A combination of biomarkers for evaluating the degree of parasitic infection in bony fish and its application

Abstract

This invention provides a combination of biomarkers for evaluating the degree of parasitic infection in bony fish and its application, belonging to the field of aquatic disease monitoring technology. The biomarker combination includes... klf9、rsad2、 il10、egr1 and f5 Five core genes. This invention screened core genes covering four dimensions—stress, immunity, apoptosis, and physical damage—through gene co-expression network analysis, and constructed a gill infection severity index (BISI) model based on Z-score normalization and weighted average. This biomarker combination and evaluation index model can be used to prepare products for assessing the degree of physiological damage, disease progression, and drug efficacy after parasitic infections in bony fish (especially Cryptocaryon irritans infection in large yellow croaker). This invention solves the technical problem of existing evaluation systems for fish parasite infections such as Cryptocaryon irritans lacking sensitivity and multi-dimensional quantitative standards, and has high accuracy and application value.

Description

Technical Field

[0001] This invention belongs to the field of aquatic disease monitoring and molecular biotechnology, and relates to a combination of biomarkers for evaluating the degree of parasite infection in bony fishes and their application. Background Technology

[0002] Irritating Cryptocaryon ( Cryptocaryon irritan s) is one of the main pathogens causing outbreaks of parasitic diseases in bony fish such as large yellow croaker, due to its high mortality rate and strong infectivity. The gills are the main target organ for this parasite, and the degree of damage to them directly determines the survival probability of the fish.

[0003] Currently, the main assessment methods for Cryptocaryon irritans infection include: (1) pathogen counting method: observing the number of parasites in the gills under a microscope, but this method cannot reflect the degree of physiological damage and immune response status inside the fish; (2) histopathological analysis: although it can observe tissue damage, the slide preparation cycle is long and destructive, making it difficult to achieve real-time dynamic monitoring of the disease process; (3) detection of single molecular markers: although it has high sensitivity, due to the multiple complex dimensions involved in the infection process such as stress, immune escape, cell apoptosis and physical barrier damage, single indicators often have the limitation of "being blinded by a leaf", making it difficult to provide quantitative comprehensive evaluation results.

[0004] Therefore, there is an urgent need in this field for a combination of biomarkers and an evaluation model that can integrate multi-physiological information, be quantifiable, and reflect the essential characteristics of infection, in order to achieve accurate grading of disease course and standardized assessment of the efficacy of anti-insect drugs. Summary of the Invention

[0005] The purpose of this invention is to address the shortcomings of existing technologies by providing a combination of biomarkers for evaluating the degree of parasitic infection in bony fishes and their applications. This scheme is based on a multi-gene co-expression network and can be used for quantitative grading of disease course and evaluation of drug efficacy. It aims to solve the technical defects of existing evaluation technologies, such as single dimension, inability to quantify, and strong lag.

[0006] The technical solution adopted in this invention is as follows: This invention, through co-expression network analysis (WGCNA) and association studies with pathological phenotypes, screened and identified core gene combinations highly correlated with the course of parasitic infections in bony fishes: klf9 (Related to stress response) rsad2 (Antiviral / Innate Immune Related) il10 (related to inflammation regulation) egr1 (Cell growth is related to apoptosis) and f5 (Blood clotting is related to tissue repair), forming a combination of biomarkers for evaluating the degree of parasitic infection in bony fish.

[0007] This invention also provides the application of a reagent for detecting the expression levels of the above-mentioned biomarker combinations in the preparation of products for evaluating the degree of parasitic infection in bony fishes. The reagent comprises substances that can detect the expression levels of biomarker combinations using methods such as sequencing, nucleic acid hybridization, and nucleic acid amplification.

[0008] The present invention also provides a kit comprising reagents for detecting the expression levels of each gene in the combination of biomarkers described above.

[0009] Furthermore, the reagents include one or more of primer pairs, probes, reagents for nucleic acid amplification, reagents for detecting nucleic acid amplification products, total RNA extraction reagents, and reverse transcription reagents; the nucleotide sequences of the primer pairs are shown in SEQ ID NO.1-12, respectively.

[0010] This invention also provides a method for constructing an index to evaluate the degree of parasitic infection in bony fish, comprising the following steps: S1. Obtain the expression level data of each gene in the biomarker combination in the bony fish sample to be tested; S2. Standardize the expression levels of each gene to obtain the standardized score of each gene; S3. The standardized scores of each gene are weighted and calculated with their corresponding preset weight coefficients to obtain the Biological Infection Index (BISI) for parasites in bony fishes.

[0011] Furthermore, in step S2, the standardization process is Z-score standardization, and the background dataset is sample data of healthy bony fish.

[0012] Furthermore, in step S3, the preset weighting coefficient is preset based on the strength of the co-expression relationship of each gene in the biomarker combination during the infection process and its association with the infection phenotype.

[0013] Furthermore, the parasite is Cryptocaryon irritans, Shield-ciliates, Thygrafia gracilis, or Eye-spot Amygdaloides, and the sample is the gills, spleen, kidney, or skin of a bony fish.

[0014] The present invention also provides a system for evaluating the degree of gill parasite infection in bony fishes, comprising: The detection module is used to acquire the expression level data of each gene in the combination of the above-mentioned biomarkers in the sample to be tested; The processing module is used to calculate the BISI index according to the method described in any of the preceding items; The evaluation output module is used to determine the severity level of infection based on the comparison result between the BISI index and a preset threshold, and then output the result.

[0015] The present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the functions of the system described above.

[0016] The present invention also provides the use of the above-described combination of markers, the kit as described in any of the preceding claims, or the method as described in any of the preceding claims in any of the following aspects: (1) Prepare products for the diagnosis or assessment of Cryptocaryon irritans infection of the gills of large yellow croaker; (2) Screening or efficacy evaluation of anti-irritant cryptocaryon drugs in large yellow croaker; (3) Screening of large yellow croaker strains resistant to Cryptocrystic worms.

[0017] The beneficial effects of this invention are: This invention uses gene co-expression network analysis to screen core genes covering four dimensions: stress, immunity, apoptosis, and physical damage, and constructs an Infection Severity Index (BISI) model based on Z-score normalization and weighted average. This biomarker combination and evaluation index model can be used to develop products assessing the degree of physiological damage, disease progression, and drug efficacy after parasitic infections in bony fish (especially Cryptocaryon irritans infection in large yellow croaker). This invention solves the technical problem of existing evaluation systems for fish parasite infections such as Cryptocaryon irritans lacking sensitivity and multi-dimensional quantitative standards, and possesses high accuracy and application value. Attached Figure Description

[0018] Figure 1 The TMM (Trimmed Mean of M-values) values ​​of the klf9 gene at different time points after gill infection are shown.

[0019] Figure 2 The TMM values ​​of the rsad2 gene at different time points after gill infection are shown.

[0020] Figure 3 The TMM values ​​of the il10 gene at different time points after gill infection are shown.

[0021] Figure 4 The TMM values ​​of the egr1 gene at different time points after gill infection.

[0022] Figure 5 The TMM values ​​of the f5 gene at different time points after gill infection are shown.

[0023] Figure 6 The change of BISI value over time after infection of the gills of large yellow croaker with Cryptocaryon irritans.

[0024] Figure 7 These are gill tissue injuries at different times following infection in bony fish.

[0025] Figure 8 Histological scores of gill infection in bony fish at different time points.

[0026] Figure 9 The ROC curve for the constructed BISI index. Detailed Implementation

[0027] The present invention will be further described below with reference to the accompanying drawings and specific embodiments, but the scope of protection of the present invention is not limited thereto: Example 1. Screening of marker combinations Experimental animals and infection procedures: Healthy large yellow croakers were randomly divided into a control group and a parasite infection group. The infection group was inoculated with Cryptocaryon irritans using the immersion infection method. Gill tissue samples were collected at 0h (control), 24h, 48h, and 72h post-infection, with at least three biological replicates for each group.

[0028] RNA extraction and sequencing: Total RNA was extracted from each sample using TRIzol reagent, and the samples were then subjected to bulk RNA sequencing (bulk RNA-Seq).

[0029] Co-expression network analysis and biomarker screening: Differential expression analysis was performed on RNA-Seq data to screen for genes whose expression levels changed persistently and significantly during infection. For example... Figure 1-5 As shown, based on the expression profile data of samples containing different infection stages, the co-expression relationship network between biomarkers was used to quantify their contribution to the infection phenotype. Combining gene function annotation (GO) and pathway analysis (KEGG), focusing on pathways such as stress response, immunity, apoptosis, and coagulation repair, candidate biomarkers including klf9, rsad2, il10, egr1, and f5 were initially screened.

[0030] This combination of candidate biomarkers can be used to comprehensively assess different physiological and pathological stages of infection: Early stress biomarker: klf9 (Krüppel-like factor 9) gene, whose upregulation reflects the body's early perception and initiation of a stress response to parasitic infection; Immune burst biomarker: rsad2 (Viperin) gene, whose upregulation reflects strong activation of the innate immune system, particularly the type I interferon pathway; Anti-inflammatory balance biomarker: il10 (interleukin 10) gene, whose expression changes are used to assess the body's ability to initiate immune regulation and feedback inhibition to control excessive inflammatory damage; Apoptosis biomarker: egr1 (early growth response protein 1) gene, whose upregulation suggests that gill tissue cells may be at increased risk of programmed cell death due to direct infection damage or immune-mediated processes; Tissue damage biomarker: f5 (coagulation factor V) gene, whose upregulation is closely related to gill microvascular endothelial damage, local coagulation activation, and the degree of tissue physical damage.

[0031] Example 2

[0032] This embodiment provides a kit that includes reagents for detecting the expression levels of each gene in a biomarker combination as described in Example 1.

[0033] The reagents may include one or more of the following: primer pairs, probes, reagents for nucleic acid amplification, reagents for detecting nucleic acid amplification products, total RNA extraction reagents, and reverse transcription reagents; wherein the primer pairs are shown in Table 1: Table 1. Primer pairs involved in the kit

[0034] Example 3. Construction and Validation of the BISI Index This embodiment provides an Infection Severity Index (BISI) model constructed based on the above gene combination. This model eliminates differences in background expression among individuals through Z-score normalization and performs a weighted summation based on the contribution weights of each gene in the disease course, thereby transforming complex molecular expression information into intuitive and continuous quantitative values. The specific steps are as follows: 1) Collect gill tissue samples from the large yellow croaker to be evaluated, extract total RNA, and detect the expression levels of the five biomarker genes (klf9, rsad2, il10, egr1, f5) using real-time quantitative PCR (qRT-PCR) or transcriptome sequencing (RNA-Seq). Expression levels are expressed as TMM, FPKM, or relative to internal reference genes (such as β-actin, gapdh) (2). -ΔΔCt )express.

[0035] 2) Data Standardization: The expression levels of each biomarker were transformed using log2(TMM+1), and the Z-score standardization method was employed to calculate the standardized score (Zi) of each biomarker's expression value in the test sample relative to a pre-set background dataset (such as a healthy control group or an infection 0-hour group). The calculation formula is as follows:

[0036] in, Let be the log2 transformed expression level of the i-th gene in the sample. and These represent the mean and standard deviation of the gene expression level in the background dataset, respectively.

[0037] 3) Weighted composite index calculation: The co-expression relationship network between biomarkers is used to quantify their contribution to the infection phenotype, and this is used as the weighting coefficient. Specifically, it includes the following steps: First, based on expression profile data from samples at different infection stages, the pairwise expression correlations between all genes were obtained by calculating Pearson correlation coefficients, forming a similarity matrix. Subsequently, an adjacency matrix was constructed and optimized. A power function transformation was used to convert the similarity matrix into an adjacency matrix, where the soft threshold parameter β was chosen to approximate a scale-free topology, strengthening strong correlations and weakening weak correlations. To more accurately characterize the functional associations of genes in the network, the Topological Overlap Measure (TOM) was calculated. Hierarchical clustering was performed based on the TOM matrix, dividing genes with highly similar co-expression patterns into different modules. The correlation coefficient between the feature vector (Module Eigengene, ME) of each module and the infection time was calculated, and the modules with the most significant associations were selected as "key infection response modules." For biomarker genes belonging to this key module, the correlation coefficient between their expression profile and the module feature vector ME was calculated, serving as the module membership degree (kMEi) of that gene, i.e., kMEi = cor(gene i expression level, ME). Finally, the module membership degree of each marker is normalized to obtain the final weight coefficient: wi = kMEi / Σ(kMEj).

[0038] The BISI index is calculated using the following formula:

[0039] In this embodiment, specifically: Data processing: The expression data obtained from RNA-seq in Example 1 were transformed using log2(TMM+1).

[0040] Standardization: Using the 0h control group data as a background, calculate the Z-score (Z0) of gene expression levels in samples at each time point. i ).

[0041] Weight Calculation: Based on co-expression network analysis, the module membership degree (kME value) of each gene in the co-expression network is calculated, and the final weight coefficients are obtained after normalization. w i In this embodiment, the weights are: klf9:0.22, rsad2:0.25, il10:0.18, egr1:0.20, f5:0.15.

[0042] Calculate BISI: According to the formula Calculate the BISI value for each sample.

[0043] The results are as follows Figure 6 As shown, the BISI value increases with the duration of infection (in hours). Figure 6The dynamic curves of the BISI value of the three biological replicates at each time point over time are shown. It can be seen that the BISI value exhibits a significant and regular increasing trend as the infection time (in hours) progresses.

[0044] In addition, based on experimental research, a judgment threshold can be preset. In this embodiment, the infection level is determined according to the numerical range of the BISI index: healthy (BISI<1.0), mild stress (1.0 ≤ BISI<7.0), and severely damaged (BISI ≥7.0).

[0045] The BISI value shows a regular increase with infection time, exhibiting a dynamic characteristic highly consistent with the disease progression in infected bony fish. Because this protocol incorporates Z-score transformation based on 0-hour healthy data in the standardization process, the BISI index value can accurately characterize the deviation and degree of damage of the organism compared to its healthy state. Furthermore, the weight coefficients of the five core genes in the co-expression network are determined by kME values. Multidimensional collaborative computation eliminates measurement noise caused by individual differences or non-specific stress in single biomarkers, thus ensuring the accuracy and reproducibility of the assessment scheme at the statistical and systems biology levels.

[0046] Example 4

[0047] This embodiment provides a system for evaluating the degree of gill parasite infection in bony fish, including: The detection module is used to acquire the expression level data of each gene in the combination of markers as described above in the sample to be tested; in other embodiments of the present invention, the detection module may also directly call the externally acquired expression level data or support manual input by the user; The processing module is used to calculate the BISI index; The evaluation output module is used to determine the severity level of infection based on the comparison result between the BISI index and a preset threshold, and then output the result.

[0048] In other embodiments of the present invention, the biomarker combination involved in the present invention, or the constructed BISI index, may also be applied to the preparation of products for diagnosing or evaluating Cryptocaryon irritans infection in large yellow croaker, or to the screening or efficacy evaluation of drugs against Cryptocaryon irritans in large yellow croaker, or to the screening of disease-resistant breeding strains of large yellow croaker.

[0049] Example 5. Histological examination and scoring of gill tissue after parasite infection in bony fish. Gill tissue from healthy bony fish and bony fish infected with parasites (such as Cryptocaryon irritans) was fixed in 4% paraformaldehyde solution, embedded in paraffin blocks, cut into 5 μm thick sections, stained with hematoxylin and eosin (HE), observed and photographed under a microscope, and histological damage was scored. Normal bony fish gill filaments consist of connective tissue, capillaries, and gill lamellae, with the gill lamellae arranged in a comb-like pattern and neatly arranged; no obvious histological changes were observed, representing normal and healthy tissue morphology. After parasite infection, the morphology of gill filament tissue changes significantly and gradually worsens with prolonged infection time. Based on gill characteristics, this invention establishes the following quantitative scoring criteria for histological damage: Table 2

[0050] Figure 7 In section A, the gill tissue of the control group (0h) is from bony fish. Figure 7 The images from the middle and lower BD images show damage to the gill tissue of bony fish at 24h, 48h, and 72h after parasite infection. Figure 8 Display histological score.

[0051] Example 6. Correlation analysis between BISI value and gill histological score The CORREL function was used to analyze the correlation between changes in the BISI index and gill histological damage scores. A correlation coefficient close to 1 or -1 indicates a strong correlation, close to 1 indicates a positive correlation, close to -1 indicates a negative correlation, a correlation coefficient of 0 indicates no correlation, a coefficient below 0.4 indicates a low correlation, 0.4–0.7 indicates a significant correlation, and a coefficient above 0.7 indicates a high correlation. The results showed that the correlation index between the BISI index and gill histological damage scores was 0.98376, indicating a high correlation between changes in this gene combination and the histological score.

[0052] Example 7. Validation of the correlation between gene expression and colon histological score To establish the infection model in Example 1, gill tissue samples were collected at 0 h (control), 24 h, 48 h, and 72 h post-infection, with at least three biological replicates per group. Total RNA was extracted using TRIzol reagent (Invitrogen). Quantitative real-time PCR (qRT-PCR) was performed using the primers provided in Example 2 to determine the relative mRNA levels of candidate genes. qPCR amplification was performed in a total volume of 10 μL according to the kit instructions, with each primer at a concentration of 10 μM. 2 -ΔΔCT β-actin was used as an internal control, with three replicates per sample. Data analysis and BISI index construction were performed based on the mean threshold (Ct). ROC curves were plotted to validate the BISI index constructed from the gene combination, using BISI values ​​and histological scores.

[0053] The results analysis showed that the AUC value of the ROC curve was 90.5%. P <0.0001. The ROC curve is as follows: Figure 9 As shown.

[0054] The above embodiments are merely illustrative of the principles and effects of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in the present invention should still be covered by the claims of the present invention.

Claims

1. A combination of biomarkers for evaluating the degree of parasitic infection in bony fish, characterized in that, The combination includes the following genes: klf9 Gene, rsad2 Gene, il10 Gene, egr1 Genes and f5 The gene, the bony fish mentioned is the large yellow croaker, and the parasite is Cryptocaryon irritans.

2. The application of the reagent for detecting the expression level of the biomarker combination described in claim 1 in the preparation of a product for evaluating the degree of parasitic infection in bony fish, wherein the bony fish is large yellow croaker and the parasite is Cryptocaryon irritans.

3. A reagent kit, characterized in that, Includes reagents for detecting the expression levels of each gene in the biomarker combination of claim 1.

4. The reagent kit according to claim 3, characterized in that, The reagents include one or more of primer pairs, probes, reagents for nucleic acid amplification, reagents for detecting nucleic acid amplification products, total RNA extraction reagents, and reverse transcription reagents; the nucleotide sequences of the primer pairs are shown in SEQ ID NO.1-12, respectively.

5. A method for constructing an index to evaluate the degree of parasitic infection in bony fish, characterized in that, The bony fish in question is the large yellow croaker, and the parasite is Cryptocaryon irritans. The method includes the following steps: S1. Obtain the expression level data of each gene in the biomarker combination described in claim 1 in the sample of bony fish to be tested; S2. Standardize the expression levels of each gene to obtain the standardized score of each gene; S3. The standardized scores of each gene are weighted and calculated with their corresponding preset weight coefficients to obtain the Biological Infection Index (BISI) for parasites in bony fishes.

6. The method according to claim 5, characterized in that, In step S2, the standardization process is Z-score standardization, and the background dataset is sample data of healthy bony fish.

7. The method according to claim 5, characterized in that, In step S3, the preset weighting coefficient is preset based on the strength of the co-expression relationship of each gene in the biomarker combination during the infection process and its association with the infection phenotype.

8. A system for evaluating the degree of gill parasite infection in bony fishes, characterized in that, The bony fish in question is the large yellow croaker, and the parasite is Cryptocaryon irritans, including: The detection module is used to acquire the expression level data of each gene in the biomarker combination of claim 1 in the sample to be tested; The processing module is configured to execute the method according to any one of claims 5 to 7 to calculate the BISI index; The evaluation output module is used to determine the severity level of infection based on the comparison result between the BISI index and a preset threshold, and then output the result.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the functions of the system as described in claim 8.

10. The use of the biomarker combination of claim 1, the kit of any one of claims 3-4, or the method of any one of claims 5-7 in any of the following aspects: (1) Prepare products for the diagnosis or assessment of Cryptocaryon irritans infection of the gills of large yellow croaker; (2) Screening or efficacy evaluation of anti-irritant cryptocaryon drugs in large yellow croaker; (3) Screening of large yellow croaker strains resistant to Cryptocrystic worms.

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