Intestinal microorganism marker combination and system for detecting fish liver injury and application

Abstract

The invention discloses an intestinal microbial marker combination and system for detecting fish liver injury and application, and belongs to the technical field of molecular detection of fish liver injury. The invention relates to an enteric microbial marker combination, which is characterized in that the enteric microbial marker combination comprises the following components: cetyl, pseudomonas and Escherichia-Shigella, and the enteric microbial marker combination comprises the following components: bacillus, pseudomonas and Escherichia-Shigella, and the enteric microbial marker combination comprises the following components: bacillus, pseudomonas and Escherichia-Shigella. By utilizing the intestinal microbial marker combination, non-invasive diagnosis of liver injury can be realized on the premise of not invading a fish body, the accuracy is high, missing detection is avoided, and particularly, the condition that liver injury is misjudged to cause excessive treatment is avoided. A judgment basis is provided for breeding environment regulation and control and precise disease prevention and treatment, the breeding survival rate of the pelteobagrus fulvidraco is increased, and the breeding benefit is improved.

Description

Technical Field

[0001] The present application relates to the technical field of molecular detection of fish liver damage, and in particular to a combination, system and application of intestinal microbial markers for detecting fish liver damage. Background Art

[0002] The water bodies where yellow catfish are farmed are often eutrophic, leading to excessive growth of cyanobacteria and algal blooms. Algal toxins are secondary metabolites of cyanobacteria and have significant liver toxicity. Long-term exposure can cause disorders of sugar and lipid metabolism in the liver of yellow catfish, leading to liver diseases such as non-alcoholic fatty liver disease and fatty hepatitis, causing huge economic losses.

[0003] Yellow catfish liver damage can be cured in the early stage, and the self-healing of damaged liver cells can be promoted through environmental control, feeding management, and drug bait mixing, thereby reducing the mortality rate of yellow catfish farming. However, the superficial symptoms of early liver damage are not obvious, and can only be detected through blood routine, liver tissue sections, liver color ultrasound and other methods, which are difficult to implement in aquaculture. In the process of fish farming, liver damage is often difficult to detect, resulting in missing the best treatment period and causing irreversible losses. Summary of the invention

[0004] In order to solve at least one of the above technical problems, the technical solution adopted in this application is as follows.

[0005] The first aspect of the present application provides a combination of intestinal microbial markers for detecting whether fish have liver damage, wherein the combination of intestinal microbial markers includes Ceti ( Cetobacterium ), Pseudomonas ( Pseudomonas ) and Escherichia coli-Shigella spp. ( Escherichia-Shigella ).

[0006] The liver-gut axis is a two-way interactive digestive system in the body of yellow catfish. Liver damage can lead to abnormal bile excretion, which in turn disrupts the balance of intestinal flora; intestinal flora imbalance can further affect liver glucose and lipid metabolism, aggravating the degree of liver cell damage. Research results show that liver damage in yellow catfish is often accompanied by intestinal microbial disorders, excessive growth of opportunistic pathogens, and a significant reduction in probiotics. Based on this, this application uses the genus Ceti ( Cetobacterium ), Pseudomonas ( Pseudomonas ) and Escherichia coli-Shigella spp. ( Escherichia-Shigella ) is an intestinal microbial marker for detecting liver damage. By detecting changes in its abundance, it is possible to determine whether the liver is damaged, which has important guiding significance for disease prevention and control and environmental regulation during yellow catfish farming.

[0007] In the present application, if Cetobacterium in the intestinal microbiota of the fish to be tested is significantly reduced, and Pseudomonas and Escherichia-Shigella are significantly increased, it indicates liver damage.

[0008] In the present application, the terms "determine", "judge", "diagnose", "detect", and "assay" have the same meaning, and are all used to determine whether a fish has liver damage.

[0009] The second aspect of the present application provides a system for detecting whether a fish has liver damage, including the following modules: A data input module, configured to input the abundance data of the intestinal microbiota biomarker combination described in claim 1 in the obtained intestinal sample of the fish to be tested; A database storage module, configured to store the abundance data of the intestinal microbiota biomarker combination in the intestinal samples of a group of fish, where the group of fish includes a group of fish with healthy livers and a group of fish with liver damage; A liver damage detection module, connected to the data input module and the database storage module respectively, and configured to: Construct a prediction model using the abundance data of the intestinal microbiota biomarker combination in the intestinal samples of the group of fish, and based on the abundance data of the intestinal microbiota biomarkers in the intestinal sample of the fish to be tested, detect whether the fish to be tested has liver damage.

[0010] In some embodiments of the present application, the intestinal sample is an excrement sample.

[0011] In some embodiments of the present application, the abundance refers to relative abundance, which is the proportion of the number of a specific intestinal microbiota in the total number of intestinal microbiota. In some specific embodiments of the present application, the number is characterized by the reads obtained by sequencing that can be aligned to the corresponding intestinal microbiota. Further, the alignment refers to alignment with a microbial database, and the database includes but is not limited to: Silva (https: / / ftp.arb-silva.de), Unite (https: / / unite.ut.ee / repository.php), Greengenes (ftp: / / greengenes.microbio.me / greengenes_release).

[0012] In some embodiments of the present application, the abundance data of the intestinal microbiota biomarker combination is obtained based on 16S rRNA gene sequencing or metagenomic sequencing methods.

[0013] In some embodiments of the present application, the steps of the 16S rRNA gene sequencing method are as follows: Obtain the excrement of the fish to be tested, and extract the microbial genomic DNA of the excrement; Amplify using 16S rRNA gene amplification primers, construct a library and sequence the amplification products to obtain sequencing data; Compare the sequencing data with a microbial database and statistically obtain the abundance data of the intestinal microbial biomarker combination.

[0014] In some possible implementation schemes of the present application, in the liver injury detection module, the steps of constructing a prediction model using the abundance data of the intestinal microbial biomarker combination in the intestinal samples of the group of fish include: Perform linear regression in the healthy liver fish group and the liver injury fish group respectively to obtain the coefficients and constants of each intestinal microbial biomarker, and obtain two determination equations: F1 = a1 × A C + b1 × A P + c1 × A E +d1 F2 = a2 × A C + b2 × A P + c2 × A E +d2 Wherein, a1, b1, c1, a2, b2 and c2 represent coefficients; d1 and d2 represent constants; A C represents the relative abundance of Cetobacterium, A P represents the relative abundance of Pseudomonas, A E represents the relative abundance of Escherichia-Shigella, Substitute the abundance data of the intestinal microbial biomarkers in the intestinal samples of the fish to be tested into the two determination equations respectively to obtain two function values F1 and F2. If F1 ≥ F2, it is determined that the liver of the fish to be tested is healthy; if F1 < F2, it is determined that the liver of the fish to be tested is damaged.

[0015] In some specific implementation schemes of the present application, the values of a1, b1, c1 and d1 are respectively: 0.32, 0.781, 8.002 and -11.576; the values of a2, b2, c2 and d2 are respectively: 0.022, 1.906, 26.081 and -3.57.

[0016] In some other possible implementation schemes of the present application, in the liver injury detection module, the steps of constructing a prediction model using the abundance data of the intestinal microbial biomarker combination in the intestinal samples of the group of fish include: S1. Randomly divide the abundance data of the intestinal microbial biomarker combination in the intestinal samples of the group of fish into two groups, one as a training set and the other as a test set. Each group includes the abundance data of the intestinal microbial biomarker combination of fish with healthy livers and fish with liver damage. S2. Use the training set data to construct a liver damage detection model based on a machine learning algorithm. S3. In the test set, verify the obtained prediction model.

[0017] In some possible implementation schemes of the present application, the machine learning algorithm is selected from any one of the following algorithms: Logistic regression algorithm, random forest algorithm, neural network algorithm, support vector machine algorithm, Bayesian classification algorithm, gradient boosting algorithm, K-nearest neighbor algorithm, and decision tree algorithm.

[0018] The third aspect of the present application provides the application of the reagent for detecting the abundance of the intestinal microbial biomarker combination described in the first aspect of the present application in the preparation of a kit for detecting whether fish have liver damage.

[0019] In some implementation schemes of the present application, the detection reagent includes 16S rRNA gene amplification primers, PCR amplification buffer, DNA polymerase, and second-generation sequencing library construction reagents.

[0020] In some specific implementation schemes of the present application, the 16S rRNA gene amplification primers are 343F: 5'-TACGGRAGGCAGCAG-3'; 798R: 5'-AGGGTATCTAATCCT-3.

[0021] In some implementation schemes of the present application, the detection reagent further includes a DNA extraction reagent.

[0022] In the present application, the fish is Pelteobagrus fulvidraco.

[0023] Compared with the prior art, the present application has the following beneficial effects: The present application provides an intestinal microbial biomarker combination for detecting whether fish have liver damage. Using this intestinal microbial biomarker combination, non-invasive diagnosis of liver damage can be achieved without invading the fish body, with high accuracy, no missed detection, and especially no misjudgment as liver damage, thus avoiding over-treatment. It provides a judgment basis for aquaculture environment regulation and precise disease prevention and control, which is beneficial to improving the survival rate of Pelteobagrus fulvidraco farming and enhancing the farming efficiency.

[0024] It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present application, nor is it used to limit the scope of the present application. Other features of the present application will become easily understood through the following description. Brief Description of the Drawings

[0025] By referring to the following detailed description with reference to the accompanying drawings, the above and other objects, features and advantages of the exemplary embodiments of the present application will become readily understood. In the drawings, several embodiments of the present application are shown in an exemplary rather than restrictive manner, where: Figure 1 Shows the observation results of liver tissue section staining of yellow catfish with healthy liver and liver injury in Example 1 of the present application; Figure 2 Shows the screening of intestinal microbial biomarkers based on the random forest method in Example 1 of the present application; Figure 3 Shows the relative abundances of three types of intestinal microbial biomarkers obtained by screening in each treatment group in Example 1 of the present application; Figure 4 Shows the observation results of liver tissue section staining of yellow catfish in Example 3 of the present application, and the numbers represent sample numbers. Detailed Embodiments

[0026] In order to make the technical problems, technical solutions and beneficial effects solved by the present application clearer and more understandable, the present application will be further described in detail below in conjunction with embodiments.

[0027] The following examples are used here to demonstrate the preferred implementation schemes of the present application. Those skilled in the art will understand that the technologies disclosed in the following examples represent the technologies that the inventors have found can be used to implement the present application, and therefore can be regarded as the preferred schemes for implementing the present application. However, those skilled in the art should understand from this specification that many modifications can be made to the specific embodiments disclosed here, and still obtain the same or similar results, without departing from the spirit or scope of the present application.

[0028] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which the present application belongs. The materials cited herein and the materials they cite will be incorporated by reference in the manner cited.

[0029] Those skilled in the art will realize or can understand through routine experiments many equivalent technologies of many specific embodiments of the inventions described herein. These equivalents will be included in the claims.

[0030] The experimental methods in the following embodiments are all conventional methods unless otherwise specified. The instrument and equipment used in the following embodiments are all conventional laboratory instrument and equipment unless otherwise specified; the test materials used in the following embodiments are all obtained from conventional biochemical reagent stores unless otherwise specified.

[0031] Example 1: Screening of Intestinal Microbial Markers for Liver Injury in Yellow Catfish 1. Construction of a Liver Injury Model in Yellow Catfish Two experimental groups with different microcystin concentrations were set up: C was the control group with a microcystin concentration of 0 μg / L; T was the microcystin stress group with a microcystin concentration of 5 μg / L and a stress time of 28 days. The stress time and concentration were set with reference to the duration of cyanobacterial blooms and the microcystin concentration in ponds in aquaculture.

[0032] 2. Diagnosis of Liver Injury in Yellow Catfish after Microcystin Stress Paraffin section technology was used to perform HE staining on liver tissue sections of yellow catfish in different experimental groups. The results of section observation are as Figure 1 shown.

[0033] As can be seen from Figure 1 , in the control group, the liver tissue structure of yellow catfish was tight, the cell structure was clear, and no liver injury was detected (the left figure in Figure 1 ), while in the microcystin group, the liver tissues of all yellow catfish were congested, and a large number of blood cells were interspersed among liver cells, showing liver injury phenomena such as cell lysis (the right figure in Figure 1 ).

[0034] 3. 16S rRNA Sequencing of Intestinal Microbes Universal primers 343F (5'-TACGGRAGGCAGCAG-3') and 798R (5'-AGGGTATCTAATCCT-3') were used to amplify the V3-V4 variable region of the 16S rRNA gene, and the amplification results were sequenced for bacterial diversity analysis.

[0035] The sequencing sequences were compared and annotated with the Silva database (https: / / ftp.arb-silva.de) to obtain the intestinal microbial species information corresponding to the gene sequences. The gene abundances of various microbes were calculated through the number of reads, and then the relative abundances of each microbe (the ratio of the number of reads of a certain microbe to the total number of reads of all microbes) were obtained.

[0036] Based on random forest analysis (see Figure 2 ), the top three microbes with the greatest differential contribution between the control group and the microcystin group (importance score > 0.5) were determined as intestinal microbial markers, namely: Cetobacterium ( Cetobacterium ), Pseudomonas ( Pseudomonas ), and Escherichia-Shigella ( Escherichia-Shigella ). Through the Mann-Whitney U test (as in Figure 3As shown in the figure, the significant differences and correlations of three intestinal microbial markers between the control group and the microcystin group were analyzed. Among them, the relative abundance of Cetobacterium was positively correlated with liver health, suggesting a protective factor; the relative abundances of Pseudomonas and Escherichia-Shigella were positively correlated with liver injury, suggesting risk factors (as shown in Table 1 and Figure 3 as shown).

[0037] Table 1 Differential microorganisms in the microcystin group and the control group

[0038] Thus, it can be seen that Cetobacterium, Pseudomonas, and Escherichia-Shigella in the intestines of fish such as Pelteobagrus fulvidraco can be used as markers of liver injury. Specifically, by detecting the relative abundances of these three types of intestinal bacteria, it is possible to detect whether fish have liver injury. Among them, Cetobacterium can be used as an independent marker. As a simple judgment method, the abundance data of Cetobacterium in the fish groups with healthy livers and the fish groups with liver injury were statistically analyzed separately to obtain the 95% confidence interval, as shown in Table 2: Table 2 95% confidence interval of the relative abundance of Cetobacterium in the liver injury group and the liver healthy group

[0039] If the relative abundance of Cetobacterium in the fish to be tested is not less than the lower limit of the 95% confidence interval of the relative abundance of Cetobacterium in the fish group with healthy livers, it is determined that the fish to be tested has a healthy liver; if the relative abundance of Cetobacterium in the fish to be tested is less than the lower limit of the 95% confidence interval of the relative abundance of Cetobacterium in the fish group with healthy livers, it is determined that the fish to be tested has liver injury.

[0040] Example 2 Detection of liver injury based on changes in the abundance of intestinal microbial markers On the basis of Example 1, the inventor further performed discriminant analysis on the relative abundances of Cetobacterium, Pseudomonas, and Escherichia-Shigella in the intestines in Table 1 to obtain the classification function coefficients for liver injury detection as shown in Table 3, and used Wilks' Lambda test to analyze the significance of the classification coefficients, and the results are shown in Table 4.

[0041] Table 3 Classification coefficients for liver injury detection

[0042] Table 4 Significance analysis of classification coefficients for liver injury detection

[0043] Based on the above classification function coefficients, a liver injury determination equation was established: F1 = 0.32 × A C + 0.781 ×A P +8.002× A E -11.576 F2 = 0.022× A C +1.906× A P +26.081× A E -3.57 Among them, A C represents the relative abundance of Cetobacterium, A P represents the relative abundance of Pseudomonas, A E represents the relative abundance of Escherichia coli - Shigella.

[0044] Substituting the relative abundances of Cetobacterium, Pseudomonas, and Escherichia coli - Shigella in the intestine into the equations, two functions F1 and F2 are obtained. When F1 ≥ F2, the liver health is detected; when F1 < F2, liver injury is detected.

[0045] Substituting the data in Table 1 into the two determination equations, F1 and F2 values are obtained respectively. Further detection shows that the number of tails detected for healthy liver individuals is 13, and the number of tails detected for liver - injured individuals is 14, as shown in Table 5.

[0046] Table 5 Liver Injury Detection Results

[0047] As can be seen from Table 5, using the determination equation constructed in this example, the detection accuracy rate is 100%.

[0048] Example 3 Application of Detecting Fish Liver Injury Based on Intestinal Microbiome Biomarkers 1. Fish Selection Select the pond for culturing Pelteobagrus fulvidraco where cyanobacterial blooms occur. After 4 hours of feeding, randomly select 30 Pelteobagrus fulvidraco for detection, and number them from 1 to 30 respectively.

[0049] 2. Collection of Intestinal Excreta Gently press the abdomen of the fish to be tested with the thumb, and slowly push from the upper abdomen to the anus. After a small amount of excreta is discharged from the anus, collect it using a sterile sample tube.

[0050] 3. Genomic DNA Extraction Take 0.5 g of excreta, and use an extraction kit to extract the genomic DNA of the sample. Then, detect the DNA concentration through agarose gel electrophoresis and NanoDrop2000.

[0051] 4. 16S rRNA gene sequencing of intestinal microbiota (1)Using the extracted genomic DNA as a template, PCR amplification was performed using the above-mentioned universal primers 343F and 798R to construct a sequencing library.

[0052] (2)The library was sequenced using a second-generation high-throughput sequencing platform to obtain reads data of approximately 250 bp.

[0053] (3)After quality filtering, noise reduction, splicing, and chimera removal of the sequencing data, quality control analysis was performed, and then the data was compared and annotated with a database to obtain information on the species and abundance of microorganisms.

[0054] 5. Result analysis and liver injury detection According to the comparison results, the relative abundances of Cetobacterium, Pseudomonas, and Escherichia-Shigella were obtained respectively. The relative abundances of the three bacteria were substituted into the two determination equations constructed in Example 1 to obtain two function values, F1 and F2. The relative abundance information and detection results of the three bacteria are shown in Table 6.

[0055] Table 6 Detection results of liver injury in Pelteobagrus fulvidraco

[0056] 6. Liver tissue section To verify the reliability and stability of using intestinal microbiota markers to detect liver injury, the inventor further performed tissue section detection on the liver of Pelteobagrus fulvidraco to verify the degree of liver injury. It should be noted that the tissue section detection is only used to verify the reliability of the technical solution of this application. In actual applications, this step is not required.

[0057] Take a small amount of liver tissue from the test sample, fix it with 4% paraformaldehyde, dehydrate it with ethanol, embed it in paraffin, cut it into 4 μm sections, stain with HE, and observe under a microscope.

[0058] The inventor compared the liver tissue sections of the first 10 samples in Table 6, and the results are as Figure 4 shown. It can be seen from Figure 4 that: The liver tissue cell structure of the 1st and 9th test samples is tight, the boundaries are clear, and the cell nuclei are clearly visible, indicating that the liver is healthy. The liver tissue spaces of the 2nd, 4th, 6th, 7th, and 10th samples are enlarged, and some cells show dissolution (indicated by the arrows in the figure). Among them, inflammatory factor infiltration was detected in the 7th sample section; in addition to observing enlarged tissue spaces and cell dissolution (indicated by the arrows in the figure) in the tissue sections of the 3rd, 5th, and 8th samples, blood cells were observed between liver cells, indicating that these three samples had symptoms of liver congestion. The tissue section results indicate that Pelteobagrus fulvidraco of No. 2-8 and No. 10 all have symptoms of liver injury.

[0059] The above results show that using Cetobacterium alone poses a risk of misjudging liver damage (No. 9), which may lead to over-treatment. However, the results of detecting liver damage using the relative abundances of Cetobacterium, Pseudomonas, and Escherichia-Shigella are completely consistent with the results of observing liver tissue sections, with an accuracy rate as high as 100%, and there will be no misjudgment of liver damage, thus avoiding over-treatment. This fully demonstrates that using Cetobacterium, Pseudomonas, and Escherichia-Shigella as intestinal microbial markers for detecting liver damage in fish such as Pelteobagrus fulvidraco is highly scientific, accurate, and specific.

[0060] In addition, it should be understood that after reading the above teachings of the present application, those skilled in the art can make various changes or modifications to the present application, and these equivalent forms also fall within the scope defined by the appended claims of the present application.

Claims

1. A combination of gut microbial markers for detecting whether fish have liver damage, characterized in that, The intestinal microbial biomarker combination includes Cetobacterium ( Cetobacterium ), Pseudomonas ( Pseudomonas ), and Escherichia-Shigella ( Escherichia-Shigella ).

2. A system for detecting whether fish have liver damage, characterized in that, comprises the following modules: A data input module, configured to input the abundance data of the intestinal microbial biomarker combination described in claim 1 in the obtained intestinal samples of the fish to be tested; A database storage module, configured to store the abundance data of the intestinal microbial biomarker combination in the intestinal samples of the population of fish, wherein the population of fish includes a group of fish with healthy livers and a group of fish with liver injuries; A liver injury detection module, connected to the data input module and the database storage module respectively, and configured to: Construct a prediction model by using the abundance data of the intestinal microbial biomarker combination in the intestinal samples of the population of fish, and detect whether the fish to be tested has liver injury based on the abundance data of the intestinal microbial biomarkers in the intestinal samples of the fish to be tested.

3. The system according to claim 2, wherein The abundance data of the intestinal microbial biomarker combination is obtained based on the 16S rRNA gene sequencing or metagenomic sequencing method.

4. The system according to claim 2, wherein In the liver injury detection module, the step of constructing a prediction model by using the abundance data of the intestinal microbial biomarker combination in the intestinal samples of the population of fish includes the following steps: Perform linear regression on the healthy liver fish and the liver injury fish respectively to obtain the coefficients and constants of each intestinal microbial biomarker, and obtain two determination equations: F1 = a1 × A C + b1 × A P + c1 × A E +d1 F2 = a2 × A C + b2 × A P + c2 × A E +d2 Among them, a1, b1, c1, a2, b2, and c2 represent coefficients; d1 and d2 represent constants; A C represents the abundance of Blautia, A P represents the abundance of Pseudomonas, A E represents the abundance of Escherichia-Shigella, Substitute the abundance data of the intestinal microbial biomarkers in the intestinal samples of the fish to be tested into the two determination equations respectively to obtain two function values F1 and F2. If F1≥F2, it is determined that the liver of the fish to be tested is healthy; if F1<F2, it is determined that the liver of the fish to be tested is damaged.

5. The system according to claim 4, characterized in that, The values of a1, b1, c1 and d1 are 0.32, 0.781, 8.002 and -11.576 respectively; the values of a2, b2, c2 and d2 are 0.022, 1.906, 26.081 and -3.57 respectively.

6. The system according to claim 2, wherein In the liver injury detection module, the step of constructing a prediction model by using the abundance data of the intestinal microbial biomarker combination in the intestinal samples of the population of fish includes the following steps: S1, randomly divide the abundance data of the intestinal microbial biomarker combination in the intestinal samples of the population of fish into two groups, one group is the training set and the other group is the test set. Each group includes the abundance data of the intestinal microbial biomarker combination of the healthy liver fish and the liver injury fish; S2, use the training set data to construct a liver injury detection model based on the machine learning algorithm; S3, verify the obtained prediction model in the test set.

7. The system according to claim 6, characterized in that, The machine learning algorithm is selected from any one of the following algorithms: Logistic regression algorithm, random forest algorithm, neural network algorithm, support vector machine algorithm, Bayesian classification algorithm, gradient boosting algorithm, K-nearest neighbor algorithm and decision tree algorithm.

8. Use of the reagent for detecting the abundance of the intestinal microbial biomarker combination according to claim 1 in the preparation of a kit for detecting whether a fish has liver injury.

9. The application according to claim 8, wherein The detection reagent includes 16S rRNA gene amplification primers, PCR amplification buffer, DNA polymerase and second-generation sequencing library construction reagents.

10. The application according to claim 8 or 9, characterized in that, The fish is Pelteobagrus fulvidraco.

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