CN113493847B - Antibiotic resistance gene based on PMA high-throughput sequencing and PICRUSt and identification method of potential host bacteria - Google Patents

Antibiotic resistance gene based on PMA high-throughput sequencing and PICRUSt and identification method of potential host bacteria Download PDF

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CN113493847B
CN113493847B CN202110707911.8A CN202110707911A CN113493847B CN 113493847 B CN113493847 B CN 113493847B CN 202110707911 A CN202110707911 A CN 202110707911A CN 113493847 B CN113493847 B CN 113493847B
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pathogenic bacteria
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host bacteria
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CN113493847A (en
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樊晓燕
张忠兴
高玉玺
赵君如
徐是龙
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Beijing University of Technology
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    • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
    • C12Q1/6888—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for detection or identification of organisms
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    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6869—Methods for sequencing
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Abstract

The invention provides an antibiotic resistance gene based on PMA high-throughput sequencing and PICRUSt and a method for identifying potential host cells thereof, belonging to the technical field of identification of Xinxing genotype pollutants and host bacteria thereof in environmental samples. The method comprises the following steps: obtaining species annotation information of living microorganisms by using a PMA-high-throughput sequencing technology aiming at an environmental sample; identifying ARGs using PICRUSt functional prediction software in conjunction with KOs data sets of ARGs; the identification of the potential active host bacteria of the ARGs effectively reduces the detection cost of the ARGs and improves the detection comprehensiveness compared with the detection of the ARGs which mainly uses metagenome sequencing; finally, the identification of the ARGs and the tracing of the active host bacteria of the ARGs are realized. The invention provides a method for solving the problems that the detection cost of the existing emerging genotype pollutants in an environmental sample is high, the detection is not comprehensive and the identification of active host bacteria is difficult.

Description

Antibiotic resistance gene based on PMA high-throughput sequencing and PICRUSt and identification method of potential host bacteria
Technical Field
The invention belongs to the technical field of identification of Xinxing genotype pollutants and host bacteria thereof in an environmental sample, and particularly relates to an identification method of Antibiotic Resistance Genes (ARGs) and potential active host bacteria thereof in the environmental sample.
Background
Antibiotics are widely used in the prevention/treatment of human or animal and plant diseases and in animal breeding, and China is one of the countries in the world where antibiotics are most produced and consumed. The metabolism rate of antibiotics in organisms is low (10-40%), most of antibiotics can be discharged out of the body in the form of original drugs or metabolites through feces/urine, and great potential harm is caused to the environment and human health. The problems of bacterial Resistance induced by antibiotics and the like have attracted much attention from the world of society, and Antibiotic Resistance Genes (ARGs) have become hot spots of current research.
ARGs are a novel genetic contaminant that is more difficult to study and control than traditional chemical contamination, and can be continuously retained in the environment, transferred, transformed and propagated between flora, with great harm to the ecological environment. The World Health Organization (WHO) has regarded ARGs as one of the most significant challenges threatening human health in the 21 st century. ARGs are widely distributed in various environmental media such as activated sludge, soil, biofilms, water bodies, sediments, air media, etc., and can achieve vertical transmission or horizontal transfer between microbial genera, and host bacteria (multi-drug resistant bacteria) carrying ARGs can simultaneously resist multiple antibiotics, and their transmission and spread may lead to more extensive diseases and infections. Therefore, research on occurrence characteristics, propagation, reduction, and the like of ARGs in various environments has been a hot issue in the field of environmental pollution control.
Real-time fluorescent quantitative PCR (qPCR), high-throughput qPCR (HT-qPCR) and metagenomic sequencing are common methods for studying the occurrence characteristics of ARGs in samples in different environments. But all have some disadvantages:
(1) qPCR is time consuming, laborious and requires specific plasmid standards (which require a series of intricate molecular biological processes to obtain), often limiting its use in quantifying all ARGs;
(2) HT-qPCR has a great improvement in throughput compared with qPCR, but still depends on known ARGs primers, and has a certain defect in unknown ARGs in an exploration environment.
(3) The ARGs are compared and annotated by combining metagenomic sequencing with a resistance gene database (ARDB and CARD), so that abundance data information of the ARGs can be comprehensively obtained. However, metagenomic sequencing is expensive, and it would cost a lot of money to obtain the occurrence of ARGs in environmental samples in large scale of time and space.
Therefore, there is a need to develop a method for comprehensively and economically characterizing the distribution of ARGs in environmental samples. Although gene annotation information can be obtained by functional prediction software based on high throughput sequencing (e.g., PICRUSt, genomic information of communities by retrieval of unobserved states). However, previous studies were mainly based on genomic DNA, and obtained are community data of "dead-live" microorganisms, whereas live microorganisms have an important role in facilitating the spread of ARGs. Therefore, it is highly necessary to excavate active host bacteria of ARGs. Propidium azide (PMA) is a photosensitive reaction DNA binding dye with high affinity, can selectively generate a cross-linking reaction with intracellular DNA, and can be combined with a molecular biology section to effectively inhibit DNA amplification signals of dead bacteria, thereby realizing the detection of living microorganisms. In conclusion, the method of combining PMA-based high-throughput sequencing technology with PICRUSt is provided to realize comprehensive and low-cost identification of ARGs information and active host bacteria thereof, so that the pollution and the spread of the ARGs in the environment are deeply and systematically understood, and a basis is provided for the control and reduction of the ARGs.
Disclosure of Invention
The invention aims to provide a comprehensive and economic method for identifying ARGs and potential active host bacteria in an environmental sample.
The method comprises the following specific steps:
1) collecting environmental samples:
and collecting samples according to sampling methods and requirements of different environmental samples (activated sludge, atmospheric particulates, soil, biological membranes and the like).
2) Environmental sample PMA treatment:
before extracting genomic DNA of an environmental sample, carrying out pretreatment on the environmental sample by adopting azide propidium bromide (PMA), wherein the pretreatment mainly comprises the following steps: preparing and storing a PMA reagent; pre-treating PMA; PMA photocrosslinking operation;
wherein, PMA (2mM) reagent is prepared and stored as follows: dissolving 1mg PMA in 980. mu.L MilliQ, and storing in a refrigerator under dark condition (-20 ℃); the PMA pretreatment steps are as follows: thawing 2mM PMA reagent, accurately measuring a certain volume of PMA solution, mixing with a certain amount of environmental sample to maintain the final concentration of PMA at 3-4 μ g/mL, mixing well, culturing in dark for 15min, and mixing evenly by inverting every 4-5min to make PMA enter damaged cells to the maximum extent; PMA photocrosslinking operation: the cultured environmental sample is placed in a blue light crosslinking device (460nm) or placed on ice, exposed by a 500W halogen lamp for 18min, crosslinked at a distance of 20cm from the light source, and then centrifuged (10000g, 5 min).
3) PMA-high throughput sequencing:
taking a certain amount of the environment sample treated in the step 2), extracting genome DNA by using a DNA extraction kit, measuring the concentration of the genome DNA by using a trace ultraviolet spectrophotometer, and detecting the quality of the extracted genome DNA by using 1% lipotrop gel electrophoresis; selecting a bacterium 16S V3-V4 region for high-throughput sequencing analysis; sequencing analysis mainly comprises the following steps: designing a synthetic primer joint according to the number of samples, carrying out PCR amplification and product purification, quantifying and homogenizing the PCR product, preparing a sequencing library, building a library, carrying out on-machine sequencing and carrying out off-machine analysis;
4) active non-pathogenic and pathogenic bacteria notes:
performing subsequent analysis by removing low-quality sequences and chimeras in the sequencing original sequence; carrying out species annotation after homogenizing treatment on the effective sequences to obtain the microbial community composition information of the environmental sample; potential pathogenic bacteria are screened from a sequencing result based on pathogenic bacteria genus information provided by a pathogenic bacteria virulence factor database (VFDB, http:// www.mgc.ac.cn/VFs/main. htm), so that identification of active non-pathogenic bacteria and pathogenic bacteria is realized.
Wherein the pathogenic bacteria of VFDB include: acinetobacter, Aeromonas, Anaplama, Bacillus, Bartonella, Bordetella, Brucella, Burkholderia, Camphyllobacter, Chlamydia, Clostridium, Corynebacterium, Coxiella, Enterococcus, Escherichia, Francisella, Haemophilus, Helicobacter, Klebsiella, Leginella, Listeria, Mycobacterium, Mycoplasma, Neisseria, Pseudomonas, Rickettsia, Salmonella, Shigella, Staphyloccocus, Streptococcus, Vibrio, and Yersinia.
5) Identification of ARGs:
analyzing bacterial 16S sequences obtained by high-throughput sequencing based on PICRUSt; the (KEGG) ordologies (kos) data is obtained by four script commands (normal _ by _ copy _ number. py, preset _ messages. py, category _ by _ function. py and metadata _ controls. py). And then, according to specific KOs corresponding to the ARGs, the ARGs are selected from the data, so that the identification of the ARGs corresponding to different types of antibiotics of the environmental sample is realized.
KOs for the different types of antibiotics ARGs are among others as follows: aac (K18816); aac3-VI (K19277); aac (6') -II (K18815); aac (6') -Iy (K18816); AacA/AphD (K00663); aacC (K19277); aacC1 (K03395); aacC4 (K00663); aacC2 (K00662); aadA1 (K00984); aadE (K05593); spcN (K18844); ampC (K01467, K20319, K20320); bla1 (K17836); blaCMY-2-1 (K19096); blaCTX-M-14 (K17836); blaCTX-M-15 (K18767); blasHV-11 (K18699); blaTEM-1 (K18698); BlaZ (K18766); cepA (K17836); cfxA (K01624); pbp2X (K12556); pbp5 (K18149); penA (K03587); acrF (K18142); adeA/cmeA (K03585); mexD (K18296); catB3 (K00638); cmlA1 (K18552); cmx (K18553); EmrB/QacA (K03446); mexE (K19586); mexF (K19585); oprJ (K08721); yidY/mdtL (K08163); basR (K07771); qnrA/B/C/D/S (K18555); qepA (K08167); oqxA (K19586); oqxB (K19585); oqxB (K19594); ereA (K06880); erm (34)/(35)/(36), ermB/C/F/X (K00561); ermK (K03543); lmrA (K18939); lnuA (K19545); mdtA (K07799); mefA (K08217); MphB (K06979); vgaB (K18231); vgb (K18235); sul1 (K18974); sul2 (K18824); tetL/K (K08168); tetM/O/W/P/Q/S/T (K18220); tetA (K08151); tetG/H/J (K08151); tetK (K08168); tetV (K18215); tetX (K18221); vanHB (K18347); vanRB (K18344); vanSB (K18345); vanTG (K18348); vanWB (K18346); vanXB (K08641); vanYB (K07260); vanJ (K18353); vanW (K18346); vanS (K18351); vanK (K18354); vraR (K07694); acrR (K03577); catB8 (K00638); emrD (K08154); fosX (K21252); marR (K03712); mdtE/yhiU (K18898); mtrCDE (K18991); a positive multiduggen (K07799); EmrB/QacA (K07786); qacEdelta1 (K18975); qacH (K03297); sdeB (K19585); emrK (K07797); ttgA (K03585); ttgB (K18324); yceE/mdtG (K08161); yceL/mdtH (K08162); bcr/tcaB (K07552); lmrP (K08152); blt (K08153); TPO1 (K08157); MDR1/FLR1/CAF5 (K08158); ATR1 (K08165); yebQ (K08169); norB/norC (K08170); yitG/ymfD/yfmO (K08221); smr3 (K09771); ebrA (K11814); ebrB (K11815); mdtO (K15547); mdtD (K18326); lmrS (K18934); sdrM (K18935); mdeA (K18936); bmr (K19578); marC (K05595); marB (K13630); ykkC (K18924).
6) Identification of potentially active host bacteria for ARGs:
after the steps 3), 4) and 5), obtaining data of active non-pathogenic bacteria, potential pathogenic bacteria and ARGs in the environmental sample, and further determining potential active host bacteria of the ARGs through network analysis. The network analysis comprises the following steps: and (3) performing correlation analysis on data of active non-pathogenic bacteria, potential pathogenic bacteria and ARGs by adopting a Spearman grade correlation coefficient non-parametric correlation method (SRCC), further screening results that the SRCC is more than or equal to 0.6 and p is less than 0.05, and performing visual analysis through network analysis to obtain potential host bacteria of the ARGs.
The invention has the beneficial effects that: the identification method of the ARGs and the active host bacteria (non-pathogenic bacteria and pathogenic bacteria) thereof can comprehensively and economically determine the occurrence characteristics and the propagation risk of the ARGs in a large scale range of large-scale environmental samples; meanwhile, typical and representative environmental samples are screened based on the results, and other common methods (such as qPCR, HT-qPCR and metagenomic sequencing) are utilized for quantitative analysis of the ARGs. The comprehensive analysis of the results can provide solid basic data and biological basis for the reduction and control of the ARGs in different environments, and has very important significance.
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The following describes embodiments of the present invention in further detail with reference to the accompanying drawings.
FIG. 1: schematic diagram for identifying environment sample ARGs and active host bacteria thereof;
FIG. 2 is a drawing: ARGs identification results in different environmental samples;
FIG. 3: identification of potentially active host bacteria for ARGs in different environmental samples.
Detailed Description
In order to make the technical solution of the present invention more apparent, the present invention is described in detail with reference to the following specific examples. However, the present invention is not limited to the following examples.
Example 1
1. Environmental sample
The environmental sample is activated sludge taken from an aeration tank of a certain regenerated sewage treatment plant in Beijing.
2. Environmental sample PMA treatment
Before extracting genome DNA of an environmental sample, adopting azide propidium bromide (PMA) to pretreat activated sludge, wherein the pretreatment mainly comprises the following steps: preparing and storing a PMA reagent; pre-treating PMA; PMA photocrosslinking operation. Wherein, PMA (2mM) reagent is prepared and stored as follows: dissolving 1mg of PMA in 980 mu of LMilliQ, and storing in a refrigerator at the temperature of minus 20 ℃; the PMA pretreatment steps are as follows: thawing 2mM PMA reagent, accurately measuring a certain volume of PMA solution, mixing with a certain amount of activated sludge to maintain the final concentration of PMA at 3-4 μ g/mL, uniformly mixing, culturing in dark for 15min, and reversing and uniformly mixing every 4-5min to ensure that PMA enters damaged cells to the maximum extent; PMA photocrosslinking operation: the cultured environmental sample is placed in a blue light crosslinking device (460nm) or placed on ice, exposed by a 500W halogen lamp for 18min, crosslinked at a distance of 20cm from the light source, and then centrifuged (10000g, 5 min).
PMA-high throughput sequencing
Taking a certain amount of the activated sludge sample treated in the step 2), extracting genome DNA by using a DNA extraction kit according to a standard process, measuring the concentration of the genome DNA by using a trace ultraviolet spectrophotometer, and detecting the quality of the extracted genome DNA by using 1% lipotrop gel electrophoresis. Bacterial 16S V3-V4 regions were selected for high throughput sequencing analysis. Sequencing analysis is completed by a sequencing company, and mainly comprises the following steps: designing and synthesizing primer joints according to the number of samples, carrying out PCR amplification and product purification, quantifying and homogenizing PCR products, preparing a sequencing library, building a library, carrying out on-machine sequencing and carrying out off-machine analysis.
4. Notes on active non-pathogenic and pathogenic bacteria
Subsequent analysis was performed by removing low quality sequences and chimeras from the original sequence sequenced. And performing species annotation after homogenization treatment on the effective sequences to obtain the microbial community composition information of the activated sludge. Potential pathogenic bacteria are screened from a sequencing result based on pathogenic bacteria genus information provided by a pathogenic bacteria virulence factor database (VFDB, http:// www.mgc.ac.cn/VFs/main. htm), so that identification of active non-pathogenic bacteria and pathogenic bacteria is realized. Wherein the pathogenic bacteria of VFDB include: acinetobacter, Aeromonas, Anaplama, Bacillus, Bartonella, Bordetella, Brucella, Burkholderia, Camphyllobacter, Chlamydia, Clostridium, Corynebacterium, Coxiella, Enterococcus, Escherichia, Francisella, Haemophilus, Helicobacter, Klebsiella, Leginella, Listeria, Mycobacterium, Mycoplasma, Neisseria, Pseudomonas, Rickettsia, Salmonella, Shigella, Staphyloccocus, Streptococcus, Vibrio, and Yersinia.
ARGs identification
Analysis of bacterial 16S sequences obtained by high throughput sequencing based on picrub gave (KEGG) genetics (kos) data by four script commands (normal _ by _ copy _ number. py, predict _ metals. py, category _ by _ function. py and metals _ controls. py). And further, the ARGs are screened out from the data according to the specific KOs corresponding to the ARGs, so that the identification of the ARGs corresponding to different types of antibiotics in the activated sludge sample is realized.
KOs for the different types of antibiotics ARGs are among others as follows: aac (K18816); aac3-VI (K19277); aac (6') -II (K18815); aac (6') -Iy (K18816); AacA/AphD (K00663); aacC (K19277); aacC1 (K03395); aacC4 (K00663); aacC2 (K00662); aadA1 (K00984); aadE (K05593); spcN (K18844); ampC (K01467, K20319, K20320); bla1 (K17836); blaCMY-2-1 (K19096); blaCTX-M-14 (K17836); blaCTX-M-15 (K18767); blasHV-11 (K18699); blaTEM-1 (K18698); BlaZ (K18766); cepA (K17836); cfxA (K01624); pbp2X (K12556); pbp5 (K18149); penA (K03587); acrF (K18142); adeA/cmeA (K03585); mexD (K18296); catB3 (K00638); cmlA1 (K18552); cmx (K18553); EmrB/QacA (K03446); mexE (K19586); mexF (K19585); oprJ (K08721); yidY/mdtL (K08163); basR (K07771); qnrA/B/C/D/S (K18555); qepA (K08167); oqxA (K19586); oqxB (K19585); oqxB (K19594); ereA (K06880); erm (34)/(35)/(36), ermB/C/F/X (K00561); ermK (K03543); lmrA (K18939); lnuA (K19545); mdtA (K07799); mefA (K08217); MphB (K06979); vgaB (K18231); vgb (K18235); sul1 (K18974); sul2 (K18824); tetL/K (K08168); tetM/O/W/P/Q/S/T (K18220); tetA (K08151); tetG/H/J (K08151); tetK (K08168); tetV (K18215); tetX (K18221); vanHB (K18347); vanRB (K18344); vanSB (K18345); vanTG (K18348); vanWB (K18346); vanXB (K08641); vanYB (K07260); vanJ (K18353); vanW (K18346); vanS (K18351); vanK (K18354); vraR (K07694); acrR (K03577); catB8 (K00638); emrD (K08154); fosX (K21252); marR (K03712); mdtE/yhiU (K18898); mtrCDE (K18991); a positive multiduggen (K07799); EmrB/QacA (K07786); qacEdelta1 (K18975); qacH (K03297); sdeB (K19585); emrK (K07797); ttgA (K03585); ttgB (K18324); yceE/mdtG (K08161); yceL/mdtH (K08162); bcr/tcaB (K07552); lmrP (K08152); blt (K08153); TPO1 (K08157); MDR1/FLR1/CAF5 (K08158); ATR1 (K08165); yebQ (K08169); norB/norC (K08170); yitG/ymfD/yfmO (K08221); smr3 (K09771); ebrA (K11814); ebrB (K11815); mdtO (K15547); mdtD (K18326); lmrS (K18934); sdrM (K18935); mdeA (K18936); bmr (K19578); marC (K05595); marB (K13630); ykkC (K18924).
Identification of potentially active host bacteria for ARGs:
after the steps 3), 4) and 5), obtaining data of active nonpathogenic bacteria, potential pathogenic bacteria and ARGs in the activated sludge sample, and further determining potential active host bacteria of the ARGs through network analysis. The network analysis comprises the following steps: and (3) performing correlation analysis on data of active non-pathogenic bacteria, potential pathogenic bacteria and ARGs by adopting a Spearman grade correlation coefficient non-parametric correlation method (SRCC), further screening results that the SRCC is more than or equal to 0.6 and p is less than 0.05, and performing visual analysis through network analysis to obtain potential host bacteria of the ARGs.
The method for identifying antibiotic resistance genes and their potential host bacteria in the high throughput sequencing and picrub based environmental sample of experiment 1 is shown in figure 1; based on the obtained KOs data and according to the specific KOs corresponding to the ARGs mentioned in the method, the ARGs possibly existing in the activated sludge sample are screened out as shown in the figure 2; network analysis of data for active non-pathogenic bacteria, potential pathogenic bacteria and ARGs is shown in figure 3.
FIG. 2 is a high throughput sequencing of activated sludge samples pretreated with PMA against ARGs datasets comparing the amount of different types of ARGs present; as can be seen from FIG. 2, the content of ARGs in different types can be screened at various orders of magnitude (even at ultra-trace level, constant level, etc.), wherein 10 aminoglycoside ARGs (the content of ARGs is 10) are detected1-103Order of magnitude), 12 fluoroquinolone ARGs (content is 10)1-105Order of magnitude), 5 Beta-lactam ARGs (content 10)1-104Order of magnitude), 3 sulfonamides ARGs (content 10)1-102Order of magnitude), 8 MLSB ARGs (content is 10)1-104Magnitude order), 5 tetracyclines ARGs (content is 10)1-104Magnitude order), 5 vancomycin ARGs (content is 10)1-103Magnitude order), 12 multidrug resistance proteins (ARGs) (content is 10)1-104Order of magnitude) and 11 unclassified ARGs (content 10)1-104An order of magnitude).
FIG. 3 is a network analysis established by ARGs in an activated sludge sample obtained by screening, active non-pathogenic bacteria and active potential pathogenic bacteria. As can be seen from FIG. 3, the potential pathogenic bacteria of 7 were identified together by PMA high throughput sequencing and identification of antibiotic resistance genes and their potential host bacteria in environmental samples based on the size of linkage (>6) of active bacteria to ARGs, including Acinetobacter, Aeromonas, Bacillus, Burkholderia, Mycobacterium, Streptococcus and Yersinia, 18 active non-pathogenic bacteria such as Hypermicobium, Halianium, Stenotropillobacter, Ferriginibacter, etc.
Analysis shows that the identification method of antibiotic resistance genes and potential host bacteria in environmental samples based on high-throughput sequencing and PICRUSt can obtain different types of ARGs existing in activated sludge samples and determine potential host bacteria.
The objects and functions of the present invention and methods for accomplishing the same will be apparent by reference to the exemplary embodiments. However, the present invention is not limited to the exemplary embodiments disclosed above; it can be implemented in different forms. The nature of the description is merely to assist those skilled in the relevant art in a comprehensive understanding of the specific details of the invention.

Claims (3)

1. The method for identifying antibiotic resistance genes and potential host bacteria based on PMA high-throughput sequencing and PICRUSt is characterized by comprising the following steps:
1) collecting environmental samples:
collecting samples according to sampling methods and requirements of samples in different environments;
2) environmental sample PMA treatment:
before extracting genome DNA of an environmental sample, pretreating the environmental sample by adopting azide propidium bromide PMA, wherein the pretreatment mainly comprises the following steps: preparing and storing a PMA reagent; pre-treating PMA; PMA photocrosslinking operation;
wherein, the 2mM PMA reagent is prepared and stored as follows: dissolving PMA in MilliQ water, and storing in a refrigerator at-20 deg.C in dark place; the PMA pretreatment steps are as follows: thawing 2mM PMA reagent, accurately measuring a certain volume of PMA solution, mixing with a certain amount of environmental sample to maintain the final concentration of PMA at 3-4 μ g/mL, mixing well, culturing in dark for 15min, and mixing evenly by inverting every 4-5min to make PMA enter damaged cells to the maximum extent; PMA photocrosslinking operation: placing the cultured environmental sample in a 460nm blue light crosslinking device or placing the sample on ice, selecting a 500W halogen lamp for exposure, performing crosslinking operation for 18min at a distance of 20cm from a light source, and then performing centrifugal operation;
3) PMA-high throughput sequencing:
taking a certain amount of the environment sample treated in the step 2), extracting genome DNA by using a DNA extraction kit, determining the concentration of the genome DNA by using a trace ultraviolet spectrophotometer, and detecting the quality of the extracted genome DNA by using 1% agarose gel electrophoresis; selecting a bacterium 16S V3-V4 region for high-throughput sequencing analysis; sequencing analysis mainly comprises the following steps: designing a synthetic primer joint, carrying out PCR amplification and product purification, quantifying and homogenizing a PCR product, constructing a sequencing library, and carrying out on-machine sequencing and off-machine analysis according to the number of samples;
4) active non-pathogenic and pathogenic bacteria notes:
performing subsequent analysis by removing low-quality sequences and chimeras in the sequencing original sequence; carrying out species annotation after homogenizing treatment on the effective sequences to obtain the microbial community composition information of the environmental sample; potential pathogenic bacteria are screened out from the sequencing result based on pathogenic bacteria genus information provided by a pathogenic bacteria virulence factor database VFDB, so that identification of active non-pathogenic bacteria and pathogenic bacteria is realized;
wherein the pathogenic bacteria of VFDB include: acinetobacter, Aeromonas, Anaplama, Bacillus, Bartonella, Bordetella, Brucella, Burkholderia, Camphyllobacter, Chlamydia, Clostridium, Corynebacterium, Coxiella, Enterococcus, Escherichia, Francisella, Haemophilus, Helicobacter, Klebsiella, Leginella, Listeria, Mycobacterium, Mycoplasma, Neisseria, Pseudomonas, Rickettsia, Salmonella, Shigella, Staphyloccocus, Streptococcus, Vibrio, and Yersinia;
5) identification of ARGs:
analysis of bacterial 16S sequences obtained by high throughput sequencing based on PICRUSt, resulting in Kyoto Encyclopedia of Genes and genomics organisations (KOs) data; further, establishing a corresponding relation of the ARGs-KOs: aac-K18816; aac 3-VI-K19277; aac (6') -II-K18815; aac (6') -Iy-K18816; AacA/AphD-K00663; aacC-K19277; aacC 1-K03395; aacC 4-K00663; aacC 2-K00662; aadA 1-K00984; aadE-K05593; spcN-K18844; ampC-K01467/K20319/K20320; bla 1-K17836; blaCMY-2-1-K19096; blaCTX-M-14-K17836; blaCTX-M-15-K18767; blasHV-11-K18699; blaTEM-1-K18698; BlaZ-K18766; cepA-K17836; cfxA-K01624; pbp 2X-K12556; pbp 5-K18149; penA-K03587; acrF-K18142; adeA/cmeA-K03585; mexD-K18296; catB 3-K00638; cmlA 1-K18552; cmx-K18553; EmrB/QacA-K03446; mexE-K19586; mexF-K19585; oprJ-K08721; yidY/mdtL-K08163; basR-K07771; qnrA/B/C/D/S-K18555; qepA-K08167; oqxA-K19586; oqxB-K19585; oqxB-K19594; ereA-K06880; erm-34/35/36/ermB/C/F/X-K00561; ermK-K03543; lmrA-K18939; lnuA-K19545; mdtA-K07799; mefA-K08217; MphB-K06979; vgAB-K18231; vgb-K18235; sul 1-K18974; sul 2-K18824; tetL/K-K08168; tetM/O/W/P/Q/S/T-K18220; tetA-K08151; tetG/H/J-K08151; tetK-K08168; tetV-K18215; tetX-K18221; van HB-K18347; vanRB-K18344; vanSB-K18345; vanTG-K18348; vanWB-K18346; vanXB-K08641; vanYB-K07260; vanJ-K18353; vanW-K18346; vanS-K18351; vanK-K18354; vraR-K07694; acrR-K03577; catB 8-K00638; emrD-K08154; fosX-K21252; marR-K03712; mdtE/yhiU-K18898; mtrCDE-K18991; a positive multiduggene-K07799; EmrB/QacA-K07786; qacEdelta 1-K18975; qacH-K03297; sdeB-K19585; emrK-K07797; ttgA-K03585; ttgB-K18324; yceE/mdtG-K08161; yceL/mdtH-K08162; bcr/tcaB-K07552; lmrP-K08152; blt-K08153; TPO 1-K08157; MDR1/FLR1/CAF 5-K08158; ATR 1-K08165; yebQ-K08169; norB/norC-K08170; yitG/ymfD/yfmO-K08221; TC.SMR3-K09771; ebrA-K11814; ebrB-K11815; mdtO-K15547; mdtD-K18326; lmrS-K18934; sdrM-K18935; mdeA-K18936; bmr-K19578; marC-K05595; marB-K13630; ykkC-K18924, and selecting ARGs from the data to identify the ARGs corresponding to different types of antibiotics in the environmental sample;
6) identification of potentially active host bacteria for ARGs:
after the steps 3), 4) and 5), obtaining data of active non-pathogenic bacteria, potential pathogenic bacteria and ARGs in the environmental sample, and further determining potential active host bacteria of the ARGs through network analysis.
2. The method for identifying antibiotic resistance genes and potential host bacteria based on PMA high throughput sequencing and PICRUSt of claim 1, wherein step 1) the environmental sample is selected from one or more of activated sludge, air particulate, soil, biofilm.
3. The method for identifying antibiotic resistance genes and potential host bacteria based on PMA high throughput sequencing and PICRUSt according to claim 1, wherein step 6) network analysis comprises: performing correlation analysis on the data of the active non-pathogenic bacteria, the potential pathogenic bacteria and the ARGs by adopting a Spearman grade correlation coefficient nonparametric correlation method, further screening the result that the Spearman grade correlation coefficient is more than or equal to 0.6 and p is less than 0.05, and performing visual analysis through network analysis to obtain the potential host bacteria of the ARGs.
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