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Effects of morphological traits on body weight and analysis of growth-related genes of Parabramis pekinensis at different ages

BMC Zoology volume 8, Article number: 13 (2023) Cite this article

Abstract

Parabramis pekinensis was treated as research object in order to investigate the correlation between morphological traits and body weight. We measured 9 morphological indexes including total length (X1), body length (X2), body height (X3), head length (X4), snout length (X5), eye diameter (X6), eye distance (X7), caudal stalk length (X8) and caudal stalk height (X9). The principal morphological traits affecting body weight were screened out and the regression equation was established. The regression equation of Y1 (age 1 group) shape character (X) and weight (Y) was Y = − 169.183 + 32.544 × 3 + 10.263 × 4 + 15.655 × 7. The regression equation of Y2 (age 2 group) shape character (X) and weight (Y) was Y = − 694.082 + 7.725 × 1 + 72.822 × 3 + 77.023 × 6, the regression equation of Y3 (age 3 group) shape character (X) and weight (Y) was Y = − 1161.512 + 26.062 × 1 + 22.319 × 2- 107.218 × 5 + 83.901 × 7. Gene expression was consistent with these conclusions. TOR signaling pathway expression raised in Y1 then width increased. And GH-IGF-1 signaling pathway expression raised in Y2 then the length increased. In conclusion, the paper could prove that P. pekinensis showed a growth trend, which was increasing width first and length later. In some sense, the study not only enriched the basic biological data of P. pekinensis, but also provided waiting morphological traits for selective breeding of P. pekinensis artificial breeding in future.

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Introduction

Parabramis pekinensis, also called White bream or Beijing white bream, belongs to the Cypriniformes, Cyprinidae, Culterinae, Parabramis. It was a plentiful fish species that lived in Heilongjiang, Yangtze, Pearl River and other major water systems. But little studies were reported it. With the continuous improvement of people’s living standard, high-quality fish species such as P. pekinensis have captured an increasing amount of attention, and became progressively popular in the market of the Yangtze River Delta. Because of its fresh meat, rich protein and fat content, P. pekinensis was favored by consumers, with broad market prospect and desirable industry development momentum. However, P. pekinensis resources have been seriously threatened due to overfishing [1], damaged habitats and environmental adverse effects, which lead to reduced quantity, smaller body size, slower growth rate and weaker resistance to environmental changes. In order to reduce the persecution of P. pekinensis germplasm resources, researchers began to study the artificial propagation techniques and variety breeding in the hope of obtaining cultivars with excellent traits for cultivation and promoting. At present, the work of artificial breeding by catch wild P. pekinensis has reached the third generation. The slow growth rate and unstable breeding benefits were still difficult to overcome. As a result, it was urgent that we need to select and breed fast-growing species with better morphological traits to increase production and income for the aquaculture industry [2, 3].

Body weight, as a crucial growth index of fish, was also an important indicator for judging the efficacy of fish breeding. It was reported that the growth traits of fish were bound up with body weight [4, 5]. Therefore, it was significant for enriching the research on fish genetics and breeding by using statistical analysis methods to regression equations and explore the correlation between fish growth traits and body weight [6].

GH and IGF-1 were well known in aquaculture. In previous study, Oncorhynchus keta transgenic to GH gene had faster growth rate than control group [7]. In addition, other studies have shown GH could promote growth by injection and food supplementation [8, 9]. GH also could induce expression of liver IGF-1. Similar to GH, IGF-1 was also able to promote growth [10]. For example, increasing IGF-1 concentrations in tissues and serum of Oreochromis niloticus, Oreochromis mossambicus, Oncorhynchus keta and Cirrhinus molitorella promoted growth of the fish organism [11,12,13]. These studies confirmed that IGF-1 was a growth promoter for fish. But GH and IGF-1 promoted growth reflected in morphological parameters still unknown. It was found that the expression of TOR pathway differed at different growth stages. Because it could affect protein synthesis, which brought varying degrees of impact on growth [14]. Some studies have shown that when amino acids were abundant, TOR senses amino acid levels and was activated to enhance protein synthesis by affecting the downstream core genes AKT, S6, and 4EBBP1 [15, 16].

In this study, we used correlation analysis, path analysis and multiple regression analysis to investigate the growth traits associated with the body weight of P. pekinensis at different ages. Combined with regression equation and growth-related mRNA expression, we learnt to P. pekinensis growth pattern. And we hoped to established the corresponding to enrich the data for P. pekinensis breeding research.

Methods

Materials

Three different years old P. pekinensis (Y1, Y2 and Y3 represented respectively the age of 1, 2 and 3) were randomly selected from Binjiang Aquaculture Breeding Farm in Jingjiang City, Jiangsu Province on September 26, 2022. The number of them were 48, 53 and 54 respectively. After anesthesia with 40 mg/L MS-222, 9 morphological indexes including total length (X1), body length (X2), body height (X3), head length (X4), snout length (X5), eye diameter (X6), eye distance (X7), caudal stalk length (X8) and caudal stalk height (X9) were measured by vernier caliper, accurate to 0.01 cm [17, 18]. The body weight (Y) was weighed with an electronic balance, accurate to 0.01 g. Specific parts of morphological traits as Fig. 1.

Fig. 1
figure 1

Morphometric traits of P. pekinensis

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Gene expression

After the breeding experiment, the fishes were all starved for 24 h. Then the fishes were dissected to obtain liver samples, which were stored in cryopreservation tubes for gene expression analysis. The related genes mRNA levels of protein synthesis were determined by qRT-PCR analysis, which was referred to Liang [19]. First, RNA extractionwas operated by the Bacteria RNA Extraction Kit (Vazyme BiotechCo., Ltd, China). Second, the thermo Scientific NanoDrop- 2000 Spectrophotometers (Thermofisher Scientific, USA) was used for testing the quantity and quality of RNA (Keep the OD260nm/OD280nm value at 1.8 ~ 2.0). Third, cDNA was synthesized by reverse transcription using Vazyme’s HiScripte ll 1st Strand cDNA Synthesis Kit (+ gDNA wiper) and then stored at -20 °C. Finally, qRT-PCR was analyzed using a CFX96 Touch Real-Time PCR Detection System (Bio-Rad, USA). The reaction system was showed in Table 1.

Table 1 Reaction system
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The reaction was performed as follows: Pre-denaturation at 95 °C for 30s; Cyclic denaturation at 95 °C for 10s, annealing and denaturation at 60 °C for 30s with 40 cycles; Melting reaction was 95 °C for 15s, 60 °C for 60s, then 95 °C for 10s, and read the plate for 30s. The primers for the analysis of mRNA levels were shown in Table 2. The Glyceraldehyde-3-phosphate dehydrogenase (GAPDH) was selected as internal reference gene, and the levels of mRNA were calculated from the standard curve, normalized against GAPDH and quantified using a relative standard curve method [20].

Table 2 Primers used for quantitative RT-PCR (qPCR)
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Statistical analysis method

The data of 3 dissimilar ages and 10 diverse traits were sorted out by Excel software. Referring to the analysis method of Du [21], SPSS 22.0 software was also used for analyzing data by one-way analysis of variance (ANOVA) and Tukey’s multiple comparisons test. Path analysis and determination coefficient calculation refer to the method of Song [22]. In addition, all the analyzed data were presented as the means with S.E.M. P < 0.05, which was considered to be a significant difference.

Results

Statistics of phenotypic parameters and normal distribution test (body weight) of P. pekinensis

P.pekinensis phenotypic traits parameters of diverse ages were shown in Table 3. The coefficient of variation on 1 age body weight (Y) was the highest, which was 29.76%, and the head length (X4) was the lowest, which was 7.93%. The coefficient of variation on 2 ages body weight (Y) was the highest, which was 11.73%, and body length (X2) was the lowest, which was 4.24%. The coefficient of variation on 3 ages eye distance (X7) was the highest, which was 12.54%, and body length (X2) was the lowest, which was 3.99%.

Table 3 Parameter statistics of phenotypic traits in P. pekinensis
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The normal distribution test results of body weight were shown in Table 4. Kolmogorov-Smirnov and Shapiro-Wilk and other methods were adopted to test P. pekinensis weight’s normal distribution, and the obtained significance levels were all greater than 0.05, which indicated that body weight of three groups obeys normal distribution. It suggested that the dates satisfied the condition of regression analysis.

Table 4 The normal distribution test of body weight in P. pekinensis
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Correlation analysis of phenotypic traits in P. pekinensis at different ages

The correlation coefficients with P. pekinensis 45 pairs of dissimilar ages were shown in Table 5. In Y1, the results showed that there was significant association between the phenotypic traits of 45 pairs (P < 0.05), all 9 morphological traits were remarkably correlated with body weight (P < 0.05); In Y2, there was a remarkable correlation between 39 pairs of phenotypic traits (P < 0.05), except for tail stalk length (X8), there was no significant correlation with body weight (P > 0.05), the other 8 pairs of phenotypic traits were noticeably correlated with body weight (P < 0.01); In Y3, There was a striking association between the phenotypic traits of 43 pairs (P < 0.05), except for tail stalk length (X8), there was no significant correlation with body weight (P > 0.05), the other 8 pairs of phenotypic traits were markedly correlated with body weight (P < 0.01). As indicated by comprehensive analysis, the correlation of shape traits was consistent to some extent between juvenile and adult, except tail stalk length (X8), the other traits had high correlation with body weight, which required further analysis.

Table 5 Correlation analysis of phenotypic traits in P. pekinensis
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Path analysis and determination coefficients of morphological traits on body weight in P. pekinensis

In accordance with results of correlation analysis, the path analysis was carried out on the shape character and body weight (Table 6). The direct effect of these three morphological traits on weight was body height (X3) > head length (X4) > eye distance (X7) in sequence, and the indirect effect was in the same order with direct effect. Morphological traits retained in Y2 were body height (X3), head length (X4) and caudal stalk height (X9). The direct effects of these morphological traits on body weight were body height (X4) > head length (X4) > caudal stalk height (X9), and the indirect effects were in the same order with direct effects. Morphological traits retained in Y3 were total length (X1), body length (X2), snout length (X5) and eye distance (X7). The order of direct effect on body weight was full length (X1) > body length (X2) > eye distance (X7) > kiss length (X5), and the order of indirect effect was total length (X1) > eye distance (X7) > body length (X2) > snout length (X5). The indirect effect of snout length was negatively correlated with body weight.

Table 6 Path analysis of morphological traits on body weight in P. pekinensis
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The determining coefficient of shape traits on weight in diverse ages was shown in Table 7. The determining effect of traits on weight was dissimilar in diverse ages. In Y1, the determining effect of body height on weight was 0.618. Under the combined condition of 2 forms, the determining effect of body height (X3) and head length (X4) on weight was 0.162. Followed by body height (X3) and eye distance (X7) on weight was 0.158. Both of them were higher than combined determining effect of head length (X4) and eye distance (X7). In Y2, the determination effect of total length (X1) on weight was the most importance, which was 0.524, far exceeds of other two forms. Under the combined condition of 2 forms and traits, the determination effect of total length (X1) and height (X3) on weight was the highest, which was 0.209. In Y3, the total length (X1) had the most remarkable decisive effect on weight, which was 0.258, and snout length (X5) had the least determining effect on weight, which was 0.056. Under the combined condition of two forms and traits, the full length (X1) and body length (X2) had the most noticeable decisive effect on weight, which was 0.342.

Table 7 Determination coefficients of morphological traits on body weight in P. pekinensis
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Regression analysis of P. pekinensis morphological traits on weight

Taking the morphological traits retained by P. pekinensis in the trialling analysis of dissimilar ages as independent variables, the stepwise regression analysis method was adopted. New variables were introduced into the regression equation successively and new regression analysis models are built constantly until the optimal regression equation was obtained. The multiple correlation analysis results of dissimilar regression analysis models were shown in Table 8. With the introduction of new variables into the regression analysis model, the multiple correlation coefficient R of each age stage was gradually increasing. When morphological traits were completely introduced into the regression analysis, the multiple correlation coefficient R were 0.990, 0.951 and 0.963. The error probability P were 0.040, 0.038 and 0.019 separately, which have statistical significance. It indicated that the retained variable was the primary morphological character affecting body weight in this age stage.

Table 8 Multiple-correlation analysis of the morphometric traits to the body weight in P. pekinensis Note: Y. body weight; X1. total length; X2. body length; X3. body high; X4. head length; X5. snout length; X6. eye diameter; X7. eye distance
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In line with the results of the multiple correlation analysis, the significance test was carried out on the bias regression coefficient and regression constant of the regression analysis model which introduced all morphological traits (Table 9) (P < 0.05). By comparing the regression coefficients of distinct forms and traits in the regression analysis model, it was found that P. pekinensis shape and traits were influenced by dissimilar forms in diverse ages. In Y1, the regression coefficients of form traits were body height (X3), head length (X4) and eye distance (X7), which were arranged in a descending order. In Y2, the regression coefficient of form traits was body height (X3), total length (X1) and eye diameter (X6) in descending order. In Y3, form traits regression coefficient was total length (X1), body length (X2), eye distance (X7) and snout length (X5) in descending order. The result indicated that body height (X3) worked as the highest trait influence in Y1 and Y2. While total length (X1) worked as the highest trait in Y3, which corresponded with the results of path analysis and determination coefficient analysis. In accordance with the partial regression coefficient of distinct forms, the multiple regression equations of P. pekinensis form traits and weight in diverse ages were established.

Table 9 Significance test of partial regression coefficient and regression constant
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Age 1: Y=-169.183 + 32.544 × 3 + 10.263 × 4 + 15.655 × 7

Age 2: Y=-694.082 + 7.725 × 1 + 72.822 × 3 + 77.023 × 6

Age 3: Y=-1161.512 + 26.062 × 1 + 22.319 × 2-107.218 × 5 + 83.901 × 7

In formula, Y represents weight, X1 represents total length, X2 represents body length, X3 represents body height, X4 represents head length, X5 represents snout length, X7 represents eyes distance, and X9 represents tail stalk height.

Growth gene expression analysis

Y2 showed significantly increased mRNA levels of growth hormone (GH) and insulin-like growth factor-1 (IGF-1) (P < 0.05) (Fig. 2(a) and 2(b)). Furthermore, target of rapamycin (TOR) and ribosomal protein S6 (S6) expressed the same trend which Y1 showed significantly higher mRNA levels (P < 0.05) (Fig. 3(a) and 3(c)). AKT performed similar trend with TOR and S6, but it showed significantly difference between Y2 and Y3 (P < 0.05) (Fig. 3(b)). However, no significant effects on the mRNA levels of eukaryotic initiation factor 4E binding protein 1 (4EBP1) were found in liver of P. pekinensis with different ages (P > 0.05) (Fig. 3(d)).

Fig. 2
figure 2

The relative expressions of GH-IGF-1 axis in liver of P. pekinensis with different ages. Data are expressed as means with S.E.M.; value with different superscripts are significantly different (P < 0:05)

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Fig. 3
figure 3

The relative expressions of TOR signaling pathway in liver of P. pekinensis with different ages. Data are expressed as means with S.E.M.; value with different superscripts are significantly different (P < 0:05)

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Discussion

Correlation between morphological traits and body weight

In the study of fish biology, body weight was often considered as the uppermost indicator to measure the body size of fish, followed by body length [23, 24]. As revealed by comparing the statistical results of diverse ages, the average weight of P. pekinensis grows from 80.54 to 699.55 g from 1 to 3 ages, with growth rate of 769%, which indicated that P. pekinensis grew tremendously fast. With the growth of age, the coefficient of variation on P. pekinensis weight and body length lessens progressively. The coefficient of variation on weight decreases from 29.76 to 11.81% and body length decreases from 11.44 to 3.99%. As indicated by other experimental findings, the individual of P. pekinensis differs immensely from adult stage, in which weight and body length could be affected by environmental factors. In juvenile stage, the growth of fish body was affected by the environment lightly and often correlated with fish body; while in adult stage, the influence of environmental factors on fish growth was enhanced, and the fish population showed homogeneity. In breeding work, juvenile stage had more selective potential than adult stage. If environmental difference was not significant, further analysis was needed.

Correlation analysis could define correlation between multiple things, help find more closely related variables, eliminate irrelevant variables, and make statistical judgments [25]. Other study severally explored the association between morphological traits and body weight of male and female Scatophagus argus [26]. The study showed that correlation coefficient was affected by sex, and it was dissimilar between morphological traits and body weight. And another study showed the correlation between morphological traits and body weight of 1+~3+ ages Thymallus arcticus, and showed morphological traits correlated with body weight are dissimilar in different ages. The number of traits decreased with the increased of ages [27]. Similarly, Liu studied the association between body weight and morphological traits of Larimichthys crocea in 13 and 20 months [28]. It indicated that the correlation coefficients between body weight and morphological traits were dissimilar at different ages. Similar results have been found in Macrobrachium rosenbergii [29] and Phoxinus lagowskii [30]. As the P. pekinensis was growing with the same speed [31], there was little difference in morphological traits between different ages. With the ages increased, the correlation coefficient progressively lessens became smaller, showing that the growth rate was faster in juvenile stage. In this study, the correlation coefficients between Y1 morphological traits and weight from high to low were body height (X3), total length (X1), tail stalk length (X8), head length (X4), eye diameter (X6) and body length (X2) correlation coefficient > 0.90), while the Y2 only body height (X3) had correlation coefficient higher than 0.9. The correlation coefficient total length (X1) and body length (X2) were higher than 0.9 in Y3 and total length (X1) was higher than body length (X2). It could be seen that the correlation between different traits and body weight differs tremendously, and the correlation between same morphological traits and body weight was also dissimilar at diverse growth stages. As persuasively illustrated in the research completed by Huang [32], the trait which could directly exert the most conspicuous influence on body weight of 2-month-old yellow croaker was the total length, while body height was the key trait affected body weight of 18-month-old yellow croaker. Other study held a standpoint that body length was the chief trait affecting body quality of Masu salmon at 6 months, while body height, caudal stalk height and head height were also the trait affecting body quality of Masu salmon at 18 months [33]. As a consequence, it was imperative to investigate the effects of morphological traits on body weight in three age groups. In this experiment, the correlation coefficients including body length (X2) and body height (X3) showed high trends with body weight in three age groups, indicating that body length, body height and body weight maintained the highest level of correlation. The conclusion coincided with the biological characteristics of P. pekinensis [34], because P. pekinensis paid attention to both lateral and vertical growth during the growth stage.

Main morphological traits affecting body weight

Correlation analysis could obtain simple association between morphological traits and body weight. Path analysis was adopted in the study to probe deep into the direct and indirect effects between traits [35], so as to obtain more reliable data support and basis. In the study of Ruditapes philippinarum, Wang found that shell length, shell height, shell width and shell thickness were the main factors affecting live weight, while gonads were a dominant factor affecting soft weight [36]. Chen [37] analyzed the effects of morphological traits on body weight of flounder at distinct periods, and found that the length from the base of pelvic fin to the terminal of dorsal fin had the highest diameter coefficient on body weight at 8 months and 14 months, which were 0.439 (8 months) and 0.752 (14 months). In the study, the direct path coefficients of 3 morphological traits on body weight reached extremely substantial level in 1 and Y2, while 4 traits in Y3. In 1 and 2 ages growth stages, the diameter coefficient of body height (X3) on body weight was the highes. Then it was followed by head length (X4). In 3 ages growth stage, the maximum diameter coefficients of total length (X1) and body height (X3) on body weight were 0.789 and 0.775. The results of direct effects coincided with above conclusions. In 1 and 2 ages, body height (X3) had the largest direct effect on body weight. The direct effect of total length (X1) and body length (X2) on body weight in Y3 were 0.508 and 0.364. As a consequence, it was essential to select diverse morphological traits as breeding materials at distinct ages. As for P. pekinensis, body height was the selection traits before 2 years old, and selecting total length and body length after 2 years old were suitable.

Multiple regression analysis was conducted on the basis of path analysis, which could reinforce the reliability of regression equation [38]. The determining coefficient could be employed to judge whether retained shape traits were the dominant influencing factor of body weight in the path analysis. Haiweis [39] believed that the sum of determining coefficient Σ ≥ 0.850 indicated that retained shape traits were the predominant influencing factor of weight. The total determination coefficients of three different groups were 0.984, 0.888 and 0.926, which indicated that selected morphological traits of 3 ages were the principal morphological traits affecting body weight. The effects of morphological traits on fish body weight were dissimilar at different ages. This phenomenon has been reported in Nibea albiflora [40], Ctenopharyngodon idella [41] and Cynoglossus semilaevis [42]. As demonstrated by correlation analysis, path analysis and multiple regression analysis, in Y1, the predominant morphological traits affecting body weight were body height (X3) and head length (X4). In Y2, the primary morphological traits affecting body weight were total length (X1) and body height (X3). Finally, in Y3, the chief morphological traits affecting body weight were total length (X1) and body length (X2).

TOR and GH-IGF-1 signaling pathway analysis

The GH-IGF-1 axis had an important role in regulating somatic growth [43]. It has been shown that the expression of GH, IGF-1 and TOR signaling pathways ccould represent not only growth level, but also the increase in body weight and production [44]. In the study, the mRNA levels of GH and IGF-1 were significantly upregulated in Y2, which partly represented that P. pekinensis vertical grew rapidly in 2 years old. TOR signaling system could sense amino acid abundance [45]. In this study, TOR, S6 and AKT gene expression significantly increased in Y1. And AKT gene expression showed difference in 3 years. Previous studies have confirmed that TOR signaling pathway and protein synthesis could affect growth performance in grass carp and blunt snout bream, which affected the downstream gene expression to regulate protein synthesis [46, 47]. However, the expressions of TOR signaling pathway were the opposite trend with the mRNA levels of GH and IGF-1. The conclusions suggested that P. pekinensis growth could be affected by expression of these genes, but the degree of gene expression varies from different ages, and the growth pattern was consistent with above study of morphology and shape. During the whole process, the width was increased at age 0 ~ 1, while the length was increased after at age 1 ~ 3 (Fig. 4). The result might be related to absorbed nutrients and gonad development. Relevant research needed further demonstrated.

Fig. 4
figure 4

Diagram of different ages growth patterns

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Conclusions

The traits affecting body weight varied in different ages. Individual differences in juvenile P. pekinensis were greater than adult stage, and it became stable in growth progress. Correlation coefficients also differed in three ages, but body length and body height always maintained a high correlation with body weight. According to path analysis and multiple regression analysis, main morphological traits affecting body weight in different ages were diverse. Y1 was body height and head length; Y2 was total length and body height; Y3 was total length and body length. Gene expression was consistent with these conclusions. TOR signaling pathway expression raised in Y1 then width increased. And GH-IGF-1 signaling pathway expression raised in Y2 then the length increased. In conclusion, the paper could prove that P. pekinensis showed a growth trend, which was increasing width first and length later. The study provided morphological traits for selective breeding of P. pekinensis artificial breeding in future.

Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

References

  1. Yang HL, Shen L, He YF, Tian HW, Gao L, Wu JM, Mei ZG, Wei N, Wang L, Zhu TB, Hu FF, Gong JL, Du HC, Duan XB, Deng HT, Wang DQ, Zhu FY, Li YF, Wu F, Ru HJ, Zhang Y, Li JY, Yang JL, Zhou YT, Fang DD, Wang YP, Run DQ, Yang YP, Li PJ, Liu SL, Yang J, Zhuang P, Wang SK, Zhang T, Yang G, Yang WB, Yuan LL, Cao K, Xu S, Liu HY, Liang ZQ, Wang ZR, Li H, Yuan XP, Yang X, Fu YL, Zhang YP, Zhang HX, Tao ZY, Wang S, Gao XP, Jin BS, Li KM, Wang GJ, Jian SL, Li YQ, Xue CJ, Lei CY, Xue SW, Sun Y, Zhu B, Shao K, Hu XK, Xiong MH, Du J, He B, Yan T, Huang YY, Zhou YC, Xie BW, Wang YM, Li B, Liu F, Zhang YY, Fan F, Wang ZJ, Huang J, Gu HR, Ge HL, Dan Y, Li Y, Wang SJ, Zhang C, Zhou L, Wang X, Zeng S, Xiang Y, He XG, Tan JH, Xia CX, Hou J, Shi YF, Gao LF, Zhu ZQ, Shen HB, Du Y, Duan XJ, Xiong JW, Yang DG, Liu SP, Ni C, Zhang H, Liu H, Zhao K, Li F, Wang YR. J W, Wei Q W. Status of aquatic organism resources and their environments in Yangtze River system (2017–2021). Journal of Fisheries of China. 2022, 1–28.

  2. Guang WZ, Zheng GD, Wu CB. Comparative analysis of growth and morphological variations among Megalobrama amblycephala, M. terminalis, Parabramis pekinensis and their hybrids. J Fish Sci China. 2017;24(01):31–9.

    Google Scholar 

  3. Gu HL, Feng YM, Yang ZJ, et al. Histological Observation of gonadal development of White Bream Parabramis pekinensis. Chin J Fisheries. 2022;35(03):44–50.

    Google Scholar 

  4. Zhang XM, Cheng SF. Path analysis and growth curve fitting of the morphological traits and body weight of Conger myriaster. J Fish Sci China. 2020;27(10):1167–75.

    Google Scholar 

  5. Liu YX, Liu Y, Liu YJ, et al. Allometric analysis of body weight and morphological traits for japanese flounder (Paralichthys olivaceus). South China Fisheries Science. 2016;12(1):36–42.

    Google Scholar 

  6. Paloheimo JE, Dickie LM. Food and Growth of Fishes. II. Effects of Food and temperature on the relation between metabolism and body weight. J Fisheries Board Can. 1996;23:6.

    Google Scholar 

  7. Robert HD, Timothy YY, Carlo AB, Edward MD, Penny S, Woon-Khiong C. Extraordinary salmon growth Nature. 1994;371(6494):209–10.

    Google Scholar 

  8. Benzion C, Bruria F, Thomas TC, Lucia IG, Manfred S. Effect of growth hormone on the growth rate of the gilthead sea bream (Sparus aurata), and use of different constructs for the production of transgenic fish. Aquaculture. 1993;111(1):189–97. https://doi.org/10.1016/B978-0-444-81527-9.50022-1.

    Article  Google Scholar 

  9. Ewen M, Edward MD, Emin T, Lawrence MS. Growth enhancement following dietary delivery of recombinant porcine somatotropin to diploid and triploid coho salmon (Oncorhynchus kisutch). Fish Physiol Biochem. 1993;11(1–6):363–9. https://doi.org/10.1007/s10695-005-0267-4.

    Article  CAS  Google Scholar 

  10. Reinecke M. Influences of the environment on the endocrine and paracrine fish growth hormone-insulin-like growth factor-I system. J Fish Biol. 2010;76(6):1233–54. https://doi.org/10.1111/j.1095-8649.2010.02605.x.

    Article  CAS  PubMed  Google Scholar 

  11. Emmanuel MVC, Christopher LB, Adam J, Matthew L, Remedios EP, Russell BB. Insulin-like growth factor-I cDNA cloning, gene expression and potential use as a growth rate indicator in Nile tilapia, Oreochromis niloticus. Aquaculture. 2006;251(2):585–95. https://doi.org/10.1016/j.aquaculture.2005.06.039.

    Article  CAS  Google Scholar 

  12. McCormick SD, Kelley KM, Young G, Nishioka RS, Bern HA. Stimulation of coho salmon growth by insulin-like growth factor I. Gen Comp Endocrinol. 1992;86(3):398–40614.

    Article  CAS  PubMed  Google Scholar 

  13. Kajimura S, Uchida K, Yada T, Riley LG, Byatt JC, Collier RJ, Aida K, Hirano T, Grau EG. Stimulation of insulin-like growth factor-I production by recombinant bovine growth hormone in Mozambique tilapia, Oreochromis mossambicus. Fish Physiol Biochem. 2001;25(3):221–30. https://doi.org/10.1023/A:1022268811599.

    Article  CAS  Google Scholar 

  14. Zhou QL, Habte-Tsion HM, Ge XP, Liu B, Xie J, Ren MC, Miao LH, Pan LK. Growth performance and TOR pathway gene expression of juvenile blunt snout bream, Megalobrama amblycephala, fed with diets replacing fish meal with cottonseed meal. Aquac Res. 2017;48(7):3693–704. https://doi.org/10.1111/are.13195.

    Article  CAS  Google Scholar 

  15. Wilson-Arop OM, Liang H, Ge X, Ren M, Habte-Tsion HM, Ji K. Dietary histidine requirement of juvenile blunt snout bream (Megalobrama amblycephala). Aquacult Nutr. 2019;24(1):1122–32. https://doi.org/10.1111/anu.12651.

    Article  CAS  Google Scholar 

  16. Feng RN, Niu YC, Sun XW, Li Q, Zhao C, Wang C, Guo FC, Sun CH, Li Y. Histidine supplementation improves insulin resistance through suppressed inflammation in obese women with the metabolic syndrome: a randomised controlled trial. Diabetologia. 2013;56(5):985–94. https://doi.org/10.1007/s00125-013-2839-7.

    Article  CAS  PubMed  Google Scholar 

  17. Liang HL, Ji K, Ge XP, Xi BW, Ren MC, Zhuang L, Chen XR. Tributyrin plays an important role in regulating the growth and health status of juvenile blunt snout bream (Megalobrama amblycephala), as evidenced by pathological examination. Front Immunol. 2021;12:652294. https://doi.org/10.3389/fimmu.2021.652294.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Stone DAJ, Gaylord TG, Johansen KA, Overturf K, Sealey WM, Hardy RW. Evaluation of the effects of repeated fecal collection by manual stripping on the plasma cortisol levels, TNF-α gene expression, and digestibility and availability of nutrients from hydrolyzed poultry and egg meal by rainbow trout, Oncorhynchus mykiss. (Walbaum) Aquaculture. 2008;275:1–4. https://doi.org/10.1016/j.aquaculture.2008.01.003.

    Article  CAS  Google Scholar 

  19. Yu C, Fan X, Hu ZH, Xia X, Zhao YC, Li RQ, Bai Y. Segmentation and measurement scheme for fish morphological features based on Mask R-CNN. Information Processing in Agriculture. 2020, 2214–3173: 523–34. https://doi.org/10.1016/j.inpa.2020.01.002.

  20. Brosse S, Charpin N, Su GH, Toussaint A, Herrera-R G A, Tedesco PA, Villéger S. FISHMORPH: a global database on morphological traits of freshwater fishes. Glob Ecol Biogeogr. 2021;30(12):2330–6. https://doi.org/10.1111/geb.13395.

    Article  Google Scholar 

  21. Du JJ, Chen ZW. Method of path analysis with SPSS linear regression. Bull Biology. 2010;45(2):4–6.

    Google Scholar 

  22. Song XY, Zhu ZY, Liu YW, Zhao HJ. Application of path analysis in Stepwise Linear Regression SPSS. Arid Zone Research. 2016;33(01):108–13.

    Google Scholar 

  23. Froese R. Cube law, condition factor and weight-length relationships: history, meta-analysis and recommendations. J Appl Ichthyol. 2006;22(4):241–53. https://doi.org/10.1111/j.1439-0426.2006.00805.x.

    Article  Google Scholar 

  24. Zhan YY, Zhang WJ, Ge C, Lin K, Li G, Song J, Chang YQ. Relationships between body weight and other morphological traits in young sea cucumbers Apostichopus japonicas. J Oceanol Limnol. 2019;37:759–66.

    Article  Google Scholar 

  25. Li ZH, Luo P. The Tutorial of Statistical Analysis of SPSS for Windows. Beijing: Publishing House of Electronics Industry; 2005. pp. 213–6.

    Google Scholar 

  26. Chen HP, Li ZY, Wang YR, Yang W, Shi HJ, Zhu CH, Li GL. Relationship between Body Weight and morphological traits in female and male spotted scat (Scatophagus argus). Pakistan J Zool. 2022;54(4):1501–2001. https://doi.org/journal.pjz/20210305150335.

    Article  Google Scholar 

  27. Ma K, Tong GX, Kuang YY, Yi JS, Zhang YQ. Effect of morphological traits on body weight of Thymallus arcticus. J Fish Sci China. 2022;29(06):825–33.

    Google Scholar 

  28. Liu XD, Cai MY, Wang ZY, Zhao GT, Wu XW, Yao LC. Correlation analysis of morphometric traits and body weight of large yellow croaker (Pseudosciaena crocea) at different growth stage. J Trop Oceanogr. 2010;29(5):159–63.

    Google Scholar 

  29. Xing QQ, Yang MM, Chen GZ, Tang QY, Xia ZL, Cai LY, Yang J, Du HK, Gao QX, Yi SK, Yang GL. Path analysis of phenotypic traits on body weight of Macrobrachium rosenbergii before and after sexual maturity. J Fisheries China. 2022;46(09):1632–45.

    Google Scholar 

  30. Zhang YQ, Bai QL, Guo WX, Feng CC, Yi JS. Effects of morphological traits on body mass of Phoxinus lagowskii Dybowskii. Chin J Ecol. 2013;32(11):3063–8.

    Google Scholar 

  31. Liu SL, Wang YP, Li PJ, Cao G, Wang L, Dai P, Liu K. Estimation of growth and population parameters of freshwater bream Parabramis pekinensis in Zhenjiang section of the Yangtze River. J Dalian Ocean Univ. 2019;34(05):725–32.

    Google Scholar 

  32. Huang WQ, Yu ZJ. Correlation analysis of morphometric traits on body weight of spotted maigre Nibea albiflora at different growth stages. Fish Sci. 2016;35(5):557–61.

    Google Scholar 

  33. Li PL, Liu W, Wang JL, Tang FJ, Cui KC, Gao WY. Effects of morphometric traits on body weight of masou salmon Oncorhynchus masou. J Dalian Ocean Univ. 2018;33(05):600–6.

    Google Scholar 

  34. Zhao C, Chen JH, Yi ZQ, Wu JH, Chen L, Yang HC, Pan XW, Zhao SY, Sun JF. Growth characteristics of Parabramis pekinensis in Xiaolangdi Reservoir of the Yellow River. Anhui Agricultural Science Bulletin. 2019;25(01):64–7.

    Google Scholar 

  35. Wu LF, Zhang LP, Shen Q, Hu CQ. The effects of morphometric traits on body weight of different families of Litopenaeus vannamei. Trans Oceanol Limnol. 2010(2): 37–48.

  36. Wang SL, Xu XH, Tu K, Liu ZH, Zhang TS, Wu B, Zhou LQ, Sun XJ. Effects of Shell morphology on the weight traits of Manila Clam (Ruditapes philippinarum) in different geographical populations. Progress in Fishery Sciences. 2022;1–14. https://doi.org/10.19663/j.issn2095-9869.20220412001.

  37. Chen HL, Tian YS, Liu F, Li H, Li XK, Song LN, Chen SL. Path analysis and curve estimates of morphometric traits and body weight of Paralichthys olivaceus at different growth stages. J Fish Sci China. 2016;23(1):64–76.

    Google Scholar 

  38. Herath H, Radampola K, Herath S. Morphological variation and length weight relationship of Oreochromis mossambicus in three Brackish Water Systems of Southern Sri Lanka. Int J Res Agric Food Sci. 2014;2:11–22.

    Google Scholar 

  39. Xia WW, Liu HY, Ou M, Luo Q, Huang SJ, Zhao XJ, Xiong BY, Wu GH, Chen KC, Zhao J. Analysis of the relationship between mass and morphological traits of the backup parents of Channa maculata. J South Agric. 2022;12–27:1–11.

    Google Scholar 

  40. He A, Zhu QC. Relationship analysis between morphometric attributes and body weight of Nibea coibor in Arafura Sea of Indonesia. Trans Oceanol Limnol. 2012, (1): 41–8.

  41. Sun JL, Shen YB, Fu JJ. Effects of main morphological traits on body weight of grass carp (Ctenopharyngodon idellus) at different months of age. Abstracts of 2014 annual meeting of China Fisheries Society. China Fisheries Society. 2014: 1.

  42. Liu F, Chen SL, Liu XF, Liu Y, Cui ZK, Deng H. Correlation and path coefficient analysis for body mass and morphometric traits in the half-smooth tongue sole (Cynoglossus semilaevis). Acta Oceanol Sin. 2015;37(4):94–102.

    Google Scholar 

  43. Dixit M, Poudel SB, Yakar S. Effects of GH/IGF axis on bone and cartilage. Mol Cell Endocrinol. 2021;519:111052.

    Article  CAS  PubMed  Google Scholar 

  44. Ndandala CB, Dai MS, Mustapha UF, Li XM, Liu JL, Huang H, Li GL, Chen HP. Current research and future perspectives of GH and IGFs family genes in somatic growth and reproduction of teleost fish. Aquaculture Rep. 2022;26:101289, 2352–5134. https://doi.org/10.1016/j.aqrep.2022.101289.

    Article  Google Scholar 

  45. Gallinetti J, Harputlugil E, Mitchell JR. Amino acid sensing in dietary-restriction-mediated longevity: roles of signal-transducing kinases GCN2 and TOR. Biochem J. 2013;449(1):1–10.

    Article  CAS  PubMed  Google Scholar 

  46. Jiang WD, Feng L, Qu B, Wu P, Kuang SY, Jiang J, Tang L, Tang WN, Zhang YA, Zhou XQ, Liu Y. Changes in integrity of the gill during histidine deficiency or excess due to depression of cellular anti-oxidative ability, induction of apoptosis, inflammation and impair of cell-cell tight junctions related to Nrf2, TOR and NF-κB signaling in fish. Fish and Shellfish Immunology. 2016;56:111–22.

    Article  CAS  PubMed  Google Scholar 

  47. Wu P, Qu B, Feng L, Jiang WD, Kuang SY, Jiang J, Tang L, Zhou XQ, Liu Y. Dietary histidine deficiency induced flesh quality loss associated with changes in muscle nutritive composition, antioxidant capacity, Nrf2 and TOR signaling molecules in on-growing grass carp (Ctenopharyngodon idella). Aquaculture. 2020;526:735399.

    Article  CAS  Google Scholar 

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Acknowledgements

We would like to thank the members of the Taizhou Institute of Agricultural Sciences for their help. We would like to express our respect and gratitude to workers of the breeding base. I would like to express my deepest gratitude to the many P. pekinensis that have grown so well even with my poor breeding techniques.

Funding

Jiangsu Province seed industry revitalization unveiled the list of hanging projects: JBGS (2021) 138; Taizhou 2022 Seed Industry R&D PR Project: TZZY (2022) 03.

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Authors and Affiliations

  1. Taizhou Institute of Agricultural Science, Jiangsu Academy of Agricultural Sciences, Taizhou, 225300, China

    Wentao Xu, Yaming Feng, Zhengyan Gu, Shuanglin Zhang, Zhijing Yang, Ye Xu & Hailong Gu

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  1. Wentao Xu
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  2. Yaming Feng
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Contributions

WTX and HLG conceived and designed the study. WTX, ZYG and YX collected the fish and prepared the samples. WTX drafted the manuscript YMF critically reviewed the manuscript and revised the manuscript. ZJY and SLZ did project matching and logistical support. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Hailong Gu.

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All procedures performed were in accordance with the National Institutes of Health Guide for the Care and Use of Laboratory Animals (NIH Publications No. 8023, revised 1978) and approved by the Experimental Animal Ethics Committee of Taizhou Academy of Agricultural Sciences (Taizhou, China).

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The authors declare that they have no conflict of interest.

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Xu, W., Feng, Y., Gu, Z. et al. Effects of morphological traits on body weight and analysis of growth-related genes of Parabramis pekinensis at different ages. BMC Zool 8, 13 (2023). https://doi.org/10.1186/s40850-023-00174-9

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Keywords

BMC Zoology

ISSN: 2056-3132

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