Exosome protein combination, screening method thereof and method for judging growth potential of grass carp

Abstract

This invention discloses an exosome protein composition consisting of 11 proteins: GYG2, EBAG9, RPZ, SLC2A2, BHMT, CLDN7B, DBT, DHTKD1, OAT, RALGAPB, and TYMP. The invention also discloses a screening method for this composition. Furthermore, this invention discloses a method for determining the growth potential of grass carp. This invention has the advantage of enabling early and accurate identification of individuals with rapid growth potential in high-density grass carp farming environments.

Description

Technical Field

[0001] This invention relates to the field of fish genetics and breeding, and in particular to an exosome protein combination and its screening method, and a method for determining the growth potential of grass carp. Background Technology

[0002] Grass carp is one of the most important freshwater aquaculture species in my country, and its growth rate is a key factor determining the profitability of its aquaculture. Currently, the selection of grass carp growth traits mainly relies on traditional phenotypic measurements, which are time-consuming and inefficient. Although marker-assisted selection (MAS) technologies, such as those based on genome-wide SNPs or specific miRNAs, have been introduced, these methods have limitations: the functional significance of SNP markers is unclear; and single nucleic acid markers cannot fully reflect the complex and systemic physiological regulatory states of an organism.

[0003] Exosomes are vesicles secreted by cells and serve as carriers of intercellular communication. In recent years, research has begun to focus on exosome contents (such as miRNAs) as biomarkers. However, current techniques mostly analyze certain molecules within exosomes in isolation, or simply treat exosomes as "molecular mirrors" of their source tissues. They fail to reveal, from the perspective of systemic integration and active regulation, how exosomes act as central messengers, coordinating functions between different distant tissues (such as the brain, liver, and muscles) to form a systemic regulatory network influencing complex traits (such as growth). This cognitive limitation hinders the development of more precise and mechanistically explanatory tools for determining growth potential. Summary of the Invention

[0004] To address the aforementioned problems, the present invention aims to provide an exosomal protein combination and identification method.

[0005] This invention provides a method for screening exosomal protein combinations, achieved through the following technical measures, comprising the following steps: a. Select the large grass carp group and the small grass carp group with significant growth differences; b. Blood was extracted from each grass carp in the large and small grass carp groups, and DIA-LC-MS / MS proteomics analysis was performed on the plasma exosomes of the two groups to select the proteins that were significantly upregulated and significantly downregulated in the large grass carp group. c. Detect the proteins in the brain, liver and muscle tissue of each grass carp in the large grass carp group and the small grass carp group respectively, and select the protein data that are different in each tissue between the large grass carp group and the small grass carp group. d. Proteins that were significantly downregulated or significantly upregulated in all tissues and plasma exosomes of the grass carp group were selected into the fast growth marker protein group.

[0006] As a preferred method, the specific steps for step a, which involves selecting the large and small grass carp groups with significant growth differences, are as follows: a1. Stock grass carp fry in the fishpond at a density of 40,000-60,000 fish per acre; a2. After one month of breeding, adjust the density of grass carp in the fishpond to 2000-2100 kg / mu; a3. After 8-10 months of breeding, those with a weight greater than 450g / fish were selected as the large grass carp group, and those with a weight less than 100g / fish were selected as the small grass carp group. As a preferred method, the dissolved oxygen in the fish pond in step a1 is 2-4 mg / L.

[0007] The present invention also discloses an exosomal protein assembly, which consists of the following 11 proteins: EBAG9 (estrogen receptor binding site-associated antigen 9), GYG2 (glycogen and glycogen), RPZ (Rapunzel protein), SLC2A2 (solute carrier family 2 member 2), BHMT (betaine-homocysteine ​​S-methyltransferase), CLDN7B (tight junction protein 7b), DBT (dihydrolipoamide branched-chain transaminases E2), DHKD1 (dehydrogenase E1 and transketolase domain 1), OAT (ornithine aminotransferase), RALGAPB (Ral GTPase activator protein non-catalytic subunit β), and TYMP (thymidine phosphorylase).

[0008] This invention also discloses a method for determining the growth potential of grass carp, comprising the following steps: a. Calculate the expression values ​​Xᵢ of GYG2, EBAG9, RPZ, SLC2A2, BHMT, CLDN7B, DBT, DHTKD1, OAT, RALGAPB and TYMP in grass carp plasma exosomes; b. Data standardization: Z-score standardization was performed on each protein expression value to obtain the standardized value Zᵢ; c. Model calculation: Substitute the standardized data into the discriminant model z = Σ(Wᵢ * Zᵢ) + b, calculate the linear score z, and calculate the probability value P(L) of the sample using the Sigmoid function P(L) = 1 / (1 + e^(-z)); where Wᵢ is the weight coefficient of each protein, and b is the intercept; d. Result determination: Based on the threshold rule of whether P(L) is greater than 0.5; when P(L) is greater than 0.5, it is determined to be a fast-growing type, and when P(L) is less than 0.5, it is determined to be a slow-growing type.

[0009] As a preferred approach, step b, data standardization, is specifically defined as: Zᵢ = σ / (Xᵢ−μ), where μ is the mean of the dataset and σ is the standard deviation of the dataset.

[0010] As a preferred approach, the weighting coefficients Wᵢ of GYG2, EBAG9, RPZ, SLC2A2, BHMT, CLDN7B, DBT, DHTKD1, OAT, RALGAPB, and TYMP in step c are -0.2052, -0.2673, -0.3502, -0.2335, -0.3349, -0.3157, -0.3599, -0.1846, -0.3531, -0.3096, and 0.3242, respectively; the intercept b is -0.00238.

[0011] This invention enables the early and accurate identification of individuals with rapid growth potential during grass carp farming, especially in high-density environments. By selectively cultivating these superior individuals, the overall growth rate of the farmed population can be effectively increased, the farming cycle shortened, and feed costs reduced, thereby directly improving farming efficiency. Simultaneously, this method provides a highly efficient molecular-assisted selection (MAS) tool for the genetic breeding of new varieties (strains), potentially significantly shortening the approximately 20-year cycle required for traditional grass carp breeding and accelerating the development of high-quality, fast-growing grass carp varieties. Attached Figure Description

[0012] Figure 1 This is a volcano diagram of differentially expressed proteins in exosomes of grass carp plasma, as described in an embodiment of the present invention.

[0013] Figure 2 This is a graph showing the functional enrichment analysis of upregulated proteins in the grass carp group according to an embodiment of the present invention.

[0014] Figure 3 This is a graph showing the functional enrichment analysis of downregulated proteins in the grass carp group according to an embodiment of the present invention.

[0015] Figure 4 This is a schematic diagram illustrating cross-tissue protein synergistic changes in an embodiment of the present invention.

[0016] Figure 5 This is a flowchart of the discrimination method according to an embodiment of the present invention.

[0017] Figure 6 This is a user-interactive discrimination interface in an embodiment of the present invention.

[0018] Figure 7 This is the user-interactive judgment result interface of Embodiment 1 of the present invention.

[0019] Figure 8 This is another user-interactive judgment result interface according to an embodiment of the present invention. Detailed Implementation

[0020] The present invention will now be described in further detail with reference to the embodiments and the accompanying drawings.

[0021] This invention provides a method for determining the growth potential of grass carp based on cross-tissue synergistic changes in proteins from plasma exosomes. The core of this method lies in discovering and utilizing the crucial role of these proteins in synergistic changes across different tissue functions. Details are as follows: The discovery of cross-tissue protein synergistic changes and the identification of key biomarker combinations were achieved by constructing a plasma exosome-mediated cross-tissue protein synergistic change network for the first time through multi-omics analysis of grass carp large fish group (L) and grass carp small fish group (S) with significant growth differences.

[0022] A method for screening exosomal protein combinations includes the following steps: a. Select the large grass carp group and the small grass carp group with significant growth differences; b. Blood was extracted from each grass carp in both the large and small grass carp groups, and DIA-LC-MS / MS proteomics analysis was performed on the exosomes in the plasma of both groups. Figure 1 A volcano plot of differentially expressed proteins in exosomes of grass carp plasma was generated, showing the distribution of significantly differentially expressed proteins in exosomes of large and small grass carp groups (based on FDR<0.05, |FC|>2), visually displaying the number of upregulated and downregulated proteins; a total of 4001 proteins were obtained with valid data. Figure 2 A bubble chart was used to show the immune / signaling processes of upregulated proteins in the exosomes of large fish plasma. Among them, the 331 proteins that were significantly upregulated in the large fish group were mainly enriched in processes such as immune cell migration, inflammatory response, and signal transduction. Figure 3 A bubble chart was used to show the metabolic / muscle enrichment of downregulated proteins in the exosomes of large fish plasma. The 222 proteins that were significantly downregulated were mainly enriched in processes such as calcium ion transport, glycolysis, and muscle structure development, which suggests that exosome protein loading has a clear functional bias. c. Detect the proteins in the brain, liver and muscle tissue of each grass carp in the large grass carp group and the small grass carp group respectively, and select the protein data that are different in each tissue between the large grass carp group and the small grass carp group. d. Proteins that were significantly downregulated or significantly upregulated in all tissues and plasma exosomes of the grass carp group were selected into the fast growth marker protein group; Figure 4 This diagram illustrates the coordinated changes in proteins across tissues, revealing 11 proteins whose expression showed significant coordinated changes in plasma exosomes and specific tissues: (1) GYG2, EBAG9, RPZ, and SLC2A2 were all significantly downregulated in brain tissue and plasma exosomes of large fish; (2) BHMT, CLDN7B, DBT, DHTKD1, OAT, and RALGAPB were all significantly downregulated in the liver tissue and plasma exosomes of large fish; (3) GYG2 (glycogen 2): It was significantly downregulated in the muscle tissue and plasma exosomes of large fish; (4) TYMP (thymidine phosphorylase): It was significantly upregulated in the muscle tissue and plasma exosomes of large fish.

[0023] In particular, the simultaneous downregulation of GYG2 protein in the brain, muscle, and exosomes provides direct molecular evidence that plasma exosomes can act as protein delivery carriers for long-distance trans-tissue (brain-peripheral) communication.

[0024] In a specific embodiment, step a, selecting the grass carp large fish group and the grass carp small fish group with significant growth differences, specifically involves the following steps: a1. Stock grass carp fry in the fishpond at a density of 40,000-60,000 fish per acre; a2. After one month of breeding, adjust the density of grass carp in the fishpond to 2000-2100 kg / mu; a3. After 8-10 months of breeding, those with a weight greater than 450g / fish were selected as the large grass carp group, and those with a weight less than 100g / fish were selected as the small grass carp group. In a more specific embodiment, the dissolved oxygen in the fish pond in step a1 is 2-4 mg / L.

[0025] To improve the objectivity, accuracy, and automation of the discrimination method, this invention further provides a method for judging the growth potential of grass carp: a. Calculate the expression values ​​Xᵢ of GYG2, EBAG9, RPZ, SLC2A2, BHMT, CLDN7B, DBT, DHTKD1, OAT, RALGAPB and TYMP in grass carp plasma exosomes; b. Data standardization: Z-score standardization was performed on each protein expression value to obtain the standardized value Zᵢ; c. Model calculation: Substitute the standardized data into the discriminant model z = Σ(Wᵢ * Zᵢ) + b, calculate the linear score z, and calculate the probability value P(L) of the sample using the Sigmoid function P(L) = 1 / (1 + e^(-z)); where Wᵢ is the weight coefficient of each protein, and b is the intercept; d. Result determination: Based on the threshold rule of whether P(L) is greater than 0.5; when P(L) is greater than 0.5, it is determined to be a fast-growing type, and when P(L) is less than 0.5, it is determined to be a slow-growing type.

[0026] In a specific embodiment, step b, data standardization, is specifically: Zᵢ = σ / (Xᵢ−μ), where μ is the mean of the dataset and σ is the standard deviation of the dataset.

[0027] In a specific embodiment, the weighting coefficients Wᵢ of GYG2, EBAG9, RPZ, SLC2A2, BHMT, CLDN7B, DBT, DHTKD1, OAT, RALGAPB, and TYMP in step c are -0.2052, -0.2673, -0.3502, -0.2335, -0.3349, -0.3157, -0.3599, -0.1846, -0.3531, -0.3096, and 0.3242, respectively; the intercept b is -0.00238.

[0028] To improve the objectivity, accuracy, and automation of the discrimination method, this invention further provides a technical solution for constructing a quantitative discrimination model based on the aforementioned co-variant protein combination and integrating it into an interactive discrimination system. This solution first uses the expression data of the 11 core co-variant proteins (GYG2, EBAG9, RPZ, SLC2A2, BHMT, CLDN7B, DBT, DHTKD1, OAT, RALGAPB, TYMP) as input features, and trains a classification model using machine learning algorithms (such as logistic regression). After training, the model achieves 100% accuracy in both the training set and 5-fold cross-validation. The weight coefficients learned by the model have clear biological significance; TYMP (highly expressed in fast-growing individuals) receives a positive weight, while the remaining proteins, highly expressed in normal individuals, receive negative weights, which is entirely consistent with the aforementioned biological findings on cross-tissue communication networks. As a preferred and specific implementation of the method of this invention, an interactive discrimination system based on the aforementioned co-variant protein combination and the above-mentioned quantitative discrimination model is provided. This system aims to provide users with an intuitive and convenient interface to quickly determine the growth potential of grass carp online. Its technical solution is as follows:

[0029] The system adopts a client-server (B / S) architecture, referencing Figure 5 ,include: (1) Front-end interactive interface: Presented in the form of a web page, providing users with a data input interface. This interface contains data input boxes corresponding one-to-one with the 11 core covariant proteins (GYG2, EBAG9, RPZ, SLC2A2, BHMT, CLDN7B, DBT, DHTKD1, OAT, RALGAPB, TYMP), used to receive the expression values ​​of exosomal proteins in grass carp plasma to be tested input by the user.

[0030] (2) Backend processing server: Deploys the trained quantitative discriminant model (such as logistic regression model) and all its parameters (including the standardized parameters μ and σ of each protein, and weight coefficients W1 to W). 11and intercept b). After receiving the protein expression data submitted by the front end, the server automatically executes the following calculation process: ① Data standardization: According to the pre-stored parameters, the Z-score standardization is performed on each input protein expression value.

[0031] ② Model calculation: Substitute the standardized data into the discriminant model to calculate the linear score z = Σ(Wᵢ * Xᵢ)+ b, and use the Sigmoid function P(L) = 1 / (1 + e^(-z)) to calculate the probability value P(L) that the sample belongs to the "fast-growing type (big fish)".

[0032] ③ Result discrimination: Determine the growth potential type based on the threshold rule of whether P(L) is greater than 0.5.

[0033] (3) Result feedback module: The backend calculates the discrimination result ("fast-growing type" or "slow-growing type") and the corresponding predicted probability value P(L), and returns and displays it on the front-end interactive interface in real time to complete one discrimination process.

[0034] When using it, users only need to... Figure 6 In the webpage input interface, enter the expression level data of the specific protein combination in the exosomes of the grass carp plasma to be tested, obtained through experimental detection. After submission, refer to... Figure 7 and Figure 8 The system automatically completes the entire process from data standardization and model calculation to result judgment in the background, without requiring users to perform any complex calculations or analyses, thus achieving one-click judgment.

[0035] This interactive system productizes and packages the core discrimination method of this invention (cross-tissue protein communication network markers + high-precision model) into a tool. It enables farmers and breeding technicians without bioinformatics or computer science backgrounds to easily and quickly apply this invention, significantly lowering the technical barrier to entry and enhancing the accessibility and practical value of the method. This is a key implementation method for moving this invention from laboratory results to industrial application.

[0036] Compared to existing technologies that rely solely on single-molecule exosomes or isolated tissue markers, this invention is the first to utilize plasma exosomes as "central messengers" to connect synergistically changing proteins across multiple tissues from a systemic regulatory perspective, thereby identifying traits. This is not merely a simple aggregation of biomarkers, but an innovative application of the mechanisms of growth regulation systems, demonstrating a greater depth and sophistication in its technological concept.

[0037] This invention demonstrates both high accuracy and reliability: the quantitative discriminant model constructed based on a core protein combination validated across tissues achieves 100% accuracy in both the training set and rigorous 5-fold cross-validation. This proves the extreme specificity and stability of covariant proteins as a discriminant criterion, providing near-perfect reliability assurance for practical applications.

[0038] This invention provides a complete solution from mechanism analysis to industrial application, demonstrating strong practicality: It not only reveals novel exosome-mediated cross-tissue regulation mechanisms (such as brain-muscle communication of GYG2), but also transforms them into a specific, automated discrimination method. This method only requires non-invasive plasma collection and detection of specific protein combinations in exosomes to efficiently and accurately assess growth potential. It provides a highly valuable new tool for early, high-throughput screening in aquaculture breeding, and is expected to significantly shorten breeding cycles and improve industrial efficiency.

Claims

1. A method for screening exosomal protein combinations, characterized in that... Includes the following steps: a. Select the large grass carp group and the small grass carp group with significant growth differences; b. Blood was extracted from each grass carp in the large and small grass carp groups, and DIA-LC-MS / MS proteomics analysis was performed on the plasma exosomes of the two groups to select the proteins that were significantly upregulated and significantly downregulated in the large grass carp group. c. Detect the proteins in the brain, liver and muscle tissue of each grass carp in the large grass carp group and the small grass carp group respectively, and select the protein data that are different in each tissue between the large grass carp group and the small grass carp group. d. Proteins that were significantly downregulated or significantly upregulated in all tissues and plasma exosomes of the grass carp group were selected into the fast growth marker protein group; The protein assembly consists of the following 11 proteins: GYG2, EBAG9, RPZ, SLC2A2, BHMT, CLDN7B, DBT, DHTKD1, OAT, RALGAPB, and TYMP.

2. The method for screening exosomal protein combinations according to claim 1, characterized in that... The specific steps for step a, which involves selecting the large and small grass carp groups with significant growth differences, are as follows: a1. Stock grass carp fry in the fishpond at a density of 40,000-60,000 fish per acre; a2. After one month of breeding, adjust the density of grass carp in the fishpond to 2000-2100 kg / mu; a3. After 8-10 months of rearing, those weighing more than 450g / fish were selected as the large grass carp group, and those weighing less than 100g / fish were selected as the small grass carp group.

3. The method for screening exosomal protein combinations according to claim 2, characterized in that: The dissolved oxygen in the fish pond in step a1 is 2-4 mg / L.

4. A method for determining the growth potential of grass carp, characterized in that... Includes the following steps: a. Calculate the expression values ​​Xᵢ of GYG2, EBAG9, RPZ, SLC2A2, BHMT, CLDN7B, DBT, DHTKD1, OAT, RALGAPB and TYMP in grass carp plasma exosomes; b. Data standardization: Z-score standardization was performed on each protein expression value to obtain the standardized value Zᵢ; c. Model calculation: Substitute the standardized data into the discriminant model z = Σ(Wᵢ * Zᵢ) + b, calculate the linear score z, and calculate the probability value P(L) of the sample using the Sigmoid function P(L) = 1 / (1 + e^(-z)); where Wᵢ is the weight coefficient of each protein, and b is the intercept. d. Result determination: Based on the threshold rule of whether P(L) is greater than 0.5; when P(L) is greater than 0.5, it is determined to be a fast-growing type, and when P(L) is less than 0.5, it is determined to be a slow-growing type.

5. The method for determining the growth potential of grass carp according to claim 4, characterized in that... In step c, the weighting coefficients Wᵢ for GYG2, EBAG9, RPZ, SLC2A2, BHMT, CLDN7B, DBT, DHTKD1, OAT, RALGAPB, and TYMP are -0.2052, -0.2673, -0.3502, -0.2335, -0.3349, -0.3157, -0.3599, -0.1846, -0.3531, -0.3096, and 0.3242, respectively; the intercept b is -0.00238.

Images