High-density aquaculture grass carp fast-growing marker combination based on plasma exosome miRNA, screening method and high-density grass carp fast-growing species identification method

Abstract

This invention discloses a biomarker combination for rapid growth in high-density cultured grass carp based on plasma exosomal miRNAs, consisting of three miRNAs: dre-miR-146b, efu-miR-143, and spu-miR-133. The invention also discloses a screening method for this biomarker. Furthermore, this invention discloses a method for identifying fast-growing grass carp species in high-density culture. This invention has the advantage of enabling early and accurate identification of individuals with rapid growth potential in high-density grass carp culture environments.

Description

Technical Field

[0001] This invention relates to the field of fish genetics and breeding, and in particular to a combination of fast-growing biomarkers for high-density cultured grass carp based on plasma exosome miRNA, a screening method thereof, and a method for identifying fast-growing high-density grass carp species. Background Technology

[0002] Grass carp is the most produced aquaculture fish in my country. Grass carp reach sexual maturity in 3-4 years, and breeding superior varieties takes about 20 years. Accelerating the breeding of superior grass carp is one of the challenges in aquatic research. During the aquaculture period, early screening to obtain grass carp individuals suitable for high-density environments and targeted cultivation are urgent technical issues that need to be addressed to improve aquaculture efficiency and efficiently select high-quality, fast-growing broodstock.

[0003] Currently, the main method for screening fast-growing and high-quality parents is to detect SNP loci in the whole genome of grass carp for genotyping. However, most SNP loci are located in intergenic or non-coding regions, and most have no biological function or significance. Furthermore, the heritability of weight traits is low and they are greatly affected by the environment, resulting in low efficiency of this method. There is an urgent need to discover a biomarker that can transmit signals related to fish growth and effectively reflect the growth status of individual grass carp for auxiliary screening. Summary of the Invention

[0004] To address the aforementioned problems, the present invention aims to provide a method for effectively screening high-density, fast-growing grass carp fry based on plasma exosome miRNAs, and to provide specific combinations of plasma exosome miRNAs obtained using this method.

[0005] This invention is achieved through the following technical measures: a method for screening combinations of fast-growing biomarkers in high-density cultured grass carp based on plasma exosome miRNA, comprising the following steps:

[0006] It includes the following steps:

[0007] a. Stock grass carp fry in the fishpond at a density of 40,000-60,000 fish per acre;

[0008] b. After one month of breeding, adjust the density of grass carp in the fishpond to 2000-2100 kg / mu;

[0009] c. After 8-10 months of rearing, select the fish with a weight greater than 450g / fish into the large fish group and the fish with a weight less than 100g / fish into the small fish group.

[0010] d. Blood was drawn from each grass carp in the large fish group and the small fish group respectively. Plasma exosome miRNAs were extracted and detected. The miRNAs that showed differences between the large fish group and the small fish group were selected as plasma differential miRNAs.

[0011] e. Detect miRNAs in the brain, liver, and muscle tissues of each grass carp in the large and small fish groups respectively, and select the miRNAs that differ between the large and small fish groups in each tissue as tissue-differential miRNAs.

[0012] f. Comparative analysis of tissue-differential miRNAs and plasma-differential miRNAs was performed to exclude miRNAs with inconsistent up- and down-regulation in the tissues and plasmas of the large and small fish groups. The final selected miRNAs were the combination of plasma exosomal miRNAs that served as markers of rapid growth.

[0013] As a preferred method, the dissolved oxygen in the fish pond in step a is 2-4 mg / L.

[0014] As a preferred method, the detection method for plasma differential miRNAs in step d is as follows: Blood is extracted from grass carp in both the large and small fish groups, and plasma exosomal miRNAs of each grass carp are extracted and detected. Using edgeR software, primary differential miRNAs are obtained by using the expression fold difference (FC) ≥ 1.5 and P Value ≤ 0.05 between the large and small fish groups as screening criteria. Secondary differential miRNAs are then obtained by using FDR ≤ 0.05 as the standard. Among the secondary differential miRNAs, miRNAs with expression values ​​higher than 100 are selected as plasma differential miRNAs.

[0015] As a preferred method, the detection method for tissue-differential miRNAs in step e is as follows: detect miRNAs in the brain, liver, and muscle tissues of each grass carp in both the large and small fish groups, and screen for miRNAs that show significant differences between the large and small fish groups in the brain tissue, extremely significant differences between the large and small fish groups in the liver tissue, and significant differences between the large and small fish groups in the muscle tissue as tissue-differential miRNAs.

[0016] This invention also discloses a high-density cultured grass carp growth marker combination based on plasma exosomal miRNA, wherein the high-density cultured grass carp growth marker combination based on plasma exosomal miRNA consists of the following three miRNAs: dre-miR-146b (TGAGAACTGAATTCCAAGGGTG).

[0017] efu-miR-143 (GTCTGAGATGAAGCACTGTAGCTC)

[0018] and spu-miR-133 (TTTGGTCCCCTTCAACCAGCCGT).

[0019] This invention also discloses a method for identifying high-density, fast-growing grass carp species. The method involves calculating and detecting the expression levels of dre-miR-146b, efu-miR-143, and spu-miR-133 in grass carp plasma exosomes. The value of D is calculated using a linear discriminant analysis function D(z) = 0.1133 × z1 – 0.8756 × z2 + 0.4696 × z3. A value greater than 0 indicates the grass carp is a large fish, while a value less than 0 indicates a small fish. Here, z1, z2, and z3 are the standardized values ​​of the expression levels of dre-miR-146b, efu-miR-143, and spu-miR-133, respectively.

[0020] As a preferred method, exosomes and their miRNAs are first extracted within one day using a rapid plasma exosome extraction reagent. Then, multiple reverse transcription is performed using specific stem-loop primers provided in the kit to simultaneously convert the three target miRNAs (dre-miR-146b, efu-miR-143, and spu-miR-133) and the internal reference gene into cDNA. Finally, quantitative detection is performed using qPCR premixed solution to calculate the expression levels of dre-miR-146b, efu-miR-143, and spu-miR-133 in grass carp plasma exosomes.

[0021] This invention enables the early and accurate identification of individuals with rapid growth potential during grass carp farming, especially in high-density farming 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 (lines), 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

[0022] Figure 1 This is a schematic diagram illustrating the sampling and sorting of individuals with significant size differences into large fish groups and small fish groups according to an embodiment of the present invention.

[0023] Figure 2 Electron micrographs of plasma exosomes from the large fish group and small fish group in this embodiment of the invention.

[0024] Figure 3 The particle size distribution of plasma exosomes in the large fish group and small fish group of this invention is shown in the embodiment of the invention.

[0025] Figure 4 This is a diagram illustrating the identification and analysis of protein markers in plasma exosomes from the large fish group and the small fish group in this embodiment of the invention.

[0026] Figure 5This is a volcano diagram showing the significant expression differences of 177 plasma exosome miRNAs in the large fish group and the small fish group in Example 1 of the present invention.

[0027] Figure 6 This is a GO and KEGG enrichment analysis diagram of 2056 target genes of 177 miRNAs in Example 1 of the present invention.

[0028] Figure 7 This is a flowchart illustrating the process of identifying high-density, fast-growing grass carp species using plasma exosomes, as described in this invention.

[0029] Figure 8 This invention utilizes plasma exosomes to identify high-density, fast-growing grass carp species. The projection score distribution of large and small fish is shown in the simulated data test. Detailed Implementation

[0030] The present invention will now be described in further detail with reference to the embodiments and the accompanying drawings.

[0031] Exosomes are nanoscale vesicles, 30–150 nm in diameter, secreted by almost all cell types, with a lipid bilayer structure. Originating from late endosomes within cells, they are released extracellularly after fusing with the plasma membrane via multivesicular bodies. Widely distributed in bodily fluids such as blood, urine, and saliva, they are rich in various bioactive molecules, including nucleic acids and proteins, and possess functions for material and information transmission. Among these, miRNAs carried by exosomes play a crucial role in intercellular communication. miRNAs are 18–25 nucleotide non-coding RNA molecules that mediate post-transcriptional gene expression repression. Protected by exosomes, miRNAs can reach target cells via blood circulation, regulating gene expression and protein synthesis, thereby influencing metabolic and physiological processes such as growth and development in fish.

[0032] The inventors have discovered that by comparing and analyzing the plasma exosomes of grass carp with significant growth differences, different miRNA information transmitted by the exosomes can be screened and used as an effective biomarker to characterize the growth status of grass carp. This can be used to efficiently select grass carp individuals with high-density, fast-growing traits, thereby improving the economic benefits of grass carp farming and accelerating the breeding of new grass carp varieties.

[0033] A method for screening a combination of biomarkers for rapid growth in high-density cultured grass carp based on plasma exosome miRNA includes the following steps:

[0034] a. Stock grass carp fry in the fishpond at a density of 40,000-60,000 fish per acre;

[0035] b. After one month of breeding, adjust the density of grass carp in the fishpond to 2000-2100 kg / mu;

[0036] c. After 8-10 months of rearing, select the fish with a weight greater than 450g / fish into the large fish group and the fish with a weight less than 100g / fish into the small fish group.

[0037] d. Blood was drawn from each grass carp in the large fish group and the small fish group respectively. Plasma exosome miRNAs were extracted and detected. The miRNAs that showed differences between the large fish group and the small fish group were selected as plasma differential miRNAs.

[0038] e. Detect miRNAs in the brain, liver, and muscle tissues of each grass carp in the large and small fish groups respectively, and select the miRNAs that differ between the large and small fish groups in each tissue as tissue-differential miRNAs.

[0039] f. Comparative analysis of tissue-differential miRNAs and plasma-differential miRNAs was performed to exclude miRNAs with inconsistent up- and down-regulation in the tissues and plasmas of the large and small fish groups. The final selected miRNAs were the combination of plasma exosomal miRNAs that served as markers of rapid growth.

[0040] In one specific embodiment, a high-density aquaculture experiment was conducted starting in September of the first year, with approximately 12,000 fish stocked over a 0.23-acre area at a density of 2,000-2,100 kg / acre, more than twice that of the normal density group (870-1,100 kg / acre). The experiment lasted for nine months. During this period, the dissolved oxygen level in the high-density group was 2-4 mg / L, significantly lower than the 5-7 mg / L in the normal density group. The experiment concluded in June of the second year, with 9,400 fish surviving at the high density. Those weighing more than 450g / fish were selected as the large fish group, and those weighing less than 100g / fish were selected as the small fish group.

[0041] Figure 1 This diagram illustrates the sampling and sorting of individuals with significant size differences into a large fish group (L group) and a small fish group (S group) in the same high-density pond after 9 months of rearing.

[0042] Plasma exosomes from the large fish group (L group) and the small fish group (S group) were extracted by ultracentrifugation and identified by particle size, electron microscopy and marker proteins. Figure 2 Electron micrographs of plasma exosomes from the large and small fish groups. Figure 3 The particle size distribution of plasma exosomes is shown for the large and small fish groups. Figure 4 The diagram shows the identification and analysis of protein markers in plasma exosomes from the large and small fish groups.

[0043] Plasma exosomal miRNA sequencing detected a total of 3325 miRNAs. Comparison with the miRBase database yielded 2925 known miRNAs and 400 newly predicted miRNAs.

[0044] Table 1: Statistical table of plasma exosome miRNA sequencing from major and minor fish groups

[0045]

[0046] Using edgeR software, a total of 2701 differentially expressed miRNAs were obtained based on a fold change (FC) ≥ 1.5 and a p-value ≤ 0.05 between the large and small fish groups. Further, using an FC ≤ 0.05 criterion, 311 miRNAs with the most significant differences were identified. For ease of rapid detection in practical applications, 177 highly expressed miRNAs (expression levels above 100 in any sample) were further selected.

[0047] Figure 5 A volcano plot showing significant differences in the expression of 177 plasma exosomal miRNAs between the large and small fish groups was presented.

[0048] Table 2: Expression levels and sequence listing of the first 67 of the 177 miRNAs in plasma exosomals

[0049]

[0050] To further understand the mechanisms by which these 177 miRNAs affect fish growth in high-density aquaculture environments, a total of 2056 target genes were predicted using miRanda and RNAhybrid software, and GO and KEGG enrichment analyses were performed.

[0051] These target genes were found to be mainly enriched in GO terms and KEGG pathways, including cell proliferation and migration (e.g., extracellular matrix-related pathways such as collagen trimer, ECM-receptor interaction, and focal adhesion), amino acid metabolism (e.g., arginine and proline metabolism, cysteine ​​and methionine metabolism, and histidine metabolism), and neural development (e.g., brain development, central nervous system morphogenesis, axon development, and axon guide).

[0052] Figure 6 GO and KEGG enrichment analysis of 2056 target genes of 177 miRNAs revealed the mechanism by which plasma exosomes regulate the growth of grass carp in high-density aquaculture environments.

[0053] To further screen for plasma exosomal miRNAs that can indicate tissue physiological status, the above miRNAs were further analyzed in brain, liver, and muscle tissues.

[0054] 143 miRNAs were detected in brain tissue, including 1 miRNA with significant differences between the large and small fish groups (p<0.05). 146 miRNAs were detected in liver tissue, including 29 miRNAs with significant differences between the large and small fish groups (p<0.05) and 3 miRNAs with highly significant differences (p<0.01). 163 miRNAs were detected in muscle tissue, including 2 miRNAs with significant differences between the large and small fish groups (p<0.05). Furthermore, using miRNAs with significant differences across tissues as candidates, we excluded miRNAs with inconsistent up- and down-regulation in tissues and plasma (only the six miR-145 types in the liver, marked in the table below) as well as miRNAs with similar or low expression levels. Finally, three miRNAs were selected (in bold in the table below). Among them, dre-miR-146b was significantly upregulated in the brain of large fish, while efu-miR-143 and spu-miR-133 were significantly downregulated in the liver and muscle of large fish, respectively. This significant up- and down-regulation relationship was also consistently reflected in the exosomes of grass carp plasma. Therefore, it can be used as a biomarker to identify the growth rate of individual grass carp.

[0055] Table 3: Three miRNAs significantly associated with the brain, liver, and muscles

[0056]

[0057] The construction process for predicting fast-growing grass carp species based on the above plasma exosomal miRNA combinations using an LDA (linear discriminant analysis) discriminant model is as follows (refer to...). Figure 7 ):

[0058] (I) Data Preparation and Standardization

[0059] Objective: To eliminate the dimensional differences between the expression levels of different miRNAs and make the data comparable.

[0060] Before modeling, the raw detection data needs to be standardized and preprocessed. In this embodiment, three key miRNA biomarkers, dre-miR-146b, efu-miR-143, and spu-miR-133, were selected as discriminant indicators.

[0061] Standardized parameter settings:

[0062] The mean of dre-miR-146b is 786.6067, and the standard deviation is 758.8048.

[0063] The mean of efu-miR-143 was 1111.2633, and the standard deviation was 450.8341.

[0064] The mean of spu-miR-133 is 392.36, and the standard deviation is 212.2454.

[0065] Standardization formula: The original expression levels of the three miRNAs for each sample are standardized using Z-score, i.e.:

[0066] Standardized value = (Original value - Mean) / Standard deviation

[0067] Standardization transforms miRNA expression data of different magnitudes to the same scale, laying the foundation for subsequent calculations.

[0068] (ii) Calculate the mean vector for each category

[0069] Objective: To determine the central locations of the "large fish group" (fast-growing type) and the "small fish group" (normal type) in the three-dimensional miRNA feature space.

[0070] Based on the standardized data, calculate the mean vectors for the two categories respectively:

[0071] The mean vector for the small fish group is [-0.5728, 0.9623, 0.9453], representing the average expression levels of normal-growing individuals across the three dimensions of dre-miR-146b, efu-miR-143, and spu-miR-133.

[0072] The mean vector for the large fish group: [0.5728, -0.9623, -0.9453], represents the average expression level of fast-growing individuals across three dimensions;

[0073] The mean vector shows that the expression level of dre-miR-146b in the large fish group is high (positive value), while the expression levels of efu-miR-143 and spu-miR-133 are low (negative value). This is completely consistent with the previous study's conclusion that "dre-miR-146b is significantly upregulated and efu-miR-143 and spu-miR-133 are significantly downregulated in large fish".

[0074] (III) Calculate the within-class scatter matrix Sw

[0075] Objective: To measure the dispersion of samples within each category and reflect intra-class differences.

[0076] The intra-class scatter matrix Sw is calculated by summing the squared deviations of each class's samples from its class mean. The formula is as follows:

[0077] Sw = Σ(x-μᵢ)(x-μᵢ)ᵀ (sum over all samples in category i)

[0078] The Sw matrix calculated in this study is a 3×3 symmetric matrix.

[0079] Sw=[[4.0316,0.2172,-0.3802],

[0080] [0.2172, 0.4438, 0.4595],

[0081] [-0.3802, 0.4595, 0.6384]]

[0082] The Sw matrix reflects the covariance structure of samples within the same category across the three miRNA dimensions. The diagonal elements represent the variance of each category in that dimension, while the off-diagonal elements represent the covariance between different dimensions.

[0083] (iv) Calculate the between-class scatter matrix Sb

[0084] Objective: To measure the degree of separation between different categories and reflect inter-class differences.

[0085] The between-class scatter matrix Sb is calculated by the deviation of each class mean from the population mean, and its formula is as follows:

[0086] Sb=Σnᵢ(μᵢ-μ)(μᵢ-μ)ᵀ

[0087] Where nᵢ is the number of samples in the i-th class, μᵢ is the mean vector of the i-th class, and μ is the overall mean vector.

[0088] The Sb matrix calculated in this study is:

[0089] Sb=[[1.9684,-3.307,-3.2486],

[0090] [-3.307, 5.5562, 5.458],

[0091] [-3.2486,5.458,5.3616]]

[0092] The Sb matrix reflects the degree of separation between the centroids of the large fish group and the small fish group. The larger the value of Sb, the more significant the difference between the two groups.

[0093] (v) Solving for the projection direction

[0094] Objective: To find the optimal linear projection direction that maximizes the inter-class distance and minimizes the intra-class distance after projection.

[0095] The core idea of ​​LDA is to find a projection vector w that maximizes the ratio of between-class divergence to within-class divergence, i.e.:

[0096] J(w)=(wᵀSbw) / (wᵀSww)

[0097] The solution to this optimization problem can be obtained by solving the generalized eigenvalue problem:

[0098] Sbw=λSww

[0099] The optimal projection direction w is the eigenvector corresponding to the largest eigenvalue of Sw⁻¹Sb.

[0100] The projection direction vector obtained by matrix operations in this study is:

[0101] w=[0.1133,-0.8756,0.4696]

[0102] This projection direction has the following biological significance:

[0103] The weight of dre-miR-146b is +0.1133 (positive weight): this miRNA is upregulated in large fish and makes a positive contribution to the identification of "large fish";

[0104] The weight of efu-miR-143 is -0.8756 (negative weight, largest absolute value): this miRNA is downregulated in large fish and is an important indicator for discrimination;

[0105] The weight of spu-miR-133 is +0.4696 (positive weight): this miRNA is downregulated in large fish and makes a negative contribution to the identification of "large fish".

[0106] (vi) Generating LDA discriminant models

[0107] Objective: To establish a mathematical discriminant function that can be used for practical prediction.

[0108] Based on the obtained projection direction vector, a linear discriminant function is constructed: D(x) = 0.1133 × (dre - miR - 146b_scaled) - 0.8756 × (efu - miR - 143_scaled) + 0.4696 × (spu - miR - 133_scaled)

[0109] Judgment rules:

[0110] If D(x) > 0, then the sample is predicted to be a large fish (fast-growing type).

[0111] If D(x)≤0, then the sample is predicted to be a small fish (normal type).

[0112] This discriminant function projects three-dimensional miRNA expression data onto a one-dimensional discriminant axis and achieves binary classification by setting a threshold of 0.

[0113] (vii) Model Validation

[0114] After the model is built, it needs to be rigorously validated to ensure its reliability and generalization ability. This study uses two validation methods:

[0115] 1. Validation of raw data

[0116] The model was back-substitution test was performed using the original sample data used for modeling to verify the model's fit to the training data:

[0117]

[0118] Validation results: The training set accuracy reached 100%, and all samples were correctly classified, indicating that the model has good fitting ability.

[0119] 2. Simulated data testing

[0120] To further verify the model's generalization ability, 100 simulated samples were generated based on the statistical characteristics (mean, standard deviation, covariance structure) of the original data for independent testing.

[0121] Test results: The accuracy of the simulated data reached 94%.

[0122] Figure 8 To illustrate the projection score distribution of large and small fish in a simulated test using plasma exosomes to identify high-density, fast-growing grass carp species:

[0123] The horizontal axis represents the sample index (1-100), and the vertical axis represents the projected score D(x).

[0124] The green dashed line represents the discrimination threshold (D(x)=0).

[0125] The blue dots represent small fish samples, whose projected scores are mainly distributed below the threshold (negative value region).

[0126] The red dots represent large fish samples, whose projected scores are mainly distributed above the threshold (positive value region).

[0127] The two types of samples show a clear separation trend on the projection axis, with only a few samples showing misclassification.

[0128] Simulation results demonstrate that the LDA model has good discriminative ability and stability, and can maintain a high prediction accuracy on independent datasets.

[0129] The entire LDA model construction process can be summarized as follows: data standardization → calculation of class means → construction of the divergence matrix → solving for the optimal projection → establishment of the discriminant function → model validation. This process systematically integrates the expression information of three plasma exosomal miRNA markers into a concise discriminant function, enabling rapid and accurate prediction of the fast-growing trait in grass carp, and providing an effective molecular tool for early screening in aquaculture.

[0130] A linear discriminant analysis (LDA) model based on plasma exosomal miRNA expression profiles was chosen to classify grass carp individuals exhibiting rapid growth. This was primarily based on previous screening findings that dre-miR-146b, efu-miR-143, and spu-miR-133 showed significant expression differences between rapidly growing individuals (large fish) and normal individuals (small fish), exhibiting a synergistic change pattern. This provided a reliable molecular marker basis for model construction. The LDA model was chosen primarily due to its advantages in handling small-sample, multi-feature classification problems, providing clear discriminant functions and feature weights for easy biological interpretation. The model eliminated dimensional differences through standardized preprocessing and automatically optimized the discriminant weights of each miRNA. The results showed that dre-miR-146b received a positive weight (upregulated in large fish), while efu-miR-143 and spu-miR-133 received negative weights (downregulated in large fish), fully consistent with previous findings. This model not only achieves 100% accurate classification on the training set, but also outputs probabilistic confidence assessments. It only requires the detection of three specific miRNAs to complete the prediction, and has the advantages of simple operation and low cost. It provides a non-invasive, quantifiable and easily scalable practical detection scheme for molecular-assisted selection of the fast-growing trait of grass carp.

[0131] This invention also provides a rapid growth trait screening kit capable of detecting exosomal miRNA biomarkers in grass carp plasma.

[0132] The kit contains specific stem-loop reverse transcription primers and TaqMan probes for these three target miRNAs. The dre-miR-146b primer is designed based on its mature sequence "TGAGAACTGAATTCCAAGGGTG", the efu-miR-143 primer is designed based on its mature sequence "GTCTGAGATGAAGCACTGTAGCTC", and the spu-miR-133 primer is designed based on its mature sequence "TTTGGTCCCCTTCAACCAGCCGT". To ensure accuracy, the kit also includes a detection system for the internal reference gene U6 and contains standardized plasma exosome RNA extraction reagents, reverse transcription premix, qPCR reaction solution, and standard curve preparation materials, forming a complete detection system. This kit overcomes the technical bottleneck of early identification of fast-growing traits in aquaculture breeding, enabling growth potential prediction at the fry stage through non-invasive blood sampling, shortening the traditional growth observation cycle of months or even years to within a week.

[0133] At the application level, this kit establishes a standardized three-step detection process: First, exosomes and their miRNAs are enriched within one day using a rapid plasma exosome extraction reagent; then, multiple reverse transcription is performed using specific stem-loop primers provided in the kit to simultaneously convert the three target miRNAs and the internal reference gene into cDNA; finally, quantitative detection is performed using an optimized qPCR premix, and a unique primer-probe design ensures consistent amplification efficiency for the three targets under the same reaction conditions. In application, simply substitute the Cq values ​​of the three detected miRNAs into the accompanying discriminant algorithm software to calculate the miRNA expression level. After standardizing the expression level data, based on the established D(z) discriminant function score, a "fast-growing type" is output when D(z) is greater than 0, and a "normal type" is output when D(z) is less than 0.

[0134] This invention provides a highly specific and sensitive molecular discrimination tool: based on a screened specific plasma exosomal miRNA combination (dre-miR-146b, efu-miR-143, spu-miR-133) and a constructed linear discriminant analysis (LDA) model, accurate identification of growth traits in grass carp individuals is achieved. Using this LDA model to classify the analyzed samples, a classification accuracy of 100% was achieved on the training set. The model has a clear discriminant function (specifically, the discriminant function is: D(z) = 0.1133 × z1 – 0.8756 × z2 + 0.4696 × z3, where z1, z2, and z3 are the standardized dre-miR-146b, efu-miR-143, and spu-miR-133, respectively). It only needs to detect the expression abundance of three miRNAs to output the discrimination result of "large fish (fast-growing type)" or "small fish (normal type)," providing a stable and quantifiable molecular basis for the early screening of the fast-growing trait of grass carp.

[0135] This invention also establishes a high-throughput, non-invasive, rapid detection solution: a screening kit developed based on the aforementioned miRNA biomarkers overcomes the technical bottlenecks of traditional methods that rely on long-term growth performance observation or genomic SNP genotyping (which is inefficient and highly susceptible to environmental influences). This kit uses specific stem-loop reverse transcription primers combined with TaqMan probes for qPCR detection, completing the entire process from plasma sampling to result analysis within one week, shortening the traditional growth observation cycle of months or even years to less than a week. This non-invasive blood testing method can predict growth potential in the early stages of fish fry development, facilitating large-scale screening.

[0136] In summary, this invention provides a complete technical system from biomarker discovery and discrimination model construction to the development of practical detection kits. This system is simple to operate, low in cost, and highly accurate, effectively meeting the urgent need of the aquaculture industry for early, rapid, and large-scale identification of the fast-growing trait in grass carp. It has significant industrial application value and promising prospects for widespread adoption.

[0137] List of 177 significantly differentially expressed miRNA sequences from grass carp plasma exosomes:

[0138] ACTCCCAATCCTTGTACCAGTG TAGGTAGTCTCATGTTGTGGCAATGACACGTTTTCCCCGGGATTG TGTAGTGTTTCCTACTTTATG TAGGTAGTTTCACGTTGTTGGTGTCAGTTTGTCAAATATACAA TAGTAATGGTAAGGGTTCT GTCCAGTTTTCCCAGGGAATCCGTGAAATGTTTAGGACCACTT ATTGGTCCCCTTCAACCCTTGTACCAGGTGT TACCCTGTAGATCCGGATTTGTGCATAAAGTAGAGACAGCACTACT TGGAATGTAAAAGTAGTGA CCCATAAAGTAGAAAGCACTATAGGTAGTTTCCTGTTGTTGGGA GTAGTGTTTCCCTACTTTATGG TCCTTCATTCCACCGGAGTTGGAATGTAAAAGTATGCGT TTTGGTCCCCTTCAACCAGCTGC TAGTGTTTCCTACTTTATGGATGAGAACTGAATTCCATGGTG TGGAATGTTAAGAAGTATGTATT TTTGGTCCCCTTCAACCAGCTGTTGGAATGTTAAGAAGTATGTAT GTGAAATGTTTAGGACCACTTGA GTTGTGTCAGTTTATCAAACCCTACCCTGTAGGTCCGAATTTGT TACCCTGTAGAACCGAATTTGTGT TGGAATGTAAAAAGTATGTTGGAATGTAAAAGTATGTAG ACCCTGTAGATCCGAATTTGTGTA ACCCTGTAGATCCGAATTTGTTTTGGTCCCCTTCCAACCAGCTGT TGGAATGTAAAGAAGTATGGAG TAGGTAGTTTCATGTTGTTGGCACCCTGTAGATCCGAATTTGT TACCCTGTAGATCCGAATTTGT TACCCTGTAGATCCGGATTTGTCATAAAGTAGACAGCACTACTA GTCCAGTTTTCCCAGGAATCCCTGAGAACTGAATTCCAAGGGTGTCTCCCAATCCTTGTACCAGTG AAGCAGCGCGTCATGGTTTTC TAGGTAGTTTCAAGTTGTTGGGTAGGTAGTTTGATGTTGTTGGG TAGGTAGTTTTATGTTGTTGGG GTGAAATGTTCAGGACCACTTGTAACGGAACCCATAATGCAGCT TTCAGTCATTGTTTCTAGTAGT AATGACACGTTTTCTCCCGGATCGTTTGGTCCCCTTCAACCAGCTGTA GTCTGAGATGAAGCACTGTAGCTC GTCCAGTTTTCCCAGGAATCCCTTATGGAATGTAAGGAAGTGTGTGGT TTCCCTTTGTCATCCTTTGCCTCGG TGTCAGTTTGTCAAATACCCCAATACCCTGTAGAACCGAATTTGT TTGGTCCCCTTCAACCAGCTA TTGGTCCCCTTCAACCAGCTGCTGTAGTGTTTCCTACTTTATGG CCCATAAAGTAGAAAGCACTAC TAGGTAGTTTCATGTTGTTGGTGGAATGTTAAGAAGTATGTA GTGAAATGTTTAGGACCACTTG TTTGGTCCCCTTCAACCAGCTGAGAACTGAATTCCATGGGT TCTCCCAATCCTTGTACCAGTGT CAGGTAGTTTCATGTTGTTGGGTGAAATGTTTAGGACCACTTA TAACGGAACCCATAATACAG TAGGTAGTCTCATGTTGTTGGGTACCCTGTAGATCCGAATTTGTG TAGGTAGTTTCATGTTGTTGGG TAGGTAGTTTCCTGTTGTTGGGTGTCAGTTTGTCAAATACCCCA GTTGTGTCAGTTTATCAAAC TAGGTAGTTTCATGTTGTTGGGATAGGTAGTTTTATGTTGTTGGGC TAACGGAACCCATAATGCAGCTG ATACCCTGTAGAACCGAATTTGTTCTCCCAACCCTTGTACCAGAGT TAGGTAGTTTCCTGTTGTTGGTGGAATGTAAGGAAGTGTGTGTACCCTGTAGAACCGAATTTGTG TTTGGTCCCCTTCAACCAGCTG TTTGGTCCCCTTCAACCAGCTATGTAGTGTTTCCTACTTTATGGA CATAAAGTAGAAAGCACTACT TGAGATGAAGCACTGTAGCTCGTCCAGTTTTCCCAGGAATCCCT TCTCCCAACCCTTGTACCAGTG TGGAATGTAAAGAAGTATGTATGTGAAATGTTTAGGACCACTAG TTCCCTTTGTCATCCTATGCCT TCCTTCATTCCACCGGAGTCTGTGGAATGTAAGGAAGTGTGTGG TTCCCTTTGTCATCCTTTGCCT TTGTGTCAGTTTATCAAACACTCCCAATCCTTGTACCAGTGTC TTTGGTCCCCTTCAACCGGCTG TGAGAACTGAATTCCACGGGTTTCCTTCATTCCACCGGAGTCT TGTCAGTTTGTCAAATACCCTA TACCCTGTAGATCCGAATTTGACCCTGTAGAACCGAATTTGTG TGGAATGTAAAGAAGTATGTGA TACCCTGTAGAACCGAATTTGTGTGTGGTCCCCTTCAACCAGC TCCATAAAGTAGAAAGCACTAC TGAGATGAAGCACTGTAGCTTCCAGTTTTCCCAGGAAT ACTCCCAATCCTTGTACCAGT TTCCCTTTGTCATCCTATGCTGTCAGTTTGTCAAATACCC GTAACGGAACCCATAATGC ACCCTGTAGATCCGAATTTGTGTGAAATGTTTAGGACCACTTG TCTCCCAACCCTTGTACCAGT TAGGTAGTTTCTTGTTGTTGGGTTGTGTCAGTTTATCAAACC TTTGGTCCCCTTCAACCAGCTGG TCCATAAAGTAGAAAGCACTATAGGTAGTTTCACGTTGTTGGGA TTCCCTTTGTCATCCTATGCCTG TTGGTCCCCTTCAACCAGCTTGTTTGGTCCCCTTCAACCAGCTC TGAGATGAAGCACTGTAACTCCTAGGTAGTATCATGTTGTTGGGTGGAATGTAAAGAAGTACGTTG TGGAATGTAAAGAAGTATGGGT TGGAATGTAAAGAAGTATGTACGTGAAATGTTTAGGACCACTGG CCCTTCATTCCACCGGAGTCTGT AACCCTGTAGATCCGAATTTGTTTGGTCCCCTTCAACCAGCTG CCCTGTAGAACCGAATTTGTGT TGGAATGTAAAGAAGTGTGTATTGAGATGAAGCACTGTAGCTCA TAGGTAGTTTCGTGTTGTTGGG TCCTTCATTCCACCGGAGTCTGTTGTCAGTTTGTCAAATACCCC TACCCTGTAGATCCGAAT TACCCTGTAGAACCGAATTTTGGTCCCCTTCAACCAG TGGAATGTAATGAAGTATGTAT TTGGTCCCCGTCAACCAGCTGTAACCCTGTAGATCCGAATTTGTG TTTGGTCCCCTTCAACCAGCCGT CACCCTGTAGAACCGAATTTGTTGTAGTGTTTCCTACTTTATGGAT TGAGAACTGAATTCCAAGGGTT TCTCCCAATCCTTGTACCAGTTGAAATGTTTAGGACCACTCG TAACGGAACCCATAAAGCAGCTG TTCAGTCATTGTTTCTGGTAGTAATGACACGTTTTCTCCCGGATT TTTGGTCCCCTTCAACCAGCTAT GTCCAGTTTTCCCAGGAATCCCTTTGGAATGTAAGGAAGTGTGTGA TGGAATGTAAAGAAGTATGCA TTTGGTCCCCTTCAACCAGCTGTAGTGTTTCCTACTTTATGGA CCCATAAAGTAGAAAGCACT TAGGTAGTTTCATGTTGTTGCATAAAGTAGAAAGCACTACTA TTCCCTTTGTTATCCTATGCCT CACCCTGTAGATCCGAATTTGTTACCCTGTAGATCCGAATTTGTGT CACCCTGTAGAATCGAATTTGT TTGGTCCCCTTCAACCAGCCGCCATAAAGTAGAAAGCACTACTGAGATGAAGCACTGTAGCTCG TCTCCCAACCCTTGTACCAGAGTAGGTAGTTTTATGTTGTTGG

Claims

1. A method for screening biomarkers for rapid growth in high-density cultured grass carp based on plasma exosome miRNA, characterized in that... Includes the following steps: a. Stock grass carp fry in the fishpond at a density of 40,000-60,000 fish per acre; b. After one month of breeding, adjust the density of grass carp in the fishpond to 2000-2100 kg / mu; c. After 8-10 months of rearing, select the fish with a weight greater than 450g / fish into the large fish group and the fish with a weight less than 100g / fish into the small fish group. d. Blood was drawn from each grass carp in the large fish group and the small fish group respectively. Plasma exosome miRNAs were extracted and detected. The miRNAs that showed differences between the large fish group and the small fish group were selected as plasma differential miRNAs. e. Detect miRNAs in the brain, liver, and muscle tissues of each grass carp in the large and small fish groups respectively, and select the miRNAs that differ between the large and small fish groups in each tissue as tissue-differential miRNAs. f. Comparative analysis of tissue-differential miRNAs and plasma-differential miRNAs was performed to exclude miRNAs with inconsistent up- and down-regulation in the tissues and plasmas of the large and small fish groups. The final selected miRNAs were the combination of plasma exosomal miRNAs that served as markers of rapid growth.

2. The method for screening high-density cultured grass carp rapid growth biomarker combinations based on plasma exosome miRNA, as described in claim 1, is characterized in that: The dissolved oxygen in the fish pond in step a is 2-4 mg / L.

3. The method for screening high-density cultured grass carp rapid growth biomarker combinations based on plasma exosome miRNA, as described in claim 1, is characterized in that: The detection method for plasma differential miRNAs in step d is as follows: Blood was extracted from grass carp in the large fish group and the small fish group, and plasma exosomal miRNAs of each grass carp were extracted and detected. Using edgeR software, the primary differential miRNAs were obtained with the expression fold difference (FC) ≥ 1.5 and P Value ≤ 0.05 between the large fish group and the small fish group. Secondary differential miRNAs were then obtained with FDR ≤ 0.

05. Among the secondary differential miRNAs, miRNAs with expression values ​​higher than 100 were selected as plasma differential miRNAs.

4. The method for screening high-density cultured grass carp rapid growth biomarker combinations based on plasma exosome miRNA, as described in claim 1, is characterized in that: The method for detecting tissue-differential miRNAs in step e is as follows: miRNAs in the brain, liver, and muscle tissues of each grass carp in both the large and small fish groups are detected separately. MiRNAs that show significant differences between the large and small fish groups in brain tissue, extremely significant differences between the large and small fish groups in liver tissue, and significant differences between the large and small fish groups in muscle tissue are identified as tissue-differential miRNAs.

5. A high-density biomarker combination for rapid growth in cultured grass carp based on plasma exosome miRNA, characterized in that: The biomarker combination for rapid growth of grass carp in high-density culture consists of the following three miRNAs: dre-miR-146b: TGAGAACTGAATTCCAAGGGTG, efu-miR-143:GTCTGAGATGAAGCACTGTAGCTC and spu-miR-133: TTTGGTCCCCTTCAACCAGCCGT.

6. A method for identifying high-density, fast-growing grass carp species, characterized in that: The expression levels of dre-miR-146b, efu-miR-143, and spu-miR-133 in grass carp plasma exosomes were calculated. The value of D was calculated using the linear discriminant analysis function D(z) = 0.1133×z1 – 0.8756×z2 + 0.4696×z3. A value greater than 0 for D(z) predicted that the grass carp was a large fish, while a value less than 0 predicted that the grass carp was a small fish. Here, z1, z2, and z3 are the standardized values ​​of the expression levels of dre-miR-146b, efu-miR-143, and spu-miR-133, respectively.

7. The method for identifying high-density, fast-growing grass carp species according to claim 6, characterized in that: First, exosomes and their miRNAs were extracted within one day using a rapid plasma exosome extraction reagent. Then, multiple reverse transcription was performed using specific stem-loop primers provided in the kit to simultaneously convert the three target miRNAs (dre-miR-146b, efu-miR-143, and spu-miR-133) and the internal reference gene into cDNA. Finally, quantitative detection was performed using qPCR premixed solution to calculate the expression levels of dre-miR-146b, efu-miR-143, and spu-miR-133 in grass carp plasma exosomes.

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