Kit for detecting drug resistance gene of mycobacterium tuberculosis complex and drug resistance detection method

Abstract

The invention discloses a kit for detecting drug resistance genes of a mycobacterium tuberculosis complex and a drug resistance detection method. The kit comprises polynucleotide primers as shown in SEQ ID NO.1-SEQ ID NO.40; the drug resistance genes comprise embA, rpsL, ahpC, gid, embB, katG, inhA, pncA, rrs, rpoB, gyrB, eis, Rv0678, gyrA, rplC, ddn, tlyA, rrl and ethA, and the strain identification gene is hsp65.

Description

Technical Field

[0001] The present invention belongs to the field of molecular biology detection technology, and in particular to the diagnosis technology of bacterial drug resistance. More specifically, the present invention provides a method for accurately and rapidly sequencing the drug resistance genes of Mycobacterium tuberculosis complex using targeted nanopore sequencing technology and implementing drug resistance prediction. This method can effectively identify multiple drug resistance genes, provide important drug resistance information for clinical treatment, and thus promote the formulation of personalized medicine and precision treatment strategies. Background Art

[0002] Tuberculosis remains a major challenge in global public health. According to statistics in 2022, there are 10.6 million cases of tuberculosis worldwide, with 7.5 million newly confirmed cases. The drug resistance of Mycobacterium tuberculosis complex (MTBC) makes the control and treatment of the disease more complicated. Currently, there are approximately 410,000 people suffering from drug-resistant tuberculosis worldwide, but only about 40% of cases are accurately diagnosed and appropriately treated.

[0003] Although traditional culture-based phenotypic drug susceptibility testing (pDST) can provide drug sensitivity diagnostic information, it is time-consuming and complex to operate. Molecular diagnostic technologies, such as Cepheid Xpert MTB / RIF and Ultra tests, can quickly provide sensitive diagnostic results, especially for suspected tuberculosis cases. However, these methods can only detect limited drug-resistant mutations and may miss other key genomic targets. Therefore, there is an urgent need for a comprehensive detection method that can comprehensively evaluate drug resistance characteristics and ensure effective treatment. Targeted next-generation sequencing technology is considered by the World Health Organization (WHO) as a key technology for managing drug-resistant tuberculosis. It can quickly provide comprehensive drug sensitivity information and greatly accelerate the clinical decision-making process. Short amplicon targeted sequencing methods based on second-generation sequencing platforms may lead to missed detection of key variants due to the limitation of sequencing read length, and their high complexity in primer design and data processing increases the difficulty of operating the method. In contrast, targeted sequencing technologies using long amplicons are based on long-read sequencing platforms, such as nanopore sequencing platforms, which can cover longer gene fragments, simplify primer design and data processing processes, and provide more comprehensive genetic information. In addition, its library construction and sequencing operation process is also more concise, effectively reducing the overall complexity of the experiment. However, the accuracy of the original sequencing data of the nanopore sequencing platform is low. In order to ensure the reliability of the detection, a deeper sequencing depth is usually required, which may increase the time and cost of the detection. In addition, the platform also faces the challenge of lacking a mature and integrated data analysis process, which is particularly critical for quickly and accurately identifying drug-resistant gene variants.

[0004] The present invention relates to a long amplicon targeted sequencing method based on a nanopore sequencing platform, which aims to directly amplify nearly 20 key drug resistance genes of the Mycobacterium tuberculosis complex from clinical samples through multiplex PCR technology, and predict its resistance to 16 commonly used anti-tuberculosis drugs. The present invention also includes the development of an integrated data analysis process that can automatically extract information from raw sequencing data and generate a detailed drug resistance prediction report. By optimizing key parameters, the present invention improves the accuracy of detection and reduces the sequencing depth required to obtain accurate results, effectively shortens the detection time, and reduces the detection cost. The technical solution of the present invention optimizes the experimental and data analysis processes, enhances the operability and efficiency of the method, and provides technical support for the formulation of more accurate treatment strategies. The present invention has broad application prospects in the field of drug resistance detection of Mycobacterium tuberculosis complex, and is expected to become an important part of the laboratory detection system for drug-resistant tuberculosis. In addition, the cost-effectiveness and practicality of the present invention will have a significant impact on the field of public health. Summary of the invention

[0005] The present invention aims to provide a method for detecting drug resistance of Mycobacterium tuberculosis complex.

[0006] The present invention relates to a long amplicon targeted sequencing method based on a nanopore sequencing platform, which uses multiplex PCR technology to directly amplify key drug resistance genes of Mycobacterium tuberculosis complex from clinical samples and detect the drug resistance of samples to 16 commonly used anti-tuberculosis drugs.

[0007] This technical solution aims to improve the accuracy and efficiency of detection by simplifying the experimental process and optimizing the data analysis process, thereby accelerating the process of clinical decision-making. First, the targeted sequencing method of the present invention can be directly applied to clinical samples without the need to isolate and culture the Mycobacterium tuberculosis complex, which greatly saves time and cost. In addition, about 20 genes related to drug resistance can be sequenced simultaneously in one experiment, which significantly improves the experimental efficiency. The entire experimental process is simple and fast, and has strong applicability. Secondly, the present invention also includes a set of integrated data analysis processes, which use advanced bioinformatics tools to automatically extract key information from the original sequencing data and generate a detailed drug resistance prediction report. Through parameter optimization, such as read length coverage, quality score threshold and variation frequency threshold, it is ensured that high-accuracy results can be achieved even at a lower sequencing depth, significantly improving the sensitivity and specificity of variation detection. Through these technical optimizations, the present invention not only makes up for the limitations of the prior art, but also helps to further study the evolution of drug resistance in the Mycobacterium tuberculosis complex and discover new variations. This new method has broad application prospects in the field of drug resistance detection of Mycobacterium tuberculosis complex, and is expected to become an important part of the laboratory detection system for drug-resistant tuberculosis. The application of this technology can provide timely and accurate scientific basis for the formulation of personalized and precise treatment plans, and promote the development of precision medicine and personalized treatment strategies.

[0008] The present invention provides a kit for sequencing drug resistance-related genes of Mycobacterium tuberculosis complex.

[0009] The kit of the present invention comprises polynucleotides of SEQ ID NO.1 to SEQ ID NO.40.

[0010] Among them, SEQ ID NO.1-SEQ ID NO.40 are listed as follows:

[0011]

[0012]

[0013] As required, the kit of the present invention may also include reagents that are beneficial to laboratory operations, such as solvents, buffer solvents, auxiliary materials, etc.

[0014] The kit of the present invention may include reagents prepared from the above components. The preparation methods of the reagents are all conventional techniques. It only requires mixing various raw materials evenly at room temperature without the need for special equipment and conditions.

[0015] The kit of the present invention can contain different reagents separately and then package them together in the same packaging box, and when used, it is operated according to the method described in the instructions.

[0016] The present invention further provides a method for using the kit of the present invention and a method for detecting drug-resistant genes, the method comprising the following steps:

[0017] Step 1, sample acquisition:

[0018] The sputum of the patient is mainly collected as the test sample.

[0019] Step 2, DNA extraction:

[0020] DNA extraction technology is used to extract DNA from samples.

[0021] Step 3, multiplex PCR amplification:

[0022] PCR products (amplicons) were obtained by multiplex PCR amplification.

[0023] Step 4, sequencing by Oxford Nanopore Technologies (ONT) GridON nanopore sequencer:

[0024] The PCR products (amplicons) were sequenced using ONT's GridON nanopore sequencer.

[0025] Step 5, data analysis: Based on the sequencing results, it can be determined whether the sample contains drug resistance-related mutations.

[0026] This can help determine which drugs the sample is resistant to.

[0027] Among them, the drug resistance genes are listed as follows:

[0028] Table 1

[0029] Gene name Correlation with drug resistance of Mycobacterium tuberculosis complex rpoB Associated with rifampicin resistance eMB Associated with ethambutol resistance embB Associated with ethambutol resistance pncA Associated with pyrazinamide resistance rrs Associated with resistance to streptomycin, capreomycin, kanamycin, and amikacin gid Associated with streptomycin resistance rPv Associated with streptomycin resistance katG Associated with isoniazid resistance aiP Associated with isoniazid resistance iA Associated with resistance to isoniazid, ethionamide, and prothionamide ethA Associated with resistance to ethionamide and prothionamide yrA Associated with resistance to levofloxacin and moxifloxacin ikB Associated with resistance to levofloxacin and moxifloxacin rrl Associated with linezolid resistance R Associated with linezolid resistance ddn Associated with delamanid resistance tlyA Associated with capreomycin resistance eis Associated with resistance to kanamycin and amikacin Rv0678 Associated with resistance to bedaquiline and clofazimine

[0030] The drug resistance refers to resistance to the following drugs:

[0031] Rifampicin, ethambutol, pyrazinamide, streptomycin, capreomycin, kanamycin, amikacin, isoniazid, ethionamide, prothionamide, levofloxacin, moxifloxacin, linezolid, delamanid, bedaquiline, clofazimine.

[0032] Among them, the drug-resistant genes include: embA, rpsL, ahpC, gid, embB, katG, inhA, pncA, rrs, rpoB, gyrB, eis, Rv0678, gyrA, rplC, ddn, tlyA, rrl, ethA, and the strain identification gene is hsp65.

[0033] The multiplex PCR amplification in step 3 of the present invention requires the use of specially designed PCR primers of the present invention. The PCR primers of 19 drug resistance-related genes and 1 bacterial species identification gene are divided into forward primers and reverse primers. The primer list of each drug resistance gene is as follows:

[0034] Table 2

[0035]

[0036] Preferably, the detection method of the present invention comprises the following steps:

[0037] Step 1, sample acquisition:

[0038] The sputum of the patient is mainly collected as the test sample.

[0039] Step 2, DNA extraction:

[0040] DNA extraction technology is used to extract DNA from samples.

[0041] Step 3, multiplex PCR amplification:

[0042] 1) Multiplex PCR reaction system:

[0043] This method can be directly applied to clinical sputum sample DNA. The PCR system and reaction conditions used are shown in Tables 3 and 4.

[0044] Table 3: Multiplex PCR reaction system

[0045] Element Add volume Kappa Ready Mix 12.5μL Primer pool 2μL DNA template variable Nuclease-free water variable Total volume 25μL

[0046] Table 4: Multiplex PCR reaction conditions

[0047] name Reaction conditions Pre-denaturation 95℃300s transsexual 98℃20s annealing 65℃15s extend 72℃105s Number of cycles 35 Supplementary extension 72℃105s

[0048] 2) Concentration of each primer pair in the primer pool:

[0049] Through multiple preliminary experiments, we optimized the amount of primer pairs added to the primer pool for amplifying different genes (Table 5). After optimization, the PCR amplification efficiency of each gene can be basically consistent.

[0050] Table 5: Primer pool composition

[0051]

[0052]

[0053]

[0054] Step 4, sequencing with nanopore GridON sequencer:

[0055] After the multiplex PCR reaction is completed, the products are first quantified for nucleic acid. The products in the two PCR product tubes for each sample are designed as different gene sets to ensure the comprehensiveness of the test. Therefore, it is necessary to mix the products in the two tubes in equal amounts, so that the sequencing library of each sample contains all target genes. Subsequently, according to the quantitative results, equal amounts of mixed PCR products are prepared for each sample for library construction. In the library construction stage, the PCR products of each sample are first connected to the adapter fragments with barcodes, which is crucial for the subsequent accurate distinction and tracking of each sample in the bioinformatics process. After the barcode adapter fragments are connected, the PCR products of all samples are appropriately mixed and processed by magnetic bead purification technology to complete the construction of the sequencing library. Finally, the constructed library is loaded onto the sequencing chip and sequencing is started on the nanopore GridION sequencer. This process ensures comprehensive gene coverage from each sample, providing comprehensive and detailed genetic information for subsequent analysis and interpretation.

[0056] Step 5, data analysis:

[0057] First, we ensured that we had enough raw data to support in-depth and comprehensive analysis, based on the number of samples involved in the sequencing. Figure 1 The analysis process shown uses specific bioinformatics software to perform preliminary processing on the raw sequencing data. This stage of processing includes data quality assessment, correction of read data, and necessary data filtering to eliminate technical errors and biases. After data processing, it enters a detailed bioinformatics analysis stage, which includes further analysis of the data after comparison to identify and verify genetic variations associated with drug resistance. In this process, special attention is paid to the differences between samples and any quality issues that may affect data interpretation. Suspected data anomalies are corrected by using appropriate statistical and computational methods to ensure high accuracy and reliability of the analysis results. Finally, all analysis data are integrated to generate a detailed drug resistance test report. This report will provide a scientific basis for clinical decision-making and help doctors choose the most appropriate treatment plan. The entire analysis process is designed to provide the most accurate assessment of drug resistance through high-standard data processing and precise genetic analysis.

[0058] The core of the present invention is that the present invention provides a new primer, and the design method of the primer is as follows:

[0059] The primer design method of the present invention adopts bioinformatics tools and software to design primers according to the sequence characteristics of specific target genes. First, by analyzing the conserved region and the variable region of the target gene, a sequence suitable for amplification is selected as a primer binding site. Then, software such as Primer3 is used to calculate the factors that may affect the PCR reaction, such as the ideal length, Tm (melting temperature), GC content of the primer, and whether there are dimers and hairpin structures. Afterwards, primer specificity verification is performed to ensure that the designed primers will not produce non-specific binding with non-target gene sequences. Finally, the amplification efficiency and specificity of the primers are verified by experiments to ensure its effectiveness and accuracy in practical applications.

[0060] In addition, the primer design of the present invention also takes into account the optimization of the following key parameters:

[0061] 1. Sequence specificity: Ensure that the primers bind specifically to the target sequence only and avoid cross-reactions.

[0062] 2. Amplification efficiency: Improve PCR amplification efficiency by optimizing primer length and GC content.

[0063] 3. Melting temperature matching: Design the melting temperatures of the primer pairs to match as much as possible to optimize the temperature cycling conditions of the PCR reaction.

[0064] The detection method of the present invention includes the following important terms:

[0065] PCR reaction: polymerase chain reaction is a molecular biology technique for amplifying specific DNA fragments in vitro. Through temperature cycling, DNA polymerase is used to replicate the target DNA sequence.

[0066] Library construction: The process of preparing DNA or RNA samples into a collection of molecules that can be sequenced. It includes steps such as DNA fragmentation, end repair, and adapter ligation to prepare for subsequent sequencing.

[0067] Sequencing reaction: Sequencing reaction refers to the use of sequencing technology to determine the nucleotide sequence in DNA or RNA samples. This process includes the preparation of nucleic acid templates, the synthesis of nucleic acid chains, and the reading of data, and ultimately outputs the precise genetic information of biological samples.

[0068] Library construction using amplicon fragments: PCR technology is used to specifically amplify the target DNA region (amplicon), and then these amplicons are processed and integrated into the sequencing library. This method is often used for selective sequencing of target genes.

[0069] Nanopore sequencing technology: a single-molecule sequencing technology that determines the base sequence by detecting the change in current generated when a DNA molecule passes through a protein nanopore. It has the characteristics of long read length and real-time sequencing.

[0070] Method optimization: Improve the sensitivity, specificity, accuracy or efficiency of a method by adjusting experimental conditions, improving experimental steps or analysis algorithms.

[0071] Illumina platform test results: data obtained using Illumina's sequencer. Usually refers to high-throughput, short-read sequencing data with high accuracy.

[0072] Optimization of multiple key parameters: Adjustment and improvement of multiple important factors affecting the performance of the method, including PCR primer design, reaction conditions, library preparation method, sequencing depth, data analysis process, etc. The detection method of the present invention has significant advantages over the prior art:

[0073] The present invention provides an innovative method for predicting drug resistance of Mycobacterium tuberculosis, which directly amplifies genes related to drug resistance from clinical samples through a long-fragment multiplex PCR amplicon sequencing strategy. Compared with traditional culture-based drug resistance phenotypic testing (pDST) and existing molecular diagnostic techniques, this method avoids the problems of complex and time-consuming operations, and can detect a wider range of drug-resistant mutations and provide complete drug sensitivity information. In addition, other next-generation sequencing products on the market, such as Deeplex Myc-TB, although capable of high-coverage targeted sequencing, usually rely on short amplicons, which may limit their ability to detect complex gene regions or large fragment insertions / deletions. The present invention, by using long amplicons, can not only cover a wider range of genomic regions, but also improve the ability to detect rare variants, thereby enhancing the comprehensiveness and accuracy of the test. The present invention also shows significant advantages in data processing, especially in the automation and optimization of bioinformatics analysis processes. By using advanced bioinformatics tools and algorithms, the present invention can quickly and accurately extract useful information from a large amount of complex sequencing data, and perform efficient data interpretation and analysis. The data processing flow used in the present invention includes automated error correction and data filtering steps, which helps to improve the accuracy and reliability of the final results. By performing quality control on the original sequencing data, low-quality or deviating data is automatically removed to ensure that the analysis results are based on high-quality data sets. The data analysis platform of the present invention can automatically perform complex alignment, mutation detection and drug resistance prediction algorithms. This highly automated process reduces the need for manual intervention, allowing non-professionals to easily operate, thereby reducing the technical threshold and the possibility of operational errors.

[0074] The data analysis process of the present invention is specially customized for nanopore targeted sequencing technology, forming a complete integrated analysis framework to improve the efficiency and accuracy of data processing. The entire process covers key steps such as quality control, variant identification, functional annotation, and final report generation. During the data processing process, we optimized multiple key parameters including read length coverage, quality score threshold, and variant frequency threshold to ensure that key variants can be effectively identified and confirmed even in complex gene regions and low sequencing depth conditions.

[0075] The process first conducts strict quality control to exclude low-quality read fragments and non-specific alignments to ensure the purity of the data. Then, the high-quality read fragments are accurately mapped to the reference genome, potential mutation points are identified, and detailed biological annotations are performed on the identified mutations to analyze their possible effects on protein function, providing a scientific basis for clinical decision-making. Finally, the system automatically integrates all analysis results and generates a detailed drug resistance prediction report to provide reliable decision support for medical providers. This integrated process ensures high efficiency of operations and high accuracy of data through continuous technical optimization and parameter adjustment, thereby significantly improving the accuracy of clinical diagnosis and treatment.

[0076] The detection method of the present invention focuses on the following steps:

[0077] 1) Design multiplex PCR reaction primers and determine reaction conditions:

[0078] The present invention designs specific primers for key genes associated with resistance to 16 commonly used anti-tuberculosis drugs (see Table 1 for details). We have developed a set of multiplex PCR primers (see Table 2 for details) to comprehensively cover known anti-tuberculosis drug resistance sites. When designing these multiplex PCR primers, we pay special attention to the specificity and comprehensiveness of the primers to ensure that all target gene regions can be accurately amplified. In addition, we also accurately set the PCR reaction conditions to optimize the amplification efficiency and the reliability of the results, ensuring that high-quality data can be obtained in each experiment.

[0079] 2) Experimental process of library construction and sequencing reaction:

[0080] When using nanopore sequencing technology for library construction and sequencing reactions, we are facing a relatively new sequencing technology, and its practical experience is not as extensive as that of mature second-generation sequencing technology. In particular, there is currently a lack of official recommended standards or mature operating procedures for library construction using amplicon fragments. Therefore, we need to customize the library construction and sequencing strategy suitable for this study based on the results of preliminary experiments. When choosing a suitable strategy, we comprehensively considered key factors such as data accuracy, experimental complexity, and time efficiency required. Given the characteristics of nanopore sequencing technology and its potential for application in tuberculosis research, we paid special attention to evaluating these technical parameters to ensure that the experimental schemes used can not only meet research needs but also produce reliable and accurate results. Careful design and optimization of this process is essential to improve the efficiency and quality of drug-resistant tuberculosis detection.

[0081] 3) Establishment of bioinformatics analysis process:

[0082] When performing bioinformatics analysis, although the basic processing workflows for different sequencing platforms are similar, data generated by each platform may require the use of specific software or adjustments to existing workflows. The first step in the analysis is to convert the raw sequencing data into a format and classify them according to barcodes, which is key to ensuring the accuracy of subsequent analysis. Subsequently, data quality control is performed to remove low-quality reads to enhance the reliability of the analysis. The quality-controlled data will be used for alignment analysis, where the data will be aligned against a reference genome or a specific target gene library. Through alignment, sequencing reads can be correctly classified to the corresponding genes. Reads belonging to the same gene amplicon are corrected against each other to ensure the accuracy and completeness of the final sequence data. To achieve efficient execution of this complex process, we have developed automated scripts to improve the speed and accuracy of data processing. These bioinformatics analysis steps are crucial for a deep understanding of the sequence data of tuberculosis-related genes, which can provide accurate genetic information for the study of disease mechanisms and the formulation of treatment strategies. During the analysis process, we also need to consider multiple aspects such as experimental design, sample processing, and result interpretation to ensure the overall reliability and scientificity of the study.

[0083] 4) Method optimization:

[0084] In our detection process, method optimization focuses on two key aspects. First, since multiplex PCR technology is used to amplify multiple genes simultaneously, there is a problem of uneven amplification efficiency. To ensure that each gene can be fully amplified, we divide the samples into two groups for amplification. Nevertheless, further optimization is still needed to address the significant differences in the abundance of amplified fragments between different genes. In particular, by adjusting the concentration of primers, we can ensure that the amplification efficiency of each gene is more uniform, thereby improving the balance and overall quality of the data.

[0085] Secondly, to ensure the accuracy of the test results, we used the test results of the Illumina platform as the gold standard, and based on this, we optimized the parameters of the data analysis process of the nanopore sequencing platform, especially in terms of read length coverage, quality score threshold, and variant frequency threshold. We optimized several key parameters to improve the processing quality of nanopore sequencing data. These adjustments significantly improved the efficiency and accuracy of the overall sequencing solution, while also effectively reducing costs. Through these optimization steps, we provide more accurate and reliable genetic information for tuberculosis drug resistance research, thereby supporting scientific research and clinical applications in this field.

[0086] The detection method of the present invention has significant advantages over the prior art:

[0087] The present invention provides an innovative method for predicting drug resistance of Mycobacterium tuberculosis, which directly amplifies genes related to drug resistance from clinical samples through a long-fragment multiplex PCR amplicon sequencing strategy. Compared with traditional culture-based drug resistance phenotypic testing (pDST) and existing molecular diagnostic techniques, this method avoids the problems of complex and time-consuming operations, and can detect a wider range of drug-resistant mutations and provide complete drug sensitivity information. In addition, other next-generation sequencing products on the market, such as Deeplex Myc-TB, although capable of high-coverage targeted sequencing, usually rely on short amplicons, which may limit their ability to detect complex gene regions or large fragment insertions / deletions. The present invention, by using long amplicons, can not only cover a wider range of genomic regions, but also improve the ability to detect rare variants, thereby enhancing the comprehensiveness and accuracy of the test. The present invention also shows significant advantages in data processing, especially in the automation and optimization of bioinformatics analysis processes. By using advanced bioinformatics tools and algorithms, the present invention can quickly and accurately extract useful information from a large amount of complex sequencing data, and perform efficient data interpretation and analysis. The data processing flow used in the present invention includes automated error correction and data filtering steps, which helps to improve the accuracy and reliability of the final results. By performing quality control on the original sequencing data, low-quality or deviating data is automatically removed to ensure that the analysis results are based on high-quality data sets. The data analysis platform of the present invention can automatically perform complex alignment, mutation detection and drug resistance prediction algorithms. This highly automated process reduces the need for manual intervention, allowing non-professionals to easily operate, thereby reducing the technical threshold and the possibility of operational errors. BRIEF DESCRIPTION OF THE DRAWINGS

[0088] Figure 1 :Data analysis process DETAILED DESCRIPTION

[0089] The present invention is further described by the following specific examples, but is not intended to be limiting of the present invention.

[0090] Embodiment 1, the detection method of the present invention

[0091] 1) Primer design

[0092] We selected 20 target genes for amplification, including 19 resistance-related genes (embA, rpsL, ahpC, gid, embB, katG, inhA, pncA, rrs, rpoB, gyrB, eis, Rv0678, gyrA, rplC, ddn, tlyA, rrl, ethA) and 1 bacterial species identification gene (hsp65) (Table 1). The primer sequences are shown in Table 2.

[0093] 2) Multiplex PCR reaction system

[0094] This method can be directly applied to clinical sputum sample DNA. The PCR system and reaction conditions are shown in Tables 3 and 4.

[0095] 3) Optimization of primer concentration in the primer pool

[0096] Through multiple preliminary experiments, we optimized the amount of primers added to the primer pool for amplifying different genes (Table 5). After optimization, the PCR amplification efficiency of each gene can be basically consistent.

[0097] 4) Sequencing using the Nanopore GridON Sequencer:

[0098] After the multiplex PCR reaction is completed, the products are first quantified for nucleic acid. The products in the two PCR product tubes for each sample are designed as different gene sets to ensure the comprehensiveness of the test. Therefore, it is necessary to mix the products in the two tubes in equal amounts, so that the sequencing library of each sample contains all target genes. Subsequently, according to the quantitative results, equal amounts of mixed PCR products are prepared for each sample for library construction. In the library construction stage, the PCR products of each sample are first connected to the adapter fragments with barcodes, which is crucial for the subsequent accurate distinction and tracking of each sample in the bioinformatics process. After the barcode adapter fragments are connected, the PCR products of all samples are appropriately mixed and processed by magnetic bead purification technology to complete the construction of the sequencing library. Finally, the constructed library is loaded onto the sequencing chip and sequencing is started on the ONT GridION sequencer. This process ensures comprehensive gene coverage from each sample, providing comprehensive and detailed genetic information for subsequent analysis and interpretation.

[0099] 5) Data analysis

[0100] First, we ensured that we had enough raw data to support in-depth and comprehensive analysis, based on the number of samples involved in the sequencing. Figure 1 The analysis process shown uses specific bioinformatics software to perform preliminary processing on the raw sequencing data. This stage of processing includes data quality assessment, correction of read data, and necessary data filtering to eliminate technical errors and biases. After data processing, it enters a detailed bioinformatics analysis stage, which includes further analysis of the data after comparison to identify and verify genetic variations associated with drug resistance. In this process, special attention is paid to the differences between samples and any quality issues that may affect data interpretation. Suspected data anomalies are corrected by using appropriate statistical and computational methods to ensure high accuracy and reliability of the analysis results. Finally, all analysis data are integrated to generate a detailed drug resistance test report. This report will provide a scientific basis for clinical decision-making and help doctors choose the most appropriate treatment plan. The entire analysis process is designed to provide the most accurate assessment of drug resistance through high-standard data processing and precise genetic analysis.

[0101] Test Example 1: Detection Effect of the Present Invention

[0102] 1. Accuracy of the method

[0103] 1) Evaluation of the accuracy of variant identification:

[0104] In this study, we first used Illumina sequencing technology to sequence 20 target genes in sputum samples positive for Mycobacterium tuberculosis complex, and used the obtained drug resistance-related variants as a benchmark. Subsequently, the same samples were sequenced using nanopore sequencing technology, and the variant data obtained by the two methods were compared. The results showed that the consistency of the variant detection results between nanopore sequencing and Illumina sequencing reached 100%, showing the high accuracy of nanopore sequencing in identifying drug resistance variants.

[0105] 2) Evaluation of the accuracy of drug resistance prediction:

[0106] The present invention shows extremely high consistency in predicting the drug resistance of Mycobacterium tuberculosis complex compared with traditional phenotypic drug sensitivity tests and Xpert MTB / RIF tests. Specifically, the consistency rate with the results of traditional phenotypic drug sensitivity tests was 98.35%, while the consistency rate with the results of Xpert MTB / RIF tests reached 100%. This remarkable result not only verifies the accuracy of the method of the present invention, but also highlights its great application potential in the rapid prediction of tuberculosis drug resistance.

[0107] 2. Sensitivity of the method

[0108] In order to accurately evaluate the sensitivity of the method of the present invention, we conducted a series of experiments, using negative clinical samples without Mycobacterium tuberculosis complex DNA as the basic background, and added different concentrations of Mycobacterium tuberculosis complex strain DNA to prepare gradient concentration simulated infection samples. Through systematic testing of these gradient concentration samples, we determined that the sensitivity of this method can reach 500 copies of the genome per reaction. This sensitivity level fully meets the needs of clinical application and can ensure effective drug resistance detection even under low bacterial load conditions.

[0109] 3. Anti-interference ability of the method

[0110] In order to evaluate the anti-interference ability of the method of the present invention in clinical applications, we designed a series of experiments, using clinical samples carrying other common pathogens as the basis, and adding DNA of Mycobacterium tuberculosis complex strains to these samples to prepare simulated co-infection samples. Through the systematic detection of these simulated co-infection samples, the experiment verified that even in the presence of complex common clinical pathogens, this method can still effectively detect the drug resistance variation of Mycobacterium tuberculosis complex without significant interference from other pathogens. This result not only proves the high accuracy of the method of the present invention, but also demonstrates its reliability and robustness in clinical settings.

[0111] Sequence Listing

[0112] SEQ ID NO.1

[0113] ACCAACGATGGTGTGTCCAT

[0114] SEQ ID NO.2

[0115] CTTGTCGAACCGCATACCCT

[0116] SEQ ID NO.3

[0117] CAACTCACCGGTGATGGATCA

[0118] SEQ ID NO.4

[0119] GCGGTGATCTGGGTGATGTT

[0120] SEQ ID NO.5

[0121] GACGAATCGAGTTTGAGGCAAG

[0122] SEQ ID NO.6

[0123] TCAAGCGCACCATAAACAATGC<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr"> SEQ ID NO.7<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr">

[0124] <h2 style=";text-align:left;direction:ltr"> GGCTCATATCGAGAATGCTTGC<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr"> SEQ ID NO.8<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr">

[0125] <h2 style=";text-align:left;direction:ltr"> GCGGCCTTGAGCTTTTCTATAC<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr"> SEQ ID NO.9<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr">

[0126] <h2 style=";text-align:left;direction:ltr"> TCTCGAGAGCGGAGAATGTTTC<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr"> SEQ ID NO.10<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr">

[0127] <h2 style=";text-align:left;direction:ltr"> CACGTGAAACATTCATCGTCGG<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr"> SEQ ID NO.11<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr">

[0128] <h2 style=";text-align:left;direction:ltr"> TGATATTCGGCTTCCTGCTCTG<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr"> SEQ ID NO.12<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr">

[0129] <h2 style=";text-align:left;direction:ltr"> GAAGAACATGGTGCCGAAGATG<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr"> SEQ ID NO.13<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr">

[0130] <h2 style=";text-align:left;direction:ltr"> ATGGGCGGACCTGATTGTTTTC<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr"> SEQ ID NO.14<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr">

[0131] <h2 style=";text-align:left;direction:ltr"> CTGTGAGACAGTCAATCCCGAT<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr"> SEQ ID NO.15<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr">

[0132] <h2 style=";text-align:left;direction:ltr"> CGTACACGTCTTTATGTAGCGC<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr"> SEQ ID NO.16<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr">

[0133] <h2 style=";text-align:left;direction:ltr"> AGATATAGCTCGCATCCTCGGA<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr"> SEQ ID NO.17<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr">

[0134] <h2 style=";text-align:left;direction:ltr"> GCGTCATGGACCCTATATCTGT<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr"> SEQ ID NO.18<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr">

[0135] <h2 style=";text-align:left;direction:ltr"> GTAACCGGTCGTAGGTCATAGC<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr"> SEQ ID NO.19<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr">

[0136] <h2 style=";text-align:left;direction:ltr"> AAACCTCTTTCACCATCGACGA<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr"> SEQ ID NO.20<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr">

[0137] CCACTACAGACAAGAACCCCTC SEQ ID NO.21

[0138] AACAACACCGGTGAGATCAAGA SEQ ID NO.22

[0139] GAGACGTCCATGTAGTCCACC SEQ ID NO.23

[0140] AACACCGAGGTCAAATCGTTTG SEQ ID NO.24

[0141] AACAGCGTGGAAATATGTTGGC SEQ ID NO.25

[0142] CGACGGAAAACTTGTTCTGGTC SEQ ID NO.26

[0143] GATGAAATCGGTGAAACTGGCC SEQ ID NO.27

[0144] GCTTGAGAGTTCCAATCATCGC SEQ ID NO.28

[0145] GTGACCCCACAATCGATAACCT SEQ ID NO.29

[0146] TGCATCGCCGGGTGCTCTAT SEQ ID NO.30

[0147] GTCCTCAATGTTGGAAATGCCG SEQ ID NO.31

[0148] GCTCACCGCATAAGTACAAGGA SEQ ID NO.32

[0149] TTTTGAGTGTCTTCTGCTCTTGC SEQ ID NO.33

[0150] ATCGAGCGGATTTAACCTAGCT SEQ ID NO.34

[0151] GATGTCACTGGGAAGGTATTCGA SEQ ID NO.35

[0152] GCATCGCACGTCGTCTTTC SEQ ID NO.36

[0153] AAGCGCAATTTTATTGTCGCCC SEQ ID NO.37

[0154] GAATATCGTGAACACCCTTGCG SEQ ID NO.38

[0155] GTCTGACCTATTGAACCCGTGT SEQ ID NO.39

[0156] GTCACCCTGGCAGCTTACTAC SEQ ID NO.40

[0157] GGCATCATCGTCGTCTGACTAT

Claims

1. A kit for detecting drug-resistant genes of Mycobacterium tuberculosis complex, characterized in that: The kit contains polynucleotide primers of SEQ ID NO.1 to SEQ ID NO.

40.

2. The kit according to claim 1, characterized in that The drug-resistant genes include: embA, rpsL, ahpC, gid, embB, katG, inhA, pncA, rrs, rpoB, gyrB, eis, Rv0678, gyrA, rplC, ddn, tlyA, rrl, ethA, and the bacterial species identification gene is hsp65.

3. The kit according to claim 1, characterized in that in, The drug resistance refers to resistance to the following drugs: rifampicin, ethambutol, pyrazinamide, streptomycin, capreomycin, kanamycin, amikacin, isoniazid, ethionamide, prothionamide, levofloxacin, moxifloxacin, linezolid, delamanid, bedaquiline, and clofazimine.

4. Use of the kit according to claim 1 in detecting drug-resistant genes of Mycobacterium tuberculosis complex.

5. The method for using the kit according to claim 1, characterized in that: The method comprises the following steps: Step 1, sample acquisition: The sputum of the patient is mainly collected as the test sample. Step 2, DNA extraction: DNA extraction technology is used to extract DNA from samples. Step 3, multiplex PCR amplification: PCR products (amplicons) were obtained by multiplex PCR amplification. Step 4, sequencing using the GridON nanopore sequencer of Oxford Nanopore Technologies (ONT): The PCR product (amplicon) is sequenced using the GridON nanopore sequencer of ONT. Step 5, data analysis: Based on the sequencing results, analyze whether the sample contains drug-resistant genes.

6. The method for using the kit according to claim 5, characterized in that: The method comprises the following steps: Step 1, sample acquisition: The sputum of the patient is mainly collected as the test sample. Step 2, DNA extraction: DNA extraction technology is used to extract DNA from samples. Step 3, multiplex PCR amplification: 1) Multiplex PCR reaction system: This method can be directly applied to clinical sputum sample DNA. The PCR system and reaction conditions used are as follows: Multiplex PCR reaction system Multiplex PCR reaction conditions 2) Concentration of each primer pair in the primer pool: Step 4, sequencing with nanopore GridON sequencer: First, the PCR products are quantified for nucleic acid; according to the quantitative results, equal amounts of mixed PCR products are prepared for each sample for library construction, and the PCR products of each sample are connected to adapter fragments with barcodes. After the barcode adapter fragments are connected, the PCR products of all samples are appropriately mixed and processed by magnetic bead purification technology to complete the construction of the sequencing library. The constructed library is loaded onto the sequencing chip and sequencing is started on the nanopore GridION sequencer; Step 5: Analyze whether the sample contains drug resistance-related mutations based on the sequencing results.

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