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

Abstract

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

Technical Field

[0001] The present invention relates to a method for predicting calf diarrhea resistance, and in particular to a method for predicting calf diarrhea resistance based on intestinal microorganism information. Background Art

[0002] Ruminants such as cattle and sheep can use cellulose that humans cannot digest and absorb to provide humans with meat, milk and other products, and are grain-saving livestock. As people's demand for meat and milk increases, the number of cattle and sheep raised in my country has increased year by year.

[0003] The feeding of calves and lambs is an extremely important part of ruminant production. The health status of young cattle and sheep directly affects the production level of cattle and sheep in adulthood, which in turn affects the economic benefits of the ranch. However, the health status of calves and lambs in my country is worrying, with a diarrhea rate of nearly 100%, and 56.6% of diarrhea can lead to the death of calves and lambs. In order to prevent and treat diarrhea, calves are continuously fed with tetracycline and β-lactam antibiotics before 5-8 days of age after being fed colostrum, but after stopping the drug, calves often suffer from diarrhea (9-15 days of age).

[0004] Calf diarrhea can be caused by pathogenic and non-pathogenic factors. Antibiotics have a good therapeutic effect on pathogenic diarrhea, but antibiotics inhibit / treat pathogenic diarrhea, which can lead to the retardation of intestinal microbial flora development, disorder of intestinal microbial flora, and increase susceptibility to subsequent diseases. Intestinal microorganisms play an important role in resisting the invasion of pathogens and maintaining intestinal health. Therefore, using fecal microorganisms to assess diarrhea resistance in calves after birth can help select calves and reduce the negative effects of antibiotics in preventing and treating diarrhea. Summary of the invention

[0005] The object of the present invention is to provide a method for predicting calf diarrhea resistance based on intestinal microbial information to solve the problems described in the background technology.

[0006] The technical solution adopted by the present invention to solve its technical problem is:

[0007] A method for predicting calf diarrhea resistance based on intestinal microbial information, the method specifically comprising the following steps:

[0008] S1. Collect calf fecal samples from which microbiome DNA can be extracted;

[0009] S2. Detecting the presence and abundance of each microorganism of the microbial community in the fecal sample of the calf, wherein the microbial community includes 34 microorganisms with nucleotide sequences of SEQ ID No. 1 to SEQ ID No. 34 and belonging to the non-resistant group and the resistant group, respectively, and calculating the interaction intensity index of the microbial community and the total abundance of different groups;

[0010] S3. Construct a random forest machine learning model based on the detection information and the calculation information;

[0011] S4. Set the interaction intensity index threshold and the total abundance threshold of different groups, and predict the diarrhea resistance of the test calves based on the interaction intensity index of the microbiome in the fecal samples of the test calves, the total abundance of different groups and the random forest machine learning model.

[0012] Preferably, in step S1, the method for extracting the DNA of the microbial community is a phenol-chloroform extraction method.

[0013] Preferably, in step S2, the method for detecting the presence and abundance of a specific ASV includes 16S rDNA high-throughput sequencing and / or fluorescent quantitative PCR, the fragment region of the 16S rDNA high-throughput sequencing method is the V3-V4 region, and the amplification primers used are 341F (5'-CCTAYGGGRBGCASCAG-3') and 806R (5'-GGACTACNNGGGTATCTAAT-3').

[0014] Preferably, in the 16S rDNA high-throughput sequencing method, data analysis is performed in an R environment, and the raw sequencing data is processed using DADA2 and Phyloseq R packages.

[0015] Preferably, in step S2, the interaction strength index is calculated using the following formula:

[0016] Y=β a X ASVa +β b X ASVb +β c X ASVc +...+β x X ASVx ,

[0017] Where Y is the interaction strength index, X ASVx is the relative abundance of ASVx in the resistance group, β x is the interaction strength coefficient of the corresponding ASVx.

[0018] Preferably, in step S3, constructing a random forest machine learning model comprises the following steps:

[0019] S3.1. Evaluate calf diarrhea based on the appearance of calf feces samples;

[0020] 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;

[0021] S3.3. The accuracy of the random forest machine learning model is evaluated through the receiver operating characteristic curve. When the accuracy reaches the standard, an effective random forest machine learning model is obtained.

[0022] Preferably, in step S3.1, the appearance of the calf feces sample includes normal feces sample, soft and shapeless feces sample, watery feces sample, and watery feces sample with mucus and blood. When the feces sample is normal and the feces sample is soft and shapeless, the calf diarrhea condition is no diarrhea. When the feces sample is watery and the feces sample is watery with mucus and blood, the calf diarrhea condition is diarrhea.

[0023] Preferably, in step S4, the interaction intensity index threshold is set as follows: when the interaction intensity index is higher than 1.26, the calf has diarrhea resistance, and when the interaction intensity index is lower than 0.03, the calf has no diarrhea resistance; the total abundance thresholds of different groups are set as follows: when the total abundance of the resistance group is greater than 18.71% and the total abundance of the non-resistant group is less than 17.5%, the calf has diarrhea resistance, and when the total abundance of the resistance group is less than 2.4% and the total abundance of the non-resistant group is greater than 51.32%, the calf has no diarrhea resistance.

[0024] The beneficial effects of the present invention are:

[0025] The present invention is based on the close relationship between the presence, abundance, interaction strength of specific types of microorganisms in calf fecal microbial flora and calf diarrhea resistance. The presence, abundance and interaction strength of these specific types of microorganisms are detected and analyzed, and a random forest machine learning model is constructed on this basis. The model can effectively predict the calf's resistance 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. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0027] Figure 1 is a ROC curve diagram of the random forest machine learning model of the present invention,

[0028] Figure 2 These are the predicted results for calves with diarrhea resistance under three conditions in the application example. DETAILED DESCRIPTION

[0029] The technical solution of the present invention is further described in detail below through specific embodiments and in conjunction with the accompanying drawings. It should be understood that the implementation of the present invention is not limited to the following embodiments, and any form of modification and / or change made to the present invention will fall within the protection scope of the present invention.

[0030] In the present invention, unless otherwise specified, all parts and percentages are weight units, and the equipment and raw materials used can be purchased from the market or are commonly used in the art. The methods in the following embodiments, unless otherwise specified, are conventional methods in the art. The components or equipment in the following embodiments, unless otherwise specified, are universal standard parts or components known to those skilled in the art, and their structures and principles are known to those skilled in the art through technical manuals or conventional experimental methods.

[0031] Example 1

[0032] A method for predicting calf diarrhea resistance based on intestinal microbial information, the method specifically comprising the following steps:

[0033] S1. Collect calf fecal samples from which microbiota DNA can be extracted;

[0034] S2. Detecting the presence and abundance of each microorganism of the microbial community in the fecal sample of the calf, wherein the microbial community includes 34 microorganisms with nucleotide sequences of SEQ ID No. 1 to SEQ ID No. 34 and belonging to the non-resistant group and the resistant group, respectively, and calculating the interaction intensity index of the microbial community and the total abundance of different groups;

[0035] S3. Construct a random forest machine learning model based on the detection information and the calculation information;

[0036] S4. Set the interaction intensity index threshold and the total abundance threshold of different groups, and predict the diarrhea resistance of the test calves based on the interaction intensity index of the microbiome in the fecal samples of the test calves, the total abundance of different groups and the random forest machine learning model.

[0037] Table 1 Microorganism numbers and nucleotide sequences

[0038]

[0039]

[0040]

[0041]

[0042]

[0043]

[0044]

[0045]

[0046]

[0047]

[0048] Example 2

[0049] A method for predicting calf diarrhea resistance based on intestinal microbial information, the method specifically comprising the following steps:

[0050] S1. Collect calf fecal samples from which microbiome DNA can be extracted;

[0051] S2. Detecting the presence and abundance of each microorganism of the microbial community in the fecal sample of the calf, wherein the microbial community includes 34 microorganisms numbered ASV1 to ASV34 having nucleotide sequences of SEQ ID No. 1 to SEQ ID No. 34 as shown in Table 1 and belonging to the non-resistant group and the resistant group, respectively, and calculating the interaction intensity index of the microbial community and the total abundance of different groups;

[0052] S3. Construct a random forest machine learning model based on the detection information and the calculation information, including:

[0053] S3.1. Evaluate the calf diarrhea condition based on the appearance of the calf feces sample. The appearance of the calf feces sample includes normal feces sample, soft and unformed feces sample, watery feces sample, and watery feces sample with mucus and blood streaks. When the feces sample is normal and the feces sample is soft and unformed, the calf diarrhea condition is no diarrhea. When the feces sample is watery and the feces sample is watery with mucus and blood streaks, the calf diarrhea condition is diarrhea.

[0054] 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;

[0055] S3.3. Evaluate the accuracy of the random forest machine learning model through the receiver operating characteristic curve, and obtain an effective random forest machine learning model when the accuracy reaches the standard;

[0056] S4. Set the interaction strength index threshold and the total abundance threshold of different groups. The interaction strength index threshold is set as follows: when the interaction strength index is higher than 1.26, the calf has diarrhea resistance, and when the interaction strength index is lower than 0.03, the calf has no diarrhea resistance; the total abundance threshold of different groups is set as follows: when the total abundance of the resistance group is greater than 18.71% and the total abundance of the non-resistant group is less than 17.5%, the calf has diarrhea resistance, and when the total abundance of the resistance group is less than 2.4% and the total abundance of the non-resistant group is greater than 51.32%, the calf has no diarrhea resistance; the diarrhea resistance of the test calf is predicted based on the interaction strength index of the microbiota in the fecal samples of the test calves, the total abundance of different groups and the random forest machine learning model.

[0057] In step S1, the method for extracting the DNA of the microbial community is the phenol-chloroform extraction method.

[0058] In step S2, the method for detecting the presence and abundance of a specific ASV includes 16S rDNA high-throughput sequencing and / or fluorescent quantitative PCR. The fragment region of the 16S rDNA high-throughput sequencing method is the V3-V4 region, and the amplification primers used are 341F (5'-CCTAYGGGRBGCASCAG-3') and 806R (5'-GGACTACNNGGGTATCTAAT-3'). In the 16S rDNA high-throughput sequencing method, data analysis is performed in the R environment, and the raw sequencing data is processed by the DADA2 and Phyloseq R packages.

[0059] The interaction strength index in step S2 is calculated using the following formula:

[0060] Y=β a X ASVa +β b X ASVb +β c X ASVc +...+β x X ASVx

[0061] Where Y is the interaction strength index, X ASVx is the relative abundance of ASVx in the resistance group, β x is the interaction strength coefficient of the corresponding ASVx.

[0062] Verification of the accuracy of the prediction model in application examples

[0063] 1. Test method validation The test selected 43 healthy Holstein calves in the early lactation period, and the calves were fed according to the pasture feeding procedures without intervention. The calves were fed through an automatic feeding system, and the amount of full milk feeding gradually increased from 7L / day at 8 days of age to 7.6L / day at 18 days of age. Fecal samples were collected from calves from 8 days of age to 18 days of age. After the samples were collected, they were placed in 2mL sterile centrifuge tubes and immediately frozen in liquid nitrogen, and then transferred to the laboratory -80℃ ultra-low temperature refrigerator for long-term storage for subsequent analysis. During the fecal sample collection process, daily fecal scores were recorded. Based on the fecal score, the diarrhea of ​​the calf was evaluated as a routine evaluation standard for calf diarrhea resistance.

[0064] After fecal sample collection, sample DNA was extracted, and the V3-V4 region was amplified using primers 341F and 806R for 16S rDNA high-throughput sequencing and sequencing data processing. The specific steps include: (1) Quality control of the sequence, removal of barcode and primer fragments, as well as low-quality (containing N, expected error greater than 2, and quality score less than 2) sequences and sequences that are too short. (2) De-noised differential sequences (sequence variants) were obtained using a sample inference algorithm. The forward sequence and the reverse sequence were spliced ​​(with a minimum overlap of 20 bases) to obtain the 'contig' sequence. (3) Chimeras sequences were removed to obtain an ASVs (amplicon sequence variants) abundance table. (4) By comparing the SILVA database (version 132), the species annotation table of ASVs was obtained. (5) The ASV abundance table and species annotation table were imported into the Phyloseq package for subsequent statistical analysis. (6) The Phyloseq package was used to remove archaea and sequences that appeared only once (singletons), and the sequences were flattened to obtain the same sequencing depth.

[0065] Verification of the prediction model was carried out in the R environment. According to the ASV sequences given in Table 1 above, the target ASVs (34) in the obtained ASV abundance table were screened out. The sum of the abundances of the 34 target ASVs in the non-resistant group and the resistant group in each sample was calculated, and the interaction intensity index of each sample was calculated according to the interaction intensity coefficient and the interaction intensity calculation formula given in Table 1. Compared with the corresponding abundance threshold and interaction intensity threshold, they were used as conditions one and two for predicting calf diarrhea resistance. The 34 target ASV abundance tables were used as test data sets, and the predict function in the stats R package was substituted into the above-mentioned random forest classification model to obtain condition three for the random forest model to predict calf diarrhea resistance. Combined with the three conditions of the prediction model, as the final result of predicting calf diarrhea, the prediction results were compared with the conventional evaluation criteria to verify the accuracy of the model.

[0066] 2. Test results: According to the model prediction, among the 261 samples of 43 calves, a total of 123 fecal samples met the three conditions of abundance threshold, interaction strength index threshold and random forest prediction and were judged to be resistant to diarrhea (see Figure 2 ), that is, it is predicted that the calf did not have diarrhea at the time corresponding to the fecal sample collection. Compared with the conventional diarrhea results (see Table 2), 92 samples predicted to be resistant to diarrhea did not cause diarrhea in the calves at the corresponding age, with an accuracy of 74.80%. At the same time, there were 106 samples predicted to be non-resistant, and the calves had diarrhea at the corresponding age, with an accuracy of 76.81%. The overall accuracy of the prediction model was 75.81%. The above results show that the 34 ASVs selected from Table 1 can effectively predict the diarrhea resistance of calves with high accuracy, sensitivity and specificity.

[0067] Table 2 Comparison of model prediction results with conventional standards

[0068]

[0069] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be 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 parts can be referred to the method part.

[0070] The above is a detailed introduction to a method for predicting calf diarrhea resistance based on intestinal microbial information provided by the present invention. Specific examples are used in this article to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. It should be pointed out that for ordinary technicians in this technical field, without departing from the principles of the present invention, the present invention can also be improved and modified in a number of ways, and these improvements and modifications also fall within the scope of protection of the claims of the present invention. Sequence Listing <110> Zhejiang University <120> A method for predicting calf diarrhea resistance based on intestinal microbial information <130> ZJDX202110 <160> 34 <170> SIPOSequenceListing 1.0 <210> 1 <211> 407 <212> DNA <213> ASV1 <400> 1 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 aatgcggggc 240 tcaactccgt attgcgttgg aaactgctaa actagagtac tggagaggtg ggcggaacta 300 caagtgtaga ggtgaaattc gtagatattt gtaggaatgc cgatggggaa gccagcccac 360 tggacagata ctgacgctaa agcgcgaaag cgtgggtagc aaacagg 407 <210> 2 <211> 407 <212> DNA <213> ASV2 <400> 2 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 aatgcggggc 240 tcaactccgt attgcgttgg aaactgtcaa actagagtac tggagaggtg ggcggaacta 360. caagtgtaga ggtgaaattc gtagatattt gtaggaatgc cgatgggga gccagcccac tggacagata ctgacgctaa agcgcgaaag cgtgggtagc aaacagg 407 <210> 3 <211> 424 <212> DNA <213> ASV4 <400> 3 tgaggaat tggtcaatgg acgggagtct gaaccagcca agtagcgtgc aggatgacgg ccctatgggt tgtaaactgc ttttataggg ggataaagtg tgccacgtgt ggcatattgc 120 aggtacccta tgaatagga ccggctaatt ccgtgccagc agccgcggta atacggaagg tccgggcgtt atccggattt attgggttta aagggagcgt aggccgtctt ataagcgtgt 240 tgtgaaatgt cggggctcaa cctgggcatt gcagcgcgaa ctgtgagact tgagtgcgca ggaagtaggc ggaattcgtc gtgtagcggt gaatgctta gatatgacga agaactccga ttgcgaaggc agcctgctgt agcgcaactg acgctgaagc tcgaaagcgt gggtatcgaa 420 cagg 424 <210> 4 <211> 407 <212> DNA <213> ASV5 <400> 4 tgggaatt tggacaatgg accaaagtc tgatccagca attctgtgtg cacgatgaag 60 ttttcggaa tgtaaagtgc ttcagttgg gacgaagtaa gtgacggtac cacagaaga 120 agcgacggct aaatacgtgc cagcagccgc ggtaatacgt atgtcgcaag cgttatccgg 180 atttattggg cgtaaagcgc gtctaggcgg tttggtaagt ctgatgtgaa atgcggggc 240 tcaactccgt attgcgttgg aactgccaa actagagtac tggaggtg ggcggaacta 300 caagtgtaga ggtgaaatttc gtagatattt gtaggaatgc caatggggaa gccagcccac 360 tggacagata ctgacgctaa agcgcgaaag cgtgggtagc aaacagg 407 <210> 5 <211> 404 <212> DNA <213> US11 <400> 5 tggggaatat tgcacaatgg gggaaaccct gatgcagcaa cgccgcgtga gtgatgaagg 60 ttttcggatc gtaaagctct gtctttgggg agataatga cggtacccaa ggaggaagcc 120 acggctaact acgtgccagc agccgcggta atacgtaggt ggcgagcgtt atccggattt 180 actgggcgta aagggagcgt aggcggatga ttaagtggga tgtgaatac ccgggctcaa 240 cttgggtgct gcattccaaa ctggttatct agagtgcagg agaggagt ggaattccta 300 gtgtagcggt gaatgcgta gagattagga agacaccag tggcgaggc gactctctgg 360 actgtaactg acgctgaggc tcgaagcgt ggggagcaaa cagg 404 <210> 6 <211> 424 <212> DNA <213> USA14 <400> 6 tgaggaatat tggtcaatgg acgcaagtct gaaccagcca agtagcgtgc aggacgacgg 60 ccctccggt tgtaaactgc ttttagttgg gaataagtg cagctcgtga gctgttttgt 120 atgtaccatc agaaaaagga ccggctaatt ccgtgccagc agccgggta atacggaagg 180 tccgggcgtt atccggattt attgggtttta aagggagcgc aggcggactc ttaagtcagt 240 tgtgaaatac ggcggctcaa ccgtcggact gcagttgata ctgggagtct tgagtgcaca 300 cagggatgct ggaattcatg gtgtagcggt gaatgctca gatatcatga agaactccga 360 tcgcgaaggc aggtatccgg gtgcaactg acgctgaggc tcgaaagtgc gggtatcaa 420 step 424 <210> 7 <211> 407 <212> DNA <213> ASV15 <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 tcaactccgt attgcgttgg aaactgctaa actagagtac tggagaggtg ggcggaacta 300 caagtgtaga ggtgaaattc gtagatattt gtaggaatgc cgatggggaa gccagcccac 360 tggacagata ctgacgctaa agcgcgaaag cgtgggtagc aaacagg 407 <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 ttctatggat tgtaaacttc ttttatacgg gaataaaacc tcccacgtgt gggagcttgt atgtaccgta tgaatagca 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 ttgcgaaggc agcacactaa gccgtaactg acgttcatgc tcgaaagtgt gggtatcaaa cagg 424 <210> 11 <211> 424 <212> DNA <213> ASV26 <400> 11 tgaggaatat tggtcaatgg acgagagtct gaaccagcca agtagcgtgc aggacgacgg ccctatgggt tgtaaactgc ttttataggg ggataaagtg tgccacgtgt ggcatattgc 120 aggtacccta tgaatagga ccggctaatt ccgtgccagc agccgcggta atacggaagg tccgggcgtt atccggattt attgggttta aagggagcgt aggccgtctt ataagcgtgt 240 tgtgaaatgt cggggctcaa cctgggcatt gcagcgcgaa ctgtgagact tgagtgcgca ggaagtaggc ggaattcgtc gtgtagcggt gaatgctta gatatgacga agaactccga ttgcgaaggc agcctgctgt agcgcaactg acgctgaagc tcgaaagcgt gggtatcgaa 420 cagg 424 <210> 12 <211> 424 <212> DNA <213> ASV28 <400> 12 tgaggaat tggtcaatgg acgcaagtct gaaccagcca agtagcgtgc aggatgacgg ccctccgggt tgtaaactgc ttttagttgg gaataaagtg cagctcgtga gctgttttgt atgtaccatc agaaaaagga ccggctaatt ccgtgccagc agccgcggta atacggaagg tccgggcgtt atccggattt attgggttta aagggagcgc aggcggactc ttaagtcagt 240 300. tgtgaaatac ggcggctcaa ccgtcggact gcagttgata ctgggagtct tgagtgcaca cagggatgct ggaattcatg gtgtagcggt gaatgctca gatatcatga agaactccga tcgcgaaggc aggtatccgg ggtgcaactg acgctgaggc tcgaaagtgc gggtatcaaa 420 cagg 424 <210> 13 <211> 424 <212> DNA <213> ASV29 <400> 13 tgaggaat tggtcaatgg acgcaagtct gaaccagcca agtagcgtgc aggatgacgg ccctccgggt tgtaaactgc ttttagttgg gaataaagtg cagctcgtga gctgttttgt atgtaccatc agaaaaagga ccggctaatt ccgtgccagc agccgcggta atacggaagg tccgggcgtt atccggattt attgggttta aagggagcgc aggcggactc ttaagtcagt 240 300. tgtgaaatac ggcggctcaa ccgtcggact gcagttgata ctgggggtct tgagtgcaca cagggatgct ggaattcatg gtgtagcggt gaatgctca gatatcatga agaactccga tcgcgaaggc aggtatccgg ggtgcaactg acgctgaggc tcgaaagtgc gggtatcaaa 420 cagg 424 <210> 14 <211> 424 <212> DNA <213> ASV30 <400> 14 tgaggaat tggtcaatgg acgcaagtct gaaccagcca agtagcgtgc aggatgacgg ccctccgggt tgtaaactgc ttttagttgg gaataaagtg cagctcgtga gctgttttgt atgtaccatc agaaaaagga ccggctaatt ccgtgccagc agccgcggta atacggaagg 180 tccgggcgtt atccggattt attgggttta aagggagcgc aggcggactc ttaagtcagt 240 300. tgtgaaatac ggcggctcaa ccgtcgaact gcagttgata ctgggagtct tgagtgcaca cagggatgct ggaattcatg gtgtagcggt gaatgctca gatatcatga agaactccga 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 ttctatggat tgtaaacttc ttttatacgg gaataaaacc ttccacgtgt gggagcttgt atgtaccgta tgaataagca tcggctaact ccgtgccagc agccgcggta atacggagga 180 tgcgagcgtt atccggattt attgggttta aagggagcgc agacgggaga ttaagtcagc 240 tgtgaaagtt tgcggctcaa ccgtaaaatt gcagttgata ctggtttcct tgagtgcggt tgaggtgtgc ggaattcgtg gtgtagcggt gaatgctta gatatcacga agaaccccga ttgcgaaggc agcacactaa gccgtaactg acgttcatgc tcgaaagtgt gggtatcaaa cagg 424 <210> 16 <211> 424 <212> DNA <213> ASV38 <400> 16 tgaggaat tggtcaatgg acgagagtct gaaccagcca agtagcgtga aggatgaagg tcctacggat tgtaaacttc ttttataagg gaataaaccc tcccacgtgt gggagcttgt 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 ttgcgaaggc agcacactaa gccgtaactg acgttcatgc tcgaaagtgt gggtatcaaa cagg 424 <210> 17 <211> 424 <212> DNA <213> ASV40 <400> 17 tgaggaatat tggtcaatgg gcgagagcct gaaccagcca agtagcgtga aggatgaagg ttctatggat tgtaaacttc ttttatacgg gaataaaacc tcccacgtgt gggagtttgt atgtaccgta tgaatagca 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 ttgcgaaggc agcacactaa gccgtaactg acgttcatgc tcgaaagtgt gggtatcaaa cagg 424 <210> 18 <211> 424 <212> DNA <213> ASV43 <400> 18 tgaggaatat tggtcaatgg acgagagtct gaaccagcca agtagcgtgc aggaagacgg ccctatgggt tgtaaactgc ttttataagg gaataaagtg agtctcgtga gactttttgc atgtacctta tgaataagga ccggctaatt ccgtgccagc agccgcggta atacggaagg tccgggcgtt atccggattt attgggttta aagggagcgt aggccggaga ttaagcgtgt 240 tgtgaaatgt agacgctcaa cgtctgcact gcagcgcgaa ctggtttcct tgagtacgca caaagtgggc ggaattcgtg gtgtagcggt gaaatgctta gatatcacga agaactccga ttgcgaaggc agctcactgg agcgcaactg acgctgaagc tcgaaagtgc gggtatcgaa 420 cagg 424 <210> 19 <211> 404 <212> DNA <213> ASV48 <400> 19 60. tggggaatat tgcacaatgg gggaaaccct gatgcagcaa cgccgcgtga gtgatgacgg ccttcgggtt gtaaagctct gtctttgggg acgataatga cggtacccaa ggaggaagcc acggctaact acgtgccagc agccgcggta atacgtaggt ggcaagcgtt gtccggattt 180 actgggcgta aagggagcgt aggcggattt ttagtggga tgtgaaatac ccgggctcaa cctgggtgct gcattccaaa ctggaaatct agagtgcagg aggggaagt ggaattccta gtgtagcggt gaaatgcgta gagattagga agaacaccag tggcgaaggc gactttctgg actgtaactg acgctgaggc tcgaaagcgt ggggagcaaa cagg 404 <210> 20 <211> 428 <212> DNA <213> ASV50 <400> 20 tggggaatct tccgcaatgg gcgcaagcct gacggagcaa cgccgcgtga gtgaagaagg ttttcggatc gtaaagctct gttgagaggg acgagaggca aggctaggaa atgagctttg taggacggta cctttcgagg aagccacggc taactacgtg ccagcagccg cggtaatacg taggtggcga gcgttgtccg gaattattgg gcgtaaaggg agcgcaggtg ggaaagtaag tcagtcttaa aagtgcgggg ctcaaccccg tgaggggatt gaaactactt ttcttgagtg caggagagga aagcggaatt cctagtgtag cggtgaaatg cgtagatatt aggaggaca 420. ccgtggcga aggcggcttt ctggactgta actgacactg aggctcgaaa gccaggggag cgaacggg 428 <210> 21 <211> 424 <212> DNA <213> ASV52 <400> 21 tgaggaatat tggtcaatgg gcgagagcct gaaccagcca agtagcgtgc aggaagacgg ccctatgggt tgtaaactgc ttttataagg gaataaagtg agtctcgtga gactttttgc atgtacctta tgaataagga ccggctaatt ccgtgccagc agccgcggta atacggaagg tccgggcgtt atccggattt attgggttta aagggagcgt aggccggaga ttaagcgtgt 240 tgtgaaatgt agacgctcaa cgtctgcact gcagcgcgaa ctggtttcct tgagtacgca caaagtgggc ggaattcgtg gtgtagcggt gaaatgctta gatatcacga agaactccga ttgcgaaggc agctcactgg agcgcaactg acgctgaagc tcgaaagtgc gggtatcgaa 420 cagg 424 <210> 22 <211> 404 <212> DNA <213> ASV57 <400> 22 60. tggggaatat tgcacaatgg gggaaaccct gatgcagcaa cgccgcgtga gtgatgacgg ccttcgggtt gtaaagctct gtcttcaggg acgataatga cggtacctga ggaggaagcc acggctaact acgtgccagc agccgcggta atacgtaggt ggcgagcgtt gtccggattt 180 actgggcgta aagggagcgt aggcggactt ttaagtgaga tgtgaaatac ccgggctcaa cttgggtgct gcatttcaaa ctggaagtct agagtgcagg agggagaat ggaattccta gtgtagcggt gaaatgcgta gagattagga agaacaccag tggcgaaggc gattctctgg actgtaactg acgctgaggc tcgaaagcgt ggggagcaaa cagg 404 <210> 23 <211> 404 <212> DNA <213> ASV60 <400> 23 60. tggggaatat tgcacaatgg gggaaaccct gatgcagcaa cgccgcgtga gtgatgacgg tcttcggatt gtaaagctct gtctttaggg acgataatga cggtacctaa ggaggaagcc acggctaact acgtgccagc agccgcggta atacgtaggt ggcaagcgtt gtccggattt 180 actgggcgta aagggagcgt aggtggatat ttaagtggga tgtgaaatac ccggggcttaa cctgggtgct gcattccaaa ctggatatct agagtgcagg agaggaagg agattccta gtgtagcggt gaaatgcgta gagataggagaataccag tggcgaaggc gcctttctgg 360 actgtaactg acacctgaggc tcgaagcgt ggggagcaa cagg 404 <210> 24 <211> 404 <212> DNA <213> USA63 <400> 24 tggggaatat tgcacaatgg gggaaaccct gatgcagcaa cgccgcgtga gtgatgaagg 60 tttcggatc gtaaagctct gtctcaggg acgataatga cggtacctga ggaggaagcc 120 acggctaact acgtgccagc agccgcggta atacgtaggt ggcgagcgtt gtccggattt 180 actgggcgta aagggagcgt aggcggatttt ttaagtgaga tgtgaatac ccgggctcaa 240 cttgggtgct gcattcaaa ctggaagtct agagtgcagg agaggagt ggaattccta 300 gtgtagcggt gaatgcgta gagattagga agacaccag tggcgaggc gactctctgg 360 actgtaactg acgctgaggc tcgaagcgt ggggagcaaa cagg 404 <210> 25 <211> 424 <212> DNA <213> US69 <400> 25 tgaggaatat tggtcaatgg acgcaagtct gaaccagcca agtagcgtgc aggatgacgg 60 ccctccggt tgtaaactgc ttttagttgg gaataagtg cagctcgtga gctgttttgt 120 atgtaccatc agaaaaagga ccggctaatt ccgtgccagc agccgggta atacggaagg 180 tccgggcgtt atccggattt attgggtttta aagggagcgc aggcggactc ttaagtcagt 240 tgtgaaatac ggcggctcaa ccgtcggact gcagttgata ctgggagtct tgagtgcaca 300 cagggatgct ggaattcatg gtgtagcggt gaatgctca gatatcatga agaactccaa 360 tcgcgaaggc aggtatccgg ggtgcaactg acgctgaggc tcgaaagtgc gggtatcaa 420 step 424 <210> 26 <211> 404 <212> DNA <213> USA87 <400> 26 tggggaatat tgcacaatgg gggaaaccct gatgcagcaa cgccgcgtga gtgatgaagg 60 ttttcggatc gtaaagctct gtctttgggg agataatga cggtacccaa ggaggaagcc 120 acggctaact acgtgccagc agccgcggta atacgtaggt ggcgagcgtt atccggattt 180 actgggcgta aagggagcgt aggcggataa ttaagtggga tgtgaatac ccgggctcaa 240 cttgggtgct gcattccaaa ctggttatct agagtgcagg agaggagt ggaattccta 300 gtgtagcggt gaaatgcgta gagattagga agaacaccag tggcgaaggc gactctctgg actgtaactg acgctgaggc tcgaaagcgt ggggagcaaa cagg 404 <210> 27 <211> 424 <212> DNA <213> ASV89 <400> 27 tgaggaatat tggtcaatgg gcgagagcct gaaccagcca agtagcgtgc aggatgacgg ccctatgggt tgtaaactgc ttttataagg gaataaagtg agagtcgtga ctctttttgc atgtacctta tgaataagga ccggctaatt ccgtgccagc agccgcggta atacggaagg tccgggcgtt atccggattt attgggttta aagggagcgt aggccggaga ttaagcgtgt 240 300. tgtgaaatgt agatgctcaa catctgaact gcagcgcgaa ctggtttcct tgagtacgca caaagtgggc ggaattcgtg gtgtagcggt gaaatgctta gatatcacga agaactccga ttgcgaaggc agctcactgg agcgcaactg acgctgaagc tcgaaagtgc gggtatcgaa 420 cagg 424 <210> 28 <211> 424 <212> DNA <213> ASV91 <400> 28 tgaggaat tggtcaatgg acgagagtct gaaccagcca agtagcgtgc aggatgacgg ccctatgggt tgtaaactgc ttttataagg gaataaagtg agtctcgtga gactttttgc atgtacctta tgaataagga ccggctaatt ccgtgccagc agccgcggta atacggaagg tccgggcgtt atccggattt attgggttta aagggagcgt aggccggaga ttaagcgtgt 240 tgtgaaatgt agacgctcaa cgtctgcact gcagcgcgaa ctggtttcct tgagtacgca caaagtgggc ggaattcgtg gtgtagcggt gaaatgctta gatatcacga agaactccga ttgcgaaggc agctcactgg agcgcaactg acgctgaagc tcgaaagtgc gggtatcgaa 420 cagg 424 <210> 29 <211> 424 <212> DNA <213> ASV92 <400> 29 tgaggaat tggtcaatgg acgagagtct gaaccagcca agtagcgtga aggatgaagg tcctacggat tgtaaacttc ttttataagg gaataaaccc tcccacgtgt gggagcttgt atgtaccttg tgaatagca tcggctaact ccgtgccagc agccgcggta atacggagga 180 tgcgagcgtt atccggattt attgggttta aagggagcgc agacgggtcg ttaagtcagc 240 tgtgaaagtt tggggctca ccttaaaatt gcagttgata ctggcgtcct tggtgcggt 300 tgaggtgtgc ggaattcgtg gtgtagcggt gaatgctta gatatcacga agaactccga 360 ttgcgaaggc agcacactaa tccgtaactg acgttcatgc tcgaaagtgt gggtatcaa 420 step 424 <210> 30 <211> 404 <212> DNA <213> US114 <400> 30 tggggaatat tgcacaatgg gggaaaccct gatgcagcaa cgccgcgtga gtgatgaagg 60 tttcggatt gtaaagctct gtctttgggg agataatga cggtacccaa ggaggaagcc 120 acggctaact acgtgccagc agccgcggta atacgtaggt ggcgagcgtt atccggattt 180 actgggcgta aagggagcgt aggcggatga ttaagtggga tgtgaatac ccgggctcaa 240 cttgggtgct gcattccaaa ctggttatct agagtgcagg agaggagt ggaattccta 300 gtgtagcggt gaatgcgta gagattagga agacaccag tggcgaggc gactctctgg 360 actgtaactg acgctgaggc tcgaagcgt ggggagcaaa cagg 404 <210> 31 <211> 408 <212> DNA <213> USA151 <400> 31 tgggaatt tggacaatgg accaaagtc tgatccagca attctgtgtg cacgatgaag 60 ttttcggaa tgtaaagtgc ttcagttgg gacgaagtaa gtgacggtac cacagaaga 120 agcgacggct aaatacgtgc cagcagccgc ggtaatacgg agggtgcaag cgttaatcgg 180 aattactggg cgtaaagcgc acggcaggcgg ttgttaagt cagatgtgaa atccccgggc 240 tcaacctggg aactgcatct gatactggca agcttgagtc tcgtagaggg gggtagaatt 300 ccaggtgtag cggtgaatg cgtagagatc tggagata ccggtggcga aggcggcccc 360 ctggacgaag actgacgctc aggtgcgaaa gcgtggggag caacagg 408 <210> 32 <211> 404 <212> DNA <213> USA183 <400> 32 tggggaatat tgcacaatgg gggaaaccct gatgcagcaa cgccgcgtga gtgatgaagg 60 ttttcggatc gtaaagctct gtctttgggg agataatga cggtacccaa ggaggaagcc 120 acggctaact acgtgccagc agccgcggta atacgtaggt ggcgagcgtt atccggattt 180 actgggcgta aagggagcgt aggcggatga ttaagtggga tgtgaatac ccgggctcaa 240 cttgggtgct gcattccaaa ctggttatct agagtgcagg agaggagt ggaattccta 300 gtgtagcggt gaatgcgta gagatctga ggaataccgg tggcgaggc ggccccctgg 360 acgaagactg acgctcaggt gcgaaagcgt ggggagcaaa cagg 404 <210> 33 <211> 404 <212> DNA <213> USA196 <400> 33 tggggaatat tgcacaatgg gggaaaccct gatgcagcaa cgccgcgtga gtgatgaagg 60 ttttcggatc gtaaagctct gtctttgggg agataatga cggtacccaa ggaggaagcc 120 acggctaact acgtgccagc agccgcggta atacggaggg tgcaagcgtt aatcggaatt 180 actgggcgta aagcgcacgc aggcggttg ttaagtcaga tgtgaatcc ccgggctcaa 240 cctgggaact gcatctgata ctggcaagct tgagtctcgt agaggggggt agaattccag 300 gtgtagcggt gaatgcgta gagatctga ggaataccgg tggcgaggc 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

1. A method for predicting calf diarrhea resistance based on intestinal microbial information, characterized in that The method specifically comprises the following steps: S1. Collect calf fecal samples from which microbiota DNA can be extracted; 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; 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'; The interaction strength index was calculated using the following formula: Y = β a X ASVa + b b X ASVb + b c X ASVc + ... + b x X ASVx , Where Y is the interaction strength index, X ASVx is the relative abundance of ASVx in the resistance group, β x is the interaction intensity coefficient of the corresponding ASVx; 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. Evaluate calf diarrhea based on the appearance of calf feces samples; The appearance of calf feces samples included normal feces, soft and shapeless feces, watery feces, and watery feces with mucus and blood. When the fecal sample is normal and the fecal sample is soft and unformed, the calf is considered to have no diarrhea. 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. 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. 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. 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; 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; 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. 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 the DNA of the microbial community is the phenol-chloroform extraction method.

3. The 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 was performed in the R environment, and the raw sequencing data were processed using the DADA2 and Phyloseq R packages.

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