CN114854884A - Method for early warning or noninvasive diagnosis of fatty liver dairy cow by using fecal microorganisms belonging to level - Google Patents

Method for early warning or noninvasive diagnosis of fatty liver dairy cow by using fecal microorganisms belonging to level Download PDF

Info

Publication number
CN114854884A
CN114854884A CN202210584152.5A CN202210584152A CN114854884A CN 114854884 A CN114854884 A CN 114854884A CN 202210584152 A CN202210584152 A CN 202210584152A CN 114854884 A CN114854884 A CN 114854884A
Authority
CN
China
Prior art keywords
fatty liver
bifidobacterium
roseburia
cow
microorganisms
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN202210584152.5A
Other languages
Chinese (zh)
Inventor
师科荣
孙浩铭
刘廷俊
张璇
侯宪朋
宋旭阳
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Shandong Agricultural University
Original Assignee
Shandong Agricultural University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Shandong Agricultural University filed Critical Shandong Agricultural University
Priority to CN202210584152.5A priority Critical patent/CN114854884A/en
Publication of CN114854884A publication Critical patent/CN114854884A/en
Priority to US18/112,518 priority patent/US20230384321A1/en
Pending legal-status Critical Current

Links

Images

Classifications

    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
    • C12Q1/6888—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for detection or identification of organisms
    • C12Q1/689—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for detection or identification of organisms for bacteria
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6869—Methods for sequencing
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
    • G01N33/569—Immunoassay; Biospecific binding assay; Materials therefor for microorganisms, e.g. protozoa, bacteria, viruses
    • G01N33/56911—Bacteria
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/68—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids
    • G01N33/6803—General methods of protein analysis not limited to specific proteins or families of proteins
    • G01N33/6848—Methods of protein analysis involving mass spectrometry
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/124—Animal traits, i.e. production traits, including athletic performance or the like
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2333/00—Assays involving biological materials from specific organisms or of a specific nature
    • G01N2333/195—Assays involving biological materials from specific organisms or of a specific nature from bacteria
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2333/00—Assays involving biological materials from specific organisms or of a specific nature
    • G01N2333/195—Assays involving biological materials from specific organisms or of a specific nature from bacteria
    • G01N2333/305—Assays involving biological materials from specific organisms or of a specific nature from bacteria from Micrococcaceae (F)
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2570/00—Omics, e.g. proteomics, glycomics or lipidomics; Methods of analysis focusing on the entire complement of classes of biological molecules or subsets thereof, i.e. focusing on proteomes, glycomes or lipidomes

Abstract

The invention discloses a method for early warning or non-invasive diagnosis of fatty liver cows by using fecal microorganisms belonging to the same level. The biomarkers are: the genus level of the fecal microorganisms is anaerobacter plush (Lachnoaerobacter), Roseburia (Roseburia), Bifidobacterium (Bifidobacterium). The diagnosis capability verifies that the AUC value of the marker accords with the diagnostic significance and has higher clinical diagnosis application value; more importantly, the diagnosis capability of the combined analysis of the three groups of microorganisms has better effect on moderate fatty liver and severe fatty liver. The biomarker is used for diagnosing, identifying and monitoring the fatty liver dairy cows, has low cost and simple operation, is a non-invasive and non-invasive detection means, accords with the concepts of animal welfare and healthy breeding, can be widely applied to the large-scale breeding of the dairy cows in the future, and promotes the development of the dairy industry.

Description

Method for early warning or noninvasive diagnosis of fatty liver dairy cow by using fecal microorganisms belonging to level
Technical Field
The invention relates to the technical field of analytical chemistry and clinical medicine, in particular to a method for early warning or non-invasive diagnosis of fatty liver dairy cows by using fecal microorganisms belonging to the same level.
Background
Foreign scholars refer to the specific stage of the dairy cows from late gestation to early lactation, namely 15 days before and 15 days after birth, as the transition period, also called the perinatal period. In the important period of perinatal period, the energy intake is reduced due to the reduction of the food intake, the exercise amount is also greatly reduced, the production and the lactation need a large amount of energy support, the fat mobilization is changed, the body consumes the fat, the concentration of free fatty acid (NEFA) in the blood is increased, the rate of the liver taking the free fatty acid is accelerated, the NEFA enters the liver and is esterified to form Triglyceride (TG), and the TG can be secreted out of the liver in a hydrolysis mode or in a Very Low Density Lipoprotein (VLDL) mode; but the secretion into the blood in the form of VLDL is inefficient. When the synthesis rate of TG is higher than the transport rate, TG accumulation in the liver is caused, and fat is accumulated in the liver more than the normal content of the liver, which is called fatty liver. The fatty liver disease of the dairy cow is one of the metabolic disorder diseases frequently occurring in the perinatal period of the dairy cow, can seriously cause ketosis, postpartum paralysis and the like, and seriously influences the milk production performance, the reproductive performance and the service life of the dairy cow. The incidence of the disease is particularly frequent in the perinatal period of the dairy cows, the incidence rate is relatively high (5-10% of the dairy cows suffer from severe fatty liver, and 30-40% of the dairy cows suffer from moderate or mild fatty liver), and huge economic loss is caused to the dairy industry.
Fatty liver exists as a common metabolic disorder disease for a long time and causes considerable economic loss to the dairy industry, but an effective diagnostic method is still lacking. The only reliable diagnosis method at present is liver biopsy, which is the measurement of fat content by taking liver tissue from a milk cow living body. The method is an invasive method, has snow frosting effect on the health of milk cow, and is not beneficial to animal welfare; moreover, poor prognosis can also lead to concurrent infectious diseases. The application number 201910873316.4 discloses a novel biomarker for noninvasive identification/early warning of fatty liver cows, which uses L-alpha-aminobutyric acid and behenic acid in feces, and 3-nitrotyrosine in urine as a biomarker, so that the feces of the cows and the urine of the cows are needed, the urine collection is troublesome, and if the feces convenient to collect can be used, the microorganisms in the feces are used as a marker source, the identification of the fatty liver of the cows is simpler, quicker and noninvasive. Meanwhile, a diagnosis method which is non-invasive, high in specificity, good in sensitivity and accuracy and capable of meeting requirements of veterinary clinics and cattle production practices is sought, and becomes a bottleneck which needs to be broken through urgently in prevention and treatment of fatty liver diseases of dairy cows.
Disclosure of Invention
In view of the above prior art, the present invention aims to provide a method for early warning or non-invasive diagnosis of fatty liver cows by using fecal microorganisms belonging to the genus level. The method utilizes the microorganisms to diagnose and identify the fatty liver dairy cows, has low cost and simple operation, is a non-invasive and non-invasive detection means, accords with the concepts of animal welfare and healthy breeding, can be widely applied to the large-scale breeding of the dairy cows in the future, and promotes the healthy and efficient development of the dairy industry.
In order to achieve the purpose, the invention adopts the following technical scheme:
in a first aspect of the invention there is provided the use of a microbial marker in faeces selected from a combination of at least two of the genera anaerobacterium lanchoanaerobacterium, Roseburia or Bifidobacterium in the manufacture of a reagent or kit for the identification of fatty liver disease in a cow.
Preferably, the microbial marker is a combination of the genera anaerobacter plush (Lachnoaerobacter), Roseburia (Roseburia) and Bifidobacterium (Bifidobacterium).
In a second aspect of the invention, the use of a reagent for detecting a microbial marker in a cow's feces for the preparation of a product for non-invasive identification of fatty liver disease in a perinatal cow is provided;
the microbial marker is selected from a combination of at least two of the genera anaerobacter plush (Lachnoaroacterium), Roseburia (Roseburia) or Bifidobacterium (Bifidobacterium).
Preferably, the microbial marker is a combination of the genera anaerobacter plush (Lachnoaerobacter), Roseburia (Roseburia) and Bifidobacterium (Bifidobacterium).
Preferably, the reagent is a reagent for detecting the relative abundance of the microbial marker in the dairy cow dung.
Preferably, the reagents are reagents for metagenomic sequencing, 16S sequencing or qPCR sequencing.
Preferably, the method for non-invasively identifying perinatal cow fatty liver disease comprises:
(1) collecting feces from the perinatal cows to be detected;
(2) detecting the relative abundance of the microbial markers in the dairy manure;
(3) and identifying the fatty liver disease of the perinatal cow to be detected based on the relative abundance of the detected microbial markers for diagnosis or early warning.
The invention has the beneficial effects that:
(1) the invention provides a microorganism for non-invasive identification and identification of fatty liver disease cows for the first time based on a macroproteomics technology. The diagnosis capability proves that the AUC of each marker is higher, and the clinical diagnosis application value is higher.
(2) The microorganism disclosed by the invention is used for identifying, identifying and monitoring the fatty liver dairy cows, is low in cost and simple to operate, is a non-invasive and non-invasive detection means, accords with the concepts of animal welfare and healthy breeding, can be widely applied to large-scale breeding of the dairy cows in the future, and promotes the healthy and efficient development of the dairy industry.
Drawings
FIG. 1. the microorganisms Roseburia (Ross. sp.) + Bifidobacterium (Bifidobacterium) in the MFLvsNorm group were analysed in combination in the ROC for MFLvsNorm;
FIG. 2. Combined analysis of the microorganisms Lachnoaerobacterium (Acerobacterium lanuginosus) + Bifidobacterium (Bifidobacterium) in the SFLvsNorm group in ROC.
Detailed Description
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, 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 belongs.
As introduced in the background art, the fatty liver disease of dairy cows, which is one of the metabolic disorders frequently occurring in the perinatal period of dairy cows, can seriously cause ketosis, postpartum paralysis and the like, and seriously affects the milk production performance, reproductive performance and service life of the dairy cows. Causing huge economic loss to the milk industry. Such losses can be effectively avoided if early identification or corresponding warning measures are possible. However, the only reliable diagnostic method at present is liver biopsy, which is the determination of fat content by taking liver tissue from a live cow. The method is an invasive method, has snow frosting effect on the health of milk cow, and is not beneficial to animal welfare; moreover, poor prognosis can also lead to concurrent infectious diseases; therefore, the noninvasive method for early diagnosis of the fatty liver cows has important significance and value.
Proteomes cover a wide range of foods, oceans, soils and intestinal tracts. With recent advances, there has been an increase and development in sample processing and mass spectrometry. With the combination of macroproteomics and bioinformatics becoming more and more mature, the macroproteomics also have high changes in the analysis of intestinal changes caused by NAFLD. In particular, it may reveal changes in the classification, function and metabolic pathways of the intestinal microbiota.
(1) The invention adopts 4D proteomics technology to carry out macroproteomics analysis on the feces. The advantage of macroproteomics is the comprehensive analysis of the classification and function of microorganisms in the feces.
The method comprises the following specific steps:
extracting protein. Taking out the sample from-80 ℃, weighing a proper amount of tissue sample into a mortar precooled by liquid nitrogen, adding liquid nitrogen, and fully grinding the tissue sample into powder. The samples were sonicated by adding powdered 4 volumes phenol extraction buffer (containing 10mM dithiothreitol, 1% protease inhibitor) to each sample. Adding equal volume of Tris equilibrium phenol, centrifuging at 4 deg.C and 5500g for 10min, collecting supernatant, adding 5 times volume of 0.1M ammonium acetate/methanol to precipitate overnight, and washing protein precipitate with methanol and acetone respectively. And finally, re-dissolving the precipitate by using 8M urea, and determining the protein concentration by using a BCA kit.
② enzymolysis by pancreatin. Performing enzymolysis on each sample protein in equal amount, adding an appropriate amount of standard protein, adjusting the volume to be consistent with the lysate, slowly adding TCA with the final concentration of 20%, uniformly mixing by vortex, and precipitating for 2h at 4 ℃. 4500g, centrifuging for 5min, discarding the supernatant, and washing the precipitate with precooled acetone for 2-3 times. Air drying the precipitate, adding TEAB with final concentration of 200mM, ultrasonically breaking the precipitate, adding trypsin at a ratio of 1:50 (protease: protein, m/m), and performing enzymolysis overnight. Dithiothreitol (DTT) was added to give a final concentration of 5mM, and the mixture was reduced at 56 ℃ for 30 min. Iodoacetamide (IAA) was then added to give a final concentration of 11 mM.
And thirdly, liquid chromatography-mass spectrometry combined analysis. The peptide fragment was dissolved in mobile phase A (0.1% (v/v) formic acid aqueous solution) by liquid chromatography, and then separated by using EASY-nLC 1000 ultra performance liquid system. The mobile phase A is an aqueous solution containing 0.1 percent of formic acid and 2 percent of acetonitrile; mobile phase B was an aqueous solution containing 0.1% formic acid and 90% acetonitrile. Setting a liquid phase gradient: 0-40min, 5% -25% B; 40-52min, 25% -35% B; 52-56min, 35% -80% B; 56-60min, 80% B, the flow rate is maintained at 500 nL/min.
The peptide fragments are separated by an ultra-high performance liquid phase system, injected into an NSI ion source for ionization and then analyzed by Q ExactivetTM Plus mass spectrum. The ion source voltage was set at 2.1kV and both the peptide fragment parent ion and its secondary fragment were detected and analyzed using the high resolution Orbitrap. The scanning range of the primary mass spectrum is set to be 350-1800m/z, and the scanning resolution is set to be 70,000; the secondary mass spectral scan range was then fixed with a starting point of 100m/z and the secondary scan resolution was set to 17,500. The data acquisition mode uses a data-dependent scanning (DDA) program, i.e., after a primary scan, the first 10 peptide fragment parent ions with the highest signal intensity are selected to enter the HCD collision cell in sequence and are fragmented by 28% of fragmentation energy, and secondary mass spectrometry is also performed in sequence. To improve the effective utilization of the mass spectra, the Automatic Gain Control (AGC) was set to 5E4, the signal threshold was set to 20000ions/s, the maximum injection time was set to 100ms, and the dynamic exclusion time of the tandem mass spectrometry scan was set to 30 seconds to avoid repeated scans of the parent ions.
Fourthly, searching and comparing the database. Secondary mass spectral data were retrieved using Maxquant (v1.5.2.8). And (3) retrieval parameter setting: adding a reverse library into the database to calculate false positive rate (FDR) caused by random matching, and adding a common pollution library into the database to eliminate the influence of pollution protein in the identification result; the enzyme cutting mode is set as Trypsin/P; the number of missed cutting sites is set to 2; the First-level parent ion mass error tolerance of the First search and the Main search is set to be 20ppm and 5ppm respectively, and the mass error tolerance of the second-level fragment ions is 0.02 Da. Cysteine alkylation was set as a fixed modification, variable modifications were oxidation of methionine, acetylation of the N-terminus of the protein, deamidation (NQ). The FDR of protein identification and PSM identification is set to be 1%.
(2) Firstly, rejecting unqualified or unreasonable data by data repeatability detection and mass spectrum quality control detection of macro proteome data; the reliability of the screening model is verified by a Pearson correlation coefficient and an OPLS-DA method, and then differential expression proteins between a normal group and a diseased group are screened out. Species annotation utilizes the software Unipept (v.2.0.0https:// Unipept. element. be/datasets).
(3) The biomarkers of the invention are subjected to a stringent screening process.
(4) Analysis was performed on 31 individual cows using macroproteomics. Compared with other methods for identifying fatty liver individuals by using fecal microorganism indexes, the three microorganisms screened at the genus level and the microorganisms screened at the species level have good verification capability.
The invention finally discovers 3 fecal microbe markers with diagnostic value.
3 microorganisms with diagnostic value found by the invention come from the excrement, and are identified by using microorganisms generated by normal metabolism of the dairy cows, so that the method is time-saving, convenient and fast, and saves the diagnostic cost; the diagnosis does not need traditional blood sample collection or operation puncture, is a painless identification marker, and has important significance for the health and safety of the dairy cows and the welfare of animals; the marker provided by the invention is used for identifying and diagnosing the fatty liver disease of the dairy cow, so that the production of the dairy cow is not influenced, and the negative effects of reduction of the yield of the dairy cow, health stress, even concurrent infection and the like caused by blood sample collection, surgical puncture and the like are avoided. Saving diagnosis and treatment cost and promoting high yield and synergism.
The biomarker adopted by the invention is derived from excrement, is a microorganism in intestinal tracts of the dairy cow, can well indicate the metabolic condition of the dairy cow, and can accurately indicate the metabolic condition of the dairy cow through the change of relative abundance content of the microorganism in the excrement for fatty liver of the dairy cow.
In order to make the technical solutions of the present application more clearly understood by those skilled in the art, the technical solutions of the present application will be described in detail below with reference to specific embodiments.
The test materials used in the examples of the present invention are all conventional in the art and commercially available.
Example 1: verification of the ability to identify microorganisms
The experimental object is a Chinese Holstein cow within 1-2 weeks after calving. The experimental place is a certain scale dairy cow farm in Shandong, and the feeding management conditions of the dairy cows collected during the experiment are consistent. And finally obtaining the dairy cattle with severe fatty liver (marked as SFL), the dairy cattle with moderate fatty liver (marked as MFL) and the dairy cattle with normal liver (marked as Norm) by using a serological detection preliminary screening method and a specific dairy cattle liver biopsy confirmation method. The basis of the liver biopsy diagnosis is the percentage of fat deposition cells in a unit area after oil red O staining, wherein n is 12 heads, n is 9 heads and n is 10 heads in the severe fatty liver group, the moderate fatty liver group and the normal liver group respectively. For three different groups of cows, samples of empty faeces were collected before morning feeding and frozen in liquid nitrogen for subsequent macroprotein analysis.
The relative abundance of the twenty microorganisms before abundance at the genus level was identified (table 1), and changes in the abundance of the microorganisms were analyzed by ROC analysis in SPSS software by the relative abundance of the microorganisms, and genus levels of Bifidobacterium, Roseburia, and lachnoaerobacterium were found in feces. Their AUC values were 0.833, 0.811, and 0.767, respectively. This shows that these three groups of microorganisms have higher discriminatory power in mflvs norm. (see Table 2) however, the AUC was not high for the three groups of microorganisms in the SFLvsNorm group.
TABLE 1 relative abundance of the first 20 microorganisms at genus level (mean. + -. standard deviation) 1
Figure BDA0003665193930000061
1 Note: the dairy cow fecal microorganisms belong to the microorganisms with the first 20 abundance levels.
TABLE 2 accuracy validation of genus level fecal microbiology diagnosis of cow metabolic status (MFLvsNorm) 1
Figure BDA0003665193930000062
1 Note: the dairy cow fecal microorganisms belong to the microorganisms with the first 20 abundance levels.
TABLE 3 accuracy validation of genus level fecal microbiology diagnosis of cow metabolic status (SFLvsNorm)1
Figure BDA0003665193930000071
1 Note: the dairy cow fecal microorganisms belong to the microorganisms with the first 20 abundance levels.
Example 2: abundance changes of microorganisms in the Norm-MFL-SFL three groups
Three groups of microorganisms selected by ROC, namely, Bifidobacterium (genus Bifidobacterium), Roseburia (genus rosenbia) and lachnoaerobacterium (genus anoxybacter), were found to be present in the diseased group in a large increase in abundance from 2.20% in Norm to 18.51% in MFL to 11.42% in SFL. Changes in the abundance of Roseburia (ross) and lachnoaerobaculum (anoxybacter) in Norm-MFL-SFL also increased in the diseased group, but the abundance ratios were smaller (table 4).
TABLE 4 variation of relative proportions of faecal microbiota levels abundances of cows of different metabolic states (mean) 1
Figure BDA0003665193930000072
1 Note: the proportion of microorganisms in the feces of the cow is 20% of the total abundance of these microorganisms in the normal state (Norm).
Example 3: the combination of microbial markers can improve the validation ability
To improve the validation ability of microorganisms among groups, we performed a combined analysis of 3 microorganisms at the genus level (see tables 5, 6). And (3) performing different permutation and combination on the three microorganism variables by utilizing binary logistic regression in the SPSS, fitting a predicted value after combination, and performing ROC analysis to finally obtain the optimal joint performance of the Lachnoaerobacter, Roseburia and Bifidobacterium on the genus level. The AUC obtained by the combined analysis of three groups of microorganisms in the MFLvsNorm group was 1, and the AUC obtained by the combined analysis of two groups of microorganisms, Lachnoaerobacterium and Bifidobacterium in the SFLvsNorm group was 0.867.
TABLE 5 analysis of the accuracy of the combined diagnosis of the metabolic status (MFLvsNorm) of cows at different genus levels by microorganisms
Figure BDA0003665193930000081
TABLE 6 analysis of the accuracy of the combined diagnosis of the metabolic status (SFLvsNorm) of cows at different levels of microorganisms
Figure BDA0003665193930000082
Table 2 the data obtained in the single microorganism ROC partition licensing are results in the MFLvsNorm group, where three groups of microorganisms lachnoaerobacter (anoxybacterium), Roseburia (ross), Bifidobacterium (Bifidobacterium) all have good results in the MFLvsNorm, but the three microorganisms in the SFLvsNorm group do not score high (see table 3), but the combined analysis AUC of the three groups of microorganisms in the SFLvsNorm group is higher than the AUC value of each microorganism.
In the dairy cow breeding application, potential fatty liver affected dairy cows can be identified and detected by detecting the relative abundance of the 3 microorganisms in the dairy cow excrement. When the abundances of Bifidobacterium and Lachnoaerobacterium in fecal microorganisms show a trend of increasing at the same time, especially when the three strains (additionally Roseburia, Roseburia) increase at the same time, the possibility of possible metabolic disturbance is early warned, and the feeding scheme should be adjusted in time or adjustment measures should be taken, so that loss can be effectively avoided. The biomarker provides a new technology and a new method for noninvasive detection and diagnosis of fatty liver diseases of cows in the future.
The above description is only a preferred embodiment of the present application and is not intended to limit the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims (7)

1. Use of a microbial marker in faeces selected from the group consisting of a combination of at least two of the genera lahnoaneabacterium (lahnoaneabacterium), Roseburia (Roseburia), Bifidobacterium (Bifidobacterium) in the manufacture of a reagent or kit for identifying fatty liver disease in a cow.
2. Use according to claim 1, characterized in that the microbial marker is a combination of the genera anaerobacter plush (Lachnoaerobacter), Roseburia (Roseburia) and Bifidobacterium (Bifidobacterium).
3. The application of a reagent for detecting a microbial marker in the feces of a cow in preparing a product for non-invasively identifying fatty liver disease of the cow in the perinatal period;
the microbial marker is selected from a combination of at least two of the genera anaerobacter plush (Lachnoaroacterium), Roseburia (Roseburia) or Bifidobacterium (Bifidobacterium).
4. Use according to claim 3, characterized in that the microbial marker is a combination of the genera Lachnoanearobacterium (Lachnaerobacter), Roseburia (Roseburia) and Bifidobacterium (Bifidobacterium).
5. Use according to claim 3, wherein the reagent is a reagent for detecting the relative abundance of a microbial marker in dairy cow dung.
6. Use according to claim 3 or 5, wherein the reagents are reagents for metagenomic sequencing, 16S sequencing or qPCR sequencing.
7. The use according to claim 3 or 4, wherein the method for non-invasively identifying perinatal cow fatty liver disease comprises:
(1) collecting feces from the perinatal cows to be tested;
(2) detecting the relative abundance of the microbial markers in the dairy manure;
(3) and identifying the fatty liver disease of the perinatal cow to be detected based on the relative abundance of the detected microbial markers for diagnosis or early warning.
CN202210584152.5A 2022-05-27 2022-05-27 Method for early warning or noninvasive diagnosis of fatty liver dairy cow by using fecal microorganisms belonging to level Pending CN114854884A (en)

Priority Applications (2)

Application Number Priority Date Filing Date Title
CN202210584152.5A CN114854884A (en) 2022-05-27 2022-05-27 Method for early warning or noninvasive diagnosis of fatty liver dairy cow by using fecal microorganisms belonging to level
US18/112,518 US20230384321A1 (en) 2022-05-27 2023-02-22 Application of microbiological markers in non-invasive identification/early warning of fatty liver in perinatal cows

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202210584152.5A CN114854884A (en) 2022-05-27 2022-05-27 Method for early warning or noninvasive diagnosis of fatty liver dairy cow by using fecal microorganisms belonging to level

Publications (1)

Publication Number Publication Date
CN114854884A true CN114854884A (en) 2022-08-05

Family

ID=82641185

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202210584152.5A Pending CN114854884A (en) 2022-05-27 2022-05-27 Method for early warning or noninvasive diagnosis of fatty liver dairy cow by using fecal microorganisms belonging to level

Country Status (1)

Country Link
CN (1) CN114854884A (en)

Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108135944A (en) * 2014-11-25 2018-06-08 伊夫罗生物科学公司 Probiotics and prebiotic compositions and its method and purposes for adjusting microorganism group
US20190127781A1 (en) * 2016-04-20 2019-05-02 Human Longevity, Inc. Use of a microbiome profile to detect liver disease
CN110501443A (en) * 2019-09-17 2019-11-26 山东农业大学 Noninvasive identification/early warning fatty liver cows new biomarker object
CN111549155A (en) * 2020-05-13 2020-08-18 朱彧 Analysis technology for predicting liver diseases through relative abundance of intestinal flora
US20210052674A1 (en) * 2017-09-28 2021-02-25 Kobiolabs, Inc. Composition for diagnosis and treatment of alcoholic liver disease, using change in short-chain fatty acid producing gut bacterial community
CN112852916A (en) * 2021-02-19 2021-05-28 王普清 Marker combination for intestinal microecology, auxiliary diagnosis model and application of marker combination
CN114373505A (en) * 2021-12-29 2022-04-19 浙江大学 System for early prediction of postpartum subclinical ketosis of dairy cow based on intestinal microorganisms
CN114381534A (en) * 2021-11-25 2022-04-22 杭州拓宏生物科技有限公司 Fatty liver marker microorganism and application thereof

Patent Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108135944A (en) * 2014-11-25 2018-06-08 伊夫罗生物科学公司 Probiotics and prebiotic compositions and its method and purposes for adjusting microorganism group
US20190127781A1 (en) * 2016-04-20 2019-05-02 Human Longevity, Inc. Use of a microbiome profile to detect liver disease
US20210052674A1 (en) * 2017-09-28 2021-02-25 Kobiolabs, Inc. Composition for diagnosis and treatment of alcoholic liver disease, using change in short-chain fatty acid producing gut bacterial community
CN110501443A (en) * 2019-09-17 2019-11-26 山东农业大学 Noninvasive identification/early warning fatty liver cows new biomarker object
CN111549155A (en) * 2020-05-13 2020-08-18 朱彧 Analysis technology for predicting liver diseases through relative abundance of intestinal flora
CN112852916A (en) * 2021-02-19 2021-05-28 王普清 Marker combination for intestinal microecology, auxiliary diagnosis model and application of marker combination
CN114381534A (en) * 2021-11-25 2022-04-22 杭州拓宏生物科技有限公司 Fatty liver marker microorganism and application thereof
CN114373505A (en) * 2021-12-29 2022-04-19 浙江大学 System for early prediction of postpartum subclinical ketosis of dairy cow based on intestinal microorganisms

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
TIPHAINE LE ROY 等: "Intestinal microbiota determines development of non-alcoholic fatty liver disease in mice", GUT, vol. 62, pages 1787 - 1794 *
YUNG-TSUNG CHEN 等: "Sugary Kefir Strain Lactobacillus mali APS1 Ameliorated Hepatic Steatosis by Regulation of SIRT-1/Nrf-2 and Gut Microbiota in Rats", MOL. NUTR. FOOD RES., vol. 62, pages 1 - 10 *

Similar Documents

Publication Publication Date Title
Ebrahimie et al. A large-scale study of indicators of sub-clinical mastitis in dairy cattle by attribute weighting analysis of milk composition features: highlighting the predictive power of lactose and electrical conductivity
CN101403740B (en) Mass spectrum model used for detecting liver cancer characteristic protein and preparation method thereof
Verma et al. Challenges and opportunities of bovine milk analysis by mass spectrometry
CN110749687B (en) Schistosomiasis japonica early diagnosis urine biomarker and screening method and application thereof
CN112858454B (en) Characteristic polypeptide composition for diagnosing new coronary pneumonia
Xie et al. Study on the potential biomarkers of maternal urine metabolomics for fetus with congenital heart diseases based on modified gas chromatograph-mass spectrometer
Kalaitzakis et al. Evaluation of ornithine carbamoyl transferase and other serum and liver-derived analytes in diagnosis of fatty liver and postsurgical outcome of left-displaced abomasum in dairy cows
CN110501443B (en) Novel biomarker for noninvasive identification/early warning of fatty liver cows
CN114854884A (en) Method for early warning or noninvasive diagnosis of fatty liver dairy cow by using fecal microorganisms belonging to level
CN110554204B (en) Method for diagnosing/early warning perinatal fatty liver dairy cows by using 3 serum biochemical indexes
CN110763795B (en) Serum biomarker for early diagnosis of schistosomiasis japonica, screening method and application
US20230384321A1 (en) Application of microbiological markers in non-invasive identification/early warning of fatty liver in perinatal cows
RU2703280C1 (en) Method of preparing samples of material for identification of nematode species by maldi toff biotyper method
CN115011706A (en) Application of fecal microorganism marker in noninvasive identification/early warning of perinatal fatty liver cows
Duffield et al. Herd level indicators for the prediction of high-risk dairy herds for subclinical ketosis
CN111812309A (en) Urine protein marker for tumor bone metastasis and application thereof
CN114371280A (en) Plasma biomarker for broiler AFB1 poisoning diagnosis and application thereof
He et al. Metabolic alterations in dairy cattle with lameness revealed by untargeted metabolomics of dried milk spots using direct infusion-tandem mass spectrometry and the triangulation of multiple machine learning models
US20230112866A1 (en) Biomarkers for clear cell renal cell carcinoma
CN111549108B (en) Method for analyzing composition and functional difference of intestinal microorganisms of laying ducks by different residual feed intake
JP2023514809A (en) Biomarkers for diagnosing ovarian cancer
WO2017058897A1 (en) Lipodomic biomarkers of metabolic diseases in dairy cows
CN114858904A (en) Mass spectrometry model comprising characteristic polypeptides for diagnosing neocoronary pneumonia
US20200217854A1 (en) Multiplexed protein assay for the detection of mitochondrial protein signatures
CN113655225B (en) Application of protein and composition for identifying early pregnancy of Tibetan pigs

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination