CN114703265A - Method for detecting biological pollution of soil pathogenic bacteria based on 16SrRNA amplicon sequencing - Google Patents
Method for detecting biological pollution of soil pathogenic bacteria based on 16SrRNA amplicon sequencing Download PDFInfo
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Abstract
The invention discloses a method for detecting biological pollution of soil pathogenic bacteria based on 16SrRNA amplicon sequencing, which comprises the following steps: step one, constructing a soil non-redundant pathogenic bacteria database; step two, completing the extraction of the DNA of the soil/plant sample; step three, completing 16S rRNA gene sequencing of the sample to obtain sequencing off-line data; step four, adopting a DADA2 analysis process to package a soil non-redundant pathogenic bacteria library to obtain a representative sequence; and fifthly, comparing the representative sequence with a soil non-redundant pathogenic bacteria database to annotate pathogenic bacteria to obtain a final ASV species table, and performing visual mapping on the composition, diversity characteristics and the like of the pathogenic bacteria of different samples in the ASV species table through an R language. The invention can quickly and accurately detect pathogenic bacteria, is not easily affected by sample pollution, and can realize the aim of quickly, comprehensively and accurately detecting the pathogenic bacteria.
Description
Technical Field
The invention relates to the field of soil biology and bioinformatics, in particular to a method for accurately identifying and quantifying pathogenic bacteria biological pollution in a soil-plant ecosystem by constructing a non-redundant database of plant and human and livestock pathogenic bacteria and aiming at comparing and annotating high-throughput sequencing data.
Background
With the aggravation of the influence of human activities, the intensive land utilization mode continuously increases the types and the number of soil-borne plant pathogenic bacteria and zoonosis pathogens, and seriously threatens agricultural production and human health. The current methods for detecting pathogenic bacteria mainly comprise a PCR method and a genome analysis method. However, although the PCR method has the advantages of strong targeting property and wide application, it is limited by the number of the primers and is prone to false positive. In addition, genome sequencing can accurately identify drug resistance genes and virulence genes carried by pathogenic bacteria, but is expensive and takes a long time to sequence. And the high-throughput sequencing for the 16S rRNA gene of the bacteria has the advantages of high detection specificity, low sequencing cost and the like, and can perform species annotation based on a relatively complete Silva database. In addition, a large number of research papers have been published in the fields of biodiversity and community ecology by the sequencing method, and the obtained results have reliable guarantee. However, detection of pathogenic bacteria is currently just beginning. In some researches, a probe method is adopted for detection, but the requirement on operators is high, and only 307 pathogenic bacteria can be detected. However, only 300 clinical pathogenic bacteria are collected by a16S amplicon sequencing method for establishing a library for detection, and a method for detecting pathogenic bacteria in a soil-plant ecosystem is lacked. Therefore, a new method for detecting soil pathogenic bacteria based on high-throughput sequencing is constructed, thousands of pathogenic bacteria can be detected at one time, an important method and technology are provided for targeted reduction of pathogenic pollution of a soil-plant ecosystem and maintenance of global integrated health, and the method and the technology have important significance.
Disclosure of Invention
The invention aims to provide a method for detecting biological pollution of soil pathogenic bacteria based on 16SrRNA amplicon sequencing, which obtains a current relatively complete bacterial pathogenic bacteria library through a network and literature resource means, and constructs a soil non-redundant pathogenic bacteria database for high-throughput sequencing analysis by comparing sequence resources of a latest Silva16S bacterial database, thereby realizing rapid and accurate detection of abundance and composition characteristics of the soil pathogenic bacteria based on 16S rRNA gene sequencing analysis, and providing research technology and strategy for reducing pathogenic pollution of a soil-plant ecosystem.
The technical scheme adopted by the invention is as follows: a method for detecting biological contamination of soil pathogenic bacteria based on sequencing of 16SrRNA amplicon, comprising the following steps:
step one, constructing a soil non-redundant pathogenic bacteria database;
step two, completing the extraction of the DNA of the soil/plant sample;
step three, completing 16S rRNA gene sequencing of the sample to obtain sequencing off-line data;
step four, adopting a DADA2 analysis process to package a soil non-redundant pathogenic bacteria library to obtain a representative sequence; the DADA2 is not clustered by similarity any more, but a biological sequence which does not contain amplification and sequencing errors and contains no chimera is obtained by noise reduction, and the clustering accuracy is superior to that of the traditional clustering method. Therefore, by comparing and annotating the representative sequence obtained by the DADA2 process with a soil non-redundant pathogenic bacterium database, the abundance and community diversity characteristics of pathogenic bacteria in a sample can be accurately detected and identified.
And fifthly, comparing the representative sequence with a soil non-redundant pathogenic bacteria database to annotate pathogenic bacteria, and obtaining a final ASV species table.
Further, in the second step, the extraction of the DNA of the soil/plant sample is completed by a FastDNA kit; in the third step, 16S rRNA gene sequencing of the sample is completed by using an Illumina PE250 sequencing platform.
Further, in the fourth step, the data of the sequencing machine is processed by the DADA2 process, wherein the processing comprises the steps of removing joints, controlling quality, reducing noise, combining double ends and removing chimeras, and a representative sequence is obtained.
Further, in the fifth step, by using uclust software, setting a similarity threshold value of > -90%, comparing the representative sequence with a soil non-redundant pathogenic bacterium database, and annotating pathogenic bacteria to obtain an ASV species table.
Further, after the fifth step, the composition and diversity characteristics of pathogenic bacteria of different samples in the ASV species table are visually mapped through an R language.
Furthermore, the abundance and diversity index of the ASV species table are calculated and visualized through the R language, so as to further define the diversity characteristics of pathogenic bacteria in the sample; in calculating the abundance of pathogenic bacteria, log-transforming the annotations to the ASV abundance of pathogenic bacteria;
PR＝log10∑Psum
H＝-∑Pi(ln Pi)
wherein PR represents the abundance of pathogenic bacteria, H represents the diversity index, PsumRepresenting the absolute abundance of ASV, P, of pathogenic bacteria in the sampleiRepresenting the proportion of the i-th individual in the sample, if the total individual number of the sample is N and the i-th individual number is ni, then PiThe more uniform the individual distribution, the greater the H value, between each; the diversity index is maximal if each individual belongs to a different species; if each individual belongs to the same species, its diversity index is minimal.
Compared with the prior art, the invention has the following beneficial effects:
(1) the traditional pathogenic bacteria detection is a bacteria separation culture and serology method, but the method has long time consumption, complex operation and low sensitivity, and the culture time generally needs more than 7 days. The invention does not need separate culture, directly identifies pathogenic bacteria through a DNA sequence, can greatly shorten the detection time and improve the sensitivity.
(2) The PCR amplification method can simply and quickly detect pathogenic bacteria, but is limited by the defects of pathogenic bacteria detection type, easy trace pollution, false positive and the like, and usually needs more than 3 times of repeated verification. The invention is based on a high-throughput sequencing analysis process, can obtain massive species data at one time, and can quickly and accurately detect pathogenic bacteria.
(3) The gene chip method can detect 307 pathogenic bacteria at one time, but a specific probe needs to be designed, and the types of the detected pathogenic bacteria are limited by the number of the probes. The invention amplifies the 16S rRNA gene shared by bacteria, and annotates the gene based on the constructed soil non-redundant pathogenic bacteria database, so that thousands of pathogenic bacteria can be detected at one time.
(4) The gene chip method is also affected by the chip specification and sample contamination. The method can only detect less than 5 samples at one time due to the limitation of chip specification. And after the sample is polluted, false positive and false negative are easy to appear. The invention is based on a high-throughput sequencing platform, can detect large-scale samples at one time, and is not easily affected by sample pollution.
(5) The invention is based on a self-built soil non-redundant pathogenic bacteria database. There is currently a large body of species composition characterization studies based on 16S amplicon sequencing, however, the study of pathogen information is still lacking. In addition, the database can be continuously upgraded according to the deep research of pathogenic bacteria, so that the aim of quickly, comprehensively and accurately detecting the pathogenic bacteria can be fulfilled.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
Drawings
FIG. 1 is a flow chart of detection of soil pathogenic bacteria.
FIG. 2 is a diagram of the structure of a pathogenic bacteria database.
FIG. 3 is a table of ASV species of pathogenic bacteria in the samples of examples.
FIG. 4 is a comparison of disease onset with healthy soil pathogens in example one of the embodiments; wherein a is the comparison of the abundance of pathogenic bacteria in the disease and healthy soil; b is the comparison of the diversity of pathogenic bacteria in the disease and healthy soil.
FIG. 5 is a comparison of abundance versus species composition of different treatment pathogens in example two of the examples; wherein a is the comparison of the abundance of different treatment pathogenic bacteria; b is the species composition of the different handling pathogens.
Detailed Description
The invention is described in detail below by way of examples, and experimental procedures not specifically identified herein under specific conditions are generally carried out under conventional conditions or as recommended by the manufacturer of the apparatus. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art.
Referring to fig. 1 and 2, the present invention provides a method for detecting biological contamination of soil pathogenic bacteria based on 16SrRNA amplicon sequencing, comprising the following specific steps:
(1) constructing a soil non-redundant pathogenic bacteria database based on network resources and literature ways;
(2) completing the extraction of the DNA of the soil/plant sample by a FastDNA kit;
(3) completing 16S rRNA gene sequencing of a sample by utilizing an Illumina PE250 sequencing platform (genome DNA extraction, PCR amplification, fluorescence quantification, Illumina PE250 library construction and Illumina PE250 sequencing), and obtaining sequencing off-line data;
(4) adopting a DADA2 flow to carry out the steps of removing joints (primers and barcode), controlling quality (filtering bases with tail quality lower than 20), reducing noise (correcting errors of sequencing and amplification), merging two ends, removing chimeras and the like on the sequencing off-line data to obtain a representative sequence;
(5) comparing the representative sequence with a soil non-redundant pathogenic bacteria database to annotate pathogenic bacteria by using uclust software (the default similarity threshold is > - < 80%), setting the similarity threshold to be > - < 90%, and obtaining an ASV species table;
(6) and (4) carrying out visual mapping on the composition, diversity characteristics and the like of pathogenic bacteria of different samples in the ASV species table through the R language.
The following is further illustrated with reference to specific examples:
example of the present embodiment-12 parts of soil from Channa Hu south, which was chronically exposed to Ralstonia solanacearum and 12 parts of Healthy soil, the soil of each region was subjected to Diseased (Diseased soil group) and health (Healthy soil group), respectively. Example two, an indoor greenhouse tomato potting experiment is adopted, the treatment is divided into pathogen inoculation (disease) and sterile water treatment (health), and after 4 weeks of pathogen inoculation (stable disease condition), Diseased and Healthy rhizosphere soil is collected respectively. The first and second examples jointly implement a novel method for detecting soil pathogenic bacteria, comprising the following specific steps:
and (5) constructing a soil non-redundant pathogenic bacterium database. Corresponding subspecies and strain information is obtained by collecting 21 species-level pathogenic bacterium Latin names in a thesis/database website where pathogenic bacteria are out, removing redundancy and comparing with a Silva species library.
And extracting sample DNA and performing library construction and sequencing. Extracting the DNA of the soil sample by adopting a FastDNA kit, and then finishing 16S rRNA gene sequencing of the sample by utilizing an Illumina PE250 sequencing platform to obtain sequencing off-line data.
The DADA2 analysis procedure was used to package the soil non-redundant database of pathogenic bacteria. And (3) performing steps of removing joints, controlling quality, reducing noise, merging double ends, removing chimeras and the like on the data of the sequencing machine, and then obtaining a representative sequence. And then comparing the representative sequence with a soil non-redundant pathogenic bacteria database, wherein uclust is adopted in the comparison method, the parameter setting similarity > is 90%, and the annotated species are attached back to the sequence table to obtain a final ASV species table.
The results of the ASV species table obtained are shown in FIG. 3. The diversity of pathogenic bacteria in the diseased and healthy soil in example one is compared as shown in fig. 4. The results of comparison of abundance and species composition under different treatments are shown in FIG. 5.
The foregoing illustrates and describes the principles, general features, and advantages of the present invention. It should be understood by those skilled in the art that the above embodiments do not limit the scope of the present invention in any way, and all technical solutions obtained by using equivalent substitution methods fall within the scope of the present invention.
The parts not involved in the present invention are the same as or can be implemented using the prior art.
Claims (6)
1. A method for detecting biological pollution of soil pathogenic bacteria based on 16SrRNA amplicon sequencing is characterized by comprising the following steps:
step one, constructing a soil non-redundant pathogenic bacteria database;
step two, completing the extraction of the DNA of the soil/plant sample;
step three, completing 16S rRNA gene sequencing of the sample to obtain sequencing off-line data;
step four, adopting a DADA2 analysis process to package a soil non-redundant pathogenic bacteria library to obtain a representative sequence;
and fifthly, comparing the representative sequence with a soil non-redundant pathogenic bacteria database to annotate pathogenic bacteria, and obtaining a final ASV species table.
2. The method for detecting the biological pollution of the soil pathogenic bacteria based on the sequencing of the 16SrRNA amplicon according to claim 1, wherein in the second step, the extraction of the DNA of the soil/plant sample is completed by a FastDNA kit; in the third step, 16S rRNA gene sequencing of the sample is completed by using an Illumina PE250 sequencing platform.
3. The method for detecting soil pathogenic bacteria biological contamination based on 16SrRNA amplicon sequencing of claim 1, wherein in the fourth step, the DADA2 process is adopted to perform the steps of removing the linker, controlling the quality, reducing the noise, merging the double ends and removing the chimera on the sequencing off-machine data to obtain the representative sequence.
4. The method for detecting the biological pollution of the soil pathogenic bacteria based on the sequencing of the 16SrRNA amplicon, which is claimed in claim 1, wherein in the fifth step, the similarity threshold is set to be > 90% through uclust software, and the representative sequence is compared with a soil non-redundant pathogenic bacteria database to annotate the pathogenic bacteria, so as to obtain an ASV species table.
5. The method for detecting soil pathogenic bacteria biological contamination based on 16SrRNA amplicon sequencing of claim 1, wherein after step five, the pathogenic bacteria composition and diversity characteristics of different samples in the ASV species table are visually mapped through R language.
6. The method for detecting the biological pollution of the pathogenic bacteria in the soil based on the sequencing of the 16SrRNA amplicon, which is characterized in that the abundance and the diversity index of the ASV species table are calculated and visualized through an R language so as to further clarify the diversity characteristics of the pathogenic bacteria in the sample; in calculating the abundance of pathogenic bacteria, log-transforming the annotations to the ASV abundance of pathogenic bacteria;
PR＝log10ΣPsum
H＝-∑Pi(ln Pi)
wherein PR represents the abundance of pathogenic bacteria, H represents the diversity index, PsumRepresenting the absolute abundance of ASV, P, of pathogenic bacteria in the sampleiRepresenting the proportion of the i-th individual in the sample, if the total individual number of the sample is N and the i-th individual number is ni, then PiThe more uniform the individual distribution, the greater the H value, between each; the diversity index is maximal if each individual belongs to a different species; if each individual belongs to the same species, its diversity index is minimal.
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