Effects of different culture modes on growth, muscle nutrition, and intestinal microbiota of largemouth bass

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D. Hu, H. D. Hu, Y. X. Deng, Y. J. Wu, Y. M. You, H.C. Sun This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4767517/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 To study the differences in morphological characteristics, muscle nutrition, and intestinal microbiota of largemouth bass under different farming modes, healthy largemouth bass with an initial body weight of 50.0 (± 2.0) g were selected and reared for 180 d under traditional pond farming (Group A), flow-through farming (Group B), and high-position pond farming (Group C) modes. The results showed that: (1) the condition factor, hepatosomatic index, and visceral somatic index of largemouth bass in Group B were significantly higher than those in Group C ( P < 0.05); (2) the crude fat content in muscle of fish in Group B was significantly lower than that in Group A ( P < 0.05). However, the crude protein content was significantly higher than that in Group A ( P < 0.05). The total amino acid content, total non-essential amino acids, total umami amino acids, and total aromatic amino acids in muscle of fish in Group B were significantly higher than those in Groups A and C ( P < 0.05). The monounsaturated fatty acids, polyunsaturated fatty acids, and DHA + EPA contents in muscle of fish in Group B were significantly higher than those in Group A ( P < 0.05) and extremely significantly higher than those in Group C ( P < 0.01), and; (3) alpha diversity analysis showed that the intestinal microbiota diversity of largemouth bass in Group B was higher than that of the other two groups. At the phylum level, the dominant bacterial phyla in largemouth bass intestines were Fusobacteriota, Firmicutes, Proteobacteria, Bacteroidota, and Actinobacteria. At the genus level, the dominant bacterial genera were Mycoplasma , Cetobacterium , and Acinetobacter . Principal coordinate analysis based on operational taxonomic units indicated that the microbiota distribution of Group B differed slightly from that of Group A and differed significantly from that of Group C. This indicated that the species diversity of the intestinal microbiota of largemouth bass varied under different farming modes. In conclusion, the farming mode affected the growth, muscle nutritional quality, and intestinal microbiota of largemouth bass. This study provides a theoretical basis for understanding the relationships between farming modes, growth performance, muscle nutrition, and intestinal microbiota in largemouth bass. flow-through farming high-position pond farming microbial species diversity traditional pond farming Micropterus salmoides Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Largemouth bass ( Micropterus salmoides ), also known as California bass, belongs to order Perciformes, family Centrarchidae, and genus Micropterus . This species was first introduced in China in the 1980s (Yu et al., 2024 ). Because of its short farming cycle, appetizing taste, and lack of intermuscular bones, it has been widely farmed and has become an important freshwater aquaculture species in China (Edwards, 2015 ). Largemouth bass are mainly farmed in traditional ponds, which have advantages of short farming cycles, high yields, and low entry barriers. However, traditional pond farming is characterized by extensive development, outdated facilities, increasing production costs, and increased environmental pressure. In recent years, aquaculture has gradually moved towards “green, healthy, and ecological farming.” Flow-through farming (i.e., microflow aquaculture within ponds) and high-position pond farming are common farming modes that achieve low-carbon, efficient, and green farming effects more effectively than pond farming, with higher yields per unit farming area and more sustainable ecological benefits (Martins et al., 2010). Morphological characteristics, muscle nutrition, and flavor of aquatic products vary among farming models used in the aquaculture industry (Wang, 2021 ; Duan et al., 2021 ; Jones et al., 2022 ). For example, Jia et al. ( 2022 ) studied largemouth bass under traditional farming and land-based water-pushing container farming models and found that bass reared in land-based containers had a more elongated body shape and significantly higher crude protein, saturated fatty acid (SFA), and polyunsaturated fatty acid (PUFA) contents than those reared in ponds. This demonstrates the correlation between the morphological characteristics and muscle nutritional components of aquatic products and their farming models. Different farming models for aquatic products also affect the gut microbiota structure. Luo et al. ( 2022 ) investigated the gut microbiota structure of crayfish reared in rice fields and ponds and found that Proteobacteria abundance in the gut of crayfish reared in rice fields was higher than in those reared in ponds; whereas, Tenericutes abundance was the opposite. At present, the research on the intestinal microbiota structure of largemouth bass in farming mode can only be found in Liu et al.(2021) : they analyzed the intestinal microbiota structure of largemouth bass in static and flowing water groups under the pond internal circulation water farming mode and found that the intestinal microbiota structures of the two groups were different. Similarly, there were differences in the morphological characteristics and muscle nutritional components of largemouth bass under internal circulation water pond farming and conventional pond farming modes. However, comprehensive comparative analyses of the morphological characteristics, muscle nutrition, and intestinal microbiota of largemouth bass in traditional pond, flow-through, and high-position pond farming modes are lacking. In view of this, the present study focused on largemouth bass under three farming modes and explored the differences in morphological characteristics, muscle nutrition, and intestinal microbiota structure and diversity and provides technical support for different farming modes of largemouth bass. Materials and methods Experimental materials Largemouth bass were purchased from Yongchuan District, Chongqing City, and were from the same batch of fry. The pond group (Group A) had a farming density of 20 kg/m³, the flow-through group (Group B) had a farming density of 20 kg/m³, and the high-position pond group (Group C) had a farming density of 30 kg/m³. The initial stocking weight was 50 (± 2.75) g, and the three groups were stocked simultaneously. The rearing period was 180 d, during which the fish were fed the same brand and specification of feed (crude protein ≥ 48.0%; crude fat ≥ 6.0%; crude fiber ≤ 6.0%; crude ash ≤ 16.0%). The average weight per fish of Group A was 438.36 (± 40.54) g, that of Group B was 370.39 (± 42.27) g, and that of Group C was 284.51 (± 19.98) g. Methods Determination of morphology and body indexes At the beginning of the test, 15 tails experimental fish were randomly sampled from each group. After accurately measuring their body weights and lengths, their internal organs and livers were dissected and weighed to calculate the liver-to-somatic ratio (hepatopancreas somatic index [HSI]) and visceral-to-somatic ratio (visceral somatic index [VSI]), and the degree of fatness (condition factor [CF]) was calculated based on the body lengths and weights. The relevant formulae are as follows: Hypertrophy (condition factor [CF] g/cm 3 ) = 100 × Wh/L3; Hepatopancreas somatic index (HSI [%]) = 100 × Wh/W; and Visceral somatic index (VCI [%]) = 100 × Wn/W; where W is fish mass (g); L is fish body length (cm ); Wh is hepatopancreas weight (g), and; Wn is viscera weight (g) Muscle nutrient composition Ten largemouth bass were randomly selected from each group. Dorsal muscles were dissected, cleaned of attached debris, and weighed immediately. Part of the dissected muscle was temporarily stored in a -80 ℃ freezer for determination of the general nutrient composition and the amino acid and fatty acid compositions and contents. To determine the general nutrient composition, the moisture, crude fat, crude protein, and crude ash contents were measured according to GB 5009.3–2016, GB 5009.4–2016, GB/T 14772 − 2016, and GB 5009.5–2016, respectively. Total amino acid content was determined following the method specified in GB 5009.124–2016. Amino acid determination with reference to GB 5009.124–2016. Samples were hydrolyzed with acid (6 mol/L HCl solution), and amino acids were analyzed using high-performance liquid chromatography. The tryptophan content was determined separately after alkaline hydrolysis (5 mol/L NaOH solution) using a Sykam automatic amino acid analyzer S433D and Agilent liquid chromatography column (4.6 x 150 mm, 5 µm). The fatty acid composition and content were determined following GB 5009.168–2016. Gas chromatography was performed using a Shimadzu GC2010 instrument equipped with a hydrogen flame ionization detector and a Supelco SP-2560 gas capillary column. Intestinal microbiota detection Eight largemouth bass were randomly selected from each group and disinfected by wiping with 75% ethanol followed by washing thrice with sterile saline. The entire intestine was extracted in a sterile environment, and the intestinal contents (> 100 mg) were placed in 2-mL cryogenic vials, rapidly frozen in liquid nitrogen, and stored at -80 ℃. Samples were transported on dry ice to Nanjing Aoweisen Gene Technology Co. Ltd. for sequencing. statement: (i) The experimental protocol was approved by the Experimental Animal Ethics Committee, College of College of Smart Agriculture, Technology Innovation Center of Ecological Fishery Industrialization, Chongqing University of Arts and Sciences, China; (ii) All experiments were performed in accordance with guidelines and regulations. Data processing and statistics Data were processed using Microsoft Excel 2010. Significance analysis of the data was performed using independent sample t-tests in SPSS 18.0. The results were expressed as mean ± standard deviation (mean ± SD). Statistical significance was set at P < 0.05, and P < 0.01 indicated a highly significant difference. Results Differences in largemouth bass morphological indices In Table 1 , the CF, HSI, and VSI of largemouth bass in Group B exhibited significantly greater values compared to those in Group C (P < 0.05). Furthermore, the VSI in Group B was significantly higher than that in Group A (P 0.05). CF did not differ significantly between Groups A and B (P > 0.05). Table 1 Differences in morphometric indices of Micropterus salmoides for Groups A, B, and C (n = 15) A B C CF/(g/cm³) 2.09 ± 016a 1.99 ± 0.06a 1.81 ± 0.05b HSI (%) 1.61 ± 0.24ab 1.94 ± 0.32a 1.55 ± 0.15b VSI (%) 9.79 ± 1.03b 11.70 ± 1.75a 9.63 ± 0.85b Values are reported as means ± SD. Values with different superscript letters within the same row indicate significant differences (P 0.05). HSI - hepatopancreas somatic index; VSI - visceral somatic index; A: traditional pond farming; B: flow-through farming; C: high-position pond farming Differences in muscle nutrient composition of largemouth bass under different farming models There were no significant differences in the muscle moisture content among the groups ( P > 0.05; Table 2 ). The crude muscle ash content in Group B was significantly lower than that in Group A ( P 0.05). The muscle crude fat contents in Groups B and C were significantly lower than that in Group A ( P < 0.05). The muscle protein content in Group B was significantly higher than that in Group A ( P 0.05). Table 2 Differences in routine nutritional components in Micropterus salmoides muscle in Groups A, B, and C (n = 10) Group Crude moisture Crude ash Crude fat Crude protein A 77.24 ± 0.83a 1.30 ± 0.08a 1.71 ± 0.08a 17.97 ± 0.71b B 76.97 ± 0.89a 1. 15 ± 0.07b 1.33 ± 0.15b 19. 12 ± 0.90a C 77. 18 ± 0.68a 1.25 ± 0.12ab 1.48 ± 0.11b 18.66 ± 0.47ab Values are reported as means ± SD. Values with different superscript letters within the same row indicate significant differences (P 0.05); A: traditional pond farming; B: flow-through farming; C: high-position pond farming; Differences in amino acids in largemouth bass muscles The amino acid composition and muscle content of the three largemouth bass groups are listed in Table 3 . Eighteen amino acids were detected in muscles of fish in all three groups, including eight essential, two semi-essential, and eight non-essential amino acids. Among these, total amino acids, total non-essential amino acids, total umami amino acids (including glutamic acid and alanine), and total aromatic amino acids were significantly higher in Group B than in Groups A and C ( P < 0.05). Except for glutamic acid, alanine, lysine, and arginine, which were significantly higher in Group B than in Groups A and C ( P 0.05). The proportion of each amino acid was similar among the three groups, with glutamic acid having the highest content, followed by aspartic acid, lysine, leucine, and alanine, and cystine having the lowest content. The ratios of essential amino acids to total amino acids (EAA/TAA) in the A、B and C groups were 41.1, 41.08, and 41.07%, respectively; the ratios of total essential amino acids to total non-essential amino acids (EAA/NEAA) were 80.05, 81.01, and 81.01%, respectively, and; the ratios of total umami amino acids to total amino acids (DAA/TAA) were 38.08, 38.05, and 38.04%, respectively. Table 3 Amino acid composition in Micropterus salmoides muscle in Groups A, B, and C (n = 10) Group Amino acid A B C Leu 1.55 ± 0.09a 1.57 ± 0.04a 1.55 ± 0.04a Ile 0.87 ± 0.02a 0.89 ± 0.01a 0.88 ± 0.02a Phe○ 0.82 ± 0.01a 0.85 ± 0.01a 0.82 ± 0.01a Lys 1.78 ± 0.01b 1.84 ± 0.02a 1.76 ± 0.01b Met 0.60 ± 0.03a 0.63 ± 0.01a 0.61 ± 0.02a Thr 0.89 ± 0.02a 0.89 ± 0.02a 0.89 ± 0.01a Val 0.92 ± 0.02b 0.96 ± 0.03a 0.96 ± 0.01a Try 0. 18 ± 0.01a 0. 18 ± 0.02a 0. 17 ± 0.01a His 0.52 ± 0.02a 0.55 ± 0.02a 0.50 ± 0.02a Arg 1. 12 ± 0.04b 1. 15 ± 0.02a 1. 11 ± 0.03b Glu* 3. 19 ± 0.07b 3.22 ± 0.03a 3. 19 ± 0.04b Asp* 2.00 ± 0.02a 2.05 ± 0.02a 2.01 ± 0.02a Gly* 0.86 ± 0.01a 0.87 ± 0.01a 0.81 ± 0.01a Ala* 1. 15 ± 0.02b 1. 19 ± 0.03a 1. 16 ± 0.02b Ser 0.77 ± 0.01a 0.78 ± 0.01a 0.78 ± 0.02a Pro 0.63 ± 0.01a 0.63 ± 0.01a 0.62 ± 0.02a Tyr○ 0.68 ± 0.02a 0.69 ± 0.01a 0.68 ± 0.02a Cys 0.22 ± 0.01a 0.22 ± 0.01a 0.22 ± 0.02a ∑TAA 18.75 ± 0.28b 19. 16 ± 0.21a 18.72 ± 0.21b ∑EAA 7.61 ± 0.09a 7.81 ± 0.11a 7.64 ± 0.10a ∑NEAA 9.50 ± 0.13b 9.65 ± 0.06a 9.47 ± 0.07b ∑HEAA 1.64 ± 0.06a 1.70 ± 0.04a 1.61 ± 0.05a ∑DAA 7.20 ± 0.13b 7.33 ± 0.07a 7. 17 ± 0.07b ∑AAA 1.50 ± 0.03b 1.54 ± 0.00a 1.50 ± 0.03b ∑EAA/TAA 0.41 ± 0.10a 0.41 ± 0.08a 0.41 ± 0.07a ∑EAA/NEAA 0.80 ± 0.05a 0.81 ± 0.01a 0.81 ± 0.01a ∑DAA/TAA 0.38 ± 0.08a 0.38 ± 0.05a 0.38 ± 0.04a Values are reported as means ± SD. Values with different superscript letters within the same row indicate significant differences (P 0.05); “*” represents delicious amino acids (DAA); “○” represents aromatic amino acid; ∑TAA is total amino acids; ∑EAA is total essential amino acids; ∑NEAA is total non-essential amino acids; ∑HEAA is total semi-essential amino acids; ∑DAA is total fresh flavor amino acids; ∑AAA is an aromatic amino acid; A: traditional pond farming; B: flow-through farming; C: high-position pond farming; Differences in fatty acid composition in largemouth bass muscle under different farming models The amino acid compositions and muscle contents of the three groups of largemouth bass are listed in Table 4 . Fifteen fatty acids were identified in muscles of fish in all three groups, including four saturated fatty acids (SFA), four monounsaturated fatty acids (MUFA), and seven polyunsaturated fatty acids ( PUFA). There were significant differences in the SFA content among the three groups ( P < 0.05), with Group C having the highest content, followed by Groups B and A. The MUFA content in Group B was markedly lower than that in Group A ( P < 0.05) and extremely markedly lower than that in Group C ( P < 0.01). The PUFA content in Group B was markedly higher than that in Group A ( P < 0.05) and extremely markedly higher than that in Group C ( P < 0.01). The main SFA in the muscles of the three groups of largemouth bass was palmitic acid, the main MUFA was oleic acid, and the main PUFA were linoleic acid and DHA. Additionally, the relative content of eicosapentaenoic acid, docosahexaenoic acid sum (EPA + DHA) in muscles of fish in Group B was markedly higher than those in Groups A and C ( P < 0.05). Table 4 Fatty acid composition in Micropterus salmoides muscle in Groups A, B, and C (n = 10) Group Fatty acid A B C C14:0 2.14 ± 0.07b 2.24 ± 0.05a 2.11 ± 0.04c C16:0 20.95 ± 0.17b 22.03 ± 0.12a 22.32 ± 0.21a C18:0 2.97 ± 0.07 a 2.17 ± 0.08 c 2.71 ± 0.05 b C20:0 0.27 ± 0.00 a 0.25 ± 0.00 a 0.22 ± 0.00 b ΣSFA 26.34 ± 0.15a 26.71 ± 0.14b 27.38 ± 0.15a C14:1 0.0031 ± 0.00b 0.0039 ± 0.00a 0.0027 ± 0.00c C16:1 4.94 ± 0.03 b 2.31 ± 0.02 c 5.14 ± 0.05 a C18:1 35.76 ± 0.23 a 27.74 ± 0.27 b 35.73 ± 0.34 a C20:1 1.23 ± 0.02c 1.45 ± 0.02a 1.33 ± 0.04b ΣMUFA 41.94 ± 0.19b 31.51 ± 0.21c 42.22 ± 0.27a C18:2 19.12 ± 0.23 b 24.70 ± 0.27 a 18.78 ± 0.19 b C18:3 2.47 ± 0.07 a 2.15 ± 0.09 b 2.39 ± 0.03 a C20:2 0.48 ± 0.01a 0.47 ± 0.00a 0.35 ± 0.00b C20:3 0.36 ± 0.00a 0.25 ± 0.00c 0.33 ± 0.00b C20:4 (ARA) 1.27 ± 0.02 a 1.07 ± 0.01 b 1.10 ± 0.01 b C20:5 (EPA) 0.95 ± 0.00 c 0.97 ± 0.00 b 0.99 ± 0.00 a C22:6 (DHA) 7.14 ± 0.08b 11.64 ± 0.14a 6.67 ± 0.07c ΣPUFA 31.82 ± 0.17b 41.27 ± 0.23a 30.65 ± 0.16c Values are reported as means ± SD. Values with different superscript letters within the same row indicate significant differences (P 0.05); ΣSFA is saturated fatty acid; ΣMUFA is monounsaturated fatty acid; ΣPUFA is polyunsaturated fatty acid; A: traditional pond farming; B: flow-through farming; C: high-position pond farming; Effects of farming models on largemouth bass intestinal microbiota Analysis of rarefaction curves and intestinal microbiota diversity differences The rarefaction and Shannon‒Wiener curves for all samples were nearly flat (Fig. 1), indicating that the sequencing depth and data volume were sufficient to reflect the biological information of the bacterial communities in each sample. Chao1, Simpson, and Shannon indexes were selected to reflect the α-diversity of the intestinal microbiota of largemouth bass under different farming models (Fig. 2). According to the Chao1 index, the species richness of the intestinal microbiota of largemouth bass under the three farming models decreased from Group A > Group B > Group C. According to the Shannon index, the diversity of the intestinal microbiota of largemouth bass under different farming models decreased from Group B > Group A > Group C. Differences in intestinal microbiota composition After conducting high-throughput sequencing and processing of largemouth bass intestinal microbiota data across three different farming models, 1152 operational taxonomic units (OTUs) were identified (Fig. 3). The Venn diagram analysis showed that the number of OTUs in Groups A, B, and C were 750, 782, and 623, respectively. Among these, 296 OTUs were shared by the intestinal microbiota across the three farming models, and 84, 128, and 233 unique OTUs were found in Groups A, B, and C, respectively. Based on the species annotation results at the phylum and genus levels for all the samples, relative abundance bar plots were created at the phylum (Fig. 4) and genus (Fig. 5) levels. At the phylum level, the predominant bacterial communities in Group A were Fusobacteria (43.90%), Proteobacteria (29.59%), Firmicutes (22.84%), Bacteroidetes (1.58%), and Actinobacteria (0.89%). In Group B, the dominant bacterial communities were Fusobacteria (37.83%), Proteobacteria (25.77%), Bacteroidetes (21.47%), Firmicutes (12.37%), and Actinobacteria (1.98%). The predominant bacterial communities in Group C were Firmicutes (77.03%), Fusobacteria (10.11%), Proteobacteria (9.50%), Bacteroidetes (2.09%), and Gemmatimonadetes (0.23%). Fusobacteria and Proteobacteria indicated that the dominant bacterial communities in largemouth bass under the three farming models were similar at the phylum level; however, their abundances differed. At the genus level, the top five genera in Group A were Cetobacterium (43.90%), uncultured (19%), Aeromonas (13.33%), Plesiomonas (11.52%), and Mycoplasma (4.44%). In Group B, the top five genera were Cetobacterium (37.83%), Acinetobacter (12.25%), Mycoplasma (12.04%), Chryseobacterium (7.81%), and Flavobacterium (6.54%). In Group C, the top five genera were Mycoplasma (76.87%), Cetobacterium (10.07%), Rheinheimera (3.00%), Plesiomonas (1.71%), and Aeromonas (1.19%). Evidently, there were differences in the types and proportions of dominant genera in the intestinal microbiota of largemouth bass under the three farming models. Principal co-ordinates analysis (PCoA) A PCoA based on Bray‒Curtis was performed to analyze whether there were differences in the intestinal microbial composition of largemouth bass among the three farming models. Principal component 1 (PCo1) served as the first principal coordinate, with a representative contribution rate of 52.51% to the total intestinal microbiota detected; whereas, principal component 2 (PCo2) was the second principal coordinate, with a contribution rate of 18.88% (Fig. 6). Closer points indicated higher similarity with regard to the representative flora composition. Group A was mainly clustered in the upper left, Group B was mainly clustered in the lower left, and Group C was mostly clustered in the lower right. Group C was far from Groups A and B, indicating that the community structure of the intestinal microbiota of largemouth bass under the three farming models differed, with significant differences in the intestinal microbiota structure between Groups C and B. Discussion Effect of farming models on largemouth bass morphological indices The morphological indices of fish (e.g., CF, HSI, and VSI) reflected their nutritional status to a certain extent. CF reflects the degree of fatness and growth of fish, HSI indicates the health status of fish, and VSI reflects the edible portion of fish(Schwitzguébel and Wang, 2007 ; Feng et al., 2024 ). Fish that are healthy and well-shaped and lower fat content, and more edible parts are the most popular amongst consumers. The findings of the current study demonstrated that the CF of Group C was significantly lower than that of Group A; whereas, VSI and HSI exhibited differences that were not significant. These results are similar to those reported by Jia et al.(2022) for largemouth bass raised in recirculating aquaculture and traditional pond systems, indicating that the largemouth bass in high-position pond farming had a superior appearance and more edible parts. This was mainly due to the high density and certain flow rate environment in Group C, which required considerable exercise during feeding and other activities. High physical activity led to the breakdown of fats to provide energy, inhibiting fat deposition and resulting in lower CF, HSI, and VSI, thus making the fish more streamlined. CF, HSI, and VSI of Group B were higher than those of Groups A and C. This result differs from those of previous studies, which may have been due to differences in flow intensity. Relevant research indicates that appropriate exercise intensity can increase fat deposition in fish, leading to higher CF, HSI, and VSI (Young and Cech Jr., 1994 ; Bjørnevik et al., 2003 ; Merino et al., 2007 ). Li et al ( 2013 ) found that exercise at speeds of 1.0 and 1.5 bl/s is conducive to fat deposition in fish, resulting in increased CF, HSI, and VSI. Therefore, different farming modes affected the morphological characteristics of fish, revealing the relationship between the flow rate and fat deposition to determine the optimal model for ensuring largemouth bass yield. Effect of farming models on largemouth bass muscle composition The composition and content of conventional nutrients are indicators of the nutritional value of fish muscle, and are important for evaluating fish muscle quality. Factors such as the living environment, feed nutrition, and fish feeding methods can all affect their nutritional composition (Tang et al., 2012 ; Hanssen et al., 2012 ; Zhao et al., 2018 ). In this experiment, muscles of largemouth bass in Group B had a higher crude protein content and lower crude fat content than those in Groups A and C. This result is consistent with that of a previous study which found that yellow catfish ( Pelteobagrus fulvidraco ) reared in recirculating pond systems had lower fat and higher protein contents than those reared in traditional ponds (Hanssen et al., 2012 ), and may have been because the largemouth bass in Group B were more physically active, resulting in a higher metabolic rate and increased energy consumption. This leads to significant depletion of glycogen in muscles and breakdown of stored fat, thereby reducing the fat content. Additionally, exercise promotes an increase in the protein synthesis rate, whereby synthesis exceeds decomposition, resulting in an increased protein content (Yuan et al., 2019 ). The nutritional value of muscle is primarily linked to essential amino acids, while the taste quality of muscle is associated with the composition and content of flavor amino acids (Glencross, 2009 ; Wang et al., 2017 ; Ryu et al., 2021a ). In the present study, the total amounts of essential and flavor amino acids (aspartic acid, glutamic acid, alanine, and glycine) in largemouth bass muscle in Group B were higher than those in Groups A and C. This indicated that muscle of largemouth bass in Group B was richer in essential and flavor amino acids than in Groups A and C, resulting in an improved flavor. The main reason for these differences may have been the higher exercise intensity in the Group B rearing system, leading to adaptive changes in metabolism and enzyme activity, which affect the storage of substances in fish and thereby influence the amino acid composition (Gaye-Siessegger et al., 2006 ). This finding was confirmed in a previous study on the effect of swimming training on the flesh quality of Chinese perch ( Siniperca chuatsi ) (Zhu et al., 2023 ). Fatty acids are essential nutrients for the human body (Pyz-Łukasik et al., 2020 ). MUFA can lower blood sugar, regulate blood lipids, and reduce cholesterol levels; whereas, PUFA can regulate lipids, prevent cardiovascular diseases, and promote growth and development (Aronis et al., 2012 ). Previous studies have shown that palmitic and oleic acids lower cholesterol levels and alleviate thrombosis (Temme et al., 1999 ). EPA and DHA have significant effects on lowering blood lipids and cholesterol and anti-inflammatory properties (Zárate, 2017 ). However, only a small portion of linoleic acid in the human body can be converted into EPA and DHA; and, more needs to be obtained through the diet (Ahmed et al., 2022 ). In the present study, largemouth bass muscles in all three groups were rich in palmitic and oleic acids, and the EPA + DHA content in Group B was significantly higher than that in Groups A and C. This indicated that the nutritional value of the largemouth bass muscle in Group B was higher than that in Groups A and C. In conclusion, recirculating aquaculture systems can enhance the quality of largemouth bass muscle more effectively than ponds or high-density tanks. Gut microbiota composition and community structure differences Gut microbiota play important roles in the nutrition, physiology, and health of aquatic animals (Uma et al., 2020 ). Studies on Acanthopagrus schlegelii (Sun et al., 2021 ), Labeo rohita (Sun et al., 2021 ), and Apostichopus japonicus (Wang et al., 2018 ) found that different farming environments have significant effects on the composition and community structure of aquatic animal gut microbiota, consistent with the results of the present study. Gut microbiota plays a key role in regulating host immunity and maintaining immune homeostasis. The main bacteria in fish intestines are Proteobacteria , Firmicutes , Fusobacteria , and Actinobacteria (Li et al., 2021 ; Wang et al., 2024 ). In the present study, the dominant phyla in the gut of largemouth bass in farming models A, B, and C were Fusobacteria , Proteobacteria , and Firmicutes , which are the main bacterial phyla in fish intestines. Generally, common bacteria in the digestive tracts of marine fish include Vibrio , Pseudomonas , and Acinetobacter (Egerton et al., 2018 ); whereas, common bacteria in the digestive tracts of freshwater fish include Aeromonas , Plesiomonas , Enterobacter , and Pseudomonas (Austin, 2006 ). Notably, the dominant genus in Groups A and B was Cetobacterium ; whereas, that in Group C was Mycoplasma , which differs from the common microbiota in the gut of freshwater fish. Cetobacterium is a bacterium predominantly found in the gut of many fish as a commensal organism. (Larsen et al., 2014 ; Ray et al., 2017 ; Xie et al., 2022 ). Cetobacterial fermentation products regulate lipid metabolism and improve digestion efficiency, immune response, and gut health in fish (Xie et al., 2021 ). Adding Cetobacterium fermentation products to carp feed improved gut and liver health and reduced lipid deposition in the liver(Xie et al., 2022 ). Cetobacterium fermentation products can also regulate the bacteria and virus composition in the gut of Nile tilapia ( Oreochromis niloticus ) (Zhou et al., 2022 ), enrich beneficial bacteria, and increase pathogen resistance. Therefore, farming models used in Groups A and B positively influenced lipid metabolism, digestion, and disease resistance in largemouth bass. Mycoplasma is widely present in various life forms, ranging from plants to higher animals (Pawar et al., 2012 ), and is a major component of the gut microbiota of farmed and wild salmon (Holben et al., 2002 ). Although Mycoplasma is traditionally considered pathogenic, recent studies have found that it is a part of normal gut microbiota in some fish species (Biasato et al., 2022 ). In the present study, the dominant genus in the gut of largemouth bass in Group C was Mycoplasma ; however, no disease was observed, indicating that its role in the fish gut requires further research. Conclusions In conclusion, the largemouth bass in Group B had significantly higher protein, flavor amino acid, PUFA, and DHA + EPA contents and higher gut microbiota diversity than the other two farming models. These differences were closely related to the farming water environment, exercise intensity, and water flow rate. These characteristics resulted in superior nutritional quality and flavor of largemouth bass in Group B compared to those in Groups A and C. Declarations Ethics approval: The experimental protocol was approved by the Experimental Animal Ethics Committee, College of College of Smart Agriculture, Technology Innovation Center of Ecological Fishery Industrialization, Chongqing University of Arts and Sciences, China. Conflicts of interest: The authors declare that there are no conflicts of interest regarding the publication of the work described in this manuscript. Author contributions: G.D. Hu.: Investigation, Data curation, Writing-review & editing. H.D. Hu.: Formal analysis, writing-original draft preparation. Y.X. Deng.: Methodology, Formal analysis. Y.J. Wu: software, Literature comparative dialogue. Y.M. You: software, Literature comparative dialogue. H.C. Sun.: Writing-review & editing, Project administration, and Funding acquisition. Acknowledgements: None Financial support: This work was supported by the Chongqing Aquatic Science and Technology Innovation Key Project (CQFTIU2024-10) and Major Science and Technology Research Project of Chongqing Municipal Education Commission (KJZD-M202401301). Data availability statement: The datasets used and analysed during the current study available from the corresponding author on reasonable request. Arrive statement: The study is reported according to the ARRIVE guidelines , and the Author Checklist - Full has been uploaded to the Related Files section. References Ahmed I, Jan K, Fatma S and Dawood MAO 2022. 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Uma A, Subash P and Abraham TJ 2020. Importance of gut microbiota in fish – A review. Indian Journal of Animal Health 59, 181–194. Wang C 2021. A comparative study on growth, muscle cellularity and flesh quality of farmed and imitative ecological farming loach, Misgurnus anguillicaudatus. Aquaculture 543, 736933. Wang L, Li J, Jin JN, Zhu F, Roffeis M and Zhang XZ 2017. A comprehensive evaluation of replacing fishmeal with housefly ( Musca domestica ) maggot meal in the diet of Nile tilapia ( Oreochromis niloticus ): growth performance, flesh quality, innate immunity and water environment. Aquaculture Nutrition 23, 983–993. Wang Z-Z, Wang Z-T, Wang W-L, Lei K-K and Zhou J-S 2024. Effects of Different Farming Modes on Salmo trutta fario Growth and Intestinal Microbial Community. Microorganisms 12, 1082. Wang Q, Zhang X, Chen M, Li W and Zhang P 2018. Comparison of intestinal microbiota and activities of digestive and immune-related enzymes of sea cucumber Apostichopus japonicus in two habitats. Journal of Oceanology and Limnology 36, 990–1001. Xie M, Xie Y, Li Y, Zhou W, Zhang Z, Yang Y, Olsen RE, Ringø E, Ran C and Zhou Z 2022. Stabilized fermentation product of Cetobacterium somerae improves gut and liver health and antiviral immunity of zebrafish. Fish & Shellfish Immunology 120, 56–66. Xie M, Zhou W, Xie Y, Li Y, Zhang Z, Yang Y, Olsen RE, Ran C and Zhou Z 2021. Effects of Cetobacterium somerae fermentation product on gut and liver health of common carp (Cyprinus carpio) fed diet supplemented with ultra-micro ground mixed plant proteins. Aquaculture 543, 736943. Young PS and Cech Jr. JJ 1994. Optimum Exercise Conditioning Velocity for Growth, Muscular Development, and Swimming Performance in Young-of-the-Year Striped Bass ( Morone saxatilis ). Canadian Journal of Fisheries and Aquatic Sciences 51, 1519–1527. Yu P, Chen H, Liu M, Zhong H, Wang X, Wu Y, Sun Y, Wu C, Wang S, Zhao C, Luo C, Zhang C, Hu F and Liu S 2024. Current status and application of largemouth bass ( Micropterus salmoides ) germplasm resources. Reproduction and Breeding 4, 73–82. Yuan J, Ni M, Liu M, Wang H, Zhang C, Mi G and Gu Z 2019. Analysis of the growth performances, muscle quality, blood biochemistry and antioxidant status of Micropterus salmoides farmed in in-pond raceway systems versus usual-pond systems. Aquaculture 511, 734241. Zárate R 2017. Significance of long chain polyunsaturated fatty acids in human health. Zhao H, Xia J, Zhang X, He X, Li L, Tang R, Chi W, Li D and Li D 2018. Diet Affects Muscle Quality and Growth Traits of Grass Carp (Ctenopharyngodon idellus): A Comparison Between Grass and Artificial Feed. Frontiers in Physiology 9. Zhou W, Xie M, Xie Y, Liang H, Li M, Ran C and Zhou Z 2022. Effect of dietary supplementation of Cetobacterium somerae XMX-1 fermentation product on gut and liver health and resistance against bacterial infection of the genetically improved farmed tilapia (GIFT, Oreochromis niloticus). Fish & Shellfish Immunology 124, 332–342. Zhu T, Yang R, Xiao R, Ni W, Liu L, Zhao J and Ye Z 2023. Effect of swimming training on the flesh quality in Chinese Perch (Siniperca chuatsi) and its relationship with muscle metabolism. Aquaculture 577, 739926. Liu M, Lian Q, Ni M, Guo A and Yuan J 2021. Effects of inner-pond raceway aquaculture on the growth performance, antioxidant enzymes, digestive enzymes, digestive tract structure, and bacterial flora of largemouth bass (Micropterus salmoides) 45, 2011–2028. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4767517","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":342626607,"identity":"d7a911a3-0a23-4be9-847c-8889c1a95be0","order_by":0,"name":"G. D. Hu","email":"","orcid":"","institution":"Chongqing University of Arts and Sciences","correspondingAuthor":false,"prefix":"","firstName":"G.","middleName":"D.","lastName":"Hu","suffix":""},{"id":342626609,"identity":"a70b22c7-8e43-49b0-ab9c-f2ed8ee2254a","order_by":1,"name":"H. D. 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10:27:30","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4767517/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4767517/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":62956249,"identity":"277449a1-ff6f-46af-b875-9bc48cd62cbc","added_by":"auto","created_at":"2024-08-21 12:21:10","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":345342,"visible":true,"origin":"","legend":"\u003cp\u003eComposition by genera of \u003cem\u003eMicropterus salmoides\u003c/em\u003e intestinal microflora\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4767517/v1/c99a345a75b929c7f3a29445.png"},{"id":62954228,"identity":"c4976405-4f3c-46b6-8808-da9a40cb7c33","added_by":"auto","created_at":"2024-08-21 11:57:10","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":101292,"visible":true,"origin":"","legend":"\u003cp\u003eAlpha diversity index of intestinal microbiota under different farming modes\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4767517/v1/99a55d18a149cc29269834ad.png"},{"id":62954813,"identity":"b169067f-fbb8-4ff6-a09e-66186a9ec87c","added_by":"auto","created_at":"2024-08-21 12:05:10","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":31752,"visible":true,"origin":"","legend":"\u003cp\u003eComposition of operational taxonomic units (OTUs) in largemouth bass intestinal microbiota under different farming modes\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4767517/v1/03596c3a54bab5f9d880b768.png"},{"id":62954816,"identity":"d0b64503-c757-4427-baa6-757964a028c6","added_by":"auto","created_at":"2024-08-21 12:05:10","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":130630,"visible":true,"origin":"","legend":"\u003cp\u003eComposition by phylum of intestinal microflora of \u003cem\u003eMicropterus salmoides\u003c/em\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4767517/v1/0e39769e59fa8f70a45e0c66.png"},{"id":62955529,"identity":"a473b6f7-5594-46fb-84ab-9382de709c52","added_by":"auto","created_at":"2024-08-21 12:13:10","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":126093,"visible":true,"origin":"","legend":"\u003cp\u003eComposition by genus of intestinal microflora of \u003cem\u003eMicropterus salmoides\u003c/em\u003e\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4767517/v1/47a1f77b7f354bb6ff9e8555.png"},{"id":62954235,"identity":"8460c2cf-45a9-4d2d-a64d-181d7feb77d6","added_by":"auto","created_at":"2024-08-21 11:57:10","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":62765,"visible":true,"origin":"","legend":"\u003cp\u003ePrincipal co-ordinates analysis (PCoA) based on operational taxonomic unit (OTU) levels under different farming modes\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-4767517/v1/5bc800ea0d3590315429ff56.png"},{"id":77059123,"identity":"5073450d-3c2f-4777-9641-c86f897ab4ff","added_by":"auto","created_at":"2025-02-24 17:08:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2002416,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4767517/v1/fd075baa-4f22-4463-8f65-e965addeec26.pdf"},{"id":62954233,"identity":"52ee9e69-4e89-40a0-8963-0f1521207f7c","added_by":"auto","created_at":"2024-08-21 11:57:10","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":303172,"visible":true,"origin":"","legend":"","description":"","filename":"AuthorChecklistFulld1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4767517/v1/8712d93f53630476647c58f3.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Effects of different culture modes on growth, muscle nutrition, and intestinal microbiota of largemouth bass","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLargemouth bass (\u003cem\u003eMicropterus salmoides\u003c/em\u003e), also known as California bass, belongs to order Perciformes, family Centrarchidae, and genus \u003cem\u003eMicropterus\u003c/em\u003e. This species was first introduced in China in the 1980s (Yu et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Because of its short farming cycle, appetizing taste, and lack of intermuscular bones, it has been widely farmed and has become an important freshwater aquaculture species in China (Edwards, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Largemouth bass are mainly farmed in traditional ponds, which have advantages of short farming cycles, high yields, and low entry barriers. However, traditional pond farming is characterized by extensive development, outdated facilities, increasing production costs, and increased environmental pressure. In recent years, aquaculture has gradually moved towards \u0026ldquo;green, healthy, and ecological farming.\u0026rdquo; Flow-through farming (i.e., microflow aquaculture within ponds) and high-position pond farming are common farming modes that achieve low-carbon, efficient, and green farming effects more effectively than pond farming, with higher yields per unit farming area and more sustainable ecological benefits (Martins et al., 2010).\u003c/p\u003e \u003cp\u003eMorphological characteristics, muscle nutrition, and flavor of aquatic products vary among farming models used in the aquaculture industry (Wang, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Duan et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Jones et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). For example, Jia et al. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) studied largemouth bass under traditional farming and land-based water-pushing container farming models and found that bass reared in land-based containers had a more elongated body shape and significantly higher crude protein, saturated fatty acid (SFA), and polyunsaturated fatty acid (PUFA) contents than those reared in ponds. This demonstrates the correlation between the morphological characteristics and muscle nutritional components of aquatic products and their farming models. Different farming models for aquatic products also affect the gut microbiota structure. Luo et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) investigated the gut microbiota structure of crayfish reared in rice fields and ponds and found that Proteobacteria abundance in the gut of crayfish reared in rice fields was higher than in those reared in ponds; whereas, Tenericutes abundance was the opposite.\u003c/p\u003e \u003cp\u003eAt present, the research on the intestinal microbiota structure of largemouth bass in farming mode can only be found in Liu et al.(2021) : they analyzed the intestinal microbiota structure of largemouth bass in static and flowing water groups under the pond internal circulation water farming mode and found that the intestinal microbiota structures of the two groups were different. Similarly, there were differences in the morphological characteristics and muscle nutritional components of largemouth bass under internal circulation water pond farming and conventional pond farming modes. However, comprehensive comparative analyses of the morphological characteristics, muscle nutrition, and intestinal microbiota of largemouth bass in traditional pond, flow-through, and high-position pond farming modes are lacking. In view of this, the present study focused on largemouth bass under three farming modes and explored the differences in morphological characteristics, muscle nutrition, and intestinal microbiota structure and diversity and provides technical support for different farming modes of largemouth bass.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eExperimental materials\u003c/h2\u003e \u003cp\u003eLargemouth bass were purchased from Yongchuan District, Chongqing City, and were from the same batch of fry. The pond group (Group A) had a farming density of 20 kg/m\u0026sup3;, the flow-through group (Group B) had a farming density of 20 kg/m\u0026sup3;, and the high-position pond group (Group C) had a farming density of 30 kg/m\u0026sup3;. The initial stocking weight was 50 (\u0026plusmn;\u0026thinsp;2.75) g, and the three groups were stocked simultaneously. The rearing period was 180 d, during which the fish were fed the same brand and specification of feed (crude protein\u0026thinsp;\u0026ge;\u0026thinsp;48.0%; crude fat\u0026thinsp;\u0026ge;\u0026thinsp;6.0%; crude fiber\u0026thinsp;\u0026le;\u0026thinsp;6.0%; crude ash\u0026thinsp;\u0026le;\u0026thinsp;16.0%). The average weight per fish of Group A was 438.36 (\u0026plusmn;\u0026thinsp;40.54) g, that of Group B was 370.39 (\u0026plusmn;\u0026thinsp;42.27) g, and that of Group C was 284.51 (\u0026plusmn;\u0026thinsp;19.98) g.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eMethods\u003c/h2\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003eDetermination of morphology and body indexes\u003c/h2\u003e \u003cp\u003eAt the beginning of the test, 15 tails experimental fish were randomly sampled from each group. After accurately measuring their body weights and lengths, their internal organs and livers were dissected and weighed to calculate the liver-to-somatic ratio (hepatopancreas somatic index [HSI]) and visceral-to-somatic ratio (visceral somatic index [VSI]), and the degree of fatness (condition factor [CF]) was calculated based on the body lengths and weights. The relevant formulae are as follows:\u003c/p\u003e \u003cp\u003eHypertrophy (condition factor [CF] g/cm\u003csup\u003e3\u003c/sup\u003e)\u0026thinsp;=\u0026thinsp;100 \u0026times; Wh/L3;\u003c/p\u003e \u003cp\u003eHepatopancreas somatic index (HSI [%])\u0026thinsp;=\u0026thinsp;100 \u0026times; Wh/W; and\u003c/p\u003e \u003cp\u003eVisceral somatic index (VCI [%])\u0026thinsp;=\u0026thinsp;100 \u0026times; Wn/W;\u003c/p\u003e \u003cp\u003ewhere W is fish mass (g); L is fish body length (cm ); Wh is hepatopancreas weight (g), and; Wn is viscera weight (g)\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eMuscle nutrient composition\u003c/h2\u003e \u003cp\u003eTen largemouth bass were randomly selected from each group. Dorsal muscles were dissected, cleaned of attached debris, and weighed immediately. Part of the dissected muscle was temporarily stored in a -80 ℃ freezer for determination of the general nutrient composition and the amino acid and fatty acid compositions and contents.\u003c/p\u003e \u003cp\u003eTo determine the general nutrient composition, the moisture, crude fat, crude protein, and crude ash contents were measured according to GB 5009.3\u0026ndash;2016, GB 5009.4\u0026ndash;2016, GB/T 14772\u0026thinsp;\u0026minus;\u0026thinsp;2016, and GB 5009.5\u0026ndash;2016, respectively. Total amino acid content was determined following the method specified in GB 5009.124\u0026ndash;2016.\u003c/p\u003e \u003cp\u003eAmino acid determination with reference to GB 5009.124\u0026ndash;2016. Samples were hydrolyzed with acid (6 mol/L HCl solution), and amino acids were analyzed using high-performance liquid chromatography. The tryptophan content was determined separately after alkaline hydrolysis (5 mol/L NaOH solution) using a Sykam automatic amino acid analyzer S433D and Agilent liquid chromatography column (4.6 x 150 mm, 5 \u0026micro;m). The fatty acid composition and content were determined following GB 5009.168\u0026ndash;2016. Gas chromatography was performed using a Shimadzu GC2010 instrument equipped with a hydrogen flame ionization detector and a Supelco SP-2560 gas capillary column.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eIntestinal microbiota detection\u003c/h2\u003e \u003cp\u003eEight largemouth bass were randomly selected from each group and disinfected by wiping with 75% ethanol followed by washing thrice with sterile saline. The entire intestine was extracted in a sterile environment, and the intestinal contents (\u0026gt;\u0026thinsp;100 mg) were placed in 2-mL cryogenic vials, rapidly frozen in liquid nitrogen, and stored at -80 ℃. Samples were transported on dry ice to Nanjing Aoweisen Gene Technology Co. Ltd. for sequencing.\u003c/p\u003e \u003cp\u003e statement: (i) The experimental protocol was approved by the Experimental Animal Ethics Committee, College of College of Smart Agriculture, Technology Innovation Center of Ecological Fishery Industrialization, Chongqing University of Arts and Sciences, China; (ii) All experiments were performed in accordance with guidelines and regulations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eData processing and statistics\u003c/h2\u003e \u003cp\u003eData were processed using Microsoft Excel 2010. Significance analysis of the data was performed using independent sample t-tests in SPSS 18.0. The results were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD). Statistical significance was set at \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01 indicated a highly significant difference.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\"\u003e\n \u003ch2\u003eDifferences in largemouth bass morphological indices\u003c/h2\u003e\n \u003cp\u003eIn Table\u0026nbsp;\u003cspan\u003e1\u003c/span\u003e, the CF, HSI, and VSI of largemouth bass in Group B exhibited significantly greater values compared to those in Group C (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Furthermore, the VSI in Group B was significantly higher than that in Group A (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), with the HSI showing a slightly higher value in Group B compared to Group A, though this difference was not statistically significant (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). CF did not differ significantly between Groups A and B (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 1\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eDifferences in morphometric indices of \u003cem\u003eMicropterus salmoides\u003c/em\u003e for Groups A, B, and C (n\u0026thinsp;=\u0026thinsp;15)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCF/(g/cm\u0026sup3;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.09\u0026thinsp;\u0026plusmn;\u0026thinsp;016a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.99\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.81\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05b\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHSI (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.61\u0026thinsp;\u0026plusmn;\u0026thinsp;0.24ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.94\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.55\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15b\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVSI (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.79\u0026thinsp;\u0026plusmn;\u0026thinsp;1.03b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.70\u0026thinsp;\u0026plusmn;\u0026thinsp;1.75a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.63\u0026thinsp;\u0026plusmn;\u0026thinsp;0.85b\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003eValues are reported as means\u0026thinsp;\u0026plusmn;\u0026thinsp;SD. Values with different superscript letters within the same row indicate significant differences (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), whereas values with the same superscript letters within the same row indicate no significant differences (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). HSI - hepatopancreas somatic index; VSI - visceral somatic index; A: traditional pond farming; B: flow-through farming; C: high-position pond farming\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\"\u003e\n \u003ch2\u003eDifferences in muscle nutrient composition of largemouth bass under different farming models\u003c/h2\u003e\n \u003cp\u003eThere were no significant differences in the muscle moisture content among the groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05; Table\u0026nbsp;\u003cspan\u003e2\u003c/span\u003e). The crude muscle ash content in Group B was significantly lower than that in Group A (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), but was not significantly lower than that in Group C (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). The muscle crude fat contents in Groups B and C were significantly lower than that in Group A (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The muscle protein content in Group B was significantly higher than that in Group A (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), but was not significantly higher than that in Group C (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 2\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eDifferences in routine nutritional components in \u003cem\u003eMicropterus salmoides\u003c/em\u003e muscle in Groups A, B, and C (n\u0026thinsp;=\u0026thinsp;10)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGroup\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCrude moisture\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCrude ash\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCrude fat\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCrude protein\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e77.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.83a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.71\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.97\u0026thinsp;\u0026plusmn;\u0026thinsp;0.71b\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76.97\u0026thinsp;\u0026plusmn;\u0026thinsp;0.89a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1. 15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19. 12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.90a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e77. 18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.68a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.48\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.66\u0026thinsp;\u0026plusmn;\u0026thinsp;0.47ab\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003eValues are reported as means\u0026thinsp;\u0026plusmn;\u0026thinsp;SD. Values with different superscript letters within the same row indicate significant differences (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), whereas values with the same superscript letters within the same row indicate no significant differences (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05); A: traditional pond farming; B: flow-through farming; C: high-position pond farming;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\"\u003e\n \u003ch2\u003eDifferences in amino acids in largemouth bass muscles\u003c/h2\u003e\n \u003cp\u003eThe amino acid composition and muscle content of the three largemouth bass groups are listed in Table\u0026nbsp;\u003cspan\u003e3\u003c/span\u003e. Eighteen amino acids were detected in muscles of fish in all three groups, including eight essential, two semi-essential, and eight non-essential amino acids. Among these, total amino acids, total non-essential amino acids, total umami amino acids (including glutamic acid and alanine), and total aromatic amino acids were significantly higher in Group B than in Groups A and C (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Except for glutamic acid, alanine, lysine, and arginine, which were significantly higher in Group B than in Groups A and C (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05); there were no significant differences in other amino acids (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). The proportion of each amino acid was similar among the three groups, with glutamic acid having the highest content, followed by aspartic acid, lysine, leucine, and alanine, and cystine having the lowest content. The ratios of essential amino acids to total amino acids (EAA/TAA) in the A、B and C groups were 41.1, 41.08, and 41.07%, respectively; the ratios of total essential amino acids to total non-essential amino acids (EAA/NEAA) were 80.05, 81.01, and 81.01%, respectively, and; the ratios of total umami amino acids to total amino acids (DAA/TAA) were 38.08, 38.05, and 38.04%, respectively.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 3\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eAmino acid composition in \u003cem\u003eMicropterus salmoides\u003c/em\u003e muscle in Groups A, B, and C (n\u0026thinsp;=\u0026thinsp;10)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGroup\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAmino acid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLeu\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.55\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.57\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.55\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.87\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.89\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.88\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePhe○\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.82\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.85\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.82\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLys\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.78\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.84\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.76\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01b\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMet\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.60\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.63\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.61\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThr\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.89\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.89\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.89\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.92\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.96\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.96\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTry\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0. 18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0. 18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0. 17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.52\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.55\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.50\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eArg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1. 12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1. 15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1. 11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03b\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGlu*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3. 19\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3. 19\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04b\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAsp*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGly*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.86\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.87\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.81\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAla*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1. 15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1. 19\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1. 16\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02b\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.77\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.78\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.78\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePro\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.63\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.63\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.62\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTyr○\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.68\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.69\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.68\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCys\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026sum;TAA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.75\u0026thinsp;\u0026plusmn;\u0026thinsp;0.28b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19. 16\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.72\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21b\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026sum;EAA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.61\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.81\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.64\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026sum;NEAA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.50\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.65\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.47\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07b\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026sum;HEAA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.64\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.70\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.61\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026sum;DAA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.20\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7. 17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07b\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026sum;AAA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.50\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.54\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.50\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03b\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026sum;EAA/TAA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.41\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.41\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.41\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026sum;EAA/NEAA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.80\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.81\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.81\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026sum;DAA/TAA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.38\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.38\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.38\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003eValues are reported as means\u0026thinsp;\u0026plusmn;\u0026thinsp;SD. Values with different superscript letters within the same row indicate significant differences (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), whereas values with the same superscript letters within the same row indicate no significant differences (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05); \u0026ldquo;*\u0026rdquo; represents delicious amino acids (DAA); \u0026ldquo;○\u0026rdquo; represents aromatic amino acid; \u0026sum;TAA is total amino acids; \u0026sum;EAA is total essential amino acids; \u0026sum;NEAA is total non-essential amino acids; \u0026sum;HEAA is total semi-essential amino acids; \u0026sum;DAA is total fresh flavor amino acids; \u0026sum;AAA is an aromatic amino acid; A: traditional pond farming; B: flow-through farming; C: high-position pond farming;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\"\u003e\n \u003ch2\u003eDifferences in fatty acid composition in largemouth bass muscle under different farming models\u003c/h2\u003e\n \u003cp\u003eThe amino acid compositions and muscle contents of the three groups of largemouth bass are listed in Table\u0026nbsp;\u003cspan\u003e4\u003c/span\u003e. Fifteen fatty acids were identified in muscles of fish in all three groups, including four saturated fatty acids (SFA), four monounsaturated fatty acids (MUFA), and seven polyunsaturated fatty acids ( PUFA). There were significant differences in the SFA content among the three groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), with Group C having the highest content, followed by Groups B and A. The MUFA content in Group B was markedly lower than that in Group A (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and extremely markedly lower than that in Group C (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). The PUFA content in Group B was markedly higher than that in Group A (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and extremely markedly higher than that in Group C (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). The main SFA in the muscles of the three groups of largemouth bass was palmitic acid, the main MUFA was oleic acid, and the main PUFA were linoleic acid and DHA. Additionally, the relative content of eicosapentaenoic acid, docosahexaenoic acid sum (EPA\u0026thinsp;+\u0026thinsp;DHA) in muscles of fish in Group B was markedly higher than those in Groups A and C (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 4\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eFatty acid composition in \u003cem\u003eMicropterus salmoides\u003c/em\u003e muscle in Groups A, B, and C (n\u0026thinsp;=\u0026thinsp;10)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGroup\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFatty acid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC14:0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.14\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04c\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC16:0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.95\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.32\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC18:0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.97\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.71\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC20:0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026Sigma;SFA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.34\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.71\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.38\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC14:1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0031\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0039\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0027\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00c\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC16:1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.94\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.31\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.14\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC18:1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.76\u0026thinsp;\u0026plusmn;\u0026thinsp;0.23\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.74\u0026thinsp;\u0026plusmn;\u0026thinsp;0.27\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.73\u0026thinsp;\u0026plusmn;\u0026thinsp;0.34\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC20:1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.45\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04b\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026Sigma;MUFA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41.94\u0026thinsp;\u0026plusmn;\u0026thinsp;0.19b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.51\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.27a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC18:2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.23\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.70\u0026thinsp;\u0026plusmn;\u0026thinsp;0.27\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.78\u0026thinsp;\u0026plusmn;\u0026thinsp;0.19\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC18:3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.47\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.39\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC20:2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.48\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.47\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.35\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00b\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC20:3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.36\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00b\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC20:4 (ARA)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC20:5 (EPA)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.95\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.97\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC22:6 (DHA)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.14\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.64\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.67\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07c\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026Sigma;PUFA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.82\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.23a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.65\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16c\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003eValues are reported as means\u0026thinsp;\u0026plusmn;\u0026thinsp;SD. Values with different superscript letters within the same row indicate significant differences (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), whereas values with the same superscript letters within the same row indicate no significant differences (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05); \u0026Sigma;SFA is saturated fatty acid; \u0026Sigma;MUFA is monounsaturated fatty acid; \u0026Sigma;PUFA is polyunsaturated fatty acid; A: traditional pond farming; B: flow-through farming; C: high-position pond farming;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\"\u003e\n \u003ch2\u003eEffects of farming models on largemouth bass intestinal microbiota\u003c/h2\u003e\n \u003cdiv id=\"Sec15\"\u003e\n \u003ch2\u003eAnalysis of rarefaction curves and intestinal microbiota diversity differences\u003c/h2\u003e\n \u003cp\u003eThe rarefaction and Shannon‒Wiener curves for all samples were nearly flat (Fig.\u0026nbsp;1), indicating that the sequencing depth and data volume were sufficient to reflect the biological information of the bacterial communities in each sample. Chao1, Simpson, and Shannon indexes were selected to reflect the \u0026alpha;-diversity of the intestinal microbiota of largemouth bass under different farming models (Fig.\u0026nbsp;2). According to the Chao1 index, the species richness of the intestinal microbiota of largemouth bass under the three farming models decreased from Group A\u0026thinsp;\u0026gt;\u0026thinsp;Group B\u0026thinsp;\u0026gt;\u0026thinsp;Group C. According to the Shannon index, the diversity of the intestinal microbiota of largemouth bass under different farming models decreased from Group B\u0026thinsp;\u0026gt;\u0026thinsp;Group A\u0026thinsp;\u0026gt;\u0026thinsp;Group C.\u003c/p\u003e\n \u003cdiv\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\"\u003e\n \u003ch2\u003eDifferences in intestinal microbiota composition\u003c/h2\u003e\n \u003cp\u003eAfter conducting high-throughput sequencing and processing of largemouth bass intestinal microbiota data across three different farming models, 1152 operational taxonomic units (OTUs) were identified (Fig.\u0026nbsp;3). The Venn diagram analysis showed that the number of OTUs in Groups A, B, and C were 750, 782, and 623, respectively. Among these, 296 OTUs were shared by the intestinal microbiota across the three farming models, and 84, 128, and 233 unique OTUs were found in Groups A, B, and C, respectively.\u003c/p\u003e\n \u003cp\u003eBased on the species annotation results at the phylum and genus levels for all the samples, relative abundance bar plots were created at the phylum (Fig.\u0026nbsp;4) and genus (Fig.\u0026nbsp;5) levels. At the phylum level, the predominant bacterial communities in Group A were Fusobacteria (43.90%), Proteobacteria (29.59%), Firmicutes (22.84%), Bacteroidetes (1.58%), and Actinobacteria (0.89%).\u003c/p\u003e\n \u003cdiv\u003e\n \u003c/div\u003e\n \u003cp\u003eIn Group B, the dominant bacterial communities were Fusobacteria (37.83%), Proteobacteria (25.77%), Bacteroidetes (21.47%), Firmicutes (12.37%), and Actinobacteria (1.98%). The predominant bacterial communities in Group C were Firmicutes (77.03%), Fusobacteria (10.11%), Proteobacteria (9.50%), Bacteroidetes (2.09%), and Gemmatimonadetes (0.23%). Fusobacteria and Proteobacteria indicated that the dominant bacterial communities in largemouth bass under the three farming models were similar at the phylum level; however, their abundances differed. At the genus level, the top five genera in Group A were \u003cem\u003eCetobacterium\u003c/em\u003e (43.90%), uncultured (19%), \u003cem\u003eAeromonas\u003c/em\u003e (13.33%), \u003cem\u003ePlesiomonas\u003c/em\u003e (11.52%), and \u003cem\u003eMycoplasma\u003c/em\u003e (4.44%). In Group B, the top five genera were \u003cem\u003eCetobacterium\u003c/em\u003e (37.83%), \u003cem\u003eAcinetobacter\u003c/em\u003e (12.25%), \u003cem\u003eMycoplasma\u003c/em\u003e (12.04%), \u003cem\u003eChryseobacterium\u003c/em\u003e (7.81%), and \u003cem\u003eFlavobacterium\u003c/em\u003e (6.54%). In Group C, the top five genera were \u003cem\u003eMycoplasma\u003c/em\u003e (76.87%), \u003cem\u003eCetobacterium\u003c/em\u003e (10.07%), \u003cem\u003eRheinheimera\u003c/em\u003e (3.00%), \u003cem\u003ePlesiomonas\u003c/em\u003e (1.71%), and \u003cem\u003eAeromonas\u003c/em\u003e (1.19%). Evidently, there were differences in the types and proportions of dominant genera in the intestinal microbiota of largemouth bass under the three farming models.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\"\u003e\n \u003ch2\u003ePrincipal co-ordinates analysis (PCoA)\u003c/h2\u003e\n \u003cp\u003eA PCoA based on Bray‒Curtis was performed to analyze whether there were differences in the intestinal microbial composition of largemouth bass among the three farming models. Principal component 1 (PCo1) served as the first principal coordinate, with a representative contribution rate of 52.51% to the total intestinal microbiota detected; whereas, principal component 2 (PCo2) was the second principal coordinate, with a contribution rate of 18.88% (Fig.\u0026nbsp;6). Closer points indicated higher similarity with regard to the representative flora composition. Group A was mainly clustered in the upper left, Group B was mainly clustered in the lower left, and Group C was mostly clustered in the lower right. Group C was far from Groups A and B, indicating that the community structure of the intestinal microbiota of largemouth bass under the three farming models differed, with significant differences in the intestinal microbiota structure between Groups C and B.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eEffect of farming models on largemouth bass morphological indices\u003c/h2\u003e \u003cp\u003eThe morphological indices of fish (e.g., CF, HSI, and VSI) reflected their nutritional status to a certain extent. CF reflects the degree of fatness and growth of fish, HSI indicates the health status of fish, and VSI reflects the edible portion of fish(Schwitzgu\u0026eacute;bel and Wang, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Feng et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Fish that are healthy and well-shaped and lower fat content, and more edible parts are the most popular amongst consumers. The findings of the current study demonstrated that the CF of Group C was significantly lower than that of Group A; whereas, VSI and HSI exhibited differences that were not significant. These results are similar to those reported by Jia et al.(2022) for largemouth bass raised in recirculating aquaculture and traditional pond systems, indicating that the largemouth bass in high-position pond farming had a superior appearance and more edible parts. This was mainly due to the high density and certain flow rate environment in Group C, which required considerable exercise during feeding and other activities. High physical activity led to the breakdown of fats to provide energy, inhibiting fat deposition and resulting in lower CF, HSI, and VSI, thus making the fish more streamlined. CF, HSI, and VSI of Group B were higher than those of Groups A and C. This result differs from those of previous studies, which may have been due to differences in flow intensity. Relevant research indicates that appropriate exercise intensity can increase fat deposition in fish, leading to higher CF, HSI, and VSI (Young and Cech Jr., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e1994\u003c/span\u003e; Bj\u0026oslash;rnevik et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Merino et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Li et al (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) found that exercise at speeds of 1.0 and 1.5 bl/s is conducive to fat deposition in fish, resulting in increased CF, HSI, and VSI. Therefore, different farming modes affected the morphological characteristics of fish, revealing the relationship between the flow rate and fat deposition to determine the optimal model for ensuring largemouth bass yield.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eEffect of farming models on largemouth bass muscle composition\u003c/h2\u003e \u003cp\u003eThe composition and content of conventional nutrients are indicators of the nutritional value of fish muscle, and are important for evaluating fish muscle quality. Factors such as the living environment, feed nutrition, and fish feeding methods can all affect their nutritional composition (Tang et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Hanssen et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Zhao et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In this experiment, muscles of largemouth bass in Group B had a higher crude protein content and lower crude fat content than those in Groups A and C. This result is consistent with that of a previous study which found that yellow catfish (\u003cem\u003ePelteobagrus fulvidraco\u003c/em\u003e) reared in recirculating pond systems had lower fat and higher protein contents than those reared in traditional ponds (Hanssen et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), and may have been because the largemouth bass in Group B were more physically active, resulting in a higher metabolic rate and increased energy consumption. This leads to significant depletion of glycogen in muscles and breakdown of stored fat, thereby reducing the fat content. Additionally, exercise promotes an increase in the protein synthesis rate, whereby synthesis exceeds decomposition, resulting in an increased protein content (Yuan et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe nutritional value of muscle is primarily linked to essential amino acids, while the taste quality of muscle is associated with the composition and content of flavor amino acids (Glencross, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Ryu et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021a\u003c/span\u003e). In the present study, the total amounts of essential and flavor amino acids (aspartic acid, glutamic acid, alanine, and glycine) in largemouth bass muscle in Group B were higher than those in Groups A and C. This indicated that muscle of largemouth bass in Group B was richer in essential and flavor amino acids than in Groups A and C, resulting in an improved flavor. The main reason for these differences may have been the higher exercise intensity in the Group B rearing system, leading to adaptive changes in metabolism and enzyme activity, which affect the storage of substances in fish and thereby influence the amino acid composition (Gaye-Siessegger et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). This finding was confirmed in a previous study on the effect of swimming training on the flesh quality of Chinese perch (\u003cem\u003eSiniperca chuatsi\u003c/em\u003e) (Zhu et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFatty acids are essential nutrients for the human body (Pyz-Łukasik et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). MUFA can lower blood sugar, regulate blood lipids, and reduce cholesterol levels; whereas, PUFA can regulate lipids, prevent cardiovascular diseases, and promote growth and development (Aronis et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Previous studies have shown that palmitic and oleic acids lower cholesterol levels and alleviate thrombosis (Temme et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). EPA and DHA have significant effects on lowering blood lipids and cholesterol and anti-inflammatory properties (Z\u0026aacute;rate, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). However, only a small portion of linoleic acid in the human body can be converted into EPA and DHA; and, more needs to be obtained through the diet (Ahmed et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In the present study, largemouth bass muscles in all three groups were rich in palmitic and oleic acids, and the EPA\u0026thinsp;+\u0026thinsp;DHA content in Group B was significantly higher than that in Groups A and C. This indicated that the nutritional value of the largemouth bass muscle in Group B was higher than that in Groups A and C. In conclusion, recirculating aquaculture systems can enhance the quality of largemouth bass muscle more effectively than ponds or high-density tanks.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eGut microbiota composition and community structure differences\u003c/h2\u003e \u003cp\u003eGut microbiota play important roles in the nutrition, physiology, and health of aquatic animals (Uma et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Studies on \u003cem\u003eAcanthopagrus schlegelii\u003c/em\u003e (Sun et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), \u003cem\u003eLabeo rohita\u003c/em\u003e (Sun et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and \u003cem\u003eApostichopus japonicus\u003c/em\u003e (Wang et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) found that different farming environments have significant effects on the composition and community structure of aquatic animal gut microbiota, consistent with the results of the present study.\u003c/p\u003e \u003cp\u003eGut microbiota plays a key role in regulating host immunity and maintaining immune homeostasis. The main bacteria in fish intestines are \u003cem\u003eProteobacteria\u003c/em\u003e, \u003cem\u003eFirmicutes\u003c/em\u003e, \u003cem\u003eFusobacteria\u003c/em\u003e, and \u003cem\u003eActinobacteria\u003c/em\u003e (Li et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In the present study, the dominant phyla in the gut of largemouth bass in farming models A, B, and C were \u003cem\u003eFusobacteria\u003c/em\u003e, \u003cem\u003eProteobacteria\u003c/em\u003e, and \u003cem\u003eFirmicutes\u003c/em\u003e, which are the main bacterial phyla in fish intestines. Generally, common bacteria in the digestive tracts of marine fish include \u003cem\u003eVibrio\u003c/em\u003e, \u003cem\u003ePseudomonas\u003c/em\u003e, and \u003cem\u003eAcinetobacter\u003c/em\u003e (Egerton et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2018\u003c/span\u003e); whereas, common bacteria in the digestive tracts of freshwater fish include \u003cem\u003eAeromonas\u003c/em\u003e, \u003cem\u003ePlesiomonas\u003c/em\u003e, \u003cem\u003eEnterobacter\u003c/em\u003e, and \u003cem\u003ePseudomonas\u003c/em\u003e (Austin, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Notably, the dominant genus in Groups A and B was \u003cem\u003eCetobacterium\u003c/em\u003e; whereas, that in Group C was \u003cem\u003eMycoplasma\u003c/em\u003e, which differs from the common microbiota in the gut of freshwater fish. Cetobacterium is a bacterium predominantly found in the gut of many fish as a commensal organism. (Larsen et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Ray et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Xie et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCetobacterial fermentation products regulate lipid metabolism and improve digestion efficiency, immune response, and gut health in fish (Xie et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Adding \u003cem\u003eCetobacterium\u003c/em\u003e fermentation products to carp feed improved gut and liver health and reduced lipid deposition in the liver(Xie et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). \u003cem\u003eCetobacterium\u003c/em\u003e fermentation products can also regulate the bacteria and virus composition in the gut of Nile tilapia (\u003cem\u003eOreochromis niloticus\u003c/em\u003e) (Zhou et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), enrich beneficial bacteria, and increase pathogen resistance. Therefore, farming models used in Groups A and B positively influenced lipid metabolism, digestion, and disease resistance in largemouth bass.\u003c/p\u003e \u003cp\u003e \u003cem\u003eMycoplasma\u003c/em\u003e is widely present in various life forms, ranging from plants to higher animals (Pawar et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), and is a major component of the gut microbiota of farmed and wild salmon (Holben et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Although \u003cem\u003eMycoplasma\u003c/em\u003e is traditionally considered pathogenic, recent studies have found that it is a part of normal gut microbiota in some fish species (Biasato et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In the present study, the dominant genus in the gut of largemouth bass in Group C was \u003cem\u003eMycoplasma\u003c/em\u003e; however, no disease was observed, indicating that its role in the fish gut requires further research.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn conclusion, the largemouth bass in Group B had significantly higher protein, flavor amino acid, PUFA, and DHA + EPA contents and higher gut microbiota diversity than the other two farming models. These differences were closely related to the farming water environment, exercise intensity, and water flow rate. These characteristics resulted in superior nutritional quality and flavor of largemouth bass in Group B compared to those in Groups A and C.\u003c/p\u003e \u003cp\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval:\u003c/strong\u003e The experimental protocol was approved by the Experimental Animal Ethics Committee, College of College of Smart Agriculture, Technology Innovation Center of Ecological Fishery Industrialization, Chongqing University of Arts and Sciences, China.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest:\u003c/strong\u003e The authors declare that there are no conflicts of interest regarding the publication of the work described in this manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions:\u003c/strong\u003e G.D. Hu.: Investigation, Data curation, Writing-review \u0026amp; editing. H.D. Hu.: Formal analysis, writing-original draft preparation. Y.X. Deng.: Methodology, Formal analysis. Y.J. Wu: software, Literature comparative dialogue. Y.M. You: software, Literature comparative dialogue. H.C. Sun.: Writing-review \u0026amp; editing, Project administration, and Funding acquisition.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eAcknowledgements:\u0026nbsp;\u003c/strong\u003eNone\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFinancial support:\u0026nbsp;\u003c/strong\u003e This work was supported by the Chongqing Aquatic Science and Technology Innovation Key Project (CQFTIU2024-10) and Major Science and Technology Research Project of Chongqing Municipal Education Commission (KJZD-M202401301).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement:\u0026nbsp;\u003c/strong\u003eThe datasets used and analysed during the current study available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eArrive\u003c/strong\u003e \u003cstrong\u003estatement:\u0026nbsp;\u003c/strong\u003eThe study is reported according to the \u003cstrong\u003eARRIVE guidelines\u003c/strong\u003e, and the Author Checklist - Full has been uploaded to the Related Files section.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAhmed I, Jan K, Fatma S and Dawood MAO 2022. 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Effects of inner-pond raceway aquaculture on the growth performance, antioxidant enzymes, digestive enzymes, digestive tract structure, and bacterial flora of largemouth bass (Micropterus salmoides) 45, 2011\u0026ndash;2028.\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":"flow-through farming, high-position pond farming, microbial species diversity, traditional pond farming, Micropterus salmoides","lastPublishedDoi":"10.21203/rs.3.rs-4767517/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4767517/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTo study the differences in morphological characteristics, muscle nutrition, and intestinal microbiota of largemouth bass under different farming modes, healthy largemouth bass with an initial body weight of 50.0 (\u0026plusmn;\u0026thinsp;2.0) g were selected and reared for 180 d under traditional pond farming (Group A), flow-through farming (Group B), and high-position pond farming (Group C) modes. The results showed that: (1) the condition factor, hepatosomatic index, and visceral somatic index of largemouth bass in Group B were significantly higher than those in Group C (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05); (2) the crude fat content in muscle of fish in Group B was significantly lower than that in Group A (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). However, the crude protein content was significantly higher than that in Group A (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The total amino acid content, total non-essential amino acids, total umami amino acids, and total aromatic amino acids in muscle of fish in Group B were significantly higher than those in Groups A and C (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The monounsaturated fatty acids, polyunsaturated fatty acids, and DHA\u0026thinsp;+\u0026thinsp;EPA contents in muscle of fish in Group B were significantly higher than those in Group A (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and extremely significantly higher than those in Group C (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and; (3) alpha diversity analysis showed that the intestinal microbiota diversity of largemouth bass in Group B was higher than that of the other two groups. At the phylum level, the dominant bacterial phyla in largemouth bass intestines were Fusobacteriota, Firmicutes, Proteobacteria, Bacteroidota, and Actinobacteria. At the genus level, the dominant bacterial genera were \u003cem\u003eMycoplasma\u003c/em\u003e, \u003cem\u003eCetobacterium\u003c/em\u003e, and \u003cem\u003eAcinetobacter\u003c/em\u003e. Principal coordinate analysis based on operational taxonomic units indicated that the microbiota distribution of Group B differed slightly from that of Group A and differed significantly from that of Group C. This indicated that the species diversity of the intestinal microbiota of largemouth bass varied under different farming modes. In conclusion, the farming mode affected the growth, muscle nutritional quality, and intestinal microbiota of largemouth bass. This study provides a theoretical basis for understanding the relationships between farming modes, growth performance, muscle nutrition, and intestinal microbiota in largemouth bass.\u003c/p\u003e","manuscriptTitle":"Effects of different culture modes on growth, muscle nutrition, and intestinal microbiota of largemouth bass","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-21 11:57:05","doi":"10.21203/rs.3.rs-4767517/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":"220f9d3a-0ea9-4afb-a6cf-8b4dab49f057","owner":[],"postedDate":"August 21st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-02-24T17:08:21+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-21 11:57:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4767517","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4767517","identity":"rs-4767517","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}