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CN113889190B - A method for predicting calf diarrhea resistance based on intestinal microbial information - Google Patents

A method for predicting calf diarrhea resistance based on intestinal microbial information Download PDF

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CN113889190B
CN113889190B CN202111232337.1A CN202111232337A CN113889190B CN 113889190 B CN113889190 B CN 113889190B CN 202111232337 A CN202111232337 A CN 202111232337A CN 113889190 B CN113889190 B CN 113889190B
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calf
diarrhea
abundance
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CN113889190A (en
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王佳堃
陈宏伟
杨斌
黄开朗
柳娅璐
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Zhejiang University ZJU
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Abstract

本发明提供一种基于肠道微生物信息预测犊牛腹泻抗性的方法，具体包括以下步骤：采集可提取微生物群DNA的犊牛粪便样品；检测犊牛粪便样品中微生物群各微生物的存在和丰度，计算微生物群的互作强度指数以及不同组别的总丰度；根据检测信息和计算信息构建随机森林机器学习模型；根据受试犊牛粪便样品中微生物群的互作强度指数、不同组别的总丰度和随机森林机器学习模型预测受试犊牛腹泻抗性。本发明可以有效预测犊牛对腹泻的抗性，具有较高的准确度、灵敏度和特异性，采用本发明的方法在初生后利用粪便微生物评估犊牛的腹泻抗性，有助于犊牛群体选育，减少抗生素预防和治疗腹泻带来的负面作用。The present invention provides a method for predicting calf diarrhea resistance based on intestinal microbial information, specifically comprising the following steps: collecting calf feces samples from which microbial DNA can be extracted; detecting the presence and abundance of each microorganism of the microbial community in the calf feces sample, calculating the interaction intensity index of the microbial community and the total abundance of different groups; constructing a random forest machine learning model based on the detection information and the calculation information; predicting the diarrhea resistance of the tested calf based on the interaction intensity index of the microbial community in the feces sample of the tested calf, the total abundance of different groups and the random forest machine learning model. The present invention can effectively predict the resistance of calves to diarrhea, with high accuracy, sensitivity and specificity. The method of the present invention is used to evaluate the diarrhea resistance of calves using fecal microorganisms after birth, which is helpful for the selection and breeding of calf groups and reduces the negative effects of antibiotics in preventing and treating diarrhea.

Description

Method for predicting calf diarrhea resistance based on intestinal microbial information
Technical Field
The invention relates to a method for predicting calf diarrhea resistance, in particular to a method for predicting calf diarrhea resistance based on intestinal microbial information.
Background
Ruminants such as cattle and sheep can utilize cellulose which is not digested and absorbed by human beings to provide meat, milk and other products for human beings, and are grain-saving livestock. With the increase of the demand of people for meat and milk, the number of cattle and sheep cultivated in China rises year by year.
Feeding calves and lambs is an extremely important part of ruminant production. The health status of young ages directly influences the production level of cattle and sheep after adults, and further influences the economic benefit of pastures. However, the health condition of calves and lambs in China is cause anxiety, the diarrhea rate is nearly one hundred percent, and 56.6 percent of diarrhea can cause death of the calves and the lambs. For preventing and treating diarrhea, the calf is continuously fed with antibiotics such as tetracyclines and beta lactams before 5-8 days of age after feeding colostrum, but diarrhea (9-15 days of age) tends to occur intensively after stopping the administration of the antibiotics.
The occurrence of calf diarrhea is divided into pathogenic diarrhea and non-pathogenic diarrhea, and antibiotics have good treatment effect on pathogenic diarrhea, but after the pathogenic diarrhea is inhibited/treated by the antibiotics, the development of intestinal microbiota is retarded, the disorder of intestinal microbiota is caused, and the susceptibility of subsequent diseases is increased. Intestinal microorganisms play an important role in protecting against pathogenic bacteria invasion and maintaining intestinal health. Therefore, after the calves are born, the diarrhea resistance of the calves is evaluated by utilizing fecal microorganisms, which is helpful for breeding the calves, and reduces the negative effects caused by antibiotic prevention and treatment of diarrhea.
Disclosure of Invention
The invention aims to provide a method for predicting calf diarrhea resistance based on intestinal microbial information, which solves the problems in the background technology.
The technical scheme adopted for solving the technical problems is as follows:
a method for predicting calf diarrhea resistance based on intestinal microbial information, which specifically comprises the following steps:
s1, collecting a calf feces sample capable of extracting microbiota DNA;
S2, detecting the existence and the abundance of each microorganism of a microorganism group in a calf feces sample, wherein the microorganism group comprises 34 microorganisms with nucleotide sequences of SEQ ID No.1 to SEQ ID No.34 and respectively belonging to a non-resistant group and a resistant group, and calculating the interaction intensity index of the microorganism group and the total abundance of different groups;
s3, constructing a random forest machine learning model according to the detection information and the calculation information;
S4, setting an interaction intensity index threshold and total abundance thresholds of different groups, and predicting diarrhea resistance of the tested calf according to the interaction intensity index of the microbiota in the tested calf feces sample, the total abundance of different groups and a random forest machine learning model.
Preferably, in the step S1, the method for extracting the microbiota DNA is a phenol chloroform extraction method.
Preferably, in the step S2, the method for detecting the existence and abundance of a specific ASV comprises a 16S rDNA high-throughput sequencing method and/or a fluorescent quantitative PCR method, wherein the fragment area of the 16S rDNA high-throughput sequencing method is a V3-V4 region, and the amplification primers are 341F (5 '-CCTAYGGGRBGCASCAG-3') and 806R (5 '-GGACTACNNGGGTATCTAAT-3').
Preferably, in the 16S rDNA high throughput sequencing method, data analysis is performed in an R environment, and sequencing raw data is processed through DADA2 and Phyloseq R packets.
Preferably, in the step S2, the interaction strength index is calculated by the following formula:
Y=βaXASVa+βbXASVb+βcXASVc+...+βxXASVx,
where Y is the interaction intensity index, X ASVx is the relative abundance of resistance group ASVx, and β x is the interaction intensity coefficient of the corresponding ASVx.
Preferably, in the step S3, the construction of the random forest machine learning model includes the following steps:
s3.1, evaluating calf diarrhea according to the appearance of the calf feces sample;
s3.2, constructing a random forest machine learning model by taking detection information and calculation information as training sets and calf diarrhea condition as an indication;
S3.3, evaluating the accuracy of the random forest machine learning model through the working characteristic curve of the test subject, and obtaining the effective random forest machine learning model after the accuracy reaches the standard.
Preferably, in the step S3.1, the calf feces sample appearance includes that the feces sample is normal, the feces sample is softer and not formed, the feces sample is water-sample and mucus is attached to blood silk, the calf diarrhea condition is that diarrhea does not occur when the feces sample is normal and the feces sample is softer and not formed, and the calf diarrhea condition is that diarrhea occurs when the feces sample is water-sample and mucus is attached to blood silk.
Preferably, in the step S4, the threshold value of the interaction intensity index is set to be that calves have diarrhea resistance when the interaction intensity index is higher than 1.26, calves do not have diarrhea resistance when the interaction intensity index is lower than 0.03, and the threshold value of the total abundance of different groups is set to be that calves have diarrhea resistance when the total abundance of the resistant group is higher than 18.71% and the total abundance of the non-resistant group is lower than 17.5%, and calves do not have diarrhea resistance when the total abundance of the resistant group is lower than 2.4% and the total abundance of the non-resistant group is higher than 51.32%.
The beneficial effects of the invention are as follows:
the invention is based on the existence, abundance, interaction intensity and the tight connection between the specific species of microorganisms of calf fecal microbiota and calf diarrhea resistance, detects and analyzes the existence, abundance, interaction intensity and the like of the specific species of microorganisms, builds a random forest machine learning model on the basis, can effectively predict the calf resistance to diarrhea, has higher accuracy, sensitivity and specificity, adopts fecal microorganisms to evaluate the calf diarrhea resistance after the birth, is beneficial to calf population breeding, and reduces the negative effects caused by antibiotic prevention and diarrhea treatment.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required to be used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only embodiments of the present invention, and that other drawings can be obtained according to the provided drawings without inventive effort for a person skilled in the art.
Figure 1 is a ROC graph of the random forest machine learning model of the present invention,
FIG. 2 shows the results of the prediction of calves with diarrhea resistance under three conditions in the application example.
Detailed Description
The technical scheme of the invention is further specifically described below through specific embodiments and with reference to the accompanying drawings. It should be understood that the practice of the invention is not limited to the following examples, but is intended to be within the scope of the invention in any form and/or modification thereof.
In the present invention, unless otherwise specified, all parts and percentages are by weight, and the equipment, materials, etc. used are commercially available or are conventional in the art. The methods in the following examples are conventional in the art unless otherwise specified. The components and devices in the following examples are, unless otherwise indicated, all those components and devices known to those skilled in the art, and their structures and principles are known to those skilled in the art from technical manuals or by routine experimentation.
Example 1
A method for predicting calf diarrhea resistance based on intestinal microbial information, which specifically comprises the following steps:
s1, collecting a calf feces sample capable of extracting microbiota DNA;
S2, detecting the existence and the abundance of each microorganism of a microorganism group in a calf feces sample, wherein the microorganism group comprises 34 microorganisms with nucleotide sequences of SEQ ID No.1 to SEQ ID No.34 and respectively belonging to a non-resistant group and a resistant group, and calculating the interaction intensity index of the microorganism group and the total abundance of different groups;
s3, constructing a random forest machine learning model according to the detection information and the calculation information;
S4, setting an interaction intensity index threshold and total abundance thresholds of different groups, and predicting diarrhea resistance of the tested calf according to the interaction intensity index of the microbiota in the tested calf feces sample, the total abundance of different groups and a random forest machine learning model.
TABLE 1 microorganism numbering and nucleotide sequence thereof
Example 2
A method for predicting calf diarrhea resistance based on intestinal microbial information, which specifically comprises the following steps:
s1, collecting a calf feces sample capable of extracting microbiota DNA;
S2, detecting the existence and abundance of each microorganism of a microorganism group in a calf feces sample, wherein the microorganism group comprises 34 numbered ASV1 to ASV34 with nucleotide sequences shown in table 1 as SEQ ID No.1 to SEQ ID No.34 and respectively belongs to a non-resistant group and a resistant group, and calculating the interaction intensity index of the microorganism group and the total abundance of different groups;
s3, constructing a random forest machine learning model according to the detection information and the calculation information, wherein the method comprises the following steps:
S3.1, evaluating calf diarrhea according to the appearance of a calf feces sample, wherein the calf feces sample appearance comprises normal feces sample, softer and unshaped feces sample, water sample of the feces sample and blood silk attached to mucus, the calf diarrhea is not diarrhea when the feces sample is normal and the feces sample is softer and unshaped, and the calf diarrhea is diarrhea when the feces sample is water sample and the blood silk attached to mucus;
s3.2, constructing a random forest machine learning model by taking detection information and calculation information as training sets and calf diarrhea condition as an indication;
s3.3, evaluating the accuracy of the random forest machine learning model through the working characteristic curve of the test subject, and obtaining an effective random forest machine learning model after the accuracy reaches the standard;
S4, setting an interaction intensity index threshold and a total abundance threshold of different groups, wherein the interaction intensity index threshold is set to be that calves have diarrhea resistance when the interaction intensity index is higher than 1.26, calves do not have diarrhea resistance when the interaction intensity index is lower than 0.03, the total abundance threshold of different groups is set to be that calves have diarrhea resistance when the total abundance of the resistant group is higher than 18.71% and the total abundance of the non-resistant group is lower than 17.5%, calves do not have diarrhea resistance when the total abundance of the resistant group is lower than 2.4% and the total abundance of the non-resistant group is higher than 51.32%, and the diarrhea resistance of the test calves is predicted according to the interaction intensity index of microbiota in a test calf feces sample, the total abundance of different groups and a random forest machine learning model.
In step S1, the method for extracting the microbiota DNA is phenol chloroform extraction.
In step S2, the method for detecting the existence and abundance of a specific ASV comprises a 16S rDNA high-throughput sequencing method and/or a fluorescent quantitative PCR method, wherein the fragment area of the 16S rDNA high-throughput sequencing method is a V3-V4 region, and the amplification primers are 341F (5 '-CCTAYGGGRBGCASCAG-3') and 806R (5 '-GGACTACNNGGGTATCTAAT-3'). In the 16S rDNA high throughput sequencing method, data analysis was performed in the R environment and the sequencing raw data was processed through DADA2 and Phyloseq R packets.
The interaction strength index in step S2 is calculated using the following formula:
Y=βaXASVa+βbXASVb+βcXASVc+...+βxXASVx
where Y is the interaction intensity index, X ASVx is the relative abundance of resistance group ASVx, and β x is the interaction intensity coefficient of the corresponding ASVx.
Verification of accuracy of application case prediction model
1. The test method verifies that 43 calves of the healthy Holstein mother calf in the early lactation period are selected, and the feeding of the calves is carried out according to the pasture feeding procedure without intervention. Calves are fed by an automatic feeding system, and the total milk feeding quantity gradually increases from 7L/day at 8 days to 7.6L/day at 18 days. From 8 days old, the calf collects the fecal sample to 18 days old, after the sample collection, the calf is placed in a 2mL sterile centrifuge tube and immediately frozen in liquid nitrogen, and then the calf is transferred to a laboratory-80 ℃ ultralow temperature refrigerator for long-term storage for subsequent analysis. During stool sample collection, daily stool scores were recorded. And evaluating calf diarrhea condition according to the fecal scores, and taking the calf diarrhea condition as a conventional evaluation standard of calf diarrhea resistance.
After the fecal sample is collected, sample DNA is extracted, and V3-V4 regions are amplified through 341F and 806R primers, and 16S rDNA high-throughput sequencing and sequencing data processing are performed. The specific steps include (1) quality control of the sequences, removal of Barcode and primer fragments, and low quality (containing N, expected errors greater than 2 and mass fractions less than 2) and over-short sequences. (2) A noise-reduced difference sequence (sequence variants) is obtained via a sample-and-extrapolate algorithm (SAMPLE INFERENCE algorithm). And splicing the forward sequence with the reverse sequence (minimum of 20 bases overlap) to obtain the 'contig' sequence. (3) The chimeric (chimeras) sequence was removed to obtain ASVs (amplicon sequence variants) abundance table. (4) By comparing the SILVA database (version 132), a ASVs species annotation table is obtained. (5) ASV abundance and species annotation tables were imported into the Phyloseq package for subsequent statistical analysis. (6) The archaea was removed by Phyloseq bags and the sequence was only once present (singleton) and the sequence was leveled to obtain the same sequencing depth.
Verification of the predictive model is performed in the R environment. Target ASVs (34) in the obtained ASV abundance table were screened out according to the ASV sequences given in table 1 above. The sum of the abundance of the non-resistant and resistant groups of 34 target ASVs in each sample was calculated, and the interaction strength index for each sample was calculated according to the interaction strength coefficient and interaction strength calculation formula given in table 1. And comparing with the corresponding abundance threshold and interaction intensity threshold to obtain a first condition and a second condition for predicting calf diarrhea resistance. And substituting the 34 target ASV abundance tables as a test data set into the random forest classification model through predict functions in a stats R package to obtain a condition III of predicting calf diarrhea resistance by the random forest model. And combining three conditions of the prediction model, and comparing the prediction result with a conventional evaluation standard to verify the accuracy of the model as a final result of predicting calf diarrhea.
2. The test result is predicted by a model, and out of 261 samples of 43 calves, 123 stool samples in total meet three conditions of an abundance threshold, an interaction intensity index threshold and random forest prediction, and the calves are judged to have diarrhea resistance (see figure 2), namely the calves are predicted not to have diarrhea at the time corresponding to the stool sample collection. Compared with the conventional diarrhea results (see Table 2), 92 samples predicted to have diarrhea resistance, calves not have diarrhea at the corresponding age of day, the accuracy is 74.80%, 106 samples predicted to have no diarrhea at the corresponding age of day, the accuracy is 76.81%, and the overall accuracy of the prediction model is 75.81%. The results show that the diarrhea resistance of calves can be effectively predicted through 34 ASVs selected from the first table, and the calves have higher accuracy, sensitivity and specificity.
Table 2 comparison of model predictions with conventional standards
In this specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, so that the same or similar parts between the embodiments are referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant points refer to the description of the method section.
The method for predicting calf diarrhea resistance based on intestinal microbial information provided by the invention is described in detail. The principles and embodiments of the present invention have been described herein with reference to specific examples, the description of which is intended only to facilitate an understanding of the method of the present invention and its core ideas. It should be noted that it will be apparent to those skilled in the art that various modifications and adaptations of the invention can be made without departing from the principles of the invention and these modifications and adaptations are intended to be within the scope of the invention as defined in the following claims.
Sequence listing
<110> University of Zhejiang
<120> Method for predicting calf diarrhea resistance based on intestinal microbial information
<130> ZJDX202110
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agcgacggct aaatacgtgc cagcagccgc ggtaatacgt atgtcgcaag cgttatccgg 180
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agcgacggct aaatacgtgc cagcagccgc ggtaatacgt atgtcgcaag cgttatccgg 180
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tcaactccgt attgcgttgg aaactgtcaa actagagtac tggagaggtg ggcggaacta 300
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<210> 3
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<400> 3
tgaggaatat tggtcaatgg acgggagtct gaaccagcca agtagcgtgc aggatgacgg 60
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aggtacccta tgaataagga ccggctaatt ccgtgccagc agccgcggta atacggaagg 180
tccgggcgtt atccggattt attgggttta aagggagcgt aggccgtctt ataagcgtgt 240
tgtgaaatgt cggggctcaa cctgggcatt gcagcgcgaa ctgtgagact tgagtgcgca 300
ggaagtaggc ggaattcgtc gtgtagcggt gaaatgctta gatatgacga agaactccga 360
ttgcgaaggc agcctgctgt agcgcaactg acgctgaagc tcgaaagcgt gggtatcgaa 420
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<210> 4
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agcgacggct aaatacgtgc cagcagccgc ggtaatacgt atgtcgcaag cgttatccgg 180
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<210> 5
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acggctaact acgtgccagc agccgcggta atacgtaggt ggcgagcgtt atccggattt 180
actgggcgta aagggagcgt aggcggatga ttaagtggga tgtgaaatac ccgggctcaa 240
cttgggtgct gcattccaaa ctggttatct agagtgcagg agaggagagt ggaattccta 300
gtgtagcggt gaaatgcgta gagattagga agaacaccag tggcgaaggc gactctctgg 360
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<400> 6
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ccctccgggt tgtaaactgc ttttagttgg gaataaagtg cagctcgtga gctgttttgt 120
atgtaccatc agaaaaagga ccggctaatt ccgtgccagc agccgcggta atacggaagg 180
tccgggcgtt atccggattt attgggttta aagggagcgc aggcggactc ttaagtcagt 240
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cagggatgct ggaattcatg gtgtagcggt gaaatgctca gatatcatga agaactccga 360
tcgcgaaggc aggtatccgg ggtgcaactg acgctgaggc tcgaaagtgc gggtatcaaa 420
cagg 424
<210> 7
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<212> DNA
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<400> 7
tggggaatat tggacaatgg accaaaagtc tgatccagca attctgtgtg cacgatgaag 60
tttttcggaa tgtaaagtgc tttcagttgg gacgaagtaa gtgacggtac caacagaaga 120
agcgacggct aaatacgtgc cagcagccgc ggtaatacgt atgtcgcaag cgttatccgg 180
atttattggg cgtaaagcgc gtctaggcgg tttggtaagt ctgatgtgaa aatacggggc 240
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caagtgtaga ggtgaaattc gtagatattt gtaggaatgc cgatggggaa gccagcccac 360
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<210> 8
<211> 407
<212> DNA
<213> ASV17
<400> 8
tggggaatat tggacaatgg accaaaagtc tgatccagca attctgtgtg cacgatgaag 60
tttttcggaa tgtaaagtgc tttcagttgg gacgaagtaa gtgacggtac cagcagaaga 120
agcgacggct aaatacgtgc cagcagccgc ggtaatacgt atgtcgcaag cgttatccgg 180
atttattggg cgtaaagcgc gtctaggcgg tttggtaagt ctgatgtgaa aatgcggggc 240
tcaactccgt attgcgttgg aaactgccaa actagagtac tggagaggtg ggcggaacta 300
caagtgtaga ggtgaaattc gtagatattt gtaggaatgc caatggggaa gccagcccac 360
tggacagata ctgacgctaa agcgcgaaag cgtgggtagc aaacagg 407
<210> 9
<211> 424
<212> DNA
<213> ASV24
<400> 9
tgaggaatat tggtcaatgg acgcaagtct gaaccagcca agtagcgtgc aggacgacgg 60
ccctccgggt tgtaaactgc ttttagttgg gaataaagtg cagctcgtga gctgttttgt 120
atgtaccatc agaaaaagga ccggctaatt ccgtgccagc agccgcggta atacggaagg 180
tccgggcgtt atccggattt attgggttta aagggagcgc aggcggactc ttaagtcagt 240
tgtgaaatac ggcggctcaa ccgtcggact gcagttgata ctgggggtct tgagtgcaca 300
cagggatgct ggaattcatg gtgtagcggt gaaatgctca gatatcatga agaactccga 360
tcgcgaaggc aggtatccgg ggtgcaactg acgctgaggc tcgaaagtgc gggtatcaaa 420
cagg 424
<210> 10
<211> 424
<212> DNA
<213> ASV25
<400> 10
tgaggaatat tggtcaatgg gcgagagcct gaaccagcca agtagcgtga aggatgaagg 60
ttctatggat tgtaaacttc ttttatacgg gaataaaacc tcccacgtgt gggagcttgt 120
atgtaccgta tgaataagca tcggctaact ccgtgccagc agccgcggta atacggagga 180
tgcgagcgtt atccggattt attgggttta aagggagcgc agacgggaga ttaagtcagc 240
tgtgaaagtt tgcggctcaa ccgtaaaatt gcagttgata ctggtttcct tgagtgcggt 300
tgaggtgtgc ggaattcgtg gtgtagcggt gaaatgctta gatatcacga agaaccccga 360
ttgcgaaggc agcacactaa gccgtaactg acgttcatgc tcgaaagtgt gggtatcaaa 420
cagg 424
<210> 11
<211> 424
<212> DNA
<213> ASV26
<400> 11
tgaggaatat tggtcaatgg acgagagtct gaaccagcca agtagcgtgc aggacgacgg 60
ccctatgggt tgtaaactgc ttttataggg ggataaagtg tgccacgtgt ggcatattgc 120
aggtacccta tgaataagga ccggctaatt ccgtgccagc agccgcggta atacggaagg 180
tccgggcgtt atccggattt attgggttta aagggagcgt aggccgtctt ataagcgtgt 240
tgtgaaatgt cggggctcaa cctgggcatt gcagcgcgaa ctgtgagact tgagtgcgca 300
ggaagtaggc ggaattcgtc gtgtagcggt gaaatgctta gatatgacga agaactccga 360
ttgcgaaggc agcctgctgt agcgcaactg acgctgaagc tcgaaagcgt gggtatcgaa 420
cagg 424
<210> 12
<211> 424
<212> DNA
<213> ASV28
<400> 12
tgaggaatat tggtcaatgg acgcaagtct gaaccagcca agtagcgtgc aggatgacgg 60
ccctccgggt tgtaaactgc ttttagttgg gaataaagtg cagctcgtga gctgttttgt 120
atgtaccatc agaaaaagga ccggctaatt ccgtgccagc agccgcggta atacggaagg 180
tccgggcgtt atccggattt attgggttta aagggagcgc aggcggactc ttaagtcagt 240
tgtgaaatac ggcggctcaa ccgtcggact gcagttgata ctgggagtct tgagtgcaca 300
cagggatgct ggaattcatg gtgtagcggt gaaatgctca gatatcatga agaactccga 360
tcgcgaaggc aggtatccgg ggtgcaactg acgctgaggc tcgaaagtgc gggtatcaaa 420
cagg 424
<210> 13
<211> 424
<212> DNA
<213> ASV29
<400> 13
tgaggaatat tggtcaatgg acgcaagtct gaaccagcca agtagcgtgc aggatgacgg 60
ccctccgggt tgtaaactgc ttttagttgg gaataaagtg cagctcgtga gctgttttgt 120
atgtaccatc agaaaaagga ccggctaatt ccgtgccagc agccgcggta atacggaagg 180
tccgggcgtt atccggattt attgggttta aagggagcgc aggcggactc ttaagtcagt 240
tgtgaaatac ggcggctcaa ccgtcggact gcagttgata ctgggggtct tgagtgcaca 300
cagggatgct ggaattcatg gtgtagcggt gaaatgctca gatatcatga agaactccga 360
tcgcgaaggc aggtatccgg ggtgcaactg acgctgaggc tcgaaagtgc gggtatcaaa 420
cagg 424
<210> 14
<211> 424
<212> DNA
<213> ASV30
<400> 14
tgaggaatat tggtcaatgg acgcaagtct gaaccagcca agtagcgtgc aggatgacgg 60
ccctccgggt tgtaaactgc ttttagttgg gaataaagtg cagctcgtga gctgttttgt 120
atgtaccatc agaaaaagga ccggctaatt ccgtgccagc agccgcggta atacggaagg 180
tccgggcgtt atccggattt attgggttta aagggagcgc aggcggactc ttaagtcagt 240
tgtgaaatac ggcggctcaa ccgtcgaact gcagttgata ctgggagtct tgagtgcaca 300
cagggatgct ggaattcatg gtgtagcggt gaaatgctca gatatcatga agaactccga 360
tcgcgaaggc aggtatccgg ggtgcaactg acgctgaggc tcgaaagtgc gggtatcaaa 420
cagg 424
<210> 15
<211> 424
<212> DNA
<213> ASV34
<400> 15
tgaggaatat tggtcaatgg gcgagagcct gaaccagcca agtagcgtga aggatgaagg 60
ttctatggat tgtaaacttc ttttatacgg gaataaaacc ttccacgtgt gggagcttgt 120
atgtaccgta tgaataagca tcggctaact ccgtgccagc agccgcggta atacggagga 180
tgcgagcgtt atccggattt attgggttta aagggagcgc agacgggaga ttaagtcagc 240
tgtgaaagtt tgcggctcaa ccgtaaaatt gcagttgata ctggtttcct tgagtgcggt 300
tgaggtgtgc ggaattcgtg gtgtagcggt gaaatgctta gatatcacga agaaccccga 360
ttgcgaaggc agcacactaa gccgtaactg acgttcatgc tcgaaagtgt gggtatcaaa 420
cagg 424
<210> 16
<211> 424
<212> DNA
<213> ASV38
<400> 16
tgaggaatat tggtcaatgg acgagagtct gaaccagcca agtagcgtga aggatgaagg 60
tcctacggat tgtaaacttc ttttataagg gaataaaccc tcccacgtgt gggagcttgt 120
atgtacctta tgaataagca tcggctaact ccgtgccagc agccgcggta atacggagga 180
tgcgagcgtt atccggattt attgggttta aagggagcgc agacgggtcg ttaagtcagc 240
tgtgaaagtt tggggctcaa ccttaaaatt gcagttgata ctggcgtcct tgagtgcggt 300
tgaggtgtgc ggaattcgtg gtgtagcggt gaaatgctta gatatcacga agaactccga 360
ttgcgaaggc agcacactaa gccgtaactg acgttcatgc tcgaaagtgt gggtatcaaa 420
cagg 424
<210> 17
<211> 424
<212> DNA
<213> ASV40
<400> 17
tgaggaatat tggtcaatgg gcgagagcct gaaccagcca agtagcgtga aggatgaagg 60
ttctatggat tgtaaacttc ttttatacgg gaataaaacc tcccacgtgt gggagtttgt 120
atgtaccgta tgaataagca tcggctaact ccgtgccagc agccgcggta atacggagga 180
tgcgagcgtt atccggattt attgggttta aagggagcgc agacgggaga ttaagtcagc 240
tgtgaaagtt tgcggctcaa ccgtaaaatt gcagttgata ctggtttcct tgagtgcggt 300
tgaggtgtgc ggaattcgtg gtgtagcggt gaaatgctta gatatcacga agaaccccga 360
ttgcgaaggc agcacactaa gccgtaactg acgttcatgc tcgaaagtgt gggtatcaaa 420
cagg 424
<210> 18
<211> 424
<212> DNA
<213> ASV43
<400> 18
tgaggaatat tggtcaatgg acgagagtct gaaccagcca agtagcgtgc aggaagacgg 60
ccctatgggt tgtaaactgc ttttataagg gaataaagtg agtctcgtga gactttttgc 120
atgtacctta tgaataagga ccggctaatt ccgtgccagc agccgcggta atacggaagg 180
tccgggcgtt atccggattt attgggttta aagggagcgt aggccggaga ttaagcgtgt 240
tgtgaaatgt agacgctcaa cgtctgcact gcagcgcgaa ctggtttcct tgagtacgca 300
caaagtgggc ggaattcgtg gtgtagcggt gaaatgctta gatatcacga agaactccga 360
ttgcgaaggc agctcactgg agcgcaactg acgctgaagc tcgaaagtgc gggtatcgaa 420
cagg 424
<210> 19
<211> 404
<212> DNA
<213> ASV48
<400> 19
tggggaatat tgcacaatgg gggaaaccct gatgcagcaa cgccgcgtga gtgatgacgg 60
ccttcgggtt gtaaagctct gtctttgggg acgataatga cggtacccaa ggaggaagcc 120
acggctaact acgtgccagc agccgcggta atacgtaggt ggcaagcgtt gtccggattt 180
actgggcgta aagggagcgt aggcggattt ttaagtggga tgtgaaatac ccgggctcaa 240
cctgggtgct gcattccaaa ctggaaatct agagtgcagg aggggaaagt ggaattccta 300
gtgtagcggt gaaatgcgta gagattagga agaacaccag tggcgaaggc gactttctgg 360
actgtaactg acgctgaggc tcgaaagcgt ggggagcaaa cagg 404
<210> 20
<211> 428
<212> DNA
<213> ASV50
<400> 20
tggggaatct tccgcaatgg gcgcaagcct gacggagcaa cgccgcgtga gtgaagaagg 60
ttttcggatc gtaaagctct gttgagaggg acgagaggca aggctaggaa atgagctttg 120
taggacggta cctttcgagg aagccacggc taactacgtg ccagcagccg cggtaatacg 180
taggtggcga gcgttgtccg gaattattgg gcgtaaaggg agcgcaggtg ggaaagtaag 240
tcagtcttaa aagtgcgggg ctcaaccccg tgaggggatt gaaactactt ttcttgagtg 300
caggagagga aagcggaatt cctagtgtag cggtgaaatg cgtagatatt aggaggaaca 360
ccagtggcga aggcggcttt ctggactgta actgacactg aggctcgaaa gccaggggag 420
cgaacggg 428
<210> 21
<211> 424
<212> DNA
<213> ASV52
<400> 21
tgaggaatat tggtcaatgg gcgagagcct gaaccagcca agtagcgtgc aggaagacgg 60
ccctatgggt tgtaaactgc ttttataagg gaataaagtg agtctcgtga gactttttgc 120
atgtacctta tgaataagga ccggctaatt ccgtgccagc agccgcggta atacggaagg 180
tccgggcgtt atccggattt attgggttta aagggagcgt aggccggaga ttaagcgtgt 240
tgtgaaatgt agacgctcaa cgtctgcact gcagcgcgaa ctggtttcct tgagtacgca 300
caaagtgggc ggaattcgtg gtgtagcggt gaaatgctta gatatcacga agaactccga 360
ttgcgaaggc agctcactgg agcgcaactg acgctgaagc tcgaaagtgc gggtatcgaa 420
cagg 424
<210> 22
<211> 404
<212> DNA
<213> ASV57
<400> 22
tggggaatat tgcacaatgg gggaaaccct gatgcagcaa cgccgcgtga gtgatgacgg 60
ccttcgggtt gtaaagctct gtcttcaggg acgataatga cggtacctga ggaggaagcc 120
acggctaact acgtgccagc agccgcggta atacgtaggt ggcgagcgtt gtccggattt 180
actgggcgta aagggagcgt aggcggactt ttaagtgaga tgtgaaatac ccgggctcaa 240
cttgggtgct gcatttcaaa ctggaagtct agagtgcagg agaggagaat ggaattccta 300
gtgtagcggt gaaatgcgta gagattagga agaacaccag tggcgaaggc gattctctgg 360
actgtaactg acgctgaggc tcgaaagcgt ggggagcaaa cagg 404
<210> 23
<211> 404
<212> DNA
<213> ASV60
<400> 23
tggggaatat tgcacaatgg gggaaaccct gatgcagcaa cgccgcgtga gtgatgacgg 60
tcttcggatt gtaaagctct gtctttaggg acgataatga cggtacctaa ggaggaagcc 120
acggctaact acgtgccagc agccgcggta atacgtaggt ggcaagcgtt gtccggattt 180
actgggcgta aagggagcgt aggtggatat ttaagtggga tgtgaaatac ccgggcttaa 240
cctgggtgct gcattccaaa ctggatatct agagtgcagg agaggaaagg agaattccta 300
gtgtagcggt gaaatgcgta gagattagga agaataccag tggcgaaggc gcctttctgg 360
actgtaactg acactgaggc tcgaaagcgt ggggagcaaa cagg 404
<210> 24
<211> 404
<212> DNA
<213> ASV63
<400> 24
tggggaatat tgcacaatgg gggaaaccct gatgcagcaa cgccgcgtga gtgatgaagg 60
ttttcggatc gtaaagctct gtcttcaggg acgataatga cggtacctga ggaggaagcc 120
acggctaact acgtgccagc agccgcggta atacgtaggt ggcgagcgtt gtccggattt 180
actgggcgta aagggagcgt aggcggattt ttaagtgaga tgtgaaatac ccgggctcaa 240
cttgggtgct gcatttcaaa ctggaagtct agagtgcagg agaggagagt ggaattccta 300
gtgtagcggt gaaatgcgta gagattagga agaacaccag tggcgaaggc gactctctgg 360
actgtaactg acgctgaggc tcgaaagcgt ggggagcaaa cagg 404
<210> 25
<211> 424
<212> DNA
<213> ASV69
<400> 25
tgaggaatat tggtcaatgg acgcaagtct gaaccagcca agtagcgtgc aggatgacgg 60
ccctccgggt tgtaaactgc ttttagttgg gaataaagtg cagctcgtga gctgttttgt 120
atgtaccatc agaaaaagga ccggctaatt ccgtgccagc agccgcggta atacggaagg 180
tccgggcgtt atccggattt attgggttta aagggagcgc aggcggactc ttaagtcagt 240
tgtgaaatac ggcggctcaa ccgtcggact gcagttgata ctgggagtct tgagtgcaca 300
cagggatgct ggaattcatg gtgtagcggt gaaatgctca gatatcatga agaactccaa 360
tcgcgaaggc aggtatccgg ggtgcaactg acgctgaggc tcgaaagtgc gggtatcaaa 420
cagg 424
<210> 26
<211> 404
<212> DNA
<213> ASV87
<400> 26
tggggaatat tgcacaatgg gggaaaccct gatgcagcaa cgccgcgtga gtgatgaagg 60
ttttcggatc gtaaagctct gtctttgggg aagataatga cggtacccaa ggaggaagcc 120
acggctaact acgtgccagc agccgcggta atacgtaggt ggcgagcgtt atccggattt 180
actgggcgta aagggagcgt aggcggataa ttaagtggga tgtgaaatac ccgggctcaa 240
cttgggtgct gcattccaaa ctggttatct agagtgcagg agaggagagt ggaattccta 300
gtgtagcggt gaaatgcgta gagattagga agaacaccag tggcgaaggc gactctctgg 360
actgtaactg acgctgaggc tcgaaagcgt ggggagcaaa cagg 404
<210> 27
<211> 424
<212> DNA
<213> ASV89
<400> 27
tgaggaatat tggtcaatgg gcgagagcct gaaccagcca agtagcgtgc aggatgacgg 60
ccctatgggt tgtaaactgc ttttataagg gaataaagtg agagtcgtga ctctttttgc 120
atgtacctta tgaataagga ccggctaatt ccgtgccagc agccgcggta atacggaagg 180
tccgggcgtt atccggattt attgggttta aagggagcgt aggccggaga ttaagcgtgt 240
tgtgaaatgt agatgctcaa catctgaact gcagcgcgaa ctggtttcct tgagtacgca 300
caaagtgggc ggaattcgtg gtgtagcggt gaaatgctta gatatcacga agaactccga 360
ttgcgaaggc agctcactgg agcgcaactg acgctgaagc tcgaaagtgc gggtatcgaa 420
cagg 424
<210> 28
<211> 424
<212> DNA
<213> ASV91
<400> 28
tgaggaatat tggtcaatgg acgagagtct gaaccagcca agtagcgtgc aggatgacgg 60
ccctatgggt tgtaaactgc ttttataagg gaataaagtg agtctcgtga gactttttgc 120
atgtacctta tgaataagga ccggctaatt ccgtgccagc agccgcggta atacggaagg 180
tccgggcgtt atccggattt attgggttta aagggagcgt aggccggaga ttaagcgtgt 240
tgtgaaatgt agacgctcaa cgtctgcact gcagcgcgaa ctggtttcct tgagtacgca 300
caaagtgggc ggaattcgtg gtgtagcggt gaaatgctta gatatcacga agaactccga 360
ttgcgaaggc agctcactgg agcgcaactg acgctgaagc tcgaaagtgc gggtatcgaa 420
cagg 424
<210> 29
<211> 424
<212> DNA
<213> ASV92
<400> 29
tgaggaatat tggtcaatgg acgagagtct gaaccagcca agtagcgtga aggatgaagg 60
tcctacggat tgtaaacttc ttttataagg gaataaaccc tcccacgtgt gggagcttgt 120
atgtaccttg tgaataagca tcggctaact ccgtgccagc agccgcggta atacggagga 180
tgcgagcgtt atccggattt attgggttta aagggagcgc agacgggtcg ttaagtcagc 240
tgtgaaagtt tggggctcaa ccttaaaatt gcagttgata ctggcgtcct tgagtgcggt 300
tgaggtgtgc ggaattcgtg gtgtagcggt gaaatgctta gatatcacga agaactccga 360
ttgcgaaggc agcacactaa tccgtaactg acgttcatgc tcgaaagtgt gggtatcaaa 420
cagg 424
<210> 30
<211> 404
<212> DNA
<213> ASV114
<400> 30
tggggaatat tgcacaatgg gggaaaccct gatgcagcaa cgccgcgtga gtgatgaagg 60
ttttcggatt gtaaagctct gtctttgggg aagataatga cggtacccaa ggaggaagcc 120
acggctaact acgtgccagc agccgcggta atacgtaggt ggcgagcgtt atccggattt 180
actgggcgta aagggagcgt aggcggatga ttaagtggga tgtgaaatac ccgggctcaa 240
cttgggtgct gcattccaaa ctggttatct agagtgcagg agaggagagt ggaattccta 300
gtgtagcggt gaaatgcgta gagattagga agaacaccag tggcgaaggc gactctctgg 360
actgtaactg acgctgaggc tcgaaagcgt ggggagcaaa cagg 404
<210> 31
<211> 408
<212> DNA
<213> ASV151
<400> 31
tggggaatat tggacaatgg accaaaagtc tgatccagca attctgtgtg cacgatgaag 60
tttttcggaa tgtaaagtgc tttcagttgg gacgaagtaa gtgacggtac caacagaaga 120
agcgacggct aaatacgtgc cagcagccgc ggtaatacgg agggtgcaag cgttaatcgg 180
aattactggg cgtaaagcgc acgcaggcgg tttgttaagt cagatgtgaa atccccgggc 240
tcaacctggg aactgcatct gatactggca agcttgagtc tcgtagaggg gggtagaatt 300
ccaggtgtag cggtgaaatg cgtagagatc tggaggaata ccggtggcga aggcggcccc 360
ctggacgaag actgacgctc aggtgcgaaa gcgtggggag caaacagg 408
<210> 32
<211> 404
<212> DNA
<213> ASV183
<400> 32
tggggaatat tgcacaatgg gggaaaccct gatgcagcaa cgccgcgtga gtgatgaagg 60
ttttcggatc gtaaagctct gtctttgggg aagataatga cggtacccaa ggaggaagcc 120
acggctaact acgtgccagc agccgcggta atacgtaggt ggcgagcgtt atccggattt 180
actgggcgta aagggagcgt aggcggatga ttaagtggga tgtgaaatac ccgggctcaa 240
cttgggtgct gcattccaaa ctggttatct agagtgcagg agaggagagt ggaattccta 300
gtgtagcggt gaaatgcgta gagatctgga ggaataccgg tggcgaaggc ggccccctgg 360
acgaagactg acgctcaggt gcgaaagcgt ggggagcaaa cagg 404
<210> 33
<211> 404
<212> DNA
<213> ASV196
<400> 33
tggggaatat tgcacaatgg gggaaaccct gatgcagcaa cgccgcgtga gtgatgaagg 60
ttttcggatc gtaaagctct gtctttgggg aagataatga cggtacccaa ggaggaagcc 120
acggctaact acgtgccagc agccgcggta atacggaggg tgcaagcgtt aatcggaatt 180
actgggcgta aagcgcacgc aggcggtttg ttaagtcaga tgtgaaatcc ccgggctcaa 240
cctgggaact gcatctgata ctggcaagct tgagtctcgt agaggggggt agaattccag 300
gtgtagcggt gaaatgcgta gagatctgga ggaataccgg tggcgaaggc ggccccctgg 360
acgaagactg acgctcaggt gcgaaagcgt ggggagcaaa cagg 404
<210> 34
<211> 428
<212> DNA
<213> ASV203
<400> 34
tggggaatat tgcacaatgg gcgcaagcct gatgcagcca tgccgcgtgt atgaagaagg 60
ccttcgggtt gtaaagtact ttcagcgggg aggaagggag taaagttaat acctttgctc 120
attgacgtta cccgcagaag aagcaccggc taactccgtg ccagcagccg cggtaatacg 180
gagggtgcaa gcgttaatcg gaattactgg gcgtaaagcg cgtctaggcg gtttggtaag 240
tctgatgtga aaatgcgggg ctcaactccg tattgcgttg gaaactgtca aactagagta 300
ctggagaggt gggcggaact acaagtgtag aggtgaaatt cgtagatatt tgtaggaatg 360
ccgatgggga agccagccca ctggacagat actgacgcta aagcgcgaaa gcgtgggtag 420
caaacagg 428

Claims (3)

1.一种基于肠道微生物信息预测犊牛腹泻抗性的方法，其特征在于该方法具体包括以下步骤：1. A method for predicting calf diarrhea resistance based on intestinal microbial information, characterized in that the method specifically comprises the following steps: S1. 采集可提取微生物群DNA的犊牛粪便样品；S1. Collect calf fecal samples from which microbiota DNA can be extracted; S2. 检测犊牛粪便样品中微生物群各微生物的存在和丰度，所述微生物群包括34个核苷酸序列为SEQ ID No.1至SEQ ID No.34的ASV且分别归属于非抗性组和抗性组的微生物，计算微生物群的互作强度指数以及不同组别的总丰度；S2. Detecting the presence and abundance of each microorganism of the microbiome in the fecal sample of the calf, wherein the microbiome includes 34 ASVs with nucleotide sequences of SEQ ID No. 1 to SEQ ID No. 34 and microorganisms belonging to the non-resistant group and the resistant group, respectively, and calculating the interaction intensity index of the microbiome and the total abundance of different groups; 检测特定ASV存在和丰度的方法包括16S rDNA高通量测序法和/或荧光定量PCR法，所述16S rDNA高通量测序法的片段区域为V3-V4区，所用扩增引物为核苷酸序列5'-CCTAYGGGRBGCASCAG-3'的341F和核苷酸序列5'- GGACTACNNGGGTATCTAAT-3'的806R；The method for detecting the presence and abundance of a specific ASV includes 16S rDNA high-throughput sequencing and/or fluorescent quantitative PCR, wherein the fragment region of the 16S rDNA high-throughput sequencing is the V3-V4 region, and the amplification primers used are 341F with a nucleotide sequence of 5'-CCTAYGGGRBGCASCAG-3' and 806R with a nucleotide sequence of 5'-GGACTACNNGGGTATCTAAT-3'; 互作强度指数采用以下公式计算得出：The interaction strength index was calculated using the following formula: Y = βaXASVa + βbXASVb + βcXASVc + ... + βxXASVx，Y = β a X ASVa + β b X ASVb + β c X ASVc + ... + β x X ASVx , 式中，Y为互作强度指数，XASVx为抗性组ASVx的相对丰度，βx为相应ASVx的互作强度系数；In the formula, Y is the interaction intensity index, X ASVx is the relative abundance of ASVx in the resistance group, and β x is the interaction intensity coefficient of the corresponding ASVx; S3. 根据检测信息和计算信息构建随机森林机器学习模型，构建随机森林机器学习模型包括以下步骤：S3. Construct a random forest machine learning model based on the detection information and the calculation information. Constructing the random forest machine learning model includes the following steps: S3.1. 根据犊牛粪便样品外观评测犊牛腹泻情况；S3.1. Evaluate calf diarrhea based on the appearance of calf feces samples; 犊牛粪便样品外观包括粪便样品正常、粪便样品较软且不成形、粪便样品呈水样、粪便样品呈水样且黏液附着血丝，The appearance of calf feces samples included normal feces, soft and shapeless feces, watery feces, and watery feces with mucus and blood. 当粪便样品正常和粪便样品较软且不成形时犊牛腹泻情况为未发生腹泻，When the fecal sample is normal and the fecal sample is soft and unformed, the calf is considered to have no diarrhea. 当粪便样品呈水样和粪便样品呈水样且黏液附着血丝时犊牛腹泻情况为发生腹泻；The calf was considered to have diarrhea when the fecal sample was watery and when the fecal sample was watery with blood streaks in the mucus; S3.2. 以检测信息和计算信息为训练集，以犊牛腹泻情况为指征，构建随机森林机器学习模型；S3.2. Using the detection information and calculation information as training sets and the calf diarrhea condition as an indication, a random forest machine learning model was constructed; S3.3. 通过受试者工作特征曲线对随机森林机器学习模型进行准确率评估，准确率达标后获得有效的随机森林机器学习模型；S3.3. The accuracy of the random forest machine learning model is evaluated by the receiver operating characteristic curve. When the accuracy reaches the standard, an effective random forest machine learning model is obtained. S4. 设定互作强度指数阈值和不同组别的总丰度阈值，根据受试犊牛粪便样品中微生物群的互作强度指数、不同组别的总丰度和随机森林机器学习模型预测受试犊牛腹泻抗性；S4. Set the threshold of interaction strength index and total abundance threshold of different groups, and predict the diarrhea resistance of the tested calves based on the interaction strength index of the microbiome in the fecal samples of the tested calves, the total abundance of different groups and the random forest machine learning model; 互作强度指数阈值设定为：当互作强度指数高于1.26时犊牛具有腹泻抗性，当互作强度指数低于0.03时犊牛不具有腹泻抗性；The threshold of the interaction intensity index was set as follows: when the interaction intensity index was higher than 1.26, the calf had diarrhea resistance, and when the interaction intensity index was lower than 0.03, the calf had no diarrhea resistance; 不同组别的总丰度阈值设定为：当抗性组总丰度大于18.71%且非抗性组总丰度小于17.5%时犊牛具有腹泻抗性，当抗性组总丰度小于2.4%且非抗性组总丰度大于51.32%时犊牛不具有腹泻抗性。The total abundance thresholds of different groups were set as follows: when the total abundance of the resistant group was greater than 18.71% and the total abundance of the non-resistant group was less than 17.5%, the calf was resistant to diarrhea; when the total abundance of the resistant group was less than 2.4% and the total abundance of the non-resistant group was greater than 51.32%, the calf was not resistant to diarrhea. 2.根据权利要求1所述的一种基于肠道微生物信息预测犊牛腹泻抗性的方法，其特征在于：所述步骤S1中，提取微生物群DNA的方法为酚氯仿提取法。2. A method for predicting calf diarrhea resistance based on intestinal microbial information according to claim 1, characterized in that: in the step S1, the method for extracting microbial population DNA is phenol-chloroform extraction method. 3. 根据权利要求1所述的一种基于肠道微生物信息预测犊牛腹泻抗性的方法，其特征在于：所述16S rDNA高通量测序法中，数据分析在R环境下进行，通过DADA2和Phyloseq R包对测序原始数据进行处理。3. A method for predicting calf diarrhea resistance based on intestinal microbial information according to claim 1, characterized in that: in the 16S rDNA high-throughput sequencing method, data analysis is performed in the R environment, and the sequencing raw data is processed by DADA2 and Phyloseq R packages.
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