Method for identifying young pigeons and laying pigeons based on metagenome sequencing

Abstract

The invention relates to the technical field of biological detection, in particular to a method for identifying young pigeons and laying pigeons based on metagenome sequencing. According to the method, on the basis of metagenome sequencing of the DNA sample of the pigeon faeces, the intestinal microbial flora characteristics and the antibiotic resistance gene characteristics are used as identification indexes, the young pigeons and the laying pigeons can be effectively distinguished and identified, meanwhile, a macro whole genome marker is provided for analysis of the intestinal microorganisms of the pigeons, and assistance is provided for research on healthy feeding and breeding of meat pigeons. By utilizing the method disclosed by the invention, the microbial assembly with higher resolution can be generated, and the microbial classification group with lower abundance and the function thereof can be identified, so that more microbial diversity in the samples and among the samples can be revealed.

Description

Technical Field

[0001] The present invention relates to the field of biological detection technology, and in particular to a method for identifying young pigeons and laying pigeons based on metagenomic sequencing. Background Art

[0002] The intestinal flora has rich biodiversity and plays a vital role in maintaining intestinal physiological balance. It can not only help the host complete nutrient absorption and energy intake, regulate metabolism, promote intestinal development, etc., but some intestinal flora can also participate in immune regulation and enhance the body's resistance. Therefore, the intestinal flora is closely related to the health and disease of the host. In recent years, with the large-scale use of antibiotics in the aquaculture industry to promote growth and prevent diseases, since the animal intestine cannot completely digest these antibiotics, more and more antibiotic-resistant bacteria (ARB) have been found in animal feces, which has brought great pressure to my country's aquaculture industry. More importantly, the use of antibiotics will produce genetic selection effects on microorganisms, leading to the enrichment of antibiotic resistance genes (ARGs). Studies have found that the intestinal microbiome of animals is a huge reservoir of resistance genes. When resistance genes are exposed and spread, they will become a major health problem worldwide and may cause ecological damage, which needs to be paid attention to.

[0003] The intestinal microbiome is considered to be the host's second genome and plays an important role in maintaining host metabolism, immunity and physiological functions. Traditional microbiology research relies on bacterial isolation and culture techniques, but approximately 85% to 99% of intestinal microorganisms cannot be cultured in the laboratory, limiting the in-depth understanding of bacterial functions, including those related to antibiotic resistance, and making it extremely difficult to fully utilize microbial diversity for disease detection and treatment research.

[0004] In recent years, with the development of sequencing platforms such as next-generation sequencing (NGS) and the rapid decline in sequencing costs, people have begun to conduct in-depth research on the diversity and function of the intestinal flora of various livestock. Metagenomics mainly studies the genetic material of environmental or host-associated microbiota to determine microbial diversity and its functions. Compared with 16S rRNA gene sequencing, metagenomics can produce assemblies with higher resolution, identify microbial taxa with lower abundance and their functions, and thus reveal more microbial diversity within and between samples. Therefore, metagenomics is increasingly used to study the characteristics of livestock intestinal flora and antibiotic resistance genes to assess the risks of resistance genes to human health.

[0005] Pigeons are the fourth largest poultry in my country after chickens, ducks, and geese. There are about 40 million pairs of breeding pigeons in my country, and more than 600 million commercial pigeons are sold each year. As nutritious foods, pigeon meat and eggs are becoming increasingly popular among consumers in Europe, the United States, and Japan. In addition, pigeons are also popular racing pigeons and ornamental birds in many regions. However, with the continuous expansion of pigeon farming, pigeon infectious diseases are becoming increasingly serious. However, there are few reports on the characteristics of pigeon intestinal flora, the diversity and function of antibiotic resistance genes, and the impact on host health and even human health. The intestinal flora of pigeons is a key factor in maintaining host health and resisting diseases. It is affected by multiple factors such as nutritional ratio, disease occurrence, and the maturity of the pigeons themselves. Therefore, the identification of its intestinal microbial flora is of great significance for disease prevention and promoting the sustainable development of the pigeon farming industry. Summary of the invention

[0006] The technical problem to be solved by the present invention is to analyze the composition and characteristics of the intestinal microbial flora of meat pigeons and the characteristics of antibiotic resistance genes through metagenomic analysis, and provide a method for identifying young pigeons and breeding pigeons based on metagenomic sequencing, and provide a reference for the healthy breeding of meat pigeons based on the discovered antibiotic resistance genes.

[0007] The technical problem that the present invention also solves is to provide a high-throughput detection system for identifying young pigeons and breeding pigeons based on metagenomic sequencing.

[0008] The technical problem to be finally solved by the present invention is to provide a test kit for distinguishing young pigeons and laying pigeons.

[0009] In order to solve the above technical problems, the technical solution adopted by the present invention is as follows:

[0010] A method for identifying young pigeons and breeding pigeons based on metagenomic sequencing. The method comprises collecting pigeon feces and extracting DNA samples, performing metagenomic sequencing on the DNA samples and performing bioinformatics analysis. Intestinal microbial flora characteristics and antibiotic resistance gene characteristics are used as identification indicators to distinguish young pigeons from breeding pigeons.

[0011] Wherein, the DNA concentration of the DNA sample is >10 ng / μL and the total amount is >1 μg.

[0012] The metagenomic sequencing of the DNA sample specifically involves using a high-throughput sequencing platform to perform double-end 150bp high-throughput sequencing on the metagenomic sequencing library constructed using the DNA sample.

[0013] Specifically, the NEBNext Ultra DNA Library Prep Kit for Illumina (NEB, USA) was used to construct a metagenomic sequencing library with high-quality DNA (DNA concentration>10 ng / μL, total amount>1 μg). The pigeon feces DNA library was subjected to double-end 150 bp high-throughput sequencing (PE150) using the Illumina Hiseq 2500 sequencing platform.

[0014] Wherein, the bioinformatics analysis comprises the following steps:

[0015] (1) Quality control and assembly of the original reads of metagenomic sequencing: remove reads with high alignment similarity from the host genome or contamination, assemble the clean reads after quality control to generate contigs, and filter sequences less than 500 bp;

[0016] (2) Gene prediction and construction of non-redundant gene sets: Prodigal was used to predict the open reading frames of contigs, and genes with a length of ≥100 bp were selected as candidate gene sets. The gene sequences predicted from all samples were clustered using CD-HIT software, and the longest gene in each cluster was taken as the representative sequence to construct a non-redundant gene set.

[0017] (3) The gene set protein sequences predicted after metagenomic sequencing were compared with the non-redundant protein database by blastp homology to obtain functional annotations and homologous species information; according to the NCBI microbial taxonomy information database, the species classification annotation information of the genes was obtained, and the relative abundance of species was counted at each taxonomic level: kingdom, phylum, class, order, family, genus, and species;

[0018] (4) Compare the gene set protein sequences predicted after metagenomic sequencing with the KEGG database to obtain the KO number corresponding to the sequence, obtain the pathway and module annotation information of the sequence based on the connection between KO and pathway and module, and count the abundance of each functional level of KEGG in each sample;

[0019] (5) Compare the gene set protein sequences predicted after metagenomic sequencing with the carbohydrate-active enzyme database CAZy to obtain the corresponding carbohydrate-active enzyme annotation information, and count the abundance of each functional level of CAZy;

[0020] (6) The gene set protein sequences predicted after metagenomic sequencing were compared with the SEED database by blastp homology to obtain functional annotations and homologous species information. According to the functional classification level of SEED Subsystem, the SEED classification annotation information of the gene was obtained, and the abundance of each functional level of SEED in each sample was counted;

[0021] (7) Compare the gene set protein sequences predicted after metagenomic sequencing with the antibiotic resistance gene database CARD to obtain the types and numbers of corresponding antibiotic resistance genes, and count the abundance of resistance genes and antibiotics in each sample;

[0022] (8) Compare the intestinal microbial flora characteristics of young pigeons and breeding pigeons according to the abundance of steps (3) to (6), and compare the antibiotic resistance gene characteristics of young pigeons and breeding pigeons according to the abundance of step (7), so as to distinguish young pigeons from breeding pigeons.

[0023] Among them, the intestinal microbial flora characteristics include: any one or a combination of intestinal flora composition and structure characteristics, intestinal microbial gene functions, intestinal flora gene function signal pathways, and intestinal microbial carbohydrate metabolism characteristics; the antibiotic resistance gene characteristics are antibiotic resistance gene diversity characteristics and / or intestinal microbial antibiotic resistance gene composition.

[0024] Specifically,

[0025] (I) in the composition and structure characteristics of the intestinal flora, when Bacillus, tail phage and Pseudomonas are the dominant microbial bacterial phyla, and Streptococcus, unclassified tail virus and Enterococcus are the dominant microbial genera, the identified pigeon is a young pigeon; when Pseudomonas and tail phage are the dominant microbial bacterial phyla, and Escherichia coli, unclassified Enterobacter and Carlton Yer virus are the dominant microbial bacterial genus, the identified pigeon is a brooding pigeon;

[0026] or,

[0027] (II) Among the intestinal microbial gene functions, when the ribosomal LSU bacteria, DNA replication and universal GTPases gene functions are the dominant gene functions, the identified pigeons are young pigeons; when the DNA replication, peptidoglycan biosynthesis, bacterial glycerolipid and glycerophospholipid metabolism gene functions are the dominant gene functions, the identified pigeons are breeding pigeons;

[0028] or,

[0029] (III) In the intestinal flora gene function signal pathway, when the metabolic pathway, biosynthesis of secondary metabolites and biosynthesis of amino acids are the dominant KEGG signal pathways, the identified pigeons are young pigeons; when the metabolic pathway, biosynthesis of secondary metabolites and microbial metabolism in different environments are the dominant KEGG signal pathways, the identified pigeons are breeding pigeons;

[0030] or,

[0031] (IV) In the intestinal microbial carbohydrate metabolism characteristics, when glycoside hydrolase 1, glycosyltransferase 35 and glycoside hydrolase 13 are the dominant carbohydrate metabolism types, the pigeon is identified as a young pigeon; when glycosyltransferase 2, glycosyltransferase 35 and glycosyltransferase 1 are the dominant carbohydrate metabolism types, the pigeon is identified as a breeding pigeon.

[0032] In some embodiments of the present invention, the abundance of Bacillus and Streptococcus is the highest in young pigeons, and the abundance of Pseudomonas and Escherichia coli is the highest in breeding pigeons.

[0033] In some embodiments of the present invention, in the intestinal microbial carbohydrate metabolism characteristics, it was also found that pigeon intestinal microorganisms contained 164 glycoside hydrolase (GH) genes, 78 auxiliary activity enzymes (AA) genes, 70 glycosyltransferase (GT) genes, 56 carbohydrate esterase (CE) genes, 3 polysaccharide lyase (PL) genes and 2 carbohydrate binding module (CBM) genes.

[0034] The diversity characteristics of antibiotic resistance genes include any one or a combination of antibiotic resistance gene family types, antibiotic resistance types and antibiotic resistance mechanism types.

[0035] Specifically,

[0036] (i) Among the antibiotic resistance gene family types, when ANT(4'), tetracycline resistance ribosomal protection protein and major promoter superfamily antibiotic efflux pump are the dominant antibiotic resistance gene family types, the identified pigeons are young pigeons; when resistance nodule cell division antibiotic efflux pump, major promoter superfamily antibiotic efflux pump and ATP binding cassette antibiotic efflux pump are the dominant antibiotic resistance gene family types, the identified pigeons are breeding pigeons;

[0037] or,

[0038] (ii) among the antibiotic resistance types, when aminoglycoside antibiotics, tetracycline antibiotics and fluoroquinolone antibiotics are the dominant antibiotic resistance types, the identified pigeons are young pigeons; when fluoroquinolone antibiotics, penicillin, disinfectants and antiseptics are the dominant antibiotic resistance types, the identified pigeons are breeding pigeons;

[0039] or,

[0040] (iii) among the antibiotic resistance mechanism types, when antibiotic inactivation, antibiotic target protection and antibiotic efflux are the dominant antibiotic resistance mechanism types, the identified pigeons are young pigeons; when antibiotic efflux, antibiotic target change and antibiotic inactivation are the dominant antibiotic resistance mechanism types, the identified pigeons are breeding pigeons;

[0041] or,

[0042] (iv) In the composition of intestinal microbial antibiotic resistance genes, when tetracycline-tetW, aminoglycoside-aadD and tetracycline-tetL were the dominant antibiotic resistance genes, the identified pigeons were young pigeons; when multidrug-acrB, multidrug-mdtF and multidrug-transporter were the dominant antibiotic resistance genes, the identified pigeons were breeding pigeons.

[0043] The present invention also obtains 142 antibiotic resistance genes by identifying the intestinal microbial flora of young pigeons and laying pigeons, among which adeJ, mdtC, tet (M) and udg are the main resistance gene types. The 142 antibiotic resistance genes are shown in Table 1.

[0044] Table 1 Gene names of 142 antibiotic resistance genes

[0045]

[0046]

[0047] Pigeons are a poultry product that is sold in a short period of time. The antibiotic problem requires special attention. Excessive use of antibiotics will not only lead to antibiotic residues, but also antibiotic resistance. In pigeon breeding and management, it is necessary not only to strengthen deworming and disinfection, maintain environmental hygiene, but also pay special attention to drug management. Therefore, the antibiotic resistance gene characteristics of the present invention can be used to distinguish and identify young pigeons and breeding pigeons, and can also provide effective guidance for the healthy feeding and breeding of meat pigeons in combination with the antibiotic resistance gene type.

[0048] A high-throughput detection system for identifying young pigeons and breeding pigeons based on metagenomic sequencing is also within the scope of protection of the present invention.

[0049] Specifically, the high-throughput detection system includes a processor and a memory; wherein the memory is used to store instructions, and when the processor executes the instructions, the high-throughput detection system implements the steps of any one of the above methods.

[0050] A test kit for distinguishing young pigeons and laying pigeons is also within the scope of protection of the present invention.

[0051] Specifically, the kit includes a DNA sample extraction reagent from pigeon feces, a DNA sample metagenomic sequencing reagent, and a DNA sample metagenomic sequencing data analysis software.

[0052] Beneficial effects:

[0053] (1) The method of the present invention does not require additional sampling steps and can directly use pigeon feces samples. Through the metagenomic distribution characteristics of fecal DNA: intestinal microbial flora characteristics and antibiotic resistance gene characteristics, young pigeons and laying pigeons are identified, and a metagenome marker is provided for analyzing pigeon intestinal microorganisms. Based on the metagenomic characteristics, compared with the identification of some intestinal microorganisms only through bacterial isolation and culture technology, the types of microorganisms studied can be greatly expanded and the sensitivity of detection can be improved. Compared with 16S rRNA gene sequencing, the data used in the present invention is metagenomic sequencing of fecal DNA, which can produce microbial assemblies with higher resolution, identify microbial taxa with lower abundance and their functions, and thus reveal more microbial diversity within and between samples.

[0054] (2) The characteristics of the intestinal microbial flora and antibiotic resistance genes of young pigeons and laying pigeons identified in the present invention can still be verified by bacterial isolation and culture, PCR and 16S rRNA gene sequencing;

[0055] (3) The method of the present invention can not only distinguish between young pigeons and breeding pigeons, providing a basis for healthy pigeon breeding, but also expand its application to other livestock and poultry breeding fields. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] The present invention will be further described in detail below with reference to the accompanying drawings, and the above and / or other advantages of the present invention will become more clear.

[0057] Figure 1 Taxonomic annotation of the gut microbiota of young pigeons and laying pigeons. (A) Venn diagram showing the number of consistent and unique bacterial communities in young pigeons and laying pigeons. (B) Venn diagram showing the number of consistent and unique bacterial genera in young pigeons and laying pigeons. (C) Relative abundance of gut microbiota at the phylum level. (D) Relative abundance of gut microbiota at the genus level. (E) Heat map of the taxonomic abundance of the top 10 dominant phylum-level gut microbiota. (F) Heat map of the taxonomic abundance of the top 10 dominant genus-level gut microbiota.

[0058] Figure 2Gene function annotations for the intestinal flora of young pigeons and breeding pigeons. (A) Statistical graph of functional genes related to GO entries. The horizontal axis is the GO entry classification; the vertical axis is the relative percentage of the corresponding functional genes. (B) Circos graph of the relative abundance of GO entries. The right side of the circle indicates the group information, and the left side indicates the GO entry information; the outer circle is the distribution ratio of unique genes, and the inner circle indicates different groups. (C) SEED functional gene classification histogram. The horizontal axis is the SEED entry classification; the vertical axis is the relative number of corresponding functional genes. (D) Heat map of the third-level abundance of the top 10 SEED entries.

[0059] Figure 3 Annotation of the functional pathways of the intestinal flora of young pigeons and pigeons. Among them, (A) KEGG pathway related functional gene secondary statistical chart. The horizontal axis is the primary and secondary classification of KEGG pathways, and the vertical axis is the relative number of corresponding functional genes. (B) KEGG pathway third-level relative abundance. (C) Heat map of the third-level abundance of the top 10 dominant KEGG pathways. (D) eggNOG functional gene classification histogram. The horizontal axis is eggNOG classification, and the vertical axis is the relative number of corresponding functional genes.

[0060] Figure 4 The characteristics of carbohydrate enzymes in the intestinal flora of young pigeons and breeding pigeons. Among them, (A) CAZy functional classification histogram. The horizontal axis is the CAZy functional classification; the vertical axis is the relative number of corresponding functional genes. (B) CAZy functional classification relative abundance Circos diagram. The right side of the circle represents the group information, and the left side represents the CAZy functional classification information; the outer circle is the distribution ratio of unique genes, and the inner circle represents different groups. (C) The relative abundance of CAZy functional items at the classification level. (D) The relative abundance of CAZy functional items at the grouping level. (E) The relative abundance of CAZy functional items at the activation level.

[0061] Figure 5 The abundance of antibiotic resistance genes in the intestinal flora of young pigeons and pigeons. Among them, (A) Classification histogram of antibiotic resistance genes. The horizontal axis is the classification content of antibiotic resistance genes, and the vertical axis is the corresponding number of functional genes. (B) Relative abundance of ARGs at the gene family level. (C) Relative abundance of ARGs at the drug class level. (D) Relative abundance of ARGs at the resistance mechanism level. (E) ARGs type statistics. The horizontal axis is the classification content of ARGs, and the vertical axis is the number of corresponding ARGs. (F) Heat map of the top 10 abundances in ARGs types. (G) Heat map of the top 10 abundances in ARGs subtypes.

[0062] Figure 6Low-throughput PCR verification of intestinal flora indicators of young pigeons and breeding pigeons. Among them, (A) Bacillota microbial phylum PCR detection electrophoresis. (B) Tetracycline-tetL antibiotic resistance gene PCR detection electrophoresis. (C) Tetracycline-tetW antibiotic resistance gene PCR detection electrophoresis. (D) Multidrug-acrB antibiotic resistance gene PCR detection electrophoresis. (E) Multidrug-mdtF antibiotic resistance gene PCR detection electrophoresis. M: DL2000DNAmarker; 1-3: amplification results of DNA samples from young pigeon feces; 4-6: amplification results of DNA samples from breeding pigeon feces; 7: ddH2O negative control. DETAILED DESCRIPTION

[0063] The present invention will be further described in detail below in conjunction with specific implementations, and the above and / or other advantages of the present invention will become more clear.

[0064] The experimental methods described in the following examples are conventional methods unless otherwise specified; the reagents and materials described are commercially available unless otherwise specified.

[0065] Example 1 A method for identifying young pigeons and laying pigeons based on metagenomic sequencing

[0066] 1. Materials and Methods

[0067] 1.1 Sample Source

[0068] 15 fecal samples were collected from young pigeons (2-5 months old) from a pigeon farm in Liuhe District, Nanjing, and designated as the YP group; 15 fecal samples were collected from pigeons (over 5 months old) from a pigeon farm in Gaochun District, Nanjing, and designated as the OP group. During the collection process, care was taken to prevent fecal material from contacting the ground. The collected fecal samples were transported to the laboratory on ice and stored at -80°C.

[0069] 1.2 DNA extraction and high-throughput sequencing

[0070] DNA was extracted from each pigeon feces sample using TIANamp Stool DNAKit (Tiangen, China). The degree of DNA degradation was analyzed using 1% agarose gel. The concentration of DNA was measured using a NanoDrop 2000 spectrophotometer (ThermoFisher, USA). Subsequently, the NEBNext Ultra DNA Library Prep Kit for Illumina (NEB, USA) was used to construct a metagenomic sequencing library with high-quality DNA (DNA concentration >10 ng / μL, total amount >1 μg). The pigeon feces DNA library was subjected to double-end 150 bp high-throughput sequencing (PE150) using the Illumina Hiseq 2500 sequencing platform. The YP group contained 1 sequencing sample, labeled YP; the OP group contained 1 sequencing sample, labeled OP.

[0071] 1.3 Quality control analysis and metagenomic assembly

[0072] Fastp was used to perform quality control and filtering on the raw reads obtained from metagenomic sequencing. Bowtie2 was used to align the reads to the host genome, and reads with high alignment similarity from the host genome or contamination were removed. For the high-quality clean reads obtained, MEGAHIT splicing software was used to perform multi-sample mixed splicing; then, the clean reads of each sample were aligned to the assembled contigs using bowtie2 to obtain unmapped PEreads; SPAdes was used to mix and splice the unaligned reads; for the contigs generated by the two splicing assemblies, sequences less than 500 bp were filtered.

[0073] 1.4 Gene prediction and non-redundant gene set construction

[0074] Prodigal was used to predict the open reading frames (ORFs) of the spliced ​​contigs, and genes with a length of ≥100bp were selected as candidate gene sets. The gene sequences predicted from all samples were clustered using CD-HIT software (parameters: 95% identity, 90% coverage). The longest gene in each class was taken as the representative sequence to construct a non-redundant gene catalog to describe the overall information of all genes in this type of environment. Bowtie2 was used to align the Clean Reads of each sample with the non-redundant gene set, and Samtools was used to count the number of reads aligned to each gene. Considering the length of the gene, the abundance of each gene in the sample was calculated and summarized into a gene abundance table for all samples.

[0075] 1.5 Species taxonomic annotation

[0076] DIAMOND was used to perform blastp homology comparison between the gene set protein sequences and the Nr (NCBI non-redundant protein sequences) database to obtain functional annotations and homologous species information, with the screening conditions of E-value <1e-5, Score> 60. At the same time, based on the NCBI microbial taxonomy information database, the species classification annotation information of the genes was obtained, and the relative abundance of species was counted at each taxonomic level of Kingdo (kingdom), Phylum (phylum), Class (class), Order (order), Family (family), Genus (genus), and Species (species).

[0077] 1.6 Functional Annotation

[0078] DIAMOND was used to compare the gene set protein sequence with the KEGG (Kyoto Encyclopedia of Genes and Genomes) database to obtain the KO number corresponding to the sequence. The pathway and module annotation information of the sequence were obtained based on the connection between KO and pathway and module, and the abundance of each functional level of KEGG in each sample was counted.

[0079] DIAMOND was used to compare the protein sequences of the gene set with the eggNOG (evolutionary genealogy of genes: Non-supervised Orthologous Groups) database to obtain the COG (Clusters of Orthologous Groups of proteins) corresponding to the genes. The screening conditions were: E-value < 1e-5, Score > 60. On this basis, the gene set was functionally annotated and classified, and the abundance of each functional level of COG in each sample was counted.

[0080] HMMER3 was used to compare the gene set protein sequences with the carbohydrate-active enzyme database CAZy (Carbohydrate-Active enZYmes Database) to obtain the corresponding carbohydrate-active enzyme annotation information. The screening condition was E-value < 1e-5, and the abundance of each functional level of CAZy was counted.

[0081] The protein sequences of the gene set were aligned with the Uniprot database using DIAMOND, and the GO (Gene Ontology) term corresponding to the gene set was obtained based on the aligned Uniprot ID. The screening conditions were: E-value < 1e-5, Score > 60, and the abundance of each functional level of GO in each sample was counted.

[0082] DIAMOND was used to perform blastp homology comparison between the gene set protein sequences and the SEED database to obtain functional annotations and homologous species information. The screening conditions were: E-value < 1e-5, Score > 60. At the same time, according to the functional classification level of SEED Subsystem, the SEED classification annotation information of the gene was obtained, and the abundance of each functional level of SEED in each sample was counted.

[0083] 1.7 Antibiotic resistance gene annotation

[0084] DIAMOND was used to compare the gene set protein sequences with the Comprehensive Antibiotic Resistance Database (CARD) to obtain the types and numbers of the corresponding antibiotic resistance genes. Screening conditions: E-value < 1e-5, Score > 60, and the abundance of resistance genes and antibiotics in each sample was counted.

[0085] 1.8 Statistical analysis and visualization

[0086] Tutools platform (https: / / www.cloudtutu.com) was used to draw Venn diagrams; alpha diversity, including Shannon-Wiener index, Simposon index, and Chao1, were calculated to characterize microbial gene diversity.

[0087] Using R-ggplot2 software, all sample structure component graphs were compared together to draw a bar graph based on the relative abundance of species or functions. Due to the large number of species classifications, only the top 10 species or functional classifications in abundance were selected and included in the bar graph.

[0088] The R package circlize package was used to draw a collinearity diagram to describe the visual circle diagram of the correspondence between samples and species and functions. This diagram not only reflects the proportion of dominant species composition in each sample, but also reflects the distribution ratio of each dominant species among different samples.

[0089] Use the R package gplots package to draw a heatmap. Cluster the data according to species, function or sample similarity as needed, and display the clustered data on the heatmap. High peaks and low abundance species or functions can be clustered in blocks, and the color gradient and degree of similarity can be used to reflect the similarities and differences in community composition or function of multiple samples at each classification level.

[0090] 2. Results and Analysis

[0091] 2.1 Metagenomic sequencing data collation

[0092] 38,220,082 original sequences corresponding to 5,733,012,300 bases were obtained from metagenomic sequencing of intestinal feces of young pigeons (referred to as YP samples); 40,093,458 original sequences corresponding to 6,014,018,700 bases were obtained from metagenomic sequencing of intestinal feces of breeding pigeons (referred to as OP samples). Low-quality sequence filtering was performed on the sequencing data of YP samples and OP samples. 36,035,534 valid sequences (clean reads) were retained in the YP sample, with a valid sequence ratio of 94.28%; 37,528,722 valid sequences (clean reads) were retained in the OP sample, with a valid sequence ratio of 93.60%. After sequencing data splicing and prediction, a total of 147,020 common genes were identified in the YP and OP samples. Gene alpha diversity analysis showed that the gene abundance and diversity of breeding pigeons were higher than those of young pigeons. The Chao1, ACE and Shannon indexes of breeding pigeons were 81253, 81679 and 8.9, respectively, while those of young pigeons were 69101, 69477 and 7.6, respectively.

[0093] 2.2 Composition and structural characteristics of intestinal flora

[0094] The assembled genomes were compared with a non-redundant protein database to obtain information on the composition and relative abundance of the microbial community. Venn diagram analysis showed that only eight and one unique bacterial phyla were identified from the gut microbiota of young pigeons and breeding pigeons, respectively ( Figure 1 A in Figure 3); 84 and 235 unique bacterial genera were identified from the gut microbiota of young pigeons and breeding pigeons, respectively ( Figure 1 Among them, Bacillota, Uroviricota and Pseudomonadota were the dominant microbial phyla in young pigeons, accounting for 58.58%, 25.19% and 9.62% respectively; Pseudomonas and Uroviricota were the dominant microbial phyla in breeding pigeons, accounting for 75.29% and 22.93% respectively. Figure 1C in the figure). Streptococcus, unclassified Caudoviricetes, and Enterococcus were the dominant microbial genera in young pigeons, accounting for 37.39%, 22.18%, and 7.30%, respectively; Escherichia, unclassified Enterobacteriaceae, and Carltongylesvirus were the dominant microbial genera in breeding pigeons, accounting for 39.53%, 21.28%, and 6.09%, respectively. Figure 1 D in Figure 1). Heat map analysis showed that the abundance of Bacillus was the highest in young pigeons, while the abundance of Pseudomonas was the highest in breeding pigeons ( Figure 1 Streptococcus was most abundant in young pigeons, while Escherichia coli was most abundant in breeding pigeons ( Figure 1 F).

[0095] 2.3 Gut microbial gene function annotation results

[0096] The functions of intestinal microbial genes in young pigeons and pigeons were annotated based on the GO and SEED databases. Gene function classification analysis showed that in pigeon intestinal microorganisms, transcription, translation and proteolysis were the main enriched GO terms for biological processes; cytoplasm, membrane components and plasma membrane were the main enriched GO terms for cell components; ATP binding, DNA binding and metal ion binding were the main enriched GO terms for molecular functions ( Figure 2 A).

[0097] The Circos diagram was used to represent the overall distribution of GO terms. The results showed that the main enriched GO terms in young pigeons and breeding pigeons were membrane components, ATP binding, and DNA binding ( Figure 2 B in the figure). SEED annotation results showed that the number of genes for carbohydrates, amino acids and their derivatives, protein metabolism, clustering-based subsystems, and cofactors, vitamins, cofactors, pigments, etc. accounted for a large proportion in the intestinal microorganisms of young pigeons and breeding pigeons ( Figure 2 C); SEED classification third level heat map analysis showed that the abundance of ribosomal LSU bacteria, DNA replication, and universal GTPases gene functions were highest in young pigeon samples, accounting for 1.72%, 1.66%, and 1.55%, respectively; while in breeding pigeon samples, DNA replication (1.19%), peptidoglycan biosynthesis (0.84%), and bacterial glycerolipid and glycerophospholipid metabolism (0.74%) were the highest in abundance ( Figure 2 D).

[0098] 2.4 Gene Function Signaling Pathway Annotation

[0099] The functional signal pathways of pigeon gut microbiota genes were annotated based on the KEGG and eggNOG databases. KEGG signal pathway classification analysis showed that in the six categories of cellular processes, environmental information processing, genetic information processing, human diseases, metabolism, and organism systems, the signal pathways with relatively more functional genes were cell community prokaryotes, membrane transport, translation, infectious disease bacteria, global and overview maps, and endocrine system pathways ( Figure 3 Metabolic pathways (17.19%), biosynthesis of secondary metabolites (9.07%), and biosynthesis of amino acids (4.98%) were the most enriched KEGG functional signaling pathways in young pigeon samples; metabolic pathways (18.22%), biosynthesis of secondary metabolites (7.4%), and microbial metabolism in different environments (5.78%) were the most enriched KEGG functional signaling pathways in breeding pigeon samples ( Figure 3 Heat map analysis showed that the abundance of metabolic pathways in the gut microbiota of young and old pigeons was higher than that of other KEGG functional signaling pathways ( Figure 3 In addition, the eggNOG annotation results showed that, in addition to the unknown functions, amino acid transport and metabolism, carbohydrate transport and metabolism, energy production and conversion, replication recombination and repair, and inorganic ion transport and metabolism were the main functions of pigeon intestinal microorganisms, and the number of genes for these eggNOG functions were 3406, 2597, 2493, 2356, and 2120, respectively ( Figure 3 D).

[0100] 2.5 Characteristics of Carbohydrate Metabolism of Pigeon Intestinal Microorganisms

[0101] In order to study the metabolic characteristics of carbohydrates in the gut microbiota of young pigeons and laying pigeons, the functions of their gut microorganisms were annotated based on the CAZy database. The results showed that there were 164 glycoside hydrolase (GH) genes, 78 auxiliary enzyme (AA) genes, 70 glycosyltransferase (GT) genes, 56 carbohydrate esterase (CE) genes, 3 polysaccharide lyase (PL) genes and 2 carbohydrate binding module (CBM) genes in the gut microbiota of pigeons ( Figure 4 A).

[0102] The Circos diagram was used to display the overall distribution of CAZy entries. The results showed that the main types of CAZy functions in young pigeons were GH1, GT35, and GH13; the main types of CAZy functions in breeding pigeons were GH2, GT35, and GH1 ( Figure 4 B in the figure). CAZy functional classification analysis showed that glycoside hydrolases, glycosyltransferases and carbohydrate esterases were the main enriched CAZy types in the intestinal microorganisms of young pigeons and breeding pigeons ( Figure 4C in the figure). GH1, GT35 and GH13 were the main enriched CAZy groups in the intestinal microorganisms of young pigeons, accounting for 31.06%, 11.8% and 6.5% respectively; GH2 (8.45%), GT35 (8.06%) and GH1 (8.05%) were the main enriched CAZy groups in the intestinal microorganisms of breeding pigeons ( Figure 4 In addition, β-galactosidase, β-mannosidase and β-glucuronidase were the main enriched CAZy activity types in the intestinal microorganisms of young pigeons and breeding pigeons ( Figure 4 E).

[0103] 2.6 Analysis of the abundance and diversity of antibiotic resistance genes (ARGs) in pigeon intestinal microorganisms

[0104] Compared with the CARD database, a total of 142 antibiotic resistance genes (ARGs) were screened from the pigeon intestinal flora. The top 12 ARGs in relative abundance were displayed in a bar graph, among which adeJ, mdtC, tet(M) and udg were the most important resistance types ( Figure 5 A).

[0105] The identified ARGs were further classified according to gene family, drug class and resistance mechanism. The results showed that (a) ANT (4') (52.29%), tetracycline resistance ribosomal protection protein (23.60%) and major facilitator superfamily (MFS) antibiotic efflux pump (15.45%) were the most enriched gene family types in the intestinal microorganisms of young pigeons; (b) resistance nodule cell division (RND) antibiotic efflux pump (37.04%), major facilitator superfamily (MFS) antibiotic efflux pump (33.97%) and ATP binding cassette (ABC) antibiotic efflux pump (8.65%) were the most enriched gene family types in the intestinal microorganisms of breeding pigeons. Figure 5 (ii) Aminoglycoside antibiotics (48.34%), tetracycline antibiotics (31.59%) and fluoroquinolone antibiotics (2.83%) were the main types of resistance enriched in young pigeons; fluoroquinolone antibiotics (13.35%), penicillin (9.25%) and disinfectants and preservatives (8.78%) were the main types of resistance enriched in breeding pigeons ( Figure 5 The main types of antibiotic resistance mechanisms in young pigeons were antibiotic inactivation (54.83%), antibiotic target protection (24.34%), and antibiotic efflux (19.63%); the main types of antibiotic resistance mechanisms in breeding pigeons were antibiotic efflux (82.26%), antibiotic target change (8.11%), and antibiotic inactivation (3.70%). Figure 5 D).

[0106] The antibiotic resistance genes were further divided into 10 categories, including: unclassified, tetracyclines, sulfonamides, polymyxins, multidrugs, macrolides-lincosamides-streptomycins, chloramphenicol, β-lactamases, bacitracins, and aminoglycosides, and the classification distribution of ARGs in pigeon intestinal microorganisms was displayed in the form of a histogram. The results showed that among the above 10 categories, the most enriched types of ARGs in the intestinal flora of young pigeons and breeding pigeons were: unclassified-sdiA, tetracyclines-tetracycline resistance protein, sulfonamides-sul1, polymyxins-arnA, multidrugs-semD, macrolides-ceramide receptor macB, chloramphenicol-loR, β-lactamases-C-type β-lactamases, bacitracins-_bacA and aminoglycosides-aph(6)-I( Figure 5 In addition, the ARGs type heat map showed that the abundance of tetracyclines was the highest in young pigeons, while the abundance of multidrugs was the highest in breeding pigeons ( Figure 5 F in Figure ). The heat map of ARGs subtypes showed that the abundance of tetracycline-tetW, aminoglycoside-aadD, and tetracycline-tetL was highest in young pigeons, while the abundance of multidrug-acrB, multidrug-mdtF, and multidrug-transporter was highest in breeding pigeons ( Figure 5 G in.

[0107] Example 2: Low-throughput PCR verification of intestinal flora indicators of young pigeons and breeding pigeons.

[0108] 1. Materials and Methods

[0109] 1.1 Sample Source

[0110] Three fecal samples were collected from young pigeons (2-5 months old) from a pigeon farm in Liuhe District, Nanjing, and designated as the YP-validation group; three fecal samples were collected from pigeons (over 5 months old) from a pigeon farm in Gaochun District, Nanjing, and designated as the OP-validation group. During the collection process, care was taken to prevent fecal material from contacting the ground. The collected fecal samples were transported to the laboratory on ice and stored at -80°C.

[0111] 1.2 DNA extraction

[0112] DNA was extracted from each pigeon feces sample using TIANamp Stool DNA Kit (Tiangen, China). The degree of DNA degradation was analyzed using 1% agarose gel, and the concentration of DNA was determined using a fluorescent multi-mode microplate reader Infinite M Nano (Tecan, Switzerland).

[0113] 1.3 Primer design and synthesis

[0114] Select some of the young pigeon and pigeon indicators identified in Example 1, including the dominant microbial phylum Bacillus in young pigeon samples, the most abundant antibiotic resistance genes tetracycline-tetL and tetracycline-tetW in young pigeon samples, and the most abundant antibiotic resistance genes multidrug-arcB and multidrug-mdtF in pigeon samples, and use Primer Premier 5.0 software to design corresponding PCR specific primers for low-throughput verification. The designed PCR primer information is shown in Table 2.

[0115] Table 2 PCR primer information

[0116]

[0117] 1.4 PCR detection

[0118] A PCR amplification instrument (Analytic Jena, Germany) was used to perform low-throughput detection of the above young pigeon and breeding pigeon indicators. The PCR reaction system (20 μL) was: PCR Master Mix 10μL (YEASEN), 0.5μL of upstream and downstream primers (10μM), 1μL of DNA, supplemented with nuclease-free ddH2O to 20μL. The PCR reaction program is: 94℃ pre-denaturation for 5min; 94℃ denaturation for 20s, 58℃ annealing for 30s, 72℃ extension for 1min, a total of 35 cycles; 72℃ final extension for 8min. After the PCR is completed, 1.5% agarose gel electrophoresis is used for detection.

[0119] 2. Results and Analysis

[0120] 2.1PCR test results

[0121] The results of electrophoresis analysis showed that the genes of Bacillus microorganisms could be detected in the DNA samples of young pigeon feces ( Figure 6 A in the figure); tetracycline-tetL antibiotic resistance gene can be detected in DNA samples of young pigeon feces ( Figure 6 B); tetracycline-tetW antibiotic resistance gene can be detected in young pigeon fecal DNA samples ( Figure 6 C); the multidrug-arcB antibiotic resistance gene can be detected in pigeon fecal DNA samples ( Figure 6 D in the figure); the multidrug-mdtF antibiotic resistance gene can be detected in the DNA samples of pigeon feces ( Figure 6 E). It has been verified that the above low-throughput verification results are consistent with the results of the metagenomic sequencing analysis in Example 1.

[0122] The present invention provides a method and idea for identifying young pigeons and laying pigeons based on metagenomic sequencing. There are many methods and approaches to implement the technical solution. The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the protection scope of the present invention. All components not specified in this embodiment can be implemented by existing technologies.

Claims

1. A method for identifying young pigeons and laying pigeons based on metagenomic sequencing, characterized in that: Pigeon feces were collected and DNA samples were extracted. Metagenomic sequencing and bioinformatics analysis were performed on the DNA samples. Intestinal microbial flora characteristics and antibiotic resistance gene characteristics were used as identification indicators to distinguish young pigeons from breeding pigeons.

2. The method according to claim 1, characterized in that The DNA concentration of the DNA sample is >10 ng / μL and the total amount is >1 μg.

3. The method according to claim 1, characterized in that The metagenomic sequencing of the DNA sample specifically uses a high-throughput sequencing platform to perform double-end 150bp high-throughput sequencing on the metagenomic sequencing library constructed using the DNA sample.

4. The method according to claim 1, characterized in that: The bioinformatics analysis comprises the following steps: (1) Quality control and assembly of the original reads of metagenomic sequencing: remove reads with high alignment similarity from the host genome or contamination, assemble the clean reads after quality control to generate contigs, and filter sequences less than 500 bp; (2) Gene prediction and construction of non-redundant gene sets: Prodigal was used to predict the open reading frames of contigs, and genes with a length of ≥100 bp were selected as candidate gene sets. The gene sequences predicted from all samples were clustered using CD-HIT software, and the longest gene in each cluster was taken as the representative sequence to construct a non-redundant gene set. (3) The gene set protein sequences predicted after metagenomic sequencing were compared with the non-redundant protein database by blastp homology to obtain functional annotations and homologous species information; according to the NCBI microbial taxonomy information database, the species classification annotation information of the genes was obtained, and the relative abundance of species was statistically analyzed at the kingdom, phylum, class, order, family, genus, and species levels; (4) Compare the gene set protein sequences predicted after metagenomic sequencing with the KEGG database to obtain the KO number corresponding to the sequence, obtain the pathway and module annotation information of the sequence based on the connection between KO and pathway and module, and count the abundance of each functional level of KEGG in each sample; (5) Compare the gene set protein sequences predicted after metagenomic sequencing with the carbohydrate-active enzyme database CAZy to obtain the corresponding carbohydrate-active enzyme annotation information, and count the abundance of each functional level of CAZy; (6) The gene set protein sequences predicted after metagenomic sequencing were compared with the SEED database by blastp homology to obtain functional annotations and homologous species information. According to the functional classification level of SEED Subsystem, the SEED classification annotation information of the gene was obtained, and the abundance of each functional level of SEED in each sample was counted; (7) Compare the gene set protein sequences predicted after metagenomic sequencing with the antibiotic resistance gene database CARD to obtain the types and numbers of corresponding antibiotic resistance genes, and count the abundance of resistance genes and antibiotics in each sample; (8) Compare the intestinal microbial flora characteristics of young pigeons and breeding pigeons according to the abundance of steps (3) to (6), and compare the antibiotic resistance gene characteristics of young pigeons and breeding pigeons according to the abundance of step (7), so as to distinguish young pigeons from breeding pigeons.

5. The method according to claim 1 or 4, characterized in that: The intestinal microbial flora characteristics include: any one or a combination of intestinal flora composition and structure characteristics, intestinal microbial gene functions, intestinal flora gene function signal pathways, and intestinal microbial carbohydrate metabolism characteristics; the antibiotic resistance gene characteristics are antibiotic resistance gene diversity characteristics and / or intestinal microbial antibiotic resistance gene composition.

6. The method according to claim 5, characterized in that (I) in the composition and structure characteristics of the intestinal flora, when Bacillus, tail phage and Pseudomonas are the dominant microbial bacterial phyla, and Streptococcus, unclassified tail virus and Enterococcus are the dominant microbial genera, the identified pigeon is a young pigeon; when Pseudomonas and tail phage are the dominant microbial bacterial phyla, and Escherichia coli, unclassified Enterobacter and Carlton Yer virus are the dominant microbial bacterial genus, the identified pigeon is a brooding pigeon; or, (II) Among the intestinal microbial gene functions, when the ribosomal LSU bacteria, DNA replication and universal GTPases gene functions are the dominant gene functions, the identified pigeons are young pigeons; when the DNA replication, peptidoglycan biosynthesis, bacterial glycerolipid and glycerophospholipid metabolism gene functions are the dominant gene functions, the identified pigeons are breeding pigeons; or, (III) In the intestinal flora gene function signal pathway, when the metabolic pathway, biosynthesis of secondary metabolites and biosynthesis of amino acids are the dominant KEGG signal pathways, the identified pigeons are young pigeons; when the metabolic pathway, biosynthesis of secondary metabolites and microbial metabolism in different environments are the dominant KEGG signal pathways, the identified pigeons are breeding pigeons; or, (IV) In the intestinal microbial carbohydrate metabolism characteristics, when glycoside hydrolase 1, glycosyltransferase 35 and glycoside hydrolase 13 are the dominant carbohydrate metabolism types, the pigeon is identified as a young pigeon; when glycosyltransferase 2, glycosyltransferase 35 and glycosyltransferase 1 are the dominant carbohydrate metabolism types, the pigeon is identified as a breeding pigeon.

7. The method according to claim 5, characterized in that The antibiotic resistance gene diversity characteristics include any one or a combination of antibiotic resistance gene family types, antibiotic resistance types and antibiotic resistance mechanism types.

8. The method according to claim 7, characterized in that (i) Among the antibiotic resistance gene family types, when ANT(4'), tetracycline resistance ribosomal protection protein and major promoter superfamily antibiotic efflux pump are the dominant antibiotic resistance gene family types, the identified pigeons are young pigeons; when resistance nodule cell division antibiotic efflux pump, major promoter superfamily antibiotic efflux pump and ATP binding cassette antibiotic efflux pump are the dominant antibiotic resistance gene family types, the identified pigeons are breeding pigeons; or, (ii) among the antibiotic resistance types, when aminoglycoside antibiotics, tetracycline antibiotics and fluoroquinolone antibiotics are the dominant antibiotic resistance types, the identified pigeons are young pigeons; when fluoroquinolone antibiotics, penicillin, disinfectants and antiseptics are the dominant antibiotic resistance types, the identified pigeons are breeding pigeons; or, (iii) among the antibiotic resistance mechanism types, when antibiotic inactivation, antibiotic target protection and antibiotic efflux are the dominant antibiotic resistance mechanism types, the identified pigeons are young pigeons; when antibiotic efflux, antibiotic target change and antibiotic inactivation are the dominant antibiotic resistance mechanism types, the identified pigeons are breeding pigeons; or, (iv) In the composition of intestinal microbial antibiotic resistance genes, when tetracycline-tetW, aminoglycoside-aadD and tetracycline-tetL were the dominant antibiotic resistance genes, the identified pigeons were young pigeons; when multidrug-acrB, multidrug-mdtF and multidrug-transporter were the dominant antibiotic resistance genes, the identified pigeons were breeding pigeons.

9. A high-throughput detection system for identifying young pigeons and breeding pigeons based on metagenomic sequencing, characterized in that: The high-throughput detection system includes a processor and a memory; wherein the memory is used to store instructions, and when the processor executes the stored instructions, the high-throughput detection system implements the steps of the method described in any one of claims 1 to 8.

10. A kit for distinguishing young pigeons and laying pigeons, characterized in that: The kit comprises a DNA sample extraction reagent from pigeon feces, a DNA sample metagenomic sequencing reagent, and a DNA sample metagenomic sequencing data analysis software.

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