WO2021088306A1 - 一种病原微生物药敏检测方法 - Google Patents

一种病原微生物药敏检测方法 Download PDF

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WO2021088306A1
WO2021088306A1 PCT/CN2020/081943 CN2020081943W WO2021088306A1 WO 2021088306 A1 WO2021088306 A1 WO 2021088306A1 CN 2020081943 W CN2020081943 W CN 2020081943W WO 2021088306 A1 WO2021088306 A1 WO 2021088306A1
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phylogenetic tree
drug
resistance
sample
pathogenic microorganisms
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PCT/CN2020/081943
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French (fr)
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任绪义
陈书韵
吕江峰
俞岳峰
周静
杨狄
潘彩霞
施宏
杨燚超
陈贻王
袁凯
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杭州迪安医学检验中心有限公司
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Priority to EP20884037.1A priority Critical patent/EP4056709A4/en
Publication of WO2021088306A1 publication Critical patent/WO2021088306A1/zh
Priority to US17/662,651 priority patent/US20220380829A1/en

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Definitions

  • the invention belongs to the technical field of microbial diagnosis, and relates to the identification of common clinical pathogenic microorganisms and the rapid diagnosis of drug sensitivity thereof, in particular to a method for detecting drug susceptibility of pathogenic microorganisms and related applications.
  • the lowest bacteriostasis The method of concentration or minimum bactericidal concentration is the current gold standard for susceptibility testing of bacteria and fungi.
  • the advantage of the culture method is that both quantitative and qualitative results can be obtained, and the results are relatively accurate.
  • the disadvantage is that different inoculation densities will affect the MIC value after the drug concentration is constant, which will cause tailing and edge effects.
  • the prerequisite for all the above identification and drug susceptibility test schemes is to obtain a single clone after cultivation.
  • the traditional live bacteria culture and identification of pathogens take 2-3 days, and the drug sensitivity incubation time is 24-48 hours.
  • the total time for identification and drug susceptibility is usually 3-5 days. During this period, the patient's condition may have changed, and the test report has lost value for the treatment of the patient. Therefore, the clinical enthusiasm for the application of microbial detection technology has been greatly reduced.
  • the choice of medication based on experience is the most direct cause of antibiotic abuse.
  • Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry is a new type of soft ionization biological mass spectrometry developed in recent years. It takes the time of flight for the molecules formed after the sample and the matrix co-crystallize after laser irradiation to pass through the vacuum tube. It has been widely used in the field of microbial identification to identify large molecules such as protein and polypeptide, but it has not yet established a mature detection method in the field of drug sensitivity diagnosis. At the same time, because this method detects large molecules such as proteins and peptides, macromolecules are often not the most direct participants in cell metabolism, which limits the improvement of its detection sensitivity and specificity.
  • Molecular diagnostic technology is a revolutionary technology in the field of microbial pathogen identification. It is not only fast and sensitive, but also can detect several pathogens at the same time.
  • microbial identification products using Sanger sequencing method, pyrosequencing method, PCR-melting curve fluorescent probe hybridization method, gene chip method and other technologies have become increasingly mature.
  • the identification of pathogenic bacteria based on whole-genome sequencing has been rapidly developed in the past ten years, and has been promoted and applied in the fields of infection traceability, infectious disease monitoring, research and development of new drugs and vaccines, and clinical treatment.
  • whole-genome sequencing technology with drug resistance gene identification as the main direction has always been difficult and technical barriers in the field of microbial drug resistance diagnosis.
  • the purpose of the present invention is to provide a method for detecting drug susceptibility of pathogenic microorganisms based on the biomarker information of the sample to be tested and related applications in view of the deficiencies of the prior art. Further, the present invention also provides a kit that uses liquid chromatography-tandem mass spectrometry technology and whole genome sequencing technology for rapid identification and drug sensitivity diagnosis of pathogenic microorganisms; using the kit to detect colony cultures and clinical samples Non-diagnostic methods, such as those used in pharmaceutical research and data statistics.
  • Liquid chromatography tandem mass spectrometry technology effectively combines the advantages of liquid chromatography for compound separation and purification with the high resolution and superior qualitative and quantitative analysis performance of mass spectrometry for compounds. Combined with metabolomics analysis methods, it can be used at the level of small molecule polar compounds. Analyze the metabolic level of pathogenic microorganisms, and accurately identify metabolites and structural compounds from the perspectives of the composition, expression, and signal pathways involved in the metabolites, so as to realize the distinguishing and identification of different pathogens.
  • Whole genome sequencing is to sequence the whole genome sequence of an individual, which can analyze the base sequence of the genome comprehensively and accurately, so as to obtain the biological information contained in it.
  • the second-generation sequencing technology that is, high-throughput sequencing technology, can sequence hundreds of thousands to millions of DNA in a single time, making whole-genome sequencing convenient and easy, but there are also high equipment costs that cannot be fully promoted.
  • the advancement of third-generation sequencing over second-generation sequencing is the realization of single-molecule sequencing, so that the sequencing process does not need to rely on PCR amplification.
  • Nanopore sequencing technology belongs to the third-generation sequencing technology, and is currently replacing the application of second-generation sequencing in the field of infectious pathogen research at a rapid rate. It reads sequence information by passing long DNA strands through small holes called nanopores and detecting small changes in the current caused by the four nucleotide components of DNA. It has three outstanding advantages, namely: real-time single-molecule sequencing without amplification; Mb-level sequencing read length significantly improves the efficiency and accuracy of genome splicing; fast sequencing speed, low instrument cost, compact and portable, and clinical acceptance And the degree of promotion is high.
  • the detection object of the former is a colony culture
  • the detection object of the latter is a clinical sample or a colony culture.
  • the liquid chromatography tandem mass spectrometry technology can take advantage of its short detection time (within 30 minutes), simple analysis process, and low cost of reagents and consumables.
  • the nanopore sequencing technology can break through the bottleneck of low culture positive rate and long culture time, and achieve high-throughput and efficient pathogen detection directly from clinical samples.
  • the invention also includes identification and drug susceptibility detection methods based on liquid-mass spectrometry technology and nanopore sequencing technology, and the optimal detection method can be selected according to different sample types and timeliness requirements.
  • the present invention provides a pathogenic microorganism identification and/or drug susceptibility detection method.
  • the method includes obtaining biomarker information of a sample to be tested, and determining the position of the sample to be tested in a phylogenetic tree based on the biomarker information. Location, determine the species of the sample to be tested and the judgment rule of drug resistance according to the location, and obtain the drug resistance of the pathogenic microorganism according to the judgment rule.
  • biomarker information is information about metabolites in pathogenic microorganisms and/or sequence information of pathogenic microorganisms.
  • the information of the metabolites in the pathogenic microorganism is mass spectrometry information of the metabolites in the pathogenic microorganism.
  • the mass spectrometry information of the metabolites in the pathogenic microorganism is one of mass-to-charge ratio, retention time, and abundance. A combination of two or more.
  • the pathogenic microorganism identification and/or drug susceptibility detection method of the present invention can be a diagnostic method or a non-diagnostic method; including the simultaneous use of phylogenetic trees and biomarkers when determining the microbial species and its antibiotic resistance , Based on the combination of the three independent judgment rules of different branches of the phylogenetic tree; among them, the location of the phylogenetic tree is used to identify the species of microorganisms; the location of the phylogenetic tree and the judgment rules of different branches, combined with biomarkers, use To analyze antibiotic resistance.
  • the phylogenetic tree is obtained by liquid chromatography tandem mass spectrometry technology and/or whole genome sequencing technology.
  • the construction form of the phylogenetic tree includes, but is not limited to: a metabolic profile phylogenetic tree based on differences in the composition and abundance of metabolites, a nucleic acid sequence phylogenetic tree based on microbial genome SNP and InDel, and /Or, a nucleic acid sequence type phylogenetic tree constructed based on the core drug resistance elements of the microbial genome and its upstream and downstream environments.
  • biomarkers include, but are not limited to: metabolic compound biomarkers such as amino acids, organic acids, fatty acids, sugar derivatives and their retention time and mass-to-charge ratio, drug resistance genes, plasmids, transpositions Nucleic acid sequences of drug-resistant element-type biomarkers such as integrons, integrons, and insert sequences.
  • the types of independent judgment rules based on different branches of the phylogenetic tree include, but are not limited to: metabolic profile phylogenetic trees based on differences in the composition and abundance of metabolites. Different judgment standards are applied to different branches of the phylogenetic tree. , And/or, the nucleic acid sequence type phylogenetic tree constructed based on the core drug resistance elements of the microbial genome and its upstream and downstream environments applies different judgment standards on its different branches.
  • the metabolites used to construct the phylogenetic tree are the sum of water-soluble metabolites with a mass-to-charge ratio between 50-1500 Da and an abundance value greater than 2000.
  • the phylogenetic tree is a rootless phylogenetic tree constructed from the genome sequence of the representative clone obtained by local sequencing.
  • the deviation range of the retention time of the metabolite compound biomarker is ⁇ 0.5 min, and the deviation range of the mass-to-charge ratio is ⁇ 0.05 Da.
  • resistant element type biomarkers include but are not limited to:
  • abarmA abAPH(3')-Ia, abOXA239, abNDM-10, abgyrA, abSUL-1, abSUL-2, abSUL-3, kpCTX-M-65, kpTEM-1b, kpIMP-4, kpKPC-2, kprmtB, kpAAC(3')-Iid, kpQNR-S, kpgyrA, kpparC, kptetA, kptetD, kpSUL-1, kpSUL-2, kpSUL-3, ecrmtB, ecAAC(3')-Iid, ecgyrA, ectetA, ectetB, ecSUL- 1.
  • the criteria for judging the metabolic profile phylogenetic tree on its different branches include but not limited to:
  • the positive cocci is Staphylococcus bacterium: when the sample to be tested is located in the mecA+ branch, follow the principle of phylogenetic tree interpretation prior to specific markers, and directly determine whether it is penicillin, oxacillin, and cephalosporin.
  • the positive cocci is Enterococcus faecalis: when the sample to be tested contains specific markers that meet the specific clone strain, the drug resistance spectrum directed by the specific markers has priority over the phylogenetic tree interpretation Principle: The specific markers of Enterococcus faecalis ST4 clones are preferred to the markers of all other clones, which can directly determine that Enterococcus faecalis is resistant to penicillin drugs.
  • the criteria for judging the different branches of the nucleic acid sequence type phylogenetic tree include but not limited to:
  • the determination of the resistance of fungi to triazole drugs is related to the branch of the phylogenetic tree that is located and follows the principle of phylogenetic tree to interpret priority resistance genes, and the other types of drugs follow the principle of interpreting resistance genes. .
  • the common clinical bacteria are divided into four categories: enterobacteria, non-fermentation-negative bacilli, positive cocci, and fastidious bacteria to construct a drug resistance database. These four types of bacteria account for more than 90% of common clinical pathogens.
  • the present invention also provides an application of a pathogenic microorganism phylogenetic tree in the preparation of pathogenic microorganism identification and/or drug sensitivity diagnostic products.
  • the phylogenetic tree adopts liquid chromatography tandem mass spectrometry technology and/or whole genome Obtained by sequencing technology.
  • the construction form of the phylogenetic tree is selected from a metabolic profile phylogenetic tree based on differences in the composition and abundance of metabolites, a nucleic acid sequence phylogenetic tree constructed based on microbial genome SNP and InDel, or, based on microbial genome Nucleic acid sequence type phylogenetic tree constructed by core drug resistance elements and their upstream and downstream environments.
  • the pathogenic microorganism identification and/or drug susceptibility diagnosis product further includes reagents or equipment for obtaining biomarker information of the sample to be tested.
  • the device for obtaining biomarker information of the sample to be tested is selected from a liquid chromatography tandem mass spectrometry or a whole genome sequencing device.
  • the reagent for obtaining biomarker information of the sample to be tested is selected from a kit for identification of pathogenic microorganisms and drug sensitivity diagnosis based on liquid chromatography tandem mass spectrometry technology or a reagent for identification of pathogenic microorganisms and drug sensitivity diagnosis based on whole genome sequencing technology box.
  • the present invention provides a kit for identification and drug sensitivity diagnosis of common clinical pathogenic microorganisms, which is characterized in that it comprises the following two sets of kit components:
  • KIT1 Pathogen identification and drug sensitivity diagnosis kit based on liquid chromatography tandem mass spectrometry technology
  • KIT2 A kit for pathogen identification and drug sensitivity diagnosis based on whole-genome sequencing technology.
  • the kit also includes a phylogenetic tree of pathogenic microorganisms.
  • the pathogenic microorganism identification and drug sensitivity diagnosis kit based on liquid chromatography tandem mass spectrometry technology includes a bacterial external standard solution, a fungal external standard solution, an extraction solvent, and a double solution.
  • kit for pathogenic microorganism identification and drug sensitivity diagnosis based on whole-genome sequencing technology includes blood cell lysate, primer mixture, fungal lysate, fungal digestion, library building reagents, native barcode, and sequencing reagents.
  • the bacterial external standard solution is a methanol solution containing 128 ng/mL 5-fluorocytosine, pre-cooled at 2-8°C.
  • the fungus external standard solution is a methanol water (volume ratio 80:20) solution containing 126ng/mL ampicillin, pre-cooled at 2-8°C.
  • the extraction solvent is a methanol-acetonitrile mixture with a volume ratio of 2:1, and precooling at -20 to -80°C.
  • the compound solution is a mixture of water-acetonitrile-formic acid, with a volume ratio of 98:2:0.05, and precooling at 2-8°C.
  • the blood cell lysate is an aqueous solution containing 0.02% (m/v) saponin.
  • the applicable sample type of the pathogenic microorganism identification and drug sensitivity diagnosis kit based on liquid chromatography tandem mass spectrometry technology is colony culture; the pathogenic microorganism identification and drug sensitivity diagnosis kit based on whole genome sequencing technology Applicable sample types are clinical samples or colony cultures.
  • the present invention provides the application of the above-mentioned clinical common pathogenic microorganism identification and drug susceptibility diagnosis kit in pathogen identification and drug susceptibility detection.
  • Figure 1a-c is a phylogenetic tree-type metabolic profile identification database constructed by representatives of 24 bacterial strains in 82 cases;
  • Figure 2a-b shows the location and distribution of 16 blind samples in the metabolic profile identification database.
  • the branches of the phylogenetic tree representing different species are marked by different colors on the left, and the black marks represent blind samples;
  • Figure 3a-b is a phylogenetic tree-type metabolic profile drug susceptibility database constructed by 87 cases of Acinetobacter baumannii representative clones;
  • Figure 4a-b shows the location and distribution of 16 cases of blind Acinetobacter baumannii in the metabolic profile drug susceptibility database.
  • the branches of the phylogenetic tree representing different drug resistance profiles are marked by different colors on the left, and the black marks represent blind samples;
  • Figure 5 is a phylogenetic tree-type metabolic profile drug susceptibility database constructed by 36 representative clones of Enterococcus faecalis;
  • Figure 6 shows the location and distribution of 6 cases of Enterococcus faecalis in the metabolic profile drug susceptibility database.
  • the branches of the phylogenetic tree representing different drug resistance profiles are marked by different colors on the left, and the black marks represent blind samples;
  • Figure 7 is a phylogenetic tree-type metabolic profile drug susceptibility database constructed by 18 representative clones of Streptococcus pneumoniae;
  • Figure 8 shows the location and distribution of 2 cases of Blind Streptococcus pneumoniae in the metabolic profile drug susceptibility database.
  • the branches of the phylogenetic tree representing different drug resistance profiles are marked by different colors on the left, and the black marks represent blind samples;
  • Figure 9a-b is a phylogenetic tree-type metabolic profile identification database constructed by representative of 115 cases of 22 fungal species;
  • Figure 10a-b shows the location and distribution of 8 cases of blind fungi in the metabolic profile identification database.
  • the branches of the phylogenetic tree representing different species are marked by different colors on the left, and the black marks represent blind samples;
  • Figure 11 shows the location and distribution of 6 cases of blind Candida tropicalis in the metabolic profile drug susceptibility database.
  • the branches of the phylogenetic tree representing different drug resistance spectrums are marked by different colors on the left, blue is the azole-sensitive tropical zone, yellow is the azole-resistant tropical zone, and the black mark represents the blind sample;
  • Figure 12a-c is a phylogenetic tree-type genome identification and drug resistance database constructed by representative clones of 165 cases of Klebsiella pneumoniae.
  • the branches of the phylogenetic tree representing different drug resistance profiles are marked by different colors on the right, where the yellow marked area is the ST11 partition, the red marked area is the ST15 partition, and the blue marked area is the sensitive plant partition (including the ST23 partition);
  • Figure 13 is a database of phylogenetic tree-type genome identification and drug resistance constructed by 93 representative clones of Staphylococcus aureus. The branches of the phylogenetic tree representing different drug resistance profiles are marked by different colors on the right;
  • Figure 14 shows the phylogenetic tree-type genome identification and drug resistance database constructed by representative clones of 25 cases of Streptococcus pneumoniae and the location and distribution of 2 blind samples in the genome database.
  • the branches of the phylogenetic tree representing different drug resistance profiles are marked by different colors on the right side, where the yellow marked area is the sensitive plant partition, the blue marked area is the penicillin resistant partition, and the black mark represents the blind sample;
  • Figure 15 is a phylogenetic tree-type genome identification and drug resistance database constructed by representative clones of 107 Candida albicans.
  • the branches of the phylogenetic tree representing different drug resistance spectrums are marked by different colors on the right side, among which red is triazole-resistant white, yellow is 5-fluorocytosine-resistant white, and green is echinocandin-resistant Bai Nian, gray is amphotericin B-mediated Bai Nian, blue is fully sensitive Bai Nian;
  • Figure 16 shows the location of a respiratory tract sputum sample on the phylogenetic tree of the Acinetobacter baumannii genome
  • Figure 17 shows the location of a case of respiratory sputum sample on the phylogenetic tree of Klebsiella pneumoniae genome.
  • the present invention will be described in detail below with reference to specific embodiments, but the protection scope of the present invention is not limited to the following embodiments.
  • the R in each table (Table 1-Table 24) represents that the corresponding drug resistance gene can be judged as drug resistance; S represents that the corresponding drug resistance gene can be judged as sensitive; the blank space represents no interpretation.
  • Example 1 Construction and verification of a bacterial identification metabolic profile database based on liquid chromatography-tandem mass spectrometry
  • step (4) Mass spectrometry detection: the residue obtained in step (4) is further reconstituted with 140 ⁇ L of the reconstituted solution, vortexed to fully dissolve, centrifuged at high speed for 15 minutes, and then transferred the supernatant to a new centrifuge tube, repeated high-speed centrifugation for 15 minutes , Transfer 100 ⁇ L of supernatant to high resolution liquid chromatography tandem mass spectrometry system, and inject 4 ⁇ L for detection and analysis; among them, high resolution liquid chromatography tandem mass spectrometry is Waters Q-TOF Synapt G2-Si quadrupole-time-of-flight mass spectrometer.
  • the separation conditions of the test sample established by gradient elution reversed-phase chromatography are as follows: water-acetonitrile-formic acid is used as the mobile phase system, the flow rate of the mobile phase is 0.4ml/min, and the column temperature is 40°C.
  • the chromatographic column is a Waters HSS T3 column with a particle size of 1.8 ⁇ m, an inner diameter of 2.1mm, and a column length of 100mm.
  • Mass detection adopts electrospray ionization source (ESI), positive ion mode, multiple reaction monitoring scan mode (MRM) and MSeContinnum data independent acquisition mode.
  • ESI electrospray ionization source
  • MRM multiple reaction monitoring scan mode
  • MSeContinnum data independent acquisition mode MSeContinnum data independent acquisition mode.
  • Differential compound screening Use Progenesis QI software to perform peak alignment, peak extraction, compound identification, and normalization processing on the raw data collected by mass spectrometry, and output compound mass-to-charge ratio (m/z), retention time, abundance, etc. Characteristic information. Set bacteria of different species into different groups, and set the screening parameters for differential compounds as: fold change>10, VIP>1, pvalue ⁇ 0.05, CV ⁇ 30%. A total of 258 differential compounds were screened:
  • the same clones were merged, and 82 representative clones of each strain were selected to construct the database.
  • Select Analysis-Classification-System Clustering the statistical parameters are set to "centralized planning", the graph parameters are set to "pedigree diagram, horizontal direction”, the method parameters are set to "connection between groups”, and the measurement interval parameter is set to "Euclidean distance” , The conversion value standardization parameter is set to "Z score", and a tree diagram is generated, as shown in Figure 1.
  • Blind Metabolic profile database prediction results Gold standard (sequencing method) identification results Is it consistent blind-1 Pseudomonas aeruginosa Pseudomonas aeruginosa Yes blind-2 Escherichia Coli Escherichia coli Yes blind-3 Escherichia Coli Escherichia coli Yes blind-4 Stenotrophomonas maltophilia Stenotrophomonas maltophilia Yes blind-5 Acinetobacter peter Acinetobacter pittii Yes blind-6 Enterococcus faecalis Enterococcus faecium Yes blind-7 Klebsiella mutans Klebsiella varicola Yes blind-8 Acinetobacter baumannii Acinetobacter baumannii Yes blind-9 Acinetobacter baumannii Acinetobacter baumannii Yes blind-10 Enterobacter cloacae Enterobacter cloacae Yes blind-11 Staphylococcus aureus Staphylococcus aureus
  • Example 2 Construction and verification of drug susceptibility database of Acinetobacter baumannii metabolic profile based on liquid chromatography-tandem mass spectrometry
  • Acinetobacter baumannii was selected as the representative of gram-negative bacilli, and the method of constructing the metabolic profile drug susceptibility database was explained in detail.
  • Other Gram-negative bacilli such as enterobacteria and non-fermenting bacteria can be constructed and analyzed with reference to this method.
  • Antimicrobial susceptibility verification of Acinetobacter baumannii Use the Mérieux Vitek2 Compact30 automatic microbial analyzer and the N335 antimicrobial susceptibility card to test all Acinetobacter baumannii used in the test, and use the results as the comparison standard.
  • step (4) Mass spectrometry detection: the residue obtained in step (4) is further reconstituted with 140 ⁇ L of the reconstituted solution, vortexed to fully dissolve, centrifuged at high speed for 15 minutes, and then transferred the supernatant to a new centrifuge tube, repeated high-speed centrifugation for 15 minutes , Transfer 100 ⁇ L of supernatant to high resolution liquid chromatography tandem mass spectrometry system, and inject 4 ⁇ L for detection and analysis; among them, high resolution liquid chromatography tandem mass spectrometry is Waters Q-TOF Synapt G2-Si quadrupole-time-of-flight mass spectrometer.
  • the separation conditions of the test sample established by gradient elution reversed-phase chromatography are as follows: water-acetonitrile-formic acid is used as the mobile phase system, the flow rate of the mobile phase is 0.4ml/min, and the column temperature is 40°C.
  • the chromatographic column is a Waters HSS T3 column with a particle size of 1.8 ⁇ m, an inner diameter of 2.1mm, and a column length of 100mm.
  • Mass detection adopts electrospray ionization source (ESI), positive ion mode, multiple reaction monitoring scan mode (MRM) and MSeContinnum data independent acquisition mode.
  • ESI electrospray ionization source
  • MRM multiple reaction monitoring scan mode
  • MSeContinnum data independent acquisition mode MSeContinnum data independent acquisition mode.
  • Differential compound screening Use Progenesis QI software to perform peak alignment, peak extraction, compound identification, and normalization processing on the raw data collected by mass spectrometry, and output compound mass-to-charge ratio (m/z), retention time, abundance, etc. Characteristic information. Set Acinetobacter baumannii into different groups according to the difference in their drug resistance spectrum, and set the screening parameters for differential compounds as: fold change>10, VIP>1, pvalue ⁇ 0.05, CV ⁇ 30%. A total of 102 differential compounds were screened as follows:
  • Bauman drug susceptibility metabolic profile database construction through the analysis of different compounds, the same clones were merged, and 87 representative Acinetobacter baumannii clones were selected to construct the database. Import the characteristic information of the compound (retention time, mass-to-charge ratio, and abundance) into the software IBM SPSS Statistics 23, use the retention time and mass-to-charge ratio information as the variable name, and the compound abundance as the variable value.
  • the statistical parameters are set to "centralized planning"
  • the graph parameters are set to "pedigree diagram, horizontal direction”
  • the method parameters are set to "connection between groups”
  • the measurement interval parameter is set to "Euclidean distance”
  • the conversion value standardization parameter is set to "Z score”
  • a tree diagram is generated, as shown in Figure 3.
  • blind sample 1 contains G208 specific markers (2.41_507.5750m/z, 2.41_511 .5846m/z)
  • the blind sample 5 contains F540 specific markers (3.00_1093.6160m/z, 3.00_1094.1189m/z)
  • the results of blind sample 1 and blind sample 5 are revised to G208 and F540, The revised results are consistent with the VITEK2 drug sensitivity results.
  • Example 3 Construction and verification of Enterococcus faecalis drug sensitivity metabolic profile database based on liquid chromatography-tandem mass spectrometry
  • Enterococcus faecalis was selected as the representative of Gram-positive cocci, and the method of constructing the metabolic profile drug susceptibility database was explained in detail.
  • the remaining Gram-positive cocci such as Enterococcus faecalis and Staphylococcus can be constructed and analyzed with reference to this method.
  • step (4) Mass spectrometry detection: the residue obtained in step (4) is further reconstituted with 140 ⁇ L of the reconstituted solution, vortexed to fully dissolve, centrifuged at high speed for 15 minutes, and then transferred the supernatant to a new centrifuge tube, repeated high-speed centrifugation for 15 minutes , Transfer 100 ⁇ L of supernatant to high resolution liquid chromatography tandem mass spectrometry system, and inject 4 ⁇ L for detection and analysis; among them, high resolution liquid chromatography tandem mass spectrometry is Waters Q-TOF Synapt G2-Si quadrupole-time-of-flight mass spectrometer.
  • the separation conditions of the test sample established by gradient elution reversed-phase chromatography are as follows: water-acetonitrile-formic acid is used as the mobile phase system, the flow rate of the mobile phase is 0.4ml/min, and the column temperature is 40°C.
  • the chromatographic column is a Waters HSS T3 column with a particle size of 1.8 ⁇ m, an inner diameter of 2.1mm, and a column length of 100mm.
  • Mass detection adopts electrospray ionization source (ESI), positive ion mode, multiple reaction monitoring scan mode (MRM) and MSeContinnum data independent acquisition mode.
  • ESI electrospray ionization source
  • MRM multiple reaction monitoring scan mode
  • MSeContinnum data independent acquisition mode MSeContinnum data independent acquisition mode.
  • Differential compound screening Use Progenesis QI software to perform peak alignment, peak extraction, compound identification, and normalization processing on the raw data collected by mass spectrometry, and output compound mass-to-charge ratio (m/z), retention time, abundance, etc. Characteristic information. Set Enterococcus faecalis into different groups according to their differences in drug resistance spectrum, and set the screening parameters for different compounds as: fold change>10, VIP>1, p value ⁇ 0.05, CV ⁇ 30%. A total of 51 differential compounds were screened as follows: 0.53_491.2411m/z 0.55_1409.6162m/z 0.55_1450.5881m/z
  • the statistical parameters are set to "centralized planning"
  • the graph parameters are set to "pedigree diagram, horizontal direction”
  • the method parameters are set to "connection between groups”
  • the measurement interval parameter is set to "Euclidean distance”
  • the conversion value standardization parameter is set to "Z score”
  • a tree diagram is generated, as shown in Figure 5.
  • blind sample verification Extract the abundance values of 51 target difference compounds from 6 blind samples, import them into the software IBM SPSS Statistics23, and combine and analyze with the data in the library. According to the branch location of the unknown sample in the phylogenetic tree and the corresponding drug resistance spectrum judgment rule, the type of the drug resistance spectrum of the blind sample is determined. As shown in Figure 6, the branches of the phylogenetic tree representing different drug resistance profiles are marked by different color areas on the left, and the black marks represent blind samples.
  • Example 4 Construction and verification of a drug susceptibility metabolic profile database of Streptococcus pneumoniae based on liquid chromatography-tandem mass spectrometry
  • Streptococcus pneumoniae was selected as the representative of fastidious bacteria, and the method of constructing the metabolic profile drug susceptibility database was explained in detail.
  • step (4) Mass spectrometry detection: the residue obtained in step (4) is further reconstituted with 140 ⁇ L of the reconstituted solution, vortexed to fully dissolve, centrifuged at high speed for 15 minutes, and then transferred the supernatant to a new centrifuge tube, repeated high-speed centrifugation for 15 minutes , Transfer 100 ⁇ L of supernatant to high resolution liquid chromatography tandem mass spectrometry system, and inject 4 ⁇ L for detection and analysis; among them, high resolution liquid chromatography tandem mass spectrometry is Waters Q-TOF Synapt G2-Si quadrupole-time-of-flight mass spectrometer.
  • the separation conditions of the test sample established by gradient elution reversed-phase chromatography are as follows: water-acetonitrile-formic acid is used as the mobile phase system, the flow rate of the mobile phase is 0.4ml/min, and the column temperature is 40°C.
  • the chromatographic column is a Waters HSS T3 column with a particle size of 1.8 ⁇ m, an inner diameter of 2.1mm, and a column length of 100mm.
  • Mass detection adopts electrospray ionization source (ESI), positive ion mode, multiple reaction monitoring scan mode (MRM) and MSeContinnum data independent acquisition mode.
  • ESI electrospray ionization source
  • MRM multiple reaction monitoring scan mode
  • MSeContinnum data independent acquisition mode MSeContinnum data independent acquisition mode.
  • Differential compound screening Use Progenesis QI software to perform peak alignment, peak extraction, compound identification, and normalization processing on the raw data collected by mass spectrometry, and output compound mass-to-charge ratio (m/z), retention time, abundance, etc. Characteristic information. Set Streptococcus pneumoniae into different groups according to their drug resistance spectrum differences, and set the screening parameters for differential compounds as: fold change>10, VIP>1, pvalue ⁇ 0.05, CV ⁇ 30%.
  • a total of 21 differential compounds were screened as follows: 0.66_382.0899m/z, 0.71_1355.4229m/z, 0.72_166.0547m/z, 0.72_295.0939m/z, 0.85_1487.5082m/z, 1.16_202.5253m/z , 1.16_366.0977m/z, 1.16_237.0537m/z, 1.16_404.0425m/z, 1.16_219.0439m/z, 1.78_443.7582m/z, 2.99_884.1424m/z, 2.99_1317.2267m/z, 2.98_1305.7357m/z, 2.98_1306.2387m/z, 2.99_871.1594m/z, 2.99_870.8261m/z, 2.99_878.8190m/z, 2.97_1134.1406m/z, 2.96_1275.6738m/z, 2.26 _1297.4318m/z.
  • the statistical parameters are set to "centralized planning"
  • the graph parameters are set to "pedigree diagram, horizontal direction”
  • the method parameters are set to "connection between groups”
  • the measurement interval parameter is set to "Euclidean distance”
  • the conversion value standardization parameter is set to "Z score”
  • a tree diagram is generated, as shown in Figure 7.
  • blind sample verification Extract the abundance values of 21 target difference compounds from 2 blind samples, and import them into the software IBM SPSS Statistics23, and merge and analyze with the data in the library. According to the branch location of the unknown sample in the phylogenetic tree and the corresponding drug resistance spectrum judgment rule, the type of the drug resistance spectrum of the blind sample is determined. As shown in Figure 8, the branches of the phylogenetic tree representing different drug resistance profiles are marked by different color areas on the left, and the black marks represent blind samples.
  • the branch location of the phylogenetic tree is combined with specific markers, in the above results, since the abundance value of penicillin-related markers in blind sample 1 is in the high abundance group, the judgment rule should be followed: the high abundance group corresponds to penicillin
  • the sensitivity to cephalosporin and cephalosporin is preferred to the phylogenetic tree branch location, and to the markers of all other clones, which can directly determine the sensitivity of penicillin and cephalosporin, so the original result was revised to the G-resistant spectrum. The revised results are consistent with the VITEK2 drug sensitivity results.
  • Example 5 Construction and verification of a fungal identification metabolic profile database based on liquid chromatography-tandem mass spectrometry
  • step (4) Mass spectrometry detection: the residue obtained in step (4) is further reconstituted with 140 ⁇ L of the reconstituted solution, vortexed to fully dissolve, centrifuged at high speed for 15 minutes, and then transferred the supernatant to a new centrifuge tube, repeated high-speed centrifugation for 15 minutes , Transfer 100 ⁇ L of supernatant to high resolution liquid chromatography tandem mass spectrometry system, and inject 4 ⁇ L for detection and analysis; among them, high resolution liquid chromatography tandem mass spectrometry is Waters Q-TOF Synapt G2-Si quadrupole-time-of-flight mass spectrometer.
  • the separation conditions of the test sample established by gradient elution reversed-phase chromatography are as follows: water-acetonitrile-formic acid is used as the mobile phase system, the flow rate of the mobile phase is 0.4ml/min, and the column temperature is 40°C.
  • the chromatographic column is a Waters HSS T3 column with a particle size of 1.8 ⁇ m, an inner diameter of 2.1mm, and a column length of 100mm.
  • Mass detection adopts electrospray ionization source (ESI), positive ion mode, multiple reaction monitoring scan mode (MRM) and MSeContinnum data independent acquisition mode.
  • ESI electrospray ionization source
  • MRM multiple reaction monitoring scan mode
  • MSeContinnum data independent acquisition mode MSeContinnum data independent acquisition mode.
  • Differential compound screening Use Progenesis QI software to perform peak alignment, peak extraction, compound identification, and normalization processing on the raw data collected by mass spectrometry, and output compound mass-to-charge ratio (m/z), retention time, abundance, etc. Characteristic information. Set different species of fungi as different groups, and set the screening parameters for differential compounds as: fold change>10, VIP>1, pvalue ⁇ 0.05, CV ⁇ 30%. A total of 72 differential compounds were screened as follows:
  • Example 6 Construction and verification of Candida tropicalis drug susceptibility profile database based on liquid chromatography-tandem mass spectrometry
  • Candida tropicalis is the second leading pathogen of clinically invasive fungal infections except Candida albicans, but its triazole drug resistance rate is much higher than that of Candida albicans (30% vs 5%), and the prediction of resistance is more valuable. Therefore, Candida tropicalis is selected as the representative, and the method of constructing the drug sensitivity metabolic profile database is explained in detail. The rest of Candida or yeast can refer to this method for library construction and analysis.
  • Preparation of bacterial suspension Inoculate the clinical Candida tropicalis sample on the Candida chromogenic plate (Zhengzhou Bosai Biology), and cultivate it at 37°C for 24-48 hours. Use a disposable inoculating loop to scrape an appropriate amount of colonies into a sterile physiological saline tube to prepare a uniform and even density bacterial suspension.
  • step (4) Mass spectrometry detection: the residue obtained in step (4) is further reconstituted with 140 ⁇ L of the reconstituted solution, vortexed to fully dissolve, centrifuged at high speed for 15 minutes, and then transferred the supernatant to a new centrifuge tube, repeated high-speed centrifugation for 15 minutes , Transfer 100 ⁇ L of supernatant to high resolution liquid chromatography tandem mass spectrometry system, and inject 4 ⁇ L for detection and analysis; among them, high resolution liquid chromatography tandem mass spectrometry is Waters Q-TOF Synapt G2-Si quadrupole-time-of-flight mass spectrometer.
  • the separation conditions of the test sample established by gradient elution reversed-phase chromatography are as follows: water-acetonitrile-formic acid is used as the mobile phase system, the flow rate of the mobile phase is 0.4ml/min, and the column temperature is 40°C.
  • the chromatographic column is a Waters HSS T3 column with a particle size of 1.8 ⁇ m, an inner diameter of 2.1mm, and a column length of 100mm.
  • Mass detection adopts electrospray ionization source (ESI), positive ion mode, multiple reaction monitoring scan mode (MRM) and MSeContinnum data independent acquisition mode.
  • ESI electrospray ionization source
  • MRM multiple reaction monitoring scan mode
  • MSeContinnum data independent acquisition mode MSeContinnum data independent acquisition mode.
  • Differential compound screening Use Progenesis QI software to perform peak alignment, peak extraction, compound identification, and normalization processing on the raw data collected by mass spectrometry, and output compound mass-to-charge ratio (m/z), retention time, abundance, etc. Characteristic information. Set Candida tropicalis into different groups according to the differences in their resistance spectrum to different antimicrobial drugs, and set the screening parameters for different compounds as: fold change>10, VIP>1, p value ⁇ 0.05, CV ⁇ 30%. A total of 22 differential compounds were screened as follows:
  • Candida tropicalis metabolic profile database Through the analysis of different compounds, the same clones were merged, and 60 representative Candida tropicalis clones were selected to construct the database. Among them, 27 were azole-resistant tropical and azole-sensitive (ie, fully sensitive). ) 33 cases in the tropics. Import the characteristic information of the compound (retention time, mass-to-charge ratio, and abundance) into the software IBM SPSS Statistics 23, use the retention time and mass-to-charge ratio information as the variable name, and the compound abundance as the variable value.
  • the statistical parameters are set to "centralized planning"
  • the graph parameters are set to "pedigree diagram, horizontal direction”
  • the method parameters are set to "connection between groups”
  • the measurement interval parameter is set to "Euclidean distance”
  • the conversion value standardization parameter is set to "Z score”
  • a tree diagram is generated.
  • Candida tropicalis refers to the branch location of 6 blind-like Candida tropicalis on the phylogenetic tree to predict its drug resistance spectrum.
  • the blind sample judgment rules are strictly based on the principle of similarity matching between adjacent plants. The prediction results are shown in Table 9.
  • Klebsiella pneumoniae was selected as the representative of Enterobacteriaceae, and the method of genome identification and drug susceptibility database construction was explained in detail. The remaining Enterobacteriaceae bacteria can be constructed and analyzed with reference to this method.
  • Klebsiella pneumoniae was inoculated into LB liquid medium and cultured overnight. The bacterial liquid was collected the next day, and the precipitate was collected after centrifugation at 10,000 rpm for 2 minutes.
  • Nanopopre sequencing use Oxford Nanopore MinION sequencer and MinION flow cell R9.4 sequencing chip for sequencing.
  • 800 ⁇ L starter solution to the chip through the starter port and leave it at room temperature for 5 minutes.
  • Bio-information analysis The FAST5 data collected by MinKNOW software is used for base identification and output in FASTQ format through Albacore basecalling software. Each sample is automatically separated by native bar code.
  • CANU34 (Version 1.8) to assemble the sequencing data (select default parameters), and assemble the whole genome of the sample.
  • CARD https://card.mcmaster.ca
  • CGE Center for Genomic Epidemiology
  • Collect unknown samples with samples in the database use kSNP3 (Version 3.1) to identify SNPs in the whole genome (select Standard mode, Kmer value 31), and construct a phylogenetic tree based on SNPs.
  • Example 8 Identification of Staphylococcus aureus based on nanopore sequencing and construction and verification of drug sensitive genome database
  • Staphylococcus aureus was selected as the representative of Gram-positive cocci, and the method of genome identification and drug susceptibility database construction was explained in detail.
  • Other Gram-positive cocci such as Staphylococcus and Enterococcus faecalis can be used for library construction and analysis with reference to this method.
  • Nanopopre sequencing use Oxford Nanopore MinION sequencer and MinION flow cell R9.4 sequencing chip for sequencing.
  • 800 ⁇ L starter solution to the chip through the starter port and leave it at room temperature for 5 minutes.
  • Bio-information analysis The FAST5 data collected by MinKNOW software is used for base identification and output in FASTQ format through Albacore basecalling software. Each sample is automatically separated by native bar code.
  • CANU34 (Version 1.8) to assemble the sequencing data (select default parameters), and assemble the whole genome of the sample.
  • CARD https://card.mcmaster.ca
  • CGE Center for Genomic Epidemiology
  • Collect unknown samples with samples in the database use kSNP3 (Version 3.1) to identify SNPs in the whole genome (select Standard mode, Kmer value 31), and construct a phylogenetic tree based on SNPs.
  • the above categories were used to predict drug resistance according to the four criteria of CC5mecA+, CC5mecA-, ST59mecA+ and S type respectively, that is, follow the principle of phylogenetic tree to identify priority drug resistance genes.
  • the remaining unknown samples fall into other branches outside the above-mentioned designated areas, they strictly follow the rules for the interpretation of drug resistance genes, and the phylogenetic tree does not modify the results of all drugs.
  • macrolides erythromycin/clarithromycin/azithromycin
  • lincosamides clindamycin/inducing clindamycin
  • the result statistics are based on all 20 drugs, a total of 440 drug results, of which there are 5 false negative results and 0 false positive results; the positive predictive value is 100.00% (138/138), and the negative predictive value is 98.34% (297/ 302), the sensitivity is 96.50% (138/143), and the specificity is 100.00% (297/297). All indicators meet the design requirements of the kit and the actual clinical application requirements, that is, the sensitivity and specificity are higher than 95%.
  • Streptococcus pneumoniae was selected as the representative of fastidious bacteria, and the method of genome identification and drug susceptibility database construction was explained in detail.
  • Nanopopre sequencing use Oxford Nanopore MinION sequencer and MinION flow cell R9.4 sequencing chip for sequencing.
  • 800 ⁇ L starter solution to the chip through the starter port and leave it at room temperature for 5 minutes.
  • Bio-information analysis The FAST5 data collected by MinKNOW software is used for base identification and output in FASTQ format through Albacore basecalling software. Each sample is automatically separated by native bar code.
  • CANU34 (Version 1.8) to assemble the sequencing data (select default parameters), and assemble the whole genome of the sample.
  • CARD https://card.mcmaster.ca
  • CGE Center for Genomic Epidemiology
  • Collect unknown samples with samples in the database use kSNP3 (Version 3.1) to identify SNPs in the whole genome (select Standard mode, Kmer value 31), and construct a phylogenetic tree based on SNPs.
  • Penicillins and cephalosporins are resistant by default , The rest of the drugs follow the rules for the interpretation of resistance genes; when they fall into the S zone, follow the principle of the evolutionary tree to determine the priority resistance genes, all drugs are sensitive by default; when the unknown samples fall into other branches outside the above-mentioned designated areas , Strictly follow the rules for the interpretation of drug resistance genes, and the phylogenetic tree does not modify the results of all drugs.
  • blind sample 1 is attributed to the branches other than the above-mentioned designated areas (PEN-R area and S area), and strictly follows the rules of drug resistance gene interpretation, that is, the phylogenetic tree does not modify the results of all drugs.
  • Blind sample 2 belongs to the penicillin-resistant PEN-R branch, and follows the principle of phylogenetic tree to interpret priority resistance genes. Penicillins and cephalosporins are resistant by default, and the rest of the drugs follow the rules for interpretation of resistance genes.
  • the prediction results of 2 blind samples were: penicillins, cephalosporins, erythromycin, compound trimethoprim and tetracycline multi-drug resistant sample; 1 case; erythromycin, compound new One case of Nuomin and tetracycline-resistant sample.
  • the two blinded drug resistance prediction results were completely consistent with the VITEK2 phenotype verification results, and the coincidence rate was 100%.
  • Candida albicans was selected as the representative of the fungus, and the method of genome identification and drug susceptibility database construction was explained in detail. The remaining Candida or yeast-like fungi can be constructed and analyzed with reference to this method.
  • Nanopopre sequencing use Oxford Nanopore MinION sequencer and MinION flow cell R9.4 sequencing chip for sequencing.
  • 800 ⁇ L starter solution to the chip through the starter port and leave it at room temperature for 5 minutes.
  • Bio-information analysis The FAST5 data collected by MinKNOW software is used for base identification and output in FASTQ format through Albacore basecalling software. Each sample is automatically separated by native bar code.
  • CANU34 (Version 1.8) to assemble the sequencing data (select default parameters), and assemble the whole genome of the sample.
  • CARD https://card.mcmaster.ca
  • CGE Center for Genomic Epidemiology
  • Collect unknown samples with samples in the database use kSNP3 (Version 3.1) to identify SNPs in the whole genome (select Standard mode, Kmer value 31), and construct a phylogenetic tree based on SNPs.
  • the prediction results of 12 blind samples were 4 samples with 5-fluorocytosine resistance and 2 samples with triazole resistance; no 5-fluorocytosine and Azole cross-resistant strains; no amphotericin B-resistant strains; no echinocandin-resistant strains.
  • the prediction result is completely consistent with the drug sensitivity result of the micro-method broth dilution method, and the coincidence rate is 100%.
  • Example 11 Direct identification and drug sensitivity diagnosis of clinical samples using nanopore sequencing technology and rapid library building method
  • Corynebacterium striatum 43890 Homo sapiens 15657 Acinetobacter baumannii 10,096 Corynebacterium simulans 9,247 Streptococcus mitis 3,341 Streptococcus pneumoniae 1,511 Streptococcus sp.oral taxon 431 1,354 Corynebacterium diphtheriae 1,311 Corynebacterium aurimucosum 1,056 Corynebacterium resistens 1,010 Streptococcus pseudopneumoniae 728 Streptococcus oralis 621
  • the resistance gene analysis results are shown in Table 21.
  • the Bowman strain contains resistance-related genes sul2, APH(3')-Ia, OXA239, gyrA(T), that is, it is resistant to the listed antibiotics (table twenty two);
  • Kmer parameter is set to 31, according to the evolution-drug resistance relationship table (Table 23) to query the results of drug resistance, the unknown sample in this example is located in the evolutionary tree Cluster5 ( Figure 16 ), the Acinetobacter baumannii strain is resistant to the listed antibiotics.
  • Example 12 Direct identification and drug sensitivity diagnosis of clinical samples using nanopore sequencing technology and PCR library construction method
  • the Klebsiella pneumoniae drug resistance-related genes did not have any positive predictive results, namely CTX-M-65, TEM-1B, IMP-4, KPC-2, rmtB, AAC (3' )-Iid, QNR-S, gyrA(T), tetA, tetD, sul1, sul2, sul3 were all negative.

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Abstract

本发明提供了一种病原微生物药敏检测方法，所述的方法包括获得待测样本的生物标志物信息，根据生物标志物信息确定待测样本在系统发生树中的定位，根据定位确定待测样本耐药性的判断规则，按照判断规则获得病原微生物的耐药性。本发明还提供了病原微生物系统发生树在制备病原微生物药敏诊断产品中的应用，以及包含病原微生物系统发生树的一种病原微生物鉴定与药敏诊断试剂盒。

Description

一种病原微生物药敏检测方法 技术领域
本发明属于微生物诊断技术领域，涉及临床常见病原微生物的种属鉴定及其药物敏感性的快速诊断，特别是涉及一种病原微生物药敏检测方法及其相关应用。
背景技术
从1930年首例可治疗溶血性链球菌感染的磺胺类药物商品化应用，到现在各类抗生素在临床上广泛应用，正是由于抗生素的发现和使用，使人类在面对传染性疾病时不再束手无策。但是抗生素与病原菌耐药性，就像是矛与盾的关系。每当一种新型抗生素应用之后，细菌凭借其天然的适应性，在不久后就能产生相应的耐药性，新型耐药基因正在不断形成与扩散，病原菌耐药性的问题日益严重。英国政府评估称，如果目前的病原菌耐药性得不到有效遏制，到2050年由于耐药菌引起的人类感染与死亡每年将会达到1000万，这将超过癌症与糖尿病引起人类死亡数的总和，成为影响人类可持续性发展的一种灾难性威胁。
对病原菌种属的准确鉴定及其药物敏感性的准确测试，对于指导临床及时合理的用药，有极大的参考价值；同时对避免抗生素滥用，有效控制微生物耐药性的蔓延，也至关重要。目前，以培养鉴定为基础的检测方法仍然是临床微生物实验室对病原菌诊断的金标准，相关技术包括纸片扩散法、E-test试验法、肉汤稀释法、全自动检测仪器等。基于美国临床和实验室标准协会(NCCLS)颁布指导的琼脂稀释法/肉汤稀释(常量法)经过24-48小时的体外培养后测得抗菌药物能抑制待测菌肉眼可见生长的最低抑菌浓度或最低杀菌浓度的方法，是目前细菌和真菌药敏检测的金标准。培养法的优点是能同时得到定量和定性结果，结果相对准确，缺点是不同的接种密度在药物浓度恒定后会对MIC值产生影响，会产生拖尾现象和边缘效应。基于微生物体内物质对反应底物的利用与培养后的浊度判定的各类微生物自动鉴定及药敏测试系统，如法国bioMérieux的Vitek 2 compact,Vitek ATB；美国BD的PhoneixTM-100，英国的Sensititre TMARIS等，实质是微型化的肉汤稀释实验，具备操作简单、判读客观、解放人力的优点，但因其设备和相关耗材的投入成本高而不利于推广。
上述所有鉴定与药敏试验方案的前提是培养后分纯获得单克隆。而传统的活菌培养鉴定病原菌需耗时2-3天，药敏孵育时间为24-48小时。鉴定及药敏总耗时通常为3-5天。这期间病人的病情可能已发生变化，检验报告对于病人治疗而言已经失去价值，因此大大降低了临床对微生物检测技术应用的热情，随之选择凭经验用药也是导致抗生素滥用的最直接原因。
基质辅助激光解吸/电离飞行时间质谱(MALDI-TOF MS)是近年来发展起来的一种新型软电离生物质谱，其通过样品与基质共结晶经过激光照射后形成的分子通过真空管所需的飞行时间来鉴别蛋白多肽等大分子，已在微生物鉴定领域广泛应用，但尚未在药敏诊断领域创立成熟的检测方法学。同时因该方法检测对象是蛋白质及多肽等大分子，大分子往往并不是细胞代谢最直接的参与者，限制了其检测灵敏度和特异性的提升。
分子诊断技术是微生物病原体鉴定领域的一项革命性技术，不仅速度快，灵敏度高，而且可同时检测数种病原菌。目前运用桑格测序法、焦磷酸测序法、PCR-熔解曲线荧光探针杂交法、基因芯片法等技术的微生物鉴定类产品已日趋成熟。基于全基因组测序的病原菌鉴定在近十年得到了迅速发展，并在感染溯源、传染病监控、新药与新疫苗研发、临床治疗等领域得以推广应用。但是以耐药基因识别为主要方向的全基因组测序技术在微生物耐药诊断领域一直存在难点和技术壁垒。首先耐药基因的出现并不总与耐药表型一致，同时受基因所处遗传环境的调控；其次PCR技术检测耐药基因易产生假阳性结果；再次对已知耐药基因的检测和分析，必定伴随着无法检测新的或非典型性耐药机制的局限性。
综前所述，目前仍然没有一个理想的鉴定与药敏一体化的微生物快速诊断产品，它既满足快速高效、操作简便、成本经济、灵敏度高，能实现临床样本的直接检测；同时具备结果稳定、重复性好、数据诠释方案简单信息丰富、能实现自动化的特点。
发明内容
本发明的目的在于针对现有技术的不足，提供一种基于待测样本的生物标志物信息的病原微生物药敏检测方法及其相关应用。进一步的，本发明还提供一种运用液相色谱-串联质谱技术与全基因组测序技术对病原微生物进行快速鉴定与药敏诊断的试剂盒；使用该试剂盒进行菌落培养物与临床样本进行检测的非诊断方法，如应用于药学研究和数据统计的方法。
液相色谱串联质谱技术有效结合了液相色谱对化合物的分离纯化优势和质谱对化合物的高分辨率和优越的定性与定量分析性能，结合代谢组学分析方法，能够在小分子极性化合物层面对病原微生物的代谢水平进行解析，并从代谢物的组成、表达量、所参与的信号通路等角度，同时对代谢化合物与结构化合物进行准确识别，从而实现不同病原菌的区分鉴定。
生物体完整的遗传物质的总和称为基因组。全基因组测序即是对个体的全基因组序列进行测序，能 够全面、精确地分析基因组的碱基序列，从而获取其所包含的生物信息。第二代测序技术，即高通量测序技术，单次能对几十万到几百万条DNA进行序列测序，使得全基因组测序变得方便易行，但也存在设备成本高无法全面推广，读长短以致生信分析难度高的缺点。三代测序相比二代测序的进步在于实现了单分子测序，从而使测序过程无需依赖PCR扩增。纳米孔测序技术即属于第三代测序技术，在传染病原研究领域中目前正以飞快的速度取代二代测序的应用。它通过将DNA长链穿过被称为纳米孔的小孔，并且探测由DNA的4个核苷酸组件引发的电流微小变化的方式，实现阅读序列信息。具备三大突出优势，即：无需扩增即可实现单分子实时测序；Mb级别的测序读长显著提高了基因组拼接效率与准确性；测序速度快、仪器成本低且小巧便携，临床易接受度和推广程度高。
液相色谱串联质谱技术和全基因组测序技术均可独立、同步实现病原菌的鉴定与药敏诊断。其中前者检测对象为菌落培养物，后者检测对象为临床样本或菌落培养物。当检测对象为培养后的纯菌落时，液相色谱串联质谱技术可发挥其检测耗时短(30分钟以内)，分析流程简单，试剂及耗材成本低廉的优势。当检测对象为临床样本时，纳米孔测序技术可以突破培养阳性率低和培养时间长的瓶颈，直接从临床样本中高通量和高效地实现病原菌检测。本发明同时包含基于液质联用技术与纳米孔测序技术的鉴定与药敏检测方法，可根据不同的样本类型和时效需求选择最优的检测方法。
一方面，本发明提供了一种病原微生物鉴定和/或药敏检测方法，所述的方法包括获得待测样本的生物标志物信息，根据生物标志物信息确定待测样本在系统发生树中的定位，根据定位确定待测样本的种属及其耐药性的判断规则，按照判断规则获得病原微生物的耐药性。
进一步的，所述的生物标志物信息为病原微生物中代谢物的信息和/或病原微生物的序列信息。
进一步的，所述病原微生物中代谢物的信息为病原微生物中代谢物的质谱信息，优选的，所述病原微生物中代谢物的质谱信息为质荷比、保留时间和丰度中的一种或两种以上的组合。
本发明所述病原微生物鉴定和/或药敏检测方法可以为诊断方法也可以为非诊断方法；包括在确定所述微生物种属及其抗生素耐药性时，同时运用系统发生树、生物标志物、基于系统发生树不同分支的独立判断规则三者结合进行判读；其中，系统发生树的定位用于鉴定微生物种属；系统发生树的定位及其不同分支的判断规则，结合生物标志物，用于分析抗生素耐药性。
进一步地，所述的系统发生树通过液相色谱串联质谱技术和/或全基因组测序技术获得。
进一步地，所述系统发生树的构建形式，包括但不限于：基于代谢物的组成与丰度差异的代谢谱型系统发生树，基于微生物基因组SNP及InDel构建的核酸序列型系统发生树，和/或，基于微生物基因组核心耐药元件及其上下游环境构建的核酸序列型系统发生树。
进一步地，所述生物标志物的类型，包括但不限于：氨基酸、有机酸、脂肪酸、糖衍生物等代谢化合物型生物标志物及其保留时间与质荷比，耐药基因、质粒、转座子、整合子、插入序列等耐药元件型生物标志物核酸序列。
进一步地，所述的基于系统发生树不同分支的独立判断规则的类型，包括但不限于：基于代谢物的组成与丰度差异的代谢谱型系统发生树在其不同分支上应用不同的判断标准，和/或，基于微生物基因组核心耐药元件及其上下游环境构建的核酸序列型系统发生树在其不同分支上应用不同的判断标准。
进一步地，用于构建所述的系统发生树的代谢物为质荷比在50-1500Da之间，丰度值大于2000的水溶性代谢化合物的总和。
进一步地，所述的系统发生树为由本地测序获得的代表克隆株的基因组序列构建的无根型系统发生树。
进一步地，其中代谢化合物型生物标志物保留时间的偏差范围为±0.5min，质荷比的偏差范围为±0.05Da。
进一步地，其中耐药元件型生物标志物包括但不限于：
abarmA、abAPH(3’)-Ia、abOXA239、abNDM-10、abgyrA、abSUL-1、abSUL-2、abSUL-3、kpCTX-M-65、kpTEM-1b、kpIMP-4、kpKPC-2、kprmtB、kpAAC(3’)-Iid、kpQNR-S、kpgyrA、kpparC、kptetA、kptetD、kpSUL-1、kpSUL-2、kpSUL-3、ecrmtB、ecAAC(3’)-Iid、ecgyrA、ectetA、ectetB、ecSUL-1、ecSUL-2、ecSUL-3、ecIMP-4、ecNDM-5、ecTEM-1b、ecCTX-M-14、ecCTX-M-55、ecCTX-M-65、ecCMY、paTEM-1b、paGES-1、paPER-1、paKPC-2、paOXA-246、parmtB、paAAC(3’)-Iid、paAAC(6’)-IIa、paVIM-2、pagyrA、efermB、eftetM、eftetL、efparC、efANT(6’)-Ia、stmecA、stmsrA、stermA、stermB、stermC、strpoB、stgyrA、stAAC(6’)-APH(2”)、stdfrG、sttetK、sttetL、stcfrA、spbpb1a、sppbp2x、spbpb2b、spdfr、sptetM、spermB、spgyrA、aat1a、acc1、adp1、mpib、sya1、vps13、zwf1b、fcy2、fur1、fca1、erg11、erg3、tac1、cdr1、cdr2、mdr1、pdr1、upc2a、fks1hs1、fks1hs2、fks2hs1、fks2hs2中的一种或两种以上的组合。
进一步地，其中代谢谱型系统发生树在其不同分支上的判断标准，包括但不限于：
1)在非发酵阴性细菌的耐药性判断中运用系统发生树各分支的独立判断规则：当待测样本中包含特 定克隆株的特异性标志物时，遵循特异性标志物指向的耐药谱优先于系统发生树判读的原则；当待测样本定位于S分支时，遵循系统发生树判读优先于特异性标志物的原则，可直接判定对所有内酰胺类药物均敏感；
2)在肠杆菌科细菌的耐药性判断中运用系统发生树各分支的独立判断规则：当待测样本中包含特定克隆株的特异性标志物时，遵循特异性标志物指向的耐药谱优先于系统发生树判读的原则；当待测样本定位于S分支时，遵循系统发生树判读优先于特异性标志物的原则，可直接判定对内酰胺类、头霉素类、内酰胺酶抑制剂类药物均敏感；
3)在阳性球菌的耐药性判断中运用系统发生树各分支的独立判断规则：当待测样本中包含符合特定克隆株的特异性标志物时，遵循特异性标志物指向的耐药谱优先于系统发生树判读的原则，当待测样本定位于S分支时，遵循系统发生树判读优先于特异性标志物的原则，可直接判定对所有药物均敏感；并且ST4型克隆株的特异性标志物，优先于其它所有克隆株的标志物，可直接判定粪肠球菌对青霉素类药物的药敏结果均为耐药；
优选的，所述的阳性球菌为葡萄球菌属细菌时：当待测样本定位于mecA+分支时，遵循系统发生树判读优先于特异性标志物的原则，可直接判定对青霉素、苯唑西林、头孢西丁和喹诺酮类药物为耐药；当待测样本定位于mecA-分支时，遵循系统发生树判读优先于特异性标志物的原则，可直接判定对青霉素、苯唑西林、头孢西丁为敏感；当待测样本定位于S分支时，遵循系统发生树判读优先于特异性标志物的原则，可直接判定对所有药物均敏感；当待测样本定位于除上述指定分支之外的其他分支时，严格遵循特异性标志物的判段规则，系统发生树对所有药物结果均不作修正；
优选的，所述的阳性球菌为粪肠球菌属细菌时：当待测样本中包含符合特定克隆株的特异性标志物时，遵循特异性标志物指向的耐药谱优先于系统发生树判读的原则；粪肠球菌ST4型克隆株的特异性标志物，优先于其它所有克隆株的标志物，可直接判定粪肠球菌对青霉素类药物的药敏结果均为耐药；
4)在肺炎链球菌的耐药性判断中运用系统发生树各分支的独立判断规则：当待测样本中包含符合特定克隆株的特异性标志物时，遵循特异性标志物指向的耐药谱优先于系统发生树判读的原则；青霉素相关的特异性标志物的丰度指标优先于系统发生树分支定位，优先于其它所有克隆株的标志物，可直接判定青霉素和头孢菌素的耐药与敏感；
和/或，5)在真菌的耐药性判断中运用系统发生树各分支的独立判断规则，并严格遵循相邻株相似性匹配原则进行耐药推断。
进一步地，其中核酸序列型系统发生树在其不同分支上的判断标准，包括但不限于：
1)肠杆菌科细菌对碳青霉烯和喹诺酮类药物的耐药性判断，与定位的系统发生树分支有关并遵循系统发生树判读优先耐药基因的原则，其余类别的药物遵循耐药基因的判读原则；
2)非发酵阴性细菌对头孢菌素和碳青霉烯类药物的耐药性判断，与定位的系统发生树分支有关并遵循系统发生树判读优先耐药基因的原则，其余类别的药物遵循耐药基因的判读原则；
3)阳性球菌对青霉素、氨苄西林、苯唑西林和头孢西丁的耐药性判断，与定位的系统发生树分支有关并遵循系统发生树判读优先耐药基因的原则，其余类别的药物遵循耐药基因的判读原则；
4)肺炎链球菌对青霉素类和头孢菌素类药物的耐药性判断，与定位的系统发生树分支有关并遵循系统发生树判读优先耐药基因的原则，其余类别的药物遵循耐药基因的判读原则；
和/或，5)真菌对三唑类药物的耐药性判断，与定位的系统发生树分支有关并遵循系统发生树判读优先耐药基因的原则，其余类别的药物遵循耐药基因的判读原则。
本发明所述的病原微生物鉴定和/或药敏检测方法中按照菌种亲缘关系的远近与临床差异用药的实际需求，依据革兰氏染色结果、形态特征、发酵糖的能力、对生长环境的需求等性质，将临床常见细菌分为肠杆菌、非发酵阴性杆菌、阳性球菌、苛养菌四类分别进行耐药数据库构建，这四类菌占临床常见病原菌的90％以上。本领域技术人员可以理解，鲍曼不动杆菌、粪肠球菌、肺炎链球菌、热带念珠菌、肺炎克雷伯菌、金黄色葡萄球菌等作为代表菌种表明所述的方法适用于临床常见细菌或真菌的鉴定和/或药敏检测，而其他菌种均可参照采用本发明的方法和判断规则。
另一方面，本发明还提供了一种病原微生物系统发生树在制备病原微生物鉴定和/或药敏诊断产品中的应用，所述的系统发生树通过液相色谱串联质谱技术和/或全基因组测序技术获得。
进一步的，所述系统发生树的构建形式选自基于代谢物的组成与丰度差异的代谢谱型系统发生树，基于微生物基因组SNP及InDel构建的核酸序列型系统发生树，或者，基于微生物基因组核心耐药元件及其上下游环境构建的核酸序列型系统发生树。
进一步的，所述病原微生物鉴定和/或药敏诊断产品还包括获得待测样本的生物标志物信息的试剂或设备。
进一步的，所述获得待测样本的生物标志物信息的设备选自液相色谱串联质谱或全基因组测序装置。
进一步的，所述获得待测样本的生物标志物信息的试剂选自基于液相色谱串联质谱技术的病原微生物鉴定与药敏诊断试剂盒或基于全基因组测序技术的病原微生物鉴定与药敏诊断试剂盒。
另一方面，本发明提供了一种临床常见病原微生物鉴定与药敏诊断试剂盒，其特征在于，包括以下两套试剂盒组分：
KIT1：基于液相色谱串联质谱技术的病原微生物鉴定与药敏诊断试剂盒；或者
KIT2：基于全基因组测序技术的病原微生物鉴定与药敏诊断试剂盒。
进一步的，所述的试剂盒还包括病原微生物系统发生树。
进一步地，其中基于液相色谱串联质谱技术的病原微生物鉴定与药敏诊断试剂盒包括细菌外标溶液、真菌外标溶液、萃取溶剂、复溶液。
进一步地，其中基于全基因组测序技术的病原微生物鉴定与药敏诊断试剂盒包括血细胞裂解液、引物混合液、真菌裂解液、真菌消化液、建库试剂、原生条码、测序试剂。
进一步地，所述细菌外标溶液为含128ng/mL5-氟胞嘧啶的甲醇溶液，2～8℃预冷。
进一步地，所述真菌外标溶液为含126ng/mL氨苄西林的甲醇水(体积比80：20)溶液，2～8℃预冷。
进一步地，所述萃取溶剂为甲醇-乙腈混合物，其体积比为2:1，-20～-80℃预冷。
进一步地，所述复溶液为水-乙腈-甲酸混合物，其体积比为98:2:0.05，2～8℃预冷。
进一步地，所述血细胞裂解液为含0.02％(m/v)皂苷的水溶液。进一步地，所述的基于液相色谱串联质谱技术的病原微生物鉴定与药敏诊断试剂盒的适用样本类型为菌落培养物；所述的基于全基因组测序技术的病原微生物鉴定与药敏诊断试剂盒的适用样本类型为临床样本或菌落培养物。
另一方面，本发明提供了上述临床常见病原微生物鉴定与药敏诊断试剂盒在病原菌鉴定与药敏检测中的应用。
附图说明
图1a-c为82例24种细菌菌种代表构建的系统发生树型代谢谱鉴定数据库；
图2a-b为16例盲样细菌(blind samples)在代谢谱鉴定数据库中的定位分布。系统发生树代表不同种属的分支分别由左侧不同颜色标识，黑色标识代表盲样；
图3a-b为87例鲍曼不动杆菌代表克隆株构建的系统发生树型代谢谱药敏数据库；
图4a-b为16例盲样鲍曼不动杆菌在代谢谱药敏数据库中的定位分布。系统发生树代表不同耐药谱的分支分别由左侧不同颜色标识，黑色标识代表盲样；
图5为36例粪肠球菌代表克隆株构建的系统发生树型代谢谱药敏数据库；
图6为6例盲样粪肠球菌在代谢谱药敏数据库中的定位分布。系统发生树代表不同耐药谱的分支分别由左侧不同颜色标识，黑色标识代表盲样；
图7为18例肺炎链球菌代表克隆株构建的系统发生树型代谢谱药敏数据库；
图8为2例盲样肺炎链球菌在代谢谱药敏数据库中的定位分布。系统发生树代表不同耐药谱的分支分别由左侧不同颜色标识，黑色标识代表盲样；
图9a-b为115例22种真菌菌种代表构建的系统发生树型代谢谱鉴定数据库；
图10a-b为8例盲样真菌在代谢谱鉴定数据库中的定位分布。系统发生树代表不同种属的分支分别由左侧不同颜色标识，黑色标识代表盲样；
图11为6例盲样热带念珠菌在代谢谱药敏数据库中的定位分布。系统发生树代表不同耐药谱的分支分别由左侧不同颜色标识，蓝色为唑类敏感热带，黄色为唑类耐药热带，黑色标识代表盲样；
图12a-c为165例肺炎克雷伯菌代表克隆株构建的系统发生树型基因组鉴定与耐药数据库。系统发生树代表不同耐药谱的分支分别由右侧不同颜色标识，其中黄色标记区域为ST11分区，红色标记区域为ST15分区，蓝色标记区域为敏感株分区(含ST23分区)；
图13为93例金黄色葡萄球菌代表克隆株构建的系统发生树型基因组鉴定与耐药数据库。系统发生树代表不同耐药谱的分支分别由右侧不同颜色标识；
图14为25例肺炎链球菌代表克隆株构建的系统发生树型基因组鉴定与耐药数据库及2例盲样在基因组数据库中的定位分布。系统发生树代表不同耐药谱的分支分别由右侧不同颜色标识，其中黄色标记区域为敏感株分区，蓝色标记区域为青霉素耐药分区，黑色标识代表盲样；
图15为107例白色念珠菌代表克隆株构建的系统发生树型基因组鉴定与耐药数据库。系统发生树代表不同耐药谱的分支分别由右侧不同颜色标识，其中红色为三唑类耐药白念，黄色为5-氟胞嘧啶耐药白念，绿色为棘白菌素类耐药白念，灰色为两性霉素B中介白念，蓝色为全敏感白念；
图16为一例呼吸道痰液样本在鲍曼不动杆菌基因组系统发生树上的分区定位；
图17为一例呼吸道痰液样本在肺炎克雷伯菌基因组系统发生树上的分区定位。
四、具体实施方式
下面结合具体实施例详述本发明，但本发明的保护范围不局限于以下实施例。其中，各表格(表1-表24)中的R代表由相应的耐药基因可判读为耐药；S代表由相应的耐药基因可可判读为敏感；空格代表不作判读。
实施例1基于液相色谱-串联质谱的细菌鉴定代谢谱数据库构建与验证
1.方法
(1)样本采集及菌种验证：收集2017年5月至2019年7月间来自全国42家不同医院的430例革兰氏阴性与阳性细菌菌株，分纯后保存菌株。所有菌株均经一代或三代测序鉴定菌种，作为结果比对的金标准。
(2)菌悬液制备：将各试验菌株接种于哥伦比亚血琼脂平板上，培养过夜后，用一次性接种环刮取适量菌落至无菌生理盐水管中，制备均一等密度的菌悬液。
(3)超声破碎：在180μL菌悬液中加入等体积的外标工作溶液，超声破碎80Hz 15min。
(4)代谢物萃取与浓缩：将步骤(3)得到的超声产物转移340μL至1.5ml离心管中，并加入等体积的萃取溶剂，冰浴震荡混匀5min，快速离心3-5秒，将管内液体甩至管底，置于氮吹仪中吹干。
(5)质谱检测：将步骤(4)得到的残渣，进一步用140μL复溶液复溶，涡旋使之充分溶解，高速离心15min后转移上清至新离心管中，重复1次高速离心15min后，转移上清100μL至高分辨液相色谱串联质谱系统，进样4μL进行检测分析；其中高分辨液相色谱串联质谱为Waters Q-TOF Synapt G2-Si四级杆-飞行时间质谱仪。采用梯度洗脱反相色谱法建立的待测样品分离条件如下：以水-乙腈-甲酸为流动相体系，流动相的流速为0.4ml/min，柱温40℃。色谱柱为Waters HSS T3柱，粒径为1.8μm，内径2.1mm，柱长100mm。质谱检测采用电喷雾离子源(ESI)、正离子模式、多反应监测扫描模式(MRM)和MSeContinnum数据独立型采集模式。
2.生信分析及数据库构建
(1)差异化合物筛选：使用Progenesis QI软件对质谱采集的原始数据进行峰对齐、峰提取、化合物鉴定、归一化处理，并输出化合物质荷比(m/z)、保留时间、丰度等特征信息。将不同种属的细菌设为不同组，设置差异化合物筛选参数为：fold change>10,VIP>1，p value<0.05,CV<30％。共筛选得到以下258个差异化合物：
Figure PCTCN2020081943-appb-000001
Figure PCTCN2020081943-appb-000002
Figure PCTCN2020081943-appb-000003
(2)鉴定代谢谱数据库构建：为提高数据库的代表性及可重复性，在选择数据库的菌种类型时均匀覆盖了各种肠杆菌科细菌、非发酵细菌和阳性球菌，并在各菌种中挑选不同医院来源的10株及以上的菌株进行代谢谱测试。为提高数据库的分辨率，构建数据库的菌种中选择加入了VITEK2等全自动生化鉴定系统中无法准确区分的醋酸钙-鲍曼不动杆菌复合体中的鲍曼不动杆菌、医院不动杆菌、皮特不动杆菌；克雷伯菌属中的肺炎克雷伯菌、变栖克雷伯菌、类肺炎克雷伯菌。
通过差异化合物分析，合并相同克隆株，选择82例各菌种代表性克隆株构建数据库。将化合物的特征信息(保留时间、质荷比及丰度)导入软件IBM SPSS Statistics 23中,将保留时间和质荷比信息作为变量名，化合物丰度作为变量值。选择分析-分类-系统聚类，统计参数设为“集中计划”，图参数设为“谱系图、水平方向”，方法参数设为“组间联接”，测量区间参数设为“欧氏距离”，转换值标准化参数设为“Z得分”，生成树状图，如图1所示。
3.数据库验证及结果解读
(1)盲样验证：从16例盲样中提取258个目标差异化合物的丰度值，并导入软件IBM SPSS Statistics 23中，与库内数据合并分析。通过各样本在系统发生树上的分支定位确定其种属。如图2所示，系统发生树代表不同种属的分支分别由左侧不同颜色区域标识，黑色标识代表盲样。
(2)结果解读：参照16例盲样在系统发生树上的种属分支定位，对其种属进行预测，结果如表1所示。代谢谱数据库的预测结果与金标准(测序法)鉴定完全结果一致，符合率100％。其中，对于VITEK2等生化鉴定系统无法区分的醋酸钙-鲍曼复合体中的鲍曼不动杆菌(Acinetobacter baumannii)和皮特不动杆菌(Acinetobacter pittii)，较难区分容易错判的溶血葡萄球菌(Staphylococcus haemolyticus)和表皮葡萄球菌(Staphylococcus epidermidis)，采用本发明的代谢谱数据库进行预测，结果与测序法金标准完全相符。
盲样 代谢谱数据库预测结果 金标准(测序法)鉴定结果 是否一致
blind-1 铜绿假单胞菌 Pseudomonas aeruginosa 是
blind-2 大肠埃希菌 Escherichia coli 是
blind-3 大肠埃希菌 Escherichia coli 是
blind-4 嗜麦芽窄食单胞菌 Stenotrophomonas maltophilia 是
blind-5 皮特不动杆菌 Acinetobacter pittii 是
blind-6 粪肠球菌 Enterococcus faecium 是
blind-7 变栖克雷伯菌 Klebsiella variicola 是
blind-8 鲍曼不动杆菌 Acinetobacter baumannii 是
blind-9 鲍曼不动杆菌 Acinetobacter baumannii 是
blind-10 阴沟肠杆菌 Enterobacter cloacae 是
blind-11 金黄色葡萄球菌 Staphylococcus aureus 是
blind-12 产气克雷伯菌 Klebsiella aerogenes 是
blind-13 肺炎克雷伯菌 Klebsiella pneumoniae 是
blind-14 溶血葡萄球菌 Staphylococcus haemolyticus 是
blind-15 肺炎克雷伯菌 Klebsiella pneumoniae 是
blind-16 表皮葡萄球菌 Staphylococcus epidermidis 是
表1
实施例2基于液相色谱-串联质谱的鲍曼不动杆菌代谢谱药敏数据库构建及验证
选择鲍曼不动杆菌作为革兰氏阴性杆菌的代表，详细说明代谢谱药敏数据库构建方式。其余肠杆菌、非发酵菌等革兰氏阴性杆菌，均可参照采用本方法进行建库及分析。
1.方法
(1)鲍曼不动杆菌药敏验证：使用梅里埃Vitek2 Compact30全自动微生物分析仪及N335药敏卡，对试验用的所有鲍曼不动杆菌进行药敏检测，将结果作为比对标准。
(2)菌悬液制备：将鲍曼不动杆菌接种于哥伦比亚血琼脂平板上，培养过夜。用一次性接种环刮取适量菌落至无菌生理盐水管中，制备均一等密度的菌悬液。
(3)超声破碎：在180μL菌悬液中加入等体积的外标工作溶液，超声破碎80Hz 15min。
(4)代谢物萃取与浓缩：将步骤(3)得到的超声产物转移340μL至1.5ml离心管中，并加入等体积的萃取溶剂，冰浴震荡混匀5min，快速离心3-5秒，将管内液体甩至管底，置于氮吹仪中吹干。
(5)质谱检测：将步骤(4)得到的残渣，进一步用140μL复溶液复溶，涡旋使之充分溶解，高速离心15min后转移上清至新离心管中，重复1次高速离心15min后，转移上清100μL至高分辨液相色谱串联质谱系统，进样4μL进行检测分析；其中高分辨液相色谱串联质谱为Waters Q-TOF Synapt G2-Si四级杆-飞行时间质谱仪。采用梯度洗脱反相色谱法建立的待测样品分离条件如下：以水-乙腈-甲酸为流动相体系，流动相的流速为0.4ml/min，柱温40℃。色谱柱为Waters HSS T3柱，粒径为1.8μm，内径2.1mm，柱长100mm。质谱检测采用电喷雾离子源(ESI)、正离子模式、多反应监测扫描模式(MRM)和MSeContinnum数据独立型采集模式。
2.生信分析及数据库构建
(1)差异化合物筛选：使用Progenesis QI软件对质谱采集的原始数据进行峰对齐、峰提取、化合物鉴定、归一化处理，并输出化合物质荷比(m/z)、保留时间、丰度等特征信息。将鲍曼不动杆菌按其耐药谱差异设为不同组，设置差异化合物筛选参数为：fold change>10,VIP>1，p value<0.05,CV<30％。共筛选得如下102个差异化合物：
0.67_885.3152m/z、0.80_247.3097m/z、0.82_937.3576m/z、0.82_937.8613m/z、0.86_892.2968m/z、0.87_275.1338m/z、0.87_574.2563m/z、0.87_645.1994m/z、1.46_583.3177m/z、1.95_119.0333m/z、1.95_136.0604m/z、1.95_268.1029m/z、2.18_330.0594m/z、2.18_393.1092m/z、2.24_277.5730m/z、2.35_242.5698m/z、2.39_98.0585m/z、2.41_507.5750m/z、2.41_511.5846m/z、2.42_492.0796m/z、2.60_1411.5904m/z、2.63_1133.9809m/z、2.65_908.9106m/z、2.66_1421.5652m/z、2.67_1250.6971m/z、2.67_1395.8226m/z、2.68_672.5593m/z、2.70_1343.0987m/z、2.71_102.1264m/z、2.73_1058.9230m/z、2.73_1279.0598m/z、2.73_1389.6516m/z、2.74_1070.9482m/z、2.74_1082.9834m/z、2.75_731.6471m/z、2.76_1092.0217m/z、2.76_1098.9938m/z、2.76_1132.0044m/z、2.76_119.0118m/z、2.76_1459.7094m/z、2.77_687.6290m/z、2.78_1126.0419m/z、2.78_1299.6447m/z、2.78_1316.6255m/z、2.78_1338.6192m/z、2.78_1491.7467m/z、2.85_1182.0766m/z、2.85_1329.6451m/z、2.85_1444.7037m/z、2.86_1374.1612m/z、2.86_1411.6575m/z、2.86_1471.7506m/z、2.89_1108.1168m/z、2.91_1011.8289m/z、2.91_1012.8270m/z、2.91_1371.6639m/z、2.91_1372.2017m/z、2.91_915.8016m/z、2.92_1187.1433m/z、2.92_1206.1139m/z、2.92_1293.6244m/z、2.92_1329.7157m/z、2.92_1365.6693m/z、2.93_1269.6239m/z、2.94_948.1554m/z、2.95_1342.7146m/z、2.95_947.8219m/z、2.95_948.8201m/z、2.96_1257.2006m/z、2.97_1052.0905m/z、2.97_1421.7925m/z、2.97_1443.7972m/z、2.97_708.7048m/z、2.97_837.7936m/z、2.97_838.1319m/z、2.98_1009.0445m/z、2.98_1044.0853m/z、2.98_1055.0739m/z、2.98_1055.5707m/z、2.98_1063.0585m/z、2.98_1066.0654m/z、2.98_1066.5628m/z、2.98_1074.0492m/z、2.98_1077.0566m/z、2.98_1260.7389m/z、2.98_1356.6824m/z、2.98_666.0361m/z、2.98_703.7160m/z、2.98_704.0466m/z、2.98_709.0372m/z、2.98_921.9998m/z、2.98_998.0538m/z、3.00_1036.0897m/z、3.00_1036.5737m/z、3.00_1093.1141m/z、3.00_1093.6160m/z、3.00_1094.1189m/z、3.00_1104.1054m/z、3.00_1104.6071m/z、3.00_1115.0977m/z、3.00_729.7483m/z、3.00_730.0794m/z。
(2)耐药谱分类：针对哌拉西林、头孢他啶、头孢吡肟、亚胺培南、美罗培南、庆大霉素、妥布霉素、阿米卡星、左氧氟沙星、环丙沙星、复方新诺明、米诺环素等12种抗菌药物，按鲍曼不动杆菌对其耐药与敏感的差异，构建不同的耐药谱类型，并以A～S命名。耐药谱分类及相应药物敏感性，如表2所示。
Figure PCTCN2020081943-appb-000004
表2
(3)鲍曼药敏代谢谱数据库构建：通过差异化合物分析，合并相同克隆株，选择87例代表性鲍曼不动杆菌克隆株构建数据库。将化合物的特征信息(保留时间、质荷比及丰度)导入软件IBM SPSS Statistics 23中，将保留时间和质荷比信息作为变量名，化合物丰度作为变量值。选择分析-分类-系统聚类，统计参数设为“集中计划”，图参数设为“谱系图、水平方向”，方法参数设为“组间联接”，测量区间参数设为“欧氏距离”，转换值标准化参数设为“Z得分”，生成树状图，如图3所示。
3.数据库验证及结果解读
(1)盲样验证：从16例盲样中提取102个目标差异化合物的丰度值，并导入软件IBM SPSS Statistics23中，与库内数据合并分析。通过未知样本在系统发生树上所处的分支定位及其相应的耐药谱判断规则，确定盲样的耐药谱类型。如图4所示，系统发生树代表不同耐药谱的分支分别由左侧不同颜色区域标识，黑色标识代表盲样。
(2)结果解读：参照16例盲样鲍曼在系统发生树上的分支定位，对其耐药谱进行预测。运用系统发生树各分区的独立判断规则，即当未知样本中包含符合特定克隆株的特异性标志物时，判断逻辑的优先级为：特异性标志物指向的耐药谱，优先于系统发生树分支定位的耐药谱。
盲样鲍曼对哌拉西林、头孢他啶、头孢吡肟、亚胺培南、美罗培南、庆大霉素、妥布霉素、阿米卡星、左氧氟沙星、环丙沙星、复方新诺明、米诺环素等12种抗菌药物的耐药谱预测及分析逻辑，如表3所示：
Figure PCTCN2020081943-appb-000005
Figure PCTCN2020081943-appb-000006
表3
如果只通过系统发生树的分支定位来预测耐药谱，该16例样本与VITEK2药敏结果不一致的有三例，六种抗生素耐药结果预测错误。如果将系统发生树的分支定位与特异性标志物相结合，并在不同分支内运用不同的判断规则，上述结果中，盲样1包含G208特异性标志物(2.41_507.5750m/z、2.41_511.5846m/z)，盲样5包含F540特异性标志物(3.00_1093.6160m/z、3.00_1094.1189m/z)，而由于208分支和540分支的耐药谱判断规则为：当未知样本中包含符合特定克隆株的特异性标志物时，特异性标志物指向的耐药谱优先于系统发生树分支定位的耐药谱，因此盲样1和盲样5的结果被修正为G208和F540，修正后的结果与VITEK2药敏结果一致。
最终16例盲样预测的耐药谱与VITEK2药敏结果不符的为1例，即复方新诺明的假阴性结果。以12种药物进行统计，共计192个药物结果，其中假阴性结果1个，假阳性结果无。阳性预测值为100％(168/168)，阴性预测值为95.83％(23/24)，灵敏度为99.41％(168/169)，特异度为100％(23/23)。各项指标均符合试剂盒的设计要求及临床实际应用需求，即灵敏度和特异度均高于95％。
实施例3基于液相色谱-串联质谱的粪肠球菌药敏代谢谱数据库构建及验证
选择粪肠球菌作为革兰氏阳性球菌的代表，详细说明代谢谱药敏数据库构建方式。其余粪肠球菌、葡萄球菌等革兰氏阳性球菌，均可参照采用本方法进行建库及分析。
1.方法
(1)粪肠球菌药敏验证：使用梅里埃Vitek2 Compact30全自动微生物分析仪及P639药敏卡，对试验用的所有粪肠球菌进行药敏检测，将结果作为比对标准。
(2)菌悬液制备：将粪肠球菌接种于哥伦比亚血琼脂平板上，培养过夜。用一次性接种环刮取适量菌落至无菌生理盐水管中，制备均一等密度的菌悬液。
(3)超声破碎：在180μL菌悬液中加入等体积的外标工作溶液，超声破碎80Hz 15min。
(4)代谢物萃取与浓缩：将步骤(3)得到的超声产物转移340μL至1.5ml离心管中，并加入等体积的萃取溶剂，冰浴震荡混匀5min，快速离心3-5秒，将管内液体甩至管底，置于氮吹仪中吹干。
(5)质谱检测：将步骤(4)得到的残渣，进一步用140μL复溶液复溶，涡旋使之充分溶解，高速离心15min后转移上清至新离心管中，重复1次高速离心15min后，转移上清100μL至高分辨液相色谱串联质谱系统，进样4μL进行检测分析；其中高分辨液相色谱串联质谱为Waters Q-TOF Synapt G2-Si四级杆-飞行时间质谱仪。采用梯度洗脱反相色谱法建立的待测样品分离条件如下：以水-乙腈-甲酸为流动相体系，流动相的流速为0.4ml/min，柱温40℃。色谱柱为Waters HSS T3柱，粒径为1.8μm，内径2.1mm，柱长100mm。质谱检测采用电喷雾离子源(ESI)、正离子模式、多反应监测扫描模式(MRM)和MSeContinnum数据独立型采集模式。
2.生信分析及数据库构建
(1)差异化合物筛选：使用Progenesis QI软件对质谱采集的原始数据进行峰对齐、峰提取、化合物鉴定、归一化处理，并输出化合物质荷比(m/z)、保留时间、丰度等特征信息。将粪肠球菌按其耐药谱差异设为不同组，设置差异化合物筛选参数为：fold change>10,VIP>1，p value<0.05,CV<30％。共筛选得如下51个差异化合物：0.53_491.2411m/z 0.55_1409.6162m/z 0.55_1450.5881m/z
Figure PCTCN2020081943-appb-000007
Figure PCTCN2020081943-appb-000008
(2)耐药谱分类：针对青霉素、氨苄西林、万古霉素、利奈唑胺、达托霉素、高水平庆大霉素、红霉素、左氧氟沙星、环丙沙星、替加环素、四环素等11种药物，按粪肠球菌对其耐药与敏感的差异，构建不同的耐药谱类型。耐药谱分类及相应药物敏感性，如表4所示。
Figure PCTCN2020081943-appb-000009
表4
(3)粪肠球菌代谢谱数据库构建：通过差异化合物分析，合并相同克隆株，选择36例代表性粪肠球菌克隆株构建数据库。将化合物的特征信息(保留时间、质荷比及丰度)导入软件IBM SPSS Statistics23中，将保留时间和质荷比信息作为变量名，化合物丰度作为变量值。选择分析-分类-系统聚类，统计参数设为“集中计划”，图参数设为“谱系图、水平方向”，方法参数设为“组间联接”，测量区间参数设为“欧氏距离”，转换值标准化参数设为“Z得分”，生成树状图，如图5所示。
3.数据库验证及结果解读
(1)盲样验证：从6例盲样中提取51个目标差异化合物的丰度值，并导入软件IBM SPSS Statistics23中，与库内数据合并分析。通过未知样本在系统发生树上所处的分支定位及其相应的耐药谱判断规则，确定盲样的耐药谱类型。如图6所示，系统发生树代表不同耐药谱的分支分别由左侧不同颜色区域标识，黑色标识代表盲样。
(2)结果解读：参照6例盲样粪肠在系统发生树上的分支定位，对其耐药谱进行预测。运用系统发生树各分区的独立判断规则，即当未知样本中包含符合特定克隆株的特异性标志物时，判断逻辑的优先级为：a)特异性标志物指向的耐药谱，优先于系统发生树分支定位的耐药谱；b)ST4克隆株的特异性标志物，优先于其它所有克隆株的标志物，可直接判定青霉素耐药结果。
盲样粪肠球菌对青霉素、氨苄西林、万古霉素、利奈唑胺、达托霉素、高水平庆大霉素、红霉素、左氧氟沙星、环丙沙星、替加环素、四环素等11种抗菌药物的耐药谱预测及分析逻辑，如表5所示：
Figure PCTCN2020081943-appb-000010
Figure PCTCN2020081943-appb-000011
表5
如果只通过系统发生树的分支定位来预测耐药谱，与VITEK2药敏结果不一致的共有1例(盲样6)。该样本在数据库内识别相同株时，由于共享了ST16的部分特异性标志物，定位于M16分区，若判为M16则共计产生六种抗生素耐药结果预测错误。如果将系统发生树的分支定位与特异性标志物相结合，上述结果中，由于盲样6包含ST4的特异性标志物(1.06_192.5755m/z、1.07_215.5787m/z、1.14_639.0793m/z和2.04_366.1352m/z)，应遵循判断规则：ST4的特异性标志物，优先于系统发生树分支定位，优先于其它所有克隆株的标志物，故原结果修正为A4耐药谱。，修正后的结果与VITEK2药敏结果一致。
最终6例盲样预测的耐药谱与VITEK2药敏结果的一致性为100％。
实施例4基于液相色谱-串联质谱的肺炎链球菌药敏代谢谱数据库构建及验证
选择肺炎链球菌作为苛养性细菌的代表，详细说明代谢谱药敏数据库构建方式。
1.方法
(1)肺炎链球菌药敏验证：使用梅里埃Vitek2 Compact30全自动微生物分析仪及GP68药敏卡，对试验用的所有肺炎链球菌进行药敏检测，将结果作为比对标准。其中青霉素的药敏结果采用纸片扩散法(OXOID,CT0043B)进行复核，当VITEK2结果与纸片法不一致时，以纸片法结果为准。
(2)菌悬液制备：将粪肠球菌接种于哥伦比亚血琼脂平板上，培养过夜。用一次性接种环刮取适量菌落至无菌生理盐水管中，制备均一等密度的菌悬液。
(3)超声破碎：在180μL菌悬液中加入等体积的外标工作溶液，超声破碎80Hz 15min。
(4)代谢物萃取与浓缩：将步骤(3)得到的超声产物转移340μL至1.5ml离心管中，并加入等体积的萃取溶剂，冰浴震荡混匀5min，快速离心3-5秒，将管内液体甩至管底，置于氮吹仪中吹干。
(5)质谱检测：将步骤(4)得到的残渣，进一步用140μL复溶液复溶，涡旋使之充分溶解，高速离心15min后转移上清至新离心管中，重复1次高速离心15min后，转移上清100μL至高分辨液相色谱串联质谱系统，进样4μL进行检测分析；其中高分辨液相色谱串联质谱为Waters Q-TOF Synapt G2-Si四级杆-飞行时间质谱仪。采用梯度洗脱反相色谱法建立的待测样品分离条件如下：以水-乙腈-甲酸为流动相体系，流动相的流速为0.4ml/min，柱温40℃。色谱柱为Waters HSS T3柱，粒径为1.8μm，内径2.1mm，柱长100mm。质谱检测采用电喷雾离子源(ESI)、正离子模式、多反应监测扫描模式(MRM)和MSeContinnum数据独立型采集模式。
2.生信分析及数据库构建
(1)差异化合物筛选：使用Progenesis QI软件对质谱采集的原始数据进行峰对齐、峰提取、化合物鉴定、归一化处理，并输出化合物质荷比(m/z)、保留时间、丰度等特征信息。将肺炎链球菌按其耐药谱差异设为不同组，设置差异化合物筛选参数为：fold change>10,VIP>1，p value<0.05,CV<30％。共筛选得如下21个差异化合物：0.66_382.0899m/z、0.71_1355.4229m/z、0.72_166.0547m/z、0.72_295.0939m/z、0.85_1487.5082m/z、1.16_202.5253m/z、1.16_366.0977m/z、1.16_237.0537m/z、1.16_404.0425m/z、1.16_219.0439m/z、1.78_443.7582m/z、2.99_884.1424m/z、2.99_1317.2267m/z、2.98_1305.7357m/z、2.98_1306.2387m/z、2.99_871.1594m/z、2.99_870.8261m/z、2.99_878.8190m/z、2.97_1134.1406m/z、2.96_1275.6738m/z、2.26_1297.4318m/z。
(2)耐药谱分类：针对青霉素、阿莫西林、头孢吡肟、头孢噻肟、头孢曲松、厄他培南、美罗培南、红霉素、复方新诺明、左氧氟沙星、莫西沙星、万古霉素、利奈唑胺、四环素等14种药物，按肺炎链球菌对其耐药与敏感的差异，构建不同的耐药谱类型。耐药谱分类及相应药物敏感性，如表6所示。
Figure PCTCN2020081943-appb-000012
表6
(3)肺炎链球菌代谢谱数据库构建：通过差异化合物分析，合并相同克隆株，选择18例代表性肺炎链球菌克隆株构建数据库。将化合物的特征信息(保留时间、质荷比及丰度)导入软件IBM SPSS Statistics 23中，将保留时间和质荷比信息作为变量名，化合物丰度作为变量值。选择分析-分类-系统聚类，统计参数设为“集中计划”，图参数设为“谱系图、水平方向”，方法参数设为“组间联接”，测量区间参数设为“欧氏距离”，转换值标准化参数设为“Z得分”，生成树状图，如图7所示。
3.数据库验证及结果解读
(1)盲样验证：从2例盲样中提取21个目标差异化合物的丰度值，并导入软件IBM SPSS Statistics23中，与库内数据合并分析。通过未知样本在系统发生树上所处的分支定位及其相应的耐药谱判断规则，确定盲样的耐药谱类型。如图8所示，系统发生树代表不同耐药谱的分支分别由左侧不同颜色区域标识，黑色标识代表盲样。
(2)结果解读：参照2例盲样肺炎链球菌在系统发生树上的分支定位，对其耐药谱进行预测。运用系统发生树各分区的独立判断规则，即当未知样本中包含符合特定克隆株的特异性标志物时，判断逻辑的优先级为：a)特异性标志物指向的耐药谱，优先于系统发生树分支定位的耐药谱；b)青霉素相关的特异性标志物的丰度值分为两个分组，高丰度组对应青霉素和头孢菌素的敏感，低丰度组对应青霉素和头孢菌素的耐药，该丰度指标优先于系统发生树分支定位，优先于其它所有克隆株的标志物，可直接判定青霉素和头孢菌素的耐药与敏感。
盲样肺炎链球菌针对青霉素、阿莫西林、头孢吡肟、头孢噻肟、头孢曲松、厄他培南南、美罗培南、红霉素、复方新诺明、左氧氟沙星、莫西沙星、万古霉素、利奈唑胺、四环素等14种药物的耐药谱预测及分析逻辑，如表7所示：
Figure PCTCN2020081943-appb-000013
表7
如果只通过系统发生树的分支定位来预测耐药谱，与VITEK2药敏结果不一致的有1例(盲样1)。该样本在数据库内识别相同株时，由于共享了ST271的部分特异性标志物，定位于A耐药谱分支，若判为A谱则共计产生五种抗生素耐药结果预测错误。如果将系统发生树的分支定位与特异性标志物相结合，上述结果中，由于盲样1中青霉素相关标志物的丰度值位于高丰度组，应遵循判断规则：高丰度组对应青霉素和头孢菌素的敏感，优先于系统发生树分支定位，优先于其它所有克隆株的标志物，可直接判定青霉素和头孢菌素的敏感，故原结果修正为G耐药谱。修正后的结果与VITEK2药敏结果一致。
实施例5基于液相色谱-串联质谱的真菌鉴定代谢谱数据库构建及验证
1.方法
(1)样本采集及菌种验证：收集2015年9月至2019年1月间来自全国31家不同医院的420例真菌临床分离株，分纯后保存菌株。所有菌株均经一代测序鉴定菌种，作为结果比对的金标准。
(2)菌悬液制备：将临床真菌样本接种于念珠菌显色平板(郑州博赛生物)，于37℃培养24-48小时。用一次性接种环刮取适量菌落至无菌生理盐水管中，制备均一等密度的菌悬液。
(3)超声破碎：在180μL菌悬液中加入等体积的外标工作溶液，超声破碎80Hz 15min。
(4)代谢物萃取与浓缩：将步骤(3)得到的超声产物转移340μL至1.5ml离心管中，并加入等体积的萃取溶剂，冰浴震荡混匀5min，快速离心3-5秒，将管内液体甩至管底，置于氮吹仪中吹干。
(5)质谱检测：将步骤(4)得到的残渣，进一步用140μL复溶液复溶，涡旋使之充分溶解，高速离心15min后转移上清至新离心管中，重复1次高速离心15min后，转移上清100μL至高分辨液相色谱串联质谱系统，进样4μL进行检测分析；其中高分辨液相色谱串联质谱为Waters Q-TOF Synapt G2-Si四级杆-飞行时间质谱仪。采用梯度洗脱反相色谱法建立的待测样品分离条件如下：以水-乙腈-甲酸为流动相体系，流动相的流速为0.4ml/min，柱温40℃。色谱柱为Waters HSS T3柱，粒径为1.8μm，内径2.1mm，柱长100mm。质谱检测采用电喷雾离子源(ESI)、正离子模式、多反应监测扫描模式(MRM)和MSeContinnum数据独立型采集模式。
2.生信分析及数据库构建
(1)差异化合物筛选：使用Progenesis QI软件对质谱采集的原始数据进行峰对齐、峰提取、化合物鉴定、归一化处理，并输出化合物质荷比(m/z)、保留时间、丰度等特征信息。将不同种属的真菌设为不同组，设置差异化合物筛选参数为：fold change>10,VIP>1，p value<0.05,CV<30％。共筛选得到以下72个差异化合物：
Figure PCTCN2020081943-appb-000014
(2)鉴定代谢谱数据库构建：为测试数据库的分辨率，构建数据库的菌种中选择加入了VITEK2等全自动生化鉴定系统中无法准确区分的近平滑复合体中的近平滑念珠菌(Candida parapsilosis)、拟平滑念珠菌(Candida orthopsilosis)和似平滑念珠菌(Candida metapsilosis)。
通过差异化合物分析，合并相同克隆株，选择115例各真菌菌种代表性克隆株构建数据库。将化合物的特征信息(保留时间、质荷比及丰度)导入软件IBM SPSS Statistics 23中,将保留时间和质荷比信息作为变量名，化合物丰度作为变量值。选择分析-分类-系统聚类，统计参数设为“集中计划”，图参数设为“谱系图、水平方向”，方法参数设为“组间联接”，测量区间参数设为“欧氏距离”，转换值标准化参数设为“Z得分”，生成树状图，如图9所示。
3.数据库验证及结果解读
(1)盲样验证：从8例盲样中提取72个目标差异化合物的丰度值，并导入软件IBM SPSS Statistics23中，与库内数据合并分析。通过各样本在系统发生树上的分支定位确定其种属。如图10所示，系统发生树代表不同种属的分支分别由左侧不同颜色区域标识，黑色标识代表盲样。
(2)结果解读：参照8例盲样在系统发生树上的种属分支定位，对其种属进行预测，结果如表8所示。代谢谱数据库的预测结果与金标准(测序法)鉴定完全结果一致，符合率100％。其中，对于VITEK2等生化鉴定系统无法区分的近平滑复合体中的近平滑念珠菌(Candida parapsilosis)和似平滑念珠菌(Candida metapsilosis)，采用本发明的代谢谱数据库进行预测，结果与测序法金标准完全相符。
  代谢谱数据库预测结果 金标准(测序法)鉴定结果 是否一致
blind-1 近平滑念珠菌 Candida parapsilosis 是
blind-2 似平滑念珠菌 Candida metapsilosis 是
blind-3 热带念珠菌 Candida tropicalis 是
blind-4 热带念珠菌 Candida tropicalis 是
blind-5 白色念珠菌 Candida albicans 是
blind-6 白色念珠菌 Candida albicans 是
blind-7 白色念珠菌 Candida albicans 是
blind-8 白色念珠菌 Candida albicans 是
表8
8例盲样经代谢谱数据库进化树定位分析后的预测结果与金标准(测序法)鉴定结果一致，符合率 100％。其中盲样1与盲样2结果显示，本数据库对于复合体的鉴定分辨率可达亚种水平。
实施例6基于液相色谱-串联质谱的热带念珠菌药敏代谢谱数据库构建及验证
热带念珠菌是除白色念珠菌外的临床侵袭性真菌感染第二大病原体，但其三唑类药物耐药率远高于白色念珠菌(30％vs 5％)，耐药预测更具价值。故选择热带念珠菌为代表，详细说明药敏代谢谱数据库构建方式。其余假丝酵母或酵母菌均可参照采用本方法进行建库及分析。
1.方法
(1)热带念珠菌药敏验证：遵循NCCLS的M27-A3及M27-S4标准，采用微量肉汤稀释法，对试验用的所有热带念珠菌进行药敏检测，将结果作为比对标准。
(2)菌悬液制备：将临床热带念珠菌样本接种于念珠菌显色平板(郑州博赛生物)，于37℃培养24-48小时。用一次性接种环刮取适量菌落至无菌生理盐水管中，制备均一等密度的菌悬液。
(3)超声破碎：在180μL菌悬液中加入等体积的外标工作溶液，超声破碎80Hz 15min。
(4)代谢物萃取与浓缩：将步骤(3)得到的超声产物转移340μL至1.5ml离心管中，并加入等体积的萃取溶剂，冰浴震荡混匀5min，快速离心3-5秒，将管内液体甩至管底，置于氮吹仪中吹干。
(5)质谱检测：将步骤(4)得到的残渣，进一步用140μL复溶液复溶，涡旋使之充分溶解，高速离心15min后转移上清至新离心管中，重复1次高速离心15min后，转移上清100μL至高分辨液相色谱串联质谱系统，进样4μL进行检测分析；其中高分辨液相色谱串联质谱为Waters Q-TOF Synapt G2-Si四级杆-飞行时间质谱仪。采用梯度洗脱反相色谱法建立的待测样品分离条件如下：以水-乙腈-甲酸为流动相体系，流动相的流速为0.4ml/min，柱温40℃。色谱柱为Waters HSS T3柱，粒径为1.8μm，内径2.1mm，柱长100mm。质谱检测采用电喷雾离子源(ESI)、正离子模式、多反应监测扫描模式(MRM)和MSeContinnum数据独立型采集模式。
2.生信分析及数据库构建
(1)差异化合物筛选：使用Progenesis QI软件对质谱采集的原始数据进行峰对齐、峰提取、化合物鉴定、归一化处理，并输出化合物质荷比(m/z)、保留时间、丰度等特征信息。将热带念珠菌按其对不同抗菌药物的耐药谱差异设为不同组，设置差异化合物筛选参数为：fold change>10,VIP>1，p value<0.05,CV<30％。共筛选得如下22个差异化合物：
Figure PCTCN2020081943-appb-000015
(2)耐药谱分类：热带念珠菌临床分离株中暂未获得5-氟胞嘧啶、两性霉素B、棘白菌素类耐药株。耐药株菌均为氟康唑、伊曲康唑、伏立康唑等三唑类泛耐药。故按耐药类型，将热带念珠菌分为三唑类耐药与三唑类敏感两种耐药谱。
(3)热带念珠菌代谢谱数据库构建：通过差异化合物分析，合并相同克隆株，选择60例代表性热带念珠菌克隆株构建数据库，其中唑类耐药热带27例，唑类敏感(即全敏感)热带33例。将化合物的特征信息(保留时间、质荷比及丰度)导入软件IBM SPSS Statistics 23中，将保留时间和质荷比信息作为变量名，化合物丰度作为变量值。选择分析-分类-系统聚类，统计参数设为“集中计划”，图参数设为“谱系图、水平方向”，方法参数设为“组间联接”，测量区间参数设为“欧氏距离”，转换值标准化参数设为“Z得分”，生成树状图。
3.数据库验证及结果解读
(1)盲样验证：从6例盲样中提取22个目标差异化合物的丰度值，并导入软件IBM SPSS Statistics23中，与库内数据合并分析。通过未知样本在系统发生树上所处的分支定位及其相应的耐药谱判断规则，确定盲样的耐药谱类型。如图11所示，系统发生树代表不同耐药谱的分支分别由左侧不同颜色区域标识，蓝色为唑类敏感热带，黄色为唑类耐药热带，黑色标识代表盲样。
(2)结果解读：热带念珠菌参照6例盲样热带念珠菌在系统发生树上的分支定位，对其耐药谱进行预测。运用系统发生树各分区的独立判断规则，由于唑类耐药热带和全敏感热带在系统发生树上呈独立成簇分布，盲样判定规则严格按照相邻株相似性匹配原则进行。预测结果如表9所示。
Figure PCTCN2020081943-appb-000016
Figure PCTCN2020081943-appb-000017
表9
运用代谢谱药敏数据库对盲样热带念珠菌进行耐药谱预测，结果显示6例样本中，3例为全敏感株，3例为唑类泛耐药株；与微量法肉汤稀释法药敏结果完全一致，符合率为100％。
实施例7基于纳米孔测序的肺炎克雷伯菌鉴定与药敏基因组数据库构建及验证
选择肺炎克雷伯菌作为肠杆菌科细菌的代表，详细说明基因组鉴定与药敏数据库构建方式。其余肠杆菌科细菌，均可参照采用本方法进行建库及分析。
1.方法
(1)样本采集及药敏验证：收集2018年1月至2019年3月间来自全国23家不同医院的240肺炎克雷伯菌，分纯后保存菌株。使用梅里埃Vitek2 Compact30全自动微生物分析仪及N13/N334药敏卡，对试验用的所有肺炎克雷伯菌菌进行药敏检测，将结果作为比对标准。
(2)细菌基因组DNA制备：将肺炎克雷伯菌接种于LB液体培养基中过夜培养。次日收集菌液，经10000rpm离心2分钟后，收集沉淀。使用细菌基因组DNA提取试剂盒(QIAamp DNA Mini Kit,QIAGEN)提取肺炎克雷伯基因组DNA。用Nanodrop检测DNA纯度(260/280＝～1.8,260/230＝～2.0-2.2)；用Qubit检测DNA浓度后用无核酸酶水稀释DNA至20ng/μL。
(3)文库制备：使用Oxford Nanopore Technologies Native Barcoding Kit 1D(EXP-NBD104+114)和Ligation Sequencing Kit 1D(SQK LSK109)试剂盒，并按照Oxford Nanopore Technologies的1D Genomic DNA by Ligation protocol进行文库制备。(3a)末端修复：在反应管中依次加入7μL Ultra II End-Prep reaction buffer，3μL Ultra II End-Prep enzyme mix(New England Biolabs,E7546L)，50μL 20ng/μL DNA，20℃反应5分钟，65℃反应5分钟。在反应管中加入60μL AMPure XP磁珠(Beckman Coulter,A63881)，涡旋混匀后瞬时离心，置于磁力架上3分钟。用200μL 70％的乙醇清洗磁珠两次后，瞬时离心，吸弃残留乙醇，室温下晾干30秒。将反应管从磁力架上取下，每管加入25μL去核酸酶水，室温放置2分钟。再将反应管置于磁力架上3分钟，待上清液澄清无色后，转移上清至干净的离心管中。(3b)加原生条码：在反应管中依次加入25μL Blunt/TA Ligase Master Mix(New England Biolabs,M0367L)，2.5μL Native Barcode No.1～24,和22.5μL 500ng末端修复DNA的去核酸酶水稀释产物。轻弹混匀后，室温放置10分钟。在反应管中加入50μL AMPure XP磁珠，涡旋混匀后瞬时离心，置于磁力架上3分钟。用200μL 70％的乙醇清洗磁珠两次后，瞬时离心，吸弃残留乙醇，室温下晾干30秒。将反应管从磁力架上取下，每管加入25μL去核酸酶水，室温放置2分钟。再将反应管置于磁力架上3分钟，待上清液澄清无色后，转移上清至干净的离心管中。(3c)加蛋白接头：反应管中依次加入20μL Barcode Adapter Mix(BAM),20μL NEBNext Quick Ligation Reaction Buffer(5X)，10μL Quick T4 DNA Ligase(New England Biolabs,M0202S)，以及50μL含700ng已添加原生条码的24种DNA样本混合物。轻弹混匀后，室温放置10分钟。在反应管中加入70μL AMPure XP磁珠，涡旋混匀后瞬时离心，置于磁力架上3分钟。用140μL ABB缓冲液清洗磁珠两次后，将反应管从磁力架上取下，每管加入15μL去核酸酶水，室温放置10分钟。再将反应管置于磁力架上3分钟，待上清液澄清无色后，转移上清至干净的离心管中。
(4)Nanopopre测序：使用Oxford Nanopore MinION测序仪及MinION flow cell R9.4测序芯片进行测序。将520μL RBF缓冲液与480μL去核酸酶水混合，制备启动液。将800μL启动液通过启动口加入芯片，室温放置5分钟。在反应管中依次加入35μL RBF缓冲液，25.5μL文库上样颗粒，及14.5μLDNA文库。打开SpotON加样口，并将200μL启动液通过启动口加入芯片。将75μL上机文库吹打混匀，通过SpotON加样口加入芯片后，开启测序。
(5)生信分析：MinKNOW软件采集的FAST5数据，通过Albacore basecalling软件进行碱基识别并FASTQ格式输出，各样本通过原生条码自动信号分离。使用CANU34(Version 1.8)对测序数据进行组装(选择默认参数)，拼接得到样本全基因组。使用在线数据库CARD(https://card.mcmaster.ca)和The Center for Genomic Epidemiology(CGE)(http://www.genomicepidemiology.org)进行耐药相关基因元件的识别检索。将未知样本与数据库内样本集合，使用kSNP3(Version 3.1)对全基因组范围内的SNPs位点进行识别(选择Standard模式，Kmer值31)，并构建基于SNPs的系统发生树。
2.数据库构建与分析逻辑的建立
(1)系统发生树的构建：对原始系统发生树进行修剪，剔除高度重合的冗余菌株，保留165例完整 拼接的肺炎克雷伯菌代表克隆株，最终生成的系统发生树型基因组鉴定与耐药数据库，如图12所示。系统发生树代表不同耐药谱的分支分别由右侧不同颜色区域标识，其中黄色标记区域为ST11分区，红色标记区域为ST15分区，蓝色标记区域为敏感株S分区(含ST23分区)。
(2)分析逻辑建立
(2a)菌种鉴定：确认未知样本的基因组大小符合5,200,000-5,600,000bp的范围。选取基因组范围内多个位点的不同长度的片段，进行blast比对。结果同时满足Strain Description为Klebsiella pneumoniae，Per Identity值>98％，可鉴定为肺炎克雷伯菌。
(2b)药敏诊断：在系统发生树内确认未知样本所处的进化分支。当未知样本落入ST11分区(图12黄色标记区域)时，按照以下ST11型判读规则进行23种抗菌药物的耐药性预测，即：系统发生树判读优先于耐药基因，对酶抑制剂、头孢菌素、头霉素、碳青霉烯及喹诺酮类药物，系统发生树强制判读为R；系统发生树对氨基糖苷类、四环素类、磺胺类药物不作预测，判读遵循耐药基因结果。由系统发生树判读的药物种类及耐药基因种类，如表10所示。
Figure PCTCN2020081943-appb-000018
表10
当未知样本定位于ST11型进化分支之外，则按照以下非ST11型判读规则进行23种抗菌药物的耐药性预测；其中，位于ST23型与ST15型分支的样本应运用自身独立的判断标准，如表11所示。
Figure PCTCN2020081943-appb-000019
Figure PCTCN2020081943-appb-000020
表11
3.数据库验证及结果解读
(1)盲样验证：将24例盲样的测序数据与库内数据合并，使用kSNP3(Version 3.1)对全基因组范围内的SNPs位点进行识别(选择Standard模式，Kmer值31)，并构建基于SNPs的系统发生树。基于系 统发生树分区判断，归于ST11进化分支的有10例；归于非ST11进化分支的有14例，包括1例ST15型，2例ST23型，和其它ST型共11例。将上述类别分别按照ST11、ST15、ST23、非ST11(且非ST15或ST23)型4种判断标准进行耐药性预测。分析到全敏感株7例，均不含有任何耐药元件信息；其余为泛耐药或部分耐药株。
(2)结果解读：结合系统发生树与耐药基因的判断规则，23种药物的耐药预测与VITEK2药敏结果比对的符合度，如表12所示：
Figure PCTCN2020081943-appb-000021
表12
24例样本中，哌拉西林/他唑巴坦、头孢哌酮/舒巴坦、头孢他啶、头孢吡肟、甲氧苄啶/磺胺甲恶唑分别出现1例假阴性结果；环丙沙星、左氧氟沙星分别出现1例假阳性结果。以全部23种药物进行结果统计，共计552个药物结果，其中有假阴性结果5个，假阳性结果2个；阳性预测值为99.32％(293/295)，阴性预测值为98.05％(252/257)，灵敏度为98.32％(293/298)，特异度为99.21％(252/254)。各项指标均符合试剂盒的设计要求及临床实际应用需求，即灵敏度和特异度高于95％。
实施例8基于纳米孔测序的金黄色葡萄球菌鉴定与药敏基因组数据库构建及验证
选择金黄色葡萄球菌作为革兰氏阳性球菌代表，详细说明基因组鉴定与药敏数据库构建方式。其余葡萄球菌、粪肠球菌等革兰氏阳性球菌，均可参照采用本方法进行建库及分析。
1.方法
(1)样本采集及药敏验证：收集2018年6月至2019年6月间来自全国20家不同医院的160例金黄色葡萄球菌，分纯后保存菌株。使用梅里埃Vitek2 Compact30全自动微生物分析仪及P639药敏卡，对试验用的所有金黄色葡萄球菌进行药敏检测，将结果作为比对标准。
(2)细菌基因组DNA制备：将金黄色葡萄球菌接种于LB液体培养基中过夜培养。次日收集菌液，经10000rpm离心2分钟后，收集沉淀。在菌悬液中加入终浓度20mg/mL的溶菌酶37℃孵育30-60min后，使用细菌基因组DNA提取试剂盒(QIAamp DNA Mini Kit,QIAGEN)提取金黄色葡萄球菌基因组DNA。用Nanodrop检测DNA纯度(260/280＝～1.8,260/230＝～2.0-2.2)；用Qubit检测DNA浓度后用无核酸酶水稀释DNA至20ng/μL。
(3)文库制备：使用Oxford Nanopore Technologies Native Barcoding Kit 1D(EXP-NBD104+114)和Ligation Sequencing Kit 1D(SQK LSK109)试剂盒，并按照Oxford Nanopore Technologies的1D Genomic  DNA by Ligation protocol进行文库制备。(3a)末端修复：在反应管中依次加入7μL Ultra II End-Prep reaction buffer，3μL Ultra II End-Prep enzyme mix(New England Biolabs,E7546L)，50μL 20ng/μL DNA，20℃反应5分钟，65℃反应5分钟。在反应管中加入60μL AMPure XP磁珠(Beckman Coulter,A63881)，涡旋混匀后瞬时离心，置于磁力架上3分钟。用200μL 70％的乙醇清洗磁珠两次后，瞬时离心，吸弃残留乙醇，室温下晾干30秒。将反应管从磁力架上取下，每管加入25μL去核酸酶水，室温放置2分钟。再将反应管置于磁力架上3分钟，待上清液澄清无色后，转移上清至干净的离心管中。(3b)加原生条码：在反应管中依次加入25μL Blunt/TA Ligase Master Mix(New England Biolabs,M0367L)，2.5μL Native Barcode No.1～24,和22.5μL 500ng末端修复DNA的去核酸酶水稀释产物。轻弹混匀后，室温放置10分钟。在反应管中加入50μL AMPure XP磁珠，涡旋混匀后瞬时离心，置于磁力架上3分钟。用200μL 70％的乙醇清洗磁珠两次后，瞬时离心，吸弃残留乙醇，室温下晾干30秒。将反应管从磁力架上取下，每管加入25μL去核酸酶水，室温放置2分钟。再将反应管置于磁力架上3分钟，待上清液澄清无色后，转移上清至干净的离心管中。(3c)加蛋白接头：反应管中依次加入20μL Barcode Adapter Mix(BAM),20μL NEBNext Quick Ligation Reaction Buffer(5X)，10μL Quick T4 DNA Ligase(New England Biolabs,M0202S)，以及50μL含700ng已添加原生条码的24种DNA样本混合物。轻弹混匀后，室温放置10分钟。在反应管中加入70μL AMPure XP磁珠，涡旋混匀后瞬时离心，置于磁力架上3分钟。用140μL ABB缓冲液清洗磁珠两次后，将反应管从磁力架上取下，每管加入15μL去核酸酶水，室温放置10分钟。再将反应管置于磁力架上3分钟，待上清液澄清无色后，转移上清至干净的离心管中。
(4)Nanopopre测序：使用Oxford Nanopore MinION测序仪及MinION flow cell R9.4测序芯片进行测序。将520μL RBF缓冲液与480μL去核酸酶水混合，制备启动液。将800μL启动液通过启动口加入芯片，室温放置5分钟。在反应管中依次加入35μL RBF缓冲液，25.5μL文库上样颗粒，及14.5μLDNA文库。打开SpotON加样口，并将200μL启动液通过启动口加入芯片。将75μL上机文库吹打混匀，通过SpotON加样口加入芯片后，开启测序。
(5)生信分析：MinKNOW软件采集的FAST5数据，通过Albacore basecalling软件进行碱基识别并FASTQ格式输出，各样本通过原生条码自动信号分离。使用CANU34(Version 1.8)对测序数据进行组装(选择默认参数)，拼接得到样本全基因组。使用在线数据库CARD(https://card.mcmaster.ca)和The Center for Genomic Epidemiology(CGE)(http://www.genomicepidemiology.org)进行耐药相关基因元件的识别检索。将未知样本与数据库内样本集合，使用kSNP3(Version 3.1)对全基因组范围内的SNPs位点进行识别(选择Standard模式，Kmer值31)，并构建基于SNPs的系统发生树。
2.数据库构建与分析逻辑的建立
(1)系统发生树的构建：对原始系统发生树进行修剪，剔除高度重合的冗余菌株，保留93例完整拼接的金黄色葡萄球菌代表克隆株，最终生成的系统发生树型基因组鉴定与耐药数据库，如图13所示，系统发生树代表不同耐药谱的分支分别由右侧不同颜色区域标识。
(2)分析逻辑建立
(2a)菌种鉴定：确认未知样本的基因组大小符合2,400,000-3,000,000bp的范围。选取基因组范围内多个位点的不同长度的片段，进行blast比对。结果同时满足Strain Description为Staphylococcus aureus，Per Identity值>98％，可鉴定为金黄色葡萄球菌。
(2b)药敏诊断：采用系统发生树结合耐药基因及相应判断规则的方式，对未知样本进行耐药预测。确认未知样本在所处进化分区中的位置，当其落入CC5mecA+和ST59mecA+分区时，遵循进化树判读优先耐药基因的原则，青霉素、苯唑西林、头孢西丁和喹诺酮默认为耐药；当其落入CC5mecA-和ST22mecA-分区时，遵循系统发生树判读优先耐药基因的原则，青霉素、苯唑西林、头孢西丁默认为敏感；当其落入S分区，遵循系统发生树判读优先耐药基因的原则，所有药物均默认为敏感；当未知样本落入上述各指定区域之外的其他分支时，严格遵循耐药基因判读规则，系统发生树对所有药物结果不作修正。
由系统发生树判读的药物种类及耐药基因判读规则，如表13所示。
Figure PCTCN2020081943-appb-000022
Figure PCTCN2020081943-appb-000023
表13
3.数据库验证及结果解读
(1)盲样验证：将22例盲样的测序数据与库内数据合并，使用kSNP3(Version 3.1)对全基因组范围内的SNPs位点进行识别(选择Standard模式，Kmer值31)，并构建基于SNPs的系统发生树。基于系统发生树分区判断，归于CC5mecA+进化分支的有3例；归于CC5mecA-进化分支的有2例；归于ST59mecA+进化分支的有4例；归于S区的有5例。将上述类别分别按照CC5mecA+、CC5mecA-、ST59mecA+和S型4种判断标准进行耐药性预测，即遵循系统发生树判读优先耐药基因的原则。其余未知样本由于落入上述各指定区域之外的其他分支时，严格遵循耐药基因判读规则，系统发生树对所有药物结果不作修正。
(2)结果解读：结合系统发生树与耐药基因的判断规则，20种药物的耐药预测符合度，如表14所示(无耐药样本的抗菌药物，其阳性预测值和敏感度不作统计)：
Figure PCTCN2020081943-appb-000024
表14
22例样本中，大环内脂类(红霉素/克拉霉素/阿奇霉素)、林可酰胺类(克林霉素/诱导克林霉素)分别出现1例假阴性结果；无假阳性结果。
以全部20种药物进行结果统计，共计440个药物结果，其中有假阴性结果5个，假阳性结果0个；阳性预测值为100.00％(138/138)，阴性预测值为98.34％(297/302)，灵敏度为96.50％(138/143)，特异度为100.00％(297/297)。各项指标均符合试剂盒的设计要求及临床实际应用需求，即灵敏度和特异度高于95％。
实施例9基于纳米孔测序的肺炎链球菌鉴定与药敏基因组数据库构建及验证
选择肺炎链球菌作为苛养性细菌代表，详细说明基因组鉴定与药敏数据库构建方式。
1.方法
(1)样本采集及药敏验证：收集2017年5月至2019年10月间来自全国18家不同医院的48例肺炎链球菌，分纯后保存菌株。使用梅里埃Vitek2 Compact30全自动微生物分析仪及GP68药敏卡，对试验用的所有肺炎链球菌进行药敏检测，将结果作为比对标准。其中青霉素的药敏结果采用纸片扩散法(OXOID,CT0043B)进行复核，当VITEK2结果与纸片法不一致时，以纸片法结果为准。
(2)细菌基因组DNA制备：将肺炎链球菌接种于哥伦比亚血琼脂平板，置于37℃、含5％二氧化碳的恒温培养箱中过夜培养。用一次性接种环刮取适量菌落至无菌生理盐水管中，经10000rpm离心2分钟后，收集沉淀。在菌悬液中加入终浓度20mg/mL的溶菌酶37℃孵育60-120min后，使用细菌基因组DNA提取试剂盒(QIAamp DNA Mini Kit,QIAGEN)提取肺炎链球菌基因组DNA。用Nanodrop检测DNA纯度(260/280＝～1.8,260/230＝～2.0-2.2)；用Qubit检测DNA浓度后用无核酸酶水稀释DNA至20ng/μL。
(3)文库制备：使用Oxford Nanopore Technologies Native Barcoding Kit 1D(EXP-NBD104+114)和Ligation Sequencing Kit 1D(SQK LSK109)试剂盒，并按照Oxford Nanopore Technologies的1D Genomic DNA by Ligation protocol进行文库制备。(3a)末端修复：在反应管中依次加入7μL Ultra II End-Prep reaction buffer，3μL Ultra II End-Prep enzyme mix(New England Biolabs,E7546L)，50μL 20ng/μL DNA，20℃反应5分钟，65℃反应5分钟。在反应管中加入60μL AMPure XP磁珠(Beckman Coulter,A63881)，涡旋混匀后瞬时离心，置于磁力架上3分钟。用200μL 70％的乙醇清洗磁珠两次后，瞬时离心，吸弃残留乙醇，室温下晾干30秒。将反应管从磁力架上取下，每管加入25μL去核酸酶水，室温放置2分钟。再将反应管置于磁力架上3分钟，待上清液澄清无色后，转移上清至干净的离心管中。(3b)加原生条码：在反应管中依次加入25μL Blunt/TA Ligase Master Mix(New England Biolabs,M0367L)，2.5μL Native Barcode No.1～24,和22.5μL 500ng末端修复DNA的去核酸酶水稀释产物。轻弹混匀后，室温放置10分钟。在反应管中加入50μL AMPure XP磁珠，涡旋混匀后瞬时离心，置于磁力架上3分钟。用200μL 70％的乙醇清洗磁珠两次后，瞬时离心，吸弃残留乙醇，室温下晾干30秒。将反应管从磁力架上取下，每管加入25μL去核酸酶水，室温放置2分钟。再将反应管置于磁力架上3分钟，待上清液澄清无色后，转移上清至干净的离心管中。(3c)加蛋白接头：反应管中依次加入20μL Barcode Adapter Mix(BAM),20μL NEBNext Quick Ligation Reaction Buffer(5X)，10μL Quick T4 DNA Ligase(New England Biolabs,M0202S)，以及50μL含700ng已添加原生条码的24种DNA样本混合物。轻弹混匀后，室温放置10分钟。在反应管中加入70μL AMPure XP磁珠，涡旋混匀后瞬时离心，置于磁力架上3分钟。用140μL ABB缓冲液清洗磁珠两次后，将反应管从磁力架上取下，每管加入15μL去核酸酶水，室温放置10分钟。再将反应管置于磁力架上3分钟，待上清液澄清无色后，转移上清至干净的离心管中。
(4)Nanopopre测序：使用Oxford Nanopore MinION测序仪及MinION flow cell R9.4测序芯片进行测序。将520μL RBF缓冲液与480μL去核酸酶水混合，制备启动液。将800μL启动液通过启动口加入芯片，室温放置5分钟。在反应管中依次加入35μL RBF缓冲液，25.5μL文库上样颗粒，及14.5μLDNA文库。打开SpotON加样口，并将200μL启动液通过启动口加入芯片。将75μL上机文库吹打混匀，通过SpotON加样口加入芯片后，开启测序。
(5)生信分析：MinKNOW软件采集的FAST5数据，通过Albacore basecalling软件进行碱基识别并FASTQ格式输出，各样本通过原生条码自动信号分离。使用CANU34(Version 1.8)对测序数据进行组装(选择默认参数)，拼接得到样本全基因组。使用在线数据库CARD(https://card.mcmaster.ca)和The Center for Genomic Epidemiology(CGE)(http://www.genomicepidemiology.org)进行耐药相关基因元件的识别检索。将未知样本与数据库内样本集合，使用kSNP3(Version 3.1)对全基因组范围内的SNPs位点进行识别(选择Standard模式，Kmer值31)，并构建基于SNPs的系统发生树。
2.数据库构建与分析逻辑的建立
(1)系统发生树的构建：对原始系统发生树进行修剪，剔除高度重合的冗余菌株，保留25例完整拼接的肺炎链球菌代表克隆株，生成系统发生树型基因组鉴定与耐药数据库。
(2)分析逻辑建立
(2a)菌种鉴定：确认未知样本的基因组大小符合2,000,000-2,300,000bp的范围。选取基因组范围内多个位点的不同长度的片段，进行blast比对。结果同时满足Strain Description为Streptococcus pneumoniae，Per Identity值>98％，可鉴定为肺炎链球菌。
(2b)药敏诊断：采用系统发生树结合耐药基因及相应判断规则的方式，对未知样本进行耐药预测。确认未知样本在所处进化分区中的位置，当其落入青霉素耐药(PEN-R)分区时，遵循系统发生树判读优先耐药基因的原则，青霉素类和头孢菌素类默认为耐药，其余药物遵循耐药基因判读规则；当其落入S分区，遵循进化树判读优先耐药基因的原则，所有药物均默认为敏感；当未知样本落入上述各指定区域之外的其他分支时，严格遵循耐药基因判读规则，系统发生树对所有药物结果不作修正。
由系统发生树判读的药物种类及耐药基因判读规则，如表15所示。
Figure PCTCN2020081943-appb-000025
表15
3.数据库验证及结果解读
(1)盲样验证：将2例盲样的测序数据与库内数据合并，使用kSNP3(Version 3.1)对全基因组范围内的SNPs位点进行识别(选择Standard模式，Kmer值31)，并构建基于SNPs的系统发生树。如图14所示，系统发生树代表不同耐药谱的分支分别由右侧不同颜色区域标识，其中黄色标记区域为敏感株分区，蓝色标记区域为青霉素耐药分区，黑色标识代表盲样。
基于系统发生树分区判断，盲样1归于上述各指定区域(PEN-R区与S区)之外的其他分支，严格遵循耐药基因判读规则，即系统发生树对所有药物结果不作修正。盲样2归于青霉素耐药PEN-R分支，遵循系统发生树判读优先耐药基因的原则，青霉素类和头孢菌素类默认为耐药，其余药物遵循耐药基因判读规则。
(2)结果解读：结合系统发生树与耐药基因的判断规则，14种药物的耐药预测结果与药敏表型验证结果，如表16所示：
Figure PCTCN2020081943-appb-000026
Figure PCTCN2020081943-appb-000027
表16
运用系统发生树结合耐药基因突变的判读规则，2例盲样的预测结果为：青霉素类、头孢类、红霉素、复方新诺明和四环素多重耐药样本1例；红霉素、复方新诺明和四环素耐药样本1例。两例盲样的耐药预测结果，与VITEK2表型验证结果完全一致，符合率为100％。
实施例10基于纳米孔测序的真菌鉴定与药敏基因组数据库构建及验证
选择白色念珠菌作为真菌代表，详细说明基因组鉴定与药敏数据库构建方式。其余假丝酵母或酵母样真菌，均可参照采用本方法进行建库及分析。
1.方法
(1)样本采集及药敏验证：收集2015年9月至2019年1月间来自全国31家不同医院的120例白色念珠菌临床分离株，分纯后保存菌株。遵循NCCLS的M27-A3及M27-S4标准，采用微量肉汤稀释法，对试验用的所有白色念珠菌进行药敏检测，将结果作为比对标准。
(2)真菌基因组DNA制备：将白色念珠菌接种于沙保罗平板或科马嘉显色平板中过夜培养。次日收集菌液，经12000rpm离心1分钟后，收集沉淀。向菌体中加入600μL 1.2M山梨醇磷酸钠buffer和约50U的溶壁酶，充分混匀后于30℃孵育30min。经4000rpm离心10min后，弃上清，收集沉淀。使用真菌基因组提取试剂盒GentraPuregene Yeast/Bact.kit(QIAGEN)提取白色念珠菌基因组DNA。用Nanodrop检测DNA纯度(260/280＝～1.8,260/230＝～2.0-2.2)；用Qubit检测DNA浓度后用无核酸酶水稀释DNA至20ng/μL。
(3)文库制备：使用Oxford Nanopore Technologies Native Barcoding Kit 1D(EXP-NBD104+114)和Ligation Sequencing Kit 1D(SQK LSK109)试剂盒，并按照Oxford Nanopore Technologies的1D Genomic DNA by Ligation protocol进行文库制备。(3a)末端修复：在反应管中依次加入7μL Ultra II End-Prep reaction buffer，3μL Ultra II End-Prep enzyme mix(New England Biolabs,E7546L)，50μL 20ng/μL DNA，20℃反应5分钟，65℃反应5分钟。在反应管中加入60μL AMPure XP磁珠(Beckman Coulter,A63881)，涡旋混匀后瞬时离心，置于磁力架上3分钟。用200μL 70％的乙醇清洗磁珠两次后，瞬时离心，吸弃残留乙醇，室温下晾干30秒。将反应管从磁力架上取下，每管加入25μL去核酸酶水，室温放置2分钟。再将反应管置于磁力架上3分钟，待上清液澄清无色后，转移上清至干净的离心管中。(3b)加原生条码：在反应管中依次加入25μL Blunt/TA Ligase Master Mix(New England Biolabs,M0367L)，2.5μL Native Barcode No.1～24,和22.5μL 500ng末端修复DNA的去核酸酶水稀释产物。轻弹混匀后，室温放置10分钟。在反应管中加入50μL AMPure XP磁珠，涡旋混匀后瞬时离心，置于磁力架上3分钟。用200μL 70％的乙醇清洗磁珠两次后，瞬时离心，吸弃残留乙醇，室温下晾干30秒。将反应管从磁力架上取下，每管加入25μL去核酸酶水，室温放置2分钟。再将反应管置于磁力架上3分钟，待上清液澄清无色后，转移上清至干净的离心管中。(3c)加蛋白接头：反应管中依次加入20μL Barcode Adapter Mix(BAM),20μL NEBNext Quick Ligation Reaction Buffer(5X)，10μL Quick T4 DNA Ligase(New England Biolabs,M0202S)，以及50μL含700ng已添加原生条码的24种DNA样本混合物。轻弹混匀后，室温放置10分钟。在反应管中加入70μL AMPure XP磁珠，涡旋混匀后瞬时离心，置于磁力架上3分钟。用140μL ABB缓冲液清洗磁珠两次后，将反应管从磁力架上取下，每管加入15μL去核酸酶水，室温放置10分钟。再将反应管置于磁力架上3分钟，待上清液澄清无色后，转移上清至干净的离心管中。
(4)Nanopopre测序：使用Oxford Nanopore MinION测序仪及MinION flow cell R9.4测序芯片进行测序。将520μL RBF缓冲液与480μL去核酸酶水混合，制备启动液。将800μL启动液通过启动口加入芯片，室温放置5分钟。在反应管中依次加入35μL RBF缓冲液，25.5μL文库上样颗粒，及14.5μLDNA文库。打开SpotON加样口，并将200μL启动液通过启动口加入芯片。将75μL上机文库吹打混匀，通过SpotON加样口加入芯片后，开启测序。
(5)生信分析：MinKNOW软件采集的FAST5数据，通过Albacore basecalling软件进行碱基识别并FASTQ格式输出，各样本通过原生条码自动信号分离。使用CANU34(Version 1.8)对测序数据进行组装(选择默认参数)，拼接得到样本全基因组。使用在线数据库CARD(https://card.mcmaster.ca)和The Center for Genomic Epidemiology(CGE)(http://www.genomicepidemiology.org)进行耐药相关基因元件的识别检索。将未知样本与数据库内样本集合，使用kSNP3(Version 3.1)对全基因组范围内的SNPs位点进行识别(选择Standard模式，Kmer值31)，并构建基于SNPs的系统发生树。
2.数据库构建与分析逻辑的建立
(1)系统发生树的构建：对原始系统发生树进行修剪，剔除高度重合的冗余菌株，保留107例白色念珠菌代表克隆株，最终生成的系统发生树型基因组鉴定与耐药数据库，如图15所示，系统发生树代表不同耐药谱的分支分别由右侧不同颜色区域标识，红色为三唑类耐药白念，黄色为5-氟胞嘧啶耐药白念，绿色为棘白菌素类耐药白念，灰色为两性霉素B中介白念，蓝色为全敏感白念。
(2)分析逻辑建立
(2a)菌种鉴定：确认未知样本的基因组大小符合12,000,000-16,000,000bp的范围。选取基因组范围内多个位点的不同长度的片段，进行blast比对。结果同时满足Strain Description为Candida albicans，Per Identity值>98％，可鉴定为白色念珠菌。
(2b)药敏诊断：采用系统发生树结合耐药基因及相应判断规则的方式，对未知样本进行耐药预测。按照相邻株相似性匹配原则,确认未知样本在所处进化分区中的位置。判读规则为：两性霉素B耐药仅由系统发生树定位进行预测；除两性霉素B之外的其余抗生素则通过系统发生树结合耐药基因突变进行预测，且相关耐药基因的突变优先于系统发生树分区。
耐药基因的类型及其对应药物的敏感与耐药判读规则，如表17所示：
Figure PCTCN2020081943-appb-000028
表17
3.数据库验证及结果解读
(1)盲样验证：将12例盲样的测序数据与库内数据合并，使用kSNP3(Version 3.1)对全基因组范围内的SNPs位点进行识别(选择Standard模式，Kmer值31)，并构建基于SNPs的系统发生树。基于系统发生树分区判断，无样本落入两性霉素B或棘白菌素耐药区域。12例样本的系统发生树预测及耐药基因引起的实义突变如表18所示：
Figure PCTCN2020081943-appb-000029
表18
(2)结果解读：结合系统发生树与耐药基因的判断规则，盲样白色念珠菌对7种药物的耐药预测符合度，如表19所示(无耐药样本的抗菌药物，其阳性预测值和敏感度不作统计)：
Figure PCTCN2020081943-appb-000030
Figure PCTCN2020081943-appb-000031
表19
运用系统发生树结合耐药基因突变的判读规则，12例盲样的预测结果为，5-氟胞嘧啶类耐药样本4例，三唑类耐药样本2例；无5-氟胞嘧啶与唑类交叉耐药株；无两性霉素B耐药株；无棘白菌素耐药株。预测结果与微量法肉汤稀释法药敏结果完全一致，符合率为100％。
实施例11运用纳米孔测序技术及快速建库法对临床样本的直接鉴定与药敏诊断
以2019年7月采集的一例鲍曼不动杆菌感染的呼吸道痰液样本的病原菌检测和耐药分析为例来说明本发明的具体实施方式。其余非发酵革兰氏阴性杆菌的病原菌检测和耐药性分析均可参照本实施方式。1.样本处理及核酸提取
(1)取1ml痰液样本加入等量痰液消化液，室温轻摇混匀15分钟。吸取1ml液化后的痰液1ml至1.5ml离心管中，12000rpm离心5分钟，去上清，加入1ml PBS缓冲液震荡充分重悬沉淀，用枪头
将样本吹打混匀后，取500μl样本于干净的1.5ml离心管中，12000xg离心5min，弃上清；
(2)加入500μl 1x PBS重悬沉淀，12000xg离心5min，弃上清；
(3)向沉淀中加入ddH2O 98μl和5％皂苷2μl，用枪头吹打混匀，室温静置反应10min；
(4)加入500μl 1x PBS，吹打混匀后12000xg离心5min，弃上清；
(5)加入40μl 1x PBS重悬沉淀，按下表反应体系配制反应液，混匀后于37℃反应15min；
Figure PCTCN2020081943-appb-000032
(6)使用细菌基因组DNA提取试剂盒(天根)提取样本核酸，50ul TE洗脱；
(7)取2ul提取的基因组DNA用Qubit试剂进行定量。
2.建库
(1)在0.2ml PCR反应管中按下表配制末修反应体系
Figure PCTCN2020081943-appb-000033
(2)手指轻弹混匀，瞬时离心，20℃孵育5min后65℃孵育5min,加入混匀并已平衡至室温的AMPure XP(Beckman)磁珠，将混合物转移至1.5ml离心管中，室温颠倒混匀5min；
(3)瞬时离心后，将离心管置于磁力架上，待磁珠富集后吸掉上清，用200ul新鲜配制的70％乙醇洗涤两次；
(4)将磁珠室温干燥2min后加入31ul TE buffer，离架混合孵育2min后，将离心管重新置于磁力架上，待磁珠聚集后取上清备用；
(5)在1.5ml离心管中按下表配制反应液：
Figure PCTCN2020081943-appb-000034
(6)手指轻弹混匀，瞬时离心后室温孵育10min,加入100ul XP磁珠，吹打混匀，室温持续颠倒混匀5min；
(7)瞬时离心后，将离心管置于磁力架上，待磁珠富集后吸掉上清，用200ul新鲜配制的70％乙醇洗涤两次；
(8)将磁珠室温干燥2min后加入25ul TE buffer，离架重悬后室温孵育2min，将离心管重新置于磁力架上，待磁珠聚集后取上清备用；
(9)取1ul洗脱液用Qubit试剂进行定量；
(10)在0.2ml PCR管中按下表配制反应液：
Figure PCTCN2020081943-appb-000035
(11)按下列条件进行PCR反应：
Figure PCTCN2020081943-appb-000036
(12)加入100ul XP磁珠，吹打混匀，室温持续颠倒混匀5min；
(13)瞬时离心后，将离心管置于磁力架上，待澄清后吸弃上清，用200ul新鲜配制的70％乙醇洗涤两次；
(14)将磁珠室温干燥2min后加入46ul TE buffer，离架混合孵育2min后，将离心管重新置于磁力架上，待澄清后取上清备用；
(15)取1ul洗脱液用Qubit试剂进行定量；
(16)在0.2ml PCR反应管中按下表配制末修反应体系
Figure PCTCN2020081943-appb-000037
(17)手指轻弹混匀，瞬时离心，20℃孵育5min后65℃孵育5min,加入60μl混匀并已平衡至室温的AMPure XP(Beckman)磁珠，将混合物转移至1.5ml离心管中，室温颠倒混匀5min；
(18)瞬时离心后，将离心管置于磁力架上，待澄清后吸掉上清，用200ul新鲜配制的70％乙醇洗涤两次；
(19)将磁珠室温干燥2min后加入61ul TE buffer，离架重悬后孵育2min，将离心管重新置于磁力架上，待磁珠聚集后取上清备用；
(20)在1.5ml离心管中按下表配制测序接头连接反应体系：
Figure PCTCN2020081943-appb-000038
(21)盖上管盖，用手指轻弹混匀，瞬时离心，室温孵育10min,加入40μl混匀并已平衡至室温的AMPure XP磁珠并轻弹管底混匀，室温持续颠倒混匀5min；
(22)瞬时离心后，将离心管置于磁力架上，待澄清后吸弃上清，加入250μl短片段清洗buffer(SFB)，盖上管盖，用手指轻弹至磁珠重复悬浮，瞬时离心后将离心管置回磁力架上，待澄清后吸弃上清；
(23)重复步骤22；
(24)室温干燥磁珠30秒后加入15μl洗脱buffer(buffer EB),离架轻弹重悬后室温孵育10min；
(25)瞬时离心后，将离心管置于磁力架上，待澄清后吸出上清保存于1.5ml低吸附管中备用；
(26)取1μlDNA用Qubit试剂进行定量，目标DNA总量1～20ng/μl，如果DNA浓度过高，用洗脱buffer(Buffer EB)对其进行稀释。
3.上样与测序
(1)取一支芯片冲洗buffer(FB)，加入30μl冲洗助剂(FLT)后涡旋混匀，取800μl从初始孔注入芯片，室温放置5min后翻开进样孔(Spot on)，再从初始孔注入200μl冲洗mix；
(2)在一支1.5ml管中依照下表按顺序配制测序体系:
Figure PCTCN2020081943-appb-000039
(3)用移液器轻轻吹打2次后，将75μl混合液全部通过上样孔滴入测序芯片，等混合液完全流入芯片之后，先盖上样孔，再关闭初始孔；
(4)上机测序，测序数量1G。
4.数据处理
(1)运用albacore软件对测序数据进行basecall；
(2)Basecall结束后，将FASTQ文档下载至本地硬盘空间，打开EPI2ME Agent，选择FASTQ WIMP(What’s in my pot)上传数据进行样本中的菌种鉴定分析；选择FASTQ Antimicrobial resistance上传数据进行病原体的耐药基因分析；
(3)WIMP分析结果如表20所示，鲍曼不动杆菌阳性，序列数为10096条；
种属 读长
Corynebacterium striatum 43890
Homo sapiens 15657
Acinetobacter baumannii 10,096
Corynebacterium simulans 9,247
Streptococcus mitis 3,341
Streptococcus pneumoniae 1,511
Streptococcus sp.oral taxon 431 1,354
Corynebacterium diphtheriae 1,311
Corynebacterium aurimucosum 1,056
Corynebacterium resistens 1,010
Streptococcus pseudopneumoniae 728
Streptococcus oralis 621
表20痰液标本nanopore宏基因组测序结果
(4)耐药基因分析结果如表21所示，该鲍曼株含有耐药相关基因sul2，APH(3')-Ia，OXA239，gyrA(T)，即对所列抗生素均耐药(表22)；
耐药基因 耐药基因
abeM adeL
abeS adeN
ADC-22 adeR
adeA ANT(3”)-IIb
adeB APH(3”)-Ib
adeC APH(3')-Ia
adeF APH(6)-Id
adeG mphD
adeH msrE
adeI OXA-239
adeJ sul2
adeK TEM-122
tet(B) TEM-90
tetR  
表21耐药基因
Figure PCTCN2020081943-appb-000040
表22根据耐药基因推定耐药结果
(5)进化-耐药分析，采用kSNP3软件构建进化树，Kmer参数设置为31，根据进化-耐药关系对应表(表23)查询耐药结果，本例未知样本位于进化树Cluster5(图16),该鲍曼不动杆菌菌株对所列抗生素均耐药。
Figure PCTCN2020081943-appb-000041
表23进化树-耐药对应关系表
实施例12运用纳米孔测序技术及PCR建库法对临床样本的直接鉴定与药敏诊断
以2019年8月采集的一例肺炎克雷伯菌感染的呼吸道痰液样本的病原菌检测和耐药分析为例来说明本发明的具体实施方式。其余肠杆菌的病原菌检测和耐药性分析均可参照本实施方式。
1.样本处理及核酸提取
(1)取1ml痰液样本加入等量痰液消化液，室温轻摇混匀15分钟。吸取1ml液化后的痰液1ml至1.5ml离心管中，12000rpm离心5分钟，去上清，加入1ml PBS缓冲液震荡充分重悬沉淀，用枪头将样 本吹打混匀后，取500μl样本于干净的1.5ml离心管中，12000xg离心5min，弃上清；
(2)加入500μl 1x PBS重悬沉淀，12000xg离心5min，弃上清；
(3)向沉淀中加入ddH2O 98μl和5％皂苷2μl，用枪头吹打混匀，室温静置反应10min；
(4)加入500μl 1x PBS，吹打混匀后12000xg离心5min，弃上清；
(5)加入40μl 1x PBS重悬沉淀，按下表反应体系配制反应液，混匀后于37℃反应15min；
Figure PCTCN2020081943-appb-000042
(6)使用细菌基因组DNA提取试剂盒(天根)提取样本核酸，50ul TE洗脱；
(7)取2ul提取的基因组DNA用Qubit试剂进行定量。
2.耐药基因PCR扩增
(1)在0.2ml PCR管中按下表配制反应液：
Figure PCTCN2020081943-appb-000043
(2)按下列条件进行PCR反应：
Figure PCTCN2020081943-appb-000044
3.建库
(1)在0.2ml PCR反应管中按下表配制末修反应体系
Figure PCTCN2020081943-appb-000045
(2)手指轻弹混匀，瞬时离心，20℃孵育5min后65℃孵育5min,加入混匀并已平衡至室温的AMPure XP(Beckman)磁珠，将混合物转移至1.5ml离心管中，室温颠倒混匀5min；
(3)瞬时离心后，将离心管置于磁力架上，待磁珠富集后吸掉上清，用200ul新鲜配制的70％乙醇洗涤两次；
(4)将磁珠室温干燥2min后加入31ul TE buffer，离架混合孵育2min后，将离心管重新置于磁力架上，待磁珠聚集后取上清备用；
(5)在1.5ml离心管中按下表配制反应液：
Figure PCTCN2020081943-appb-000046
(6)手指轻弹混匀，瞬时离心后室温孵育10min,加入100ul XP磁珠，吹打混匀，室温持续颠倒混匀5min；
(7)瞬时离心后，将离心管置于磁力架上，待磁珠富集后吸掉上清，用200ul新鲜配制的70％乙醇洗涤两次；
(8)将磁珠室温干燥2min后加入25ul TE buffer，离架重悬后室温孵育2min，将离心管重新置于磁力架上，待磁珠聚集后取上清备用；
(9)取1ul洗脱液用Qubit试剂进行定量；
(10)在0.2ml PCR管中按下表配制反应液：
Figure PCTCN2020081943-appb-000047
(11)按下列条件进行PCR反应：
Figure PCTCN2020081943-appb-000048
(12)加入100ul XP磁珠，吹打混匀，室温持续颠倒混匀5min；
(13)瞬时离心后，将离心管置于磁力架上，待澄清后吸弃上清，用200ul新鲜配制的70％乙醇洗涤两次；
(14)将磁珠室温干燥2min后加入46ul TE buffer，离架混合孵育2min后，将离心管重新置于磁力架上，待澄清后取上清备用；
(15)取1ul洗脱液用Qubit试剂进行定量；
(16)在0.2ml PCR反应管中按下表配制末修反应体系
Figure PCTCN2020081943-appb-000049
(17)手指轻弹混匀，瞬时离心，20℃孵育5min后65℃孵育5min,加入60μl混匀并已平衡至室温的AMPure XP(Beckman)磁珠，将混合物转移至1.5ml离心管中，室温颠倒混匀5min；
(18)瞬时离心后，将离心管置于磁力架上，待澄清后吸掉上清，用200ul新鲜配制的70％乙醇洗涤两次；
(19)将磁珠室温干燥2min后加入61ul TE buffer，离架重悬后孵育2min，将离心管重新置于磁力架上，待磁珠聚集后取上清备用；
(20)将耐药基因建库得到的产物与检测鉴定得到的产物等量混合成DNA mix；
(21)在1.5ml离心管中按下表配制测序接头连接反应体系：
Figure PCTCN2020081943-appb-000050
(22)盖上管盖，用手指轻弹混匀，瞬时离心，室温孵育10min,加入40μl混匀并已平衡至室温的AMPure XP磁珠并轻弹管底混匀，室温持续颠倒混匀5min；
(23)瞬时离心后，将离心管置于磁力架上，待澄清后吸弃上清，加入250μl短片段清洗buffer(SFB)，盖上管盖，用手指轻弹至磁珠重复悬浮，瞬时离心后将离心管置回磁力架上，待澄清后吸弃上清；
(24)重复步骤23；
(25)室温干燥磁珠30秒后加入15μl洗脱buffer(buffer EB),离架轻弹重悬后室温孵育10min；
(26)瞬时离心后，将离心管置于磁力架上，待澄清后吸出上清保存于1.5ml低吸附管中备用；
(27)取1μlDNA用Qubit试剂进行定量，目标DNA总量1～20ng/μl，如果DNA浓度过高，用洗脱buffer(Buffer EB)对其进行稀释。
4.上样与测序
(1)取一支芯片冲洗buffer(FB)，加入30μl冲洗助剂(FLT)后涡旋混匀，取800μl从初始孔注入芯片，室温放置5min后翻开进样孔(Spot on)，再从初始孔注入200μl冲洗mix；
(2)在一支1.5ml管中依照下表按顺序配制测序体系:
Figure PCTCN2020081943-appb-000051
(3)用移液器轻轻吹打2次后，将75μl混合液全部通过上样孔滴入测序芯片，等混合液完全流入芯片之后，先盖上样孔，再关闭初始孔；
(4)上机测序，测序数量1G。
5.数据处理
(1)运用albacore软件对测序数据进行basecall；
(2)Basecall结束后，将FASTQ文档下载至本地硬盘空间，打开EPI2ME Agent，选择FASTQ WIMP(What’s in my pot)上传数据进行样本中的菌种鉴定分析；选择FASTQ Antimicrobial resistance上传数据进行病原体的耐药基因分析；
(3)WIMP分析结果如表24所示，肺炎克雷伯菌阳性，序列数为88638条；
Species Reads
Klebsiella pneumoniae 88,638
Homo sapiens 66,519
Escherichia coli 4,635
Rothiamucilaginosa 2,917
Streptococcus mitis 1,044
Acinetobacter baumannii 1,001
Corynebacterium striatum 861
Klebsiella variicola 527
Streptococcus sp.oral taxon 431 444
Streptococcus pneumoniae 374
Veillonellaparvula 353
Acinetobacter nosocomialis 310
表24痰液标本nanopore宏基因组测序结果
(4)耐药基因分析结果：该肺炎克雷伯菌耐药相关基因无任何阳性预测结果，即CTX-M-65、TEM-1B、IMP-4、KPC-2、rmtB、AAC(3’)-Iid、QNR-S、gyrA(T)、tetA、tetD、sul1、sul2、sul3的扩增均为阴性。
(5)进化-耐药分析，采用kSNP3软件构建系统发生树，Kmer参数设置为31，根据进化-耐药关系对应表(见实施例7，表11)查询耐药结果，本例未知样本T811位于进化树S分区(图17),该肺炎克雷伯菌对所列抗生素均敏感。
结合系统发生树与耐药基因结果，该例盲样肺炎克雷伯菌判读为全敏感株，与VITEK2药敏结果一致。

Claims (19)

  1. 一种病原微生物药敏检测方法，其特征在于，所述的方法包括获得待测样本的生物标志物信息，根据生物标志物信息确定待测样本在系统发生树中的定位，根据定位确定待测样本耐药性的判断规则，按照判断规则获得病原微生物的耐药性。
  2. 权利要求1所述的病原微生物药敏检测方法，其特征在于，所述的生物标志物信息为病原微生物中代谢物的信息和/或病原微生物的序列信息。
  3. 权利要求2所述的病原微生物药敏检测方法，其特征在于，所述病原微生物中代谢物的信息为病原微生物中代谢物的质谱信息，优选的，所述病原微生物中代谢物的质谱信息为质荷比、保留时间和丰度中的一种或两种以上的组合。
  4. 权利要求3所述的病原微生物药敏检测方法，其特征在于，所述的代谢物为质荷比在50-1500Da之间，丰度值大于2000的水溶性代谢化合物的总和。
  5. 权利要求2所述的病原微生物药敏检测方法，其特征在于，所述的病原微生物的序列信息为耐药元件型生物标志物的核酸序列，优选的，所述的病原微生物的序列信息为耐药基因、质粒、转座子、整合子或插入序列。
  6. 权利要求5所述的病原微生物药敏检测方法，其特征在于，所述的耐药元件型生物标志物选自abarmA、abAPH(3’)-Ia、abOXA239、abNDM-10、abgyrA、abSUL-1、abSUL-2、abSUL-3、kpCTX-M-65、kpTEM-1b、kpIMP-4、kpKPC-2、kprmtB、kpAAC(3’)-Iid、kpQNR-S、kpgyrA、kpparC、kptetA、kptetD、kpSUL-1、kpSUL-2、kpSUL-3、ecrmtB、ecAAC(3’)-Iid、ecgyrA、ectetA、ectetB、ecSUL-1、ecSUL-2、ecSUL-3、ecIMP-4、ecNDM-5、ecTEM-1b、ecCTX-M-14、ecCTX-M-55、ecCTX-M-65、ecCMY、paTEM-1b、paGES-1、paPER-1、paKPC-2、paOXA-246、parmtB、paAAC(3’)-Iid、paAAC(6’)-IIa、paVIM-2、pagyrA、efermB、eftetM、eftetL、efparC、efANT(6’)-Ia、stmecA、stmsrA、stermA、stermB、stermC、strpoB、stgyrA、stAAC(6’)-APH(2”)、stdfrG、sttetK、sttetL、stcfrA、spbpb1a、sppbp2x、spbpb2b、spdfr、sptetM、spermB、spgyrA、aat1a、acc1、adp1、mpib、sya1、vps13、zwf1b、fcy2、fur1、fca1、erg11、erg3、tac1、cdr1、cdr2、mdr1、pdr1、upc2a、fks1hs1、fks1hs2、fks2hs1、fks2hs2中的一种或两种以上的组合。
  7. 权利要求1所述的病原微生物药敏检测方法，其特征在于，所述的系统发生树通过液相色谱串联质谱技术和/或全基因组测序技术获得。
  8. 权利要求7所述的病原微生物药敏检测方法，其特征在于，所述系统发生树的构建形式选自基于代谢物的组成与丰度差异的代谢谱型系统发生树，基于微生物基因组SNP及InDel构建的核酸序列型系统发生树，或者，基于微生物基因组核心耐药元件及其上下游环境构建的核酸序列型系统发生树。
  9. 权利要求8所述的病原微生物药敏检测方法，其特征在于，所述判断规则包括：基于代谢物的组成与丰度差异的代谢谱型系统发生树在系统发生树不同分支上应用不同的判断标准，和/或，基于微生物基因组核心耐药元件及其上下游环境构建的核酸序列型系统发生树在系统发生树不同分支上应用不同的判断标准。
  10. 根据权利要求9所述的病原微生物药敏检测方法，其特征在于，所述代谢谱型系统发生树在系统发生树不同分支上的判断标准选自：
    1)在非发酵阴性细菌的耐药性判断中运用系统发生树各分支的独立判断规则：当待测样本中包含特定克隆株的特异性标志物时，遵循特异性标志物指向的耐药谱优先于系统发生树判读的原则；当待测样本定位于S分支时，遵循系统发生树判读优先于特异性标志物的原则，可直接判定对所有内酰胺类药物均敏感；
    2)在肠杆菌科细菌的耐药性判断中运用系统发生树各分支的独立判断规则：当待测样本中包含特定克隆株的特异性标志物时，遵循特异性标志物指向的耐药谱优先于系统发生树判读的原则；当待测样本定位于S分支时，遵循系统发生树判读优先于特异性标志物的原则，可直接判定对内酰胺类、头霉素类、内酰胺酶抑制剂类药物均敏感；
    3)在阳性球菌的耐药性判断中运用系统发生树各分支的独立判断规则：当待测样本中包含特定克隆株的特异性标志物时，遵循特异性标志物指向的耐药谱优先于系统发生树判读的原则；当待测样本定位于S分支时，遵循系统发生树判读优先于特异性标志物的原则，可直接判定对所有药物均敏感；粪肠球菌ST4型克 隆株的特异性标志物，优先于其它所有克隆株的标志物，可直接判定粪肠球菌对青霉素类药物的药敏结果均为耐药；
    4)在肺炎链球菌的耐药性判断中运用系统发生树各分支的独立判断规则：当待测样本中包含符合特定克隆株的特异性标志物时，遵循特异性标志物指向的耐药谱优先于系统发生树判读的原则；青霉素相关的特异性标志物的丰度指标优先于系统发生树分支定位，优先于其它所有克隆株的标志物，可直接判定青霉素和头孢菌素的耐药与敏感；
    和/或，5)在真菌的耐药性判断中运用系统发生树各分支的独立判断规则，并严格遵循相邻株相似性匹配原则进行耐药推断。
  11. 根据权利要求9所述的病原微生物药敏检测方法，其特征在于，所述核酸序列型系统发生树在系统发生树不同分支上的判断标准，选自：
    1)肠杆菌科细菌对碳青霉烯和喹诺酮类药物的耐药性判断，与定位的系统发生树分支有关并遵循系统发生树判读优先耐药基因的原则，其余类别的药物遵循耐药基因的判读原则；
    2)非发酵阴性细菌对头孢菌素和碳青霉烯类药物的耐药性判断，与定位的系统发生树分支有关并遵循系统发生树判读优先耐药基因的原则，其余类别的药物遵循耐药基因的判读原则；
    3)阳性球菌对青霉素、氨苄西林、苯唑西林和头孢西丁的耐药性判断，与定位的系统发生树分支有关并遵循系统发生树判读优先耐药基因的原则，其余类别的药物遵循耐药基因的判读原则；
    4)肺炎链球菌对青霉素类和头孢菌素类药物的耐药性判断，与定位的系统发生树分支有关并遵循系统发生树判读优先耐药基因的原则，其余类别的药物遵循耐药基因的判读原则；和/或，5)真菌对三唑类药物的耐药性判断，与定位的系统发生树分支有关并遵循系统发生树判读优先耐药基因的原则，其余类别的药物遵循耐药基因的判读原则。
  12. 病原微生物系统发生树在制备病原微生物药敏诊断产品中的应用，其特征在于，所述的系统发生树通过液相色谱串联质谱技术和/或全基因组测序技术获得。
  13. 权利要求12所述的病原微生物系统发生树在制备病原微生物药敏诊断产品中的应用，其特征在于，所述系统发生树的构建形式选自基于代谢物的组成与丰度差异的代谢谱型系统发生树，基于微生物基因组SNP及InDel构建的核酸序列型系统发生树，或者，基于微生物基因组核心耐药元件及其上下游环境构建的核酸序列型系统发生树。
  14. 权利要求12所述的病原微生物系统发生树在制备病原微生物药敏诊断产品中的应用，其特征在于，所述病原微生物药敏诊断产品还包括获得待测样本的生物标志物信息的试剂或设备。
  15. 权利要求14所述的病原微生物系统发生树在制备病原微生物药敏诊断产品中的应用，其特征在于，所述获得待测样本的生物标志物信息的设备选自液相色谱串联质谱或全基因组测序装置。
  16. 权利要求14所述的病原微生物系统发生树在制备病原微生物药敏诊断产品中的应用，其特征在于，所述获得待测样本的生物标志物信息的试剂选自基于液相色谱串联质谱技术的病原微生物鉴定与药敏诊断试剂盒或基于全基因组测序技术的病原微生物鉴定与药敏诊断试剂盒。
  17. 一种病原微生物鉴定与药敏诊断试剂盒，其特征在于，包括：
    KIT1：基于液相色谱串联质谱技术的病原微生物鉴定与药敏诊断试剂盒；或者，
    KIT2：基于全基因组测序技术的病原微生物鉴定与药敏诊断试剂盒；以及，
    病原微生物系统发生树。
  18. 根据权利要求17所述的一种病原微生物鉴定与药敏诊断试剂盒，其特征在于，其中基于液相色谱串联质谱技术的病原微生物鉴定与药敏诊断试剂盒包括细菌外标溶液、真菌外标溶液、萃取溶剂和复溶液。
  19. 根据权利要求17所述的一种病原微生物鉴定与药敏诊断试剂盒，其特征在于，其中基于全基因组测序技术的病原微生物鉴定与药敏诊断试剂盒包括血细胞裂解液、引物混合液、真菌裂解液、真菌消化液、建库试剂、原生条码和测序试剂。
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