Feces metabolite composition for auxiliary diagnosis of subclinical mastitis of dairy cow and application of faeces metabolite composition

Abstract

The invention relates to an excrement metabolite composition for auxiliary diagnosis of sub-clinical mastitis of dairy cows and application of the excrement metabolite composition. The excrement metabolite combination is a first combination and / or a second combination, and the first combination is composed of 9 (S)-hydroxyl peroxide octadecadienoic acid, 25-hydroxyl cholesterol, dodecanedioic acid, deoxycytidine and cis-1, 2-dihydronaphthalene-1, 2-diol; and the second composition is composed of glycyl leucine, 3-methyl-L-tyrosine, L-3-cyano alanine, 2-(methyl amino) benzoic acid, N6-methyl lysine, 5, 6-dihydroepoxy eicosatrienoic acid, phenylethylamine, and 12, 13-dihydroxy octadecadienoic acid. The product prepared on the basis of the excrement metabolite composition and used for auxiliary diagnosis of cow subclinical mastitis is simple in use method and high in accuracy, and can be used for assisting a milk somatic cell number method to diagnose cow subclinical mastitis, the probability of false negative and false positive occurrence is reduced, and the accuracy of a subclinical mastitis diagnosis result is improved.

Description

Technical Field

[0001] The invention relates to the technical field of disease diagnosis, and in particular to a fecal metabolite combination for auxiliary diagnosis of subclinical mastitis in dairy cows and an application thereof. Background Art

[0002] Mastitis is globally recognized as one of the major diseases that affect pasture production efficiency, and has a great negative impact on dairy cow lactation performance, milk quality, reproductive performance, animal welfare and food safety. The reduction in milk production and milk quality caused by dairy cow mastitis, as well as the increase in culling rate and treatment costs, have caused huge economic losses to pastures. According to statistical reports, the global economic losses caused by dairy cow mastitis are as high as 35 billion US dollars each year. Therefore, mastitis is not only an important factor affecting the health of dairy cows, but also a major issue that is directly related to the quality and safety of dairy products and affects the high-quality development of the dairy industry.

[0003] Mastitis refers to the inflammatory reaction of mammary tissue. According to its clinical manifestations, it can be generally divided into subclinical mastitis and clinical mastitis. Cows with clinical mastitis generally show obvious abnormalities in the udder and milk. The udder appears red, swollen, hot, painful and other inflammatory manifestations. The milk often contains flocs, clots, and even bleeding. It may reduce or stop lactation, accompanied by systemic symptoms such as fever, mental depression, and decreased appetite. However, cows with subclinical mastitis generally have no obvious clinical characteristics and abnormal behavior. The cows themselves have normal mental state, body temperature, appetite, etc., no typical breast inflammation reaction, and no obvious changes in milk that can be seen with the naked eye. Although subclinical mastitis has no obvious clinical symptoms, it can cause congestion and edema of the mammary epithelium, blockage of the mammary ducts, a certain degree of reduction in milk production, and changes in physical and chemical properties such as an increase in the number of somatic cells (SCC) in milk. Therefore, in production practice, farms often manage clinical mastitis better, but tend to ignore the prevention and treatment of subclinical mastitis. Because subclinical mastitis in dairy cows has no obvious clinical symptoms and is highly hidden, it will cause the diseased cows to exist in the herd for a long time and may spread pathogens. In addition, subclinical mastitis may occur periodically and eventually escalate to clinical mastitis. Therefore, the economic losses caused by subclinical mastitis to farms far exceed those of clinical mastitis.

[0004] Milk SCC is currently the most commonly used indicator for diagnosing bovine mastitis, especially the presence and occurrence of subclinical mastitis without obvious clinical symptoms. However, the diagnosis of subclinical mastitis in dairy cows based solely on milk SCC has certain limitations. This is mainly reflected in the following three aspects: First, in different studies, the criteria for diagnosing bovine mastitis based on milk SCC are not the same. Generally, milk SCC < 20 × 10 4cells / mL is considered to be a healthy mammary gland in dairy cows (Petzer et al 2017, Sumon et al 2020). However, other studies have shown that milk SCC < 10×10 4 cells / mL is the threshold for healthy mammary glands in dairy cows. 4 cells / mL <SCC<50×10 4 When the number of cells / mL is 60×10 4 cells / mL <SCC<100×10 4 When the SCC in breast milk is >20×10 4 When the number of SCC in milk is less than 10 cells / mL and there are no obvious clinical symptoms in the udder, subclinical mastitis can be diagnosed (Petzer et al 2017, Xi et al 2017, Martins et al 2020). Secondly, milk SCC is related to factors such as lactation status, stress, season and milking frequency of dairy cows (Wellnitz and Bruckmaier 2012). The increase of SCC may not only be caused by mastitis due to infection, but other non-inflammatory factors such as mechanical damage, chemical stimulation, stress, etc. may also cause the increase of milk SCC. Mastitis is diagnosed only by the increase of SCC, which may result in false positive results. Finally, although the increase of milk SCC is usually associated with mastitis, in some cases, even if there is bacterial infection, SCC may remain within the normal range, resulting in false negative results and missed diagnosis of some cases. In view of the above limitations, although milk SCC is one of the important indicators for evaluating the health status of dairy cows' mammary glands, it is more necessary to combine other laboratory detection methods for comprehensive judgment in practical applications. Therefore, it is of great significance to explore a biomarker for auxiliary diagnosis of subclinical mastitis in dairy cows. Summary of the invention

[0005] The present invention provides a fecal metabolite combination for assisting diagnosis of subclinical mastitis in dairy cows and application thereof, so as to solve the problem in the related art that false negative and false positive results are prone to occur when determining subclinical mastitis in dairy cows by milk somatic cell count.

[0006] The technical solution provided by the present invention is specifically as follows:

[0007] In a first aspect, the present invention provides a fecal metabolite combination for assisting the diagnosis of subclinical mastitis in dairy cows, wherein the fecal metabolite combination is a first combination and / or a second combination, wherein the first combination is composed of 9(S)-hydroxyperoxyoctadecadienoic acid, 25-hydroxycholesterol, dodecanedioic acid, deoxycytidine, and cis-1,2-dihydronaphthalene-1,2-diol; and the second combination is composed of glycylleucine, 3-methyl-L-tyrosine, L-3-cyanoalanine, 2-(methylamino)benzoic acid, N6-methyllysine, 5,6-dihydroepoxyeicosatrienoic acid, phenylethylamine, and 12,13-dihydroxyoctadecadienoic acid.

[0008] In a second aspect, the present invention provides the use of the above-mentioned fecal metabolite combination in the preparation of a product for assisting the diagnosis or treatment of subclinical mastitis in dairy cows, wherein the product comprises one or more of a diagnostic reagent, a diagnostic kit, a diagnostic chip, a diagnostic system, and a diagnostic instrument.

[0009] In a third aspect, the present invention provides a kit for assisting in the diagnosis of subclinical mastitis in dairy cows, the kit comprising reagents and internal standards for detecting the concentration of a fecal metabolite combination in a fecal sample, wherein the fecal metabolite combination is a first combination and / or a second combination, the first combination consisting of 9(S)-hydroxyperoxyoctadecadienoic acid, 25-hydroxycholesterol, dodecanedioic acid, deoxycytidine, and cis-1,2-dihydronaphthalene-1,2-diol; the second combination consisting of glycylleucine, 3-methyl-L-tyrosine, L-3-cyanoalanine, 2-(methylamino)benzoic acid, N6-methyllysine, 5,6-dihydroepoxyeicosatrienoic acid, phenylethylamine, and 12,13-dihydroxyoctadecadienoic acid; the internal standard is a combination of standard substances of each fecal metabolite in the fecal metabolite combination.

[0010] In conjunction with the third aspect of the present invention, in some embodiments, the method for using the kit includes: taking a fecal sample from a test cow whose milk somatic cell count is not less than a mammary health threshold; using the kit to detect the concentration of the fecal metabolite combination in the fecal sample, and determining whether the test cow is a cow with subclinical mastitis based on the concentration of the fecal metabolite combination.

[0011] In a fourth aspect, the present invention provides a dairy cow subclinical mastitis auxiliary diagnosis system, comprising:

[0012] An acquisition module is used to obtain the relative concentration of a fecal metabolite combination in a fecal sample of a tested dairy cow; the fecal metabolite combination is a first combination and / or a second combination, the first combination is composed of 9(S)-hydroxyperoxyoctadecadienoic acid, 25-hydroxycholesterol, dodecanedioic acid, deoxycytidine, and cis-1,2-dihydronaphthalene-1,2-diol; the second combination is composed of glycylleucine, 3-methyl-L-tyrosine, L-3-cyanoalanine, 2-(methylamino)benzoic acid, N6-methyllysine, 5,6-dihydroepoxyeicosatrienoic acid, phenylethylamine, and 12,13-dihydroxyoctadecadienoic acid;

[0013] An evaluation module, used for evaluating whether the tested dairy cow suffers from subclinical mastitis according to the relative concentration;

[0014] Wherein, the acquisition module and the prediction module are connected wirelessly and / or wiredly.

[0015] In conjunction with the fourth aspect of the present invention, in some implementations, the acquisition module includes:

[0016] A detection module, used for detecting the concentration of a fecal metabolite combination in a fecal sample of a tested dairy cow;

[0017] The comparison module is used to compare the concentration of the fecal metabolite combination in the test cow fecal sample with the standard concentration and output the relative concentration.

[0018] In conjunction with the fourth aspect of the present invention, in some embodiments, the detection module detects the concentration of the fecal metabolite combination in the fecal sample by one or more of the following methods: chromatography, spectroscopy, mass spectrometry, and chemical analysis.

[0019] In conjunction with the fourth aspect of the present invention, in some embodiments, the somatic cell count in the milk of the test cow is not lower than the mammary gland health threshold.

[0020] In conjunction with the fourth aspect of the present invention, in some embodiments, the assessing whether the test cow suffers from subclinical mastitis according to the relative concentration includes: determining that the test cow is a subclinical mastitis cow when the concentration of the fecal metabolite combination in the fecal sample of the test cow meets one of the following conditions:

[0021] (1) The first combination satisfies at least one of the following conditions: 9(S)-hydroxyperoxyoctadecadienoic acid is not less than 2.50 times its standard concentration, 25-hydroxycholesterol is not less than 1.97 times its standard concentration, dodecanedioic acid is not less than 1.41 times its standard concentration, deoxycytidine is not less than 1.69 times its standard concentration, and cis-1,2-dihydronaphthalene-1,2-diol is not less than 1.30 times its standard concentration;

[0022] (2) The second combination satisfies at least one of the following conditions: glycylleucine is not higher than 0.57 times of its standard concentration, 3-methyl-L-tyrosine is not higher than 0.52 times of its standard concentration, L-3-cyanoalanine is not higher than 0.53 times of its standard concentration, 2-(methylamino)benzoic acid is not higher than 0.52 times of its standard concentration, N6-methyllysine is not higher than 0.55 times of its standard concentration, 5,6-dihydroepoxyeicosatrienoic acid is not higher than 0.53 times of its standard concentration, phenylethylamine is not higher than 0.35 times of its standard concentration, and 12,13-dihydroxyoctadecadienoic acid is not higher than 0.37 times of its standard concentration.

[0023] In a fifth aspect, the present invention provides an auxiliary diagnostic instrument for subclinical mastitis in dairy cows, and the instrument adopts the above-mentioned auxiliary diagnostic system for subclinical mastitis in dairy cows.

[0024] The beneficial effects achieved by the technical solution provided by the embodiment of the present invention include:

[0025] (1) The present invention uses 9(S)-hydroxyperoxyoctadecadienoic acid, 25-hydroxycholesterol, dodecanedioic acid, deoxycytidine, cis-1,2-dihydronaphthalene-1,2-diol, glycylleucine, 3-methyl-L-tyrosine, L-3-cyanoalanine, 2-(methylamino)benzoic acid, N6-methyllysine, 5,6-dihydroepoxyeicosatrienoic acid, phenylethylamine, and 12,13-dihydroxyoctadecadienoic acid in dairy cow feces samples as biomarkers for auxiliary diagnosis of subclinical mastitis in dairy cows. These fecal metabolites have significant specificity and sensitivity.

[0026] (2) Compared with blood or other body fluid samples, the process of collecting fecal samples does not require invasive operations, which greatly reduces the cost and complexity of collecting samples to be tested.

[0027] (3) The fecal biomarkers identified in the present invention can provide diagnostic clues for subclinical mastitis in dairy cows from multiple angles.

[0028] (4) The fecal biomarkers identified in the present invention are used to assist in the early diagnosis of subclinical mastitis in dairy cows, which can improve the accuracy of diagnosis, so as to facilitate the early identification of cows with subclinical mastitis and take timely treatment measures to reduce economic losses in dairy farming. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0030] Figure 1 : PLS-DA analysis of metabolites in each fecal sample in positive ion mode.

[0031] Figure 2 : PLS-DA analysis of metabolites in each fecal sample in negative ion mode.

[0032] Figure 3 : Volcano plot of differential metabolites in feces of healthy and subclinical mastitis dairy cows.

[0033] Figure 4 : Cluster heat map of differential metabolites in feces of healthy and subclinical mastitis dairy cows.

[0034] Figure 5 : Machine learning analysis of differential metabolites in feces of healthy and subclinical mastitis dairy cows.

[0035] Figure 6 : KEGG metabolic pathway enrichment analysis of differential metabolites in fecal markers of healthy and subclinical mastitis dairy cows.

[0036] Figure 7 : Linoleic acid metabolic pathway with significant enrichment in fecal marker differential metabolites between healthy and subclinical mastitis dairy cows.

[0037] Figure 8 : Arachidonic acid metabolic pathways significantly enriched in fecal marker differential metabolites between healthy and subclinical mastitis dairy cows.

[0038] Fig. 9 :ROC curve of 9(S)-hydroxyperoxyoctadecadienoic acid as a differential metabolite in fecal markers between healthy and subclinical mastitis dairy cows.

[0039] Fig.10 : ROC curve of 25-hydroxycholesterol as a differential metabolite in fecal markers between healthy and subclinical mastitis dairy cows.

[0040] Fig.11 : ROC curve of dodecanedioic acid as a differential metabolite in fecal markers between healthy and subclinical mastitis dairy cows.

[0041] Fig.12 : ROC curve of fecal marker metabolite deoxycytidine between healthy and subclinical mastitis dairy cows.

[0042] Fig.13 : ROC curve of fecal marker metabolite cis-1,2-dihydronaphthalene-1,2-diol between healthy and subclinical mastitis dairy cows.

[0043] Fig.14:Correlation analysis between fecal differential metabolites 9(S)-hydroxyperoxyoctadecadienoic acid, 25-hydroxycholesterol, dodecanedioic acid, deoxycytidine, cis-1,2-dihydronaphthalene-1,2-diol and milk SCC.

[0044] Fig.15 : ROC curve of glycylleucine, a differential metabolite in fecal markers between healthy and subclinical mastitis dairy cows.

[0045] Fig.16 : ROC curve of 3-methyl-L-tyrosine, a differential metabolite in fecal markers between healthy and subclinical mastitis dairy cows.

[0046] Fig.17 : ROC curve of L-3-cyanoalanine, a differential metabolite in fecal markers between healthy and subclinical mastitis dairy cows.

[0047] Fig.18 : ROC curve of 2-(methylamino)benzoic acid as a differential metabolite in fecal markers between healthy and subclinical mastitis dairy cows.

[0048] Fig.19 : ROC curve of N6-methyllysine, a differential metabolite in fecal markers between healthy and subclinical mastitis dairy cows.

[0049] Fig. 20 : ROC curve of 5,6-dihydroepoxyeicosatrienoic acid as a differential metabolite in fecal markers between healthy and subclinical mastitis dairy cows.

[0050] Fig.21 : ROC curve of gamma-tocotrienol as a fecal marker for differences between healthy and subclinical mastitis dairy cows.

[0051] Fig. 22 : ROC curve of fecal marker metabolite phenylethylamine between healthy and subclinical mastitis dairy cows.

[0052] Fig.23 : ROC curve of 5-(2-hydroxyethyl)-4-methylthiazole, a differential metabolite of fecal markers between healthy and subclinical mastitis dairy cows.

[0053] Fig.24 : ROC curve of 12,13-dihydroxyoctadecadienoic acid as a differential metabolite in fecal markers between healthy and subclinical mastitis dairy cows.

[0054] Fig.25: Correlation analysis between fecal differential metabolites glycylleucine, 3-methyl-L-tyrosine, L-3-cyanoalanine, 2-(methylamino)benzoic acid, N6-methyllysine, 5,6-dihydroepoxyeicosatrienoic acid, γ-tocotrienol, phenylethylamine, 5-(2-hydroxyethyl)-4-methylthiazole and 12,13-dihydroxyoctadecadienoic acid and milk SCC. DETAILED DESCRIPTION

[0055] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in combination with the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0056] As described in the background art, the standards for diagnosing subclinical mastitis in dairy cows by the milk somatic cell count method vary, and false positive and false negative results are prone to occur. Once certain cases are misdiagnosed or missed, the long-term presence of sick cows will spread pathogens in the herd and increase the cost of disease treatment.

[0057] To solve the above problems, the present invention uses metabolomics to quantitatively characterize some fecal metabolites in fecal samples of dairy cows whose milk somatic cell count is not lower than the mammary gland health threshold, to assist in identifying individual dairy cows suffering from subclinical mastitis.

[0058] In the present invention, the term "mammary gland health threshold" refers to the milk SCC threshold of a dairy cow with healthy mammary glands. As described in the background art, different studies have determined different thresholds for mammary gland health in dairy cows: some believe that milk SCC < 10 × 10 4 Cows with SCC of 20×10 cells / mL have healthy mammary glands. 4 Cows with SCC of 60×10 cells / mL have healthy mammary glands. 4 Cows with 10 cells / mL have healthy mammary glands. The higher the "mammary gland health threshold", the more likely it is to have a false negative result. The lower the "mammary gland health threshold", the less likely it is to have a false negative result when judging that a cow has subclinical mastitis, but it is also more likely to have a false positive result, increasing the isolation and treatment costs of cows with subclinical mastitis. Therefore, those skilled in the art can select the "mammary gland health threshold" according to their needs.

[0059] The fecal metabolite combination for assisting diagnosis of subclinical mastitis in dairy cows provided by the present invention is a first combination and / or a second combination, the first combination is composed of 9(S)-hydroxyperoxyoctadecadienoic acid, 25-hydroxycholesterol, dodecanedioic acid, deoxycytidine, and cis-1,2-dihydronaphthalene-1,2-diol; the second combination is composed of glycylleucine, 3-methyl-L-tyrosine, L-3-cyanoalanine, 2-(methylamino)benzoic acid, N6-methyllysine, 5,6-dihydroepoxyeicosatrienoic acid, phenylethylamine, and 12,13-dihydroxyoctadecadienoic acid.

[0060] These fecal metabolites have significant specificity and can be easily distinguished from similar interferences in fecal samples and accurately identified; they also have significant sensitivity and can capture subtle changes in the early stages of the disease, aiding in the early diagnosis of the disease and providing the possibility for early intervention; they can more accurately distinguish between healthy and subclinical mastitis individuals, reduce misdiagnosis (false positives) and missed diagnoses (false negatives), and obtain more accurate diagnostic results, leading to timely and effective treatment.

[0061] In the present invention, there are significant differences in the concentrations of fecal metabolites between healthy and subclinical mastitis cows: 9(S)-hydroxyperoxyoctadecadienoic acid (the content of this metabolite in the feces of cows with subclinical mastitis is greater than or equal to 2.50 times the standard concentration), 25-hydroxycholesterol (the content of this metabolite in the feces of cows with subclinical mastitis is greater than or equal to 1.97 times the standard concentration), dodecanedioic acid (the content of this metabolite in the feces of cows with subclinical mastitis is greater than or equal to 1.41 times the standard concentration), and 1,2-dihydro-2-hydroxy-1-decadienoic acid (the content of this metabolite in the feces of cows with subclinical mastitis is greater than or equal to 1.5 times the standard concentration). times the standard concentration), deoxycytidine (the content of this metabolite in the feces of cows with subclinical mastitis is greater than or equal to 1.69 times the standard concentration), cis-1,2-dihydronaphthalene-1,2-diol (the content of this metabolite in the feces of cows with subclinical mastitis is greater than or equal to 1.30 times the standard concentration), and glycylleucine (the content of this metabolite in the feces of cows with subclinical mastitis is less than or equal to 0.57 times the standard concentration), 3-methyl-L-tyrosine (the content of this metabolite in the feces of cows with subclinical mastitis is less than or equal to 0.57 times the standard concentration), and 4-hydroxy-1-nitro-2-nitro-1-nitro-2-nitro-3-ol (the content of this metabolite in the feces of cows with subclinical mastitis is less than or equal to 0.57 times the standard concentration). The content of this metabolite in the feces of dairy cows with subclinical mastitis is less than or equal to 0.52 times the standard concentration), L-3-cyanoalanine (the content of this metabolite in the feces of dairy cows with subclinical mastitis is less than or equal to 0.53 times the standard concentration), 2-(methylamino)benzoic acid (the content of this metabolite in the feces of dairy cows with subclinical mastitis is less than or equal to 0.52 times the standard concentration), N6-methyllysine (the content of this metabolite in the feces of dairy cows with subclinical mastitis is less than or equal to 0.55 times the standard concentration), 5,6-dihydroepoxyeicosatriene Acid (the content of this metabolite in the feces of cows with subclinical mastitis is less than or equal to 0.53 times the standard concentration), phenylethylamine (the content of this metabolite in the feces of cows with subclinical mastitis is less than or equal to 0.35 times the standard concentration), and 12,13-dihydroxyoctadecadienoic acid (the content of this metabolite in the feces of cows with subclinical mastitis is less than or equal to 0.37 times the standard concentration) can be used as biomarkers in fecal samples of cows with subclinical mastitis for auxiliary diagnosis or auxiliary diagnosis of subclinical mastitis in dairy cows.

[0062] The first combination is a combination of fecal metabolites that are upregulated compared to healthy levels, indicating that a certain pathological process is occurring or active, and can serve as a direct indication of disease activity; the second combination is a combination of fecal metabolites that are downregulated compared to healthy levels, which may reflect the loss of function or damage to specific cells or tissues, and is helpful for diagnosing and understanding the disease. The combined detection of the first and second combinations can provide a more comprehensive assessment of the disease status, including disease activity, progression, and the body's response.

[0063] Without intending to limit the type of dairy cow, in some embodiments of the present invention, the fecal metabolite combination is used to assist in the diagnosis of subclinical mastitis in Holstein cows. Preferably, the Holstein cows are southern Chinese Holstein cows.

[0064] The fecal metabolite combination of the present invention can be directly used for the diagnosis of subclinical mastitis in any dairy cow. In an embodiment of the present invention, the fecal metabolite combination is used to assist the milk somatic cell count method in diagnosing subclinical mastitis in dairy cows, so as to reduce the occurrence of false negative and false positive results of the milk somatic cell count method.

[0065] The present invention provides the use of the above-mentioned fecal metabolite combination in the preparation of a product for assisting the diagnosis or treatment of subclinical mastitis in dairy cows, wherein the product includes one or more of a therapeutic agent, a diagnostic reagent, a diagnostic kit, a diagnostic chip, a diagnostic system, and a diagnostic instrument. For example, in the development of a therapeutic agent, the above-mentioned fecal metabolite combination system can be used to explore the metabolic pathway of subclinical mastitis in dairy cows, thereby finding a target related to the metabolic pathway, and providing a corresponding therapeutic agent based on the target. In the clinical verification of the therapeutic agent, the efficacy of the therapeutic agent can also be evaluated by monitoring the concentration of the above-mentioned fecal metabolite combination; in the development of a diagnostic reagent, a suitable detection reagent is studied for the above-mentioned fecal metabolite combination as a diagnostic reagent.

[0066] The present invention provides a kit for assisting in the diagnosis of subclinical mastitis in dairy cows, comprising reagents and internal standards for detecting the concentration of a fecal metabolite combination in a fecal sample, wherein the fecal metabolite combination is a first combination and / or a second combination, the first combination consisting of 9(S)-hydroxyperoxyoctadecadienoic acid, 25-hydroxycholesterol, dodecanedioic acid, deoxycytidine, and cis-1,2-dihydronaphthalene-1,2-diol; the second combination consisting of glycylleucine, 3-methyl-L-tyrosine, L-3-cyanoalanine, 2-(methylamino)benzoic acid, N6-methyllysine, 5,6-dihydroepoxyeicosatrienoic acid, phenylethylamine, and 12,13-dihydroxyoctadecadienoic acid; the internal standard is a combination of standard products of each fecal metabolite in the fecal metabolite combination; the internal standard is a combination of standard products of each fecal metabolite in the fecal metabolite combination.

[0067] In some embodiments, the method of using the kit includes: taking a fecal sample of a test cow whose milk somatic cell count is not less than a mammary gland health threshold; using the kit to detect the concentration of a fecal metabolite combination in the fecal sample, and determining whether the test cow is a subclinical mastitis cow according to the concentration of the fecal metabolite combination. In the present invention, the mammary gland health threshold is milk SCC=20×10 4 cells / mL.

[0068] In some embodiments, the kit detects the concentration of each fecal metabolite based on liquid chromatography technology.

[0069] The present invention provides a dairy cow subclinical mastitis auxiliary diagnosis system, comprising:

[0070] An acquisition module is used to obtain the relative concentration of a fecal metabolite combination in a fecal sample of a tested dairy cow; the fecal metabolite combination is a first combination and / or a second combination, the first combination is composed of 9(S)-hydroxyperoxyoctadecadienoic acid, 25-hydroxycholesterol, dodecanedioic acid, deoxycytidine, and cis-1,2-dihydronaphthalene-1,2-diol; the second combination is composed of glycylleucine, 3-methyl-L-tyrosine, L-3-cyanoalanine, 2-(methylamino)benzoic acid, N6-methyllysine, 5,6-dihydroepoxyeicosatrienoic acid, phenylethylamine, and 12,13-dihydroxyoctadecadienoic acid;

[0071] An evaluation module, used for evaluating whether the tested dairy cow suffers from subclinical mastitis according to the relative concentration;

[0072] Wherein, the acquisition module and the prediction module are connected wirelessly and / or wiredly.

[0073] The present invention can quickly determine whether a tested dairy cow suffers from subclinical mastitis through the dairy cow subclinical mastitis auxiliary diagnosis system.

[0074] In some implementations, the acquisition module includes:

[0075] A detection module, used for detecting the concentration of a fecal metabolite combination in a fecal sample of a tested dairy cow;

[0076] The comparison module is used to compare the concentration of the fecal metabolite combination in the test cow fecal sample with the standard concentration and output the relative concentration.

[0077] For example, the detection module controls the operation of the detection instrument, and the detection instrument detects the fecal metabolite combination in the fecal sample of the tested dairy cow. In some embodiments, the detection module detects the concentration of the fecal metabolite combination in the fecal sample by one or more of the following methods: chromatography, spectroscopy, mass spectrometry, and chemical analysis. That is, the detection instrument can be a high performance liquid chromatography, a gas chromatography, or a mass spectrometer. The comparison module stores the standard concentration of each fecal metabolite in the fecal metabolite combination. When the comparison module receives the concentration of the fecal metabolite combination obtained by the detection module, it compares it with the standard concentration and outputs the comparison result, i.e., the relative concentration. Note: In the present invention, the term "standard concentration" refers to the average concentration of fecal metabolites of dairy cows of the same species and age with healthy mammary glands.

[0078] In some embodiments, the milk somatic cell count of the tested dairy cow is not lower than the mammary gland health threshold. Such tested dairy cows have been judged to be likely to suffer from subclinical mastitis according to the milk somatic cell count method, but false positive results are prone to occur. The present invention uses the dairy cow subclinical mastitis auxiliary diagnosis system to re-examine such dairy cows, which can reduce the probability of false positive results.

[0079] In some embodiments, the step of evaluating whether the tested dairy cow suffers from subclinical mastitis based on the relative concentration includes: determining that the tested dairy cow is a cow with subclinical mastitis when the concentration of the fecal metabolite combination in the fecal sample of the tested dairy cow meets one of the following conditions:

[0080] (1) The first combination satisfies at least one of the following conditions: 9(S)-hydroxyperoxyoctadecadienoic acid is not less than 2.50 times its standard concentration, 25-hydroxycholesterol is not less than 1.97 times its standard concentration, dodecanedioic acid is not less than 1.41 times its standard concentration, deoxycytidine is not less than 1.69 times its standard concentration, and cis-1,2-dihydronaphthalene-1,2-diol is not less than 1.30 times its standard concentration;

[0081] (2) The second combination satisfies at least one of the following conditions: glycylleucine is not higher than 0.57 times of its standard concentration, 3-methyl-L-tyrosine is not higher than 0.52 times of its standard concentration, L-3-cyanoalanine is not higher than 0.53 times of its standard concentration, 2-(methylamino)benzoic acid is not higher than 0.52 times of its standard concentration, N6-methyllysine is not higher than 0.55 times of its standard concentration, 5,6-dihydroepoxyeicosatrienoic acid is not higher than 0.53 times of its standard concentration, phenylethylamine is not higher than 0.35 times of its standard concentration, and 12,13-dihydroxyoctadecadienoic acid is not higher than 0.37 times of its standard concentration.

[0082] When one of the above metabolites is detected to meet the conditions, it can be judged that the tested cow may suffer from subclinical mastitis; when two or more metabolites meet the conditions, the possibility of the tested cow suffering from subclinical mastitis increases; the more types of metabolites that meet the conditions, the greater the possibility of the tested cow suffering from subclinical mastitis. Therefore, those skilled in the art can select the first combination or the second combination to meet 1, 2, 3, 4, 5, 6 or all of the limited conditions as needed to determine that the tested cow is a subclinical mastitis cow. The fewer conditions that the concentration of the fecal metabolite combination needs to meet, the faster the detection; the more conditions it needs to meet, the higher the accuracy of the test result.

[0083] The invention provides an auxiliary diagnosis instrument for subclinical mastitis in dairy cows, and the instrument adopts the auxiliary diagnosis system for subclinical mastitis in dairy cows.

[0084] The technical solution of the present invention is described in detail below through specific embodiments:

[0085] The following examples collect fecal samples of healthy mammary gland cows and subclinical mastitis cows for non-targeted metabolomics detection. Compare the differences in fecal metabolite profiles of healthy mammary gland cows and subclinical mastitis cows. Through machine learning analysis, the marked difference metabolites between the two groups are identified. Then, by establishing a ROC curve, it is judged that 9 (S) -hydroxyperoxy octadecadienoic acid, 25-hydroxycholesterol, dodecanedioic acid, deoxycytidine, cis-1,2-dihydronaphthalene-1,2-diol, glycyl leucine, 3-methyl-L-tyrosine, L-3-cyanoalanine, 2- (methylamino) benzoic acid, N6-methyllysine, 5,6-dihydroepoxyeicosatrienoic acid, phenylethylamine, 12,13-dihydroxyoctadecadienoic acid have significant specificity and sensitivity as fecal metabolites of subclinical mastitis in dairy cows.

[0086] Example 1

[0087] 1. Experimental Materials and Methods

[0088] 1. Experimental Animals

[0089] The experiment of the present invention was completed in an experimental demonstration dairy farm in Wuhan. Several healthy Holstein cows with similar milk production, parity and lactation days were selected (control group, abbreviated as CON group, SCC was 9.13±2.17×10 4 cells / mL, no clinical symptoms of mastitis) and several Holstein cows with subclinical mastitis (subclinical mastitis group, abbreviated as SM group, SCC was 75.25±9.50×10 4 cells / mL, no clinical signs of mastitis).

[0090] 2. Test methods

[0091] 2.1 Stool sample collection

[0092] For stool samples, fresh stool samples were collected by rectal examination using sterile long-arm gloves, quickly divided into sterile 5 mL cryotubes, immediately frozen in liquid nitrogen, and then transferred to a -80 °C refrigerator for storage.

[0093] 2.2 Non-targeted fecal metabolome analysis

[0094] 2.2.1 Extraction of metabolites

[0095] For solid samples, after slowly thawing at 4°C, weigh an appropriate amount of sample (50 mg-100 mg), add 200 μL of pre-cooled water, vortex for 60 seconds, add 800 μL of pre-cooled methanol-acetonitrile solution (1:1, v / v), vortex for 60 seconds, low-temperature ultrasonicate for 30 minutes, place at -20°C for 1 hour to precipitate protein, centrifuge at 12000×g at 4°C for 10 minutes, take the supernatant and vacuum dry, redissolve in 200 μL of 30% ACN, vortex, centrifuge at 14000×g at 4°C for 15 minutes, and take the supernatant for detection.

[0096] 2.2.2 Liquid chromatography parameters

[0097] An ultra-high performance liquid chromatography system (Vanquish, UPLC, Thermo, USA) was used. Column: Waters HSS T3 (100 mm × 2.1 mm, 1.8 μm); mobile phase: phase A is 0.1% (v / v) formic acid-water solution, phase B is 0.1% (v / v) formic acid-acetonitrile; flow rate: 0.3 mL / min; column temperature: 40 °C; injection volume: 2 μL; elution gradient: 0.0 min-1.0 min B phase maintained at 0%, 1.0 min-12.0 min B phase linearly changed from 0% to 95%, 12.0 min-13.0 min B phase maintained at 95%, 13.0 min-13.1 min B phase linearly changed from 95% to 0%, 13.1 min-17.0 min B phase maintained at 0%; the samples were placed in an automatic sampler at 4 °C during the entire analysis process. To avoid the influence of fluctuations in instrument detection signals, samples were analyzed continuously in a random order. QC samples are inserted into the sample queue to monitor and evaluate the stability of the system and the reliability of experimental data.

[0098] 2.2.3 Mass spectrometry conditions

[0099] A high-resolution mass spectrometer (Q Exactive HFX, Thermo, USA) was used to collect primary and secondary spectra. The electrospray ionization (ESI) source conditions were: sheath gas 40 arb; auxiliary gas 10 arb; ion spray voltage 3000V / -2800V; temperature 350℃; ion transfer tube temperature 320℃. The scanning mode was Full-ms-ddMS2 mode; the scanning mode was positive ion / negative ion. The primary scan range (scan m / z range): 70Da-1050Da, primary resolution 70000, secondary resolution 17500.

[0100] 2.3.4 Data Analysis

[0101] Raw mass spectrometry data were acquired on Q-Exactive using Xcalibur 4.1 (Thermo Scientific) and processed using Progenesis QI (Waters Corporation, Milford, USA). The raw data were converted into mzXML format using ProteoWizard, and then peak alignment, retention time correction, and peak area extraction were performed using XCMS software. The data extracted by XCMS were matched to the public database Human Metabolome Database (HMDB) ( http: / / www.hmdb.ca )、METLIN( http: / / metlin.scripps.edu )、Massbank( http: / / www.massbank.jp / )、Mzclound( https: / / www.mzcloud.org ) is used to identify the metabolites in biological samples based on the retention time, molecular mass (molecular mass error is within <10ppm), secondary fragmentation spectrum, collision energy and other information of the metabolites. The identification results are strictly checked and confirmed for the second time before the next step of bioinformatics analysis.

[0102] Multivariate data analysis was performed using the R software package, including principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA). The projected importance (VIP) value of each variable in the OPLS-DA model was calculated to indicate its contribution to the classification of differences between groups. Metabolites with VIP values ​​> 1 were further evaluated by Student's t test on univariate concentrations, and P < 0.05 was considered statistically significant. In this experiment, VIP > 1, P < 0.05, and fold-change ≥ 1.2 or ≤ 0.83 were used as screening conditions for differential metabolites between the two groups. The identified metabolites were annotated using the KEGG database (http: / / www.kegg.jp / kegg / compound / ), and then the annotated metabolites were mapped to the KEGG Pathway database (http: / / www.kegg.jp / kegg / pathway.html) for KEGG enrichment analysis. Advanced analysis of differential metabolites was performed using machine learning techniques provided by the MLR3verse package (v0.2.7), and feature selection and model interpretation were performed. The pROC package (v1.18.2) was used to construct receiver operating characteristic curves of differential metabolites. Spearman correlation analysis was performed using the correlation function in R software and presented as a heat map ("*" indicates P < 0.05, "**" indicates P < 0.01, and "***" indicates P < 0.001).

[0103] 2. Test results

[0104] 1. Identification of differential metabolites in feces of healthy and subclinical mastitis dairy cows

[0105] The present invention explored the characteristics and differences of the fecal metabolome of healthy and subclinical mastitis dairy cows through non-targeted metabolomics technology. The results are shown in Table 1. In the positive and negative ion modes, a total of 11,431 metabolites were detected in the feces of dairy cows in the CON group and the SM group, and a total of 623 metabolites were identified by the database. The specific methods used are shown in "2.3.4 Data Analysis". VIP>1, P<0.05 and Fold-Change≥1.2 or ≤0.83 were further used as the screening conditions for differential metabolites between the two groups. The results are shown in Table 1 and Figure 3 As shown, a total of 57 differential metabolites (27 up-regulated and 30 down-regulated) were screened in the feces of dairy cows in the CON group and the SM group.

[0106] The results obtained by partial least squares analysis (PLS-DA) of the metabolite abundance of each sample showed that the CON group and SM group samples could be effectively distinguished in both positive and negative ion modes ( Figure 1 , Figure 2 ). The results of the differential metabolite clustering heat map showed that the fecal metabolites of dairy cows in the CON group and the SM group formed obvious clusters within each group, and there was a clear partition of the differential metabolite composition between the groups ( Figure 4 ). The above experimental results showed that there were significant differences in the composition of fecal metabolites between the CON group and the SM group of dairy cows.

[0107] Table 1 Statistics of differential metabolites in feces of healthy and subclinical mastitis dairy cows

[0108]

[0109]

[0110] 2. Analysis of differential metabolites in fecal markers of healthy and subclinical mastitis dairy cows

[0111] In order to further screen the marker metabolites that can distinguish between healthy and subclinical mastitis cows, the present invention conducted machine learning analysis on the fecal differential metabolites and drew a random forest map of the differential metabolites. In the map, the horizontal axis of the column is the importance score of the metabolite for the classifier model, and the vertical axis is the metabolite name; different colors show the abundance distribution of these metabolites in each sample, red indicates high abundance, and blue indicates low abundance; the metabolites with the highest importance are the marker metabolites of the differences between the groups, and their contribution to the differences between the groups is relatively large ( Figure 5 ).

[0112] The results are as follows Figure 5As shown in Table 2, 9(S)-hydroxyperoxyoctadecadienoic acid (fold change = 2.11, P < 0.05), 25-hydroxycholesterol (fold change = 2.12, P < 0.05), dodecanedioic acid (fold change = 1.93, P < 0.05), prostaglandin B1 (fold change = 1.89, P < 0.05), 4-hydroxycinnamic acid (fold change = 2.06, P < 0.05), pipecolic acid (fold change = 1. .35, P<0.05), deoxycytidine (fold change = 2.28, P<0.05), and cis-1,2-dihydronaphthalene-1,2-diol (fold change = 1.30, P<0.01) were significantly higher in the CON group than in the CON group. In addition, glycylleucine (fold change = 0.57, P<0.05), 3-methyl-L-tyrosine (fold change = 0.52, P<0.05), L-3-cyanoalanine (fold change = 0.53, P<0.05) were significantly higher in the CON group than in the CON group. P<0.05), 2-(methylamino)benzoic acid (fold change = 0.52, P<0.01), N6-methyllysine (fold change = 0.55, P<0.01), 5,6-dihydroepoxyeicosatrienoic acid (fold change = 0.53, P<0.05), γ-tocotrienol (fold change = 0.50, P<0.05), phenethylamine (fold change = 0.35, P<0.01), 5-(2-hydroxyethyl)-4-methylthiophene The relative abundance of oxadiazole (fold change = 0.67, P < 0.05), mandelic acid (fold change = 0.66, P < 0.05), 12,13-dihydroxyoctadecadienoic acid (fold change = 0.37, P < 0.01), 3,4-dihydrocaprylin (fold change = 0.26, P < 0.05) and 11,12-dihydroxyeicosatrienoic acid (fold change = 0.73, P < 0.05) in the intestine of SM group cows was significantly lower than that of CON group cows. And the importance of these differential metabolites in the random forest model was relatively high ( Figure 5 ), is a potential marker metabolite in feces of dairy cows with subclinical mastitis.

[0113] Table 2 Relative abundance changes of differentially expressed metabolites in feces of healthy and subclinical mastitis dairy cows

[0114]

[0115]

[0116] 3. KEGG enrichment analysis of differential metabolites in fecal markers of healthy and subclinical mastitis dairy cows

[0117] KEGG (Kyoto Encyclopedia of Genes and Genomes) metabolic pathway analysis is a bioinformatics analysis method based on the KEGG database, which is used to study and explain metabolic processes in organisms. KEGG enrichment analysis combines experimental data (such as metabolite abundance, etc.) with KEGG pathway maps to identify metabolic pathways associated with specific conditions (such as diseases, etc.), which can provide a theoretical basis for the selection of new drug targets and drug design, and help achieve accurate diagnosis and treatment of diseases.

[0118] The present invention conducted a KEGG enrichment analysis on the above-mentioned fecal differential metabolites. The results showed that the differential metabolites in feces of healthy and subclinical mastitis cows were mainly enriched in fatty acid metabolism-related pathways such as linoleic acid metabolism and steroid biosynthesis ( Figure 6 ), for example, the up-regulated 9(S)-hydroxyperoxyoctadecadienoic acid, 25-hydroxycholesterol, prostaglandin B1, dodecanedioic acid and 4-hydroxycinnamic acid, and the down-regulated 5,6-dihydroepoxyeicosatrienoic acid, 12,13-dihydroxyoctadecadienoic acid and 11,12-dihydroxyeicosatrienoic acid are all involved in lipid metabolism pathways such as linoleic acid metabolism and arachidonic acid metabolism ( Figure 7 , Figure 8 The above analysis results further proved that the changes in the abundance of fatty acid metabolites and their derivatives such as 9(S)-hydroxyperoxyoctadecadienoic acid, 12,13-dihydroxyoctadecadienoic acid, 5,6-dihydroepoxyeicosatrienoic acid, and 11,12-dihydroxyeicosatrienoic acid were closely related to the occurrence of subclinical mastitis in dairy cows.

[0119] 4. ROC analysis of differential metabolites in fecal markers of healthy and subclinical mastitis dairy cows

[0120] Receiver operating characteristic (ROC) analysis is a common tool for evaluating the performance of classification models. It is widely used in the field of biomedical diagnosis to evaluate the perfection of biomarkers and inference models. The area under the ROC curve (AUC) can reflect the accuracy of the diagnostic test. When AUC <0.5, there is no diagnostic value; when AUC is 0.5-0.7, there is a lower accuracy; when AUC is 0.7-0.85, there is a certain accuracy; when AUC>0.85, there is a higher accuracy. Therefore, in order to evaluate the diagnostic value of the marked differential metabolites identified in this experiment for subclinical mastitis in dairy cows, the present invention performed ROC analysis. The ROC curve AUC results of each marked differential metabolite are shown in Table 3.

[0121] The results in Table 3 show that among them, 5 up-regulated (increased) differential metabolites were 9(S)-hydroxyperoxyoctadecadienoic acid (AUC=0.9062, Fig. 9 ), 25-hydroxycholesterol (AUC = 0.8594, Fig.10 ), dodecanedioic acid (AUC = 0.875, Fig.11 ), deoxycytidine (AUC = 0.9062, Fig.12 ) and cis-1,2-dihydronaphthalene-1,2-diol (AUC = 0.875, Fig.13 ) were all above 0.85. Furthermore, the present invention conducted a correlation analysis between the above five up-regulated fecal signature differential metabolites and the SCC in dairy cow milk. The results showed that 9(S)-hydroxyperoxyoctadecadienoic acid (P<0.05), 25-hydroxycholesterol (P<0.01), dodecanedioic acid (P<0.01), deoxycytidine (P<0.001) and cis-1,2-dihydronaphthalene-1,2-diol (P<0.05) were all significantly positively correlated with SCC ( Fig.14 ), indicating that the increase in the content of the above five metabolites in dairy cow feces is closely related to subclinical mastitis and has a high diagnostic value.

[0122] The results in Table 3 show that 10 down-regulated (decreased) differential metabolites glycylleucine (AUC = 1, Fig.15 ), 3-methyl-L-tyrosine (AUC=1, Fig.16 ), L-3-cyanoalanine (AUC=1, Fig.17 ), 2-(methylamino)benzoic acid (AUC=1, Fig.18 ), N6-methyllysine (AUC = 0.9688, Fig.19 ), 5,6-dihydroepoxyeicosatrienoic acid (AUC = 0.9062, Fig. 20 ), γ-tocotrienol (AUC = 0.9062, Fig.21 ), phenylethylamine (AUC = 0.9062, Fig. 22 ), 5-(2-hydroxyethyl)-4-methylthiazole (AUC = 0.8906, Fig.23 ) and 12,13-dihydroxyoctadecadienoic acid (AUC = 0.9062, Fig.24) had an AUC of more than 0.85. Furthermore, the present invention conducted a correlation analysis between the above 10 down-regulated fecal marker differential metabolites and cow milk SCC. The results showed that 8 of the differential metabolites, glycylleucine (P<0.01), 3-methyl-L-tyrosine (P<0.001), L-3-cyanoalanine (P<0.001), 2-(methylamino)benzoic acid (P<0.05), N6-methyllysine (P<0.001), 5,6-dihydroepoxyeicosatrienoic acid (P<0.05), phenylethylamine (P<0.05) and 12,13-dihydroxyoctadecadienoic acid (P<0.05), were significantly negatively correlated with SCC ( Fig.25 ), indicating that the reduction of the contents of the above eight metabolites in dairy cow feces is closely related to subclinical mastitis and has a high diagnostic value.

[0123] The above results show that 9(S)-hydroxyperoxyoctadecadienoic acid, 25-hydroxycholesterol, dodecanedioic acid, deoxycytidine, cis-1,2-dihydronaphthalene-1,2-diol, glycylleucine, 3-methyl-L-tyrosine, L-3-cyanoalanine, 2-(methylamino)benzoic acid, N6-methyllysine, 5,6-dihydroepoxyeicosatrienoic acid, phenylethylamine, and 12,13-dihydroxyoctadecadienoic acid are biomarkers in the feces of dairy cows with subclinical mastitis, and have a high accuracy in assisting the diagnosis of subclinical mastitis in dairy cows.

[0124] Table 3 ROC curve AUC of differential metabolites in fecal markers of healthy and subclinical mastitis cows

[0125]

Claims

1. A fecal metabolite combination for assisting diagnosis of subclinical mastitis in dairy cows, characterized in that: The fecal metabolite combination is a first combination and / or a second combination, the first combination is composed of 9(S)-hydroxyperoxyoctadecadienoic acid, 25-hydroxycholesterol, dodecanedioic acid, deoxycytidine, and cis-1,2-dihydronaphthalene-1,2-diol; the second combination is composed of glycylleucine, 3-methyl-L-tyrosine, L-3-cyanoalanine, 2-(methylamino)benzoic acid, N6-methyllysine, 5,6-dihydroepoxyeicosatrienoic acid, phenylethylamine, and 12,13-dihydroxyoctadecadienoic acid.

2. Use of the fecal metabolite combination according to claim 1 in the preparation of a product for assisting diagnosis or treatment of subclinical mastitis in dairy cows, characterized in that: The product includes one or more of a therapeutic agent, a diagnostic reagent, a diagnostic kit, a diagnostic chip, a diagnostic system, and a diagnostic instrument.

3. A kit for assisting diagnosis of subclinical mastitis in dairy cows, characterized in that: The kit comprises reagents and internal standards for detecting the concentration of a fecal metabolite combination in a fecal sample, wherein the fecal metabolite combination is a first combination and / or a second combination, the first combination consisting of 9(S)-hydroxyperoxyoctadecadienoic acid, 25-hydroxycholesterol, dodecanedioic acid, deoxycytidine, and cis-1,2-dihydronaphthalene-1,2-diol; the second combination consisting of glycylleucine, 3-methyl-L-tyrosine, L-3-cyanoalanine, 2-(methylamino)benzoic acid, N6-methyllysine, 5,6-dihydroepoxyeicosatrienoic acid, phenylethylamine, and 12,13-dihydroxyoctadecadienoic acid; and the internal standard is a combination of standard substances of each fecal metabolite in the fecal metabolite combination.

4. The kit according to claim 3, characterized in that: The method for using the kit comprises: taking a fecal sample from a test cow whose milk somatic cell count is not less than a mammary gland health threshold; using the kit to detect the concentration of a fecal metabolite combination in the fecal sample, and determining whether the test cow is a cow with subclinical mastitis based on the concentration of the fecal metabolite combination.

5. A cow subclinical mastitis auxiliary diagnosis system, characterized in that: include: An acquisition module is used to obtain the relative concentration of a fecal metabolite combination in a fecal sample of a tested dairy cow; the fecal metabolite combination is a first combination and / or a second combination, the first combination is composed of 9(S)-hydroxyperoxyoctadecadienoic acid, 25-hydroxycholesterol, dodecanedioic acid, deoxycytidine, and cis-1,2-dihydronaphthalene-1,2-diol; the second combination is composed of glycylleucine, 3-methyl-L-tyrosine, L-3-cyanoalanine, 2-(methylamino)benzoic acid, N6-methyllysine, 5,6-dihydroepoxyeicosatrienoic acid, phenylethylamine, and 12,13-dihydroxyoctadecadienoic acid; An evaluation module, used for evaluating whether the tested dairy cow suffers from subclinical mastitis according to the relative concentration; Wherein, the acquisition module and the prediction module are connected wirelessly and / or wiredly.

6. The auxiliary diagnosis system for subclinical mastitis in dairy cows according to claim 5, characterized in that: The acquisition module comprises: A detection module, used for detecting the concentration of a fecal metabolite combination in a fecal sample of a tested dairy cow; The comparison module is used to compare the concentration of the fecal metabolite combination in the test cow fecal sample with the standard concentration and output the relative concentration.

7. The auxiliary diagnosis system for subclinical mastitis in dairy cows according to claim 6, characterized in that: The detection module detects the concentration of the fecal metabolite combination in the fecal sample by one or more of the following methods: chromatography, spectroscopy, mass spectrometry, and chemical analysis.

8. The auxiliary diagnosis system for subclinical mastitis in dairy cows according to claim 5, characterized in that: The somatic cell count of the milk of the tested dairy cows is not lower than the mammary gland health threshold.

9. The auxiliary diagnosis system for subclinical mastitis in dairy cows according to claim 6, characterized in that: The step of evaluating whether the tested dairy cow suffers from subclinical mastitis according to the relative concentration includes: determining that the tested dairy cow is a dairy cow with subclinical mastitis when the concentration of the fecal metabolite combination in the fecal sample of the tested dairy cow meets one of the following conditions: (1) The first combination satisfies at least one of the following conditions: 9(S)-hydroxyperoxyoctadecadienoic acid is not less than 2.50 times its standard concentration, 25-hydroxycholesterol is not less than 1.97 times its standard concentration, dodecanedioic acid is not less than 1.41 times its standard concentration, deoxycytidine is not less than 1.69 times its standard concentration, and cis-1,2-dihydronaphthalene-1,2-diol is not less than 1.30 times its standard concentration; (2) The second combination satisfies at least one of the following conditions: glycylleucine is not higher than 0.57 times of its standard concentration, 3-methyl-L-tyrosine is not higher than 0.52 times of its standard concentration, L-3-cyanoalanine is not higher than 0.53 times of its standard concentration, 2-(methylamino)benzoic acid is not higher than 0.52 times of its standard concentration, N6-methyllysine is not higher than 0.55 times of its standard concentration, 5,6-dihydroepoxyeicosatrienoic acid is not higher than 0.53 times of its standard concentration, phenylethylamine is not higher than 0.35 times of its standard concentration, and 12,13-dihydroxyoctadecadienoic acid is not higher than 0.37 times of its standard concentration.

10. An auxiliary diagnostic instrument for subclinical mastitis in dairy cows, characterized by: The auxiliary diagnosis system for subclinical mastitis in dairy cows as described in any one of claims 5 to 7 is adopted.

Images