A method for metagenomic tracing analysis of mobile genetic elements of soil bacteria

Abstract

The present invention discloses a method for metagenomic traceability analysis of mobile genetic elements (MGEs) of soil bacteria, and the steps are as follows: Step 1, construct an MGEs database; Step 2, construct a non-redundant protein set; Step 3, use the Reads Mapping method to annotate the MGEs information of the non-redundant protein set to obtain the MGEs species traceability information; Step 4, construct an MGEs species traceability database after screening and removing redundancy; Step 5, encapsulate the MGEs species traceability database and build an analysis platform; Step 6, collect environmental samples for DNA extraction and library construction, and perform metagenomic sequencing analysis using Illumina sequencing technology to obtain raw data; Step 7, use FastQC to perform quality control on the raw data and remove human genome contamination to obtain quality-controlled data Cleandata; Step 8, input the quality-controlled data into the analysis platform to obtain the MGEs species traceability abundance table. The present invention greatly improves the speed and accuracy of MGEs analysis, and realizes the rapid analysis of MGEs species traceability based on metagenomic sequencing data.

Description

Technical Field

[0001] The present invention relates to the fields of soil biology and bioinformatics, and specifically to a method for metagenomic tracing analysis of mobile genetic elements of soil bacteria. Background Art

[0002] Antibiotic resistance genes (ARGs) are a new type of soil biological pollutant. Their enrichment and diffusion in the soil-plant-environment exacerbate the health risks of animals, plants, and humans. The spread and diffusion of ARGs must rely on the help of mobile genetic elements (MGEs) to achieve risk transmission among soil microorganisms, especially soil bacteria. MGEs mainly include plasmids, prophages, and transposable elements, etc. They usually carry a variety of functional genes on the genome that can endow the host with resistance, pathogenicity, etc., and play an important role in the evolution and adaptation of bacteria. Once soil bacteria use MGEs to transmit ARGs to highly pathogenic bacteria in the human body, it can threaten the health of humans and animals through direct contact, and can also indirectly threaten public microbial safety by contaminating food. Therefore, clarifying the abundance of MGEs and the types of host bacteria in the soil environment is crucial for preventing public risks of drug resistance.

[0003] At present, the research on MGEs in the environment mainly focuses on detection based on high-throughput sequencing technology. However, the types of MGEs detected by qPCR and MGEs gene chip technology are very limited. In recent years, with the decline in sequencing costs, shotgun metagenomic sequencing has been increasingly used for the detection of MGEs. However, the MGEs detection technology based on metagenomics is also not perfect. Although there are many MGEs sequence databases, such as mMGE, IS-Finder, etc., due to different research focuses, the databases are very scattered and not well integrated. When conducting quantitative analysis of MGEs in samples, researchers often have to compare multiple MGEs libraries separately, which brings great inconvenience and there is also a lot of redundancy in the obtained data. Although the MGEs detection method based on machine learning can avoid the above complex process, it cannot extract the MGEs sequences in the samples, and its accuracy is far less reliable than Reads alignment. In addition, the current MGEs research cannot achieve species tracing of microorganisms, and the correlation network based on MGEs and microorganisms can only determine the potential high-risk carriers of MGEs in the environment, and cannot conduct precise quantitative analysis of MGEs carried by microorganisms in the environment. Therefore, there is an urgent need to establish an accurate, fast, and efficient MGEs microbial tracing analysis method to provide theoretical and technical support for the research on the spread risk of MGEs and the risk of biological combined pollution. Summary of the Invention

[0004] The purpose of the present invention is to provide a metagenomic traceability analysis method for mobile genetic elements of soil bacteria. By establishing an amino acid and DNA traceability database and an analysis platform for mobile genetic elements (MGEs) of known bacteria, the abundance calculation and species traceability analysis of MGEs can be quickly realized by inputting metagenomic quality control data.

[0005] To achieve the above object, the technical solution adopted by the present invention is:

[0006] A metagenomic traceability analysis method for mobile genetic elements of soil bacteria, which includes the following steps:

[0007] Step 1: Construct an MGEs database;

[0008] Step 2: Construct a non-redundant protein set;

[0009] Step 3: Use the Reads Mapping method to annotate MGEs information for the non-redundant protein set to obtain MGEs species traceability information;

[0010] Step 4: Construct an MGEs species traceability database by screening and removing redundancy;

[0011] Step 5: Package the MGEs species traceability database and build an analysis platform;

[0012] Step 6: Collect environmental samples for DNA extraction and library construction, and perform metagenomic sequencing analysis using Illumina sequencing technology to obtain raw data;

[0013] Step 7: Use FastQC to perform quality control on the raw data and remove human genome contamination to obtain quality control data Cleandata;

[0014] Step 8: Input the quality control data into the analysis platform to obtain an MGEs species traceability abundance table.

[0015] Further, in the above step 1, all MGEs sequences in the public database are integrated, their structured information is sorted out, and redundant sequences are removed to obtain a non-redundant personalized MGEs database for MGEs species traceability.

[0016] Further, in the above step 2, the coding proteins of more than 120,000 reference genomes are collected from the public database, and the cd-hit software is used to remove redundancy to obtain a non-redundant coding gene set of 13,001 species; Prodigal is used to predict the proteins of the non-redundant coding gene set to obtain a non-redundant protein set of 13,001 species.

[0017] Furthermore, the steps for constructing the non-redundant protein set are as follows:

[0018] Step 1: Collect the protein-coding sequences encoded by bacterial reference genomes in the public database and then merge them;

[0019] Step 2: Remove redundant sequences from the merged gene set according to the species classification;

[0020] Step 3: Predict the non-redundant coding gene set and construct a non-redundant protein set.

[0021] Furthermore, in Step 3, the non-redundant protein set is aligned with the self-constructed MGEs database by ReadsMapping. Standard screening is used to match the sequences and species tags are added to achieve species information matching of the MGEs database.

[0022] Furthermore, in Step 5, the ARGs-OAP software is used, and based on the Perl language script, the MGEs species tracing database is encapsulated to complete the construction of the metagenomic tracing platform for soil mobile genetic elements; the Cleandata obtained after metagenomic quality control is input into the completed encapsulated analysis platform, and species tracing annotation and quantitative analysis of mobile genetic elements will be realized. Among them, the quantitative analysis method is as follows: The metagenomic sequencing reads are aligned and quantified by using the USEARCH+BLASTX built in ARGs-OAP to align the MGEs species tracing database, that is, the quality control data of all samples are used to align the MGEs species tracing database, and the reads abundance of each gene for each sample is obtained for mobile genetic elements; the abundance quantitative unit of the final result is the number of reads of the matching gene per million reads.

[0023] Furthermore, after the quality control data is input into the analysis platform, the obtained results can be visually displayed.

[0024] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0025] (1) At present, there is no good analysis method for MGEs based on metagenomics. The MGEs databases are messy and scattered, and researchers have to align the sequencing data with each database separately, and the process is cumbersome and complex. This patent systematically integrates and constructs a personalized MGEs database, which not only includes the current mainstream MGEs databases, but also reorganizes the structure. This will greatly improve the speed and accuracy of MGEs analysis.

[0026] (2) When using the non-redundant protein set for MGEs annotation, the species taxonomic annotation of MGEs in the global bacterial genomes is also obtained. This will play an important role in analyzing the MGEs map on a global scale and also provide a reference basis for explaining the horizontal gene transfer risk between microorganisms.

[0027] (3) While reconstructing the structured MGEs database, the present invention also realizes the construction of the MGEs species traceability database and the establishment of the analysis platform; breaks the current method of MGEs species traceability based only on the correlation analysis network, and realizes the rapid analysis of MGEs species traceability based on metagenomic sequencing data. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 is the flow chart of MGEs traceability analysis;

[0029] Figure 2 is the statistical chart of the biological composite pollution index in a specific example, where: Gene to ppm: the name of the MGEs gene aligned to the database, Subtype: the name of the Type of mobile genetic element, Type: the name of the Subtype of mobile genetic element;

[0030] Figure 3 is the relative abundance of MGEs (at the Type classification level) in a specific example;

[0031] Figure 4 is the relative abundance (ppm) of MGEs carried by the top 15 species in a specific example. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0032] The present invention will be described in detail below with reference to the drawings and specific embodiments.

[0033] As Figure 1 , a metagenomic traceability analysis method for mobile genetic elements of soil bacteria provided by the present invention is specifically divided into the following parts: (1) construction of a personalized MGEs database; (2) construction of a non-redundant protein set; (3) construction of an MGEs species traceability database and process encapsulation. Based on the above method, the species source tracking of mobile genetic elements can be finally realized, and based on the metagenomic quality control data, the abundance calculation of MGEs for species traceability can be realized.

[0034] The specific introduction to each part is as follows:

[0035] (1) Construction of a personalized MGEs database

[0036] At present, the MGEs databases used by researchers are relatively scattered and the classification criteria are not unified, and there is no structured and relatively comprehensive MGEs database. Therefore, the MGEs sequences in the public database were collected, manually filtered and screened to remove the redundancy between the sequences. Referring to the SARG database classification standard, its annotation information was structurally divided and integrated according to Type and Subtype, and finally a non-redundant personalized MGEs database was obtained, which will be used for MGEs species traceability.

[0037] (2) Construction of non-redundant protein set

[0038] Collect the coding proteins of more than 120,000 reference genomes from public databases, and use the cd-hit software to remove redundancy to obtain a non-redundant coding gene set of 13,001 species. Use Prodigal to predict the proteins of the non-redundant coding gene set to obtain the corresponding non-redundant protein sets of 13,001 species. The steps are as follows:

[0039] Step 1: Collect and merge the coding protein sequences of bacterial reference genomes in public databases;

[0040] Step 2: Remove redundant sequences from the merged gene set according to the species classification;

[0041] Step 3: Predict the non-redundant coding gene set to construct a non-redundant protein set.

[0042] (3) Encapsulation of the MGEs species traceability library

[0043] Perform Reads Mapping alignment on the non-redundant protein set with the self-built personalized MGEs database, screen the matched sequences using strict criteria, and label them with species tags to achieve species information matching in the MGEs database. Write a Perl language script to extract the screened sequence based on the gene ID number after rhizosphere screening to construct the MGEs species traceability Fasta database, which contains a total of 24,773 MGEs sequences. Use the ARGs-OAP software and encapsulate the MGEs species traceability database based on the Perl language script to complete the construction of the metagenomic traceability soil mobile genetic element analysis platform. Input the Cleandata obtained after metagenomic quality control into the completed analysis platform, and the species traceability annotation and quantitative analysis of mobile genetic elements will be realized. The internal quantitative analysis method of the software is as follows:

[0044] The internal algorithm of the software is to align and quantify the metagenomic sequencing reads by using the USEARCH + BLASTX in ARGs-OAP to align the MGEs species traceability database. That is, the quality control data of all samples are used to align the MGEs species traceability database with BLASTX to obtain the mobile genetic elements and calculate the reads abundance of each gene in each sample. Therefore, the abundance quantification unit of the final result is the number of reads of the matching gene per million reads (ppm).

[0045] The following will be further described in combination with specific examples:

[0046] This implementation example was collected from a tomato field in Qilin Town, Nanjing. A total of 40 healthy and diseased rhizosphere soil samples were collected in situ and then input into the soil mobile genetic element tracing analysis platform after metagenomic sequencing analysis. The specific steps are as follows:

[0047] 1. Use Perl language to complete the encapsulation of the MGEs species tracing database and build an MGEs species tracing analysis platform based on metagenomic sequencing.

[0048] 2. Collect environmental samples and extract DNA. After completing the extraction of genomic DNA, use 1% agarose gel electrophoresis to detect the extracted genomic DNA. After completing the construction of the PE library and bridge PCR, use Illunimanovaseq6000 for sequencing.

[0049] 3. Data analysis starts from the raw sequences downloaded from the sequencer. First, perform optimization processes such as adapter removal, quality trimming, and contamination removal on the raw sequences to obtain the quality-controlled data Cleandata as the input source for the analysis platform.

[0050] 4. Input the quality-controlled data into the MGEs species tracing analysis platform. Finally, an MGEs species tracing abundance information table ( Figure 2 ) will be obtained and visualized to show the abundance composition of MGEs Type species ( Figure 3 ) and the abundance composition of MGEs carried by species ( Figure 4 ).

[0051] The above shows and describes the basic principles, main features, and advantages of the present invention. Those of ordinary skill in the art should understand that the above embodiments do not limit the protection scope of the present invention in any form. Any technical solutions obtained by means of equivalent replacement and the like fall within the protection scope of the present invention.

[0052] Parts not involved in the present invention are the same as the prior art or can be implemented using the prior art.

Claims

1. A method for metagenomic tracing analysis of mobile genetic elements of soil bacteria, characterized in that it includes the following steps: Step 1: Construct an MGEs database; integrate all MGEs sequences in the public database, organize their structured information and remove redundant sequences to obtain a non-redundant personalized MGEs database for MGEs species tracing; Step 2: Construct a non-redundant protein set; collect the coding proteins of more than 120,000 reference genomes from the public database, use the cd-hit software to remove redundancy to obtain a species non-redundant coding gene set; use Prodigal to predict the proteins of the non-redundant coding gene set to obtain the corresponding species non-redundant protein set. The specific steps are as follows: Step 1: Collect and merge the coding protein sequences of bacterial reference genomes in the public database; Step 2: Remove redundant sequences from the merged gene set according to the classification of species; Step 3: Predict the non-redundant coding gene set to construct a non-redundant protein set; Step 3: Use the Reads Mapping method to annotate the MGEs information of the non-redundant protein set to obtain MGEs species tracing information; compare the non-redundant protein set with the self-constructed MGEs database by Reads Mapping, use standard screening to match the sequences and label the species to achieve species information matching of the MGEs database; Step 4: Construct an MGEs species tracing database by screening and removing redundancy; Step 5: Package the MGEs species tracing database and build an analysis platform; use the ARGs-OAP software and encapsulate the MGEs species tracing database based on Perl language scripts to complete the construction of the metagenomic tracing analysis platform for mobile genetic elements of soil; input the Cleandata obtained after metagenomic quality control into the completed packaged analysis platform, and the species tracing annotation and quantitative analysis of mobile genetic elements will be realized; the quantitative analysis method is: compare and quantify the metagenomic sequencing reads by using the USEARCH+BLASTX in ARGs-OAP to compare the MGEs species tracing database, that is, use BLASTX to compare the quality control data of all samples with the MGEs species tracing database to obtain the mobile genetic elements and calculate the reads abundance of each gene in each sample; the abundance quantitative unit of the final result is the number of reads of the matching gene per million reads; Step 6: Collect environmental samples for DNA extraction and library construction, and perform metagenomic sequencing analysis using Illumina sequencing technology to obtain raw data; Step 7: Use FastQC to perform quality control on the raw data and remove human genome contamination to obtain quality control data Cleandata; Step 8: Input the quality control data into the analysis platform to obtain the MGEs species tracing abundance table.

2. The method for metagenomic tracing analysis of mobile genetic elements of soil bacteria according to claim 1, characterized in that after the quality control data is input into the analysis platform, the obtained results are visually displayed.

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