Pheromone screening and neuro-endocrine regulation in turbot (Scophthalmus maximus) under different stocking density

Pheromone screening and neuro-endocrine regulation in turbot (Scophthalmus maximus) under different stocking density | 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 Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Pheromone screening and neuro-endocrine regulation in turbot (Scophthalmus maximus) under different stocking density Jiyuan Li, Yanfeng Wang, Teng Guo, Shihong Xu, Guang Gao, Feng Liu, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3244498/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 Pheromones play a vital role in regulating fish behavior, including reproduction, aggregation, hazard recognition, and food location. To gain a better understanding of chemical communication in fish produced by density changes, this study analyzed the metabolites released by turbot ( Scophthalmus maximus ) under different stocking density and investigated their effects on the neuroendocrine function of turbot. The experiment was conducted at low (LD: 3.01 kg/m 3 ), medium (MD: 6.62 kg/m 3 ), and high (HD: 10.84 kg/m 3 ) densities for 15 days. High-throughput non-targeted metabolomics (LC-MS/MS) was used to identify variations in metabolites released into the aquatic environment by turbot at different densities. Results showed that 29 and 47 metabolites were significantly upregulated in MD and HD groups, respectively, compared with the LD group. Among them, hexadecanedioic acid, xanthine, phenethylamine, proline, and styrene were significantly upregulated in MD vs LD, HD vs MD, and HD vs LD. The VIP diagram of OPLS-DA alignment showed that phenethylamine was the most important metabolite shared by MD vs LD, HD vs MD, and HD vs LD. To investigate the impact of phenethylamine on turbot, its concentration in the aquatic environment was set at 0 (CON), 10 − 7 (LP), 10 − 5 (HP) mol/l via exogenous addition, and turbot were exposed to these environments for 2 days. Key genetic changes in the GH/IGF-1 signaling pathway, HPI axis of turbot were studied using qRT-PCR for density treatment and phenethylamine treatment. The results demonstrated that the expression of GH, GHR, and IGF-1 was significantly lower, while the expression of CRH and ACTH was higher in the HD group. Additionally, plasma levels of cortisol, glucose, triglycerides, and T 3 were also highest in the HD group compared to the LD and MD groups and were positively correlated with density. In the phenethylamine treatment, there was a high degree of concordance between the GH/IGF-1 signaling pathway (GH, GHR, IGF-1), HPI axis (CRH, ACTH) and plasma physiological changes (cortisol, glucose, triglycerides, T 3 ) in the phenethylamine-treated group and the density-treated group. Thus, phenethylamine produced by turbot under high stocking density may act as a pheromone of density stress, and its effect is dose-dependent and trace effect. Pheromone Phenethylamine Metabolomics Scophthalmus maximus Stocking density Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction In natural environments, many organisms use chemical signals to learn information about their surroundings, especially aquatic organisms, which live in low light water environments where chemical signals are sometimes more critical than visual and auditory signal (Burnard et al., 2008 ; Sorensen and Wisenden, 2014 ). Lawrence and Smith study found that the alarm substance in the skin of a 1 cm 3 fathead minnows ( Pimephales promelas ) may produce more than 58 m 3 of active space(Lawrence and Smith, 1989). Mathuru et al. discovered that chondroitin sulfate can be perceived by zebrafish( Danio rerio ) olfaction and elicit significant startle responses(Mathuru et al., 2012 ). Kamio et al. isolated N-acetylglucosamino-1,5-lactone, a substance that causes courtship behavior in males, from the urine of mature molting female blue crabs ( Callinectes sapidus )(Kamio et al., 2014 ). Mounting evidence indicates that chemical signals exert influences on the reproduction, growth, and even survival of aquatic animals(Kamio and Derby, 2017 ). Recently, due to the gradual depletion of fishery resources, industrial recirculating aquaculture has been developing rapidly. Industrial recirculating aquaculture uses limited water and land resources, and raises the stocking density by controlling feed, water quality, environment and other factors, so as to achieve the purpose of improving aquaculture benefits (Lei et al., 2005 ). However, high stocking density is widely recognized as a stressor that affects the welfare of aquatic animals, and excessive or prolonged exposure to high stocking density can cause physiological disorders, immunosuppression, and growth inhibition in fish(Bolasina et al., 2006 ; Sadhu et al., 2014 ; Zaki et al., 2020 ). Numerous studies have demonstrated that high stocking density have a negative impact on the survival rate, antioxidant capacity and feed utilization efficiency of both marine and freshwater fish species(Ezhilmathi et al., 2022 ; Liu et al., 2017 ; Onxayvieng et al., 2021 ; Refaey et al., 2018 ). Density stress is commonly perceived as a consequence attributed to insufficiency in resources (e.g., low oxygen) or an excess accumulation of metabolites (e.g., high ammonia)(Qiang et al., 2018 ; Zaki et al., 2020 ). However, chemical communication under high stocking density has been largely neglected. Pheromone-related substances (such as stress hormones and chemical alarm signals) may accumulate with increasing fish stocking density(Ruane and Komen, 2003 ; van de Nieuwegiessen et al., 2009 ). For example, Ruane and Komen ( 2003 ) found that cortisol concentrations in water increased when the loading density of carp( Cyprinus carpio ) increased(Ruane and Komen, 2003 ). Pfuderer et al. indicated the potential existence of crowding factors, which are substances released by fish under crowded conditions that inhibit their growth and reproduction (Pfuderer et al., 1974 ). Roales study speculates that growth inhibitory factors released from crowded fish may affect the thyroid gland, leading to fat mobilization in the tissues and thus lowering total fat in these animals(Roales, 1981 ). Although most pheromones in fish remain unidentified, the few that are structurally determined are mainly low molecular metabolites such as bile salts, F-series prostaglandins, amino acids and gonadal steroids, which can be detected using metabolomics(Kawabata, 1993 ; Polkinghorne et al., 2001 ; Sorensen et al., 1990 ; Sorensen et al., 1988 ). Metabolomics is a systems approach to studying the small, endogenous metabolites in organism(Samuelsson and Larsson, 2008 ). It can detect changes in the metabolome brought on by external or internal stressors(Young and Alfaro, 2018 ). Because of its ability to perform high-throughput chemical analysis without the necessary purification steps, metabolomics (in addition to targeted screening and bioactivity-guided fractionation) has emerged as a novel approach to identify pheromones(Izrayelit et al., 2012 ; Kuhlisch and Pohnert, 2015 ; Lacalle-Bergeron et al., 2021 ). Density stress is a common phenomenon in aquaculture, yet the specific mechanisms underlying it remain unclear. Turbot, a demersal fish species, displays a preference for living on the seafloor and exhibits infrequent swimming behavior. While multilayer stacking can cause localized over-density at low stocking densities, it rarely leads to stress. However, as overall density increases, turbot growth and physiological status become significantly challenged. Pheromones may act as potential density stressors, and high stocking density may lead to a gradual accumulation of pheromones, resulting in stressful effects. Therefore, this study aimed to screen pheromone-related substances produced by turbot under high stocking density and analyze their effects on turbot's neuroendocrine function. 2. Materials and methods 2.1 Experimental system and experimental design The experiment was conducted at the Weihai Institute of Marine Biological Industry Technology, China, and fish treatment was approved by the Animal Protection and Utilization Committee of the Institute of Oceanography, Chinese Academy of Sciences. Turbot was obtained from Guoxin Oriental recirculating water culture base and reared in recirculating aquaculture systems (RAS) for 15 d to acclimatize to the experimental environment. The experimental area was equipped with three recirculating aquaculture systems, each comprising three replicated tanks (1 m 3 ), three whirl-separators, a mechanical microfilter, a protein separator, a decarbonization tower, a moving-bed biological filter, a UV disinfection. Healthy, active and non-traumatized turbot were selected for the experiment. Through pre-experiments, it was found that there was a significant difference between the small experimental system and the actual production of stocking density stress. As high as 14 kg/m 2 , serious stress already existed in the system, leading to death and food stoppage in the high-density group, so we reduced the density for the experiment according to the actual situation. Density experiments: A total of 510 fish (average individual weight 136.12 ± 27.71 g) were reared for 15 d under three stocking densities: low density (LD) with 25 fish per tank (3.01kg/m 2 at initial density), medium density (MD) with 55 fish per tank (6.62kg/m 2 at initial density), and high density (HD) with 90 fish per tank (10.84kg/m 2 at initial density). Each density was tested in triplicate. Phenethylamine treatment experiments: fish (6 per tank) were exposed to different concentrations of phenethylamine (mol/l): 0(control, CON), 10 − 7 (low phenethylamine, LP), 10 − 5 (high phenethylamine, HP) for 2 days. Each concentration was tested in triplicate. Fishes were fed a commercial pellet diet (53% crude protein, 12% crude lipids, 16.0% crude ash, 4.0% crude fiber, 12% water, 0.5% P, 2.3% lysine) at 0.5% feeding rate twice daily. Daily recordings of water parameters were taken at 09:00 am., including temperature, dissolved oxygen (DO), salinity and pH, using a Handheld Multi-Parameter Water Quality Analyzer (YSI Incorporated, Yellow Springs, Ohio, USA) and the content of total ammonia (TAN) and nitrite (NO 2− ) were measured using Nessler’s reagent colorimetric method, the N-1-Naphthylethylenediamine photometric method (GB 13580.7–92), respectively(Lin et al., 2018 ; Wu and Cao, 2013 ). During the experiment period, other water quality parameters were maintained at appropriate levels for turbot. Specifically, dissolved oxygen, pH, temperature, salinity, TAN, and NO 2− concentration varied between 7.01–7.12 mg/L, 7.41–7.46, 15.8–16.5 ℃, 29.63–31.56‰, 0.23–0.29 mg/L, 0.07–0.13 mg/L, respectively. The photoperiod was maintained at 12 h light/12 h dark. 2.2 Sample preparation To compare the composition of metabolites released into the aquatic environment by turbot at different densities (aquatic environment metabolome), we used a rational sample collection method. 100 L of water was collected from each replicate tank, rapidly filtered through a filter pump onto glass fiber filter paper, and then the membranes were stored at -80°C until extraction. After the experiment, all fish were fasted for 24 h. And then three fish were randomly collected from each culture tank (nine fish per group) and anesthetized with tricaine methane sulfonate (MS-222, Sigma Diagnostics INS, St. Louis, MO) at 40–45 mg/L. Blood was obtained from the tail vein using a syringe and collected in sodium heparin anticoagulation tubes. The collected blood samples were centrifuged at 3000 rpm for 10 minutes to obtain plasma, which was then stored at -80°C. Immediately after blood collection, the liver, hypothalamus and pituitary gland were removed from each fish, immediately frozen in liquid nitrogen and stored at -80 ℃ for gene expression analysis. 2.3 Determination of biochemical parameters Plasma glucose, triglyceride, cortisol and triiodothyronine(T 3 ) levels in fish were measured using commercial kits (#F006-1-1, #A110-2-1, #H094-1-1 and #H222-1-1) according to the instructions for use. All commercialized kits were purchased from Nanjing Jiancheng Institute of Biological Engineering. 2.4 RNA extraction and qPCR Total RNA was isolated from liver, hypothalamus and pituitary gland samples using the TRIzol reagent (Trans-Gen Biotech, Beijing, China). RNA concentration and purity were measured using a Nanodrop 2000 spectrophotometer (Gene Company Limited, Hong Kong, China), and purity was calculated using the 260/280 nm optical density ratio (purity: 2.0 ± 0.1). Reverse transcription of RNA into cDNA was then performed using the Evo M-MLV Mix Kit (Hunan Accurate Biomedical Technology Co., China). Primers were designed using Primer Premier 5.0 and NCBI online website. The primer sequences used are listed in Table 1 . The qPCR was conducted using a SYBR Green Premix Pro Taq HS qPCR Kit (Hunan Accurate Biomedical Technology Co., China), with a 20 µL reaction solution on a CFX Connet Real-Time PCR System (Bio-Rad, China). CRH, GH, IGF-1, and GHR thermal cycling conditions were 95◦ C for 15 min, followed by 35 cycles of 95◦ C for 15 s and 58◦ C for 60 s. ACTH thermal cycling conditions were 95◦ C for 15 min, followed by 35 cycles of 95◦ C for 15 s and 60◦ C for 60 s. Amplification specificity was validated by melting curve analysis. The Pfaffl method(Pfaffl, 2001 ) was used for calculations. Table 1 List of primers used for quantitative real-time PCR analysis Gene name Primer sequence(5’-3’) Annealing temperature(℃) Amplic on size(bp) IGF-1 F: TCGTGGACGAGTGCTGCTT R: CCGCCTTGCTAGTCTTGG 58 81 ACTH F: TGTGGCTATTAGTGGCTGTGG R: CCTGGCAGTTCGGATTCTC 60 81 GH F: AATAACCACGAGACACAACGCA R: GAGAACTCCCAAGACTCAACCAA 58 80 CRH F: CCTCCTCTAACGATTGAAGATTCC R: AGGGCTGTCAATAGCTCGAC 58 123 GHR F: ACACGTCCATTTGGATCCCC R: GCTCCCAGTTGACCATGACA 58 183 β-actin F: TGAACCCCAAAGCCAACAGG R: GAGGCATACAGGGACAGCAC 107 2.5 Metabolite extraction and UHPLC-MS/MS analysis For metabolite extraction, take the filter membrane sample in an EP tube, add 1000µL of 80% methanol aqueous solution, put it into liquid nitrogen for 5 minutes; thaw on ice, vortex for 30 seconds, sonicate for 6 min, centrifuge for 1 min at 5000rpm and 4°C, take the supernatant into a new centrifuge tube, lyophilize into dry powder, add 60µL of 10% methanol solution to dissolve, and feed into LC-MS for analysis. UHPLC-MS/MS analyses were performed using a Vanquish UHPLC system (ThermoFisher, Germany) coupled with an Orbitrap Q Exactive ™ HF-X mass spectrometer (Thermo Fisher, Germany) in Gene Denovo Co., Ltd. (Guangzhou, China). Samples were injected onto a Hypesil Gold column (100×2.1 mm, 1.9µm) using a 17-min linear gradient at a flow rate of 0.2mL/min. The eluents for the positive polarity mode were eluent A (0.1% FA in Water) and eluent B (Methanol).The eluents for the negative polarity mode were eluent A (5 mM ammonium acetate, pH 9.0) and eluent B (Methanol).The solvent gradient was set as follows: 2% B, 1.5 min; 2-100% B, 12.0 min; 100% B, 14.0 min;100-2% B, 14.1 min༛2% B, 17 min. Q Exactive TM HF-X mass spectrometer was operated in positive/negative polarity mode with spray voltage of 3.2 kV, capillary temperature of 320°C, sheath gas flow rate of 40 arb and aux gas flow rate of 10 arb. 2.6 Data processing and metabolite identification The raw data files generated by UHPLC-MS/MS were processed using the Compound Discoverer 3.1 (CD3.1, Thermo Fisher) to perform peak alignment, peak picking, and quantitation for each metabolite. The main parameters were set as follows: retention time tolerance, 0.2 minutes; actual mass tolerance, 5ppm; signal intensity tolerance, 30%; signal/noise ratio, 3; and minimum intensity, 100,000. After that, peak intensities were normalized to the total spectral intensity. The normalized data was used to predict the molecular formula based on additive ions, molecular ion peaks and fragment ions. And then peaks were matched with the mzCloud ( https://www.mzcloud.org/ ), mz Vaultand Mass Listdatabase to obtain the accurate qualitative and relative quantitative results. Statistical analyses were performed using the statistical software R (R version R-3.4.3), Python (Python 2.7.6 version) and CentOS (CentOS release 6.6). When data were not normally distributed, normal transformations were attempted using of area normalization method. 2.7 Statistical analysis and pathway analysis The collected metabolites were annotated using the Human Metabolome database ( http://www.hmdb.ca/ ) and the KEGG database ( http://www.genome.jp/kegg/ ). The R package gmodels was used to perform principal component analysis (PCA) analysis on the data, and the R language ropls package was used to perform supervised orthogonal partial least squares-discriminant analysis (OPLS-DA).The OPLS-DA model was further validated by cross-validation and permutation test. For cross-validation, the data was partitioned into seven subsets, where each of the subsets was then used as a validation set. A variable importance in projection (VIP) score of (O)PLS model was applied to rank the metabolites that best distinguished between two groups. The threshold of VIP was set to 1. In addition, T-test was also used as a univariate analysis for screening differential metabolites. Those with a p value of T test <0.05 and VIP ≥ 1 were considered differential metabolites between two groups. Statistical analyses comprised one-way ANOVA, followed by Tukey’s test, using IBM SPSS software (version 20.0) to examine significant differences between the groups. A significance level of P < 0.05 was used in all analyses. All data are shown as the means ± standard error (S.E.) of the treatments. 3. Result 3.1 Metabolomics principal component analysis (PCA) at different densities As a non-supervised multivariate data analysis method, PCA is always used to give a comprehensive view of the clustering trend for the multidimensional data(Gao et al., 2013 ). To screen for characteristic metabolites with significant concentration changes, the PCA approach was utilized to conduct a model with the ES + and ES − data, respectively. Unsupervised PCA showed LD and MD were significantly differentiated, and their contribution rates were 33.7% and 27.3% (POS), 31.7% and 26% (NEG), respectively (Fig. 1 ); LD and HD were significantly differentiated, and their contribution rates were 39.2% and 30.7% (POS), 58.1% and 10.9% (NEG), respectively (Fig. 1 ); MD and HD were significantly differentiated, and their contribution rates were 35.8% and 27% (POS), 47.1% and 18.9% (NEG), respectively (Fig. 1 ). It indicated that significant changes in aquatic environment metabolome occurred at different densities. To maximize the discrimination between different densities treatments, we employed OPLS-DA to identify differences in metabolite. In the positive ion mode, the OPLS-DA score plots of LDvsMD、LDvsHD、MDvsHD had the cumulative values of R2X being 76.2%, 75.8%, 55.2%, R2Y being 89.3%, 95.5%, 98.3% and Q2 being 76.8%, 63.5%, 70.5%, respectively (Table 2 ). In negative ion mode, the OPLS-DA score plots of LDvsMD、LDvsHD、MDvsHD had the cumulative values of R2X being 70.9%, 94.0%, 87.2%, R2Y being 92.4%, 98.4%, 98.3% and Q2 being 76.9%, 96.7%, 90.2%, respectively (Table 2 ). R2X and R2Y denote the explanation rate of the proposed model for X and Y matrices respectively, and Q2 denotes the predictive ability of the model. The closer the three indicators are to 1, the more stable and reliable the model is. Q2 > 0.5 indicates that the model has good predictive ability. To evaluate the accuracy of the OPLS model, we used the permutation test for verification. As shown in Fig. 2 , The intersection of the regression line at point Q2 with the vertical coordinate is < 0, indicating that the model prediction is reliable. The data and instrumental analysis system of this study are reliable and stable. Table 2 OPLS-DA model validation parameters Comparison group name R2X R2Y Q2 LD-vs-MD.POS 0.762 0.893 0.768 LD-vs-MD.NEG 0.709 0.924 0.769 LD-vs-HD.POS 0.758 0.955 0.635 LD-vs-HD.NEG 0.94 0.984 0.967 MD-vs-HD.POS 0.552 0.983 0.705 MD-vs-HD.NEG 0.872 0.983 0.902 3.2 Differential metabolites at different densities In the positive ion mode, there were 26 significant differential metabolites (SDMs) (upregulated (up):15, downregulated (down):11) in LDvsMD group, 51 SDMs (up:33, down:18) in LDvsHD group and 50 SDMs (up:34, down:16) in MDvsHD group (Fig. 3 ). 8 SDMs could be identified in LDvsMD, LDvsHD and MDvsHD group, among them, the contents of phenethylamine, proline and styrene increased with the increase of culture density (Fig. 3 ). In the negative ion mode, there were 27 SDMs (up:14, down:13) in LDvsMD group, 17 SDMs (up:14, down:3) in LDvsHD group and 11 SDMs (up:9, down:2) in MDvsHD group (Fig. 3 ). 2 SDMs could be identified in LDvsMD, LDvsHD and MDvsHD group, among them, the contents of xanthine and hexadecanedioic acid increased with the increase of culture density (Fig. 3 ). Variable importance in projection (VIP) scores ranked by partial least square discriminant analysis (PLS-DA) was shown in Fig. 4 . The top 3 most important metabolites were gamma-glutamylleucine, phenethylamine and N-benzylformamide in LDvsMD group. The top 3 most important metabolites were oleamide, arachidonoyl amide and phenethylamine in LDvsHD group. The top 3 most important metabolites were Phenethylamine, guanine and 2-amino-1,3,4-octadecanetriol in MDvsHD group. 3.3 GH/IGF-1 signaling pathway To investigate the effect of density stress and phenethylamine treatment on the function of GH/IGF-1 signaling pathway, gene expression levels of GH in the pituitary gland and GHR and IGF-1 in the liver of turbot were analyzed. The results of the density treatment showed that GH mRNA levels were significantly down-regulated ( P < 0.05) in the MD (0.47-fold) and HD groups (0-fold) compared to the LD group. Similar trends were observed for GHR expression in the liver of turbot under different density treatments. In addition, the expression of IGF-1, another key gene located downstream of the GH/IGF-1 signaling pathway, was also significantly lower ( P < 0.05) in the MD (0.67-fold) and HD (0.63-fold) groups than in the LD group. In phenethylamine treatment, a significant decrease in GH, GHR, and IGF-1 gene expression was observed in the LP and HP groups when compared to the CON group ( P < 0.05). GH expression was almost undetectable in the LP and HP groups. 3.4 HPI axis To assess the effect of density stress and phenethylamine treatment on the HPI axis of turbot, we measured the abundance of key genes (CRH and ACTH) and cortisol levels. The density treatment results revealed that the expression levels of CRH and ACTH genes increased with increasing density, and the HD group treatment exhibited a significantly higher expression than the LD group (1.65 and 1.63 times greater, respectively) ( P < 0.05). Furthermore, plasma cortisol levels at the end of the HPI axis were significantly higher in the HD group (18.67 ± 0.16 ng/mL) than in the MD group (16.33 ± 0.14 ng/mL) and LD group (16.17 ± 0.41 ng/mL) ( P < 0.05). In the phenethylamine treatment, the expression levels of CRH and ACTH genes increased with increasing phenethylamine concentration. Plasma cortisol levels at the end of the HPI axis were significantly higher in the HP group (27.95 ± 0.21 ng/mL) than in the LP group (25.98 ± 0.57 ng/mL) and the CON group (24.64 ± 0.07 ng/mL). 3.5 Physiological response of turbot plasma As shown in Fig. 7 , the effects of density stress and phenethylamine treatment on T 3 , glucose, and triglycerides. In the density treatment, plasma glucose, triglyceride, and T3 levels were significantly higher in the HD group than in the LD group ( P < 0.05), while there were no significant differences between the MD and LD groups. In phenethylamine treatment, the levels of plasma glucose, triglycerides and T 3 were significantly higher in the LP and HP groups than in the LD group ( P < 0.05), while there were no significant differences between the LP and HP groups. 4. Discussion Pheromones are chemical substances that are secreted outwardly by organisms into their surroundings and received by the same species to influence organisms' behavioral habits, growth, development and so on activities. Scott et al. found that spermine in the semen of male sea lamprey( Petromyzon marinus ) acts as a sex pheromone and attracts ovulating females(Scott et al., 2019 ). Zhu et al. showed that Large Yellow Croaker ( Larimichthys crocea ) are attracted to gut contents from conspecifics(Zhu et al., 2023 ). Pheromones can be involved in fish reproduction, migration, alarm and other behaviors that are important to the life of aquatic organisms(Kamio and Derby, 2017 ). Hexadecanedioic acid, xanthine, phenethylamine, proline and styrene in the aquatic metabolic group in this study were significantly different in MDvsLD, HDvsMD, HDvsLD, and the levels increased significantly with increasing density. Among them, hexadecanedioic acid and styrene are insoluble in water, and xanthine, phenethylamine and proline are soluble in water. Solubility determines the spatial extent of the pheromone, and since substances dissolved in water are more likely to diffuse in an aquatic environment, proline, xanthine, and phenethylamine are more likely to act as pheromones. Amino acids play a role in chemical communication as one of the few identified fish pheromones. Yambe et al. identified l-Kynurenine as a sex pheromone in the urine of ovulated female masu salmon( Oncorhynchus masou )(Yambe et al., 2006 ). Shoji et al. study finds that amino acids in stream water are essential for salmon ( Oncorhynchus keta )homing migration(Shoji et al., 2003 ). However, it is important to note that the proline screened in this study was D-proline, which has been temporarily excluded as a natural pheromone. On the other hand, xanthine, as a typical purine and an important biomolecule, plays a crucial role in purine catabolic reactions(Liu et al., 2023 ). Xanthine can be converted to uric acid by the action of xanthine oxidase, and high levels of uric acid are associated with gout(Wu et al., 2021 ; Zhong et al., 2022 ). Therefore, the level of xanthine mainly indicates the health status of an organism and its levels in serum or urine can provide valuable information for the diagnosis and medical treatment of certain metabolic disorders(Pundir and Devi, 2014 ). Phenethylamine is an endogenous amine compound that can play an important biological role in the nervous system as a chemical messenger(Boulton, 1980 ; Branchek and Blackburn, 2003 ; Premont et al., 2001 ). Low concentrations of phenethylamine produce euphoria, but high concentrations of phenethylamine may form neurotoxic compounds(Edwards and Blau, 1973 ). Plasma phenethylamine concentrations are associated with stress, but they are quickly metabolized by monoamine oxygenase (MAO)(Grimsby et al., 1997 ; Paulos and Tessel, 1982). Studies have reported that the half-life of phenethylamine is very short in dogs (1.8-3 min) and is rapidly distributed and eliminated in rats after intravenous administration(Cone et al., 1978 ; Wu and Boulton, 1975). In the absence of any treatment, phenethylamine levels in organisms' tissues are very low (Durden and Boulton, 1982). In the external environment, phenethylamine can also act as chemical cues to regulate individual animal behavior. Ferrero et al found that phenethylamine induced strong avoidance responses in rodent and herbivore species(Ferrero et al., 2011 ). Imre et al. showed that sea lamprey ( Petromyzon marinus also) showed a strong avoidance response to phenethylamine(Imre et al., 2014 ). Bredy and Barad showed that phenethylamine can act as a pheromone to communicate information about fear or threats(Bredy and Barad, 2009 ). Phenethylamine normally binds to the vertebrate trace amine-associated receptor (TAAR), and its perception by the TAAR4 olfactory receptor leads to an increase in intracellular cAMP levels. cAMP directly activates cyclic nucleotide-gated channels (CNG channels) to allow the entry of Na + and Ca 2+ , depolarizing olfactory sensory neurons (OSN) to generate action potentials and converting chemical signals into electrical signals(Mombaerts et al., 1996 ; Xu and Li, 2020).The electrical signal is transmitted to brain regions to produce olfactory perception, thus enabling the transmission of information(Lindemann and Hoener, 2005 ). Phenethylamine levels have been shown to increase in the urine of stressed animals(Paulos and Tessel, 1982; Snoddy et al., 1985 ). In this study, the levels of phenethylamine were found to increase with increasing density. Subsequently, we examined changes in key genes of the HPI axis and GH/IGF-1 signaling pathway as well as physiological indicators in turbot after density treatment and phenethylamine treatment and performed correlation analysis. The HPI axis plays an important role in the response of fish to environmental stresses(Rotllant et al., 2000 ). In a stressful state, the HPI axis is first activated, causing the body to release large amounts of cortisol in response to the stressor(Barton, 2002 ; Yusishen et al., 2020 ). Excessive cortisol will induce secondary and tertiary stress responses, resulting in physiological and other functional disorders in fish (Van Der Boon et al., 1991 ). Bi et al. found that the serum ACTH and cortisol levels of hybrid sturgeon (♀ Acipenser baerii ×♂ Acipenser schrenckii )increased with increasing stocking density (Bi et al., 2023 ). Wang et al. found that a stocking density of 24 kg/m 3 for 220 days resulted in a significant increase in plasma cortisol levels in Atlantic salmon when compared to a density of 6 kg/m 3 (Wang et al., 2019 ). In addition, changes in other environmental factors, such as ammonia exposure, nitrate exposure, and pathogenic infections, can also upregulate CRH, ACTH genes, and plasma cortisol in fish(Jia et al., 2017 ; Madison et al., 2013 ; Yu et al., 2021 ). This study also showed that plasma cortisol was significantly higher in the HD group compared to the MD and LD groups, while the expression levels of CRH and ACTH genes also increased with increasing density. Similarly, plasma cortisol was significantly higher in the HP group compared to the LP and CON groups, while the expression levels of CRH and ACTH genes also increased with increasing phenethylamine concentrations. The similarity between the phenethylamine treatment and density treatment suggests that high stocking density may activate the turbot HPI axis through phenethylamine, leading to a stress response. Under stress, fish release large amounts of cortisol, which increases the metabolism of carbohydrates, fats and proteins and controls the flow of energy in the organism in response to the stressor (Barton, 2002 ; Flik et al., 2006 ; Mommsen et al., 1999 ; Shepherd et al., 2018 ). Plasma glucose, lactate, and triglycerides were significantly elevated in both HD and HP groups in this study, which may be due to energy mobilization by the organism to resist the unfavorable external environment, corroborating with the above results. In teleost fish, the GH/IGF-1 signaling pathway regulates a variety of physiological functions, such as growth, reproduction, immunity, and osmoregulation(Blanco, 2020 ; Canosa and Bertucci, 2023 ; Pérez-Sánchez et al., 2018 ). GH levels in fish are positively associated with growth in vivo. However, under prolonged stress conditions, GH levels in fish can fluctuate, disrupting the function of the GH/IGF-1 signaling pathway. Environmental factors such as temperature, salinity, density, and other breeding-induced discomfort can cause a decrease in fish IGF-1 levels, often resulting in an impact on fish growth and development (Davis and Peterson, 2006 ; Deane et al., 2002 ; Seo and Park, 2022 ). The present study found that both GH and IGF-1 were significantly downregulated in the HD and HP groups, suggesting the inhibitory effects of high density and phenethylamine treatment on turbot growth. Thyroid hormones (TH) have also been shown to play a crucial regulatory role in fish growth, often working synergistically with other hormones(Power et al., 2001 ; Xie et al., 2015 ). In fish, TH exerts its biological function mainly through the formation of T 3 (Yamano, 2005 ). Therefore, we evaluated the T 3 levels in turbot plasma. The results showed that T 3 increased with the increase of density. Ardiansyah and Fotedar found that the T 3 of juvenile barramundi (Lates calcarifer Bloch ) decreased gradually with the increase of culture density(Ardiansyah and Fotedar, 2016). This may be due to the short duration of density treatment (15 days), where the fish are at an early level of stress and T 3 is elevated to promote energy metabolism in response to the unfavorable environment. T 3 also increased with increasing phenethylamine concentration in the phenethylamine treatment. The effects of density treatment and phenethylamine treatment on turbot growth were also highly consistent. Interestingly, in our study, GH gene expression was almost absent in both the HD and HP groups, but IGF-1 gene expression was still present. This may be because the HD and HP groups promoted IGF-1 expression by elevated T 3 acting on the liver. It has been shown that T3 increases IGF-1 mRNA expression and stimulates the release of IGF-1.(Pepene et al., 2001 ; Robson et al., 2002 ). Phenethylamine concentration was strongly correlated with stocking density, and the effects of phenethylamine treatment and density treatment on the HPI axis, GH/IGF-1 signaling pathway, and key physiological indicators (cortisol, T 3 , glucose, triglycerides) were highly similar. In the present study, phenethylamine was detected at LD, MD and HD, but only the turbot in the HD group produced significant stress and growth inhibition. This suggests that the effects of phenethylamine (harmful or beneficial) are dose dependent under specific conditions. In the phenethylamine treatment experiment, even the LP group (10 − 7 mol/l) had a significant negative effect on turbot, which indicates that phenethylamine has a trace effect. 5. Conclusion In summary, phenethylamine produced by turbot under high density may act as a pheromone to signal crowding stress, and phenethylamine has dose-dependent and trace effects. High doses of phenethylamine cause disruptions in neuroendocrine function (GH/IGF-1 signaling pathway, HPI axis) and physiology in turbot. The findings of this study provide new ideas for further exploration of density stress mechanisms. Declarations Acknowledgements This work was financially supported by China Agriculture Research System (CARS-47-G21). Special thanks to China Weihai Institute of Marine Biotechnology (Zhe Liu, Xiaoyang Ma, Jialin Li, Jingqiang Yang) for their support. References Ardiansyah, Fotedar, R., 2016. Water quality, growth and stress responses of juvenile barramundi (Lates calcarifer Bloch), reared at four different densities in integrated recirculating aquaculture systems. Aquaculture 458, 113-120. https://doi.org/10.1016/j.aquaculture.2016.03.001 Barton, B.A., 2002. 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Intestinal Bile Acids Induce Behavioral and Olfactory Electrophysiological Responses in Large Yellow Croaker (Larimichthys crocea). Fishes 8(1), 26. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About In Review Editorial Policies 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-3244498","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":225117380,"identity":"b4cdb82a-b06e-4ac9-aad0-a1585ce63abc","order_by":0,"name":"Jiyuan Li","email":"","orcid":"","institution":"Institute of Oceanology","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jiyuan","middleName":"","lastName":"Li","suffix":""},{"id":225117382,"identity":"4f5017e2-8f20-4e58-9e3b-4bd12986e5b6","order_by":1,"name":"Yanfeng Wang","email":"","orcid":"","institution":"Institute of Oceanology","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yanfeng","middleName":"","lastName":"Wang","suffix":""},{"id":225117383,"identity":"81d570ed-dbd4-47fe-b40d-60a1e7a07c12","order_by":2,"name":"Teng Guo","email":"","orcid":"","institution":"Qingdao Agricultural University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Teng","middleName":"","lastName":"Guo","suffix":""},{"id":225117385,"identity":"549f4a04-d4f8-4e69-9a12-12967c0664f5","order_by":3,"name":"Shihong Xu","email":"","orcid":"","institution":"Institute of Oceanology","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Shihong","middleName":"","lastName":"Xu","suffix":""},{"id":225117386,"identity":"54d52605-477c-44a4-bfa7-bf48e3ce4fb4","order_by":4,"name":"Guang Gao","email":"","orcid":"","institution":"Institute of Oceanology","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Guang","middleName":"","lastName":"Gao","suffix":""},{"id":225117387,"identity":"07d7e4e0-80a6-4131-a34a-46b4094a3447","order_by":5,"name":"Feng Liu","email":"","orcid":"","institution":"Institute of Oceanology","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Feng","middleName":"","lastName":"Liu","suffix":""},{"id":225117388,"identity":"5c28f7b3-4e26-4d69-a620-4fe564d7564a","order_by":6,"name":"Xiaoyang Guo","email":"","orcid":"","institution":"Qingdao Agricultural University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xiaoyang","middleName":"","lastName":"Guo","suffix":""},{"id":225117389,"identity":"db8d09e5-7966-4088-9a68-f1e90b0a48bb","order_by":7,"name":"Yanduo Wu","email":"","orcid":"","institution":"Institute of Oceanology","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yanduo","middleName":"","lastName":"Wu","suffix":""},{"id":225117390,"identity":"49b0c5b4-7382-45c0-9f24-b03eedc60392","order_by":8,"name":"Haixia Zhao","email":"","orcid":"","institution":"Institute of Oceanology","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Haixia","middleName":"","lastName":"Zhao","suffix":""},{"id":225117391,"identity":"df0d7c26-7858-4aed-a1b2-8b08238b68f3","order_by":9,"name":"Jun Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAs0lEQVRIiWNgGAWjYBACxgYGxgMJFQyMbaRoYTiQcIYULSBwAKgepJVIwDwj98CBh/OsZfvYGxg//GCwyyPssBl5CQcSt6Ubt/EcYJbsYUguJkJLjgFQy+HENokEBmmgIxMJuhCiZQ5YC/NvErQ0gLWwEWlLzxuDAwnHQH452GbZY5BMWIthe47hwx811rLz25sP3/hRYUeEFogKZgZIrBoQUg8E8gxwLaNgFIyCUTAKcAAANBg+/U3kePQAAAAASUVORK5CYII=","orcid":"","institution":"Institute of Oceanology","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Jun","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2023-08-08 07:44:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3244498/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3244498/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":41533973,"identity":"eb9724cd-2e3f-4352-941f-be260cbbb961","added_by":"auto","created_at":"2023-08-14 13:29:16","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":96747,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis result of PCA. The top and bottom are for PAC under positive and negative ion modes, respectively.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-3244498/v1/baa505acbd03f1f20e4a42cb.png"},{"id":41535664,"identity":"7d3ed217-57ae-464e-9b16-d7a44664c556","added_by":"auto","created_at":"2023-08-14 13:37:16","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":183223,"visible":true,"origin":"","legend":"\u003cp\u003eOPLS-DA replacement test charts. The top and bottom are for OPLS-DA replacement test charts under positive and negative ion modes, respectively.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-3244498/v1/84a9f5600a6de2c7437679e3.png"},{"id":41533972,"identity":"3e8933f8-e441-4d77-9f6d-115fe060550f","added_by":"auto","created_at":"2023-08-14 13:29:16","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":169459,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification of significantly differentially abundant metabolites. On the left are the abundant metabolites with significant differences in the positive ion mode, and on the right are the abundant metabolites with significant differences in the negative ion mode.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-3244498/v1/654d77e983544797351d7a6a.png"},{"id":41533977,"identity":"dfed8449-1c55-414a-a9fc-ebbe5003e144","added_by":"auto","created_at":"2023-08-14 13:29:16","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":403893,"visible":true,"origin":"","legend":"\u003cp\u003eVIP score plot in density treatment. Variable importance in Projection (VIP) scores for the top 18 metabolites. VIP scores are derived form a partial least squares discriminant analysis (PLS-DA) model and colored boxed on the right indicate the relative abundance of the corresponding metabolite in each group.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-3244498/v1/de012aadb2724f0a802a1ee5.png"},{"id":41536941,"identity":"b5b1589b-16fb-46db-8b95-3ed53723519c","added_by":"auto","created_at":"2023-08-14 13:45:16","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":99685,"visible":true,"origin":"","legend":"\u003cp\u003eRelative expression of GH in the pituitary and GHR and IGF-1 in the liver of turbot by density and phenethylamine treatment.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-3244498/v1/48afe2c9a86f0391f42340e1.png"},{"id":41533980,"identity":"94529cbe-3170-41ba-9ab7-328e07d0c019","added_by":"auto","created_at":"2023-08-14 13:29:16","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":72219,"visible":true,"origin":"","legend":"\u003cp\u003eRelative expression of hypothalamic CRH and pituitary ACTH in turbot by density and phenethylamine treatment.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-3244498/v1/0e64116ff8e59602d8c74c3f.png"},{"id":41535663,"identity":"e651d294-9af3-4578-b25c-53444ea624cf","added_by":"auto","created_at":"2023-08-14 13:37:16","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":92500,"visible":true,"origin":"","legend":"\u003cp\u003eEffect of density and phenethylamine treatment on physiological parameters of turbot plasma\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-3244498/v1/60914f3bdcd551c2e7dedd86.png"},{"id":42136513,"identity":"f0e1bf8c-fa60-4fd5-ad9e-9afd3b242bbb","added_by":"auto","created_at":"2023-08-25 13:07:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1374300,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3244498/v1/f2bcb7a3-d5e8-4c7c-b221-0a10970e7f12.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Pheromone screening and neuro-endocrine regulation in turbot (Scophthalmus maximus) under different stocking density","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eIn natural environments, many organisms use chemical signals to learn information about their surroundings, especially aquatic organisms, which live in low light water environments where chemical signals are sometimes more critical than visual and auditory signal (Burnard et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Sorensen and Wisenden, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Lawrence and Smith study found that the alarm substance in the skin of a 1 cm\u003csup\u003e3\u003c/sup\u003e fathead minnows (\u003cem\u003ePimephales promelas\u003c/em\u003e) may produce more than 58 m\u003csup\u003e3\u003c/sup\u003e of active space(Lawrence and Smith, 1989). Mathuru et al. discovered that chondroitin sulfate can be perceived by zebrafish(\u003cem\u003eDanio rerio\u003c/em\u003e) olfaction and elicit significant startle responses(Mathuru et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Kamio et al. isolated N-acetylglucosamino-1,5-lactone, a substance that causes courtship behavior in males, from the urine of mature molting female blue crabs (\u003cem\u003eCallinectes sapidus\u003c/em\u003e)(Kamio et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Mounting evidence indicates that chemical signals exert influences on the reproduction, growth, and even survival of aquatic animals(Kamio and Derby, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRecently, due to the gradual depletion of fishery resources, industrial recirculating aquaculture has been developing rapidly. Industrial recirculating aquaculture uses limited water and land resources, and raises the stocking density by controlling feed, water quality, environment and other factors, so as to achieve the purpose of improving aquaculture benefits (Lei et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). However, high stocking density is widely recognized as a stressor that affects the welfare of aquatic animals, and excessive or prolonged exposure to high stocking density can cause physiological disorders, immunosuppression, and growth inhibition in fish(Bolasina et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Sadhu et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Zaki et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Numerous studies have demonstrated that high stocking density have a negative impact on the survival rate, antioxidant capacity and feed utilization efficiency of both marine and freshwater fish species(Ezhilmathi et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Onxayvieng et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Refaey et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Density stress is commonly perceived as a consequence attributed to insufficiency in resources (e.g., low oxygen) or an excess accumulation of metabolites (e.g., high ammonia)(Qiang et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Zaki et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, chemical communication under high stocking density has been largely neglected. Pheromone-related substances (such as stress hormones and chemical alarm signals) may accumulate with increasing fish stocking density(Ruane and Komen, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; van de Nieuwegiessen et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). For example, Ruane and Komen (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2003\u003c/span\u003e) found that cortisol concentrations in water increased when the loading density of carp(\u003cem\u003eCyprinus carpio\u003c/em\u003e) increased(Ruane and Komen, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Pfuderer et al. indicated the potential existence of crowding factors, which are substances released by fish under crowded conditions that inhibit their growth and reproduction (Pfuderer et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e1974\u003c/span\u003e). Roales study speculates that growth inhibitory factors released from crowded fish may affect the thyroid gland, leading to fat mobilization in the tissues and thus lowering total fat in these animals(Roales, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e1981\u003c/span\u003e). Although most pheromones in fish remain unidentified, the few that are structurally determined are mainly low molecular metabolites such as bile salts, F-series prostaglandins, amino acids and gonadal steroids, which can be detected using metabolomics(Kawabata, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1993\u003c/span\u003e; Polkinghorne et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Sorensen et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e1990\u003c/span\u003e; Sorensen et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e1988\u003c/span\u003e). Metabolomics is a systems approach to studying the small, endogenous metabolites in organism(Samuelsson and Larsson, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). It can detect changes in the metabolome brought on by external or internal stressors(Young and Alfaro, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Because of its ability to perform high-throughput chemical analysis without the necessary purification steps, metabolomics (in addition to targeted screening and bioactivity-guided fractionation) has emerged as a novel approach to identify pheromones(Izrayelit et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Kuhlisch and Pohnert, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Lacalle-Bergeron et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDensity stress is a common phenomenon in aquaculture, yet the specific mechanisms underlying it remain unclear. Turbot, a demersal fish species, displays a preference for living on the seafloor and exhibits infrequent swimming behavior. While multilayer stacking can cause localized over-density at low stocking densities, it rarely leads to stress. However, as overall density increases, turbot growth and physiological status become significantly challenged. Pheromones may act as potential density stressors, and high stocking density may lead to a gradual accumulation of pheromones, resulting in stressful effects. Therefore, this study aimed to screen pheromone-related substances produced by turbot under high stocking density and analyze their effects on turbot's neuroendocrine function.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Experimental system and experimental design\u003c/h2\u003e \u003cp\u003eThe experiment was conducted at the Weihai Institute of Marine Biological Industry Technology, China, and fish treatment was approved by the Animal Protection and Utilization Committee of the Institute of Oceanography, Chinese Academy of Sciences. Turbot was obtained from Guoxin Oriental recirculating water culture base and reared in recirculating aquaculture systems (RAS) for 15 d to acclimatize to the experimental environment. The experimental area was equipped with three recirculating aquaculture systems, each comprising three replicated tanks (1 m\u003csup\u003e3\u003c/sup\u003e), three whirl-separators, a mechanical microfilter, a protein separator, a decarbonization tower, a moving-bed biological filter, a UV disinfection. Healthy, active and non-traumatized turbot were selected for the experiment.\u003c/p\u003e \u003cp\u003eThrough pre-experiments, it was found that there was a significant difference between the small experimental system and the actual production of stocking density stress. As high as 14 kg/m\u003csup\u003e2\u003c/sup\u003e, serious stress already existed in the system, leading to death and food stoppage in the high-density group, so we reduced the density for the experiment according to the actual situation. Density experiments: A total of 510 fish (average individual weight 136.12\u0026thinsp;\u0026plusmn;\u0026thinsp;27.71 g) were reared for 15 d under three stocking densities: low density (LD) with 25 fish per tank (3.01kg/m\u003csup\u003e2\u003c/sup\u003e at initial density), medium density (MD) with 55 fish per tank (6.62kg/m\u003csup\u003e2\u003c/sup\u003e at initial density), and high density (HD) with 90 fish per tank (10.84kg/m\u003csup\u003e2\u003c/sup\u003e at initial density). Each density was tested in triplicate.\u003c/p\u003e \u003cp\u003ePhenethylamine treatment experiments: fish (6 per tank) were exposed to different concentrations of phenethylamine (mol/l): 0(control, CON), 10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e (low phenethylamine, LP), 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e(high phenethylamine, HP) for 2 days. Each concentration was tested in triplicate.\u003c/p\u003e \u003cp\u003eFishes were fed a commercial pellet diet (53% crude protein, 12% crude lipids, 16.0% crude ash, 4.0% crude fiber, 12% water, 0.5% P, 2.3% lysine) at 0.5% feeding rate twice daily. Daily recordings of water parameters were taken at 09:00 am., including temperature, dissolved oxygen (DO), salinity and pH, using a Handheld Multi-Parameter Water Quality Analyzer (YSI Incorporated, Yellow Springs, Ohio, USA) and the content of total ammonia (TAN) and nitrite (NO\u003csup\u003e2\u0026minus;\u003c/sup\u003e) were measured using Nessler\u0026rsquo;s reagent colorimetric method, the N-1-Naphthylethylenediamine photometric method (GB 13580.7\u0026ndash;92), respectively(Lin et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Wu and Cao, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). During the experiment period, other water quality parameters were maintained at appropriate levels for turbot. Specifically, dissolved oxygen, pH, temperature, salinity, TAN, and NO\u003csup\u003e2\u0026minus;\u003c/sup\u003e concentration varied between 7.01\u0026ndash;7.12 mg/L, 7.41\u0026ndash;7.46, 15.8\u0026ndash;16.5 ℃, 29.63\u0026ndash;31.56\u0026permil;, 0.23\u0026ndash;0.29 mg/L, 0.07\u0026ndash;0.13 mg/L, respectively. The photoperiod was maintained at 12 h light/12 h dark.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Sample preparation\u003c/h2\u003e \u003cp\u003eTo compare the composition of metabolites released into the aquatic environment by turbot at different densities (aquatic environment metabolome), we used a rational sample collection method. 100 L of water was collected from each replicate tank, rapidly filtered through a filter pump onto glass fiber filter paper, and then the membranes were stored at -80\u0026deg;C until extraction. After the experiment, all fish were fasted for 24 h. And then three fish were randomly collected from each culture tank (nine fish per group) and anesthetized with tricaine methane sulfonate (MS-222, Sigma Diagnostics INS, St. Louis, MO) at 40\u0026ndash;45 mg/L. Blood was obtained from the tail vein using a syringe and collected in sodium heparin anticoagulation tubes. The collected blood samples were centrifuged at 3000 rpm for 10 minutes to obtain plasma, which was then stored at -80\u0026deg;C. Immediately after blood collection, the liver, hypothalamus and pituitary gland were removed from each fish, immediately frozen in liquid nitrogen and stored at -80 ℃ for gene expression analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Determination of biochemical parameters\u003c/h2\u003e \u003cp\u003ePlasma glucose, triglyceride, cortisol and triiodothyronine(T\u003csub\u003e3\u003c/sub\u003e) levels in fish were measured using commercial kits (#F006-1-1, #A110-2-1, #H094-1-1 and #H222-1-1) according to the instructions for use. All commercialized kits were purchased from Nanjing Jiancheng Institute of Biological Engineering.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 RNA extraction and qPCR\u003c/h2\u003e \u003cp\u003eTotal RNA was isolated from liver, hypothalamus and pituitary gland samples using the TRIzol reagent (Trans-Gen Biotech, Beijing, China). RNA concentration and purity were measured using a Nanodrop 2000 spectrophotometer (Gene Company Limited, Hong Kong, China), and purity was calculated using the 260/280 nm optical density ratio (purity: 2.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1). Reverse transcription of RNA into cDNA was then performed using the Evo M-MLV Mix Kit (Hunan Accurate Biomedical Technology Co., China). Primers were designed using Primer Premier 5.0 and NCBI online website. The primer sequences used are listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The qPCR was conducted using a SYBR Green Premix Pro Taq HS qPCR Kit (Hunan Accurate Biomedical Technology Co., China), with a 20 \u0026micro;L reaction solution on a CFX Connet Real-Time PCR System (Bio-Rad, China). CRH, GH, IGF-1, and GHR thermal cycling conditions were 95◦ C for 15 min, followed by 35 cycles of 95◦ C for 15 s and 58◦ C for 60 s. ACTH thermal cycling conditions were 95◦ C for 15 min, followed by 35 cycles of 95◦ C for 15 s and 60◦ C for 60 s. Amplification specificity was validated by melting curve analysis. The Pfaffl method(Pfaffl, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) was used for calculations.\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 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eList of primers used for quantitative real-time PCR analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrimer sequence(5\u0026rsquo;-3\u0026rsquo;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAnnealing temperature(℃)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAmplic on size(bp)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIGF-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF: TCGTGGACGAGTGCTGCTT\u003c/p\u003e \u003cp\u003eR: CCGCCTTGCTAGTCTTGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACTH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF: TGTGGCTATTAGTGGCTGTGG\u003c/p\u003e \u003cp\u003eR: CCTGGCAGTTCGGATTCTC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF: AATAACCACGAGACACAACGCA\u003c/p\u003e \u003cp\u003eR: GAGAACTCCCAAGACTCAACCAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF: CCTCCTCTAACGATTGAAGATTCC\u003c/p\u003e \u003cp\u003eR: AGGGCTGTCAATAGCTCGAC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e123\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF: ACACGTCCATTTGGATCCCC\u003c/p\u003e \u003cp\u003eR: GCTCCCAGTTGACCATGACA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e183\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβ-actin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF: TGAACCCCAAAGCCAACAGG\u003c/p\u003e \u003cp\u003eR: GAGGCATACAGGGACAGCAC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e107\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 \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Metabolite extraction and UHPLC-MS/MS analysis\u003c/h2\u003e \u003cp\u003eFor metabolite extraction, take the filter membrane sample in an EP tube, add 1000\u0026micro;L of 80% methanol aqueous solution, put it into liquid nitrogen for 5 minutes; thaw on ice, vortex for 30 seconds, sonicate for 6 min, centrifuge for 1 min at 5000rpm and 4\u0026deg;C, take the supernatant into a new centrifuge tube, lyophilize into dry powder, add 60\u0026micro;L of 10% methanol solution to dissolve, and feed into LC-MS for analysis.\u003c/p\u003e \u003cp\u003eUHPLC-MS/MS analyses were performed using a Vanquish UHPLC system (ThermoFisher, Germany) coupled with an Orbitrap Q Exactive\u003csup\u003e\u0026trade;\u003c/sup\u003e HF-X mass spectrometer (Thermo Fisher, Germany) in Gene Denovo Co., Ltd. (Guangzhou, China). Samples were injected onto a Hypesil Gold column (100\u0026times;2.1 mm, 1.9\u0026micro;m) using a 17-min linear gradient at a flow rate of 0.2mL/min. The eluents for the positive polarity mode were eluent A (0.1% FA in Water) and eluent B (Methanol).The eluents for the negative polarity mode were eluent A (5 mM ammonium acetate, pH 9.0) and eluent B (Methanol).The solvent gradient was set as follows: 2% B, 1.5 min; 2-100% B, 12.0 min; 100% B, 14.0 min;100-2% B, 14.1 min༛2% B, 17 min. Q Exactive\u003csup\u003eTM\u003c/sup\u003eHF-X mass spectrometer was operated in positive/negative polarity mode with spray voltage of 3.2 kV, capillary temperature of 320\u0026deg;C, sheath gas flow rate of 40 arb and aux gas flow rate of 10 arb.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Data processing and metabolite identification\u003c/h2\u003e \u003cp\u003eThe raw data files generated by UHPLC-MS/MS were processed using the Compound Discoverer 3.1 (CD3.1, Thermo Fisher) to perform peak alignment, peak picking, and quantitation for each metabolite. The main parameters were set as follows: retention time tolerance, 0.2 minutes; actual mass tolerance, 5ppm; signal intensity tolerance, 30%; signal/noise ratio, 3; and minimum intensity, 100,000. After that, peak intensities were normalized to the total spectral intensity. The normalized data was used to predict the molecular formula based on additive ions, molecular ion peaks and fragment ions. And then peaks were matched with the mzCloud (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.mzcloud.org/\u003c/span\u003e\u003cspan address=\"https://www.mzcloud.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), mz Vaultand Mass Listdatabase to obtain the accurate qualitative and relative quantitative results. Statistical analyses were performed using the statistical software R (R version R-3.4.3), Python (Python 2.7.6 version) and CentOS (CentOS release 6.6). When data were not normally distributed, normal transformations were attempted using of area normalization method.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Statistical analysis and pathway analysis\u003c/h2\u003e \u003cp\u003eThe collected metabolites were annotated using the Human Metabolome database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.hmdb.ca/\u003c/span\u003e\u003cspan address=\"http://www.hmdb.ca/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and the KEGG database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.genome.jp/kegg/\u003c/span\u003e\u003cspan address=\"http://www.genome.jp/kegg/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The R package gmodels was used to perform principal component analysis (PCA) analysis on the data, and the R language ropls package was used to perform supervised orthogonal partial least squares-discriminant analysis (OPLS-DA).The OPLS-DA model was further validated by cross-validation and permutation test. For cross-validation, the data was partitioned into seven subsets, where each of the subsets was then used as a validation set. A variable importance in projection (VIP) score of (O)PLS model was applied to rank the metabolites that best distinguished between two groups. The threshold of VIP was set to 1. In addition, T-test was also used as a univariate analysis for screening differential metabolites. Those with a p value of T test \u0026lt;0.05 and VIP\u0026thinsp;\u0026ge;\u0026thinsp;1 were considered differential metabolites between two groups.\u003c/p\u003e \u003cp\u003eStatistical analyses comprised one-way ANOVA, followed by Tukey\u0026rsquo;s test, using IBM SPSS software (version 20.0) to examine significant differences between the groups. A significance level of P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was used in all analyses. All data are shown as the means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard error (S.E.) of the treatments.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Result","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Metabolomics principal component analysis (PCA) at different densities\u003c/h2\u003e \u003cp\u003eAs a non-supervised multivariate data analysis method, PCA is always used to give a comprehensive view of the clustering trend for the multidimensional data(Gao et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). To screen for characteristic metabolites with significant concentration changes, the PCA approach was utilized to conduct a model with the ES\u0026thinsp;+\u0026thinsp;and ES\u0026thinsp;\u0026minus;\u0026thinsp;data, respectively. Unsupervised PCA showed LD and MD were significantly differentiated, and their contribution rates were 33.7% and 27.3% (POS), 31.7% and 26% (NEG), respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e); LD and HD were significantly differentiated, and their contribution rates were 39.2% and 30.7% (POS), 58.1% and 10.9% (NEG), respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e); MD and HD were significantly differentiated, and their contribution rates were 35.8% and 27% (POS), 47.1% and 18.9% (NEG), respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). It indicated that significant changes in aquatic environment metabolome occurred at different densities.\u003c/p\u003e \u003cp\u003eTo maximize the discrimination between different densities treatments, we employed OPLS-DA to identify differences in metabolite. In the positive ion mode, the OPLS-DA score plots of LDvsMD、LDvsHD、MDvsHD had the cumulative values of R2X being 76.2%, 75.8%, 55.2%, R2Y being 89.3%, 95.5%, 98.3% and Q2 being 76.8%, 63.5%, 70.5%, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In negative ion mode, the OPLS-DA score plots of LDvsMD、LDvsHD、MDvsHD had the cumulative values of R2X being 70.9%, 94.0%, 87.2%, R2Y being 92.4%, 98.4%, 98.3% and Q2 being 76.9%, 96.7%, 90.2%, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). R2X and R2Y denote the explanation rate of the proposed model for X and Y matrices respectively, and Q2 denotes the predictive ability of the model. The closer the three indicators are to 1, the more stable and reliable the model is. Q2\u0026thinsp;\u0026gt;\u0026thinsp;0.5 indicates that the model has good predictive ability. To evaluate the accuracy of the OPLS model, we used the permutation test for verification. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, The intersection of the regression line at point Q2 with the vertical coordinate is \u0026lt;\u0026thinsp;0, indicating that the model prediction is reliable. The data and instrumental analysis system of this study are reliable and stable.\u003c/p\u003e \u003cp\u003e \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 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOPLS-DA model validation parameters\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComparison group name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR2X\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR2Y\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLD-vs-MD.POS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.762\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.893\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.768\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLD-vs-MD.NEG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.709\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.924\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.769\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLD-vs-HD.POS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.758\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.955\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.635\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLD-vs-HD.NEG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.984\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.967\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMD-vs-HD.POS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.552\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.983\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.705\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMD-vs-HD.NEG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.872\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.983\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.902\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Differential metabolites at different densities\u003c/h2\u003e \u003cp\u003eIn the positive ion mode, there were 26 significant differential metabolites (SDMs) (upregulated (up):15, downregulated (down):11) in LDvsMD group, 51 SDMs (up:33, down:18) in LDvsHD group and 50 SDMs (up:34, down:16) in MDvsHD group (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). 8 SDMs could be identified in LDvsMD, LDvsHD and MDvsHD group, among them, the contents of phenethylamine, proline and styrene increased with the increase of culture density (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In the negative ion mode, there were 27 SDMs (up:14, down:13) in LDvsMD group, 17 SDMs (up:14, down:3) in LDvsHD group and 11 SDMs (up:9, down:2) in MDvsHD group (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). 2 SDMs could be identified in LDvsMD, LDvsHD and MDvsHD group, among them, the contents of xanthine and hexadecanedioic acid increased with the increase of culture density (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Variable importance in projection (VIP) scores ranked by partial least square discriminant analysis (PLS-DA) was shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The top 3 most important metabolites were gamma-glutamylleucine, phenethylamine and N-benzylformamide in LDvsMD group. The top 3 most important metabolites were oleamide, arachidonoyl amide and phenethylamine in LDvsHD group. The top 3 most important metabolites were Phenethylamine, guanine and 2-amino-1,3,4-octadecanetriol in MDvsHD group.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.3 GH/IGF-1 signaling pathway\u003c/h2\u003e \u003cp\u003eTo investigate the effect of density stress and phenethylamine treatment on the function of GH/IGF-1 signaling pathway, gene expression levels of GH in the pituitary gland and GHR and IGF-1 in the liver of turbot were analyzed. The results of the density treatment showed that GH mRNA levels were significantly down-regulated (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in the MD (0.47-fold) and HD groups (0-fold) compared to the LD group. Similar trends were observed for GHR expression in the liver of turbot under different density treatments. In addition, the expression of IGF-1, another key gene located downstream of the GH/IGF-1 signaling pathway, was also significantly lower (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in the MD (0.67-fold) and HD (0.63-fold) groups than in the LD group. In phenethylamine treatment, a significant decrease in GH, GHR, and IGF-1 gene expression was observed in the LP and HP groups when compared to the CON group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). GH expression was almost undetectable in the LP and HP groups.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.4 HPI axis\u003c/h2\u003e \u003cp\u003eTo assess the effect of density stress and phenethylamine treatment on the HPI axis of turbot, we measured the abundance of key genes (CRH and ACTH) and cortisol levels. The density treatment results revealed that the expression levels of CRH and ACTH genes increased with increasing density, and the HD group treatment exhibited a significantly higher expression than the LD group (1.65 and 1.63 times greater, respectively) (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Furthermore, plasma cortisol levels at the end of the HPI axis were significantly higher in the HD group (18.67\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16 ng/mL) than in the MD group (16.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14 ng/mL) and LD group (16.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.41 ng/mL) (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In the phenethylamine treatment, the expression levels of CRH and ACTH genes increased with increasing phenethylamine concentration. Plasma cortisol levels at the end of the HPI axis were significantly higher in the HP group (27.95\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21 ng/mL) than in the LP group (25.98\u0026thinsp;\u0026plusmn;\u0026thinsp;0.57 ng/mL) and the CON group (24.64\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07 ng/mL).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Physiological response of turbot plasma\u003c/h2\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, the effects of density stress and phenethylamine treatment on T\u003csub\u003e3\u003c/sub\u003e, glucose, and triglycerides. In the density treatment, plasma glucose, triglyceride, and T3 levels were significantly higher in the HD group than in the LD group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), while there were no significant differences between the MD and LD groups. In phenethylamine treatment, the levels of plasma glucose, triglycerides and T\u003csub\u003e3\u003c/sub\u003e were significantly higher in the LP and HP groups than in the LD group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), while there were no significant differences between the LP and HP groups.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003ePheromones are chemical substances that are secreted outwardly by organisms into their surroundings and received by the same species to influence organisms' behavioral habits, growth, development and so on activities. Scott et al. found that spermine in the semen of male sea lamprey(\u003cem\u003ePetromyzon marinus\u003c/em\u003e) acts as a sex pheromone and attracts ovulating females(Scott et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Zhu et al. showed that Large Yellow Croaker (\u003cem\u003eLarimichthys crocea\u003c/em\u003e) are attracted to gut contents from conspecifics(Zhu et al., \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Pheromones can be involved in fish reproduction, migration, alarm and other behaviors that are important to the life of aquatic organisms(Kamio and Derby, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Hexadecanedioic acid, xanthine, phenethylamine, proline and styrene in the aquatic metabolic group in this study were significantly different in MDvsLD, HDvsMD, HDvsLD, and the levels increased significantly with increasing density. Among them, hexadecanedioic acid and styrene are insoluble in water, and xanthine, phenethylamine and proline are soluble in water. Solubility determines the spatial extent of the pheromone, and since substances dissolved in water are more likely to diffuse in an aquatic environment, proline, xanthine, and phenethylamine are more likely to act as pheromones. Amino acids play a role in chemical communication as one of the few identified fish pheromones. Yambe et al. identified l-Kynurenine as a sex pheromone in the urine of ovulated female masu salmon(\u003cem\u003eOncorhynchus masou\u003c/em\u003e)(Yambe et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Shoji et al. study finds that amino acids in stream water are essential for salmon (\u003cem\u003eOncorhynchus keta\u003c/em\u003e)homing migration(Shoji et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). However, it is important to note that the proline screened in this study was D-proline, which has been temporarily excluded as a natural pheromone. On the other hand, xanthine, as a typical purine and an important biomolecule, plays a crucial role in purine catabolic reactions(Liu et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Xanthine can be converted to uric acid by the action of xanthine oxidase, and high levels of uric acid are associated with gout(Wu et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Zhong et al., \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Therefore, the level of xanthine mainly indicates the health status of an organism and its levels in serum or urine can provide valuable information for the diagnosis and medical treatment of certain metabolic disorders(Pundir and Devi, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Phenethylamine is an endogenous amine compound that can play an important biological role in the nervous system as a chemical messenger(Boulton, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1980\u003c/span\u003e; Branchek and Blackburn, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Premont et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). Low concentrations of phenethylamine produce euphoria, but high concentrations of phenethylamine may form neurotoxic compounds(Edwards and Blau, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1973\u003c/span\u003e). Plasma phenethylamine concentrations are associated with stress, but they are quickly metabolized by monoamine oxygenase (MAO)(Grimsby et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Paulos and Tessel, 1982). Studies have reported that the half-life of phenethylamine is very short in dogs (1.8-3 min) and is rapidly distributed and eliminated in rats after intravenous administration(Cone et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1978\u003c/span\u003e; Wu and Boulton, 1975). In the absence of any treatment, phenethylamine levels in organisms' tissues are very low (Durden and Boulton, 1982).\u003c/p\u003e \u003cp\u003eIn the external environment, phenethylamine can also act as chemical cues to regulate individual animal behavior. Ferrero et al found that phenethylamine induced strong avoidance responses in rodent and herbivore species(Ferrero et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Imre et al. showed that sea lamprey (\u003cem\u003ePetromyzon marinus\u003c/em\u003ealso) showed a strong avoidance response to phenethylamine(Imre et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Bredy and Barad showed that phenethylamine can act as a pheromone to communicate information about fear or threats(Bredy and Barad, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Phenethylamine normally binds to the vertebrate trace amine-associated receptor (TAAR), and its perception by the TAAR4 olfactory receptor leads to an increase in intracellular cAMP levels. cAMP directly activates cyclic nucleotide-gated channels (CNG channels) to allow the entry of Na\u003csup\u003e+\u003c/sup\u003e and Ca\u003csup\u003e2+\u003c/sup\u003e, depolarizing olfactory sensory neurons (OSN) to generate action potentials and converting chemical signals into electrical signals(Mombaerts et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Xu and Li, 2020).The electrical signal is transmitted to brain regions to produce olfactory perception, thus enabling the transmission of information(Lindemann and Hoener, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Phenethylamine levels have been shown to increase in the urine of stressed animals(Paulos and Tessel, 1982; Snoddy et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e1985\u003c/span\u003e). In this study, the levels of phenethylamine were found to increase with increasing density. Subsequently, we examined changes in key genes of the HPI axis and GH/IGF-1 signaling pathway as well as physiological indicators in turbot after density treatment and phenethylamine treatment and performed correlation analysis.\u003c/p\u003e \u003cp\u003eThe HPI axis plays an important role in the response of fish to environmental stresses(Rotllant et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). In a stressful state, the HPI axis is first activated, causing the body to release large amounts of cortisol in response to the stressor(Barton, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Yusishen et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Excessive cortisol will induce secondary and tertiary stress responses, resulting in physiological and other functional disorders in fish (Van Der Boon et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e1991\u003c/span\u003e). Bi et al. found that the serum ACTH and cortisol levels of hybrid sturgeon (♀\u003cem\u003eAcipenser baerii\u003c/em\u003e\u0026times;♂\u003cem\u003eAcipenser schrenckii\u003c/em\u003e)increased with increasing stocking density (Bi et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Wang et al. found that a stocking density of 24 kg/m\u003csup\u003e3\u003c/sup\u003e for 220 days resulted in a significant increase in plasma cortisol levels in Atlantic salmon when compared to a density of 6 kg/m\u003csup\u003e3\u003c/sup\u003e(Wang et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In addition, changes in other environmental factors, such as ammonia exposure, nitrate exposure, and pathogenic infections, can also upregulate CRH, ACTH genes, and plasma cortisol in fish(Jia et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Madison et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Yu et al., \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This study also showed that plasma cortisol was significantly higher in the HD group compared to the MD and LD groups, while the expression levels of CRH and ACTH genes also increased with increasing density. Similarly, plasma cortisol was significantly higher in the HP group compared to the LP and CON groups, while the expression levels of CRH and ACTH genes also increased with increasing phenethylamine concentrations. The similarity between the phenethylamine treatment and density treatment suggests that high stocking density may activate the turbot HPI axis through phenethylamine, leading to a stress response. Under stress, fish release large amounts of cortisol, which increases the metabolism of carbohydrates, fats and proteins and controls the flow of energy in the organism in response to the stressor (Barton, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Flik et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Mommsen et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Shepherd et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Plasma glucose, lactate, and triglycerides were significantly elevated in both HD and HP groups in this study, which may be due to energy mobilization by the organism to resist the unfavorable external environment, corroborating with the above results.\u003c/p\u003e \u003cp\u003eIn teleost fish, the GH/IGF-1 signaling pathway regulates a variety of physiological functions, such as growth, reproduction, immunity, and osmoregulation(Blanco, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Canosa and Bertucci, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; P\u0026eacute;rez-S\u0026aacute;nchez et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). GH levels in fish are positively associated with growth in vivo. However, under prolonged stress conditions, GH levels in fish can fluctuate, disrupting the function of the GH/IGF-1 signaling pathway. Environmental factors such as temperature, salinity, density, and other breeding-induced discomfort can cause a decrease in fish IGF-1 levels, often resulting in an impact on fish growth and development (Davis and Peterson, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Deane et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Seo and Park, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The present study found that both GH and IGF-1 were significantly downregulated in the HD and HP groups, suggesting the inhibitory effects of high density and phenethylamine treatment on turbot growth. Thyroid hormones (TH) have also been shown to play a crucial regulatory role in fish growth, often working synergistically with other hormones(Power et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Xie et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). In fish, TH exerts its biological function mainly through the formation of T\u003csub\u003e3\u003c/sub\u003e(Yamano, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Therefore, we evaluated the T\u003csub\u003e3\u003c/sub\u003e levels in turbot plasma. The results showed that T\u003csub\u003e3\u003c/sub\u003e increased with the increase of density. Ardiansyah and Fotedar found that the T\u003csub\u003e3\u003c/sub\u003e of juvenile barramundi \u003cem\u003e(Lates calcarifer Bloch\u003c/em\u003e) decreased gradually with the increase of culture density(Ardiansyah and Fotedar, 2016). This may be due to the short duration of density treatment (15 days), where the fish are at an early level of stress and T\u003csub\u003e3\u003c/sub\u003e is elevated to promote energy metabolism in response to the unfavorable environment. T\u003csub\u003e3\u003c/sub\u003e also increased with increasing phenethylamine concentration in the phenethylamine treatment. The effects of density treatment and phenethylamine treatment on turbot growth were also highly consistent. Interestingly, in our study, GH gene expression was almost absent in both the HD and HP groups, but IGF-1 gene expression was still present. This may be because the HD and HP groups promoted IGF-1 expression by elevated T\u003csub\u003e3\u003c/sub\u003e acting on the liver. It has been shown that T3 increases IGF-1 mRNA expression and stimulates the release of IGF-1.(Pepene et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Robson et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2002\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePhenethylamine concentration was strongly correlated with stocking density, and the effects of phenethylamine treatment and density treatment on the HPI axis, GH/IGF-1 signaling pathway, and key physiological indicators (cortisol, T\u003csub\u003e3\u003c/sub\u003e, glucose, triglycerides) were highly similar. In the present study, phenethylamine was detected at LD, MD and HD, but only the turbot in the HD group produced significant stress and growth inhibition. This suggests that the effects of phenethylamine (harmful or beneficial) are dose dependent under specific conditions. In the phenethylamine treatment experiment, even the LP group (10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e mol/l) had a significant negative effect on turbot, which indicates that phenethylamine has a trace effect.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn summary, phenethylamine produced by turbot under high density may act as a pheromone to signal crowding stress, and phenethylamine has dose-dependent and trace effects. High doses of phenethylamine cause disruptions in neuroendocrine function (GH/IGF-1 signaling pathway, HPI axis) and physiology in turbot. The findings of this study provide new ideas for further exploration of density stress mechanisms.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eThis work was financially supported by China Agriculture Research System (CARS-47-G21). Special thanks to China Weihai Institute of Marine Biotechnology (Zhe Liu, Xiaoyang Ma, Jialin Li, Jingqiang Yang) for their support.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eArdiansyah, Fotedar, R., 2016. Water quality, growth and stress responses of juvenile barramundi (Lates calcarifer Bloch), reared at four different densities in integrated recirculating aquaculture systems. Aquaculture 458, 113-120. https://doi.org/10.1016/j.aquaculture.2016.03.001\u003c/li\u003e\n\u003cli\u003eBarton, B.A., 2002. Stress in fishes: a diversity of responses with particular reference to changes in circulating corticosteroids. Integrative and comparative biology 42(3), 517-525. \u003c/li\u003e\n\u003cli\u003eBi, B., Yuan, Y., Zhao, Y., He, M., Song, H., Kong, L., Gao, Y., 2023. Effect of crowding stress on growth performance, the antioxidant system and humoral immunity in hybrid sturgeon. 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Fishes 8(1), 26.\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":"Pheromone, Phenethylamine, Metabolomics, Scophthalmus maximus, Stocking density","lastPublishedDoi":"10.21203/rs.3.rs-3244498/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3244498/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePheromones play a vital role in regulating fish behavior, including reproduction, aggregation, hazard recognition, and food location. To gain a better understanding of chemical communication in fish produced by density changes, this study analyzed the metabolites released by turbot (\u003cem\u003eScophthalmus maximus\u003c/em\u003e) under different stocking density and investigated their effects on the neuroendocrine function of turbot. The experiment was conducted at low (LD: 3.01 kg/m\u003csup\u003e3\u003c/sup\u003e), medium (MD: 6.62 kg/m\u003csup\u003e3\u003c/sup\u003e), and high (HD: 10.84 kg/m\u003csup\u003e3\u003c/sup\u003e) densities for 15 days. High-throughput non-targeted metabolomics (LC-MS/MS) was used to identify variations in metabolites released into the aquatic environment by turbot at different densities. Results showed that 29 and 47 metabolites were significantly upregulated in MD and HD groups, respectively, compared with the LD group. Among them, hexadecanedioic acid, xanthine, phenethylamine, proline, and styrene were significantly upregulated in MD vs LD, HD vs MD, and HD vs LD. The VIP diagram of OPLS-DA alignment showed that phenethylamine was the most important metabolite shared by MD vs LD, HD vs MD, and HD vs LD. To investigate the impact of phenethylamine on turbot, its concentration in the aquatic environment was set at 0 (CON), 10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e (LP), 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e (HP) mol/l via exogenous addition, and turbot were exposed to these environments for 2 days. Key genetic changes in the GH/IGF-1 signaling pathway, HPI axis of turbot were studied using qRT-PCR for density treatment and phenethylamine treatment. The results demonstrated that the expression of GH, GHR, and IGF-1 was significantly lower, while the expression of CRH and ACTH was higher in the HD group. Additionally, plasma levels of cortisol, glucose, triglycerides, and T\u003csub\u003e3\u003c/sub\u003e were also highest in the HD group compared to the LD and MD groups and were positively correlated with density. In the phenethylamine treatment, there was a high degree of concordance between the GH/IGF-1 signaling pathway (GH, GHR, IGF-1), HPI axis (CRH, ACTH) and plasma physiological changes (cortisol, glucose, triglycerides, T\u003csub\u003e3\u003c/sub\u003e) in the phenethylamine-treated group and the density-treated group. Thus, phenethylamine produced by turbot under high stocking density may act as a pheromone of density stress, and its effect is dose-dependent and trace effect.\u003c/p\u003e","manuscriptTitle":"Pheromone screening and neuro-endocrine regulation in turbot (Scophthalmus maximus) under different stocking density","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-08-14 13:29:11","doi":"10.21203/rs.3.rs-3244498/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":"d5bbe962-cee3-4d18-8365-76e438162d77","owner":[],"postedDate":"August 14th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-08-25T12:59:30+00:00","versionOfRecord":[],"versionCreatedAt":"2023-08-14 13:29:11","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3244498","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3244498","identity":"rs-3244498","version":["v1"]},"buildId":"iFTdqyg4nuje_uAy1AHro","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}