Method for detecting pathogenic microorganisms based on nanopore mTGS

Abstract

The invention provides a method for detecting pathogenic microorganisms based on a nanopore mTGS (mTGS). The nanopore three-generation sequencing method comprises the following steps: collecting a to-be-detected sample, and carrying out liquefaction treatment to obtain a pretreated sample; adding lysate into the pretreated sample, and performing wall-breaking grinding treatment based on a three-dimensional vibration mode to extract a nucleic acid sample; carrying out end repair treatment on the nucleic acid sample and completing library preparation to obtain a marked sample; sequencing the marked sample, and performing database comparison according to a sequencing result to obtain a comparison result; and performing classification marking processing on the comparison result according to the sample source type of the to-be-detected sample, and obtaining a detection result. The method provided by the invention is superior to the prior art in the aspects of detection sensitivity, accuracy, speed, cost, detection range, specificity and the like, and can better meet the requirements of clinical rapid diagnosis and precise treatment.

Description

Technical Field

[0001] The present invention relates to the technical field of nanopore third-generation sequencing (mTGS), and in particular to a method for detecting pathogenic microorganisms based on nanopore mTGS. Background Art

[0002] Respiratory infections pose a huge challenge to etiological diagnosis due to their high incidence and diverse pathogen types. With the frequent emergence of new pathogens, accurate and rapid identification of pathogens is crucial for effective treatment and control of infection spread. Traditional pathogen detection methods, such as microbial culture, serological testing, and polymerase chain reaction (PCR)-based technologies, although they meet clinical needs to a certain extent, have many limitations. For example, the positive detection rate of traditional microbial culture methods is usually less than 30%, and it takes a long time, often requiring more than 48 hours to obtain results, which makes it difficult to meet the needs of rapid clinical diagnosis. In addition, this method has limited detection capabilities for atypical pathogens and viruses, which can easily lead to missed diagnoses. Although serological methods and PCR technologies have improved detection speed, their detection range is usually narrow and can only detect a limited number of pathogens, and cannot fully cover the many pathogens that may be involved in respiratory infections.

[0003] In recent years, the development of metagenomic next-generation sequencing (mNGS) has brought new hope for pathogen detection. mNGS can comprehensively and rapidly detect pathogenic microorganisms in samples, breaking through the limitations of traditional methods. However, mNGS currently relies mainly on second-generation sequencing technology, which has some shortcomings. The short read length of second-generation sequencing makes it difficult to identify pathogens with high similarity. In addition, second-generation sequencing is expensive, the process is cumbersome, and it requires professional laboratories and equipment support, which limits its widespread application in primary medical institutions. Despite this, due to the long development time of second-generation sequencing technology, some clinical application guidelines and consensus based on second-generation sequencing for detecting infectious pathogens have been formed at home and abroad, and some technical parameters have been standardized, providing certain guidance for clinical application.

[0004] At the same time, nanopore third-generation sequencing technology has gradually emerged with its advantages of long read length, fast speed, flexible throughput, and real-time analysis. Metagenomic third-generation sequencing (mTGS) based on nanopore technology has shown significant advantages in ease of operation, detection cost, specificity, and analytical simplicity. It can be tested on-demand, providing strong support for rapid clinical pathogen diagnosis.

[0005] However, despite the development of nanopore third-generation sequencing technology in China since 2018, the industry still lacks technical requirements and standardized quality control processes for mTGS. This lack of technical specifications has led to limitations in clinical applications and hindered the widespread promotion and application of mTGS technology.

[0006] Therefore, how to establish a standardized mTGS detection process and determine relevant quality control parameters to improve the accuracy and reliability of detection is an urgent problem that needs to be solved. Summary of the Invention

[0007] The purpose of the present invention is to provide a method for detecting pathogenic microorganisms based on nanopore mTGS. The established detection method has significant advantages over existing technologies and conventional technologies in terms of sensitivity, accuracy, speed, cost, detection range and specificity, and can better meet the needs of clinical rapid diagnosis and precise treatment.

[0008] In order to achieve the above-mentioned purpose of the present invention, the following technical solutions are adopted:

[0009] In a first aspect, the present invention provides a method for detecting pathogenic microorganisms based on nanopore mTGS, comprising:

[0010] Collecting a sample to be tested and subjecting it to liquefaction treatment to obtain a pretreated sample;

[0011] After adding lysis solution to the pretreated sample, a wall-breaking grinding process based on a three-dimensional vibration mode is performed to extract a nucleic acid sample;

[0012] Performing end-repair treatment on the nucleic acid sample and completing library preparation to obtain a labeled sample;

[0013] Sequencing the labeled sample, and performing database comparison based on the sequencing results to obtain a comparison result;

[0014] The comparison result is subjected to classification and labeling processing of colonizing bacteria and / or pathogenic bacteria according to the sample source type of the sample to be tested, and a detection result corresponding to the sample to be tested is obtained based on the classification result of the classification and labeling processing.

[0015] In an optional embodiment, the method of collecting a sample to be tested and subjecting it to liquefaction treatment to obtain a pretreated sample further includes:

[0016] Taking the liquefied sample and performing a human nucleic acid removal process, including: dividing it into a treated sample and a non-treated sample according to volume; wherein the treated sample accounts for 80% of the total volume; the non-treated sample accounts for 20% of the total volume; performing a human nucleic acid removal operation on the treated sample to obtain a post-processed sample; and mixing the post-processed sample and the non-processed sample to obtain the pre-processed sample;

[0017] Alternatively, the sample after liquefaction treatment is taken and subjected to human origin removal treatment, including: dividing the sample into a treated sample and a non-treated sample according to the volume; wherein the treated sample accounts for 50% of the total volume; the non-treated sample accounts for 50% of the total volume; performing a human nucleic acid removal operation on the treated sample to obtain a post-processed sample; and mixing the post-processed sample and the non-processed sample to obtain the pre-processed sample.

[0018] Alternatively, the liquefied sample is used as the pre-treated sample without undergoing the humanization operation.

[0019] In an optional embodiment, the human nucleic acid removal operation includes:

[0020] The treated sample is centrifuged to remove the supernatant, and the host-free solution is added to perform mixed lysis;

[0021] The mixed lysate solution was centrifuged to remove the supernatant, and digestion buffer and digestion enzyme were added to the precipitate for digestion only;

[0022] DNA protective groups were added, and after incubation at room temperature, the human nucleic acid removal operation was completed.

[0023] In an optional embodiment, the pre-treated sample is added with a lysis solution and then subjected to a wall-breaking grinding process based on a three-dimensional vibration mode to extract a nucleic acid sample, comprising:

[0024] adding lysis solution and lysozyme to the pretreated sample;

[0025] Grinding is performed using a tissue grinding and homogenizing device based on a three-dimensional vibration mode;

[0026] Proteinase K was added and lysis was carried out at 100°C for 30 minutes;

[0027] The supernatant was collected by centrifugation, a binding solution was added, and the mixture was purified by centrifugal column to obtain the nucleic acid sample.

[0028] In an optional embodiment, the grinding method is to control the tissue grinding and homogenizing device to continuously repeat the following process for three cycles:

[0029] Each cycle consisted of 60 seconds of continuous vibration at 5 m / s, followed by a 30-second rest period.

[0030] In an optional embodiment, the nucleic acid sample is subjected to end repair treatment and library preparation is completed to obtain a labeled sample, comprising:

[0031] Adding a repair enzyme with end-repair and nick-repair capabilities to the nucleic acid sample to perform nucleic acid end-repair to obtain repaired nucleic acid;

[0032] The repaired nucleic acid is mixed with a PCR tag adapter, and the nucleic acid connected to the adapter is used as a template, and a PCR amplification reagent is added to perform a PCR amplification reaction to obtain an amplified product;

[0033] The amplified product is subjected to magnetic bead purification to obtain a purified product;

[0034] Perform Qubit quantification and mixing on the purified product;

[0035] The nucleic acid in the purified product after Qubit quantification and mixing is connected to the sequencing adapter to obtain the labeled sample.

[0036] In an optional embodiment, the magnetic bead purification process comprises:

[0037] According to the volume of the amplified product, magnetic beads are added and mixed to obtain a mixture;

[0038] The mixture is subjected to magnetic bead adsorption separation, and is washed, dried, and eluted to obtain the purified product.

[0039] In an optional embodiment, the added volume of the magnetic beads is 0.7 times the volume of the amplification product.

[0040] In an optional embodiment, the amount of sequencing data for sequencing the labeled sample is controlled at 800 MB.

[0041] In an optional embodiment, the comparison result is subjected to classification and labeling processing of colonizing bacteria and / or pathogenic bacteria according to the sample source type of the sample to be tested, and a test result corresponding to the sample to be tested is obtained based on the classification result of the classification and labeling processing, including:

[0042] Performing error value filtering on the comparison result to obtain a screening result;

[0043] Performing classification labeling processing on the screening results for colonizing bacteria and / or pathogenic bacteria to obtain classification labels;

[0044] Determining whether the classification mark contains a target pathogen mark;

[0045] If so, the target pathogen marker is used as the detection result.

[0046] Compared with the prior art, the present invention has the following beneficial effects:

[0047] The present application provides a method for detecting pathogenic microorganisms based on mTGS. By adopting a wall-breaking grinding process based on a three-dimensional vibration mode, the efficiency of nucleic acid extraction of difficult-to-extract pathogens such as Firmicutes is significantly improved, thereby improving the sensitivity of detection. At the same time, the optimized end-repair and library preparation processes can better retain short nucleic acid fragments, especially nucleic acid fragments of small pathogens such as viruses, further reducing the possibility of missed detection. The non-fragmentation and different proportions of human-derived design greatly maintain the original state of the sample and nucleic acid, achieving a double guarantee of sensitivity and specificity. In addition, the rapid sequencing and real-time analysis capabilities of mTGS technology significantly shorten the detection time, and can provide clinical diagnosis results more quickly. In terms of cost, this method simplifies the operating process, reduces equipment and labor costs, making the detection more economical and feasible, and is particularly suitable for promotion in primary medical institutions. mTGS technology can also simultaneously detect multiple pathogens such as bacteria, fungi, and viruses, providing more comprehensive pathogen information. Through the optimized detection process, the identification accuracy of similar pathogens is improved, and the specificity of detection is enhanced. This method is also highly adaptable, applicable to a variety of sample sources, and the throughput can be flexibly adjusted according to detection needs. Through optimized quality control parameters, mTGS can provide more standardized and reliable test results, reducing errors caused by operational differences.

[0048] In summary, this method system is superior to existing technologies in terms of detection sensitivity, accuracy, speed, cost, detection range and specificity, and can better meet the needs of clinical rapid diagnosis and precise treatment. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0050] Figure 1 Schematic diagram of pathogen detection efficiency in reference products before (C001) and after (C002) cell wall disruption optimization;

[0051] Figure 2 Schematic diagram of the distribution of sequencing fragment read lengths in the reference sample before (R001) and after (R002) fragment screening optimization;

[0052] Figure 3 Schematic diagram of the detection of pathogenic microorganisms in reference products under different sequencing data amounts under the conditions of 1:0 non-human origin removal, 1:1 human origin removal, and 1:4 human origin removal;

[0053] Figure 4The detection of pathogenic microorganisms in reference products under different dehumanization treatment conditions when the data volume is 800MB;

[0054] Figure 5 Schematic diagram of the average length of sequenced fragments in reference products under different experimental conditions;

[0055] Figure 6 Schematic diagram of the detection of pathogenic microorganisms in clinical pilot samples under different dehumanization conditions;

[0056] Figure 7 This is a schematic diagram of the detection of pathogenic microorganisms in clinical pilot samples under different data amounts;

[0057] Figure 8 Schematic diagram of the experimental process of Pre-mTGS and mTGS testing clinical samples before and after optimization of experimental conditions;

[0058] Figure 9 Schematic diagram of the overall detection of different types of pathogens by CMTs, mNGS, Pre-mTGS, and mTGS methods;

[0059] Figure 10 Schematic diagram of the distribution of responsible pathogens detected by CMTs, mNGS, pre-mTGS, and mTGS methods;

[0060] Figure 11 Schematic diagram of the consistency between the detection results of CMTs, mNGS, Pre-mTGS, and mTGS methods and clinical practice. DETAILED DESCRIPTION

[0061] The embodiments of the present invention will be described in detail below with reference to the examples, but it will be understood by those skilled in the art that the following examples are merely illustrative of the present invention and should not be construed as limiting the scope of the invention. Where specific conditions are not specified in the examples, the methods were performed according to conventional conditions or the conditions recommended by the manufacturer. Where the manufacturers of the reagents or instruments are not specified, they are all commercially available conventional products.

[0062] In an embodiment of the present application, a method for detecting pathogenic microorganisms based on nanopore mTGS is provided, comprising:

[0063] Step S1: Collect a sample to be tested and liquefy it to obtain a pre-treated sample.

[0064] The sample to be tested can be any biological sample containing pathogens, with the specific choice depending on the testing purpose and clinical needs. Common sample types used in mTGS-based pathogen detection methods include bronchoalveolar lavage fluid, blood, sputum, tissue, other body fluids, swabs, and feces. Each of these sample types has its own characteristics and applicable scenarios, meeting diverse clinical diagnostic needs.

[0065] For example, in this embodiment, the target is alveolar lavage fluid.

[0066] Bronchoalveolar lavage fluid (BAL) can be obtained from a patient's lungs during bronchoscopy. It is used to detect pathogens of respiratory infections, such as bacteria, fungi, and viruses. It directly reflects the severity of lung infection and is an important sample type for diagnosing pneumonia and other infectious lung diseases.

[0067] As mentioned above, liquefaction refers to the processing of the collected samples to be tested (such as alveolar lavage fluid, sputum, etc.) to make them into a uniform liquid state for subsequent operations. Its purpose is that many biological samples have a certain viscosity or contain solid components, such as mucus in sputum, cell debris in alveolar lavage fluid, etc. Liquefaction treatment can fully disperse these components, so that the microbial nucleic acids in the sample can be better released, improving the efficiency and quality of nucleic acid extraction, thereby providing a better quality sample basis for subsequent detection steps.

[0068] Step S2, adding lysis solution to the pretreated sample and performing wall-breaking grinding treatment based on a three-dimensional vibration mode to extract a nucleic acid sample.

[0069] It should be noted that previous studies have found that the detection rates of firmicutes, such as Mycobacterium tuberculosis and certain fungi, are generally low. This is mainly because the cell walls of these microorganisms are relatively complex and strong. For example, the cell wall of Mycobacterium tuberculosis is rich in lipids and long-chain fatty acids, forming a strong outer membrane, which makes it more difficult to break the cell wall during nucleic acid extraction.

[0070] Traditional nucleic acid extraction methods, such as using ordinary vibration grinders (such as Dinghaoyuan TL-Smart), have an upper limit of oscillation speed of only 2100rpm and only have a "horizontal vibration" function. Although this single vibration mode and low oscillation speed can break up cell walls to a certain extent, it is difficult to effectively destroy the cell walls of firmicutes (such as Mycobacterium tuberculosis and certain fungi), resulting in insufficient release of nucleic acids and a low nucleic acid extraction rate, which in turn affects the sensitivity and accuracy of detection.

[0071] Therefore, in order to improve the nucleic acid extraction rate and detection sensitivity of Firmicutes, the cell wall breaking treatment conditions need to be optimized.

[0072] In this embodiment, a three-dimensional vibration mode is used for cell wall breaking and grinding. This optimized method achieves multi-dimensional vibration, including up-and-down, left-and-right, and spiral rotations. This allows for a more comprehensive effect on the sample, improving cell wall breaking efficiency. It also achieves higher oscillation speeds, specifically increasing the upper limit to 5 m / s, significantly higher than the 2100 rpm of conventional equipment. This high-speed vibration more effectively destroys cell walls and releases nucleic acids.

[0073] The optimized cell wall breaking and grinding process can significantly improve the nucleic acid extraction rate of firm-walled bacteria, such as Mycobacterium tuberculosis and certain fungi; through more efficient cell wall breaking, more nucleic acids can be released, thereby improving the sensitivity of detection; and the cell wall can be more thoroughly destroyed, reducing false negative results caused by intact cell walls.

[0074] In summary, optimizing the grinding process based on a three-dimensional vibration mode significantly improves the nucleic acid extraction rate and detection sensitivity of Firmicutes by increasing the oscillation speed and adding multi-dimensional vibration. This optimization method can better meet the needs of pathogen detection, especially for the detection of Firmicutes such as Mycobacterium tuberculosis and fungi.

[0075] Step S3: performing end-repair treatment on the nucleic acid sample and completing library preparation to obtain a labeled sample.

[0076] End repair is a key step in molecular biology and high-throughput sequencing. It is primarily used to treat the ends of DNA fragments, making them suitable for subsequent operations (such as adapter ligation and sequencing). Specifically, it involves chemically and enzymatically modifying the ends of DNA fragments to make them blunt-ended, ensuring that the ends have a 5' phosphate group and a 3' hydroxyl group.

[0077] This step provides a process for performing end-repair treatment on nucleic acid samples and completing library preparation in the mTGS-based pathogen detection method. In high-throughput sequencing (NGS), the terminal state of nucleic acid fragments is crucial to the subsequent adapter connection and sequencing efficiency. The purpose of the end-repair treatment is to modify the ends of the nucleic acid fragments to blunt ends and ensure that the ends have 5' phosphate groups and 3' hydroxyl groups so that they can be efficiently connected to sequencing adapters.

[0078] The process can include blunting, i.e., using the 5'→3' DNA polymerase activity of T4 DNA polymerase or Klenow fragmentase to fill in the 5' protruding ends in the presence of dNTPs; simultaneously using its 3'→5' DNA exonuclease activity to cut the 3' protruding ends; and phosphorylation, i.e., using T4 polynucleotide kinase (PNK) to add phosphate groups to the 5' end of DNA in the presence of ATP.

[0079] Library preparation involves converting end-repaired nucleic acid fragments into a DNA library suitable for sequencing. This process includes ligating sequencing adapters and PCR amplification, ultimately yielding labeled samples ready for sequencing.

[0080] Labeled samples refer to nucleic acid libraries that have been end-repaired, ligated with sequencing adapters, and amplified by PCR, resulting in sample tags (such as barcodes or index sequences). These tags are used to distinguish sequences from different samples during bioinformatics analysis.

[0081] Step S4: sequencing the labeled sample and performing database comparison based on the sequencing results to obtain comparison results.

[0082] In this step, in the mTGS-based pathogen detection method, the labeled sample is sequenced, and the sequencing result is compared with the database to obtain a comparison result.

[0083] First, the purpose of sequencing is to convert the nucleic acid sequence in the labeled sample into readable digital information. Through sequencing, the sequence information of all nucleic acid fragments in the sample can be obtained. The specific process may include loading the labeled sample into a sequencing instrument (such as the Oxford Nanopore sequencer); starting the sequencing instrument and reading the sequence information of the nucleic acid fragments through electrophoresis or optical detection technology; data generation: The sequencing instrument generates a large amount of short sequence data (reads), which will be used for subsequent analysis.

[0084] Next, database alignment can be performed. The purpose of database alignment is to compare the sequence data generated by sequencing with a known reference database to determine the types and abundance of pathogenic microorganisms present in the sample. Among them, suitable reference databases can be selected, such as the NCBI NT database (nucleotide sequence database), UniProt (protein sequence database), or other specialized pathogenic microorganism databases.

[0085] Also, choose an appropriate alignment tool, such as BLAST (Basic Local Alignment Search Tool), DIAMOND (a fast alignment tool), or Minimap2 (an alignment tool suitable for long-read sequencing data).

[0086] A specific alignment operation may be to align the reads generated by sequencing with a reference database to generate alignment results, where the alignment results may include but are not limited to information such as the matching status of each read with the reference sequence, similarity score, E-value, etc.; the results may then be filtered, that is, based on parameters such as the similarity score and E-value of the alignment results, low-quality or mismatched alignment results are filtered out, while retaining high-confidence alignment results.

[0087] The final comparison results may include but are not limited to: species identification: determining the types of pathogenic microorganisms present in the sample, such as bacteria, fungi, viruses, etc.; abundance analysis: calculating the relative abundance of each pathogenic microorganism, that is, the proportion in the sample; functional annotation: functional annotation of the genes of pathogenic microorganisms to understand their possible biological functions and metabolic pathways.

[0088] Step S5, performing classification and labeling processing of colonizing bacteria and / or pathogenic bacteria on the comparison result according to the sample source type of the sample to be tested, and obtaining a detection result corresponding to the sample to be tested based on the classification result of the classification and labeling processing.

[0089] It should be noted that after sequencing and database comparison, the resulting alignment contains sequence information and abundance information for the various microorganisms present in the sample. However, this information requires further analysis to distinguish between colonizers and pathogens. Colonizers are microorganisms that persist in the host for a long time but typically do not cause disease, while pathogens are capable of causing disease. Classification and labeling can more accurately identify pathogens in samples, providing more valuable information for clinical diagnosis.

[0090] The classification and labeling process can be as follows: first, based on the source type of the sample to be tested (such as alveolar lavage fluid, blood, sputum, etc.), combined with the clinical background of the sample (such as the patient's symptoms, medical history, imaging examination results, etc.), a comprehensive analysis of the sequencing results is performed; certain microorganisms are common colonizers in specific parts, for example, viridans streptococci, Neisseria, Haemophilus, etc. in the mouth. If these microorganisms are detected in respiratory samples (such as sputum or alveolar lavage fluid) and their sequence numbers are low, they are generally considered to be more likely to be colonizers. In addition, for those microorganisms that are not usually present as colonizers, such as Legionella pneumophila, Chlamydia, Bordetella pertussis, Corynebacterium diphtheriae, etc., if they are detected in the sample and have a high sequence number, they are generally considered to be more likely to be pathogens.

[0091] Furthermore, even detected pathogens that are generally considered to be of low virulence may be pathogenic in immunosuppressed patients.

[0092] Based on this analysis, the microorganisms in the sample are classified and labeled as colonizers or pathogens. This process may require combining multiple pieces of information, such as the type and abundance of the microorganism, the source of the sample, and the patient's clinical symptoms.

[0093] Based on the results of the classification and labeling process, the system generates a test result corresponding to the sample, taking into account the species, abundance, and pathogenicity of the microorganisms present in the sample. This test result provides clinicians with detailed information about the pathogenic microorganisms in the sample, helping them to more accurately determine the patient's infection status and develop more appropriate treatment plans.

[0094] In some embodiments, in step S1, the sample to be tested is collected and liquefied to obtain a pre-treated sample, and further comprises any one of the following three processing methods:

[0095] (1) Method 1: Step S11, taking the sample after liquefaction treatment and performing human nucleic acid removal treatment, including: dividing it into a treated sample and a non-treated sample according to volume; wherein the treated sample accounts for 80% of the total volume fraction; the non-treated sample accounts for 20% of the total volume fraction; performing a human nucleic acid removal operation on the treated sample to obtain a post-processed sample; mixing the post-processed sample and the non-processed sample to obtain the pre-processed sample;

[0096] (2) Method 2: Step S12, taking the sample after liquefaction treatment and performing human origin removal treatment, including: dividing it into a treated sample and a non-treated sample according to volume; wherein the treated sample accounts for 50% of the total volume; the non-treated sample accounts for 50% of the total volume; performing a human origin nucleic acid removal operation on the treated sample to obtain a post-processed sample; mixing the post-processed sample and the non-processed sample to obtain the pre-processed sample;

[0097] (3) Method 3: Step S13, the liquefied sample is used as the pre-treated sample without undergoing the humanization operation.

[0098] The above three steps are in parallel, and any one of them can be used as the processing method for this step. In clinical sample testing, dehumanization of the entire sample is a conventional method, which aims to remove human-derived nucleic acid components in the sample, thereby reducing the interference of host nucleic acids, allowing sequencing resources to focus more on pathogenic microorganism nucleic acids, and improving the detection ability of low-abundance pathogens. However, this method may cause the loss of some low-abundance pathogen nucleic acids, especially those that are closely bound to human nucleic acids or are easily mistakenly removed during the dehumanization process. In addition, dehumanization operations increase the complexity and time cost of sample pre-processing, and may introduce additional operational errors.

[0099] To address these issues, this technology uses an innovative strategy, where the volume ratio of treated and untreated samples is 4:1 or 1:1. Specifically, the majority of samples (4 / 5) undergo human nucleic acid removal to reduce interference from human nucleic acids, while a small portion (1 / 5) retains human nucleic acids to ensure that low-abundance pathogen nucleic acids are not mistakenly removed.

[0100] This strategy strikes a balance between removing human nucleic acids and retaining pathogen nucleic acids, optimizing detection performance. By mixing treated samples (samples that have undergone human nucleic acid removal) with untreated samples (samples that have not undergone human nucleic acid removal) in a ratio of 4:1, human nucleic acids can be removed to a certain extent, reducing background noise, while retaining sufficient pathogen nucleic acids to avoid the loss of pathogen nucleic acids due to excessive removal of human nucleic acids, especially for those pathogens with low abundance or that bind closely to human nucleic acids.

[0101] This ratio has been experimentally validated, with results demonstrating that a 4:1 ratio can remove human nucleic acids while maintaining high pathogen detection sensitivity and reducing false-negative results. Furthermore, this mixing method effectively controls human nucleic acids without requiring complex human nucleic acid removal procedures, thereby simplifying sample pretreatment, reducing steps and time, and improving detection efficiency. This strategy not only optimizes test performance but also enhances comprehensiveness and accuracy, providing more reliable data support for clinical diagnosis and research.

[0102] In addition, although dehumanization treatment (or treatment methods such as those in Method 1 and / or Method 2) can remove or partially remove human nucleic acids in samples, thereby reducing human background noise in sequencing data to a certain extent, increasing the relative proportion of pathogen nucleic acids, and thus improving detection sensitivity and specificity, this process may also lead to the following defects:

[0103] (1) The dehumanization process may result in the loss of some low-abundance pathogen nucleic acids, especially those that are closely associated with human nucleic acids or are easily mistakenly removed during the dehumanization process. For example, the nucleic acids of some pathogens may be mistakenly identified as host nucleic acids during the dehumanization process due to their similarity to host nucleic acids and removed, resulting in a decrease in detection sensitivity.

[0104] (2) The humanization operation itself is a relatively complex process that requires additional steps and time to complete. This not only increases the difficulty of the experiment, but may also lead to extended sample processing time and affect the efficiency of the test.

[0105] (3) Any additional processing steps may introduce new sources of error. The removal of human origin may lead to incomplete removal or partial loss of pathogen nucleic acid in the sample due to improper operation or reagent differences, thereby affecting the accuracy and reliability of the test results.

[0106] Therefore, in this embodiment, the pre-processed sample is obtained without undergoing the humanization operation, mainly to improve the sensitivity and accuracy of the detection, that is, method 3, the liquefied sample is used as the pre-processed sample without undergoing the humanization operation.

[0107] In method 3, the ability to retain human nucleic acids in the sample can avoid the loss of low-abundance pathogen nucleic acids due to dehumanization operations. This is particularly important for detecting low-abundance pathogens such as Mycobacterium tuberculosis, Chlamydia psittaci, and Pneumocystis jiroveci. It can significantly improve detection sensitivity and reduce the occurrence of false negative results. In addition, retaining human nucleic acids can also provide more comprehensive sample information, reflect the interaction between the host and the pathogen, avoid the bias and errors that may be introduced by dehumanization operations, and thus improve the accuracy of the test results. This method is not only suitable for samples with low human nucleic acid content, such as cerebrospinal fluid and blood, but also simplifies the sample pre-processing process, reduces the number of operation steps and time, improves detection efficiency, and provides more accurate and comprehensive data support for clinical diagnosis and research.

[0108] In some embodiments, in step S11, the human nucleic acid removal operation includes:

[0109] Step S111: centrifuge the treated sample to remove the supernatant, add the host-free solution and perform mixed lysis.

[0110] Step S112: centrifuging the mixed lysed solution to remove the supernatant, adding digestion buffer and digestion enzyme to the precipitate for digestion treatment.

[0111] Step S113, adding a DNA protective group, incubating at room temperature, and completing the human nucleic acid removal operation.

[0112] In some embodiments, the step S2, adding a lysis solution to the pretreated sample and performing a wall-breaking grinding process based on a three-dimensional vibration mode to extract a nucleic acid sample, comprises:

[0113] Step S21: adding lysis solution and lysozyme to the pre-treated sample.

[0114] It should be noted that in conventional techniques, lysis solutions are usually used to disrupt cell structures and release nucleic acids, but lysing bacteria are not used at the same time. In this technology, lysing bacteria are added mainly to enhance the cell wall disruption effect on certain microorganisms with thick cell walls (such as bacteria and fungi), thereby more effectively releasing nucleic acids.

[0115] This method is particularly important when processing samples containing thick-walled microorganisms such as Gram-positive bacteria or fungi, because the cell walls of these microorganisms are relatively strong and lysis buffer alone may not be able to fully destroy their cell walls, resulting in incomplete nucleic acid extraction.

[0116] The addition of lysozyme further breaks down the cell wall, improving the efficiency and quality of nucleic acid extraction. The reason for using a 100°C lysis temperature is to ensure the efficiency and thoroughness of the lysis process. High temperatures accelerate chemical reactions, allowing the lysis solution and lysozyme to work more effectively, thereby more effectively disrupting the cell structure and releasing nucleic acids.

[0117] At the same time, the high temperature of 100°C also helps to inactivate potential active ingredients in the sample, reducing the risk of biohazards in subsequent operations.

[0118] In this method, the selection of a lysis temperature of 100°C has been experimentally verified and can ensure efficient nucleic acid extraction in a shorter time (e.g., 30 minutes) while maintaining the integrity and quality of the nucleic acid, providing high-quality nucleic acid samples for subsequent detection steps.

[0119] Step S22: Grinding is performed using a tissue grinding and homogenizing device based on a three-dimensional vibration mode.

[0120] In this method, a tissue grinding and homogenization device with a three-dimensional vibration mode is used to achieve more efficient and uniform sample grinding. This device uses a special three-dimensional vibration mode to make the sample move in complex space, thereby achieving more thorough grinding and homogenization. Specifically, the three-dimensional vibration mode device enables grinding beads (such as zirconia beads, steel balls, glass beads, ceramic beads, etc.) to vibrate back and forth at high frequencies, impact, and shear within the sample, thereby quickly and evenly destroying cell walls and cell membranes and releasing nucleic acids.

[0121] For example, in this embodiment, the tissue grinding and homogenization device that can be used is the TGrinder H24R tissue grinding and homogenization device. This device uses a three-dimensional high-speed vibration mode to simultaneously process 1-24 independent grinding tube samples. Using grinding media (such as zirconium oxide beads, stainless steel beads, ceramic beads, and glass beads), it effectively grinds, lyses, and homogenizes various types of biological samples (such as plant tissue, animal tissue, soil, and feces). Combined with various reagents, it can quickly and stably extract DNA / RNA / protein from the samples while preserving the integrity of the biomolecules.

[0122] In summary, tissue grinding and homogenization devices that utilize a three-dimensional vibration mode, such as the TGrinder H24R, are designed to achieve more efficient and uniform sample grinding, improve the quality and consistency of nucleic acid extraction, reduce cross-contamination, and include cryogenic grinding capabilities to protect the integrity of nucleic acids and proteins. This device is particularly suitable for processing microorganisms with thick cell walls and difficult-to-grind samples, significantly improving the sensitivity and accuracy of pathogen detection.

[0123] Step S23: adding proteinase K and performing lysis at a lysis temperature of 100° C. for 30 minutes.

[0124] Step S24, centrifuging to obtain the supernatant, adding binding solution, and purifying through a centrifugal column to obtain the nucleic acid sample.

[0125] In some embodiments, the grinding method comprises controlling the tissue grinding and homogenizing device to continuously repeat the following process for three cycles:

[0126] Each cycle consisted of 60 seconds of continuous vibration at 5 m / s, followed by a 30-second rest period.

[0127] As mentioned above, each cycle begins with 60 seconds of vibration at a speed of 5 m / s. This speed and duration are designed to ensure sufficient movement of the grinding beads within the sample, effectively disrupting cell walls and membranes and releasing nucleic acids. This is followed by a 30-second pause: after 60 seconds of vibration, the device pauses for 30 seconds. This pause allows for redistribution of sample components, preventing localized overheating or excessive shearing, thereby protecting the integrity of nucleic acids and proteins. One cycle takes 90 seconds to complete, and the total time for all three cycles is 270 seconds.

[0128] In some embodiments, step S3, performing end-repair treatment on the nucleic acid sample and completing library preparation to obtain a labeled sample, includes:

[0129] Step S31 , adding a repair enzyme with end-repair and nick-repair capabilities to the nucleic acid sample to perform nucleic acid end-repair to obtain repaired nucleic acid.

[0130] The purpose of end-repair is to blunt-end the ends of nucleic acid samples and ensure they have 5' phosphate groups and 3' hydroxyl groups, enabling efficient ligation to sequencing adapters. The repaired nucleic acids are then ligated to PCR tag adapters to introduce sample tags during subsequent PCR amplification, allowing for differentiation between sequences from different samples.

[0131] Furthermore, in some embodiments, the repair enzyme having end repair and nick repair capabilities is Novozymes N210.

[0132] It should be noted that this technology has been optimized to address the low detection rate of viruses using mTGS. Previous studies have shown that compared to bacteria and fungi, the detection rate of viruses is only 39.4%. This is primarily due to the smaller size of viral genomes, which are easily broken into short fragments during extraction, grinding, and lysis. Consequently, short fragments less than 1000 bp are filtered out during library construction, thus affecting the detection rate of viruses and resulting in an extremely low rate.

[0133] In conventional technology, a repair enzyme that only has "end repair" ability (such as Novazonic N203) is usually used to perform end repair treatment on nucleic acid samples. This repair enzyme can repair the ends of nucleic acid fragments to flat ends and ensure that the ends have 5' phosphate groups and 3' hydroxyl groups, thereby preparing for subsequent connector connection and sequencing. However, this repair enzyme only has end repair function, and the connection effect for some short nucleic acid fragments (such as viral nucleic acids) may not be ideal, especially when the fragments are short and there are cuts.

[0134] In view of this, this study optimized the fragment screening conditions and replaced the repair enzyme that only has "end repair" ability (such as Novozymes N203 used in conventional technology) with a repair enzyme with "end repair + incision repair" ability (Novozymes N210).

[0135] This repair enzyme not only repairs the ends but also fills in the gaps in the nucleic acid fragments, thereby better connecting the short fragment sequences into long fragments. This is especially important for viral nucleic acids, because the viral genome is small and easily broken into short fragments during extraction, grinding, and lysis.

[0136] In step S32, the repaired nucleic acid is mixed with a PCR tag adapter, and the nucleic acid connected to the adapter is used as a template, and a PCR amplification reagent is added to perform a PCR amplification reaction to obtain an amplified product.

[0137] In this step, the number of target nucleic acid fragments is increased through PCR amplification, and sequencing adapters are further introduced to prepare for subsequent sequencing.

[0138] Step S33: purify the amplified product with magnetic beads to obtain a purified product.

[0139] As described above, unbound primers, adapters and other impurities are removed, the purified PCR products are retained, and the sample quality is improved.

[0140] In some embodiments, the magnetic bead purification process comprises:

[0141] According to the volume of the amplified product, magnetic beads are added and mixed to obtain a mixture;

[0142] The mixture is subjected to magnetic bead adsorption separation, and is washed, dried, and eluted to obtain the purified product.

[0143] In some preferred embodiments, the added volume of the magnetic beads is 0.7 times the volume of the amplified product.

[0144] It should be noted that during library construction, the magnetic bead purification ratio was increased from 0.4X to 0.7X. This adjustment helps better retain sequence fragments less than 1000bp. The amount of magnetic beads used directly affects the lower limit of DNA length that can be purified; and the higher the multiplier, the shorter the lower limit of DNA length that can be purified.

[0145] For example, 1× magnetic beads can only efficiently purify DNA longer than 250bp, and shorter DNA will be lost in large quantities during the purification process; after increasing to 1.8×, 150bp DNA can also be efficiently purified.

[0146] By increasing the proportion of magnetic beads used, short nucleic acid fragments can be purified and enriched more effectively, reducing the situation where fragments are filtered out due to being too short during library construction.

[0147] Step S34: performing Qubit quantification and mixing on the purified product.

[0148] The purified PCR products were quantified using Qubit fluorescent quantitative reagent to ensure the consistency of nucleic acid concentration in each sample, and different samples were adjusted to the same mass concentration and then mixed.

[0149] In step S35, the nucleic acid in the purified product after Qubit quantification and mixing is connected to a sequencing adapter to obtain the labeled sample.

[0150] In this step, the purified nucleic acids are ligated to sequencing adapters to complete library preparation and generate labeled samples. Specifically, the nucleic acids in the purified product, after Qubit quantification and mixing, are ligated to sequencing adapters. The ligation reaction is performed under appropriate conditions to ensure that the sequencing adapters are successfully attached to the ends of the nucleic acid fragments, completing library preparation.

[0151] This method optimizes the end-repair and purification steps of nucleic acid samples by replacing the repair enzyme and increasing the proportion of magnetic bead purification. These improvements can better retain short nucleic acid fragments, especially viral nucleic acids, thereby improving the sensitivity and accuracy of mTGS in pathogen detection.

[0152] In some embodiments, the amount of sequencing data for sequencing the labeled sample is controlled at 800 MB.

[0153] Sequencing data volume refers to the total amount of sequence data generated during the sequencing process. It reflects the richness of the sequence information generated during the sequencing process and directly affects the sequencing depth and coverage. Based on multiple factors such as detection performance, analysis requirements, sequencing costs, and sequencing time, 800MB of sequencing data volume is the optimal sequencing data volume for mTGS in this example.

[0154] By controlling the sequencing data volume to 800MB, we can ensure sufficient sequencing depth to cover the nucleic acids of these low-abundance pathogens, thereby improving the sensitivity of detection; 800MB of sequencing data can provide sufficient sequence information, so that the nucleic acid sequence of the pathogen can be more accurately identified during the comparison process, reducing misjudgments; 800MB of data can complete sequencing in a shorter time, thereby improving detection efficiency and meeting the needs of rapid clinical diagnosis; and 800MB of data can meet the technical requirements for clinical pathogen metagenomic identification and perform well in detection sensitivity and accuracy.

[0155] In some embodiments, step S5, performing classification and labeling processing of colonizing bacteria and / or pathogenic bacteria on the comparison results according to the sample source type of the sample to be tested, and obtaining a test result corresponding to the sample to be tested based on the classification result of the classification and labeling processing, includes:

[0156] Step S51 , performing error value filtering processing on the comparison result to obtain a screening result.

[0157] In the alignment results, there may be some false positive results due to sequencing errors or alignment errors. By using error value filtering, these inaccurate results can be removed, improving the accuracy of the test. In this step, the preliminary alignment results can be screened according to a set error threshold (such as alignment score, E value, etc.), removing those with alignment scores below the threshold or E values that are too high.

[0158] For example, filtering can be performed based on a 2% threshold for cross-contamination of samples from the same batch and a 3-fold ratio of the number of negative NTC reads.

[0159] Step S52: performing classification labeling processing on the screening results for colonizing bacteria and / or pathogenic bacteria to obtain classification labels.

[0160] As mentioned above, the microbial sequences in the comparison results are marked as colonizing bacteria or pathogenic bacteria to more accurately assess the pathogen situation in the sample.

[0161] Colonizers are labeled as colonizers based on the sample source type and clinical context, combined with known information about colonizers. For example, in respiratory samples, certain common oral flora can be labeled as colonizers. Pathogens are labeled as pathogens for microorganisms that are detected in the sample with a high sequence count and are consistent with the sample source and clinical symptoms. For example, if Mycobacterium tuberculosis is detected in a bronchoalveolar lavage fluid sample with a high sequence count, it can be labeled as a pathogen.

[0162] Step S53: determine whether the classification mark contains the target pathogen mark.

[0163] Step S54: If yes, the target pathogenic bacteria is marked as the detection result.

[0164] Determine whether the target pathogens are present in the sample to provide a basis for clinical diagnosis, and generate a final test report based on the results of the classification labeling process to provide clear pathogen information for clinical diagnosis. Check whether the classification labeling results contain the label of the target pathogens. Target pathogens refer to those microorganisms known to cause disease, such as Mycobacterium tuberculosis and Streptococcus pneumoniae. Organize the classification labeling results into a test report, including sample information, test methods, comparison results, classification labeling results, and the final test results. For example, the test report can clearly indicate the type of pathogens detected in the sample and their sequence number, and indicate whether the target pathogens are present.

[0165] In summary, this step provides information on how to filter the results for errors, classify and label colonizing bacteria and / or pathogens, and then generate test results corresponding to the sample based on the classification results. This process ensures the accuracy and reliability of the test results and provides clear pathogen information for clinical diagnosis.

[0166] The present invention is further described below by way of specific examples. However, it should be understood that these examples are merely provided for more detailed description and are not to be construed as limiting the present invention in any form.

[0167] The names of the methods used in the examples and their corresponding descriptions are as follows:

[0168] 1. CMTs detection method: Conventional microbiological examination methods for BALF samples include: bacterial culture, mycobacterial culture, and fungal culture; acid-fast staining of mycobacterial smears; fungal immunofluorescence staining; Aspergillus galactomannan antigen and Cryptococcal capsular polysaccharide antigen testing; and real-time polymerase chain reaction (PCR) for Mycobacterium tuberculosis complex, Mycoplasma pneumoniae, Chlamydia pneumoniae, cytomegalovirus (CMV), adenovirus, influenza A / B virus, respiratory syncytial virus, and human metapneumovirus.

[0169] 2. mNGS (metagenomic next-generation sequencing) testing method: BALF (>5 mL, placed in a sterile sputum container) was collected and immediately transported on dry ice to the molecular laboratory of the First Affiliated Hospital, Zhejiang University School of Medicine for testing. mNGS was tested within 8 hours of collection. The detailed mNGS testing process and positive result criteria were referenced to the study by Zhou et al. [The Journal of molecμLar diagnostics: JMD, 2021, 23(10): 1259-1268].

[0170] 3. Conventional pre-mTGS method (metagenomic third-generation sequencing before optimization): Conventional method, including:

[0171] (1) Sample pretreatment: Liquefaction: Take 500 μL of viscous sample, add 100 μL of liquefaction solution (Solarbo, D1070), and mix at room temperature for 15 minutes until there is no obvious clump or stickiness. If the sample is too viscous, increase the volume of liquefaction solution. Dehumanization operation: After liquefaction, centrifuge the sample to remove the supernatant, add 100 μL of host removal solution (Zymo, D4310-1), mix at room temperature for 5 minutes to lyse the human cells, and centrifuge to remove the supernatant. Add digestion buffer and digestion enzyme (Zymo, D4310-3) to the precipitate, treat at 37°C for 30 minutes to remove human nucleic acids, then add 100 μL of DNA protectant (Zymo, R1200) to inactivate the digestion enzyme, and incubate at room temperature for 5 minutes.

[0172] (2) Nucleic acid extraction and purification: The pre-treated sample was transferred to a centrifuge tube containing grinding beads, lysis buffer (Zymo, D4300-1) was added, and the sample was shaken at 2000 rpm / min in a grinder (Dinghaoyuan TL-Smart) for 5 minutes. 10 μL of proteinase K (Zymo, D3001-2) was added, and the sample was treated at 56°C for 30 minutes and centrifuged at 10,000 g for 1 minute. The supernatant was added to the binding buffer (Zymo, D4300-2), mixed, and passed through the centrifuge column in batches to collect the nucleic acid. After the centrifuge column was washed several times, 50 μL of deionized water was added and incubated at 55°C for 5 minutes to elute into a new centrifuge tube.

[0173] (3) Library preparation: The extracted nucleic acid was repaired with an end-repair enzyme (Norvozyme N203) and then connected to a PCR tag adapter. The target nucleic acid fragment reads were amplified by PCR amplification (95°C for 3 minutes, 94°C for 15 seconds, 62°C for 15 seconds, 72°C for 2 minutes, 28 cycles; 72°C for 5 minutes, and stored at 10°C). At the same time, a tag adapter (Nanopore, EXP-PBC096) was introduced. The PCR product was purified using magnetic beads (Norvozyme N411) at a ratio of 0.4X. After purification, Qubit quantification was performed, and the samples were adjusted to the same mass and mixed. End-repair and sequencing adapter ligation (Nanopore, SQK-LSK110) were performed to complete library preparation.

[0174] (4) Loading the library: Perform quality control on the sequencing chip. Sequencing can only be performed if the number of wells is ≥800. Replace the buffer before loading the chip, replacing the preservation solution with activation solution (Nanopore, EXP-FLP002). Drop the library, along with the sequencing buffer and loading beads (Nanopore, SQK-LSK110), onto the chip. Set the sequencing parameters (select SQK-LSK110, EXP-PBC096, HACmodels, and leave the rest of the parameters as default) and start sequencing.

[0175] (5) Bioinformatics analysis: The fast5 files generated by the GridION instrument were converted into fastq sequence files using guppy 6.4.2 software. Adapter sequences were removed using porechop 0.2.4, and reads with length <200 bp and average quality <9 were filtered out using filtlong 0.2.1. The metagenome database was aligned using minimap2 2.26, and hits with similarity <85% or AS values <100 were filtered out. Preliminary classification was performed using a combination of voting and LCA algorithms. Further verification was performed using blast 2.14.0 against the nt library, and the species classification was updated.

[0176] (6) mTGS positive result criteria: Calculate the proportion of species in the same batch of samples. Batch proportions <2% are considered as cross-contamination filtration; calculate the ratio of the number of reads of the species to be tested to the number of reads of the NTC; a ratio <3 is considered as environmental or reagent background bacteria filtration; mark colonizing bacteria according to sample type, such as Neisseria and Actinomyces in respiratory samples.

[0177] 3. Optimization of mTGS detection method (metagenomic Third-generation sequencing): The optimized method in this application (referred to as the optimized method) is used. The difference from the conventional method pre-mTGS is that: (1) The dehumanization operation in step (1) adopts the method of treated sample: untreated sample 4:1, 1:1, and 1:0. (2) In step (2), a tissue grinding homogenizer TGrinder H24R based on a three-dimensional vibration mode is used. (3) In step (3), the repair enzyme with end repair and nick repair capabilities is Novagen N210. (4) In step (3), the PCR product is purified using magnetic beads (Novagen N411), and the ratio of magnetic beads used is 0.7X. (5) The amount of sequencing data for sequencing the labeled sample in step (3) is controlled at 800MB.

[0178] Example 1

[0179] In this example, the preparation of the test sample used as the reference in the subsequent examples and the optimization of the cell wall breaking treatment conditions were investigated.

[0180] Experimental methods:

[0181] Thirteen pathogenic microorganisms, including Listeria monocytogenes, Pseudomonas aeruginosa, Bacillus subtilis, Escherichia coli, Salmonella enterica, Lactobacillus fermentum, Enterococcus faecalis, Staphylococcus aureus, Saccharomyces cerevisiae, Cryptococcus neoformans (purchased from ZymoResearch, D6310), Mycobacterium tuberculosis (purchased from China Food and Drug Inspection Institute, 230034-201802), Mycobacterium avium (purchased from Zhejiang Provincial Center for Disease Control and Prevention, NTM2020037) and human papillomavirus (HPV) (purchased from Bondsheng, 203202011), were mixed with human lymphocytes (from the Laboratory Department of Zhejiang Provincial People's Hospital) at a human cell concentration of 5×10 5 copies / mL: pathogenic cells 5×10 4 The reference sample was prepared by mixing at a ratio of 10 copies / mL.

[0182] Using this reference product, each experiment was repeated three times (S1-S3) to evaluate the performance of various optimized conditions.

[0183] Optimization of cell wall breaking treatment conditions:

[0184] The detection performance of the reference product in Example 1 before optimization of the cell wall breaking treatment (represented by C001) and after optimization (represented by C002) was compared and verified.

[0185] Among them, C001 represents the use of conventional pre-mTGS method; C002 represents the use of optimized mTGS detection method, in which only the cell wall breaking conditions are optimized relative to the conventional method.

[0186] The optimization of the cell wall breaking conditions included: in step (2), replacing the tissue grinder (Dinghaoyuan TL-Smart) with a maximum oscillation speed of only 2100 rpm and only a "horizontal vibration" function with a tissue grinding homogenizer (Tiangen TGrinder H24) using a "three-dimensional high-speed vibration" mode; adjusting the grinding speed from 2000 rpm / min for 5 min to 5 m / s for 60 s (with a rest interval of 30 s and 60 s), and repeating the operation three times.

[0187] In addition to the addition of proteinase K (Zymo, D3001-2), lysozyme (Tiangen Lysozyme RT401) was added, and the lysis temperature was increased from 56°C to 100°C to further improve the efficiency of Firmicutes nucleic acid lysis.

[0188] Experimental results:

[0189] Tested, reference Figure 1 As shown, compared with C001, the optimized conditions of C002 consistently detected Mycobacterium tuberculosis, Cryptococcus neoformans, and Saccharomyces cerevisiae, with significantly higher pathogen read counts (P < 0.01). There were no significant differences in the detection of the remaining 10 pathogens. The C002 conditions were determined to be the stable reaction system for mTGS cell disruption.

[0190] Example 2

[0191] This example is based on the experiment in Example 1 and investigates the optimization of the fragment screening conditions.

[0192] Experimental methods:

[0193] The detection performance of the reference product before fragment screening optimization (represented by R001) and after optimization (represented by R002) was compared and verified.

[0194] Among them, R001 represents the use of conventional pre-mTGS method; R002 represents the use of optimized mTGS detection method, in which only the fragment screening conditions are optimized relative to the conventional method.

[0195] Optimization of fragment screening conditions: In step (3) library preparation, the repair enzyme with only "end repair" capability (Norvozyme N203) was replaced with a repair enzyme with "end repair + nick repair" capability (Norvozyme N210), which better connects short fragment sequences into long fragments. In addition, the ratio of magnetic beads (Norvozyme N411) used for purification in library construction was increased from 0.4X to 0.7X to better retain sequence fragments less than 1000bp.

[0196] Experimental results:

[0197] After testing, under R001 conditions, the sequencing read length of the reference sample fragments was concentrated in the range of 200-300bp, while under R002 conditions, the sequencing read length increased, mostly concentrated in the range of 1000bp, and the number of long fragments >3Kb increased significantly ( Figure 2 ), which was consistent with expectations. The R002 conditions were determined to be stable for subsequent use as the reaction system conditions for mTGS fragment screening.

[0198] Example 3

[0199] This example is based on the experiment of Example 1 and investigates the optimization of data volume and conditions for removing human sources.

[0200] Experimental methods:

[0201] Different sequencing data volume groups were set up to investigate the detection of reference products. In addition, different dehumanization treatment conditions were set up to investigate the dehumanization process. Specific investigation items include:

[0202] (1) Different data sizes include: 400MB, 600MB, 800MB, 1000MB and 1200MB.

[0203] (2) The optimized conditions for different dehumanization treatments are as follows: in step (1), after liquefaction treatment, compared with the conventional pre-mTGS method (complete dehumanization operation), the following different operation groups are added:

[0204] Group 1: The reference sample was divided into two equal parts at a volume ratio of 1:1, one part was treated with human origin removal, and the other part was not treated with human origin removal. The two parts were then combined into one (1:1 human origin removal) during extraction.

[0205] Group 2: The reference sample was divided into two parts at a volume ratio of 1:4, with 1 / 5 not treated with human origin removal and 4 / 5 treated with human origin removal. The two parts were then combined into one (1:4 human origin removal) during extraction.

[0206] Group 3: No human-derived DNA removal (1:0 no human-derived DNA removal). The library construction and machine loading procedures for the three groups of experiments were exactly the same.

[0207] Experimental results and analysis:

[0208] 1:0 not to human source (the third group, Figure 3 A), 1:1 dehumanization (Group 1, Figure 3 B) and 1:4 dehumanized (Group 2, Figure 3 Under conditions C), the number of reads detected for various pathogens increased linearly with sequencing data volume. The most significant increase in reads detected for Enterococcus faecalis, Lactobacillus fermentum, Mycobacterium avium, and Pseudomonas aeruginosa was observed. Based on multiple factors, including assay performance, analytical requirements, sequencing cost, and sequencing time, 800MB of sequencing data may be the optimal mTGS data volume, which will be further validated with clinical samples.

[0209] Under the same data volume of 800MB, we further evaluated the impact of different dehumanization treatment conditions on the detection of various pathogenic microorganisms in the reference products and found that compared with 1:0 non-dehumanization and 1:1 dehumanization, the 1:4 dehumanization condition had the best detection of various pathogenic microorganisms, among which the number of detection reads of Listeria monocytogenes (P < 0.01), Staphylococcus aureus (P < 0.01), Lactobacillus fermentum (P < 0.05), Bacillus subtilis (P < 0.01), Saccharomyces cerevisiae (P < 0.01), and Cryptococcus neoformans (P < 0.01) increased significantly. However, all 13 pathogenic microorganisms could be stably detected under all three conditions ( Figure 4 ).

[0210] Based on the complexity of dehumanization and the objective risk of removing pathogenic microorganisms to a certain extent, we tend to not use dehumanization as a pretreatment reaction system condition for mTGS and conduct further verification on clinical samples.

[0211] The overall results of the reference sample sequencing were summarized based on the test data under different fragment screening treatments, human-removal ratios, and data volume conditions. The analysis showed that under the R002 fragment screening condition, the effects of different human-removal treatments on the average read length of the data were not significantly different, but under the 1:4 human-removal treatment, the average read length of the sequencing fragments was the most concentrated, and the fragment analysis effect was the best ( Figure 5 ); Under the 1:4 dehumanization treatment condition, the average read length of sequencing fragments under R002 screening treatment increased significantly compared with R001 ( Figure 2 、 Figure 5 ).

[0212] Based on the analysis of the results in Examples 1-3, C002 cell wall disruption treatment, R002 sheet screening treatment, 1:0 non-human dehumanization treatment, and 800MB data volume were used as the reaction system conditions for mTGS and clinical sample verification was performed.

[0213] As can be seen from the above experiments, under the C002 cell wall breaking conditions and R002 sheet screening conditions, different dehumanization treatments have significant differences in the effects on the detection of pathogenic microorganisms in clinical samples. Among them, the 1:0 non-dehumanization treatment can significantly improve the detection of pathogenic microorganisms, especially pathogenic microorganisms with low abundance, such as Mycobacterium tuberculosis, Chlamydia psittaci, and Pneumocystis jiroveci ( Figure 6 ).

[0214] Under different data volumes, it was found that the number of reads detected for pathogens such as Pneumocystis jiroveci, Candida albicans, and cytomegalovirus was basically stable under the condition of 800MB data volume. Further increasing the data volume would not significantly increase the number of pathogen detection reads. Therefore, 800MB data volume is the plateau period for nanopore mTGS detection of clinical samples ( Figure 7 ), further increasing the amount of data will further increase the sequencing cost and sequencing time, which has no economic value.

[0215] Overall, it was determined that 1:0 without human de-humanization and 800MB of data volume can be used as the optimal reaction system conditions for mTGS testing of clinical samples and clinical evaluation research can be carried out.

[0216] Example 4

[0217] In this example, the mTGS detection method and detection limit were investigated:

[0218] In this experiment, combined with the results of the reference product and 8 clinical sample pre-experimental tests, C002 cell wall disruption, R002 plate screening, 1:0 non-human depletion treatment, and 800MB of data were used as the reaction system conditions for mTGS detection. The experimental flow chart of the Pre-mTGS and mTGS methods was also compared. Figure 8 .

[0219] Based on Example 1, the detection limits of Pre-mTGS and mTGS were verified using reference substances.

[0220] The results showed that mTGS was more effective than Pre-mTGS in detecting Bacillus subtilis, Mycobacterium tuberculosis, Mycobacterium avium, Saccharomyces cerevisiae, Cryptococcus neoformans and HPV, with a detection limit of 10 4 copies / mL increased to 10 3 copies / mL, the detection limit of Listeria monocytogenes, Lactobacillus fermentum and Salmonella enterica was increased from 10 3 copies / mL increased to 10 2 The detection sensitivity of the two methods was improved by more than 10 times, while the detection limits of other pathogens remained the same (Table 1).

[0221] Table 1. Detection limits of Pre-mTGS and mTGS for various pathogenic microorganisms in reference products

[0222]

[0223] Example 5

[0224] In this embodiment, the sample to be tested was prepared, and different detection methods were investigated.

[0225] 1. Subjects of investigation:

[0226] In this example, 156 bronchoalveolar lavage fluid specimens and clinical data from patients diagnosed with pneumonia were collected for performance evaluation. Of these, 135 patients were confirmed to have been infected, and a total of 188 pathogens were identified. These criteria were used to evaluate the detection efficacy and pathogen detection performance of various methodologies.

[0227] 2. Samples and laboratory testing:

[0228] All patients underwent bronchoscopy, and bronchoalveolar lavage fluid was collected and packaged for testing using CMTs, mNGS, conventional Pre-mTGS, and optimized mTGS.

[0229] 3. Statistical analysis

[0230] All sequencing sample results were analyzed using R software. Normally distributed quantitative data were expressed as median (M) and upper and lower quartiles (P25, P75) for those that did not. Comprehensive clinical diagnosis was used as the reference standard, and the Pearson chi-square test or Fisher's exact test was used to compare the diagnostic performance of CMTs, mNGS, pre-mTGS, and mTGS. All tests were two-tailed, and P < 0.05 was considered statistically significant. 156 samples were analyzed and statistically analyzed, and the results were presented using Venn diagrams, heat maps, and bar charts.

[0231] Experimental results and analysis:

[0232] 4. Clinical efficacy of CMTs, mNGS, pre-mTGS, and mTGS

[0233] Compared with CMTs, the sensitivity and negative predictive value of mTGS were significantly better than those of CMTs (82.96% vs. 43.70%, P < 0.01; 37.84% vs. 20.00%, P < 0.01), and the specificity was significantly lower than that of CMTs (76.19% vs. 90.48%, P < 0.01). However, the positive predictive value of mTGS was comparable (95.45% vs. 96.72%, P = 0.9). ); Compared with mNGS, the detection sensitivity of mTGS was slightly better than that of mNGS, but the difference was not significant (82.96% vs. 77.78%, P = 0.21; 95.45% vs. 94.12%, P = 0.91). The detection specificity and negative predictive value of mTGS were better than those of mNGS (76.19% vs. 66.67%, P < 0.05; 37.84% vs. 34.78%, P < 0.01).

[0234] Compared with the optimized Pre-mNGS method, the detection sensitivity and negative predictive value of the mTGS method were significantly improved (82.96% vs. 48.89%, P < 0.01; 37.84% vs. 20.69%, P < 0.01), and the positive predictive value was not significantly different between the two methods. However, the detection specificity of Pre-mNGS was superior (66.67% vs. 85.71%, P < 0.01) (Table 3).

[0235] Table 2. Diagnostic performance of CMTs, mNGS, Pre-mTGS, and mTGS for pathogen identification

[0236]

[0237] 5. CMTs, mNGS, Pre-mTGS, and mTGS pathogen detection results

[0238] Compared with CMTs, mTGS identified more bacteria, fungi, viruses and other pathogens (mTGS vs. CMTs): bacteria (68 vs. 35), fungi (57 vs. 17), viruses (15 vs. 8), and other types of pathogens including Mycoplasma pneumoniae and Chlamydia psittaci (4 vs. 0).

[0239] Using the mTGS method in this application, Pre-mTGS detected more bacteria, fungi, viruses and other pathogens (mTGS vs. Pre-mTGS): bacteria (68 vs. 42), fungi (57 vs. 22), viruses (15 vs. 12), mycobacteria (35 vs. 14), other pathogens (4 vs. 1), and the overall detection level was better than that of mNGS (mTGS vs. mNGS): bacteria (68 vs. 62), fungi (57 vs. 56), viruses (15 vs. 15), mycobacteria (35 vs. 28), other pathogens (4 vs. 5). Figure 9 .

[0240] Specifically, in terms of detecting responsible pathogenic microorganisms, the overall detection performance of mTGS and mNGS is comparable, but the detection of individual pathogens has its own advantages and disadvantages: Mycobacterium tuberculosis, non-tuberculous mycobacteria, Pseudomonas aeruginosa, and Streptococcus pneumoniae are the top four bacteria detected by both ( Figure 10 a); Pneumocystis jiroveci, Aspergillus, and Cryptococcus neoformans are the top three fungi detected in both ( Figure 10 b); Cytomegalovirus and Epstein-Barr virus are the most detected viruses ( Figure 10 c); For pathogens such as Mycoplasma hominis and Aspergillus, mNGS has a higher detection rate ( Figure 10 c. Figure 10 d); For pathogenic microorganisms such as Mycobacterium tuberculosis, Cryptococcus neoformans, and Pseudomonas aeruginosa, the detection rate of mTGS is higher ( Figure 10 a. Figure 10 b), and the optimized mTGS method has a significant improvement in the overall detection performance of the responsible pathogens compared with the pre-mTGS method before optimization ( Figure 10 ).

[0241] 6. Clinical diagnostic efficacy of CMTs, mNGS, Pre-mTGS, and mTGS

[0242] In this experiment, based on the 188 pathogens commonly found in 135 samples with confirmed infection, the clinical diagnostic consistency of CMTs, mNGS, Pre-mTGS, and mTGS (the method provided in this application) was evaluated according to the final clinical interpretation results.

[0243] The results showed that the nanopore mTGS-based pathogen detection method provided in this application had a complete consistency of 70.40% with clinical diagnosis, a partial consistency of 11.10%, and an overall consistency of 81.50%. The overall clinical consistency of mTGS was significantly improved by 31.90% compared with the pre-mTGS method (81.50% vs. 49.60%, P < 0.01).

[0244] mTGS (the method provided in this application) has better consistency with clinical diagnosis than mNGS, but the difference between the two is not significant (81.50% vs. 74.80%, P = 0.14), but both are significantly better than the conventional CMTs method (81.50% vs. 40.8%, P < 0.01; 74.80% vs. 40.8%, P < 0.01) ( Figure 11 ).

[0245] Analysis: Respiratory tract infections remain the world's deadliest infectious diseases. Delayed diagnosis of their etiology can lead to inappropriate use of broad-spectrum antibiotics, resulting in adverse treatment outcomes, longer hospital stays, and higher medical expenses. Rapid and accurate diagnosis is of great significance for achieving precise use of antibiotics, improving treatment outcomes, and optimizing antimicrobial management.

[0246] Building on previous research, this experiment optimized the nanopore metagenomic sequencing process (Pre-mTGS) for respiratory alveolar lavage fluid samples. Technological innovations were made in cell wall disruption, sieving, and human-derived DNA removal, and quality control parameters such as sequencing data volume were studied, leading to the initial construction of a relatively standardized mTGS detection reaction system. Compared to the basic process, mTGS achieved a tenfold improvement in the detection limit for pathogens such as Bacillus subtilis, Mycobacterium tuberculosis, Mycobacterium avium, Cryptococcus neoformans, and HPV (Table 1).

[0247] The diversity and structural differences of microorganisms lead to different efficiency of nucleic acid release. Especially for Gram-positive bacteria or fungi, the cell walls are relatively thick, and conventional wall-breaking methods are difficult to completely release nucleic acids, which affects the detection effect and may even lead to false negative results. Studies have shown that efficient wall breaking can improve the detection efficiency of 9 common bacteria and fungi to varying degrees, but another study found that physical bead milling will reduce the detection of RNA viruses. To this end, based on Pre-mTGS, we innovatively combined the dual wall-breaking technology of "physical vibration + biological enzymatic hydrolysis", which not only significantly improved the detection efficiency of thick-walled microorganisms such as Mycobacterium tuberculosis and Cryptococcus neoformans, but also ensured that the detection of other pathogens was not affected ( Figure 1 ).

[0248] It is worth noting that nanopore metagenomics technology has limitations in virus detection, which is mainly due to the small size of the viral genome and its fragility during extraction. To overcome this problem, we optimized a long fragment screening technology to better connect short sequences into long fragments that can be better captured by nanopore sequencing technology. After optimization, the average read length of the sequenced fragments reached about 1000bp, and the number of long fragments >3Kb was significantly increased ( Figure 2This technological innovation will greatly improve the accuracy and specificity of pathogen identification, and is particularly important for the detection of low-abundance pathogens.

[0249] Human nucleic acid in the sample is another major factor affecting the sensitivity of metagenomic pathogen detection. Although removing human nucleic acid can improve detection sensitivity, excessive human-derived nucleic acid removal may also lead to the loss of special or low-abundance pathogens. To this end, a detailed exploration of human-derived nucleic acid removal was conducted under different data volume conditions. The experiment showed that under the 1:4 human-derived nucleic acid removal condition, the detection of various pathogenic microorganisms was the most ideal ( Figure 4 However, for real clinical samples, different dehumanization treatment strategies have a significant impact on the detection of pathogenic microorganisms. Among them, the 1:0 non-dehumanization treatment can significantly improve the detection of pathogens such as Mycobacterium tuberculosis, Chlamydia psittaci, and Pneumocystis jiroveci ( Figure 6 This may be related to the reference sample's simple species structure, containing only clearly defined, high-abundance human and pathogenic microbial cells, few interfering factors, and excluding easily lost pathogens such as Pneumocystis jiroveci and RNA viruses. In contrast, clinical samples are more complex and diverse, with many interfering factors. Human nucleic acid removal significantly impacts pathogen detection sensitivity, and the content of human nucleic acid varies greatly between samples. The removal ratio needs to be adjusted based on the abundance and degree of pathogen nucleic acid contamination. Considering the complexity and potential risks of human nucleic acid removal, the ultimate decision was to retain human nucleic acid in the mTGS pretreatment system to ensure the comprehensiveness and accuracy of test results.

[0250] Furthermore, the data volume quality control standard for second-generation mNGS has been established as 20M reads per sample, but there are no guidelines recommending data volumes for nanopore metagenomic sequencing. In this experiment, we thoroughly explored the performance of different sequencing data volumes for reference and pilot samples. The results showed that the number of reads detected for various pathogens in the reference sample gradually increased with increasing sequencing data volume. However, for clinical pilot samples, such as Pneumocystis jiroveci, Candida albicans, and cytomegalovirus, the number of reads detected plateaued at 800MB of data volume, and further increasing the data volume did not significantly improve pathogen detection rates. The 800MB data volume demonstrated comparable sensitivity to the 20M reads of second-generation metagenomic sequencing during performance verification for various pathogens. Therefore, an 800MB data volume can meet the performance and technical requirements for metagenomic identification of clinical pathogens. Furthermore, at this data volume, the cost of nanopore mTGS sequencing per sample drops to a few hundred yuan, while sequencing time is reduced to less than 2 hours. This means that although further increasing the amount of data may obtain more sequence information, it will significantly increase sequencing costs and time, while the improvement in pathogen detection is not significant and has no economic value.

[0251] In summary, after improving the cell wall breaking and sieving conditions, as well as clarifying the data volume and human-source removal conditions, a stable mTGS process system was successfully established ( Figure 8 ). The detection performance of this system in bronchoalveolar lavage fluid has been comprehensively evaluated by multiple methods, and the results are encouraging. A total of 188 pathogens were identified from 135 patients with a clear diagnosis. Compared with CMTs, mTGS showed higher pathogen recognition ability, with a 39.26% increase in detection sensitivity; compared with Pre-mTGS, the detection sensitivity of mTGS increased by 34.07%; compared with mNGS, mTGS achieved a comparable level of pathogen detection sensitivity, and both maintained a high level of consistency with clinical practice (81.50% vs. 74.80%), both significantly outperforming traditional microbial detection. However, the two have their own advantages and disadvantages in the detection of individual pathogens. Among them, mNGS has a higher detection rate for Mycoplasma hominis and Aspergillus, while mTGS has a higher detection rate for Mycobacterium tuberculosis, Cryptococcus neoformans, and Pseudomonas aeruginosa. The reasons may be: (1) mNGS has a dehumanization step during the detection process, and some intracellular bacteria are easily dehumanized, such as Mycobacterium and Salmonella; (2) the pathogen content in some samples is very low, and nanopore sequencing technology has the characteristics of no amplification and long read length sequencing, which can more effectively identify low-abundance pathogens. However, after mNGS is lysed, a small number of short sequence fragments are not enriched and cannot be effectively and accurately identified, resulting in false negatives. In summary, compared with mNGS, mTGS has simpler experimental operations, shorter sequencing time, and similar costs. In addition, nanopore technology has unique advantages in rapid diagnosis of infectious pathogens and drug resistance analysis due to its long read length, rapid sequencing, and real-time analysis. It is recommended as a powerful tool for rapid diagnosis of respiratory infection bronchoalveolar lavage fluid pathogens.

[0252] In summary, this application provides a method for detecting pathogenic microorganisms based on nanopore mTGS. A relatively standardized mTGS process system and related parameters have been initially established, and clinical samples have confirmed that mTGS significantly improves the performance of pre-mTGS in detecting pathogens of lung infections. Furthermore, mTGS and mNGS have similar detection performance in detecting pathogens in the BALF of patients with lung infections, and both are significantly superior to CMTs. The mTGS method provided in this application can serve as a reliable means for the detection of pathogens of lung infections.

[0253] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for detecting pathogenic microorganisms based on nanopore mTGS, characterized in that: include: Collecting a sample to be tested and subjecting it to liquefaction treatment to obtain a pretreated sample; Adding a lysis solution to the pretreated sample and performing a wall-breaking grinding process based on a three-dimensional vibration mode to extract a nucleic acid sample; Performing end-repair treatment on the nucleic acid sample and completing library preparation to obtain a labeled sample; Sequencing the labeled sample, and performing database comparison based on the sequencing results to obtain a comparison result; The comparison result is subjected to classification and labeling processing of colonizing bacteria and / or pathogenic bacteria according to the sample source type of the sample to be tested, and a detection result corresponding to the sample to be tested is obtained based on the classification result of the classification and labeling processing.

2. The method for detecting pathogenic microorganisms based on nanopore mTGS according to claim 1, wherein: The method of collecting the sample to be tested and subjecting it to liquefaction treatment to obtain a pre-treated sample further comprises: Taking the liquefied sample and performing a human nucleic acid removal process, including: dividing it into a treated sample and a non-treated sample according to volume; wherein the treated sample accounts for 80% of the total volume; the non-treated sample accounts for 20% of the total volume; performing a human nucleic acid removal operation on the treated sample to obtain a post-processed sample; and mixing the post-processed sample and the non-processed sample to obtain the pre-processed sample; Alternatively, the sample after liquefaction treatment is taken and subjected to human origin removal treatment, including: dividing the sample into a treated sample and a non-treated sample according to the volume; wherein the treated sample accounts for 50% of the total volume; the non-treated sample accounts for 50% of the total volume; performing a human nucleic acid removal operation on the treated sample to obtain a post-processed sample; and mixing the post-processed sample and the non-processed sample to obtain the pre-processed sample. Alternatively, the liquefied sample is used as the pre-treated sample without undergoing the humanization operation.

3. The method for detecting pathogenic microorganisms based on nanopore mTGS according to claim 2, wherein: The human nucleic acid removal operation includes: The treated sample is centrifuged to remove the supernatant, and the host-free solution is added to perform mixed lysis; The mixed lysate solution was centrifuged to remove the supernatant, and digestion buffer and digestion enzyme were added to the precipitate for digestion only; DNA protective groups were added, and after incubation at room temperature, the human nucleic acid removal operation was completed.

4. The method for detecting pathogenic microorganisms based on nanopore mTGS according to claim 1, wherein: The pre-treated sample is added with a lysis solution and then subjected to a wall-breaking grinding process based on a three-dimensional vibration mode to extract a nucleic acid sample, comprising: adding lysis solution and lysozyme to the pretreated sample; Grinding is performed using a tissue grinding and homogenizing device based on a three-dimensional vibration mode; Proteinase K was added and lysis was carried out at 100°C for 30 minutes; The supernatant was collected by centrifugation, a binding solution was added, and the mixture was purified by centrifugal column to obtain the nucleic acid sample.

5. The method for detecting pathogenic microorganisms based on nanopore mTGS according to claim 4, characterized in that: The grinding method is to control the tissue grinding and homogenizing device to continuously repeat the following process for three cycles: Each cycle consisted of 60 seconds of continuous vibration at 5 m / s, followed by a 30-second rest period.

6. The method for detecting pathogenic microorganisms based on nanopore mTGS according to claim 1, characterized in that: The nucleic acid sample is subjected to end repair treatment and library preparation is completed to obtain a labeled sample, including: Adding a repair enzyme with end-repair and nick-repair capabilities to the nucleic acid sample to perform nucleic acid end-repair to obtain repaired nucleic acid; The repaired nucleic acid is mixed with a PCR tag adapter, and the nucleic acid connected to the adapter is used as a template, and a PCR amplification reagent is added to perform a PCR amplification reaction to obtain an amplified product; The amplified product is subjected to magnetic bead purification to obtain a purified product; Perform Qubit quantification and mixing on the purified product; The nucleic acid in the purified product after Qubit quantification and mixing is connected to the sequencing adapter to obtain the labeled sample.

7. The method for detecting pathogenic microorganisms based on nanopore mTGS according to claim 6, characterized in that: The magnetic bead purification process comprises: According to the volume of the amplified product, magnetic beads are added and mixed to obtain a mixture; The mixture is subjected to magnetic bead adsorption separation, and after washing, drying, and elution, the purified product is obtained; Preferably, the added volume of the magnetic beads is 0.7 times the volume of the amplified product.

8. The method for detecting pathogenic microorganisms based on nanopore mTGS according to claim 7, characterized in that: The amount of sequencing data for sequencing the labeled samples is controlled at 800MB.

9. The method for detecting pathogenic microorganisms based on nanopore mTGS according to claim 1, wherein: The comparison result is subjected to classification and labeling processing of colonizing bacteria and / or pathogenic bacteria according to the sample source type of the sample to be tested, and a detection result corresponding to the sample to be tested is obtained based on the classification result of the classification and labeling processing, including: Performing error value filtering on the comparison result to obtain a screening result; Performing classification labeling of colonizing bacteria and / or pathogenic bacteria on the screening results to obtain classification labels; Determining whether the classification mark contains a target pathogenic bacteria mark; If so, the target pathogen marker is used as the detection result.

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