Method and system for detecting respiratory tract pathogens, pathogen drug-resistant genes and pathogen virulence genes

Abstract

The invention discloses a method and system for detecting respiratory tract pathogens, pathogen drug-resistant genes and pathogen virulence genes, and relates to the technical field of gene detection.The method comprises the steps that a respiratory tract sample of a patient is collected, DNA and RNA are extracted, and total nucleic acid is obtained; carrying out first-round PCR amplification on the total nucleic acid, purifying a first-round PCR product, carrying out second-round PCR amplification, and sorting the product to obtain a library building product; performing high-throughput sequencing on the library building product to obtain sequencing data, and detecting pathogens, drug-resistant genes and virulence genes; according to the method and system for detecting the respiratory tract pathogens, the pathogen drug-resistant genes and the pathogen virulence genes, by optimizing library preparation and direct library building of nucleic acid, the process is simplified, and efficiency and accuracy are improved; the balance reagent design guarantees library quality; the kit adapts to pathogen detection requirements in Yunnan and the like, provides efficient diagnosis, shortens detection time, improves treatment effect, regularly updates pathogen information, and keeps technical frontier.

Description

Technical Field

[0001] The present invention relates to gene detection technology, and in particular to a method and system for detecting respiratory pathogens, pathogen resistance genes and pathogen virulence genes. Background Art

[0002] Respiratory infections are a common health problem worldwide, and their pathogens are diverse. Traditional detection methods such as culture and PCR have problems such as limited detection range, long time consumption, and insufficient sensitivity. Although metagenomic sequencing (mNGS) technology can detect without bias, it is costly, the data analysis is complex, and clinical interpretation is difficult. As a biodiversity hotspot and border area, Yunnan is at risk of special pathogens and unique infectious diseases. Existing technologies are difficult to meet its needs for comprehensive, rapid, and accurate diagnosis of respiratory pathogens, especially in the detection of mixed infections and drug-resistant genes. Traditional high-throughput sequencing technologies usually require quantification of extracted nucleic acids to ensure accuracy in the subsequent library construction process. However, the quantification step usually requires additional time and experimental costs, and the accuracy of quantification may be affected by improper sample handling or nucleic acid degradation. Summary of the Invention

[0003] The purpose of the present invention is to provide a method and system for detecting respiratory pathogens, pathogen resistance genes and pathogen virulence genes, so as to solve the above-mentioned deficiencies in the prior art.

[0004] In order to achieve the above object, the present invention provides the following technical solution: a method for detecting respiratory pathogens, pathogen resistance genes and pathogen virulence genes, comprising the following steps:

[0005] S1. Collect respiratory samples from patients and extract DNA and RNA from the samples simultaneously to obtain total nucleic acid;

[0006] S2. Reverse transcription of the total nucleic acid using random primers to synthesize cDNA, and then performing the first round of PCR amplification using a multiplex PCR premix containing primers for detecting respiratory pathogens, pathogen resistance genes, and pathogen virulence genes to obtain the first round of PCR products;

[0007] S3. After purifying the first-round PCR products, perform a second-round PCR amplification, and sort the second-round PCR amplification products to obtain library construction products;

[0008] S4. Perform high-throughput sequencing on the library products to obtain sequencing data. The data quality requirement is Q30 ≥ 85%, and the sequencing data volume is between 0.4M and 1M. Analyze the sequencing data through a bioinformatics analysis process to identify pathogens, drug-resistance genes, and virulence genes in the samples.

[0009] Furthermore, the pathogens described in S4 include DNA viruses, RNA viruses, bacteria, fungi, special pathogens and parasites.

[0010] Furthermore, the DNA viruses include bocavirus type 1, adenovirus type 11, bocavirus type 2, adenovirus type 12, bocavirus type 3, adenovirus type 14, human bocavirus type 4, adenovirus type 18, human polyomavirus type 1, adenovirus type 1, human polyomavirus type 2, adenovirus type 21, human polyomavirus type 4, adenovirus type 2, human herpesvirus type 1, adenovirus type 31, human herpesvirus type 2, adenovirus type 34, human herpesvirus type 3, adenovirus type 35, human herpesvirus type 4, adenovirus type 35, human herpesvirus type 5, adenovirus type 37, and human herpesvirus 6A. Adenovirus 3, human herpesvirus 6B, adenovirus 40, human herpesvirus 7, adenovirus 41, human parvovirus B19, adenovirus 4, human adenovirus A, adenovirus 52, human adenovirus B, adenovirus 55, human adenovirus C, adenovirus 5, human adenovirus D, adenovirus 67, human adenovirus E, adenovirus 7, human adenovirus F, adenovirus 8, adenovirus, adenovirus 9, human adenovirus 24, human adenovirus 27, human adenovirus 28, human adenovirus 30, human adenovirus 38, and human herpesvirus 6.

[0011] Furthermore, the RNA viruses include echovirus 11, coxsackievirus B2, echovirus 18, coxsackievirus B3, echovirus 30, coxsackievirus B4, echovirus 6, coxsackievirus B5, echovirus 9, coxsackievirus B6, rhinovirus A, rotavirus, rhinovirus B, rotavirus A, rhinovirus C, rotavirus B, enterovirus A71, rotavirus C, enterovirus A, rotavirus D, enterovirus B, enterovirus F, enterovirus C, rotavirus I, enterovirus D68, rotavirus J, enterovirus D, measles virus, dengue virus, norovirus, rubella virus, human respiratory virus 1, parechovirus A, human respiratory virus 3, influenza A virus, human respiratory syncytial virus A, influenza A virus H1 N1, human respiratory syncytial virus B, influenza A virus H3N2, human coronavirus OC43, influenza A virus H5N1, human coronavirus 229E, influenza A virus H7N9, human coronavirus, influenza B virus, human coronavirus NL63, influenza C virus, new coronavirus, coxsackievirus A10, human metapneumovirus, coxsackievirus A16, mumps virus 2, coxsackievirus A24, mumps virus 4, coxsackievirus A2, mumps virus, coxsackievirus A6, rhinovirus, influenza A virus H1N1 (2009), coxsackievirus A5, influenza B virus Victoria lineage, and influenza B virus Yamagata lineage.

[0012] Further, the bacteria include Nocardia brasiliensis, Nocardia asteroides, Corynebacterium diphtheriae, Streptococcus constellatus, Listeria monocytogenes, Nocardia asiatica, Streptococcus pneumoniae, Streptococcus anginosus, Enterococcus faecalis, Streptococcus suis, Nocardia gelsenkirchen, Mycobacterium intracellulare, Streptococcus pyogenes, Mycobacterium chelonae, Streptococcus mitis, Mycobacterium marinum, Clostridium difficile, Mycobacterium kansasii, Staphylococcus aureus, Mycobacterium avium complex, Bacillus cereus, Mycobacterium avium, Staphylococcus lugdunensis, Mycobacterium abscessus, Nocardia dermatitis, Mycobacterium fortuitum, Enterococcus faecium, Mycobacterium bovis, Streptococcus dysgalactiae, Mycobacterium tuberculosis complex, and Mycobacterium spp. Streptococci, Mycobacterium tuberculosis, Cryptobacterium haemolyticum, Corynebacterium ulcerans, Streptococcus intermedius, Enterococcus spp., Haemophilus influenzae, Bordetella pertussis, Neisseria meningitidis, Acinetobacter baumannii, Brucella bovis, Brucella spp., Proteus mirabilis, Salmonella enterica, Legionella pneumophila, Escherichia coli, Stenotrophomonas maltophilia, Klebsiella pneumoniae, Pseudomonas aeruginosa, Bordetella parapertussis, Enterobacter cloacae complex, Haemophilus parainfluenzae, Serratia marcescens, Ralstonia mannitolens, Legionella longbeach, Moraxella catarrhalis, Bordetella bronchiseptica, Fusobacterium necroticus, Neisseria gonorrhoeae, Yersinia enterocolitica, and Haemophilus influenzae type.

[0013] Furthermore, the fungi include Pneumocystis jiroveci, Cryptococcus neoformans, Aspergillus fumigatus, Talaromyces marneffei, Aspergillus terreus, Candida tropicalis, Aspergillus flavus, Candida parapsilosis, Aspergillus niger, Candida albicans and Aspergillus spp.

[0014] Furthermore, the special pathogens include Chlamydia psittaci, Ureaplasma urealyticum, Orientia tsutsugamushi, Mycoplasma pneumoniae, Ureaplasma parvum, Chlamydia pneumoniae, Chlamydia trachomatis, Coxiella burnetii, Mycoplasma hominis, Rickettsia typhi, Chlamydia abortus and Mycoplasma genitalium.

[0015] Furthermore, the parasites include Paragonimus westermani and Toxoplasma gondii.

[0016] Furthermore, the drug-resistant genes described in S4 include vancomycin resistance gene, methicillin resistance gene, β-lactam antibiotic resistance gene, macrolide antibiotic resistance gene and tetracycline resistance gene; the virulence genes described in S4 include peg-344, rmpA, rmpA2, iucA, iucB and iroB.

[0017] A system for detecting respiratory pathogens, pathogen resistance genes and pathogen virulence genes, including a nucleic acid extraction module, a library construction module, a sequencing analysis module and a data interpretation module.

[0018] Compared with the existing technology, the method and system provided by the present invention for detecting respiratory pathogens, pathogen resistance genes and pathogen virulence genes, through an optimized library preparation scheme and a special buffer system, can realize the direct input of nucleic acids into library construction operations without quantification, thereby simplifying the experimental process, improving operational efficiency and reducing experimental errors; by adopting a balanced reagent design, it can automatically adjust the library construction reaction of nucleic acids of different concentrations to ensure the consistency of library quality; it is suitable for areas with rich biodiversity such as Yunnan, and can quickly adapt to the unique pathogen and mixed infection detection needs of these areas, providing efficient and accurate diagnostic results for clinicians; by reducing detection time, it can effectively improve the timeliness of clinical treatment, thereby improving the treatment effect of patients; and it is regularly updated according to epidemiological changes in Yunnan and border areas, and new pathogen information and drug resistance genes are promptly incorporated to maintain the cutting-edge and adaptability of the technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments described in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0020] Figure 1A schematic diagram of the overall process of the detection method provided by an embodiment of the present invention;

[0021] Figure 2 A schematic diagram of a test report provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0022] In order to enable those skilled in the art to better understand the technical solution of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings.

[0023] Example 1:

[0024] See also Figure 1 A method for detecting respiratory pathogens, pathogen resistance genes, and pathogen virulence genes comprises the following steps:

[0025] S1. Collect respiratory samples (sputum, throat swab, or BALF) from patients and extract DNA and RNA simultaneously from the samples to obtain total nucleic acid;

[0026] Use appropriate instruments such as nasopharyngeal swabs or oropharyngeal swabs, follow clinical standards, penetrate deep into the corresponding part of the patient's respiratory tract, gently rotate and stay for a few seconds to fully absorb epithelial cells, possible viruses, bacteria and other microorganisms, and related secretions, then quickly place the swab into a sampling tube containing viral transport medium (VTM), break off the excess part, seal and store, mark it, and indicate the patient information, collection time, etc.

[0027] Vortex the collected respiratory sample tube for several seconds to thoroughly mix the contents. If the sample comes with a swab, squeeze the swab firmly several times in the tube to release cells and microorganisms adsorbed on the swab into the preservation solution. Then remove the swab and retain the preservation solution as the sample solution for subsequent extraction.

[0028] Take an appropriate amount of the treated sample solution (approximately 100-500 μl) and add it to the centrifuge tube containing the lysis buffer. Vortex for 10-20 seconds to thoroughly mix the lysis buffer and sample. The protein denaturant in the lysis buffer will quickly take effect, destroying the protein structures of the cells and microorganisms in the sample, including cell membranes, nuclear membranes, and viral capsid proteins, allowing the intracellular DNA and RNA to be released into the solution.

[0029] Add an appropriate amount of magnetic beads to the lysed sample solution and gently invert the centrifuge tube 5-10 times to ensure full contact between the beads and the nucleic acids. The released nucleic acids will gradually bind to the beads under the action of the chemical groups modified on the bead surface. This process can be allowed to stand at room temperature for 2-5 minutes to ensure sufficient binding.

[0030] Use a magnetic rack to attract the magnetic beads, so that the magnetic beads and the bound nucleic acids are adsorbed on the inner wall of the centrifuge tube. At this time, the supernatant is discarded to remove protein denaturation products and unbound impurities. Then, add an appropriate amount of magnetic bead washing solution, vortex and oscillate to redisperse the magnetic beads in the washing solution, and then place it on the magnetic rack. After the liquid is clarified, discard the supernatant of the washing solution. Repeat this washing process 2-3 times to improve the purity of the nucleic acid.

[0031] Finally, add an appropriate amount of elution buffer to the magnetic beads with pure nucleic acid adsorbed on them, vortex for 1-2 minutes, and heat appropriately (in a 60-70°C water bath for about 5 minutes) to fully elute the nucleic acid from the magnetic beads. Transfer the eluted solution containing nucleic acid to a new centrifuge tube. This solution is the total nucleic acid sample that can be used for subsequent library construction.

[0032] The lysis buffer contains a powerful protein denaturant that rapidly dissolves protein in the sample, releasing nucleic acids. In its presence, the released nucleic acids bind to the magnetic beads. A magnetic bead wash solution then removes proteins, inorganic salt ions, and many organic impurities. Finally, the eluent elutes the purified nucleic acids for library construction.

[0033] S2. Reverse transcription of the total nucleic acid using random primers to synthesize cDNA, and then performing the first round of PCR amplification using a multiplex PCR premix containing primers for detecting respiratory pathogens, pathogen resistance genes, and pathogen virulence genes to obtain the first round of PCR products;

[0034] Prepare the following reaction system in a sterilized PCR tube:

[0035] Components Volume (μL) Sample nucleic acid 15 RandomPrimers 1 10×RTMix 2 10×EnzymeMix 2 Total 20

[0036] Then, thoroughly vortex the reaction system in the sterilized PCR tube and mix it evenly. Centrifuge briefly and place the reaction tube in the PCR instrument. Run the reaction program as shown in the table below:

[0037] Temperature (heat cover 105℃) time 25℃ 5min 42℃ 20min 85℃ 5min 4℃ Hold

[0038] Thaw the multiplex PCR premix (200 pathogen detection panel) at room temperature, invert and mix thoroughly, and set aside. Prepare the first-round amplification reaction system as shown in the table in a sterile PCR tube and operate on ice:

[0039] Components Volume (μL) Multiplex PCR premix (detection of 200 respiratory pathogens) 10 One-chain synthesis product 10 Total 20

[0040] After thoroughly mixing the first round of amplification reaction system on a vortexer, centrifuge briefly and place the reaction tube in a PCR instrument to run the following reaction program:

[0041]

[0042]

[0043] After the reaction is completed, the first round of PCR products are obtained.

[0044] S3. After purifying the first-round PCR products, perform a second-round PCR amplification, and sort the second-round PCR amplification products to obtain library construction products;

[0045] The first-round PCR product was briefly centrifuged to purify the first-round amplification product: 30 μL Nuclease-free water was added, and then 50 μL DNA purification magnetic beads were added; after mixing evenly, the mixture was allowed to stand at room temperature for 5 minutes, and then placed on a magnetic stand for 3-5 minutes to separate the magnetic beads; after the supernatant was completely clear, the supernatant was removed; 200 μL freshly prepared 80% ethanol was added to the remaining magnetic beads, and the mixture was allowed to stand at room temperature for 30 seconds, and the supernatant was removed and repeated once; the PCR tube was capped and centrifuged for 5 seconds, and the remaining liquid was discarded; the magnetic beads were air-dried with the lid open for about 3 minutes until the surface of the magnetic beads was no longer reflective to prevent drying and cracking; 25 μL Nuclease-free water was added to resuspend the magnetic beads, and the beads were placed at room temperature for 2 minutes. After a brief centrifugation, the beads were placed on a magnetic stand for 2 minutes to separate the magnetic beads; 23 μL supernatant was transferred to a new PCR tube to obtain the purified product of the first-round amplification product.

[0046] Prepare the following second-round amplification reaction system in a sterilized PCR tube and operate on ice if possible.

[0047] Components Volume (μL) HiFi Amplification Master Mix (TX) 30 Purified product of the first round of amplification 20 Total 50

[0048] After thoroughly mixing the second round of amplification reaction system on a vortexer, centrifuge briefly and place the reaction tube in a PCR instrument to run the following reaction program:

[0049]

[0050] After the reaction is complete, briefly centrifuge and perform a second round of amplification product sorting: add 50 μL of nuclease-free water, add 65 μL of DNA purification magnetic beads to the PCR product, mix well, and let it stand at room temperature for 5 minutes. Briefly centrifuge, place on a magnetic rack, wait for the supernatant to clear, transfer it to a PCR tube containing 15 μL of DNA purification magnetic beads, mix well, and let it stand at room temperature for 5 minutes. Briefly centrifuge, place on a magnetic rack, wait for the supernatant to clear, discard the supernatant, add 200 μL of 80% ethanol, let it stand for 30 seconds, and discard the supernatant. Repeat this process once. Cap the PCR tube and centrifuge for 5 seconds. Place on a magnetic rack and discard any remaining liquid. Uncap and air-dry the magnetic beads for approximately 3 minutes until the surface is no longer reflective to prevent cracking. Resuspend the beads in 20 μL of nuclease-free water, mix well, and let it stand for 2 minutes. Briefly centrifuge, place on a magnetic rack, and transfer 18 μL of the supernatant to a new low-binding tube to obtain the library product. 1 μL of the supernatant was quantified using the Qubit dsDNA assay.

[0051] S4. Perform high-throughput sequencing on the library products to obtain sequencing data. The data quality requirement is Q30 ≥ 85%, and the sequencing data volume is between 0.4M and 1M. Analyze the sequencing data through the bioinformatics analysis process to identify pathogens, drug-resistant genes, and virulence genes in the samples. Pathogens include DNA viruses, RNA viruses, bacteria, fungi, special pathogens, and parasites. DNA viruses include bocavirus type 1, adenovirus type 11, bocavirus type 2, adenovirus type 12, bocavirus type 3, adenovirus type 14, human bocavirus type 4, adenovirus type 18, human polyomavirus type 1, adenovirus type 1, human polyomavirus type 2, adenovirus type 21, human polyomavirus type 4, adenovirus type 2, human herpesvirus type 1, adenovirus type 31, human herpesvirus type 2, adenovirus type 34, human herpesvirus type 3, adenovirus type 35, human herpesvirus type 4, adenovirus type 35, human herpesvirus type 5, and adenovirus type 5. Adenovirus 37, human herpesvirus 6A, adenovirus 3, human herpesvirus 6B, adenovirus 40, human herpesvirus 7, adenovirus 41, human parvovirus B19, adenovirus 4, human adenovirus A, adenovirus 52, human adenovirus B, adenovirus 55, human adenovirus C, adenovirus 5, human adenovirus D, adenovirus 67, human adenovirus E, adenovirus 7, human adenovirus F, adenovirus 8, adenovirus, adenovirus 9, human adenovirus 24, human adenovirus 27, human adenovirus 28, human adenovirus 30, human adenovirus 38, and human herpesvirus 6.RNA viruses include echovirus 11, coxsackievirus B2, echovirus 18, coxsackievirus B3, echovirus 30, coxsackievirus B4, echovirus 6, coxsackievirus B5, echovirus 9, coxsackievirus B6, rhinovirus A, rotavirus, rhinovirus B, rotavirus A, rhinovirus C, rotavirus B, enterovirus A71, rotavirus C, enterovirus A, rotavirus D, enterovirus B, enterovirus F, enterovirus C, rotavirus I, enterovirus D68, rotavirus J, enterovirus D, measles virus, dengue virus, norovirus, rubella virus, human respiratory virus 1, parechovirus A, human respiratory virus 3, influenza A virus, human respiratory syncytial virus A, influenza A virus H1 N1, human respiratory syncytial virus B, influenza A virus H3N2, human coronavirus OC43, influenza A virus H5N1, human coronavirus 229E, influenza A virus H7N9, human coronavirus, influenza B virus, human coronavirus NL63, influenza C virus, new coronavirus, coxsackievirus A10, human metapneumovirus, coxsackievirus A16, mumps virus 2, coxsackievirus A24, mumps virus 4, coxsackievirus A2, mumps virus, coxsackievirus A6, rhinovirus, influenza A virus H1N1 (2009), coxsackievirus A5, influenza B virus Victoria lineage, and influenza B virus Yamagata lineage. Bacteria include Nocardia brasiliensis, Nocardia asteroides, Corynebacterium diphtheriae, Streptococcus constellatus, Listeria monocytogenes, Nocardia asiatica, Streptococcus pneumoniae, Streptococcus anginosus, Enterococcus faecalis, Streptococcus suis, Nocardia gelsenkirchen, Mycobacterium intracellulare, Streptococcus pyogenes, Mycobacterium chelonae, Streptococcus mitis, Mycobacterium marinum, Clostridium difficile, Mycobacterium kansasii, Staphylococcus aureus, Mycobacterium avium complex, Bacillus cereus, Mycobacterium avium, Staphylococcus lugdunensis, Mycobacterium abscessus, Nocardia dermatitis, Mycobacterium fortuitum, Enterococcus faecium, Mycobacterium bovis, Streptococcus dysgalactiae, Mycobacterium tuberculosis complex, and Streptococcus agalactiae. , Mycobacterium tuberculosis, Cryptobacterium haemolyticum, Corynebacterium ulcerans, Streptococcus intermedius, Enterococcus spp., Haemophilus influenzae, Bordetella pertussis, Neisseria meningitidis, Acinetobacter baumannii, Brucella bovis, Brucella spp., Proteus mirabilis, Salmonella enterica, Legionella pneumophila, Escherichia coli, Stenotrophomonas maltophilia, Klebsiella pneumoniae, Pseudomonas aeruginosa, Bordetella parapertussis, Enterobacter cloacae complex, Haemophilus parainfluenzae, Serratia marcescens, Ralstonia mannitolens, Legionella longbeach, Moraxella catarrhalis, Bordetella bronchiseptica, Fusobacterium necroticus, Neisseria gonorrhoeae, Yersinia enterocolitica, and Haemophilus influenzae type.Fungi include Pneumocystis jiroveci, Cryptococcus neoformans, Aspergillus fumigatus, Talaromyces marneffei, Aspergillus terreus, Candida tropicalis, Aspergillus flavus, Candida parapsilosis, Aspergillus niger, Candida albicans, and Aspergillus species. Specific pathogens include Chlamydia psittaci, Ureaplasma urealyticum, Orientia tsutsugamushi, Mycoplasma pneumoniae, Ureaplasma parvum, Chlamydia pneumoniae, Chlamydia trachomatis, Coxiella burnetii, Mycoplasma hominis, Rickettsia typhimurium, Chlamydia abortus, and Mycoplasma genitalium. Parasites include Paragonimus westermani and Toxoplasma gondii. Drug-resistance genes include vancomycin resistance, methicillin resistance, β-lactam resistance, macrolide resistance, and tetracycline resistance; virulence genes include peg-344, rmpA, rmpA2, iucA, iucB, and iroB.

[0052] Prepare a sample pooling task list based on the sample library concentration and specific adapters. Denature the pooled library and sequence it on the selected sequencing instrument. Data quality requirements: Q30 ≥ 85%. The recommended sequencing data volume (raw read count) is between 0.5M and 1M, with a minimum of 0.4M. If the data volume is less than 0.4M, resequencing is recommended for this sample.

[0053] Data Analysis and Interpretation: We provide an automated, intelligent data analysis system that comprehensively analyzes samples using a comparison algorithm, automatically identifying pathogens and drug-resistant genes, and generating detailed analysis reports. The system can also compare against our own database to ensure rapid and accurate output of results.

[0054] The entire data analysis process includes the following four main steps:

[0055] 1. Raw Data Quality Control and Filtering: Fastp software was used to perform quality control and filtering on the raw sequencing data to ensure the accuracy and reliability of subsequent analysis. Specific filtering parameters were as follows: automatic identification and excision of sequences containing adapters or forming dimers; removal of low-quality reads with an average base quality score below 15, and exclusion of sequences with more than 5 N bases; trimming of polyG tails to a minimum of 10 bp; and trimming of polyX structures at the 3' end to a minimum of 10 bp. After these processes, high-quality Clean FASTQ format data files were generated, serving as the basis for subsequent analysis.

[0056] 2. Target Sequence Alignment and Statistics: The cleaned Clean FASTQ files were aligned with the preset target amplification region reference sequences using the BWA mem algorithm (version v0.7.17), generating a SAM format alignment result file. The SAM file was then parsed and filtered using a custom statistical script, retaining valid alignments with a similarity of ≥90%. The total number of covered sequences for each target amplification region was then counted to provide data support for subsequent species identification and drug resistance analysis.

[0057] 3. Mutation Site Detection and Analysis: Using the bcftools tool, we analyze the alignment result file (SAM format) obtained in Step 2 for single nucleotide variants (SNVs) and small insertions and deletions (InDels). This mutation information is then matched against a database of established pathogen resistance gene mutations to identify clinically significant resistance-associated mutations. The number of detected sequences and mutation frequencies at each mutation site are then counted, providing a quantitative basis for resistance assessment.

[0058] 4. Result Integration and Annotation: Customized scripts are used to integrate alignment results with mutation analysis data, and species and functional annotations are performed using an internal pathogen database. Ultimately, the results output includes the pathogen species detected in each sample, the number of corresponding sequences, the presence of drug-resistant genes, and their mutation frequencies, creating a standardized, interpretable clinical diagnosis report.

[0059] Example 2:

[0060] This embodiment provides a technical solution based on the first embodiment: a system for detecting respiratory pathogens, pathogen resistance genes and pathogen virulence genes, which is suitable for a method for detecting respiratory pathogens, pathogen resistance genes and pathogen virulence genes. The system includes a nucleic acid extraction module, a library construction module, a sequencing analysis module and a data interpretation module.

[0061] Nucleic acid extraction module: According to different sample types (sputum, throat swab or BALF), the reagent formula and operating conditions of the lysis, adsorption, washing, elution and other steps are optimized to ensure efficient extraction of nucleic acids from various complex samples. Automated nucleic acid extraction equipment is introduced, combined with magnetic bead extraction technology to achieve high-throughput and automated operation of nucleic acid extraction, minimize human errors, and improve extraction efficiency and result stability. During the nucleic acid extraction process, the concentration and purity of the nucleic acid are monitored in real time. The absorbance values ​​of the nucleic acid at 260nm and 280nm (A260 and A280) are detected by ultraviolet spectrophotometer, and the A260 / A280 ratio is calculated to evaluate the purity of the nucleic acid;

[0062] Library Construction Module: Design and synthesize highly specific primer pools. These primer pools can be customized for specific gene families, disease-related genes, microbial genes, and more, ensuring specific capture and amplification of target sequences during library construction. For pathogen detection, primer pools are designed to target conserved gene regions across different viral, bacterial, and fungal pathogens, enabling simultaneous detection of multiple pathogens, drug resistance genes, and virulence genes. High-quality library construction reagents, including DNA polymerase, ligase, and dNTPs, are selected to ensure efficient and accurate library construction reactions.

[0063] Sequencing Analysis Module: Compatible with second- and third-generation sequencing platforms, quality control analysis is performed immediately after sequencing data is generated, evaluating metrics such as Q30 (sequencing quality with base accuracy ≥99%), sequencing depth, and coverage. For low-quality sequencing data, data filtering and correction algorithms are used to remove low-quality sequences, adapter contamination, and duplicate sequences, thereby improving data usability.

[0064] Data Interpretation System: Build a rich clinical database that integrates multi-omics data, including genomic, transcriptomic, proteomic, and metabolomic data, as well as disease-related clinical information (such as symptoms, diagnostic results, and treatment responses). By mining and analyzing large amounts of clinical data, we construct association models between diseases and genetic variations, microbial infections, and other factors, providing strong support for clinical diagnosis, treatment, and prognosis. At the same time, we continuously update and improve the clinical database to ensure the timeliness and accuracy of the data.

[0065] Example 3:

[0066] This embodiment provides a technical solution based on the first embodiment: determination of the minimum concentration for stable detection.

[0067] Target nucleic acid concentrations were serially diluted to below the theoretical detection limit (LOD). Repeat the test three times to determine the lowest concentration that could be stably detected. The limit concentrations were 100 CFU / mL for bacteria, 100 CFU / mL for fungi, and 1000 CFU / mL for viruses.

[0068] Example 4:

[0069] See also Figure 2 This embodiment provides a technical solution based on the first embodiment: a method and system for detecting respiratory pathogens, pathogen resistance genes, and pathogen virulence genes, and practical applications.

[0070] 1. Collect bronchoalveolar lavage fluid from the patient's respiratory tract sample, and simultaneously extract DNA and RNA from the bronchoalveolar lavage fluid to obtain total nucleic acid; the total nucleic acid is reverse transcribed into cDNA using random primers, and then a first round of PCR amplification is performed using a multiplex PCR premix containing primers for detecting respiratory pathogens, pathogen resistance genes, and pathogen virulence genes to obtain first-round PCR products; after purifying the first-round PCR products, a second round of PCR amplification is performed, and the second-round PCR amplification products are product sorted to obtain library construction products; the library construction products are subjected to high-throughput sequencing to obtain sequencing data, with a data quality requirement of Q30 ≥ 85% and a sequencing data volume between 0.4M and 1M; the sequencing data are analyzed using a bioinformatics analysis process to identify pathogens, resistance genes, and virulence genes in the samples.

[0071] See also Figure 2 This genetic testing report mainly found several bacteria (Streptococcus mitis, Streptococcus anginosus, Staphylococcus aureus, Haemophilus parainfluenzae), fungi (Pneumocystis jiroveci) and viruses (human herpes virus type 7), and detected related drug-resistant genes and gave interpretations.

[0072] This method has a wide detection range and can simultaneously detect 52 DNA viruses, 62 RNA viruses, 61 bacteria, 11 fungi, 12 special pathogens and 2 parasites, and covers a variety of drug-resistant genes and virulence genes, achieving full coverage of respiratory infection pathogens; high detection efficiency: no nucleic acid quantification is required, library construction and sequencing can be directly performed, simplifying the experimental process, and the overall detection cycle is shortened to within 15 hours; strong applicability: supports regular updates of primer databases, can flexibly adapt to the epidemiological characteristics of different regions, and is particularly suitable for the detection of special pathogens in border areas such as Yunnan; strong mixed infection recognition ability: can accurately identify single infection and multiple infection samples, significantly reduce the clinical missed diagnosis rate, and improve diagnostic accuracy.

[0073] In summary, this method has achieved significant improvements over existing technologies in terms of detection range, detection speed, diagnostic accuracy and adaptability, and has good clinical application prospects and social value.

[0074] 2. Testing of 40 clinical specimens showed a 100% positive rate; pathogen distribution: bacteria 69.43%, viruses 23.14%, fungi 6.32%, and special pathogens 1.08%; all specimens were mixed infections with multiple pathogens; drug-resistant genes AAC(3') and ermB were detected; the consistency rate of the detection results of this method compared with the results of KingMed Respiratory 100 was >90%.

[0075] The above description is merely illustrative of certain exemplary embodiments of the present invention. It goes without saying that those skilled in the art will be able to modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and description are illustrative in nature and should not be construed as limiting the scope of protection of the claims.

Claims

1. A method for detecting respiratory pathogens, pathogen resistance genes and pathogen virulence genes, characterized in that: The steps include: S1. Collect respiratory samples from patients and extract DNA and RNA from the samples simultaneously to obtain total nucleic acid; S2. Reverse transcription of the total nucleic acid using random primers to synthesize cDNA, and then performing the first round of PCR amplification using a multiplex PCR premix containing primers for detecting respiratory pathogens, pathogen resistance genes, and pathogen virulence genes to obtain the first round of PCR products; S3. After purifying the first-round PCR products, perform a second-round PCR amplification, and sort the second-round PCR amplification products to obtain library construction products; S4. Perform high-throughput sequencing on the library products to obtain sequencing data. The data quality requirement is Q30 ≥ 85%, and the sequencing data volume is between 0.4M and 1M. Analyze the sequencing data through a bioinformatics analysis process to identify pathogens, drug-resistance genes, and virulence genes in the samples.

2. The method for detecting respiratory pathogens, pathogen resistance genes and pathogen virulence genes according to claim 1, characterized in that: S4 The pathogens described include DNA viruses, RNA viruses, bacteria, fungi, special pathogens and parasites.

3. A method for detecting respiratory pathogens, pathogen resistance genes and pathogen virulence genes according to claim 2, characterized in that: The DNA viruses include bocavirus type 1, adenovirus type 11, bocavirus type 2, adenovirus type 12, bocavirus type 3, adenovirus type 14, human bocavirus type 4, adenovirus type 18, human polyomavirus type 1, adenovirus type 1, human polyomavirus type 2, adenovirus type 21, human polyomavirus type 4, adenovirus type 2, human herpesvirus type 1, adenovirus type 31, human herpesvirus type 2, adenovirus type 34, human herpesvirus type 3, adenovirus type 35, human herpesvirus type 4, adenovirus type 35, human herpesvirus type 5, adenovirus type 37, human herpesvirus 6A, Adenovirus 3, human herpesvirus 6B, adenovirus 40, human herpesvirus 7, adenovirus 41, human parvovirus B19, adenovirus 4, human adenovirus A, adenovirus 52, human adenovirus B, adenovirus 55, human adenovirus C, adenovirus 5, human adenovirus D, adenovirus 67, human adenovirus E, adenovirus 7, human adenovirus F, adenovirus 8, adenovirus, adenovirus 9, human adenovirus 24, human adenovirus 27, human adenovirus 28, human adenovirus 30, human adenovirus 38, and human herpesvirus 6.

4. The method for detecting respiratory pathogens, pathogen resistance genes and pathogen virulence genes according to claim 2, characterized in that: The RNA viruses include echovirus 11, coxsackievirus B2, echovirus 18, coxsackievirus B3, echovirus 30, coxsackievirus B4, echovirus 6, coxsackievirus B5, echovirus 9, coxsackievirus B6, rhinovirus A, rotavirus, rhinovirus B, rotavirus A, rhinovirus C, rotavirus B, enterovirus A71, rotavirus C, enterovirus A, rotavirus D, enterovirus B, enterovirus F, enterovirus C, rotavirus I, enterovirus D68, rotavirus J, enterovirus D, measles virus, dengue virus, norovirus, rubella virus, human respiratory virus 1, parechovirus A, human respiratory virus 3, influenza A virus, human respiratory syncytial virus A, influenza A virus H1 N1, human respiratory syncytial virus B, influenza A virus H3N2, human coronavirus OC43, influenza A virus H5N1, human coronavirus 229E, influenza A virus H7N9, human coronavirus, influenza B virus, human coronavirus NL63, influenza C virus, new coronavirus, coxsackievirus A10, human metapneumovirus, coxsackievirus A16, mumps virus 2, coxsackievirus A24, mumps virus 4, coxsackievirus A2, mumps virus, coxsackievirus A6, rhinovirus, influenza A virus H1N1 (2009), coxsackievirus A5, influenza B virus Victori A lineage, and influenza B virus Yamagata lineage.

5. The method for detecting respiratory pathogens, pathogen resistance genes and pathogen virulence genes according to claim 2, characterized in that: The bacteria include Nocardia brasiliensis, Nocardia asteroides, Corynebacterium diphtheriae, Streptococcus constellatus, Listeria monocytogenes, Nocardia asiatica, Streptococcus pneumoniae, Streptococcus anginosus, Enterococcus faecalis, Streptococcus suis, Nocardia gelsenkirchen, Mycobacterium intracellulare, Streptococcus pyogenes, Mycobacterium chelonae, Streptococcus mitis, Mycobacterium marinum, Clostridium difficile, Mycobacterium kansasii, Staphylococcus aureus, Mycobacterium avium complex, Bacillus cereus, Mycobacterium avium, Staphylococcus lugdunensis, Mycobacterium abscessus, Nocardia dermatitis, Mycobacterium fortuitum, Enterococcus faecium, Mycobacterium bovis, Streptococcus dysgalactiae, Mycobacterium tuberculosis complex, Streptococcus agalactiae Bacteria, Mycobacterium tuberculosis, Cryptobacterium haemolyticum, Corynebacterium ulcerans, Streptococcus intermedius, Enterococcus spp., Haemophilus influenzae, Bordetella pertussis, Neisseria meningitidis, Acinetobacter baumannii, Brucella bovis, Brucella spp., Proteus mirabilis, Salmonella enterica, Legionella pneumophila, Escherichia coli, Stenotrophomonas maltophilia, Klebsiella pneumoniae, Pseudomonas aeruginosa, Bordetella parapertussis, Enterobacter cloacae complex, Haemophilus parainfluenzae, Serratia marcescens, Ralstonia mannitolens, Legionella longbeach, Moraxella catarrhalis, Bordetella bronchiseptica, Fusobacterium necroticus, Neisseria gonorrhoeae, Yersinia enterocolitica, and Haemophilus influenzae type.

6. The method for detecting respiratory pathogens, pathogen resistance genes and pathogen virulence genes according to claim 2, characterized in that: The fungi include Pneumocystis jiroveci, Cryptococcus neoformans, Aspergillus fumigatus, Talaromyces marneffei, Aspergillus terreus, Candida tropicalis, Aspergillus flavus, Candida parapsilosis, Aspergillus niger, Candida albicans and Aspergillus spp.

7. The method for detecting respiratory pathogens, pathogen resistance genes and pathogen virulence genes according to claim 2, characterized in that: The special pathogens include Chlamydia psittaci, Ureaplasma urealyticum, Orientia tsutsugamushi, Mycoplasma pneumoniae, Ureaplasma parvum, Chlamydia pneumoniae, Chlamydia trachomatis, Coxiella burnetii, Mycoplasma hominis, Rickettsia typhi, Chlamydia abortus and Mycoplasma genitalium.

8. The method for detecting respiratory pathogens, pathogen resistance genes and pathogen virulence genes according to claim 2, characterized in that: The parasites include Paragonimus westermani and Toxoplasma gondii.

9. The method for detecting respiratory pathogens, pathogen resistance genes and pathogen virulence genes according to claim 1, characterized in that: The drug-resistant genes described in S4 include vancomycin resistance gene, methicillin resistance gene, β-lactam antibiotic resistance gene, macrolide antibiotic resistance gene and tetracycline resistance gene; the virulence genes described in S4 include peg-344, rmpA, rmpA2, i ucA, i ucB and iroB.

10. A system for detecting respiratory pathogens, pathogen resistance genes and pathogen virulence genes, characterized in that: It is applicable to a method for detecting respiratory pathogens, pathogen resistance genes and pathogen virulence genes as described in any one of claims 1 to 9, and the system includes a nucleic acid extraction module, a library construction module, a sequencing analysis module and a data interpretation module.

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