CN115852001A - Wheat pathogenic bacteria detection method and application thereof - Google Patents
Wheat pathogenic bacteria detection method and application thereof Download PDFInfo
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- CN115852001A CN115852001A CN202211477406.XA CN202211477406A CN115852001A CN 115852001 A CN115852001 A CN 115852001A CN 202211477406 A CN202211477406 A CN 202211477406A CN 115852001 A CN115852001 A CN 115852001A
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Abstract
The application discloses a wheat pathogenic bacteria detection method and application thereof. Extracting genome DNA of a wheat sample to be detected, performing whole-genome high-throughput sequencing, performing quality control and filtration on sequencing data, comparing the sequencing data with a known wheat genome, extracting reads which cannot be compared with the known wheat genome, performing metagenome assembly, comparing the metagenome assembly with a wheat exogenous species genome database, and identifying pathogenic bacteria of the wheat sample to be detected and the abundance of each pathogenic bacteria according to a comparison result; the wheat foreign species genome database includes all known wheat pathogens and nucleic acid sequences of fungi and bacteria carried by wheat. The method can comprehensively detect all pathogenic bacteria possibly contained in the wheat sample to be detected, and has the advantages of high detection efficiency and simple and convenient operation; provides a new scheme and a new way for solving the pathogen transmission risk and the technical barrier of the wheat trade.
Description
Technical Field
The application relates to the technical field of wheat pathogenic bacteria detection, in particular to a wheat pathogenic bacteria detection method and application thereof.
Background
Pathogenic bacteria carried on wheat are introduced and colonized in a trade mode, so that great risk is brought to domestic agricultural production safety. At present, more than 200 diseases are existed on wheat all over the world, part of the diseases can be transmitted by inputting wheat, and comprise quarantine pathogens and some potential risk pathogens, and the seed-transmitted diseases are transmitted by a trade route and are the objects of major concern of plant quarantine and agricultural departments.
At present, the detection of wheat pathogenic microorganisms mainly comprises morphological, biochemical, serological and molecular biological methods. Most of these detection methods are based on analysis of a pathogen after isolation and culture. Taking international standard (ISPM 27.2006Annex 4. Just as the microorganisms which have been isolated and cultured are only a very small fraction of them, the number of pathogenic microorganisms isolated and cultured by humans is as small. Obligate parasitism, rigorous culture conditions, slow growth speed, low abundance in populations, special ecological requirements and the like influence the isolated culture of pathogenic microorganisms, and the limitations provide challenges for researching classification, diversity, pathogenic mechanism, detection method, risk analysis and prevention and control measures of pathogenic bacteria. In the field of plant quarantine, the distinction of quarantine species and similar species thereof, the omission of important pests, the formulation of quarantine treatment measures and the like are all restricted by the condition that microorganisms cannot be separated and cultured, and the restrictive factor has more special and important significance for the formulation of a detection method and the judgment of results. Therefore, there is a need to identify and analyze the pathogenic microorganisms on wheat, which cannot be separately cultured, to evaluate the harmfulness and risk thereof, and to supplement a list of harmful organisms with important concerns based on the conventional risk analysis, so as to cope with the technical barriers in the international plant and plant product trade.
The high-throughput sequencing technology has the functions of efficiently, quickly, cheaply and comprehensively analyzing complex nucleotides. Currently, high throughput testing techniques have been applied in clinical medicine to comprehensively detect mutations in disease or medication related genes, and further guide medication and prognosis.
However, the existing wheat pathogenic microorganism detection based on high-throughput sequencing only identifies species by reading a certain length of nucleic acid fragments, has low accuracy and cannot perform quantitative analysis; further, the result judgment requires confirmation in combination with morphology and isolation culture identification. More importantly, the existing wheat pathogenic microorganism detection based on high-throughput sequencing cannot find unknown pathogenic bacteria and can not predict potential risks.
Therefore, there is a need to develop a new method for detecting pathogenic bacteria of wheat more comprehensively and effectively to cope with the pathogenic bacteria propagation risk and the technical barrier of wheat trade.
Disclosure of Invention
The application aims to provide a novel wheat pathogenic bacterium detection method and application thereof.
The following technical scheme is adopted in the application:
one aspect of the application discloses a wheat pathogenic bacteria detection method, which comprises the following steps;
(1) Extracting genome DNA of a wheat sample to be detected;
(2) Carrying out whole genome high-throughput sequencing on the genome DNA;
(3) Performing quality control and filtration on the high-throughput sequencing data;
(4) Comparing the genome DNA sequencing result of the wheat sample to be detected with the known wheat genome, and extracting reads (i.e. unmapped reads) which cannot be compared with the known wheat genome;
(5) Performing metagenome assembly on reads which cannot be compared with known wheat genomes to obtain a sequence for identifying pathogenic bacteria;
(6) Comparing the sequence for identifying the pathogenic bacteria obtained in the step (5) with a genomic database of exogenous wheat species, and identifying the pathogenic bacteria of the wheat sample to be detected and the abundance of each pathogenic bacteria according to the comparison result;
wherein, the wheat exogenous species genome database comprises all known wheat pathogenic bacteria and nucleic acid sequences of fungi and bacteria carried by wheat.
The method for detecting the pathogenic bacteria of the wheat can comprehensively detect all pathogenic bacteria possibly contained in a wheat sample to be detected, and has the advantages of high detection efficiency and simple and convenient operation. Particularly, under the condition that a wheat exogenous species genome database is constructed, only genome DNA extraction and high-throughput sequencing are needed to be carried out on a wheat sample to be detected, and then sequencing data are compared and analyzed, so that comprehensive pathogenic bacteria carrying information of the wheat sample to be detected can be obtained.
In an implementation manner of the present application, the detection method of the present application further includes:
(7) And in the sequences for identifying the pathogenic bacteria, the sequences of the exogenous wheat species genome database cannot be compared, and species identification is further carried out by adopting a species specific marker gene database to obtain new wheat pathogenic bacteria.
In the detection method, a sequence which is not compared with a wheat exogenous species genome database is further compared with a species specific marker gene database, so that new wheat pathogenic bacteria are obtained through identification. It is understood that in the present application unmapped reads are actually reads that do not match the host wheat genome, and these reads can be identified as gene sequences of foreign species of wheat, i.e., wheat pathogens or fungi or bacteria carried by wheat; therefore, the unmapped reads are assembled and then compared with a genomic database of exogenous wheat species, so that information of all pathogenic bacteria or other fungi and bacteria on the wheat sample to be detected can be obtained; further, if the sequences assembled by the partial unmapped reads do not match with the genome database of the exogenous species of wheat, the sequences are not the nucleic acid sequences of the existing known wheat pathogenic bacteria or fungi or bacteria carried by wheat; therefore, it was judged as a new wheat pathogen.
In one implementation of the present application, the species specific marker gene database is MetaPhlAn; metaPhlAn contains 99500 bacterial, 500 eukaryotic species-specific marker genes, and species can be identified by short-fragment data.
In addition, metaPhlAn is the qualitative and quantitative analysis of the flora according to the sequencing data of the metameome; the basic technical route and concept are as follows: 1) analyzing sequence information of a known database to finally form a marker unique to each species, 2) comparing sequencing reads with the markers to determine species types, and 3) calculating the comparison quantity of each species and the length of the markers to obtain the content. It will be appreciated that MetaPhlAn is only a database of species specific marker genes employed in one implementation of the present application and does not exclude the possibility of using other databases or means for the identification of new pathogens of wheat.
In one implementation of the present application, the data volume of the high-throughput sequencing data of step (3) is not less than 20G.
It should be noted that the data size of the high-throughput sequencing data is only to ensure that the sequencing data can more effectively and widely cover more pathogenic bacteria; it is understood that if the data size is too small, some pathogenic bacteria may not be accurately and effectively represented in the sequencing data. Of course, the data volume of the high throughput sequencing data can also be less than 20G if the requirements for detection are low.
In one implementation of the present application, the step (3) performs quality control and filtering on the high throughput sequencing data, including performing Q20 and Q30 detection on the off-line data, and performing quality control and filtering on the sequencing data by using fastp.
It should be noted that the high-throughput sequencing data, i.e., the off-line data of the sequencer, and the specific parameters and conditions for quality control and filtering thereof, may refer to the existing off-line data processing scheme, which is not limited herein.
In one implementation of the present application, the wheat foreign species genome database comprises 3740 triticale pathogenic species, and 1584347 nucleic acid sequences.
In one implementation mode of the application, the wheat exogenous species genome database is constructed by adopting Kraken2-build in Kraken 2.
It should be noted that Kraken2-build in Kraken2 is only a method for constructing a genomic database of exogenous wheat species used in an implementation manner of the present application, and does not exclude that other methods for constructing a genomic database of exogenous wheat species may also be used.
In one implementation manner of the application, in the step (4), the genome DNA sequencing result of the wheat sample to be detected is compared with the known wheat genome by the bwa mem, and paired reads which cannot be compared with the known wheat genome are extracted by samtools.
It should be noted that bwa mem and samtools are only comparison and pair reads extraction tools specifically adopted in one implementation manner of the present application, and do not exclude that other comparison and pair reads extraction tools may also be adopted.
In one implementation manner of the present application, metagenomic assembly is performed on reads that cannot be aligned to a known wheat genome by using metasapides in step (5).
It should be noted that metagenomic libraries are only the multifunctional metagenomic splicing tool specifically adopted in one implementation manner of the present application, and it is not excluded that metagenomic assembly can be performed on unmapped reads by using other splicing tools.
In another aspect, the application discloses the use of the detection method of the present application in the detection or identification of phytopathogens.
The key point of the wheat pathogenic bacteria detection method is that genome DNA of a plant sample to be detected is extracted and subjected to whole genome sequencing, then the genome sequence of the plant to be detected is removed from sequencing data, and the remaining sequencing data can be regarded as pathogenic bacteria of the plant to be detected or fungi and bacteria carried by the pathogenic bacteria; therefore, the pathogenic bacteria information of the plant to be detected can be accurately and comprehensively obtained. Therefore, the detection method of the present application is not limited to the detection of pathogenic bacteria of wheat, and can be used for the detection or identification of other plant pathogenic bacteria.
The beneficial effect of this application lies in:
according to the wheat pathogenic bacterium detection method, whole genome sequencing is carried out on a sample to be detected, and then genome information of wheat serving as a host is removed from sequencing data, so that all pathogenic bacterium information on the sample to be detected can be obtained; therefore, all pathogenic bacteria possibly contained in the wheat sample to be detected can be comprehensively detected, the detection efficiency is high, and the operation is simple and convenient; provides a new scheme and approach for solving the pathogen transmission risk and technical barrier of wheat trade.
Detailed Description
The present application will be described in further detail with reference to specific examples. The following examples are intended to be illustrative of the present application only and should not be construed as limiting the present application.
Examples
1. Test material
The original wheat sample is provided and stored by the Shenzhen customs animal and plant inspection and quarantine technology center, and the storage number is UW-01.
2. Test procedure
1. Genomic DNA preparation
1g of wheat sample is taken, liquid nitrogen grinding is carried out on the wheat sample to be pulpous, and then the wheat genome DNA is extracted by using a plant universal genome extraction kit. This example specifically used a plant genome extraction kit, qiagen, USA.
The extracted genomic DNA was quantified using Qubit3.0 and fragment quality was checked using Aglient 2100.
The results showed that the genomic DNA concentration was 200 ng/. Mu.L, the total amount was 500ng, OD260/280:1.9, has no impurity pollution such as RNA, protein and the like, and meets the requirement of library construction and sequencing.
2. High throughput sequencing
According to different second generation sequencing platforms, matched library building kits are selected and completed according to the instruction. According to different second generation sequencing platforms, a matched sequencing kit is selected and completed according to the instruction. The data volume is not less than 20G. This example used a PCR-free shot-gun genome sequencing method. Randomly interrupting the prepared genome DNA, and using a second-generation sequencing platform BGISEQ-500 to construct a library for sequencing. This example specifically commissioned Shanghai Linn Biotechnology, inc. to perform the library construction and sequencing.
3. Data processing
1. Quality control
Detecting the off-line data such as Q20 and Q30; and (4) performing quality control on the sequencing data by using fastp, and removing sequences with low quality values and low complexity. In the embodiment, a data analysis module carried by a sequencing platform BGISEQ-500 is adopted for quality control and filtration.
2. Self-building exogenous species genome database
The method comprises the steps of sorting a list of plant pathogenic fungi and bacterial species on wheat at home and abroad, determining taxid of each species in NCBI taxonomy, and extracting all sequences of related species from an NT library according to the taxid. And (3) constructing a foreign species database, namely a wheat foreign species genome database, by using Kraken2-build in Kraken 2. The genome database of exogenous wheat species constructed in this example comprises a total of 3740 species, 1584347 sequences.
The concrete process of Kraken2-build for constructing the database is as follows:
kraken2-build--download-taxonomy--db wheat_pathon
kraken2-build--add-to-library 441_USA_taxid.uniq.fa--db wheat_pathon
3. comparison of
After high throughput sequencing data screening and filtering, the obtained sequencing result comprises 172678697 pairs of reads, and the total length is 51803609100bp.
Pairs of unmapped reads were extracted with samtools by aligning bwam with the wheat genome (iwgsc _ refseq 2.1_ assembly. Fa).
In the embodiment, 23234972 obtained by extraction can not compare with reads to obtain a wheat genome, and the total length is 6970491600bp. Reads that do not align to the wheat genome are labeled unmapped reads.
The procedure for the bwa mem alignment and samtools extraction is as follows:
bwa mem iwgsc_refseqv2.1_assembly.fa-t 56UW_R1.fq.gz UW_R2.fq.gz-oUW_wheat.sam
samtools view-f 12UW_wheat.sam-b-1-o unmapped.bam-@40
samtools fastq-1UW_unmapped_1.fq-2UW_unmapped_2.fq unmapped.bam-@10
4. metagenomic assembly
Metagenomic assembly of unmapped reads with metaSPADes, including 3233659 sequences, total 900719315bp, with N50 270bp.
The procedure for meta bands assembly is as follows:
metaspades.py-o.-1UW_unmapped_1.fq.gz-2UW_unmapped_2.fq.gz--disable-gzip-output-t 30-m 120
5. metagenome assembly result identification
Using Kraken to identify that the metagenome assembly result contains exogenous species; bracken was used to identify the abundance of each species. The assembled sequence is compared with a wheat exogenous species genome database, and pathogenic bacteria of a wheat sample to be detected and the abundance of each pathogenic bacteria are identified according to the comparison result. Finally, visualization was performed with Pavian results.
The Kraken identification procedure is as follows:
kraken2-db wheat_pathon--threads 60--classified-out classified.fa--output wheat_pathon_kraken2.output--report wheat_pathon_kraken2.report--use-names scaffolds.fasta
bracken-build-d wheat_pathon
bracken-d wheat_pathon-i wheat_pathon_kraken2.report-owheat_pathon_kraken2.count-w wheat_pathon_kraken2.report.new-l S
6. identification of unknown species
For species not in the potential list, i.e. sequences that could not be aligned to the genomic database of exogenous species in wheat, identification was performed using MetaPhlAn, which by means of a database of species-specific marker genes comprising 99500 bacteria, 500 eukaryotes, identified the species to which it corresponds from short fragment data.
4. Results
According to the provided list of harmful organisms possibly contained in the wheat and the condition of the harmful organisms on the wheat concerned in China, 43 kinds of bacteria are compared and identified on the wheat sample to be detected in the embodiment, as shown in the table 1. Among them, 19 kinds of pests which are reported to be distributed and harmful on wheat in the country where the wheat is located, and 23 kinds of quarantine pests which are concerned by China are listed in the entry of quarantine pests of plants in the republic of China.
According to the abundance value, the Xanthomonas hyalinus transparent variety Xanthomonas tranlucens pv. Translucens, claviceps purpurea and Fusarium graminearum are three species with the largest biomass, 2 fungi and 1 bacterium, and have larger potential invasion risk.
According to the concerns and hazards analysis, the Brassica napus Leptosphaeria maculans, clavibacter michiganensis subsp. Indicating that the 2 bacteria and 1 fungus should be of high interest.
TABLE 1 wheat harbours phytopathogenic fungi and bacteria
5. To summarize
The research takes all pathogenic fungi and bacteria carried on wheat as research objects, and extracts the whole genome DNA in a sample to carry out high-throughput sequencing and bioinformatics analysis, thereby analyzing the diversity of the pests in the sample and carrying out quarantine identification on the high-risk and quarantine pests which pay attention to the high-risk and quarantine pests.
Compared with the traditional metagenome analysis method, the method of the embodiment has obvious advantages in the aspects of accuracy and operability, and the comparison condition is detailed in Table 2.
TABLE 2 technical comparison of the method established in this study with the conventional metagenomic approach
Therefore, the wheat pathogenic bacteria detection method of the embodiment omits a PCR link, simplifies the library construction process and time, avoids the amplification preference, particularly reduces the repeated reads and InDel copying errors aiming at nucleic acid templates with large GC content difference, complex secondary structures and the like, improves the effective rate of data, ensures the authenticity of a sequence, and effectively reduces the diversity composition in the sample. In addition, a calculation method for accurately identifying sequencing data of a certain pest is obtained and verified in a mode of combining with a genome and multiple genes, qualitative and quantitative detection and identification of species by a bioinformatics method are realized, and the method is suitable for establishing a related detection standard method.
The foregoing is a more detailed description of the present application in connection with specific embodiments thereof, and it is not intended that the present application be limited to the specific embodiments thereof. It will be apparent to those skilled in the art from this disclosure that many more simple deductions or substitutions can be made without departing from the spirit of the disclosure.
Claims (10)
1. A method for detecting wheat pathogenic bacteria is characterized by comprising the following steps: comprises the following steps;
(1) Extracting genome DNA of a wheat sample to be detected;
(2) Carrying out whole genome high-throughput sequencing on the genome DNA;
(3) Performing quality control and filtration on the high-throughput sequencing data;
(4) Comparing a genome DNA sequencing result of a wheat sample to be detected with a known wheat genome, and extracting reads which cannot be compared with the known wheat genome;
(5) Performing metagenome assembly on reads which cannot be compared with known wheat genomes to obtain a sequence for identifying pathogenic bacteria;
(6) Comparing the sequence for identifying the pathogenic bacteria obtained in the step (5) with a genome database of exogenous species of wheat, and identifying the pathogenic bacteria of the wheat sample to be detected and the abundance of each pathogenic bacteria according to the comparison result;
wherein the wheat exogenous species genome database comprises all known wheat pathogenic bacteria and nucleic acid sequences of fungi and bacteria carried by wheat.
2. The detection method according to claim 1, characterized in that: also comprises
(7) And in the sequences for identifying the pathogenic bacteria, the sequences of the exogenous wheat species genome database cannot be compared, and species identification is further carried out by adopting a species specific marker gene database to obtain new wheat pathogenic bacteria.
3. The detection method according to claim 2, characterized in that: the species specific marker gene database is MetaPhlAn;
MetaPhlAn contains 99500 bacterial, 500 eukaryotic species-specific marker genes, and species can be identified by short-fragment data.
4. The detection method according to any one of claims 1 to 3, characterized in that: in the step (3), the data volume of the high-throughput sequencing data is not less than 20G.
5. The detection method according to any one of claims 1 to 3, characterized in that: and (3) performing quality control and filtration on the high-throughput sequencing data, including performing Q20 and Q30 detection on off-line data, and performing quality control and filtration on the sequencing data by using fastp.
6. The detection method according to any one of claims 1 to 3, characterized in that: the wheat exogenous species genome database comprises 3740 wheat pathogenic bacteria species and 1584347 nucleic acid sequences.
7. The detection method according to any one of claims 1 to 3, characterized in that: the wheat exogenous species genome database is constructed by adopting Kraken2-build in Kraken 2.
8. The detection method according to any one of claims 1 to 3, wherein: in the step (4), the genome DNA sequencing result of the wheat sample to be detected is compared with the known wheat genome by the bwa mem, and paired reads which cannot be compared with the known wheat genome are extracted by samtools.
9. The detection method according to any one of claims 1 to 3, characterized in that: in the step (5), metagenomic assembly is carried out on reads which cannot be compared with known wheat genomes by adopting metaSPADes.
10. Use of the detection method according to any one of claims 1 to 9 for the detection or identification of phytopathogens.
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