Combined transcriptomic and metabolomic analyses revealed the mechanisms by which preferential baits regulate feeding and appetite responses in Neptunea arthritica cumingii Crosse

Combined transcriptomic and metabolomic analyses revealed the mechanisms by which preferential baits regulate feeding and appetite responses in Neptunea arthritica cumingii Crosse | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Combined transcriptomic and metabolomic analyses revealed the mechanisms by which preferential baits regulate feeding and appetite responses in Neptunea arthritica cumingii Crosse 文慧 顾, 振林 郝, 俊霞 毛, 莹 田 This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6817321/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Neptunea arthritica cumingii Crosse is a large carnivorous marine snail with high economic value and aquaculture potential in the northern waters of China. Due to its low feeding response to existing feeds and other limiting factors, large-scale artificial cultivation of this species remains unachieved. To address this limitation, we used behavioral observation to compare this snail’s preferences for five different baits, and we also analyzed the biochemical composition of the baits. We also conducted transcriptomic and metabolomic analyses of the snail’s water tubes and olfactory organs using RNA-sequencing and liquid chromatography tandem mass spectrometry to identify genes and metabolites associated with feeding and fasting states. The results showed significant differences in feeding preferences among the five baits, with the highest frequency observed for Ezo scallops ( Mizuhopecten yessoensis ) and the lowest for Korean rockfish ( Sebastes schlegelii ). Comparative analysis of the bait compositions revealed that L-glycine and L-glutamic acid might be key food attractants. We found that the differentially expressed genes and differential metabolites in the snails were enriched in nutrient-related pathways, including neuroactive ligand-receptor interactions, the phosphoinositide 3-kinase–protein kinase B signaling pathway, and the oxytocin signaling pathway. After feeding on M. yessoensis , differentially expressed genes were linked to appetite stimulation, increased feeding rate, and biosynthesis of phenylalanine, tyrosine, and tryptophan. In summary, we identified the preferred bait and potential food attractants for N. cumingii , thereby establishing a theoretical basis for understanding its feeding regulation mechanism and developing artificial compound feeds, with both theoretical significance and practical application value. Neptunea arthritica cumingii Crosse Preference for bait Transcriptome Metabolomics Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Introduction Neptunea arthritica cumingii Crosse is a large temperate marine snail that is mainly distributed in the Yellow Sea and Bohai Sea of China and in coastal areas around Japan and the Korean Peninsula (Lv et al. 2024). In China, Changhai County of Liaoning Province is the main production area for this species, with its output ranking among the highest nationwide. This species has significant economic value in aquaculture. Currently, the market supply of N. cumingii depends primarily on harvesting from natural populations. In recent years, however, its high economic value, overfishing, and environmental changes have led to a sharp decline in natural sources. Therefore, developing aquaculture techniques is of great significance for achieving sustainable utilization and ecological restoration of this species. Animals' selectivity for baits is closely related to the properties of the baits, such as attractiveness and palatability (Assis et al.2021). Dang et al. ( 2022 ) investigated the feed preference of the crayfish Cherax quadricarinatus and found that among fish, corn, carrot, and unselected compound feeds, the highest feeding rate was for fish. Li et al. ( 2024 ) investigated the effects of four baits (rotifer, Artemia nauplii, Tubifex , and a micro-diet) on the feeding and growth of larvae of catfish ( Mystus macropterus ) and found that Tubifex was the most effective bait. In another study, Huang et al. (2019) investigated the feeding selectivity of snails ( Thais bronni ) for the bivalves Septifer virgatus, Sinonovacula constricta , and Mytilus galloprovincialis and small miscellaneous crabs and found that that it preferred S. virgatus . These results suggest that selecting suitable preferred baits can significantly increase the feeding rate of aquatic animals and improve their growth performance and survival rate. By stimulating the olfactory and gustatory receptors of aquatic animals, food attractants can effectively cause them to approach the feed and enhance their appetite (Kasumyan and Døving. 2003). Biswas et al. ( 2018 ) conducted comparative experiments by adding various food attractants to the feed of butter catfish ( Ompok bimaculatus ) and found that L-tryptophan improved its survival rate and specific growth rate. Yu et al. ( 2021 ) evaluated the effects of 16 food attractants on grass carp ( Ctenopharyngodon idella ) through behavioral experiments and found that glycine, L-glutamic acid, and L-arginine significantly increased feeding frequency. The screened food attractants demonstrated excellent efficacy in enhancing the feeding and growth of this species. However, no studies have yet reported on the screening of preferred baits and their attractants for N. cumingii . In natural ecosystems, N. cumingii primarily preys on bivalve mollusks and carcasses of other aquatic animals. However, the high cost of using fresh bivalves as aquaculture feed has significantly constrained the development of large-scale N. cumingii farming. Although our research group previously conducted extensive studies on compound feed formulations for this species, practical aquaculture observations revealed poor feeding responses to existing artificial feeds. Long-term cultivation monitoring further demonstrated that this species exhibits distinct feeding preferences among different bivalve species. Building upon these findings, we investigated the effects of five different baits on feeding regulation and growth in N. cumingii and explored the molecular mechanisms underlying bait preference using transcriptomic and metabolomic approaches. Furthermore, we systematically analyzed the biochemical components of the most preferred bait ( Mizuhopecten yessoensis ) to identify potential feeding attractants. These attractants could be incorporated into compound feeds to enhance palatability and feeding rates, ultimately enabling effective replacement of natural prey with formulated feeds. Our results have both theoretical significance and practical value for developing artificial compound feeds and promoting sustainable large-scale aquaculture of N. cumingii . Materials and Methods Experimental bait selection and feeding management In May 2023, 300 N. cumingii (shell height: 90–110 mm; wet weight: 90–112 g) were collected from the waters off Roe Deer Island, Dalian City, Liaoning Province, China. And the specimens were fed with the following prey species: the Ezo scallop M. yessoensis (shell height: 75 ± 1.5 mm; wet weight: 52 ± 2.1 g), M. galloprovincialis (shell height: 73 ± 2.0 mm; wet weight: 55 ± 2.7 g), the clam Ruditapes philippinarum (shell height: 23 ± 1.2 mm; wet weight: 15 ± 2.0 g), the whiteleg shrimp Litopenaeus vannamei (length: 120 ± 2.1 mm; wet weight: 25 ± 2.0 g), and the Korean rockfish S. schlegelii (body length: 97 ± 1.5 mm; wet weight: 34 ± 0.2 g). After collection, N. cumingii were transported to the Key Laboratory of Northern Seawater Enhancement Culture, Ministry of Agriculture and Rural Affairs, Dalian Ocean University, for temporary rearing. The bait organisms were frozen at − 20°C upon procurement and thawed prior to feeding. Two hundred and fifty healthy N. cumingii were selected for the experiment and randomly divided into five groups for 10-day acclimation in 300 L (180 cm × 100 cm × 70 cm) tanks. During acclimation, we maintained the following husbandry conditions: 50% water exchange twice daily, complete bottom cleaning once daily, and surimi feeding twice daily at 09:00 and 18:00. During the feeding period, the environmental indicators of the aquaculture water were as follows: pH, 7.7–8.1; dissolved oxygen content, 9.5–10.7 mg/L; salinity 31–35‰; and temperature 11–18°C. Experimental methods In the experimental tanks, the five bait species were placed in separate grid compartments separated by baffles (Fig. 1 ). Ten N. cumingii individuals were randomly selected and placed 5 cm outside the baffle area. After a 30-min acclimation period, the baffle was removed to initiate the experiment, which lasted for 24 h. Feeding behavior was observed at 30-min intervals, and the initial reaction time, satiation time, and number of N. cumingii individuals at each bait station were recorded. Upon completion of feeding, the remaining bait was collected and weighed to calculate consumption amounts and percentages. To assess bait preference, each bait type was tested five times. After each trial, the least consumed bait was eliminated from subsequent tests. The final remaining bait was identified as the preferred choice, and all five bait types were ranked according to consumption data. In the next experiment, five biochemical components of the baits (L-glycine, L-glutamic acid, L-alanine, L-aspartic acid, and L-arginine) were selected as candidate feeding attractants. For preparation, 0.3 g agar powder was weighed into a 100 mL conical flask, mixed with 30 mL of distilled water, and heated until completely dissolved. Next, 0.3 g of a test substance was added to the molten agarose solution, which was then poured into a cylindrical mold. One to five mung beans were embedded to indicate which test substance was in each gel after solidification (Fig. 2 ). The prepared gels were randomly placed in the aquarium, and we recorded the reaction time, duration of stay near each attractant, and occurrence of foraging behavior of ten snails in the tank. The experiment was repeated eight times with different N. cumingii batches to identify the most effective feeding attractant. Data processing and analysis The feeding preference of N. cumingii for different baits was quantified using feeding preference coefficients, which were calculated as follows: $$\:{\gamma\:}_{n}=\frac{{A}_{n}}{{N}_{n}}$$ where \(\:{A}_{n}\) is the proportion of the nth bait's intake relative to total consumption by N. cumingii and \(\:{N}_{n}\) is the proportion of the nth bait's quantity relative to the total quantity of all baits offered. The preference judgment follows these criteria: when \(\:{\gamma\:}_{n}\) > 1, the bait is relatively preferred; when \(\:{\gamma\:}_{n}\) = 1, N. cumingii shows no obvious selectivity; and when \(\:{\gamma\:}_{n}\) < 1, the bait is relatively non-preferred. Acquisition of experimental samples Nine N. cumingii were randomly selected from both the group ingesting M. yessoensis and the fasting-state group. The water tubes and olfactory organs from three individuals were collected and pooled in 1.5 mL centrifuge tubes, resulting in three replicates for each tissue type for each group, totaling 12 samples. Following collection, all samples were immediately frozen at − 80°C for subsequent RNA extraction. Quantitative real-time PCR (qRT-PCR) analysis Using transcriptome sequencing technology, we analyzed N. cumingii specimens that had ingested M. yessoensis along with fasting controls, identifying six differentially expressed genes (DEGs) through transcriptomic analysis. These DEGs were subsequently validated via qRT-PCR to confirm their expression trends (gene details provided in Table 1 ). For qRT-PCR analysis, RNA was reverse transcribed using the FastKing gDNA Dispelling RT SuperMix kit (TIANGEN Biochemical Science and Technology Co., Ltd., China) to generate cDNA. Each 20 µL reaction mixture contained 2 µL cDNA, 10 µL 2× FastStar Essential DNA Green Master, 6.4 µL DEPC-treated water, and 0.8 µL each of upstream and downstream primers (Table 2 ). The qRT-PCR reaction conditions were as follows: 95°C for 10 min; 95°C for 10 s, 60°C for 60 s, and 45 cycles. Post-amplification, product specificity was verified by melt curve analysis. The relative expressions of all genes were assessed by the 2 –ΔΔCt method. Table 2 Total response system of qRT-PCR Additive reagents volume of use cDNA 2 µL 2×FastStar Essential DNA Green Master 10 µL DEPC treated water 6.4 µL upstream primer 0.8 µL downstream primer 0.8 µL RNA-seq analysis TRIzol reagent (Ambion, USA) was used for total RNA extraction from hepatopancreas. To ensure that the total RNA met the requirements for transcriptome library construction, the quality of the total RNA was checked. The mRNA enriched by magnetic beads with Oligo (dT) was fragmented. The fragmented mRNA was used to synthesize the first strand of cDNA as well as the second strand of cDNA. Double-stranded cDNA was purified using 1.8X Agencourt AMPure XP Beads (Beckman Coulter, Beverly, USA). Around 250–300 bp cDNAs were screened from double-stranded cDNAs, which have undergone terminal repair and ligation of sequencing adaptors. The PCR products were purified again using AMPure XP Beads to obtain the final library. The libraries were sequenced using an Illumina NovaSeq 6000 according to the standard Illumina protocols. All libraries were sequenced for 150 bp at pair end. The low-quality data in the raw reads were filtered for quality control with the help of FASTP to obtain clean reads. The filtered reads were spliced through the HISAT software Trinity (v2.6.6) for clean reads. The filtered data were analysed for base quality and sequence comparison. All samples were subjected to PCA analysis as well as calculation of correlation coefficients among samples to assess the reproducibility of samples. The read count data were subjected to standardized analysis (DESeq method). The genes with an FDR value lower than 0.05 and a |log2(FC)| value greater than 1 was identified as DEGs. DEGs were mapped toward each GO term in the GO database. All DEGs were subjected to pathway enrichment analysis. LC-MS/MS analysis Non-targeted metabolomics (LC-MS/MS) was performed to compare N. cumingii that had ingested preferred bait with fasting controls. Water tubes and olfactory detectors were immediately collected and flash-frozen in liquid nitrogen. For extraction, tissues were removed from − 80°C storage, placed on ice, and homogenized in liquid nitrogen. The powder was then vortexed with 80% aqueous methanol and kept in an ice bath. Subsequently, the mixtures were placed at -20°C for 5 min. Finally, the stationary mixtures were centrifuged (15,000 rpm) for 20 min at 4°C and the supernatant was collected. All samples were analyzed using a Vanquish UHPLC system (Thermo Fisher, Germany) coupled to a Q Exactive™ HF mass spectrometer (Thermo Fisher, Germany). During the sample detection process, two ionization modes, namely positive ion mode (POS) and negative ion mode (NEG), were used. Raw data were processed, including identification, filtering, and alignment processing of peaks. In order to better present the results of metabolomics, the data from POS and NEG were combined and analyzed together. To assess the overall metabolic differences between sample groups and the magnitude of variability among samples within groups, the experimental samples were analyzed by multivariate statistics. Metabolites with VIP values > 1 in OPLS-DA analysis, combined with P -values < 0.05 and fold change (FC) ≥ 2 or ≤ 0.5 in univariate t-test analysis, were considered as differential metabolites (DMs). To determine the major biochemical metabolic pathways and signal transduction pathways in which the metabolites were involved, all DMs were mapped to the KEGG database. Co-analysis of transcriptome and metabolome To explore the relationship between DEGs obtained from transcriptomic analysis and DMs obtained from metabolomic analysis, transcriptomics and metabolomics were co-analyzed. The results of KEGG functional enrichment analysis for DEGs and DMs were correlated. The correlation between the transcription levels of DEGs as well as the abundance changes of DMs was analyzed with the help of the Pearson correlation coefficient. Statistics The normal distribution and homogeneity of variance of all data were evaluated (IBM SPSS Statistics, USA). All data were analyzed by one-way ANOVA. Tukey was used for multiple comparison analysis among all experimental groups. Differences were considered significant if the P -value was less than 0.05. All experimental results were presented as mean ± standard error. Table 1 Target gene qRT-PCR validation primer. Tm: primer-specific annealing temperature Category Description Unigene ID Primer squences(5’→3’) Tm(℃) Reference gene 18s F:TCTTGATTCGGTGGGTGGTG R: CCCGGACATCTAAGGGCATC mRNA Pro-neuropeptide Y-like Cluster-14163.20005 F: AGTGGTGGTAGCGGTCAGTG R: AACGCTTCGTCCAAACCTG 58.0 58.4 Pro-melanin-concentrating hormone Cluster-14163.115318 F: CAGTGCCAACAGCGTTTACA R: TAAAGCCTGCCTGACAAACAT 57.9 57.7 Prepro-orexin Cluster-14163.15450 F:TGAAACTGAAAGCATTTACACCC R: CATACAAGTCTTTTTGGCAGGC 59.0 59.5 Neuropeptide Y receptor type 6 OS Cluster-14163.146275 F: CGACGACTGGAAGATGGACT R: GCAGAGGGAGGTTGAAGACA 57.1 57.2 Hormone receptor domain Cluster-14163.65457 F: CAGGTTACAACTGGAACTTTCG R: TTCACCGTCTTCAATCCACTT 57.0 57.2 Orexin receptor Cluster-14163.87308 F: GCTGGGAAATATCCAACCG R: TGAAGCAGGGCACGAAGA 57.8 57.8 Results Comparison of feeding preferences for different baits and related food attractants Calculating the feeding preference coefficients for the five baits and then ranking them (Fig. 3 ) revealed that M. yessoensis significantly stimulated the appetite of N. cumingii and had the highest feeding frequency, whereas S. schlegelii had the lowest. Results of the attractant experiment indicated that N. cumingii had a stronger preference for gels containing L-glycine and L-glutamic acid, as they elicited shorter staying times and more frequent foraging behaviors compared to the other three substances. In contrast, the snails were almost unresponsive to gels containing L-alanine. These results suggest that L-glycine and L-glutamic acid may act as important food attractants for N. cumingii (Fig. 4 ). Statistics of RNA-seq data Twelve RNA-seq libraries were constructed from the experimental and control groups of N. cumingii , including six libraries (NXQ_1, NXQ_2, NXQ_3, and nXQ_1, nXQ_2, and nXQ_3) for the water pipes (NXQ, nXQ) and six libraries (NX_1, NX_2, NX_3, and nX_1, nX_2, nX_3) for the olfactory organs. In this experiment, 276,945,600 raw reads were obtained from transcriptome sequencing, and 264,178,671 clean reads (95.39%) remained after low-quality data were removed. The amount of raw data for each sample ranged from 6.37 to 7.52 Gb, and the amount of clean data was 6.09–7.86 Gb. The clean reads were analyzed for base composition and mass distribution. The percentages of sequenced bases with quality values reaching Q20 and Q30 levels in clean data were not less than 96.53% and 97.25%, respectively. The GC content of clean reads was 42.32–46.16%, implying a more balanced base composition for clean reads relative to raw reads. The transcriptome of N. cumingii was assembled de novo using Trinity (v2.6.6) software with the default min_kmer_cov setting of 3. Integrity assessment showed that when using all genes from the eukaryotic database, all genes obtained by splicing with BUSCO (v3.0.2) software were successfully assembled. The integrity assessment of the transcripts revealed that 87.3% were single-copy genes and 9.4% were duplicates. A total of 364,106 transcripts were obtained, with an average length of 1020 bp, and the N50 and N90 lengths of all transcripts were 1426 bp and 445 bp, respectively (Fig. 5 ). Approximately 176,871 unigenes were generated, with an average length of 932 bp and N50 and N90 lengths of 1236 bp and 420 bp, respectively. After BLAST annotation, 100% of the unigenes were annotated across seven databases (NR, NT, KO, SwissProt, PFAM, GO, KOG). A total of 19.87% of the de-redundant sequences were annotated in the NR database, 8.99% in the NT database, 7.60% in the KO database, 10.40% in the SwissProt database, 30.51% in the PFAM database, and 29.84% in the GO database. Additionally, 5.39% were annotated in the KOG database, while at least 2.45% were annotated across all databases. In total, 41.16% of the de-redundant sequences were annotated in at least one database, of which 5061 unigenes were jointly annotated in five databases (NR, NT, PFAM, GO, and KOG) (Fig. 5 ). Sample matrices based on Pearson correlation coefficients showed that the fragments per kilobase of transcript per million mapped reads correlation for three biological replicates ranged from 0.708 to 0.824 (Fig. 6 ). These results indicate that our RNA-seq data had good reproducibility and were suitable for subsequent analyses. Identification of DEGs and enrichment analysis of functions and pathways Principal component analysis (PCA) indicated that the samples within each treatment group were relatively concentrated, and they were also very reproducible. The PCA results revealed significant differences among the samples from the two treatment groups. The Venn diagram of DEGs shows that 356 DEGs were shared between the NXQ and nXQ libraries and between the NX and nX libraries. Comparisons of the transcriptomes showed that 8366 DEGs were identified in the experimental and control groups ( P -value < 0.05). Among these DEGs, 3944 were up-regulated and 4422 were down-regulated. Among the 4914 DEGs found in the NXQ and nXQ comparison, 2633 were up-regulated and 2281 were down-regulated. Among the 3452 DEGs found in the NX and nX comparison, 1311 were up-regulated and 2141 were down-regulated (Fig. 6 ). The DEGs were mapped to the GO database, and 48 significantly enriched GO terms were identified. The DEGs from the NXQ, nXQ, NX, and nX samples contained 15, 13, 16, and 22 GO terms, respectively. Many GO terms were related to cellular components, biological processes, and molecular functions, and these enriched GO terms may be related to feeding behaviors, digestive metabolism, and other biological functions (Fig. 6 ). The DEGs were also mapped to the KEGG database, and 1554 DEGs were enriched for 524 pathways (Fig. 7 ). The sulfur metabolism pathway (ko00920) was the most significantly enriched pathway in the NXQ and nXQ comparison, while the protein digestion and absorption pathway (ko04974) showed the highest enrichment in the NX and nX comparison. Further KEGG pathway analysis revealed several feeding-related pathways in N. cumingii , including the neuroactive ligand-receptor interaction pathway (ko04080), phosphoinositide 3-kinase–protein kinase B (PI3K-Akt) signaling pathway (ko04151), oxytocin signaling pathway (ko04921), and steroid hormone biosynthesis pathway (ko00140). Expression of feeding-related genes Six ingestion-related genes were screened by qRT-PCR. The relative expression of these genes was calculated using the 2 −ΔΔCt method, with 18S as the internal reference. Most target genes showed strong correlation with transcriptome sequencing results (Fig. 8 ). Identification of metabolites and enrichment analysis of pathways The score plots of OPLS-DA and PCA showed a high degree of repeatability among repeated samples within a group, and the differences between intergroup samples were quite noticeable. Metabolomics analysis showed that 1196 DMs were obtained from the DX vs dX comparison, and 100 DMs (80 up-regulated and 20 down-regulated) were significantly expressed, with an overall up-regulation trend (Fig. 9 A, C). Of the 1023 DMs obtained from the DXQ vs dXQ comparison, 247 DMs were significantly expressed (169 up-regulated and 78 down-regulated), with an overall trend of up-regulation (Fig. 9 B, D). To explore the pathways and metabolite quantities of these DMs, KEGG enrichment analysis was conducted. All DMs were enriched in three KEGG_A_classes, and 27, 5, and 1 were enriched in the KEGG_B_classes of metabolism, environmental information processing, and genetic information processing, respectively. DMs in the DX vs dX comparison were primarily associated with global metabolic profiles, including nucleotide metabolism, amino acid metabolism, and lipid metabolism (Fig. 10 ). In contrast, DMs in the DXQ vs dXQ comparison were mainly linked to overall metabolic patterns, amino acid metabolism, lipid metabolism, and nucleotide metabolism interactions. KEGG enrichment analysis also identified feeding-related pathways, such as the phenylalanine, tyrosine, and tryptophan biosynthetic pathway (map00400). These pathways were closely associated with the feeding behavior of N. cumingii , with metabolites such as L-phenylalanine, tryptophan, and dopamine exhibiting significant changes after the ingestion of preferred baits. Co-analysis of DEGs and DMs Analysis of the KEGG pathways co-enriched by all DEGs and DMs revealed that most were co-enriched in key metabolic pathways, such as the biosynthetic pathways of phenylalanine, tyrosine, and tryptophan (Fig. 11 ). The regulation of these pathways plays a crucial role in feeding behavior, energy supply, and signaling in N. cumingii. Validation of the RNA-seq results by qRT-PCR Nine randomly selected DEGs were subjected to qRT-PCR experiments to validate the RNA-seq results. The changes in the relative expression of the nine DEGs in the water pipe and olfactory organ groups were mostly consistent with the transcriptomics data (Fig. 12 ). However, individual target genes did not fully match the trends observed in RNA-seq, which might be attributed to experimental animal variability. Discussion In aquaculture, natural bait serves as a crucial pathway for energy acquisition, and the feeding selectivity of aquatic animals directly influences their growth and survival (He et al. 2024 ). The feeding preferences of N. cumingii are influenced by multiple factors, among which bait type is predominant. In this study, we investigated the feeding selectivity of N. cumingii for three bivalves ( M. yessoensis, M. galloprovincialis , and R. philippinarum ), one shrimp ( Litopenaeus vannamei ), and one fish ( S. schlegelii ). Results demonstrated a clear preference hierarchy of M. yessoensis > M. galloprovincialis > R. philippinarum > L. vannamei > S. schlegelii . These findings contrast with those reported by Li et al. ( 2023 ), who observed that N. cumingii preferentially consumed S. constricta , with subsequent preferences for R. philippinarum, Scapharca subcrenata, Crassostrea gigas, M. yessoensis, Chlamys farreri, Scomberomorus niphonius , and Atrina pectinata . This discrepancy may be attributed to geographical variations in the snails' origin. Our specimens were collected from Roe Deer Island, where M. yessoensis is abundant, whereas Li's study utilized populations from Qingdao's Shazikou coast, where M. yessoensis is unavailable. This ecological difference likely explains the observed variation in feeding preferences. Aquatic feed attractants can be categorized into various types, with amino acid-based attractants being particularly effective for inducing feeding behavior in aquatic species (Coman et al.1996; Min et al. 2001; Velez. 2007; Yasumasa et al.1980). These additives enhance feed localization, accelerate feeding initiation, and reduce nutrient leaching (Meyers. 1987). Amino acids exist in two stereoisomeric forms: L-type (levorotatory) and D-type (dextrorotatory) (Velez. 2007; Zhong et al. 2013), with L-amino acids being widely recognized as superior feeding stimulants for aquatic animals (Ling et al. 2000). Accordingly, we selected five L-amino acids as potential attractants to evaluate their effects on N. cumingii feeding behavior. We found that L-glycine exhibited the strongest attractant effect, followed by L-glutamic acid. These findings align with those of Wang et al. (2011), which showed an enhanced feeding response and reduced feeding time of the Yellow River carp ( Cyprinus carpio ) when arginine and glutamic acid were added to the diet. Similarly, LING Mengqing et al. ( 2000 ) confirmed the significant feeding stimulation effect of optimally dosed L-glycine on Chrysophrysmajor and Fugurubripes . Notably, amino acid mixtures may outperform single amino acids as feeding stimulants (Wang et al. 2011). For example, Zhou et al. ( 2005 ) observed that glycine-glutamic acid combinations produced significantly better feeding responses in pond loach ( Misgurnus anguillicaudatus ) than individual amino acids, possibly through synergistic stimulation. To date, the effects of compound amino acid supplementation on N. cumingii have not been reported, and future research should explore this issue and the mechanisms responsible for any positive effects. Good or bad feeding can significantly affect the expression levels of genes and hormones in aquatic animals and ultimately influence their growth and health (Li et al. 2024 ). In this study, N. cumingii exhibited highly active feeding behavior when consuming M. yessoensis , and transcriptome analysis revealed significant up-regulation of neuropeptide Y (NPY) analog genes, melanin-concentrating hormone (MCH) genes, and orexin (ORX) receptor genes. NPY is widely distributed in the central and peripheral nervous systems, and it regulates internal homeostasis and plays a key role in stimulating appetite (Kalra et al. 1991 ). Kaga et al. ( 2001 ) demonstrated that NPY overexpression in the brain of brown rats ( Rattus norvegicus ) enhanced feeding behavior, resulting in accelerated body weight gain. Similarly, Zarjevski et al. ( 1994 ) observed a substantial increase in appetite and body weight after injecting NPY into rat brain ventricles. Kim et al. ( 2021 ) reported analogous results in Pacific abalone ( Haliotis discus hannai ), in which NPY homolog injection significantly enhanced food intake. In our study, NPY homologs and their receptor genes were highly expressed in the water pipe and olfactory organs of N. cumingii . We hypothesize that up-regulation of these genes when feeding on preferred prey enhances appetitive responses, thereby promoting feeding behavior and increasing consumption. MCH is a critical neuropeptide in the hypothalamic feeding center, and it is a major regulator of appetite in animals (Bittencourt et al. 1992 ). Qu et al. ( 1996 ) found that MCH injection into the lateral ventricles of R. norvegicus increased food intake and body weight, aligning with our observations in N. cumingii . However, Senzui et al. (2023) observed down-regulated MCH expression in fasted specimens of the amberjack Seriola quinqueradiata relative to their normally fed counterparts. In our study, fasting N. cumingii showed higher MCH expression in water pipes than those fed preferred bait. This discrepancy may reflect differences between invertebrate and vertebrate MCH regulatory mechanisms. ORX is a hypothalamic neuropeptide and appetite-stimulating hormone that enhances appetite in vertebrates (Chen et al. 2021 ). Volkoff et al. ( 2005 ) observed that starvation significantly elevated ORX expression in goldfish ( Carassius auratus ), resulting in increased food intake. Buckley et al. ( 2010 ) demonstrated that in winter flounder ( Pleuronectes americanus ), orexigenic precursor transcripts were expressed in the hypothalamus and also in peripheral tissues, including the stomach, intestine, and gonads, with the highest expression levels observed in fasted individuals. Here, we report for the first time that the ORX precursor gene is differentially expressed in the water ducts and olfactory organs of N. cumingii . We propose that ORX signaling through receptor binding in these tissues may regulate feeding behavior, thereby enhancing food consumption. The regulation of feeding in aquatic animals involves synergistic interactions among multiple pathways, and it requires coordination of complex physiological and behavioral responses through the activation of appetite-related genes (Xie et al. 2016 ). In this study, we observed significant enrichment of DEGs in N. cumingii following ingestion of preferred bait, particularly in key signaling pathways such as neuroactive ligand-receptor interactions, PI3K-Akt, and oxytocin signaling. Previous research has established that the neuroactive ligand-receptor interaction pathway (ko04080) is a critical transmembrane signaling mechanism (Lauss et al. 2007 ). Zhuang et al. (2018) identified several appetite-regulating genes within this pathway, while Heng et al. (2023) demonstrated its role in modulating feeding behavior in larvae of largemouth bass ( Micropterus salmoides ) through regulation of appetite-related hormone secretion. Our study provides the first evidence of this pathway's involvement in the feeding behavior of N. cumingii . We propose that pathway activation may represent a key molecular mechanism underlying enhanced feeding behavior in this species. Oxytocin, a neuropeptide synthesized in the hypothalamus, plays a key role in regulating feeding behavior and energy metabolism in animals (Son et al. 2022 ). Leng et al. ( 2008 ) demonstrated that the oxytocin signaling pathway (ko04921) participates in growth and energy metabolism regulation in invertebrate neurons. Watanabe et al. ( 2007 ) further revealed that this pathway induces excitation of the esophageal sphincter in Japanese eel ( Anguilla japonica ). We found that the oxytocin signaling pathway in N. cumingii not only regulates growth but may also mediate stage-specific feeding behavior through hypothalamic-feeding feedback loops. These findings provide new insights into the evolutionary mechanisms of neuroendocrine systems in invertebrates. The PI3K-Akt signaling pathway (ko04151) is crucial for regulating feeding behavior and energy homeostasis in aquatic animals (Cota et al. 2006 ). Studies conducted by Shu et al. (2024) and Li et al. ( 2024 ) in Siberian sturgeon ( Acipenser baerii ) and mandarin fish ( Siniperca chuatsi ), respectively, showed that this pathway coordinates metabolic homeostasis and stimulates postprandial appetite. Our results indicate that N. cumingii utilizes the PI3K-Akt pathway not only for energy metabolism regulation but also potentially for modulating appetite mechanisms to maintain energy balance. These adaptive modifications underlie efficient food acquisition and physiological maintenance in this species. Feeding behavior in animals is synergistically regulated by the central nervous system through neurotransmitters and hormones (Morton et al. 2014). Extensive research has confirmed the presence of diverse neurotransmitters and hormones in shellfish (Wang. 2022). The regulatory effects of feeding-related hormones are primarily mediated through interactions with NPY family members, including 5-hydroxytryptamine (5-HT) and dopamine (Ye et al. 2024 ; Tierney. 2020; Elekes et al. 1991 ). Among these, 5-HT activates the cyclin adenosine monophosphate signaling pathway via specific receptor binding. This neuromodulator plays a pivotal role in invertebrate feeding behavior (Gillette. 2006; Voigt et al. 2015; Tecott. 2007) and also influences diverse physiological and behavioral processes in aquatic animals, including regulation of food intake (Winberg et al. 1993). In gastropods, feeding behavior is controlled by conserved neuronal circuits and electrical signaling, with tight coupling to hunger-satiety states (Tierney. 2020). Károly et al. ( 2018 ) demonstrated that 5-HT enhances feeding rhythm and promotes feeding behavior in the pond snail Lymnaea stagnalis , while Kupfermann et al. (1982) induced feeding behavior in California sea hares ( Aplysia californica ) through serotonergic stimulation of muscular excitation. Consistent with these findings, our study suggests that 5-HT produced by N. cumingii after ingesting M. yessoensis may modulate feeding processes through neuroexcitatory mechanisms. Dopamine is a key catecholamine neurotransmitter that plays a well-documented role in the regulation of aquatic animal feeding (Brown et al. 2018 ; Montague et al. 1996 ; Sawin et al. 2000 ; Schultz. 2013). Research indicates that food stimuli activate dopaminergic neurons, thereby initiating feeding behavior (Elekes et al. 1991 ; Carpenter et al. 1971 ; Wieland et al. 1983). Brown et al. ( 2018 ) localized dopamine primarily in the cephalic sensory organs of the nudibranch Pleurobranchaea californica , suggesting this neurotransmitter's functional role in gastropod sensory systems. We identified dopamine-related DMs in the water duct and olfactory organs of N. cumingii . We hypothesize that dopamine mediates feeding behavior through chemosensory receptors and that detection of preferred bait triggers neural excitation, ultimately driving locomotor and feeding responses in this snail. Integrated analysis of transcriptomics and metabolomics offers a powerful approach for elucidating biological regulatory networks, as it enables cross-validation between gene expression and metabolite profiles while identifying key regulatory pathways ( Fraga et al. 2010). In this study, we observed significant enrichment of DEGs and DMs in N. cumingii within the L-phenylalanine, tryptophan, arginine, and methionine metabolic pathways during feeding. These findings align with reports of other aquatic species. For instance, Esmaeili et al. ( 2023 ) documented similar enrichment of L-phenylalanine and tryptophan pathways in Chinook salmon ( Oncorhynchus tshawytscha ) following consumption of palatable bait. Guo et al. (2022) demonstrated that C. idella exhibits comparable pathway activation during feeding, with optimal feed concentrations significantly elevating levels of arginine and other amino acid metabolites to enhance growth and metabolic health. Parallel work by Uikawa et al. (1997) in C. carpio confirmed that dietary composition directly influences arginine and related metabolite enrichment, which are critical for immune function and developmental processes. In addition, Chen et al. ( 2024 ) and Gao et al. ( 2018 ) studied European seabass ( Dicentrarchus labrax ) juveniles and turbot ( Scophthalmus maximus ), respectively, and showed that amino acids such as methionine can effectively regulate antioxidant responses, improve growth performance, and enhance feed utilization in these fish. Similarly, our results showed that activation of specific amino acid pathways in N. cumingii enhanced their sensitivity to amino acids in the bait and their bait utilization, thereby improving feeding performance. Collectively, these findings underscore the pivotal role of amino acid metabolism in regulating feeding behavior and growth across aquatic species. In summary, DEGs and DMs were significantly enriched in feeding-related signaling pathways in N. cumingii , including phenylalanine metabolism and the biosynthetic pathways of tyrosine and tryptophan, following ingestion of M. yessoensis . Furthermore, observed fluctuations in key metabolite levels (L-phenylalanine, tryptophan, arginine, and methionine) were strongly associated with feeding regulation in this snail. Conclusions N. cumingii exhibited significant feeding preferences among different baits, with the following order of preference: M. yessoensis > M. galloprovincialis > R. philippinarum > L. vannamei > S. schlegelii . The addition of L-glycine and L-glutamic acid to the feed significantly enhanced feeding attraction. Exploring N. cumingii ’s preference for different baits and food attractants is essential for developing compound feeds. This will enable artificial breeding of the species to increase production, meeting societal development needs and market demand. Declarations Acknowledgments The authors gratefully acknowledge the valuable support and assistance provided by all colleagues at the Key Laboratory of Mariculture and Enrichment in the Northern Sea Area of China. We are also deeply indebted to Science Editors International for their professional language editing services, which significantly enhanced the quality of this manuscript. Author Contributions Wenhui Gu: Data curation, Formal analysis, Visualization, Writing—original draft, Writing—review & editing. Fengxiao Lv: Investigation, Methodology, Data curation, Validation. Yaqing Chang: Resources, Supervision, Funding acquisition, Writing—review & editing. Zhenlin Hao: Resources, Supervision, Funding acquisition, Writing—review & editing, Project administration. All authors read and approved the final manuscript. Funding This project is supported by the Joint Research Program of Liaoning Provincial Science and Technology Plan（2024JH2/102600076）,2024 Open Research Project of Hainan Provincial Key Laboratory for Efficient Utilization and Processing of Deep-Sea Fishery Resources（KLEU-2024-2），Liaoning Provincial Modern Agricultural Industry Technology System for Shellfish, and Liaoning Provincial Major Special Project (2024JH1/11700010). Data availability The RNA clean datasets generated the during the current study are available in the National Center for Biotechnology Information (NCBI) Shrot Read Archive (SRA) repository, persistent accession number to datasets PRJNA1267058. The metabolomics datasets generated in this study are available in the Metabolights database (http://www.ebi.ac.uk/metabolights ) under the persistent accession number dataset MTBLS12527. Ethics approval The animal study was approved by the Ethics Committee of Dalian Ocean University. The study was conducted in accordance with the local legislation and institutional requirements. Consent to participate Authors have permission to participate. Consent for publication Authors have permission for publication. Confict of interest The authors declare no competing interests. Authors and Afliations Wenhui Gu 1# · Junxia Mao 1# · Fengxiao Lv 1 · Xubo Wang 1 · Ping He 1 · Linxuan Cai 1 · Longwei Dai 1 · Menghao Jia 1 · Ying Tian 1* · Zhenlin Hao 1* * Zhenlin Hao [email protected] * Ying Tian [email protected] 1 Key Laboratory of Mariculture and Stock Enhancement in North China’s Sea (Dalian Ocean University), Ministry of Agriculture, Dalian 116023, China 2 Present address: College of Fisheries and Life Science, Dalian Ocean University, 52, Heishijiao Street, Shahekou District, Dalian, Liaoning Province, China References Assis D F J H, Sanches R D A, De R S O, et al. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6817321","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":469508774,"identity":"26481b18-f7d2-4d13-9c2a-80d8ced3bc5f","order_by":0,"name":"文慧 顾","email":"","orcid":"","institution":"Dalian Ocean University","correspondingAuthor":false,"prefix":"","firstName":"文慧","middleName":"","lastName":"顾","suffix":""},{"id":469508775,"identity":"83dc0c9a-7ff9-41cb-b6f8-bed20be10ade","order_by":1,"name":"振林 郝","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA40lEQVRIiWNgGAWjYBACAyBmBmIZNgbmAxIPwGIJxGnhYWNgS5BIIEkLEBkQp8Wc/fDhzwUVd3j4pHs+3kioOMzAz55jwPBzB24tlj1pCcYzzjzjYZM5u9ki4cxhBsmeNwaMvWfwOOxAjkEyb9thHjaJ3G0SiW2HGQxu5BgwM7bh0XL+jcFh3n8gLTnPwFrsCWq5kWPYzNsA1sIGsUWCoJZnycw8x0Ba0oyBfknnkTjzrOBgL16HJR/+zFNzWE5+RvLDGx8qrOX425M3PviJRwsG4AERB0jQMApGwSgYBaMACwAAhWJNqu9uzp8AAAAASUVORK5CYII=","orcid":"","institution":"Dalian Ocean University","correspondingAuthor":true,"prefix":"","firstName":"振林","middleName":"","lastName":"郝","suffix":""},{"id":469508776,"identity":"2681c7f6-2eb5-4d07-89fa-a769945b554a","order_by":2,"name":"俊霞 毛","email":"","orcid":"","institution":"Dalian Ocean University","correspondingAuthor":false,"prefix":"","firstName":"俊霞","middleName":"","lastName":"毛","suffix":""},{"id":469508778,"identity":"e21b15d3-c862-4533-a563-fd4c9de9a673","order_by":3,"name":"莹 田","email":"","orcid":"","institution":"Dalian Ocean University","correspondingAuthor":false,"prefix":"","firstName":"莹","middleName":"","lastName":"田","suffix":""}],"badges":[],"createdAt":"2025-06-04 07:08:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6817321/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6817321/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84476099,"identity":"27ba8b60-f92f-4b51-8d9d-2e819ebe8209","added_by":"auto","created_at":"2025-06-12 11:32:34","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":47763,"visible":true,"origin":"","legend":"\u003cp\u003eDiagram of the experimental tank\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-6817321/v1/530d873b5dde3d2fa9c6a1aa.png"},{"id":84477286,"identity":"bb31909f-afe3-4b0e-b9f6-c04af2b501fb","added_by":"auto","created_at":"2025-06-12 11:48:35","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":110359,"visible":true,"origin":"","legend":"\u003cp\u003eAgar plates were supplemented with different attractants\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-6817321/v1/b1b965abaa638687f2425dd0.png"},{"id":84476101,"identity":"b6480543-c1f9-4231-9695-246962d549ea","added_by":"auto","created_at":"2025-06-12 11:32:35","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":34661,"visible":true,"origin":"","legend":"\u003cp\u003eFeeding preference coefficients of \u003cem\u003eN. cumingii\u003c/em\u003e for different baits \u003cstrong\u003eA\u003c/strong\u003e In the first round of the experiment, the feeding preference coefficient for five different baits was tested, followed by \u003cstrong\u003eB \u003c/strong\u003efour in the second, \u003cstrong\u003eC\u003c/strong\u003e three in the third, and \u003cstrong\u003eD\u003c/strong\u003e two in the fourth round.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-6817321/v1/03566d41ce3e87554cf1b562.png"},{"id":84476100,"identity":"4cd1c4fd-64c5-4c29-bf6e-b13b1dc7240d","added_by":"auto","created_at":"2025-06-12 11:32:35","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":73290,"visible":true,"origin":"","legend":"\u003cp\u003eReaction of \u003cem\u003eN. cumingii\u003c/em\u003e to five food attractant substances in eight trials. The number of stays of \u003cem\u003eN. cumingii\u003c/em\u003e during 8 measurements of L - Glycine, L - Glutamic acid, L - Alanine, L - Aspartic acid, and L - Arginine. The values marked above the columns represent the feeding reaction time.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-6817321/v1/cb1df588798762f82876d1d3.png"},{"id":84476110,"identity":"c01dc6cb-d18d-465a-bf01-39425a89cd2b","added_by":"auto","created_at":"2025-06-12 11:32:35","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":268932,"visible":true,"origin":"","legend":"\u003cp\u003eResults of BUSCO evaluation of \u003cstrong\u003eA\u003c/strong\u003e spliced transcripts and \u003cstrong\u003eB\u003c/strong\u003e Venn diagram of gene annotation \u003cstrong\u003eC\u003c/strong\u003e Pearson correlation of RNA-seq data\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-6817321/v1/7bd38d663a3a23ad08356093.png"},{"id":84476911,"identity":"7c5825c3-fc80-4580-9227-c25cd4c20680","added_by":"auto","created_at":"2025-06-12 11:40:35","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":144921,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis and GO analysis of differentially expressed genes (DEGs) \u003cstrong\u003eA\u003c/strong\u003e NXQ vs nXQ, \u003cstrong\u003eB\u003c/strong\u003e NX vs nX. Volcano plot of DEGs. Red spots indicate up-regulated DEGs; green spots indicate down-regulated DEGs.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-6817321/v1/fb844520bdfb4fd51f280dfd.png"},{"id":84476105,"identity":"57d0fbd4-b13f-4c71-a1c6-b8ac644ee0e4","added_by":"auto","created_at":"2025-06-12 11:32:35","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":107269,"visible":true,"origin":"","legend":"\u003cp\u003eKyoto Encyclopedia of Genes and Genomes analysis of differentially expressed genes \u003cstrong\u003eA\u003c/strong\u003e NXQ vs nXQ, \u003cstrong\u003eB\u003c/strong\u003e NX vs nX\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-6817321/v1/2691205fc2da4b4f36d1a926.png"},{"id":84476112,"identity":"456f93a3-f9d7-43a6-a398-d96a87ee7495","added_by":"auto","created_at":"2025-06-12 11:32:35","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":119642,"visible":true,"origin":"","legend":"\u003cp\u003eExpression of differentially expressed genes related to feeding of \u003cem\u003eN. cumingii\u003c/em\u003e. Data are expressed as mean ± standard error of the mean. Not significant P \u0026gt; 0.05, *P \u0026lt; 0.05, **P \u0026lt; 0.01, ***P \u0026lt; 0.001, ****P \u0026lt; 0.0001.\u003c/p\u003e","description":"","filename":"image8.png","url":"https://assets-eu.researchsquare.com/files/rs-6817321/v1/23b16e3bbe46a9338abbca68.png"},{"id":84476119,"identity":"1481fce8-388f-495d-91e7-10cdd3782a91","added_by":"auto","created_at":"2025-06-12 11:32:35","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":114479,"visible":true,"origin":"","legend":"\u003cp\u003eVolcano plot of differential metabolites \u003cstrong\u003eA\u003c/strong\u003e DX vs dX in positive ion mode, \u003cstrong\u003eB\u003c/strong\u003e DXQ vs dXQ in positive ion mode, \u003cstrong\u003eC\u003c/strong\u003e DX vs dX in negative ion mode, \u003cstrong\u003eD\u003c/strong\u003e DXQ vs dXQ in negative ion mode\u003c/p\u003e","description":"","filename":"image9.png","url":"https://assets-eu.researchsquare.com/files/rs-6817321/v1/5bd2ffa2ac948700fbffbf73.png"},{"id":84476907,"identity":"eb94e62f-9da3-4240-8b7d-1bca7b721cea","added_by":"auto","created_at":"2025-06-12 11:40:35","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":109523,"visible":true,"origin":"","legend":"\u003cp\u003eKyoto Encyclopedia of Genes and Genomes pathway enrichment and classification of differential metabolites \u003cstrong\u003eA\u003c/strong\u003e DX vs dX, \u003cstrong\u003eB\u003c/strong\u003eDXQ vs dXQ\u003c/p\u003e","description":"","filename":"image10.png","url":"https://assets-eu.researchsquare.com/files/rs-6817321/v1/d5b06306b3520dbe96a8a2e6.png"},{"id":84476913,"identity":"3c73557c-97bc-4914-847e-0a8aee5b9ad6","added_by":"auto","created_at":"2025-06-12 11:40:35","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":184782,"visible":true,"origin":"","legend":"\u003cp\u003eCombined analysis of the effects of \u003cem\u003eM. yessoensis\u003c/em\u003e on \u003cem\u003eN. cumingii\u003c/em\u003e differentially expressed genes (DEGs) and differential metabolites (DMs) \u003cstrong\u003eA\u003c/strong\u003e Shared pathways between all DEGs and DMs, \u003cstrong\u003eB\u003c/strong\u003e Correlation analysis of all feeding-related DEGs with key DMs\u003c/p\u003e","description":"","filename":"image11.png","url":"https://assets-eu.researchsquare.com/files/rs-6817321/v1/e0097d0f323701e23c945554.png"},{"id":84477288,"identity":"508dee5a-ea6c-47e2-bc90-51b1c2634a64","added_by":"auto","created_at":"2025-06-12 11:48:35","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":25183,"visible":true,"origin":"","legend":"\u003cp\u003eValidation of the RNA-seq results by quantitative real-time PCR\u003c/p\u003e","description":"","filename":"image12.png","url":"https://assets-eu.researchsquare.com/files/rs-6817321/v1/7e20e51bd84597f940a2dbf3.png"},{"id":86161946,"identity":"104d9a44-dc9b-405b-87c5-6ba2847195d7","added_by":"auto","created_at":"2025-07-07 12:46:56","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2274400,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6817321/v1/ca5c8052-9613-4a36-a818-b8bee2022ebc.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Combined transcriptomic and metabolomic analyses revealed the mechanisms by which preferential baits regulate feeding and appetite responses in Neptunea arthritica cumingii Crosse","fulltext":[{"header":"Introduction","content":"\u003cp\u003e \u003cem\u003eNeptunea arthritica cumingii\u003c/em\u003e Crosse is a large temperate marine snail that is mainly distributed in the Yellow Sea and Bohai Sea of China and in coastal areas around Japan and the Korean Peninsula (Lv et al. 2024). In China, Changhai County of Liaoning Province is the main production area for this species, with its output ranking among the highest nationwide. This species has significant economic value in aquaculture. Currently, the market supply of \u003cem\u003eN. cumingii\u003c/em\u003e depends primarily on harvesting from natural populations. In recent years, however, its high economic value, overfishing, and environmental changes have led to a sharp decline in natural sources. Therefore, developing aquaculture techniques is of great significance for achieving sustainable utilization and ecological restoration of this species.\u003c/p\u003e \u003cp\u003eAnimals' selectivity for baits is closely related to the properties of the baits, such as attractiveness and palatability (Assis et al.2021). Dang et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) investigated the feed preference of the crayfish \u003cem\u003eCherax quadricarinatus\u003c/em\u003e and found that among fish, corn, carrot, and unselected compound feeds, the highest feeding rate was for fish. Li et al. (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) investigated the effects of four baits (rotifer, \u003cem\u003eArtemia nauplii, Tubifex\u003c/em\u003e, and a micro-diet) on the feeding and growth of larvae of catfish (\u003cem\u003eMystus macropterus\u003c/em\u003e) and found that \u003cem\u003eTubifex\u003c/em\u003e was the most effective bait. In another study, Huang et al. (2019) investigated the feeding selectivity of snails (\u003cem\u003eThais bronni\u003c/em\u003e) for the bivalves \u003cem\u003eSeptifer virgatus, Sinonovacula constricta\u003c/em\u003e, and \u003cem\u003eMytilus galloprovincialis\u003c/em\u003e and small miscellaneous crabs and found that that it preferred \u003cem\u003eS. virgatus\u003c/em\u003e. These results suggest that selecting suitable preferred baits can significantly increase the feeding rate of aquatic animals and improve their growth performance and survival rate.\u003c/p\u003e \u003cp\u003eBy stimulating the olfactory and gustatory receptors of aquatic animals, food attractants can effectively cause them to approach the feed and enhance their appetite (Kasumyan and D\u0026oslash;ving. 2003). Biswas et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) conducted comparative experiments by adding various food attractants to the feed of butter catfish (\u003cem\u003eOmpok bimaculatus\u003c/em\u003e) and found that L-tryptophan improved its survival rate and specific growth rate. Yu et al. (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) evaluated the effects of 16 food attractants on grass carp (\u003cem\u003eCtenopharyngodon idella\u003c/em\u003e) through behavioral experiments and found that glycine, L-glutamic acid, and L-arginine significantly increased feeding frequency. The screened food attractants demonstrated excellent efficacy in enhancing the feeding and growth of this species. However, no studies have yet reported on the screening of preferred baits and their attractants for \u003cem\u003eN. cumingii\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eIn natural ecosystems, \u003cem\u003eN. cumingii\u003c/em\u003e primarily preys on bivalve mollusks and carcasses of other aquatic animals. However, the high cost of using fresh bivalves as aquaculture feed has significantly constrained the development of large-scale \u003cem\u003eN. cumingii\u003c/em\u003e farming. Although our research group previously conducted extensive studies on compound feed formulations for this species, practical aquaculture observations revealed poor feeding responses to existing artificial feeds. Long-term cultivation monitoring further demonstrated that this species exhibits distinct feeding preferences among different bivalve species.\u003c/p\u003e \u003cp\u003eBuilding upon these findings, we investigated the effects of five different baits on feeding regulation and growth in \u003cem\u003eN. cumingii\u003c/em\u003e and explored the molecular mechanisms underlying bait preference using transcriptomic and metabolomic approaches. Furthermore, we systematically analyzed the biochemical components of the most preferred bait (\u003cem\u003eMizuhopecten yessoensis\u003c/em\u003e) to identify potential feeding attractants. These attractants could be incorporated into compound feeds to enhance palatability and feeding rates, ultimately enabling effective replacement of natural prey with formulated feeds. Our results have both theoretical significance and practical value for developing artificial compound feeds and promoting sustainable large-scale aquaculture of \u003cem\u003eN. cumingii\u003c/em\u003e.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eExperimental bait selection and feeding management\u003c/h2\u003e \u003cp\u003eIn May 2023, 300 N. cumingii (shell height: 90\u0026ndash;110 mm; wet weight: 90\u0026ndash;112 g) were collected from the waters off Roe Deer Island, Dalian City, Liaoning Province, China. And the specimens were fed with the following prey species: the Ezo scallop M. yessoensis (shell height: 75\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5 mm; wet weight: 52\u0026thinsp;\u0026plusmn;\u0026thinsp;2.1 g), M. galloprovincialis (shell height: 73\u0026thinsp;\u0026plusmn;\u0026thinsp;2.0 mm; wet weight: 55\u0026thinsp;\u0026plusmn;\u0026thinsp;2.7 g), the clam Ruditapes philippinarum (shell height: 23\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2 mm; wet weight: 15\u0026thinsp;\u0026plusmn;\u0026thinsp;2.0 g), the whiteleg shrimp Litopenaeus vannamei (length: 120\u0026thinsp;\u0026plusmn;\u0026thinsp;2.1 mm; wet weight: 25\u0026thinsp;\u0026plusmn;\u0026thinsp;2.0 g), and the Korean rockfish S. schlegelii (body length: 97\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5 mm; wet weight: 34\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2 g). After collection, N. cumingii were transported to the Key Laboratory of Northern Seawater Enhancement Culture, Ministry of Agriculture and Rural Affairs, Dalian Ocean University, for temporary rearing. The bait organisms were frozen at \u0026minus;\u0026thinsp;20\u0026deg;C upon procurement and thawed prior to feeding.\u003c/p\u003e \u003cp\u003eTwo hundred and fifty healthy \u003cem\u003eN. cumingii\u003c/em\u003e were selected for the experiment and randomly divided into five groups for 10-day acclimation in 300 L (180 cm \u0026times; 100 cm \u0026times; 70 cm) tanks. During acclimation, we maintained the following husbandry conditions: 50% water exchange twice daily, complete bottom cleaning once daily, and surimi feeding twice daily at 09:00 and 18:00. During the feeding period, the environmental indicators of the aquaculture water were as follows: pH, 7.7\u0026ndash;8.1; dissolved oxygen content, 9.5\u0026ndash;10.7 mg/L; salinity 31\u0026ndash;35\u0026permil;; and temperature 11\u0026ndash;18\u0026deg;C.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eExperimental methods\u003c/h3\u003e\n\u003cp\u003eIn the experimental tanks, the five bait species were placed in separate grid compartments separated by baffles (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Ten N. cumingii individuals were randomly selected and placed 5 cm outside the baffle area. After a 30-min acclimation period, the baffle was removed to initiate the experiment, which lasted for 24 h. Feeding behavior was observed at 30-min intervals, and the initial reaction time, satiation time, and number of N. cumingii individuals at each bait station were recorded. Upon completion of feeding, the remaining bait was collected and weighed to calculate consumption amounts and percentages. To assess bait preference, each bait type was tested five times. After each trial, the least consumed bait was eliminated from subsequent tests. The final remaining bait was identified as the preferred choice, and all five bait types were ranked according to consumption data.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn the next experiment, five biochemical components of the baits (L-glycine, L-glutamic acid, L-alanine, L-aspartic acid, and L-arginine) were selected as candidate feeding attractants. For preparation, 0.3 g agar powder was weighed into a 100 mL conical flask, mixed with 30 mL of distilled water, and heated until completely dissolved. Next, 0.3 g of a test substance was added to the molten agarose solution, which was then poured into a cylindrical mold. One to five mung beans were embedded to indicate which test substance was in each gel after solidification (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The prepared gels were randomly placed in the aquarium, and we recorded the reaction time, duration of stay near each attractant, and occurrence of foraging behavior of ten snails in the tank. The experiment was repeated eight times with different N. cumingii batches to identify the most effective feeding attractant.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eData processing and analysis\u003c/h3\u003e\n\u003cp\u003eThe feeding preference of \u003cem\u003eN. cumingii\u003c/em\u003e for different baits was quantified using feeding preference coefficients, which were calculated as follows:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:{\\gamma\\:}_{n}=\\frac{{A}_{n}}{{N}_{n}}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{A}_{n}\\)\u003c/span\u003e\u003c/span\u003e is the proportion of the nth bait's intake relative to total consumption by \u003cem\u003eN. cumingii\u003c/em\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{N}_{n}\\)\u003c/span\u003e\u003c/span\u003e is the proportion of the nth bait's quantity relative to the total quantity of all baits offered. The preference judgment follows these criteria: when \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\gamma\\:}_{n}\\)\u003c/span\u003e\u003c/span\u003e\u0026gt; 1, the bait is relatively preferred; when \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\gamma\\:}_{n}\\)\u003c/span\u003e\u003c/span\u003e= 1, \u003cem\u003eN. cumingii\u003c/em\u003e shows no obvious selectivity; and when \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\gamma\\:}_{n}\\)\u003c/span\u003e\u003c/span\u003e\u0026lt; 1, the bait is relatively non-preferred.\u003c/p\u003e\n\u003ch3\u003eAcquisition of experimental samples\u003c/h3\u003e\n\u003cp\u003eNine \u003cem\u003eN. cumingii\u003c/em\u003e were randomly selected from both the group ingesting \u003cem\u003eM. yessoensis\u003c/em\u003e and the fasting-state group. The water tubes and olfactory organs from three individuals were collected and pooled in 1.5 mL centrifuge tubes, resulting in three replicates for each tissue type for each group, totaling 12 samples. Following collection, all samples were immediately frozen at \u0026minus;\u0026thinsp;80\u0026deg;C for subsequent RNA extraction.\u003c/p\u003e\n\u003ch3\u003eQuantitative real-time PCR (qRT-PCR) analysis\u003c/h3\u003e\n\u003cp\u003eUsing transcriptome sequencing technology, we analyzed \u003cem\u003eN. cumingii\u003c/em\u003e specimens that had ingested \u003cem\u003eM. yessoensis\u003c/em\u003e along with fasting controls, identifying six differentially expressed genes (DEGs) through transcriptomic analysis. These DEGs were subsequently validated via qRT-PCR to confirm their expression trends (gene details provided in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003e). For qRT-PCR analysis, RNA was reverse transcribed using the FastKing gDNA Dispelling RT SuperMix kit (TIANGEN Biochemical Science and Technology Co., Ltd., China) to generate cDNA. Each 20 \u0026micro;L reaction mixture contained 2 \u0026micro;L cDNA, 10 \u0026micro;L 2\u0026times; FastStar Essential DNA Green Master, 6.4 \u0026micro;L DEPC-treated water, and 0.8 \u0026micro;L each of upstream and downstream primers (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The qRT-PCR reaction conditions were as follows: 95\u0026deg;C for 10 min; 95\u0026deg;C for 10 s, 60\u0026deg;C for 60 s, and 45 cycles. Post-amplification, product specificity was verified by melt curve analysis. The relative expressions of all genes were assessed by the 2\u003csup\u003e\u0026ndash;ΔΔCt\u003c/sup\u003e method.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTotal response system of qRT-PCR\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdditive reagents\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003evolume of use\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecDNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 \u0026micro;L\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u0026times;FastStar Essential DNA Green Master\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 \u0026micro;L\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDEPC treated water\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.4 \u0026micro;L\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eupstream primer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.8 \u0026micro;L\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edownstream primer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.8 \u0026micro;L\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eRNA-seq analysis\u003c/h2\u003e \u003cp\u003eTRIzol reagent (Ambion, USA) was used for total RNA extraction from hepatopancreas. To ensure that the total RNA met the requirements for transcriptome library construction, the quality of the total RNA was checked. The mRNA enriched by magnetic beads with Oligo (dT) was fragmented. The fragmented mRNA was used to synthesize the first strand of cDNA as well as the second strand of cDNA. Double-stranded cDNA was purified using 1.8X Agencourt AMPure XP Beads (Beckman Coulter, Beverly, USA). Around 250\u0026ndash;300 bp cDNAs were screened from double-stranded cDNAs, which have undergone terminal repair and ligation of sequencing adaptors. The PCR products were purified again using AMPure XP Beads to obtain the final library. The libraries were sequenced using an Illumina NovaSeq 6000 according to the standard Illumina protocols. All libraries were sequenced for 150 bp at pair end.\u003c/p\u003e \u003cp\u003eThe low-quality data in the raw reads were filtered for quality control with the help of FASTP to obtain clean reads. The filtered reads were spliced through the HISAT software Trinity (v2.6.6) for clean reads. The filtered data were analysed for base quality and sequence comparison. All samples were subjected to PCA analysis as well as calculation of correlation coefficients among samples to assess the reproducibility of samples. The read count data were subjected to standardized analysis (DESeq method). The genes with an FDR value lower than 0.05 and a |log2(FC)| value greater than 1 was identified as DEGs. DEGs were mapped toward each GO term in the GO database. All DEGs were subjected to pathway enrichment analysis.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eLC-MS/MS analysis\u003c/h3\u003e\n\u003cp\u003eNon-targeted metabolomics (LC-MS/MS) was performed to compare \u003cem\u003eN. cumingii\u003c/em\u003e that had ingested preferred bait with fasting controls. Water tubes and olfactory detectors were immediately collected and flash-frozen in liquid nitrogen. For extraction, tissues were removed from \u0026minus;\u0026thinsp;80\u0026deg;C storage, placed on ice, and homogenized in liquid nitrogen. The powder was then vortexed with 80% aqueous methanol and kept in an ice bath. Subsequently, the mixtures were placed at -20\u0026deg;C for 5 min. Finally, the stationary mixtures were centrifuged (15,000 rpm) for 20 min at 4\u0026deg;C and the supernatant was collected. All samples were analyzed using a Vanquish UHPLC system (Thermo Fisher, Germany) coupled to a Q Exactive\u0026trade; HF mass spectrometer (Thermo Fisher, Germany). During the sample detection process, two ionization modes, namely positive ion mode (POS) and negative ion mode (NEG), were used. Raw data were processed, including identification, filtering, and alignment processing of peaks. In order to better present the results of metabolomics, the data from POS and NEG were combined and analyzed together. To assess the overall metabolic differences between sample groups and the magnitude of variability among samples within groups, the experimental samples were analyzed by multivariate statistics. Metabolites with VIP values\u0026thinsp;\u0026gt;\u0026thinsp;1 in OPLS-DA analysis, combined with \u003cem\u003eP\u003c/em\u003e-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and fold change (FC)\u0026thinsp;\u0026ge;\u0026thinsp;2 or \u0026le;\u0026thinsp;0.5 in univariate t-test analysis, were considered as differential metabolites (DMs). To determine the major biochemical metabolic pathways and signal transduction pathways in which the metabolites were involved, all DMs were mapped to the KEGG database.\u003c/p\u003e\n\u003ch3\u003eCo-analysis of transcriptome and metabolome\u003c/h3\u003e\n\u003cp\u003eTo explore the relationship between DEGs obtained from transcriptomic analysis and DMs obtained from metabolomic analysis, transcriptomics and metabolomics were co-analyzed. The results of KEGG functional enrichment analysis for DEGs and DMs were correlated. The correlation between the transcription levels of DEGs as well as the abundance changes of DMs was analyzed with the help of the Pearson correlation coefficient.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eStatistics\u003c/h2\u003e \u003cp\u003eThe normal distribution and homogeneity of variance of all data were evaluated (IBM SPSS Statistics, USA). All data were analyzed by one-way ANOVA. Tukey was used for multiple comparison analysis among all experimental groups. Differences were considered significant if the \u003cem\u003eP\u003c/em\u003e-value was less than 0.05. All experimental results were presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard error.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTarget gene qRT-PCR validation primer. Tm: primer-specific annealing temperature\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnigene ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePrimer squences(5\u0026rsquo;\u0026rarr;3\u0026rsquo;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTm(℃)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReference gene\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e18s\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eF:TCTTGATTCGGTGGGTGGTG\u003c/p\u003e \u003cp\u003eR: CCCGGACATCTAAGGGCATC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emRNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePro-neuropeptide Y-like\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCluster-14163.20005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eF: AGTGGTGGTAGCGGTCAGTG\u003c/p\u003e \u003cp\u003eR: AACGCTTCGTCCAAACCTG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e58.0\u003c/p\u003e \u003cp\u003e58.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePro-melanin-concentrating hormone\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCluster-14163.115318\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eF: CAGTGCCAACAGCGTTTACA\u003c/p\u003e \u003cp\u003eR: TAAAGCCTGCCTGACAAACAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e57.9\u003c/p\u003e \u003cp\u003e57.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePrepro-orexin\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCluster-14163.15450\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eF:TGAAACTGAAAGCATTTACACCC\u003c/p\u003e \u003cp\u003eR: CATACAAGTCTTTTTGGCAGGC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e59.0\u003c/p\u003e \u003cp\u003e59.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eNeuropeptide Y receptor type 6 OS\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCluster-14163.146275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eF: CGACGACTGGAAGATGGACT\u003c/p\u003e \u003cp\u003eR: GCAGAGGGAGGTTGAAGACA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e57.1\u003c/p\u003e \u003cp\u003e57.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eHormone receptor domain\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCluster-14163.65457\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eF: CAGGTTACAACTGGAACTTTCG\u003c/p\u003e \u003cp\u003eR: TTCACCGTCTTCAATCCACTT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e57.0\u003c/p\u003e \u003cp\u003e57.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eOrexin receptor\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCluster-14163.87308\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eF: GCTGGGAAATATCCAACCG\u003c/p\u003e \u003cp\u003eR: TGAAGCAGGGCACGAAGA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e57.8\u003c/p\u003e \u003cp\u003e57.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eComparison of feeding preferences for different baits and related food attractants\u003c/h2\u003e \u003cp\u003eCalculating the feeding preference coefficients for the five baits and then ranking them (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) revealed that \u003cem\u003eM. yessoensis\u003c/em\u003e significantly stimulated the appetite of \u003cem\u003eN. cumingii\u003c/em\u003e and had the highest feeding frequency, whereas \u003cem\u003eS. schlegelii\u003c/em\u003e had the lowest. Results of the attractant experiment indicated that \u003cem\u003eN. cumingii\u003c/em\u003e had a stronger preference for gels containing L-glycine and L-glutamic acid, as they elicited shorter staying times and more frequent foraging behaviors compared to the other three substances. In contrast, the snails were almost unresponsive to gels containing L-alanine. These results suggest that L-glycine and L-glutamic acid may act as important food attractants for \u003cem\u003eN. cumingii\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eStatistics of RNA-seq data\u003c/h2\u003e \u003cp\u003eTwelve RNA-seq libraries were constructed from the experimental and control groups of \u003cem\u003eN. cumingii\u003c/em\u003e, including six libraries (NXQ_1, NXQ_2, NXQ_3, and nXQ_1, nXQ_2, and nXQ_3) for the water pipes (NXQ, nXQ) and six libraries (NX_1, NX_2, NX_3, and nX_1, nX_2, nX_3) for the olfactory organs. In this experiment, 276,945,600 raw reads were obtained from transcriptome sequencing, and 264,178,671 clean reads (95.39%) remained after low-quality data were removed. The amount of raw data for each sample ranged from 6.37 to 7.52 Gb, and the amount of clean data was 6.09\u0026ndash;7.86 Gb. The clean reads were analyzed for base composition and mass distribution. The percentages of sequenced bases with quality values reaching Q20 and Q30 levels in clean data were not less than 96.53% and 97.25%, respectively. The GC content of clean reads was 42.32\u0026ndash;46.16%, implying a more balanced base composition for clean reads relative to raw reads.\u003c/p\u003e \u003cp\u003eThe transcriptome of \u003cem\u003eN. cumingii\u003c/em\u003e was assembled de novo using Trinity (v2.6.6) software with the default min_kmer_cov setting of 3. Integrity assessment showed that when using all genes from the eukaryotic database, all genes obtained by splicing with BUSCO (v3.0.2) software were successfully assembled. The integrity assessment of the transcripts revealed that 87.3% were single-copy genes and 9.4% were duplicates. A total of 364,106 transcripts were obtained, with an average length of 1020 bp, and the N50 and N90 lengths of all transcripts were 1426 bp and 445 bp, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eApproximately 176,871 unigenes were generated, with an average length of 932 bp and N50 and N90 lengths of 1236 bp and 420 bp, respectively. After BLAST annotation, 100% of the unigenes were annotated across seven databases (NR, NT, KO, SwissProt, PFAM, GO, KOG). A total of 19.87% of the de-redundant sequences were annotated in the NR database, 8.99% in the NT database, 7.60% in the KO database, 10.40% in the SwissProt database, 30.51% in the PFAM database, and 29.84% in the GO database. Additionally, 5.39% were annotated in the KOG database, while at least 2.45% were annotated across all databases. In total, 41.16% of the de-redundant sequences were annotated in at least one database, of which 5061 unigenes were jointly annotated in five databases (NR, NT, PFAM, GO, and KOG) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Sample matrices based on Pearson correlation coefficients showed that the fragments per kilobase of transcript per million mapped reads correlation for three biological replicates ranged from 0.708 to 0.824 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). These results indicate that our RNA-seq data had good reproducibility and were suitable for subsequent analyses.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of DEGs and enrichment analysis of functions and pathways\u003c/h2\u003e \u003cp\u003ePrincipal component analysis (PCA) indicated that the samples within each treatment group were relatively concentrated, and they were also very reproducible. The PCA results revealed significant differences among the samples from the two treatment groups. The Venn diagram of DEGs shows that 356 DEGs were shared between the NXQ and nXQ libraries and between the NX and nX libraries. Comparisons of the transcriptomes showed that 8366 DEGs were identified in the experimental and control groups (\u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Among these DEGs, 3944 were up-regulated and 4422 were down-regulated. Among the 4914 DEGs found in the NXQ and nXQ comparison, 2633 were up-regulated and 2281 were down-regulated. Among the 3452 DEGs found in the NX and nX comparison, 1311 were up-regulated and 2141 were down-regulated (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe DEGs were mapped to the GO database, and 48 significantly enriched GO terms were identified. The DEGs from the NXQ, nXQ, NX, and nX samples contained 15, 13, 16, and 22 GO terms, respectively. Many GO terms were related to cellular components, biological processes, and molecular functions, and these enriched GO terms may be related to feeding behaviors, digestive metabolism, and other biological functions (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe DEGs were also mapped to the KEGG database, and 1554 DEGs were enriched for 524 pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). The sulfur metabolism pathway (ko00920) was the most significantly enriched pathway in the NXQ and nXQ comparison, while the protein digestion and absorption pathway (ko04974) showed the highest enrichment in the NX and nX comparison. Further KEGG pathway analysis revealed several feeding-related pathways in \u003cem\u003eN. cumingii\u003c/em\u003e, including the neuroactive ligand-receptor interaction pathway (ko04080), phosphoinositide 3-kinase\u0026ndash;protein kinase B (PI3K-Akt) signaling pathway (ko04151), oxytocin signaling pathway (ko04921), and steroid hormone biosynthesis pathway (ko00140).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eExpression of feeding-related genes\u003c/h2\u003e \u003cp\u003eSix ingestion-related genes were screened by qRT-PCR. The relative expression of these genes was calculated using the 2\u003csup\u003e\u0026minus;ΔΔCt\u003c/sup\u003e method, with 18S as the internal reference. Most target genes showed strong correlation with transcriptome sequencing results (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of metabolites and enrichment analysis of pathways\u003c/h2\u003e \u003cp\u003eThe score plots of OPLS-DA and PCA showed a high degree of repeatability among repeated samples within a group, and the differences between intergroup samples were quite noticeable. Metabolomics analysis showed that 1196 DMs were obtained from the DX vs dX comparison, and 100 DMs (80 up-regulated and 20 down-regulated) were significantly expressed, with an overall up-regulation trend (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA, C). Of the 1023 DMs obtained from the DXQ vs dXQ comparison, 247 DMs were significantly expressed (169 up-regulated and 78 down-regulated), with an overall trend of up-regulation (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eB, D).\u003c/p\u003e \u003cp\u003eTo explore the pathways and metabolite quantities of these DMs, KEGG enrichment analysis was conducted. All DMs were enriched in three KEGG_A_classes, and 27, 5, and 1 were enriched in the KEGG_B_classes of metabolism, environmental information processing, and genetic information processing, respectively. DMs in the DX vs dX comparison were primarily associated with global metabolic profiles, including nucleotide metabolism, amino acid metabolism, and lipid metabolism (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e). In contrast, DMs in the DXQ vs dXQ comparison were mainly linked to overall metabolic patterns, amino acid metabolism, lipid metabolism, and nucleotide metabolism interactions. KEGG enrichment analysis also identified feeding-related pathways, such as the phenylalanine, tyrosine, and tryptophan biosynthetic pathway (map00400). These pathways were closely associated with the feeding behavior of \u003cem\u003eN. cumingii\u003c/em\u003e, with metabolites such as L-phenylalanine, tryptophan, and dopamine exhibiting significant changes after the ingestion of preferred baits.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eCo-analysis of DEGs and DMs\u003c/h2\u003e \u003cp\u003eAnalysis of the KEGG pathways co-enriched by all DEGs and DMs revealed that most were co-enriched in key metabolic pathways, such as the biosynthetic pathways of phenylalanine, tyrosine, and tryptophan (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e). The regulation of these pathways plays a crucial role in feeding behavior, energy supply, and signaling in \u003cem\u003eN. cumingii.\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eValidation of the RNA-seq results by qRT-PCR\u003c/h2\u003e \u003cp\u003eNine randomly selected DEGs were subjected to qRT-PCR experiments to validate the RNA-seq results. The changes in the relative expression of the nine DEGs in the water pipe and olfactory organ groups were mostly consistent with the transcriptomics data (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003e). However, individual target genes did not fully match the trends observed in RNA-seq, which might be attributed to experimental animal variability.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn aquaculture, natural bait serves as a crucial pathway for energy acquisition, and the feeding selectivity of aquatic animals directly influences their growth and survival (He et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The feeding preferences of \u003cem\u003eN. cumingii\u003c/em\u003e are influenced by multiple factors, among which bait type is predominant. In this study, we investigated the feeding selectivity of \u003cem\u003eN. cumingii\u003c/em\u003e for three bivalves (\u003cem\u003eM. yessoensis, M. galloprovincialis\u003c/em\u003e, and \u003cem\u003eR. philippinarum\u003c/em\u003e), one shrimp (\u003cem\u003eLitopenaeus vannamei\u003c/em\u003e), and one fish (\u003cem\u003eS. schlegelii\u003c/em\u003e). Results demonstrated a clear preference hierarchy of \u003cem\u003eM. yessoensis\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;\u003cem\u003eM. galloprovincialis\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;\u003cem\u003eR. philippinarum\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;\u003cem\u003eL. vannamei\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;\u003cem\u003eS. schlegelii\u003c/em\u003e. These findings contrast with those reported by Li et al. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), who observed that \u003cem\u003eN. cumingii\u003c/em\u003e preferentially consumed \u003cem\u003eS. constricta\u003c/em\u003e, with subsequent preferences for \u003cem\u003eR. philippinarum, Scapharca subcrenata, Crassostrea gigas, M. yessoensis, Chlamys farreri, Scomberomorus niphonius\u003c/em\u003e, and \u003cem\u003eAtrina pectinata\u003c/em\u003e. This discrepancy may be attributed to geographical variations in the snails' origin. Our specimens were collected from Roe Deer Island, where \u003cem\u003eM. yessoensis\u003c/em\u003e is abundant, whereas Li's study utilized populations from Qingdao's Shazikou coast, where \u003cem\u003eM. yessoensis\u003c/em\u003e is unavailable. This ecological difference likely explains the observed variation in feeding preferences.\u003c/p\u003e \u003cp\u003eAquatic feed attractants can be categorized into various types, with amino acid-based attractants being particularly effective for inducing feeding behavior in aquatic species (Coman et al.1996; Min et al. 2001; Velez. 2007; Yasumasa et al.1980). These additives enhance feed localization, accelerate feeding initiation, and reduce nutrient leaching (Meyers. 1987). Amino acids exist in two stereoisomeric forms: L-type (levorotatory) and D-type (dextrorotatory) (Velez. 2007; Zhong et al. 2013), with L-amino acids being widely recognized as superior feeding stimulants for aquatic animals (Ling et al. 2000). Accordingly, we selected five L-amino acids as potential attractants to evaluate their effects on \u003cem\u003eN. cumingii\u003c/em\u003e feeding behavior. We found that L-glycine exhibited the strongest attractant effect, followed by L-glutamic acid. These findings align with those of Wang et al. (2011), which showed an enhanced feeding response and reduced feeding time of the Yellow River carp (\u003cem\u003eCyprinus carpio\u003c/em\u003e) when arginine and glutamic acid were added to the diet. Similarly, LING Mengqing et al. (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2000\u003c/span\u003e) confirmed the significant feeding stimulation effect of optimally dosed L-glycine on \u003cem\u003eChrysophrysmajor\u003c/em\u003e and \u003cem\u003eFugurubripes\u003c/em\u003e. Notably, amino acid mixtures may outperform single amino acids as feeding stimulants (Wang et al. 2011). For example, Zhou et al. (\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) observed that glycine-glutamic acid combinations produced significantly better feeding responses in pond loach (\u003cem\u003eMisgurnus anguillicaudatus\u003c/em\u003e) than individual amino acids, possibly through synergistic stimulation. To date, the effects of compound amino acid supplementation on \u003cem\u003eN. cumingii\u003c/em\u003e have not been reported, and future research should explore this issue and the mechanisms responsible for any positive effects.\u003c/p\u003e \u003cp\u003eGood or bad feeding can significantly affect the expression levels of genes and hormones in aquatic animals and ultimately influence their growth and health (Li et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In this study, \u003cem\u003eN. cumingii\u003c/em\u003e exhibited highly active feeding behavior when consuming \u003cem\u003eM. yessoensis\u003c/em\u003e, and transcriptome analysis revealed significant up-regulation of neuropeptide Y (NPY) analog genes, melanin-concentrating hormone (MCH) genes, and orexin (ORX) receptor genes. NPY is widely distributed in the central and peripheral nervous systems, and it regulates internal homeostasis and plays a key role in stimulating appetite (Kalra et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e1991\u003c/span\u003e). Kaga et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) demonstrated that NPY overexpression in the brain of brown rats (\u003cem\u003eRattus norvegicus\u003c/em\u003e) enhanced feeding behavior, resulting in accelerated body weight gain. Similarly, Zarjevski et al. (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e1994\u003c/span\u003e) observed a substantial increase in appetite and body weight after injecting NPY into rat brain ventricles. Kim et al. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) reported analogous results in Pacific abalone (\u003cem\u003eHaliotis discus hannai\u003c/em\u003e), in which NPY homolog injection significantly enhanced food intake. In our study, NPY homologs and their receptor genes were highly expressed in the water pipe and olfactory organs of \u003cem\u003eN. cumingii\u003c/em\u003e. We hypothesize that up-regulation of these genes when feeding on preferred prey enhances appetitive responses, thereby promoting feeding behavior and increasing consumption.\u003c/p\u003e \u003cp\u003eMCH is a critical neuropeptide in the hypothalamic feeding center, and it is a major regulator of appetite in animals (Bittencourt et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1992\u003c/span\u003e). Qu et al. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e1996\u003c/span\u003e) found that MCH injection into the lateral ventricles of \u003cem\u003eR. norvegicus\u003c/em\u003e increased food intake and body weight, aligning with our observations in \u003cem\u003eN. cumingii\u003c/em\u003e. However, Senzui et al. (2023) observed down-regulated MCH expression in fasted specimens of the amberjack \u003cem\u003eSeriola quinqueradiata\u003c/em\u003e relative to their normally fed counterparts. In our study, fasting \u003cem\u003eN. cumingii\u003c/em\u003e showed higher MCH expression in water pipes than those fed preferred bait. This discrepancy may reflect differences between invertebrate and vertebrate MCH regulatory mechanisms. ORX is a hypothalamic neuropeptide and appetite-stimulating hormone that enhances appetite in vertebrates (Chen et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Volkoff et al. (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) observed that starvation significantly elevated ORX expression in goldfish (\u003cem\u003eCarassius auratus\u003c/em\u003e), resulting in increased food intake. Buckley et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) demonstrated that in winter flounder (\u003cem\u003ePleuronectes americanus\u003c/em\u003e), orexigenic precursor transcripts were expressed in the hypothalamus and also in peripheral tissues, including the stomach, intestine, and gonads, with the highest expression levels observed in fasted individuals. Here, we report for the first time that the ORX precursor gene is differentially expressed in the water ducts and olfactory organs of \u003cem\u003eN. cumingii\u003c/em\u003e. We propose that ORX signaling through receptor binding in these tissues may regulate feeding behavior, thereby enhancing food consumption.\u003c/p\u003e \u003cp\u003eThe regulation of feeding in aquatic animals involves synergistic interactions among multiple pathways, and it requires coordination of complex physiological and behavioral responses through the activation of appetite-related genes (Xie et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). In this study, we observed significant enrichment of DEGs in \u003cem\u003eN. cumingii\u003c/em\u003e following ingestion of preferred bait, particularly in key signaling pathways such as neuroactive ligand-receptor interactions, PI3K-Akt, and oxytocin signaling. Previous research has established that the neuroactive ligand-receptor interaction pathway (ko04080) is a critical transmembrane signaling mechanism (Lauss et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Zhuang et al. (2018) identified several appetite-regulating genes within this pathway, while Heng et al. (2023) demonstrated its role in modulating feeding behavior in larvae of largemouth bass (\u003cem\u003eMicropterus salmoides\u003c/em\u003e) through regulation of appetite-related hormone secretion. Our study provides the first evidence of this pathway's involvement in the feeding behavior of \u003cem\u003eN. cumingii\u003c/em\u003e. We propose that pathway activation may represent a key molecular mechanism underlying enhanced feeding behavior in this species.\u003c/p\u003e \u003cp\u003eOxytocin, a neuropeptide synthesized in the hypothalamus, plays a key role in regulating feeding behavior and energy metabolism in animals (Son et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Leng et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) demonstrated that the oxytocin signaling pathway (ko04921) participates in growth and energy metabolism regulation in invertebrate neurons. Watanabe et al. (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) further revealed that this pathway induces excitation of the esophageal sphincter in Japanese eel (\u003cem\u003eAnguilla japonica\u003c/em\u003e). We found that the oxytocin signaling pathway in \u003cem\u003eN. cumingii\u003c/em\u003e not only regulates growth but may also mediate stage-specific feeding behavior through hypothalamic-feeding feedback loops. These findings provide new insights into the evolutionary mechanisms of neuroendocrine systems in invertebrates.\u003c/p\u003e \u003cp\u003eThe PI3K-Akt signaling pathway (ko04151) is crucial for regulating feeding behavior and energy homeostasis in aquatic animals (Cota et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Studies conducted by Shu et al. (2024) and Li et al. (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) in Siberian sturgeon (\u003cem\u003eAcipenser baerii\u003c/em\u003e) and mandarin fish (\u003cem\u003eSiniperca chuatsi\u003c/em\u003e), respectively, showed that this pathway coordinates metabolic homeostasis and stimulates postprandial appetite. Our results indicate that \u003cem\u003eN. cumingii\u003c/em\u003e utilizes the PI3K-Akt pathway not only for energy metabolism regulation but also potentially for modulating appetite mechanisms to maintain energy balance. These adaptive modifications underlie efficient food acquisition and physiological maintenance in this species.\u003c/p\u003e \u003cp\u003eFeeding behavior in animals is synergistically regulated by the central nervous system through neurotransmitters and hormones (Morton et al. 2014). Extensive research has confirmed the presence of diverse neurotransmitters and hormones in shellfish (Wang. 2022). The regulatory effects of feeding-related hormones are primarily mediated through interactions with NPY family members, including 5-hydroxytryptamine (5-HT) and dopamine (Ye et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Tierney. 2020; Elekes et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1991\u003c/span\u003e). Among these, 5-HT activates the cyclin adenosine monophosphate signaling pathway via specific receptor binding. This neuromodulator plays a pivotal role in invertebrate feeding behavior (Gillette. 2006; Voigt et al. 2015; Tecott. 2007) and also influences diverse physiological and behavioral processes in aquatic animals, including regulation of food intake (Winberg et al. 1993).\u003c/p\u003e \u003cp\u003eIn gastropods, feeding behavior is controlled by conserved neuronal circuits and electrical signaling, with tight coupling to hunger-satiety states (Tierney. 2020). K\u0026aacute;roly et al. (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) demonstrated that 5-HT enhances feeding rhythm and promotes feeding behavior in the pond snail \u003cem\u003eLymnaea stagnalis\u003c/em\u003e, while Kupfermann et al. (1982) induced feeding behavior in California sea hares (\u003cem\u003eAplysia californica\u003c/em\u003e) through serotonergic stimulation of muscular excitation. Consistent with these findings, our study suggests that 5-HT produced by \u003cem\u003eN. cumingii\u003c/em\u003e after ingesting \u003cem\u003eM. yessoensis\u003c/em\u003e may modulate feeding processes through neuroexcitatory mechanisms.\u003c/p\u003e \u003cp\u003eDopamine is a key catecholamine neurotransmitter that plays a well-documented role in the regulation of aquatic animal feeding (Brown et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Montague et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Sawin et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Schultz. 2013). Research indicates that food stimuli activate dopaminergic neurons, thereby initiating feeding behavior (Elekes et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1991\u003c/span\u003e; Carpenter et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1971\u003c/span\u003e; Wieland et al. 1983). Brown et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) localized dopamine primarily in the cephalic sensory organs of the nudibranch \u003cem\u003ePleurobranchaea californica\u003c/em\u003e, suggesting this neurotransmitter's functional role in gastropod sensory systems. We identified dopamine-related DMs in the water duct and olfactory organs of \u003cem\u003eN. cumingii\u003c/em\u003e. We hypothesize that dopamine mediates feeding behavior through chemosensory receptors and that detection of preferred bait triggers neural excitation, ultimately driving locomotor and feeding responses in this snail.\u003c/p\u003e \u003cp\u003eIntegrated analysis of transcriptomics and metabolomics offers a powerful approach for elucidating biological regulatory networks, as it enables cross-validation between gene expression and metabolite profiles while identifying key regulatory pathways ( Fraga et al. 2010). In this study, we observed significant enrichment of DEGs and DMs in \u003cem\u003eN. cumingii\u003c/em\u003e within the L-phenylalanine, tryptophan, arginine, and methionine metabolic pathways during feeding. These findings align with reports of other aquatic species. For instance, Esmaeili et al. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) documented similar enrichment of L-phenylalanine and tryptophan pathways in Chinook salmon (\u003cem\u003eOncorhynchus tshawytscha\u003c/em\u003e) following consumption of palatable bait. Guo et al. (2022) demonstrated that \u003cem\u003eC. idella\u003c/em\u003e exhibits comparable pathway activation during feeding, with optimal feed concentrations significantly elevating levels of arginine and other amino acid metabolites to enhance growth and metabolic health. Parallel work by Uikawa et al. (1997) in \u003cem\u003eC. carpio\u003c/em\u003e confirmed that dietary composition directly influences arginine and related metabolite enrichment, which are critical for immune function and developmental processes. In addition, Chen et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and Gao et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) studied European seabass (\u003cem\u003eDicentrarchus labrax\u003c/em\u003e) juveniles and turbot (\u003cem\u003eScophthalmus maximus\u003c/em\u003e), respectively, and showed that amino acids such as methionine can effectively regulate antioxidant responses, improve growth performance, and enhance feed utilization in these fish. Similarly, our results showed that activation of specific amino acid pathways in \u003cem\u003eN. cumingii\u003c/em\u003e enhanced their sensitivity to amino acids in the bait and their bait utilization, thereby improving feeding performance. Collectively, these findings underscore the pivotal role of amino acid metabolism in regulating feeding behavior and growth across aquatic species.\u003c/p\u003e \u003cp\u003eIn summary, DEGs and DMs were significantly enriched in feeding-related signaling pathways in \u003cem\u003eN. cumingii\u003c/em\u003e, including phenylalanine metabolism and the biosynthetic pathways of tyrosine and tryptophan, following ingestion of \u003cem\u003eM. yessoensis\u003c/em\u003e. Furthermore, observed fluctuations in key metabolite levels (L-phenylalanine, tryptophan, arginine, and methionine) were strongly associated with feeding regulation in this snail.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003e \u003cem\u003eN. cumingii\u003c/em\u003e exhibited significant feeding preferences among different baits, with the following order of preference: \u003cem\u003eM. yessoensis\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;\u003cem\u003eM. galloprovincialis\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;\u003cem\u003eR. philippinarum\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;\u003cem\u003eL. vannamei\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;\u003cem\u003eS. schlegelii\u003c/em\u003e. The addition of L-glycine and L-glutamic acid to the feed significantly enhanced feeding attraction. Exploring \u003cem\u003eN. cumingii\u003c/em\u003e\u0026rsquo;s preference for different baits and food attractants is essential for developing compound feeds. This will enable artificial breeding of the species to increase production, meeting societal development needs and market demand.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u0026nbsp;\u003c/strong\u003eThe authors gratefully acknowledge the valuable support and assistance provided by all colleagues at the Key Laboratory of Mariculture and Enrichment in the Northern Sea Area of China. We are also deeply indebted to Science Editors International for their professional language editing services, which significantly enhanced the quality of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u0026nbsp;\u003c/strong\u003eWenhui Gu: Data curation, Formal analysis, Visualization, Writing\u0026mdash;original draft, Writing\u0026mdash;review \u0026amp; editing. Fengxiao Lv: Investigation, Methodology, Data curation, Validation. Yaqing Chang: Resources, Supervision, Funding acquisition, Writing\u0026mdash;review \u0026amp; editing. Zhenlin Hao: Resources, Supervision, Funding acquisition, Writing\u0026mdash;review \u0026amp; editing, Project administration. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003eThis project is supported by the Joint Research Program of Liaoning Provincial Science and Technology Plan（2024JH2/102600076）,2024 Open Research Project of Hainan Provincial Key Laboratory for Efficient Utilization and Processing of Deep-Sea Fishery Resources（KLEU-2024-2），Liaoning Provincial Modern Agricultural Industry Technology System for Shellfish, and Liaoning Provincial Major Special Project (2024JH1/11700010).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003eThe RNA clean datasets generated the during the current study are available in the National Center for Biotechnology Information (NCBI) Shrot Read Archive (SRA) repository, persistent accession number to datasets PRJNA1267058. The metabolomics datasets generated in this study are available in the Metabolights database (http://www.ebi.ac.uk/metabolights ) under the persistent accession number dataset MTBLS12527.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u0026nbsp;\u003c/strong\u003eThe animal study was approved by the Ethics Committee of Dalian Ocean University. The study was conducted in accordance with the local legislation and institutional requirements.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e Authors have permission to participate.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e Authors have permission for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConfict of interest\u0026nbsp;\u003c/strong\u003eThe authors declare no competing interests.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eAuthors and Afliations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWenhui Gu\u003csup\u003e1#\u0026nbsp;\u003c/sup\u003e\u0026middot; Junxia Mao\u003csup\u003e1#\u0026nbsp;\u003c/sup\u003e\u0026middot; Fengxiao Lv\u003csup\u003e1\u003c/sup\u003e \u0026middot; Xubo Wang\u003csup\u003e1\u003c/sup\u003e \u0026middot; Ping He\u003csup\u003e1\u003c/sup\u003e \u0026middot; Linxuan Cai\u003csup\u003e1\u003c/sup\u003e \u0026middot; Longwei Dai\u003csup\u003e1\u003c/sup\u003e \u0026middot; Menghao Jia\u003csup\u003e1\u003c/sup\u003e \u0026middot; Ying Tian\u003csup\u003e1*\u0026nbsp;\u003c/sup\u003e\u0026middot; Zhenlin Hao\u003csup\u003e1*\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e*\u0026nbsp;Zhenlin Hao\u003c/p\u003e\n\u003cp\[email protected]\u003c/p\u003e\n\u003cp\u003e*\u0026nbsp;Ying Tian\u003c/p\u003e\n\u003cp\[email protected]\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eKey Laboratory of Mariculture and Stock Enhancement in North China\u0026rsquo;s Sea (Dalian Ocean University), Ministry of Agriculture, Dalian 116023, China\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e2\u003c/sup\u003ePresent address: College of Fisheries and Life Science, Dalian Ocean University, 52, Heishijiao Street, Shahekou District, Dalian, Liaoning Province, China\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAssis D F J H, Sanches R D A, De R S O, et al. 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BMC genomics 19 (1): 249. https://doi.org/10.1186/s12864-018-4644-7\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Neptunea arthritica cumingii Crosse, Preference for bait, Transcriptome, Metabolomics","lastPublishedDoi":"10.21203/rs.3.rs-6817321/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6817321/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e \u003cem\u003eNeptunea arthritica cumingii\u003c/em\u003e Crosse is a large carnivorous marine snail with high economic value and aquaculture potential in the northern waters of China. Due to its low feeding response to existing feeds and other limiting factors, large-scale artificial cultivation of this species remains unachieved. To address this limitation, we used behavioral observation to compare this snail\u0026rsquo;s preferences for five different baits, and we also analyzed the biochemical composition of the baits. We also conducted transcriptomic and metabolomic analyses of the snail\u0026rsquo;s water tubes and olfactory organs using RNA-sequencing and liquid chromatography tandem mass spectrometry to identify genes and metabolites associated with feeding and fasting states. The results showed significant differences in feeding preferences among the five baits, with the highest frequency observed for Ezo scallops (\u003cem\u003eMizuhopecten yessoensis\u003c/em\u003e) and the lowest for Korean rockfish (\u003cem\u003eSebastes schlegelii\u003c/em\u003e). Comparative analysis of the bait compositions revealed that L-glycine and L-glutamic acid might be key food attractants. We found that the differentially expressed genes and differential metabolites in the snails were enriched in nutrient-related pathways, including neuroactive ligand-receptor interactions, the phosphoinositide 3-kinase\u0026ndash;protein kinase B signaling pathway, and the oxytocin signaling pathway. After feeding on \u003cem\u003eM. yessoensis\u003c/em\u003e, differentially expressed genes were linked to appetite stimulation, increased feeding rate, and biosynthesis of phenylalanine, tyrosine, and tryptophan. In summary, we identified the preferred bait and potential food attractants for \u003cem\u003eN. cumingii\u003c/em\u003e, thereby establishing a theoretical basis for understanding its feeding regulation mechanism and developing artificial compound feeds, with both theoretical significance and practical application value.\u003c/p\u003e","manuscriptTitle":"Combined transcriptomic and metabolomic analyses revealed the mechanisms by which preferential baits regulate feeding and appetite responses in Neptunea arthritica cumingii Crosse","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-12 11:32:30","doi":"10.21203/rs.3.rs-6817321/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1f8d539e-b3dd-40ff-af27-b13a9602e082","owner":[],"postedDate":"June 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-07-07T12:38:37+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-12 11:32:30","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6817321","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6817321","identity":"rs-6817321","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}