A combination of gut microbiota biomarkers, a system, and its application for detecting liver damage in fish.

Abstract

This application discloses a combination of gut microbial biomarkers, a system, and its application for detecting liver injury in fish, belonging to the field of molecular detection technology for fish liver injury. The gut microbial biomarker combination includes *Cetobacter* spp. (…). Cetobacterium ), Pseudomonas spp. Pseudomonas ) and Escherichia coli-Shigella spp. Escherichia‑Shigella Using the intestinal microbial biomarker combination described in this application, non-invasive diagnosis of liver damage can be achieved without touching the fish body, with high accuracy and no missed detections, especially avoiding misdiagnosis as liver damage that could lead to overtreatment. This provides a basis for judgment in aquaculture environment control and precise disease prevention and control, and is beneficial for improving the survival rate and profitability of yellow catfish farming.

Description

Technical Field

[0001] This application relates to the field of molecular detection technology for fish liver injury, and in particular to a combination of intestinal microbial biomarkers, a system, and its application for detecting fish liver injury. Background Technology

[0002] Eutrophication often leads to excessive proliferation and algal blooms in yellow catfish farming waters. Algal toxins, secondary metabolites of cyanobacteria, have significant hepatotoxicity. Long-term exposure can cause disorders of glucose and lipid metabolism in the liver of yellow catfish, leading to liver diseases such as non-alcoholic fatty liver disease and steatohepatitis, resulting in huge economic losses.

[0003] Early-stage liver damage in yellow catfish is treatable. Environmental control, feeding management, and medicated feed can promote the self-healing of damaged liver cells, thereby reducing the incidence and mortality rates in farmed yellow catfish. However, early liver damage often presents with subtle symptoms and can only be detected through methods such as blood tests, liver tissue biopsies, and liver ultrasound. These methods are difficult to implement in aquaculture. The difficulty in detecting liver damage during fish farming often leads to missed opportunities for optimal treatment, resulting in irreversible losses. Summary of the Invention

[0004] To solve at least one of the above-mentioned technical problems, the technical solution adopted in this application is as follows.

[0005] The first aspect of this application provides a combination of gut microbial biomarkers for detecting liver damage in fish, the combination of gut microbial biomarkers including *Cetacea* spp. Cetobacterium ), Pseudomonas spp. Pseudomonas ) and Escherichia coli-Shigella spp. Escherichia-Shigella ).

[0006] The liver-gut axis is a bidirectional digestive system in the yellow catfish. Liver damage leads to abnormal bile excretion, which in turn disrupts the balance of the gut microbiota; gut microbiota dysbiosis further affects hepatic glucose and lipid metabolism, exacerbating hepatocyte damage. Research results indicate that liver damage in yellow catfish is often accompanied by gut microbiota dysbiosis, with excessive proliferation of opportunistic pathogens and a significant reduction in probiotics. Based on this, this application utilizes *Cetobacter* spp. (… Cetobacterium ), Pseudomonas spp. Pseudomonas ) and Escherichia coli-Shigella spp. Escherichia-Shigella As a marker of gut microbiota for detecting liver damage, its abundance changes can be used to determine whether the liver is damaged, which has important guiding significance for disease prevention and control and environmental regulation in the process of yellow catfish farming.

[0007] In this application, if the *Cetobacter* spp. is significantly reduced and the *Pseudomonas* spp. and *Escherichia coli*-*Shigella* spp. are significantly increased in the gut microbiota of the fish being tested, it indicates the presence of liver damage.

[0008] In this application, the terms “determination,” “judgment,” “diagnosis,” “detection,” and “testing” have the same meaning and are all used to determine whether fish have liver damage.

[0009] A second aspect of this application provides a system for detecting liver damage in fish, comprising the following modules:

[0010] The data input module is used to input the abundance data of the combination of intestinal microbial markers as described in claim 1 in the obtained intestinal sample of the fish to be tested;

[0011] A database storage module is used to store the abundance data of the combination of gut microbial markers in gut samples of a group of fish, including a group of fish with healthy livers and a group of fish with liver damage.

[0012] The liver injury detection module is connected to both the data input module and the database storage module, and is used for:

[0013] A predictive model is constructed using the abundance data of the gut microbial biomarker combination in the gut samples of the group of fish, and the presence of liver damage in the fish to be tested is detected based on the abundance data of the gut microbial biomarker in the gut samples of the fish to be tested.

[0014] In some embodiments of this application, the intestinal sample is an excrement sample.

[0015] In some embodiments of this application, the abundance refers to relative abundance, which is the proportion of a specific gut microbe to the total number of gut microbes. In some specific embodiments of this application, the quantity is characterized using reads obtained from sequencing that can be matched to the corresponding gut microbe. Further, the matching refers to matching with a microbial database, including but not limited to: Silva (https: / / ftp.arb-silva.de), Unite (https: / / unite.ut.ee / repository.php), and Greengenes (ftp: / / greengenes.microbio.me / greengenes_release).

[0016] In some embodiments of this application, the abundance data of the gut microbiota biomarker combination is obtained based on 16S rRNA gene sequencing or metagenomic sequencing methods.

[0017] In some embodiments of this application, the steps of the 16S rRNA gene sequencing method are as follows:

[0018] Obtain the excrement of the fish to be tested and extract the microbial genomic DNA from the excrement;

[0019] 16S rRNA gene amplification primers were used for amplification, and the amplification products were used to construct libraries and sequenced to obtain sequencing data.

[0020] Sequencing data were compared with a microbial database to obtain the abundance data of the gut microbial biomarker combination.

[0021] In some possible embodiments of this application, the liver injury detection module, wherein constructing a predictive model using abundance data of the gut microbiota marker combination in the gut sample of the group of fish includes the following steps:

[0022] Linear regression was performed on fish with healthy livers and fish with damaged livers to obtain the coefficients and constants of each gut microbiota marker, resulting in two decision equations:

[0023] F1=a1× A C +b1× A P +c1× A E +d1

[0024] F2=a2× A C +b2× A P +c2× A E +d2

[0025] Where a1, b1, c1, a2, b2, and c2 represent coefficients; d1 and d2 represent constants; A C Indicates the relative abundance of the genus *Cetacea*. A P Indicates the relative abundance of the genus *Pseudomonas*. A E This indicates the relative abundance of Escherichia coli and Shigella spp.

[0026] The abundance data of the intestinal microbial markers in the intestinal samples of the fish to be tested were substituted into two judgment equations to obtain two function values, F1 and F2. If F1≥F2, the liver of the fish to be tested was determined to be healthy; if F1<F2, the liver of the fish to be tested was determined to be damaged.

[0027] In some specific embodiments of this application, the values ​​of a1, b1, c1 and d1 are 0.32, 0.781, 8.002 and -11.576, respectively; and the values ​​of a2, b2, c2 and d2 are 0.022, 1.906, 26.081 and -3.57, respectively.

[0028] In some other possible embodiments of this application, the liver injury detection module, wherein constructing a predictive model using abundance data of the gut microbiota marker combination in the gut samples of the group of fish includes the following steps:

[0029] S1, the abundance data of the gut microbiota marker combination in the gut samples of the group of fish are randomly divided into two groups, one is a training set and the other is a test set. Each group includes the abundance data of the gut microbiota marker combination of fish with healthy liver and fish with liver damage.

[0030] S2, using training set data, constructs a liver injury detection model based on machine learning algorithms;

[0031] S3. Validate the obtained prediction model on the test set.

[0032] In some possible embodiments of this application, the machine learning algorithm is selected from any of the following algorithms:

[0033] 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.

[0034] The third aspect of this application provides the use of the abundance detection reagent of the intestinal microbial biomarker combination described in the first aspect of this application in the preparation of a kit for detecting liver damage in fish.

[0035] In some embodiments of this application, the detection reagent includes 16S rRNA gene amplification primers, PCR amplification buffer, DNA polymerase, and next-generation sequencing library preparation reagents.

[0036] In some specific embodiments of this application, the 16S rRNA gene amplification primers are 343F: 5'-TACGGRAGGCAGCAG-3'; 798R: 5'-AGGGTATCTAATCCT-3.

[0037] In some embodiments of this application, the detection reagent further includes a DNA extraction reagent.

[0038] In this application, the fish is the yellow catfish.

[0039] Compared with the prior art, this application has the following advantages:

[0040] This application provides a combination of gut microbial biomarkers for detecting liver damage in fish. Using this combination, non-invasive diagnosis of liver damage can be achieved without damaging the fish, with high accuracy and no missed diagnoses, especially avoiding misdiagnosis as liver damage that could lead to overtreatment. It provides a basis for judgment in aquaculture environment control and precise disease prevention and control, and is beneficial for improving the survival rate and profitability of yellow catfish farming.

[0041] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this application, nor is it intended to limit the scope of this application. Other features of this application will become readily apparent from the following description. Attached Figure Description

[0042] The above and other objects, features, and advantages of exemplary embodiments of this application will become readily apparent from the following detailed description taken in conjunction with the accompanying drawings. Several embodiments of this application are illustrated in the drawings by way of example and not limitation, in which:

[0043] Figure 1 The results of staining observations of liver tissue sections from yellow catfish with healthy and damaged livers, as shown in Example 1 of this application, are illustrated.

[0044] Figure 2 This illustrates the screening of gut microbial biomarkers based on the random forest method in Embodiment 1 of this application;

[0045] Figure 3 The relative abundance of the three types of gut microbial biomarkers screened in Example 1 of this application is shown in each treatment group;

[0046] Figure 4 The results of staining observation of yellow catfish liver tissue sections in Example 3 of this application are shown. The numbers represent sample numbers. Detailed Implementation

[0047] To make the technical problems, technical solutions and beneficial effects solved by this application clearer, the following detailed description is provided in conjunction with embodiments.

[0048] The following examples are used to illustrate preferred embodiments of this application. Those skilled in the art will understand that the techniques disclosed in the examples represent technologies discovered by the inventors that can be used to implement this application, and therefore can be considered preferred embodiments of this application. However, those skilled in the art should understand from this specification that many modifications can be made to the specific embodiments disclosed herein, still yielding the same or similar results, without departing from the spirit or scope of this application.

[0049] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains, and all materials cited herein and referenced by them are incorporated herein by reference.

[0050] Those skilled in the art will recognize, or can learn through routine experimentation, many equivalents of the specific embodiments of the invention described herein. These equivalents will be included in the claims.

[0051] Unless otherwise specified, the experimental methods used in the following examples are conventional methods. Unless otherwise specified, the instruments and equipment used in the following examples are all conventional laboratory instruments and equipment; unless otherwise specified, the experimental materials used in the following examples were all purchased from conventional biochemical reagent stores.

[0052] Example 1: Screening of gut microbial markers for liver damage in yellow catfish

[0053] 1. Constructing a liver injury model in yellow catfish.

[0054] Two experimental groups with different algal toxin concentrations were set up: C was the control group with an algal toxin concentration of 0 μg / L; T was the algal toxin stress group with an algal toxin concentration of 5 μg / L, and the stress period was 28 days. The stress time and concentration were set with reference to the duration of cyanobacterial blooms and the concentration of algal toxins in ponds in aquaculture.

[0055] 2. Diagnosis of liver damage in yellow catfish after algal toxin stress

[0056] HE staining was performed on liver sections of yellow catfish from different experimental groups using paraffin sectioning techniques. The results of the section observations are as follows: Figure 1 As shown.

[0057] from Figure 1 It can be seen that the liver tissue structure of the yellow catfish in the control group was compact and the cell structure was clear, and no liver damage was detected. Figure 1 (Middle left image), and in the algal toxin group, all yellow catfish liver tissues were congested, with a large number of blood cells interspersed between liver cells, showing liver damage phenomena such as cell lysis. Figure 1 (Right image in the middle)

[0058] 3. Gut microbial 16S rRNA sequencing

[0059] The V3-V4 variable region of the 16S rRNA gene was amplified using universal primers 343F (5'-TACGGRAGGCAGCAG-3') and 798R (5'-AGGGTATCTAATCCT-3'). The amplification results were sequenced for bacterial diversity analysis.

[0060] Sequencing sequences were compared and annotated with the Silva database (https: / / ftp.arb-silva.de) to obtain gut microbial species information corresponding to the gene sequences. Gene abundance of various microorganisms was calculated by the number of reads, and then the relative abundance of each microorganism (the ratio of the number of reads of a certain microorganism to the total number of reads of all microorganisms) was obtained.

[0061] Based on random forest analysis (see...) Figure 2 The top three microorganisms (importance score > 0.5) contributing the most to the differences between the control group and the algal toxin group were identified as gut microbial biomarkers, namely: *Cetobacter* spp. (…). Cetobacterium ), Pseudomonas spp. Pseudomonas ) and Escherichia coli-Shigella spp. Escherichia-Shigella ). Through the Mann-Whitney U test (e.g.) Figure 3 As shown in Table 1), the differences and correlations of three gut microbial markers between the control group and the algal toxin group were analyzed. The relative abundance of *Cetobacter* was positively correlated with liver health, suggesting a protective factor; the relative abundance of *Pseudomonas* and *Escherichia coli*-*Shigella* was positively correlated with liver injury, suggesting risk factors (as shown in Table 1 and...). Figure 3 (As shown).

[0062] Table 1. Differential Microorganisms between Algal Toxin Group and Control Group

[0063]

[0064] Therefore, *Cetobacter*, *Pseudomonas*, and *Escherichia coli*-*Shigella* species in the intestines of fish such as yellow catfish can serve as biomarkers for liver damage. Specifically, the relative abundance of these three types of intestinal bacteria can be used to detect whether liver damage exists in fish. *Cetobacter* can be used as an independent biomarker. As a simple diagnostic method, the abundance data of *Cetobacter* in healthy and damaged fish populations were statistically analyzed to obtain a 95% confidence interval, as shown in Table 2.

[0065] Table 2. 95% confidence intervals for the relative abundance of *Cetobacter* in the liver injury group and the liver health group.

[0066]

[0067] If the relative abundance of the *Cetobacter* genus to be tested is not less than the lower limit of the 95% confidence interval for the relative abundance of *Cetobacter* genus in healthy fish populations, the fish to be tested is considered to have a healthy liver; if the relative abundance of the *Cetobacter* genus to be tested is less than the lower limit of the 95% confidence interval for the relative abundance of *Cetobacter* genus in healthy fish populations, the fish to be tested is considered to have liver damage.

[0068] Example 2: Detection of liver injury based on changes in the abundance of gut microbial biomarkers

[0069] Based on Example 1, the inventors further conducted a discriminant analysis on the relative abundance of *Cetobacter*, *Pseudomonas*, and *Escherichia coli*-*Shigella* in Table 1, and obtained the classification function coefficients for liver injury detection as shown in Table 3. The significance of the classification coefficients was analyzed using the Wilks' Lambda test, and the results are shown in Table 4.

[0070] Table 3 Classification coefficients for liver injury detection

[0071]

[0072] Table 4. Significance analysis of classification coefficients for liver injury detection

[0073]

[0074] Based on the above classification function coefficients, an equation for determining liver damage is established:

[0075] F1 = 0.32 × A C +0.781× A P +8.002× A E -11.576

[0076] F2=0.022× A C +1.906× A P +26.081× A E -3.57

[0077] in, A C Indicates the relative abundance of the genus *Cetacea*. A P Indicates the relative abundance of the genus *Pseudomonas*. A E This indicates the relative abundance of Escherichia coli and Shigella.

[0078] Substituting the relative abundances of *Cetobacter*, *Pseudomonas*, and *Escherichia coli*-*Shigella* in the gut into the equations yields two function values, F1 and F2. When F1 ≥ F2, liver health is detected; when F1 < F2, liver damage is detected.

[0079] Substituting the data in Table 1 into the two decision equations, we obtained the F1 and F2 values ​​respectively. Further testing was conducted, and the number of tails detected in healthy individuals was 13, while the number of tails detected in individuals with liver damage was 14, as shown in Table 5.

[0080] Table 5 Liver injury detection results

[0081]

[0082] As shown in Table 5, the detection accuracy rate of the judgment equation constructed using this embodiment is 100%.

[0083] Example 3: Application of gut microbial biomarkers in detecting liver damage in fish

[0084] 1. Fish Selection

[0085] Select yellow catfish farming ponds experiencing cyanobacterial blooms. Four hours after feeding, randomly select 30 yellow catfish for testing, numbering them 1 to 30.

[0086] 2. Collection of intestinal excrement

[0087] Gently press the abdomen of the fish to be tested with your thumb, and slowly push from the upper abdomen to the anus. After a small amount of excrement is discharged from the anus, collect it using a sterile sample tube.

[0088] 3. Genomic DNA extraction

[0089] Take 0.5g of excrement and extract genomic DNA from the sample using an extraction kit. The DNA concentration is then detected by agarose gel electrophoresis and NanoDrop2000.

[0090] 4. Gut microbial 16S rRNA gene sequencing

[0091] (1) Using the extracted genomic DNA as a template, PCR amplification was performed using the above universal primers 343F and 798R to construct a sequencing library.

[0092] (2) The library was sequenced using a second-generation high-throughput sequencing platform to obtain approximately 250 bp of reads.

[0093] (3) After quality control analysis such as quality filtering, noise reduction, splicing and dechimerism, the sequencing data are compared and annotated with the database to obtain information on the types and abundance of microbial species.

[0094] 5. Results Analysis and Liver Injury Detection

[0095] Based on the comparison results, the relative abundances of *Cetobacter*, *Pseudomonas*, and *Escherichia coli*-*Shigella* were obtained. The relative abundances of the three bacteria were then substituted into the two decision 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.

[0096] Table 6 Results of liver damage detection in yellow catfish

[0097]

[0098] 6. Liver tissue section

[0099] To verify the reliability and stability of using gut microbiota markers to detect liver injury, the inventors further performed tissue section examination on the liver of yellow catfish to verify the degree of liver damage. It should be noted that the tissue section examination is only used to verify the reliability of the technical solution in this application; this step is not necessary in practical applications.

[0100] A small amount of liver tissue was taken as the test sample, fixed with 4% paraformaldehyde, dehydrated with ethanol, embedded in paraffin, sectioned into 4μm pieces, stained with hematoxylin and eosin (HE), and observed under a microscope.

[0101] The inventors compared liver tissue sections from the first 10 samples in Table 6, and the results were as follows: Figure 4 As shown. From Figure 4 The results show that samples 1 and 9 have tightly packed liver tissue cells with clear boundaries and clearly visible cell nuclei, indicating healthy livers. Samples 2, 4, 6, 7, and 10 show increased interstitial spaces in the liver tissue and some cells exhibiting lysis (indicated by arrows in the image). Sample 7 showed infiltration of inflammatory factors. Samples 3, 5, and 8, in addition to increased interstitial spaces and cell lysis (indicated by arrows in the image), also showed blood cells between liver cells, indicating liver congestion in these three samples. The tissue section results indicate that samples 2-8 and 10 (yellow catfish) all showed signs of liver damage.

[0102] The above results indicate that using *Cetobacter* alone carries the risk of misdiagnosing liver damage (No. 9), leading to overtreatment. However, using the relative abundance of *Cetobacter*, *Pseudomonas*, and *Escherichia coli-Shigella* to detect liver damage yields results completely consistent with liver tissue section observations, achieving an accuracy rate of 100%. This eliminates the risk of misdiagnosing liver damage and thus avoids overtreatment. This fully demonstrates that using *Cetobacter*, *Pseudomonas*, and *Escherichia coli-Shigella* as intestinal microbial markers for liver damage detection in fish such as yellow catfish is scientifically sound, highly accurate, and specific.

[0103] Furthermore, it should be understood that after reading the foregoing teachings of this application, those skilled in the art can make various alterations or modifications to this application, and these equivalent forms also fall within the scope defined by the appended claims.

Claims

1. A system for detecting liver damage in fish, characterized in that, Includes the following modules: The data input module is used to input the abundance data of the gut microbial biomarker combination in the obtained gut sample of the fish to be tested. The abundance refers to the relative abundance, and the gut microbial biomarker combination consists of cetaceans (Cetacea spp.). Cetobacterium ), Pseudomonas spp. Pseudomonas ) and Escherichia coli-Shigella spp. Escherichia-Shigella )composition; A database storage module is used to store the abundance data of the combination of gut microbial markers in gut samples of a group of fish, including a group of fish with healthy livers and a group of fish with liver damage. The liver injury detection module is connected to both the data input module and the database storage module, and is used for: A predictive model was constructed using the abundance data of the gut microbial biomarker combinations in the gut samples of the fish population, and the presence of liver damage in the fish under test was detected based on the abundance data of the gut microbial biomarkers in the gut samples of the fish under test. The fish in question is the yellow catfish, and the liver damage refers to liver damage caused by algal toxins.

2. The system according to claim 1, characterized in that, Abundance data for the gut microbiota biomarker ensemble were obtained based on 16S rRNA gene sequencing or metagenomic sequencing methods.

3. The system according to claim 1, characterized in that, In the liver injury detection module, the step of constructing a prediction model using the abundance data of the gut microbiota marker combination in the gut sample of the group of fish includes the following steps: Linear regression was performed on fish with healthy livers and fish with damaged livers to obtain the coefficients and constants of each gut microbiota marker, resulting in two decision equations: F1=a1× A C +b1× A P +c1× A E +d1 F2 = a2 × A C +b2× A P +c2× A E +d2 Where a1, b1, c1, a2, b2, and c2 represent coefficients; d1 and d2 represent constants; A C Indicates the abundance of the genus *Cetacea*. A P Indicates the abundance of the genus *Pseudomonas*. A E The abundance values ​​for Escherichia coli-Shigella spp. are: a1, b1, c1, and d1: 0.32, 0.781, 8.002, and -11.576, respectively; and a2, b2, c2, and d2: 0.022, 1.906, 26.081, and -3.57, respectively. The abundance data of the intestinal microbial markers in the intestinal samples of the fish to be tested were substituted into two judgment equations to obtain two function values, F1 and F2. If F1≥F2, the liver of the fish to be tested was determined to be healthy; if F1<F2, the liver of the fish to be tested was determined to be damaged.

4. The system according to claim 1, characterized in that, In the liver injury detection module, the step of constructing a prediction model using the abundance data of the gut microbiota marker combination in the gut sample of the group of fish includes the following steps: S1, the abundance data of the gut microbiota marker combination in the gut samples of the group of fish are randomly divided 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 gut microbiota marker combination of fish with healthy liver and fish with liver damage. S2, using training set data, constructs a liver injury detection model based on machine learning algorithms; S3. Validate the obtained prediction model on the test set.

5. The system according to claim 4, characterized in that, The machine learning algorithm is selected from any 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.

6. Application of an abundance assay kit for gut microbial biomarkers in the preparation of a kit for detecting liver damage in fish, wherein the gut microbial biomarker ensemble comprises Cetacea ( Cetobacterium ), Pseudomonas spp. Pseudomonas ) and Escherichia coli-Shigella spp. Escherichia-Shigella The composition is as follows: the fish is yellow catfish; the liver damage refers to liver damage caused by algal toxins; and the abundance refers to relative abundance.

7. The application according to claim 6, characterized in that, The detection reagents include 16S rRNA gene amplification primers, PCR amplification buffer, DNA polymerase, and next-generation sequencing library preparation reagents.

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