. 2022)
Pandalus_Iso0007508	MYD88 Innate immune signal transduction	(Pongsomboon et al. 2008; Yang et al. 2022)
Pandalus_Iso0027115	Glutathione S-transferase	(Pongsomboon et al. 2008; Wang et al. 2021c)
Pandalus_Iso0038653	Cytochrome c oxidase subunit 1	(Leu et al. 2007 Pongsomboon et al. 2008;)
Pandalus_Iso0000048	mitogen-activated protein kinase	(Pan et al. 2005; Zeng et al. 2013)

Interaction between DEMs and immune-related genes

To investigate the potential interactions between the 16 predicted P. hypsinotus immune-related unigenes and DEMs, we investigated whether the seed regions of the DEMs could bind to the selected immune-related genes. Additionally, interactions between DEMs and immune-related genes showing oppositely regulated expression in the hepatopancreas compared to the tail muscle were predicted based on the fact that miRNAs inhibit the expression of their target genes. A complex regulatory correlation between 29 DEMs and 16 immune-related genes was confirmed. LYZ (Pandalus_Iso0035432), CASP (Pandalus_Iso0037270) and CLEC (Pandalus_Iso0001842) showed relatively reduced expression in the hepatopancreas. Interaction analysis with DEMs predicted that six out of 17 upregulated miRNAs (phy-miR-1000, phy-miR-7, phy-miR-315, phy-miR-281, phy-miR-29b and phy-miR-12) interact with CLEC. Furthermore, downregulated LYZ and CASP were predicted to interact with six out of 12 miRNAs (phy-miR-1000, phy-miR-750, phy-miR-315, phy-miR-281, phy-miR-12 and phy-miR-184a) whose expression was upregulated in the hepatopancreas compared to the tail muscle (Figure 6(B)). Among the miRNAs validated to be upregulated, miR-1000, miR-315 and miR-281 had the potential to bind to CLEC, LYZ and CASP based on their sequences. Luciferase analysis confirmed the potential interactions between miR-1000 and CLEC, miR-315 and CASP, and miR-281 and LYZ. Relative luciferase activity decreased when cells were treated with the miR-1000 mimic; in contrast, luciferase activity increased when cells were treated with the miR-1000 inhibitor. miR-315 and miR-281 also showed decreased expression when cells were treated with mimics and increased expression when treated with inhibitors, indicating that their interactions with CASP and LYZ (Figure 7).

Figure 7.

Relative luciferase activity after co-transfection of the plasmid vector containing the microRNA binding site with the microRNA mimics and inhibitors. (A) Relative luciferase activity of Psi-CHECK2 + CLEC changed through treatment with miR-1000 mimic and inhibitor. (B) Relative luciferase activity of Psi-CHECK2 + LYZ changed through treatment with miR-281 mimic and inhibitor. (C) Relative luciferase activity of Psi-CHECK2 + CASP changed through treatment with miR-315 mimic and inhibitor (*p < 0.05; **p < 0.01, ***p < 0.001).

Discussion

Compared to genomic research on penaeid shrimps, studies on carideans, including P. hypsinotus, are notably limited. This study is the first report on the transcriptome analyses of P. hypsinotus. It presents a comprehensive transcriptome and miRNA resource for the species based on sequencing and experimental results, and identifies direct interactions between miRNAs and genes that may play a role in the immunity of P. hypsinotus. Obtaining genomic and transcriptomic data is a prerequisite for further research on P. hypsinotus; therefore, such genetic resources would be indispensable for studies on shrimp immunity and ecology. In this study, we isolated poly(A) RNAs to facilitate accurate analysis and generated a full-length transcriptome using PacBio Iso-Seq (Grabherr et al. 2011). To minimise inter-individual variation, libraries were constructed by pooling RNA samples extracted from the tissues of three individual specimens, which were then subjected to PacBio Iso-Seq analysis. Using k-mer analysis of the 405 Gb generated by Illumina short-read DNA sequencing, the genome size of P. hypsinotus was estimated to be 8.1 Gb. This result is similar to that of the related species Pandalus montagui (pink shrimp), with the estimated genome size being 8.53 Gb. Moreover, the genome sizes of previously studied penaeid shrimps, such as L. vannamei (Zhang et al. 2019), P. monodon (Uengwetwanit et al. 2021), M. japonicus (Kawato et al. 2021), and F. chinensis (Wang et al. 2021b), were smaller than that of P. hypsinotus. The genome size distribution of caridean shrimps, estimated using C-value, showed a wide range, from 4.35 Gb for Palaemonetes pugio (Jimenez et al. 2010) to 40 Gb for Sclerocrangon ferox (Rees et al. 2008; Rutz et al. 2023). However, the biological basis for these variations has not yet been elucidated. According to previous studies, the variations in genome sizes among eukaryotic species are usually related to the distribution of repetitive sequences rather than the number of coding genes (Kidwell 2002; Naville et al. 2019). These scientific findings are also supported by the presence of numerous TEs detected in the transcriptome of P. hypsinotus. This suggests that a significant portion of the P. hypsinotus genome may consist of repetitive sequences. TEs can insert into intron sequences and undergo exonisation and activation as alternatively spliced cassette exons. The domestication of TEs presents significant opportunities for species evolution (Sela et al. 2010; Schmitz et al. 2011; Jangam et al. 2017). The ratios of these exonised TEs vary among species and can serve as a standard for species identification. Therefore, we verified the distribution ratio of TEs in the transcriptome of P. hypsinotus and conducted a comparative analysis of the distribution of TEs in the transcriptome sequences of seven decapod species (H. americanus, P. trituberculatus, E. sinensis, F. chinensis, L. vannamei, P. monodon, M. japonicus), along with the outgroup D. pulex. The distribution of TEs inserted in the P. hypsinotus transcriptome was 34.16%, which was higher than that in the penaeid shrimps, P. trituberculatus, H. americanus, and D. pulex (Supplementary Figure 2). Owing to the limitations in the RepeatMasker reference database, many TEs in invertebrates were identified as repetitive but remained ‘unclassified.’ Generally, species in the infraorder Pleocyemata exhibited higher TE proportions within their genes than those in the suborder Dendrobranchiata. Notably, the exceptionally high distribution of TEs in E. sinensis, which inhabits both freshwater and saltwater environments, may represent a genomic strategy for adapting to hostile environments. Similarly, the high TE content in P. hypsinotus, which dwells at a wide depth range of 5–500 m, could be a byproduct of environmental adaptation (Carotti et al. 2023; Shao et al. 2023; Baril et al. 2025). This contrasts with the lower TE proportions observed in Dendrobranchiata shrimps, P. trituberculatus, and D. pulex, whose habitats are relatively stable.

The transcriptome of P. hypsinotus was analysed using PacBio Iso-Seq, which overcomes the limitations of short-read genome sequencing with numerous repetitive sequences (Rhoads and Au 2015). Unigenes constructed through sequencing were annotated using NCBI-nr, UniProt and InterProScan. In the unigene annotation using BLASTX, the organism with the largest number of hits was the American lobster, followed by P. clarkii, M. japonicus, C. quadricarinatus, F. chinensis, P. monodon, L. vannamei, E. sinensis, and P. trituberculatus (Figure 3).

To conduct phylogenetic analysis, we identified the phylogenetic position of P. hypsinotus. The results showed that it was closest to M. rosenbergii, which belongs to the same Caridea infraorder. Similar to the alignment results, the phylogenetic distance to H. americanus was relatively close, whereas it was far from other penaeid shrimps, such as L. vannamei and P. monodon, as well as Brachyura species, including E. sinensis and P. trituberculatus (Supplementary Figure 3).

To compare the expression of the identified transcripts in different tissue types, the reads generated from short-read sequencing of the tail muscle and hepatopancreas were mapped to the isoform sequences constructed using PacBio Iso-seq. For differential expression analysis, short-read sequencing was performed on tissues from three individuals to ensure minimum statistical significance (Horn et al. 2017; González-Castellano et al. 2019). However, as alignment and annotation were conducted based on our PacBio Iso-Seq results in the absence of a previously reported reference genome, the possibility remains that certain inter-individual variations may have gone undetected. The hepatopancreas is an important organ involved in immunity and metabolism in invertebrates (Rőszer 2014; Liang et al. 2021). Therefore, we anticipated that the differential expression analysis could offer a deeper understanding of the ecology and immune responses in P. hypsinotus. Approximately 90% of the reads were mapped to the constructed isoforms (Supplementary Table 11). Based on the count value, we identified 9,429 unigenes that were differentially expressed in the hepatopancreas compared with the tail muscle. GO analysis of the differentially expressed unigenes revealed that genes related to collagen metabolism were the most frequently represented, followed by genes involved in protein folding, positive regulation of gene expression, phosphorylation, digestion, negative regulation of gene expression and chitin metabolism. Collagen is associated with tail muscle tightness in shrimp, and chitin is highly associated with the formation of shrimp shells (Wei et al. 2018; Deng et al. 2020; Pakizeh et al. 2021; Xiao et al. 2023). Furthermore, many differentially expressed unigenes were involved in shrimp body formation and environmental adaptation, such as polysaccharide metabolism, actin skeleton, heat response and hypoxia response (Zhu et al. 2016; Wang et al. 2019). Among the four unigenes that showed the greatest increase in expression in the hepatopancreas compared to the tail muscle, the functions of three remain unclear, whereas Pandalus_Iso0021869 was identified as a haemocyanin B protein. Haemocyanin plays a role in the innate immunity of crustaceans and exhibits antibacterial and bactericidal effects upon pathogen infections (Pan et al. 2019; Zhao et al. 2021). The qPCR results showed that Pandalus_Iso0021869 exhibited significantly higher expression in the hepatopancreas across all individuals compared to the other two unigenes. This suggests that the hepatopancreas may play a crucial role in the immune and metabolic processes of P. hypsinotus. In contrast, the four genes with decreased expression had relatively well-defined functions. Pandalus_Iso0010486 and Pandalus_Iso0011835 were identified as sarcoplasmic calcium-binding proteins, Pandalus_Iso0000969 as a 300 kDa muscle-specific protein and Pandalus_Iso0010252 as myosin regulatory light chain 2, genes most closely associated with tail muscle composition (Takagi and Konish 1984; Koyama et al. 2012; Brooks et al. 2021). The qPCR results showed that these four unigenes were specifically expressed in the tail muscles across all individuals, suggesting that gene expression in tail muscle is less complex and more specialised than that in the hepatopancreas.

Additionally, considering that the expression of haemocyanin B was the most significantly altered, we focused on identifying potential immune genes whose expression was increased in the hepatopancreas among all the genes of P. hypsinotus and verified their correlation with shrimp immunity. Additionally, we identified potential immune genes among the genes of P. hypsinotus, whose expression was elevated in the hepatopancreas, and verified their correlation with immunity in shrimp. Based on the list of decapod immune genes identified in previous studies, we selected immune genes for the experiment by intersecting them with the annotated genes of P. hypsinotus. Among the various genes that could affect immunity in Decapoda, 16 immune-related genes were identified in P. hypsinotus, and the genes that showed the highest differential expression were LYZ, CLEC, and A2M (Table 3). LYZ has been studied in relation to acute hepatopancreatic necrosis disease and TSV infection response in P. monodon, M. japonicus, and L. vannamei (Fagutao et al. 2008; Yue et al. 2010; Zeng et al. 2013; Zhang et al. 2015; Soo et al. 2019; Xu et al. 2020), and A2M is known to be involved in environmental and immune responses in P. trituberculatus, Tachypleus tridentatus, and Scylla paramamosain (S. paramamosain) (Fagutao et al. 2008; Yue et al. 2010; Xu et al. 2020; Wang et al. 2021a). CLEC is known as a representative immune-related gene involved in immune responses to viral infections and environmental changes in various decapod species, such as P. monodon, L. vannamei, M. japonicus, and S. paramamosain (Pan et al. 2005; Pongsomboon et al. 2008; Zeng et al. 2013; Soo et al. 2019; Xu et al. 2020). We focused on tissue-specific miRNAs that could be associated with the differential gene expression between the hepatopancreas and the tail muscle. miRNA regulates gene expression at the post-transcriptional level by binding complementarily to the 3’ UTR of the mRNA sequence (Hwang et al. 2025; Oh et al. 2025). Many recent studies on shrimps suggest that the regulation of gene expression by miRNAs is important for physiological processes such as the response to stress, pathogen infection and environmental adaptation (González-Duarte and García-Carreño 2020; Wang et al. 2020; Liang et al. 2021). Because the miRNAs of caridean shrimps, including P. hypsinotus, have not been previously reported, we sorted RNA fragments ranging from 17 to 25 nt in size, created libraries, and conducted sequencing to assemble the generated small reads de novo. The reads had an average size of 22 nt, and ten orthologous miRNAs were identified by aligning the assembled sequences with the miRNA sequences of Homo sapiens, Mus musculus, Gallus gallus, Danio rerio, and T. cancriformis represented by miRBase. Precursor miRNAs have a stem-loop structure (Liu et al. 2016). Therefore, after mapping, the remaining contigs were screened for stem-loop structures using miRDeep2, and the generated miRNA candidates were annotated using BLASTN. miRDeep2 is a computational tool that identifies miRNA precursors with characteristic secondary structures and quantifies their expression levels from sequence data (Friedländer et al. 2012). A total of 130 sequences were predicted to form stem-loop structures, 32 of which were orthologous miRNAs that aligned with miRNA sequences of other species, and 98 were novel miRNA sequences specific to P. hypsinotus (Figure 5(A)). Of the 32 miRNAs, 17 had sequences homologous to miRNAs from other crustaceans (Supplementary Table 8). In particular, miR-1000 has been shown to be involved in the degradation of viral mRNA when M. japonicus is infected with WSSV, thereby contributing to innate immune function (Gong et al. 2018). Additionally, miR-8 has been reported to target IKKβ in L. vannamei, regulating gill cell apoptosis under ammonia-N stress (Zhuo et al. 2024). To investigate the impact of these miRNAs on immunity in P. hypsinotus, we selected miRNAs with differential expression in the hepatopancreas compared to those in the tail muscle and analysed whether they interact with immune-related unigenes showing the opposite expression.

We sought to verify the interactions between these DEMs and previously selected immune genes to finally investigate the effects of miRNAs on immunity. In the hepatopancreas, the miRNA showed an expression pattern opposite to that of the CLEC gene (Pandalus_Iso0001842) demonstrating that the most pronounced disparity in expression was observed for miR-1000. The seed region of miR-1000 could bind to two regions of CLEC sequence via complementary base pairing. In a cellular-level interaction experiment using Psi-CHECK2, miR-1000 potentially interacted with CLEC (Figure 7(A)). miR-1000 is an orthologue present in a variety of species and is involved in the immune response of shrimps, particularly during infection with WSSV (Gong et al. 2015; Gong et al. 2018). The identified immune-related miRNAs can be used as markers for infectious diseases in P. hypsinotus, and novel miRNAs can potentially be used as molecular markers specific to P. hypsinotus. To validate the interactions between the additional immune-related genes and differentially expressed miRNAs, we performed similar experiments using CASP and LYZ. The results confirmed that miR-315 and miR-281 bind complementarily to CASP and LYZ, respectively (Figure 7(B,C)). This suggests that gene expression in the hepatopancreas of P. hypsinotus is potentially regulated by miRNAs, which could serve as biomarkers for diseases and environmental changes.

It is well established that CLEC, CASP and LYZ are involved in the immune responses of closely related shrimp species. CLEC is one of the most prominent immune genes in shrimp. In F. chinensis, it acts as a pattern recognition receptor (PRR) that interacts with VP28, a major envelope protein of WSSV, to facilitate pathogen recognition. In L. vannamei, CLEC expression increases upon WSSV infection, triggering viral recognition and the initiation of innate immunity (Wang et al. 2009; Wei et al. 2012; Bi et al. 2020). Therefore, it is plausible that CLEC performs a similar innate immune function in the P. hypsinotus, a hypothesis that can be validated through viral challenge studies in future research. CASP is another well-known shrimp immune gene primarily activated during viral infections. It has been reported to be involved in the induced apoptosis of WSSV-infected cells and functions as a key component of the IMD pathway in shrimps, influencing the recognition of gram-negative bacteria and the subsequent expression of innate immunity (Wang et al. 2008; Li and Xiang 2013). Conversely, while LYZ is known to function as an antimicrobial protein in invertebrate innate immunity, its specific immune pathways in shrimps have not yet been fully elucidated. Generally, LYZ exhibits antimicrobial activity by specifically hydrolysing the β−1,4-glycosidic bonds between N-acetylmuramic acid and N-acetylglucosamine in the peptidoglycan backbone of gram-positive bacterial cell walls (Van Herreweghe and Michiels 2012; Ferraboschi et al. 2021). In L. vannamei, LYZ expression increases during Vibrio infections, and LYZ from Fenneropenaeus indicus has shown potent antimicrobial activity against Salmonella typhimurium, Pichia pastoris and Saccharomyces cerevisiae (Burge et al. 2007; Karthik et al., 2014). The expression of these three innate immune genes typically increases upon exposure to viral and bacterial pathogens. Future studies comparing the gene and miRNA expression profiles between infected and pathogen-free individuals are expected to provide definitive evidence for the immune functions of the newly identified immune genes in the spot prawn.

In addition to miR-1000, which has been validated for its expression and potential gene regulatory functions, many DEMs identified in this study may serve as biomarkers for aquaculture and ecological research on P. hypsinotus. The expression of immune-related miRNAs is known to exhibit changes during pathogen infection and stress (Zheng et al. 2018; Liang et al. 2021; Zhong et al. 2024). Therefore, numerous studies have been conducted to explore the potential use of miRNAs as biomarkers for disease detection and stress monitoring in aquaculture (Houdelet et al. 2023; SiouNing et al. 2023). It is well known that L. vannamei, which is extensively cultivated, exhibits decreased expression of miR-252 and miR-2765 under the oxidative and ammonia stress associated with aquaculture (Wang et al. 2021a; Wang et al. 2024). Additionally, the expression of miR-8, miR-10, and other miRNAs increases upon WSSV infection (Wu et al. 2019). The miR-1000 identified in this study is known to exhibit decreased expression upon WSSV infection in L. vannamei and can induce an innate immune response by degrading viral mRNA (Gong et al. 2018). Additionally, miRNA-315 expression changes significantly during WSSV infection in P. monodon and can cause the collapse of innate immunity by inhibiting prophenol oxidase during WSSV infection (Kaewkascholkul et al. 2016; Jaree et al. 2018).

Additionally, miR-10, which exhibited relatively low expression in the hepatopancreas, has been reported to be upregulated upon WSSV infection in L. vannamei, potentially facilitating viral infection (Zou et al. 2021). Similarly, let-7, which also showed low expression, is known to be upregulated in L. vannamei under hypoxic stress (Rees et al. 2008). This suggests that these miRNAs may serve as potential biomarkers for stress and pathogen infection, and therefore, could be utilised as tools for assessing shrimp health in aquaculture and ecological research.

We confirmed that upregulated miRNAs in the hepatopancreas can regulate immune-related genes, and that other orthologous miRNAs regulate immune-related genes in other crustaceans. However, this does not fully explain the changes in expression and regulatory mechanisms of miRNAs during infection in P. hypsinotus. Therefore, it is essential to identify the viruses and pathogens that infect P. hypsinotus, and to investigate the changes in gene and miRNA expression during infection. Future studies should focus on identifying pathogens through metagenomic sequencing by comparing shrimps with and without increased expression of immune genes. Given the limited research on infectious diseases in caridean organisms, pathogens carrying pathogenic genes should first be identified through viral genome or metagenomic analysis. Subsequently, immune-related miRNAs that show increased expression during infection can be catalogued, and their gene regulatory functions in immune responses can be validated through miRNA overexpression and silencing experiments using agomiR and antagomiR (Zhou et al., 2024). Taken together, future studies should focus on elucidating the biological pathways of miRNAs during infection and exploring their potential as biomarkers for disease detection.

In summary, we report the complete transcriptome and miRNAome of P. hypsinotus for the first time. The results of this study can be used for further research on the genomes and transcriptomes of various caridea species that have received less attention than Penaeidae. These findings shed light on the mechanisms of disease resistance and environmental adaptation in P. hypsinotus and provide a basis for future aquaculture applications.

Ethics approval and consent to participate

All the experiments were performed in accordance with the guidelines and regulations approved by the Pusan National University Institutional Animal Care and Use Committee (PNU-IACUC).

Author contributions

D.H.L., W.R.K. and H.S.K. conceived the project; D.H.L., W.R.K., H.J.S., E.G.P., Y.J.L. and Y. H. C. designed the experiments; D.H.L., W.R.K., H.J.S., and J.M.K. performed the experiments; D.H.L., W.R.K., H.J.S., H.S.J., H.Y.R., Y. H.C. and I.T.C. analysed the data; D.H.L., W.R.K., H.J.S., H.S.J., and H.Y.R. contributed materials and methods; and D.H.L. and W.R.K. wrote the paper. The authors read and approved the final manuscript.

Funding Statement

This work was supported by a grant from the National Institute of Biological Resources (NIBR), funded by the Ministry of Climate , Energy, Environment (MCEE) of the Republic of Korea (NIBR202605103).

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The P. hypsinotus RNA-sequencing data generated in this study have been submitted to the NCBI BioProject database (https://www.ncbi.nlm.nih.gov/bioproject/) under accession number PRJNA1055290.

Supplemental Material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/19768354.2026.2692155.