TWI865877B - Diagnostic system and method for the enrichment of microbial nucleic acids and the identification of microorganisms and/or antimicrobial resistance genes by immobilized adsorption - Google Patents

Abstract

Provided is a diagnostic system for identifying target microorganisms and/or antimicrobial resistance genes in a sample, including a cell lysis unit, a target nucleic acid enriching unit, a sequencing unit, and a sequence analyzing unit connected to the sequencing unit, wherein the cell lysis unit is configured to lyse non-target cells in the sample, the target nucleic acid enriching unit equipped with an immobilized adsorption device is configured to remove the nucleic acids of the non-target cells and to enrich the nucleic acids of the target microorganisms, and the sequencing unit and the sequence analyzing unit are configured to produce identification results of the microbial species and/or antimicrobial resistance genes from the sequences of the enriched nucleic acids of the target microorganisms. Also provided is a method for enriching target nucleic acids and a method for identifying microorganisms and/or antimicrobial resistance genes.

Description

Detection system and method for enriching microbial nucleic acid and identifying microorganisms and/or drug resistance genes by solid phase adsorption

The present disclosure relates to a detection system for identifying microorganisms and/or drug-resistant genes based on enriched target nucleic acids, which involves a method for removing non-target nucleic acids from a sample to enrich target (e.g., microorganism) nucleic acids through the principle of solid phase adsorption.

Rapid and accurate pathogen and resistance identification is critical to improving patient health and addressing the problem of microbial resistance. Currently, the "gold standard" for clinical diagnosis is phenotypic analysis based on microbial culture. However, this diagnostic method takes at least 24 hours to several days from bacterial growth to initial results analyzed in the clinical microbiology laboratory. For patients with diseases that may rapidly deteriorate and become life-threatening, such as bacteremia, sepsis, and pneumonia, this method cannot provide immediate guidance in the early stages. As a result, septic patients often face ineffective or excessive antibiotic treatment, suffer from antibiotic-related toxicity, or face the threat of various multi-drug resistant pathogens due to the evolution of resistant pathogens due to inappropriate use of antibiotics.

Currently, the technology suitable for rapid detection of pathogens is nucleic acid amplification technology (NAAT), which has been applied to the diagnosis of sepsis (such as Septifast test (Roche Diagnostics)) and respiratory tract infections (such as FilmArray respiratory test (Biofire Defense)). However, nucleic acid amplification technology is limited by primer design, so it can only plan different target pathogens and drug resistance genes for different samples. Taking FilmArray's Blood Culture Identification 2 (BCID2) platform as an example, it can only detect 33 preset target pathogens and 10 drug resistance genes, but cannot fully cover a wide variety of pathogens and drug resistance genes, let alone identify rare pathogens and special drug resistance genes, which makes it impossible for nucleic acid amplification technology to completely replace traditional culture methods. Therefore, there is an urgent need for a universal diagnostic technology that can quickly detect pathogens (such as viruses, bacteria and fungi) and as many drug resistance genes as possible.

Recently, it has been reported that the use of next-generation DNA sequencing (NGS) for microbial metagenomic sequencing and shotgun sequencing can obtain information about pathogens. For example, the Illumina sequencing platform has been shown to be an advanced technology with high sensitivity and specificity in pathogen identification research. However, Illumina sequencing uses short-read sequencing of no more than 300 base pairs, which is not easy to assemble into a complete pathogen gene sequence and is easily interfered by the human genome and colony, thus reducing its sensitivity and specificity. Therefore, it is not easy to identify the drug-resistant genes of pathogens from samples with too high a proportion of human nucleic acid. In addition, the next-generation sequencing technology platform based on Illumina takes dozens of hours to obtain sequencing results with higher pathogen genome coverage, and its analysis process is relatively complicated and costly, making it impossible to provide real-time analysis results to meet clinical needs.

Clinical samples or blood cultures are challenging samples for total genome sequencing because they usually contain a large amount of nucleic acid from humans or other animals. Therefore, only a small amount of sequences can be used to identify pathogenic bacteria and drug resistance genes, and the low abundance of target DNA sequences may lead to low sensitivity in detecting pathogens. In addition, filtering out host sequences from a large amount of raw data is very time-consuming and highly dependent on the computing power of computer equipment.

Several methods have been developed to remove non-target nucleic acids during sample processing. The MolYsis Basic 5 Kit (Molzym, Bremen, Germany) uses nucleases to degrade non-target nucleic acids; however, the extracted bacterial nucleic acid fragments are relatively short, making it difficult to generate long reads. The NEBNext Microbial Genomic DNA Enrichment Kit (New England Biolabs, Inc., USA) uses monoclonal antibodies that specifically bind to methylated CpG islands in the human genome; however, the methylation distribution on human genes is not uniform, and this kit is not cost-effective for routine testing. The QIAamp BiOstic Bacteremia DNA Kit (QIAGEN, Hilden, Germany) uses multiple centrifugation steps to separate host cells based on differences in cell density.

However, there is still a need to provide a rapid and cost-effective strategy to detect pathogens, for example in clinical settings, while simultaneously detecting characteristics associated with their pathogenicity and antibiotic resistance.

In view of this, the present disclosure provides a system and method for removing non-target nucleic acids from a sample by solid phase adsorption to enrich the target nucleic acid. The system and method provided by the present disclosure have various applications, such as identifying pathogen types and drug resistance genes after pre-treatment of biological samples from a host.

In at least one embodiment of the present disclosure, a method for enriching target nucleic acids (e.g., bacterial nucleic acids) in a sample is provided, the method comprising: providing a sample containing a target microorganism and non-target cells, wherein the target microorganism and the non-target cells are derived from different species; adding a non-ionic surfactant to the sample to lyse the non-target cells and release the non-target nucleic acids contained therein; contacting the sample with a solid phase adsorbent to bind free nucleic acids in the sample; and removing the solid phase adsorbent and the nucleic acids bound thereto, thereby enriching the target nucleic acids contained in the target microorganism in the sample.

In at least one specific embodiment of the present disclosure, a method for enriching a target nucleic acid in a sample is provided, comprising: providing a sample containing a target microorganism and a non-target cell, wherein the target microorganism and the non-target cell are derived from different species; lysing the non-target cells in the sample and releasing the non-target nucleic acid contained therein by a cell lysis unit of a detection system; and removing the non-target nucleic acid by a target nucleic acid enrichment unit of the detection system, thereby enriching the target nucleic acid contained in the target microorganism in the sample. In some specific embodiments, the target nucleic acid enrichment unit disclosed herein comprises a solid-phase nucleic acid adsorption device having a solid-phase adsorbent, and the removal of the non-target nucleic acid comprises: contacting the sample with the solid-phase adsorbent so that the non-target nucleic acid binds to the solid-phase adsorbent; and removing the solid-phase adsorbent to enrich the target nucleic acid.

In at least one specific embodiment of the present disclosure, the solid phase adsorbent is selected from the group consisting of silica beads, silica magnetic beads, column extraction filter membranes, alkyl bonded silica gels, biochar, cellulose, anion exchange resins, and any combination thereof. Hydrogen bonds, hydrophobic interactions, and electrostatic interactions between the cationic portion of the adsorbent and the negatively charged phosphate groups of nucleic acids can be the driving force for binding. In some specific embodiments, the solid phase adsorbent can be silica magnetic beads or based on silica magnetic beads. In some specific embodiments, the solid phase adsorbent can be controlled by any salt and pH value, for example, the solid phase adsorbent adsorbs nucleic acids in an alkaline environment. In some embodiments, the surface of the silicon-containing magnetic beads can be further modified with a silane polymer, wherein examples of the silane polymer include, but are not limited to, tetramethoxysilane (TMOS), tetraethoxysilane (TEOS) and 3-aminopropyltriethoxysilane (APTES). In some embodiments, the solid phase adsorbent used in the present disclosure does not include an antibody. In some embodiments, the method of the present disclosure does not include adsorbing or removing non-target nucleic acids or free nucleic acids in a sample based on the antibody-antigen principle.

In at least one embodiment of the present disclosure, the non-ionic surfactant is selected from the group consisting of saponin, Tween, Triton, polyoxyethylene (10) oleyl ether (e.g., BrijO10), polyols, polyethylene-polypropylene glycol (PEG/PPG), polyoxyethylene ethers, alkylethanolamides, glucosides, fatty alcohols, and any combination thereof. In some embodiments, the method further comprises incubating the non-ionic surfactant and the sample under alkaline conditions to lyse the non-target cells and separate the non-target nucleic acid from the non-target cells.

In at least one embodiment of the present disclosure, the target nucleic acid may be at least one of a microbial nucleic acid, a bacterial nucleic acid, a viral nucleic acid, a fungal nucleic acid, an algal nucleic acid, a protozoan nucleic acid, a pathogen nucleic acid, and a parasite nucleic acid. In some embodiments, the target nucleic acid may be a bacterial nucleic acid. In some embodiments, the target nucleic acid may be derived from bacteria, such as antibiotic-resistant bacteria. In some embodiments, the target nucleic acid may be a mycoplasma or a fragment thereof, such as an antibiotic resistance gene.

In at least one embodiment of the present disclosure, the non-target cell belongs to a eukaryotic host, such as an animal host. In some embodiments, the non-target nucleic acid is derived from an animal host. In some embodiments, the animal host is a mammalian host. In some embodiments, the sample contains mammalian host nucleic acid and nucleic acid derived from a pathogen in the mammalian host. In some embodiments, the sample is obtained from a human host and includes human host nucleic acid and non-human nucleic acid.

In at least one embodiment of the present disclosure, the sample may be an environmental sample, such as dust, soil, water, air, artificial water system or food sample. In some embodiments, the sample may be a biological sample obtained from a host suffering from or suspected of suffering from an infectious disease. In some embodiments, the infectious disease includes, but is not limited to, bacteremia, sepsis and pneumonia.

In at least one specific embodiment of the present disclosure, a detection system for identifying target microorganisms and/or drug-resistant genes in a sample is also provided, the detection system comprising: a cell lysis unit for lysing non-target cells in the sample, wherein the target microorganism and the non-target cells are derived from different species; a target nucleic acid enrichment unit comprising a solid-phase nucleic acid adsorption device for removing nucleic acids from the non-target cells after cell lysis, thereby enriching the target nucleic acid; The nucleic acid of the target microorganism in the sample; a sequencing coding unit for decoding the sequence of the nucleic acid of the target microorganism in the sample; and a sequence analysis and comparison unit, which is connected to the sequencing coding unit, for receiving the decoded data generated by the sequencing coding unit and comparing the decoded data with a microbial gene database and/or a drug-resistant gene database to obtain the identification result of the target microorganism and/or the drug-resistant gene.

In at least one embodiment of the present disclosure, the solid-phase nucleic acid adsorption device comprises a solid-phase adsorbent, and the cell lysis unit comprises a non-ionic surfactant. In some embodiments, the lysis of the non-target cells is performed in an alkaline environment. In some embodiments, the solid-phase adsorbent used in the present disclosure does not comprise an antibody. In some embodiments, the solid-phase nucleic acid adsorption device does not include an antibody-antigen principle to adsorb or remove non-target nucleic acids or free nucleic acids in the sample.

In at least one embodiment of the present disclosure, the target microorganism is selected from the group consisting of bacteria, viruses, fungi, algae, protozoa, pathogens, parasites, and any combination thereof, and the non-target cell belongs to a eukaryotic host.

In at least one embodiment of the present disclosure, the detection system further includes a target microorganism expansion unit for increasing the quantity of the target microorganism and its nucleic acid in the sample. In some embodiments, the target microorganism expansion unit includes a blood culture device.

In at least one specific embodiment of the present disclosure, the sequencing encoding unit is at least one of a next-generation sequencing platform, a high-throughput sequencing platform, a nanopore sequencing platform, a PacBio sequencing platform, and a Sanger sequencing platform.

In at least one specific embodiment of the present disclosure, the decoded data to be compared is subjected to the following procedures through microbial species comparison software and/or microbial drug resistance gene species determination software to identify microbial species, drug resistance genes and/or predict antimicrobial resistance (AMR): obtaining an index value of a sequence of a preset length in the decoded data to be compared; correcting and assembling microbial genome and bacterioblastic sequences; reading a corresponding comparison sequence from a reference gene sequence according to the index value; and determining whether the decoded data to be compared and the comparison sequence are identical to generate a determination result.

In at least one embodiment of the present disclosure, the sequence analysis and alignment unit is further used to analyze the drug resistance gene carried by the target microorganism, for example, the antimicrobial drug resistance gene. In some embodiments, the sequence analysis and alignment unit is further used to calculate at least one parameter including the number of effective alignment sequences, coverage, coverage depth, relative abundance and dispersion, so as to obtain the identification result of the type of the target microorganism and/or the drug resistance gene carried by it.

In at least one embodiment of the present disclosure, the sequencing coding unit generates decoded data with a genome coverage depth of at least 20 times the genome size of the target microorganism. In some embodiments, the decoded data generated by the sequencing coding unit within, for example, 15 minutes or the decoded data with a genome coverage depth of at least 1 times the genome size of the target microorganism is used to compare the distribution ratio of microbial populations accounting for more than 1% of the total reads as a basis for the relative abundance of the target microorganism in the sample. In some embodiments, the sequence analysis and comparison unit is further used to identify (for example within 6 hours) the types and quantities of complete drug resistance gene subtypes on the plasmid of the target microorganism and the mutation sites on the chromosome, thereby predicting the resistance of the target microorganism to antimicrobial drugs.

In at least one specific embodiment of the present disclosure, a method for using the above-mentioned detection system is also provided, including: providing a sample containing target microorganisms and non-target cells of different species or originating from different species; lysing the non-target cells in the sample by the cell lysis unit; removing the nucleic acids of the non-target cells after cell lysis by the target nucleic acid enrichment unit, thereby enriching the nucleic acids of the target microorganism in the sample; sequencing the nucleic acids of the target microorganism by the sequencing encoding unit; and obtaining the identification results of the target microorganism and/or the drug-resistant genes carried by it by the sequence analysis and comparison unit.

In at least one embodiment of the present disclosure, the cell lysis unit lyses the non-target cells by adding a non-ionic surfactant to the sample. In some embodiments, the target nucleic acid enrichment unit enriches the nucleic acid of the target microorganism by bringing a solid phase adsorbent into contact with the sample and removing the solid phase adsorbent bound to the nucleic acid of the non-target cells and/or the free nucleic acid in the sample.

In at least one specific embodiment of the present disclosure, a method for identifying a target microorganism and/or a drug-resistant gene is further provided, comprising: providing the target nucleic acid enriched by the aforementioned method, wherein the enriched target nucleic acid has a length of at least 2,000 nucleotides; decoding the sequence of the enriched target nucleic acid by sequencing detection to obtain decoding data; and comparing the decoding data with a microbial gene database and/or a drug-resistant gene database to obtain an identification result of the target microorganism and/or the drug-resistant gene it carries.

In at least one embodiment of the present disclosure, a method for identifying a pathogen in a biological sample is also provided. In some embodiments, the method of the present disclosure includes: providing a biological sample from an individual infected or suspected of being infected with a pathogen; adding a non-ionic surfactant to the biological sample and incubating under alkaline conditions; contacting the biological sample with a solid phase adsorbent to bind non-target nucleic acids from the individual; removing the solid phase adsorbent to enrich the pathogen nucleic acid in the biological sample; and sequencing the enriched pathogen nucleic acid by sequencing. In some embodiments, the solid phase adsorbent further binds free nucleic acids contained in the sample.

In at least one embodiment of the present disclosure, the biological sample is selected from the group consisting of blood, serum, plasma, urine, sputum, saliva, cerebrospinal fluid, interstitial fluid, mucus, sweat, fecal extract, feces, synovial fluid, tears, semen, peritoneal fluid, nipple aspirate, milk, vaginal fluid, and any combination thereof.

In at least one embodiment of the present disclosure, based on the amount of target nucleic acid in the biological sample, the method provided by the present disclosure optionally includes amplifying the microorganism, pathogen, target nucleic acid and/or nucleic acid of the pathogen in the biological sample before adding the non-ionic surfactant. For example, the biological sample is derived from a patient with sepsis and is a blood sample that has been cultured. In some embodiments, the sample suitable for the method of the present disclosure is a blood culture sample that is identified as positive by a continuous monitoring blood culture system (such as a blood sample that uses Gram staining to confirm the presence of microorganisms). In some embodiments, the method provided by the present disclosure further includes removing red blood cells from the blood sample.

In at least one embodiment of the present disclosure, the sequencing assay is selected from the group consisting of a next-generation sequencing assay, a high-throughput sequencing assay, a nanopore sequencing assay, a PacBio sequencing assay, a Sanger sequencing assay, and any combination thereof. In some embodiments, the sequencing assay may be a nanopore sequencing assay.

In at least one embodiment of the present disclosure, the target nucleic acid or pathogen nucleic acid enriched by the method provided by the present disclosure has a length of at least 2,000 nucleotides (nt). For example, the enriched target nucleic acid or enriched pathogen nucleic acid to be sequenced has a length of at least 2,000 nt, at least 2,500 nt, at least 3,000 nt, at least 3,500 nt, at least 4,000 nt, at least 4,500 nt, at least 5,000 nt, at least 5,500 nt, at least 6,000 nt, at least 6,500 nt, or at least 7,000 nt.

In at least one embodiment of the present disclosure, the method provided by the present disclosure results in an enrichment of the target nucleic acid or the nucleic acid of the pathogen initially contained in the biological sample by at least 10 times. For example, the method results in an enrichment of the target nucleic acid or the nucleic acid of the pathogen initially contained in the biological sample by at least 10 times, at least 10 ² times, at least 10 ³ times, at least 10 ⁴ times, or at least 10 ⁵ times. In some embodiments, by the enrichment method provided by the present disclosure, based on the total amount of nucleic acid therein, the nucleic acid of the target nucleic acid or the pathogen accounts for more than 50%, such as more than 55%, more than 60%, more than 65%, more than 70%, more than 75%, more than 80%, more than 85%, more than 90%, more than 95%, and more than 99%.

In at least one embodiment of the present disclosure, the method provided by the present disclosure further includes extracting the enriched pathogen nucleic acid from the biological sample before sequencing. In some embodiments, the method provided by the present disclosure further includes identifying the drug resistance gene carried by the pathogen based on the sequencing results. In some embodiments, the identification of drug resistance genes is performed at a genome coverage depth of at least 20 times (e.g., at least 25 times, at least 30 times, at least 40 times, at least 50 times, at least 60 times, and at least 70 times) the size of the pathogen genome.

In at least one embodiment of the present disclosure, the system and method of the present disclosure can efficiently and selectively remove non-target nucleic acids (e.g., host nucleic acids) to extract high-purity pathogen DNA, which can be used for rapid sequencing and generate long sequence reads for assembling a complete genome of the pathogen. Therefore, the present disclosure can be used to eliminate the interference of non-target nucleic acids and accelerate and enhance bioinformatics analysis, thereby enhancing the effectiveness of identifying pathogen types and their drug resistance genes.

This specification discloses some specific embodiments in detail so that a person with ordinary knowledge in the art can utilize the specific embodiments based on this disclosure. Because many steps or features of the specific embodiments will be obvious to a person with ordinary knowledge in the art based on this disclosure, all steps or features of the specific embodiments are not discussed in detail.

As used in this disclosure, the singular forms "a," "an," and "the" include plural forms unless the context indicates otherwise. As used in this disclosure, the term "and" is intended to be inclusive unless the context indicates otherwise. As used herein, the term "or" is generally used in its sense including "and/or" unless the context indicates otherwise.

As used herein, the term "about" means the degree of deviation of a specified property, composition, amount, value or parameter, such as based on experimental error, measurement error, approximate error, calculation error, standard deviation of the mean, routine fine-tuning, etc.

As used herein, the terms "comprising," "having," "including," and "containing" are to be construed as open-ended terms (ie, meaning "including, but not limited to,") unless otherwise noted.

The present disclosure relates to a method for enriching a target nucleic acid in a sample (e.g., a biological sample from an individual suffering from or suspected of suffering from an infectious disease). In at least one embodiment, the sample includes non-target nucleic acids derived from a host and target nucleic acids derived from a non-host source. In at least one embodiment, the method increases the ratio of target nucleic acids to non-target nucleic acids in the sample by at least 10 times.

As used herein, the terms "patient", "host" and "subject" are used interchangeably. The term "subject" means a human or an animal. Examples of subjects include, but are not limited to, humans, monkeys, mice, rats, woodchucks, ferrets, rabbits, hamsters, cows, horses, pigs, deer, dogs, cats, foxes, wolves, chickens, ducks, ostriches and fish. In some embodiments, the subject is a mammal, such as a primate, such as a human.

As used herein, the term "biological sample" means a sample to be processed or analyzed by any of the methods described herein, which can be any type of sample obtained from an individual to be tested. Biological samples used herein include, but are not limited to: tissue samples (such as tissue sections and tissue puncture sections); cell samples (e.g., cytological smears (such as Pap smears or blood smears) or cell samples obtained by microdissection); samples of whole organisms (such as yeast or bacterial samples); or cell fractions, fragments or organelles (such as those obtained by lysing cells and separating their components by centrifugation or other means). Other examples of biological samples include, but are not limited to, body fluid samples, such as blood, serum, plasma, urine, sputum, saliva, cerebrospinal fluid, interstitial fluid, mucus, sweat, fecal extract, feces, synovial fluid, tears, semen, peritoneal fluid, nipple aspirate, breast milk, vaginal fluid, or any combination thereof. In some embodiments, the blood sample can be whole blood or a fraction thereof, such as serum or plasma, treated with heparin or EDTA to prevent blood clotting.

The method disclosed herein includes adding a non-ionic surfactant (e.g., saponin) to a sample, such as a biological sample including host nucleic acids and non-host nucleic acids. In at least one embodiment, the host nucleic acids and non-host nucleic acids are contained in cells or particles derived from host and non-host sources, respectively. In at least one embodiment, the non-ionic surfactant selectively causes lysis of host cells and their inner membranes, and releases the host nucleic acids so that the host nucleic acids can be partially or completely bound to a solid phase adsorbent. The nucleic acids in non-host cells or particles (e.g., pathogens) remain substantially intact and are not significantly removed from the biological sample, so these nucleic acids can be subsequently collected and analyzed by, for example, sequencing. The non-host nucleic acids processed or analyzed by any of the methods described herein have an average length long enough to be identifiable; that is, the sequence and/or biological source of the non-host nucleic acids can therefore be determined. In at least one embodiment, the non-host nucleic acids enriched by the methods described herein can each have a length of at least 2,000 nucleotides.

Please refer to Figure 1, which shows a schematic diagram of a detection system of at least one specific embodiment of the present disclosure. The detection system 10 disclosed in the present disclosure includes a cell lysis unit 100, a target nucleic acid enrichment unit 200, a sequencing encoding unit 300, and a sequence analysis and comparison unit 400. The cell lysis unit 100 may include a container 101 for accommodating a sample and a non-ionic surfactant 102 disposed toward the container 101, which is used to lyse non-target cells in the sample so that the nucleic acid of the non-target cells is separated from the cells. For example, after blood culture, the blood sample to be tested collected from a human host can be extracted from a blood culture bottle into a centrifuge tube containing a non-ionic surfactant through a three-way sample extraction system.

In at least one specific embodiment of the present disclosure, the target nucleic acid enrichment unit 200 can be connected to the cell lysis unit 100 to receive the cell lysis sample processed by the cell lysis unit 100. The target nucleic acid enrichment unit 200 can include a solid-phase nucleic acid adsorption device 201 and a nucleic acid extraction device 202. The solid-phase nucleic acid adsorption device 201 includes a solid-phase adsorbent for adsorbing and removing nucleic acids of non-target cells after cell lysis, thereby enriching the nucleic acids of the target microorganisms in the sample, and then the nucleic acids of the target microorganisms are extracted by the nucleic acid extraction device 202. For example, please refer to Figure 2, a solid phase adsorbent (such as silicon-containing magnetic beads) can be added to the sample to adsorb free nucleic acids in the sample, and then the solid phase adsorbent is removed by a removal device (such as a magnetic stand) or by using density gradient differences, so that only the target microorganisms remain in the sample, and then the nucleic acid of the target microorganism is extracted.

Please refer to FIG. 1 again. In at least one embodiment of the present disclosure, the sequencing encoding unit 300 can be connected to the nucleic acid enrichment unit 200 to receive the nucleic acid of the target microorganism after being processed by the target nucleic acid enrichment unit 200. In at least one embodiment of the present disclosure, the sequencing encoding unit 300 can include a DNA library preparation kit 301 and a sequencer 302 for decoding the nucleic acid sequence of the target microorganism. In at least one embodiment, the sequencer applicable to the detection system of the present disclosure includes, but is not limited to, a Flongle sequencer and a MinION sequencer.

In at least one specific embodiment of the present disclosure, the sequence analysis and comparison unit 400 can be connected to the sequencing and coding unit 300 to receive the decoded data generated by the sequencing and coding unit 300, which includes the barcode of the preset length subsequence in the sequence to be compared (i.e., the nucleic acid sequence of the target microorganism). In addition, in at least one specific embodiment of the present disclosure, the sequence analysis and comparison unit 400 can include a microorganism identification module 401 and a drug resistance gene identification module 402. In the microorganism identification module 401, the decoded data is compared with the microorganism gene database to obtain the identification result of the target microorganism, and the drug resistance gene identification module 402 further analyzes the drug resistance gene carried by the target microorganism based on the decoded data.

In some specific embodiments of the present disclosure, when determining whether the decoded data and the reference sequence of the microbial gene database are identical, the corresponding sequence to be compared can be read from the reference sequence according to the barcode of the decoded data, and then the base pairs in the sequence to be compared and the reference sequence are compared one by one to determine whether the bases in the sequence to be compared and the reference sequence correspond to each other. When the result of the determination is identical, the index value is used as the position information of the sequence to be compared; when the result of the determination is different, it is determined that there are inserted or deleted base pairs in the sequence to be compared. In at least one specific embodiment, the microbial gene database applicable to the detection system disclosed herein includes Centrifuge and Karken2, which are clinical pathogen databases used to compare bacteria, viruses, fungi, parasites, etc., but are not limited thereto.

In some specific embodiments of the present disclosure, the database used for species sequence comparison includes a pathogen genome database and a pathogen literature database, and the original source can be a public database, such as the National Center for Biotechnology Information (NCBI). Currently, the reference sequences of the microbial gene database include 5,527 species of bacteria and archaea, 1,677 species of viruses, 5,523 species of fungi, and 865 species of parasites, and also include 62,602 species of eukaryotes, totaling 69,836 species.

In some specific embodiments of the present disclosure, the database used for drug resistance gene comparison includes the drug resistance gene database Resfinder 4.0 (Center for Genomic Epidemiology, DTU, Denmark). Currently, the reference sequence of the drug resistance gene database has a total of 2,690 drug resistance genes on plastids and 266 drug resistance gene variant sites on chromosomes, and contains 57 drugs that can predict microbial resistance.

Please refer to FIG. 3 , which shows the operation process of the detection system in at least one specific embodiment of the present disclosure, mainly including steps S1 to S4, which are cell lysis (S1), target nucleic acid enrichment (S2), sequencing encoding (S3) and sequence analysis and comparison (S4), respectively, and each is described as follows:

Cell lysis (S1) involves adding a non-ionic surfactant to a sample collected from the environment or host to lyse non-target cells in the sample;

Target nucleic acid enrichment (S2) involves using a solid phase adsorbent to bind nucleic acids from non-target cells and then extracting the target nucleic acids from the sample after removing the solid phase adsorbent;

Sequencing encoding (S3) includes constructing a sequencing library using a library preparation kit, sequencing it using a sequencer, and then generating decoding data using a base identification program; and

Sequence analysis and comparison (S4) includes comparing the decoded data with a microbial gene database and/or a drug-resistant gene database to obtain identification results of the target microorganism and/or drug-resistant gene.

The materials and other methods and procedures used in this disclosure are described in detail below.

(1) Solid-phase adsorption of host nucleic acids

When the blood culture in the BACTEC (BD) system was marked as positive, 2 mL of the blood culture was reacted with 1× red blood cell lysis buffer at room temperature for 5 minutes to remove red blood cells from the blood. Subsequently, the reacted solution was centrifuged at 3,000 × g for 10 minutes to initially remove debris. After discarding the supernatant, the pellet was resuspended with 250 μL of phosphate buffered saline (PBS). Next, add 1 mL of non-ionic surfactant (e.g. saponin, Tween, Triton, polyoxyethylene (10) oleyl ether, polyol, polyoxyethylene-polyoxypropylene, polyoxyethylene ether, alkylethanolamide, glucosides, fatty alcohols, etc.) to the suspension. For example, if 5% saponin is added to the suspension, the final concentration is 2.2%. After incubation at room temperature for 10 minutes, centrifuge at 6,000 × g for 5 minutes, discard the supernatant, and resuspend the pellet with 200 μL of phosphate buffer. Add 100 μL of solid-phase reversible immobilization (SPRI) magnetic beads to the suspension, and then pipette for 5 minutes. Collect the supernatant after standing on a magnetic stand. The supernatant was centrifuged at 3,000 × g for 3 min and the pellet was resuspended in 200 μL of phosphate buffer.

(2) Extraction of bacterial DNA

To extract bacterial DNA from the pre-treated pellet for nanopore sequencing, a commercially available kit was used that followed the procedure described in the Qiagen manual for QIAamp Genomic DNA from Blood and Tissue, but with modifications to the lysozyme and lysostaphin procedures to reduce the number of processing steps and turnaround time. Lysozyme and lysostaphin are recommended for DNA extraction from Gram-positive and "difficult to lyse" bacteria. Both enzymes disrupt the peptidoglycan cell wall, resulting in lysis. In addition, lysozyme hydrolyzes the glycosidic bond between N-acetylmuramic acid and N-acetylglucosamine, while lysostaphin breaks the glycine-glycine bond of the pentaglycine bridge in the staphylococcal cell wall. Due to differences in the cell wall structure, the staphylococcal cell wall is resistant to lysozyme, so lysostaphin is used instead of lysozyme to produce sufficient DNA from S. aureus.

After DNA extraction, SPRI magnetic beads were used to remove shorter DNA fragments and polymerase chain reaction (PCR) inhibitors and sequencing reaction inhibitors. DNA concentration was assessed using the Qubit Broad Range double-stranded DNA (dsDNA) quantification kit and Qubit fluorimeter, with a quantification range of 2 to 1,000 ng/μL. DNA purity and contamination were assessed using a NanoDrop spectrophotometer. The recommended sample purity is A ₂₆₀ /A ₂₃₀ > 2.0 and A ₂₆₀ /A ₂₈₀ > 1.8.

(3) Library preparation for nanopore sequencing

Each extracted DNA sample was adjusted to 80 ng/μL, 5 μL of the sample (about 400 ng) was pipetted and added with 2.5 μL of water to make the total volume 7.5 μL. The Rapid Barcoding Kit (SQK-RBK004) developed by Oxford Nanopore was taken out and placed on a centrifuge tube rack to dissolve at room temperature, and after sufficient shaking, the tube was briefly centrifuged and placed on an ice box for later use.

Add 7.5 μL of sample and 2.5 μL of barcode adapters 1 to 96, sequencing adapter, and kinesin to a 0.2 mL centrifuge tube. When connecting the barcode adapters, the same barcode adapter cannot be used repeatedly for 96 consecutive samples.

Next, place the sample in a polymerase chain reaction (PCR) machine for 30°C for 1 minute and 80°C for 1 minute, then place it on an ice box to mix all labeled samples. DNA purification was performed using Agencourt AMPure XP magnetic beads. The magnetic beads must be shaken by hand before use to mix them evenly. Take 60 μL of magnetic beads and add them to the reaction solution of the previous step. After lightly flicking the tube wall with your fingertips to mix evenly, place it in a mixer and mix it by inversion for 5 minutes. After that, place the centrifuge tube on the magnetic stand for 10 minutes. After the magnetic beads are completely adsorbed, remove the reaction solution in the tube and wash the magnetic beads twice with 200 μL of 70% alcohol. Then, the magnetic beads were dispersed with 25 μL of pure water without DNase to dissolve the DNA in water, and the magnetic beads were adsorbed and removed again using a magnetic stand to obtain a purified DNA library.

(4) Nanopore sequencing data analysis

Take the nanopore sequencing chip (flow cell) (R9.4.1 FLO-MIN106, Oxford Nanopore) from 4℃ to room temperature, and put it into the MinION sequencer after warming up, and perform on-machine sequencing with the Flow Cell Priming Kit. Take a tube of flush buffer (FB) and flush tether (FLT) from -20℃, warm up, shake and mix, take 30 μL of FLT and add FB buffer to prepare the sequencing priming mixture. Take 800 μL of the sequencing priming mixture and add it to the priming port on the chip, and let it stand for 5 minutes.

Take out 12 μL of the prepared DNA library and add 37.5 μL of sequencing buffer (SQB buffer) and 25.5 μL of loading beads to form a total volume of 75 μL of sequencing mixture. Mix with a micropipette and avoid bubbles. Before going on the machine, take 200 mL of sequencing starter mixture and add it to the starter well. Then slowly drip the prepared sequencing mixture into the sample well (sample port), close the reagent well and sample well, and cover the sequencer for sequencing.

Data were collected using the software MinKNOW v4.2.4. Base calling was performed using Guppy with barcode de-multiplexing and FASTQ file output. Adapter sequences were trimmed from the reads using Porechop v0.2.3, which was run with barcode de-multiplexing. Only reads identified by Guppy and Porechop barcode bins were retained to reduce the risk of cross-barcode contamination. Sequencing data generated by the MinKNOW platform and all sequences per file were exported using default settings. The first output file was generated approximately 2 hours after the start of the sequencing run and continued until the 10th hour of the run. For this work, each output file was processed individually to continuously track the time that had passed since the start of sequencing.

(5) Classification by taxonomy

Raw sequencing reads (≥ 2,000 bp) were aligned to reference gene sequences of bacteria, archaea, viruses, and humans using the alignment software Centrifuge 1.0.4 or Kraken2 with default settings (minimum length of partial hits (min_hitlen) = 22; maximum k = 5 different assignments per read; no preferred/excluded taxonomic groups) for taxonomic classification.

According to the barcode of the preset length subsequence in the obtained sequence to be compared, the corresponding comparison sequence is read from the reference gene sequence, and the obtained sequence data is classified through the clinical pathogen database of the comparison software Centrifuge 1.0.4 or Kraken2. Sequences with a comparison length greater than 80% of the total sequence length and a mismatched base number in the comparison region less than or equal to 10% are retained, and the proportion of pathogen classification is calculated. The pathogen type present in the sample is screened based on the proportion greater than 1% of the total read sequence.

(6) Overall genome assembly and drug resistance gene search

Once the sequencing data is collected, it is then pre-processed and base-called, followed by global genome assembly. A variety of assemblers are available for the assembly of long-read global genome data, including long-read assembly software such as Canu and Flye. In addition, error correction can be performed on individual long-read sequences using Racon and Medaka (which use neural networks to identify and correct nanopore homopolymer errors and produce consensus sequences) and Homopolish (which removes systematic errors in nanopore sequencing through homologous gene error correction (homologous polishing) software). Raw sequencing reads (≥ 300 bp) and plastid-tagged assembly contigs (contigs) were searched using BLAST and the ResFinder 4.0 database. Only hits with ≥ 90% similarity, E-value ≤ ^10-6 , and database entry coverage ≥ 60% were retained.

The assembled sequence is compared with a database containing microbial resistance genes, and the detection parameters of the microbial genome and resistance gene sequences that are compared are statistically analyzed to calculate the detection results including at least one parameter of the number of valid matched sequences (i.e., the number of sequences of the species and the gene in the comparison between the genus/species and resistance gene levels), coverage (i.e., the percentage of the length of the detected microbial nucleic acid sequence to the length of the entire genome sequence of the microorganism and the resistance gene), coverage depth (i.e., the average depth of bases within the coverage range on the genome), relative abundance (the proportion of microorganisms detected at the genus/species level to microorganisms of the same type detected in the entire sample) and dispersion, and the detection analysis is output.

The following specific embodiments further illustrate the features and effects of the present disclosure, but are not intended to limit the scope of the present disclosure.

Example 1: Evaluation of Removal of Non-Target Nucleic Acids

In this embodiment, human blood samples containing Klebsiella pneumoniae ( K. pneumoniae ) ST11 strain (KPC160111) or Staphylococcus aureus ( S. aureus ) are pre-treated by solid phase adsorption of human nucleic acid and then subjected to quantitative polymerase chain reaction (qPCR) and nanopore sequencing.

The results showed that the pretreatment of solid phase adsorption enriched the bacterial nucleic acid in the sample. As shown in Table 1 below, in the pretreated samples, human nucleic acid was reduced to 0.005 to 0.016 times that of the control sample, while bacterial nucleic acid increased to 2.34 to 5.78 times that of the control sample. Table 1. Host and bacterial nucleic acid amounts evaluated by qPCR Blood test substances qPCR assay Sample Copy 1 Copy 2 average C Multiple Klebsiella pneumoniae Klebsiella pneumoniae Not removed 15.02 15.04 15.03 2.53 5.78 After removal 11.21 13.79 12.50 Human Not removed 22.81 22.87 22.84 -7.69 0.005 After removal 30.76 30.29 30.53 Staphylococcus aureus Staphylococcus aureus Not removed 10.82 13.85 12.34 1.23 2.34 After removal 9.05 13.17 11.11 Human Not removed 19.21 20.13 19.67 -5.97 0.016 After removal 25.56 25.71 25.64 Cq: Quantization cycle

In addition, the results of nanopore sequencing showed that the number of reads, read length (including average read length, median read length and N50) and total base number obtained from the pre-treated samples were significantly higher than those of the control samples (Table 2). Table 2. Quality of bacterial nucleic acids in pre-treated samples Test substances in blood Sample Average reading length (bp) Average reading quality (Q) Median reading length (bp) Reading number N50 Total base Klebsiella pneumoniae Not removed 5,613 13.6 3,038 84,376 11,470 473,680,706 After removal 9,833 12.8 6,467 168,409 17,242 1,656,087,432 Staphylococcus aureus Not removed 4,774 13.7 2,570 13,959 9,765 66,640,453 After removal 8,713 13 5,536 111,447 15,610 971,092,018 N50: The length of the shortest overlapping group at 50% of the total genome length.

As for the proportion of bacterial nucleic acids after nanopore sequencing, Table 3 below shows that the proportion of non-target nucleic acids (i.e., human nucleic acids) in pre-treated samples containing Klebsiella pneumoniae was significantly reduced from 63.09% to 0.13%, and in pre-treated samples containing Staphylococcus aureus, it was reduced from 75.35% to 0.11%. On the other hand, the proportion of bacterial nucleic acids increased from 28.34% to 82.01% (Klebsiella pneumoniae) and from 20.72% to 81.14% (Staphylococcus aureus). Table 3. Proportion of bacterial nucleic acids after nanopore sequencing Blood test substances Sample General Reading Order Classifiable reading order Human Reading Target reading order Unable to sort and read Klebsiella pneumoniae Not removed 84,376 81,865 53,234 (63.09%) 23,910 (28.34%) 2,511 After removal 168,409 161,348 221 (0.13%) 138,110 (82.01%) 7,061 Staphylococcus aureus Not removed 13,959 13,573 10,518 (75.35%) 2,904 (20.72%) 386 After removal 111,447 106,364 118 (0.11%) 90,424 (81.14%) 5,083

Example 2: Identification of bacterial species and drug resistance genes

In this embodiment, human blood containing Klebsiella pneumoniae or Staphylococcus aureus was pre-treated with solid phase adsorption of human nucleic acid or commercially available kits (i.e., MolYsis Basic 5 Kit, NEBNext Microbial Genomic DNA Enrichment Kit, and QIAamp BiOstic Bacteremia DNA Kit), and then qPCR, nanopore sequencing, and drug resistance gene search were performed.

Compared with the commercially available kit, the samples pre-treated with solid phase adsorption provided by the present disclosure have the longest read length (including average read length and median read length) generated by nanopore sequencing (Table 4 and Table 5 below). Table 4. Blood samples containing Klebsiella pneumoniae (with 29 drug resistance genes) pre-treated with different methods method DNA (ng/μL) Average reading length (bp) Median reading length (bp) Reading number Total base Control group 30.7 5,074 2,413 37,619 190,883,881 Molysis 5.9 1,438 187 2,376 3,416,680 NEB 14.7 4,223 2,300 3,765 1,222,820,069 QiAamp BB 28 2,383 1,618 342,288 815,884,192 TCDC 32.8 9,921 6,788 498,615 4,946,906,836 Control group: blood samples without pretreatment to remove non-target nucleic acids; Molysis: MolYsis Basic 5 kit; NEB: NEBNext Microbial Genomic DNA Enrichment Kit; QiAamp BB: QIAamp BiOstic Bacteremia DNA Kit; TCDC: the method provided in this disclosure. Table 5. Blood samples containing Staphylococcus aureus (with 2 drug resistance genes) pretreated with different methods method DNA (ng/μL) Average reading length (bp) Median reading length (bp) Reading number Total base Control group 30.6 2,293 921 10,549 24,197,739 Molysis 93.8 821 185 7,475 6,143,528 NEB 10.2 541 221 11,929 6,459,450 QiAamp BB 94.4 1,603 888 585,887 935,519,573 TCDC 11.8 2,618 1,299 298,892 782,622,266 Control group: blood sample without pretreatment to remove non-target nucleic acids; Molysis: MolYsis Basic 5 kit; NEB: NEBNext Microbial Genomic DNA Enrichment Kit; QiAamp BB: QIAamp BiOstic Bacteremia DNA Kit; TCDC: the method provided in this disclosure.

In addition, as shown in Figures 4A and 4B, the proportion of bacterial nucleic acid in the samples pre-treated using the disclosed method is much higher than that of the samples pre-treated using other commercial kits. For example, the sequence data obtained by nanopore sequencing was used to identify species distribution using the Centrifuge database, and the results showed that in the samples pre-treated using the disclosed method, the proportion of human nucleic acid was only about 1%, while the proportion of bacterial nucleic acid could reach 85% (Klebsiella pneumoniae) or 63% (Staphylococcus aureus). It can be seen that compared with commercial kits, the disclosed method significantly increases the proportion of bacterial nucleic acid in pre-treated samples.

In addition, as shown in FIG5A , the 29 drug resistance genes carried by Klebsiella pneumoniae can be sequenced and identified within 6 hours, indicating that the read sequence sequenced within 6 hours reaches a coverage depth of 20 times the size of the Klebsiella pneumoniae genome using the pre-processing method provided by the present disclosure, which is sufficient to detect the complete drug resistance gene. Similarly, FIG5B shows that the 2 drug resistance genes carried by Staphylococcus aureus can be sequenced and identified within 2 hours, which reaches a coverage depth of 20 times the size of the Staphylococcus aureus genome. Compared with the QiAamp BB kit, which requires 6 hours of sequencing to obtain read sequences with sufficient coverage depth for detection, the read sequences obtained within 10 hours of samples pre-treated with the NEB kit are still insufficient to identify the two drug resistance genes.

Example 3: Evaluation of clinical specimens

In this embodiment, 36 human blood samples provided by the China Medical University Hospital were pre-treated by non-target nucleic acid solid phase adsorption, and then pathogen identification and drug resistance gene detection were performed.

The results are shown in Table 6 below, where the percentage represents the proportion of the reading sequence distribution. It can be found that in 36 blood samples, 33 cases showed that the pathogens identified by the method of the present disclosure were consistent with the pathogens identified by microbial culture; in addition, when the sample contains more than one pathogen (such as samples No. 4, 12 and 20) or the pathogens belong to different species of the same genus (such as samples No. 7, 13, 18, 24, 26, 31, 35 and 36), the minor pathogens or species in the sample can also be identified by the method of the present disclosure. Traditional microbial culture can usually only identify one strain in one experiment, while the method of the present disclosure can simultaneously identify all strains in the sample. Therefore, although the identification results of the three cases of samples No. 7, 14 and 24 are inconsistent with the results of microbial culture, the results identified by the disclosed method are closer to the actual infection situation.

In addition, the drug resistance genes detected by the disclosed method are consistent with the results of the traditional antibiotic susceptibility test (AST). Table 6. Comparison of traditional microbial culture and the disclosed method in pathogen identification Sample Number G Traditional microbial culture Identified by the disclosed method (classified reading order ＞ 1%) Remarks 1 - Klebsiella pneumoniae Klebsiella pneumoniae (77.7%) 2 - Escherichia coli Escherichia coli (75.6%) 3 - Acinetobacter baumannii Acinetobacter boutonii (62.9%) 4 + Staphylococcus aureus Staphylococcus aureus (53%)/ Escherichia coli (25%) MRSA 5 - Escherichia coli Escherichia coli (65.6%) 6 + Staphylococcus aureus Staphylococcus aureus (56%) MRSA 7 + Staphylococcus aureus Staphylococcus epidermidis (47%) / Staphylococcus aureus (17%) / Staphylococcus simulans (1.1%) / Lactobacillus johnsonii (3.2%) / Aerococcus urinaeequi (1.8%) / Klebsiella pneumoniae (1.1%) MRSA 8 - Proteus mirabilis Proteobacterium mirabilis (58%) 9 - Escherichia coli E. coli (58%) 10 - Escherichia coli E. coli (68%) 11 - Escherichia coli E. coli (92.8%) 12 - Escherichia coli E. coli (86%) / Enterococcus faecium (1.9%) 13 - Pseudomonas Pseudomonas BJP69 (55%) / P. putida (18.5%) / P. monteilii (1.4%) / P. aeruginosa (1.1%) / Enterobacter hormaechi (1.1%) 14 + Staphylococcus epidermidis Staphylococcus capitis (47.8%)/ Staphylococcus hominis (25.5%)/ Staphylococcus aureus (1.41%) 15 + Enterococcus faecium Faecalis (97%) 16 - Acinetobacter boutonii Acinetobacter boutonii (58.0%) CR 17 - Acinetobacter boutonii Acinetobacter boutonii (68.8%) CR 18 - Klebsiella pneumoniae Klebsiella pneumoniae (76.7%) / K. variicola (1.7%) / K. quasipneumoniae (1.2%) / Escherichia coli (2.6%) CR 19 + Staphylococcus aureus Staphylococcus aureus (94%) MRSA 20 - Acinetobacter boutonii Acinetobacter baumii (67.3%)/ Enterococcus faecium (5.0%) CR twenty one - Escherichia coli E. coli (90.4%) CR twenty two + Enterococcus faecium Faecalis (96.5%) VRE twenty three - Klebsiella aerogenes Aeromonas aeruginosa (93%) CR twenty four - Klebsiella pneumoniae Klebsiella pneumoniae (60.9%) / Klebsiella pneumoniae (7.1%) CR 25 - Klebsiella variant Klebsiella variant (86.9%) CR 26 - Klebsiella pneumoniae Klebsiella pneumoniae (67.5%) / Klebsiella variants (1.4%) CR 27 - Klebsiella pneumoniae Klebsiella pneumoniae (75.1%)/ Escherichia coli (2.9%) CR 28 - Klebsiella pneumoniae Klebsiella pneumoniae (80.6%) CR 29 - Klebsiella pneumoniae Klebsiella pneumoniae (67.6%)/ Escherichia coli (2.0%) CR 30 - Klebsiella pneumoniae Klebsiella pneumoniae (81.8%) CR 31 - Klebsiella pneumoniae Klebsiella pneumoniae (74.2%) / Klebsiella variant (1.1%) CR 32 - Klebsiella pneumoniae Klebsiella pneumoniae (62.4%)/ Escherichia coli (1.2%) CR 33 - Klebsiella pneumoniae Klebsiella pneumoniae (65.3%)/ Escherichia coli (3.2%) CR 34 - Klebsiella pneumoniae Klebsiella pneumoniae (81.5%) / Escherichia coli (3.2%) CR 35 Y Candida glabrata Candida alopecia (55.4%)/ Escherichia coli (2.2%) 36 Y Candida albicans Candida albicans (51.3%)/ Escherichia coli (1.2%) G: Gram-positive (+); Gram-negative (-); Y: yeast; MRSA: methicillin-resistant Staphylococcus aureus ; CR: carbapenem-resistant; VRE: vancomycin-resistant Enterococcus.

Furthermore, samples No. 4, 17, 19, 21, 22, and 29 were respectively tested for drug resistance genes using the disclosed method. The results are shown in FIG6 . Within a sequencing time of about 2 to 6 hours, a coverage depth of about 20 times the genome size of the individual bacterial species can be collected, and all drug resistance genes carried by the individual bacterial species can be identified.

On the other hand, to evaluate the performance accuracy of the disclosed method relative to gold standard culture and/or PCR testing for pathogen identification, 44 blood samples from 42 bacteremia patients provided by the China Medical University Hospital were pre-treated by non-target nucleic acid solid phase adsorption and then pathogen identification was performed.

The results of bacterial pathogen identification are shown in Table 7 below, where two reference standards were used: (1) the clinical gold standard - matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS); and (2) BIOFIRE blood culture identification (BCID2, Filmarray), which is a multiplex PCR system certified by the U.S. Food and Drug Administration (FDA), CE- in vitro diagnostic devices (CE-IVD) and the Australian Therapeutic Goods Administration (TGA) that can rapidly and accurately automatically detect pathogens and antibiotic resistance genes associated with bloodstream infections. Table 7. Comparison of pathogen identification between the disclosed method (i.e., TCDC detection system) and the reference standard method (i.e., MALDI-TOF MS and Filmarray BCID2) MALDI-TOF MS Number of samples for Filmarray BCID2 platform test results Number of samples with TCDC test results (classified readings > 1%) Identification Type Sample quantity Gram-negative bacteria Klebsiella pneumoniae 7 7 7 Klebsiella variicola 1 Klebsiella pneumoniae ^* 1 Citrobacter freundii 2 Enterobacteriaceae 2 Serratia marcescens 3 3 3 Serratia rubidaea 1 Enterobacteriaceae 1 Enterobacter cloacae 1 1 1 Moraxella osloensis 1 0 1 Escherichia coli 11 11 11 Pseudomonas aeruginosa 3 3 3 Acinetobacter boutonii 2 2 2 Acinetobacter guillouiae 1 0 1 Stenotrophomonas maltophilia 1 1 1 Total 34 28 34 Gram-positive bacteria Staphylococcus aureus 1 1 1 Group B Streptococcus 1 1 1 Enterococcus faecium 3 3 3 Total 5 5 5 Multiple strains Escherichia coli Klebsiella pneumoniae Enterococcus gallinarum 1 1 1 Klebsiella pneumoniae Enterococcus vaginalis 1 1 1 Enterococcus albicans 1 1 1 Proteobacterium mirabilis Klebsiella pneumoniae Staphylococcus epidermidis 1 1 1 Citrobacter cronae 1 Aerococcus Escherichia coli Aeromonas aeruginosa Citrobacter kluyveri Total 5 4 5 * Inconsistencies are indicated in text

As can be seen from the above, the performance of the disclosed method in identifying Gram-positive, Gram-negative single-species or multi-species samples is in line with expectations, and is 100% consistent with the test results of the clinical gold standard; for example, the standard test results show that the pathogens of 7 samples out of 44 samples are Klebsiella pneumoniae, and the disclosed method can also detect that the pathogens in 7 samples are Klebsiella pneumoniae.

In contrast, due to the limited abundance of detectable organisms caused by the number of primers designed in the Filmarray BCID2 platform, it was found that it was unable to identify Moraxella osloensis and Acinetobacter guillouiae in clinical samples, and it was unable to identify Citrobacter freundii and Serrita rubidaea to the species name; in addition, a sample containing Klebsiella variicola was identified as Klebsiella pneumoniae. Table 8 below shows the comparison results of these samples with inconsistent test results after being tested by other methods, and the full-length 16S rRNA sequence of nanopore sequencing was used to confirm the bacterial species classification, where the percentage represents the proportion of the sequence distribution. Table 8. Comparison of pathogen identification using the disclosed method (i.e., TCDC detection system) and the reference standard method (i.e., MALDI-TOF MS and nanopore sequencing of 16S rRNA full gene) for samples that the Filmarray BCID2 platform cannot identify pathogens Sample Number MALDI-TOF MS Filmarray BCID2 Platform Nanopore sequencing of 16S rRNA TCDC detection system (classified reading ＞ 1%) 2-1 Citrobacter freundii Enterobacteriales Citrobacter murliniae 40% Citrobacter freundii group-Citrobacter freundii 90% 53% Citrobacter gillenii twenty three% - Citrobacter portucalensis twenty four% Citrobacter freundii 18% - Citrobacter youngae 3% Citrobacter braakii 16% Escherichia coli 2% 4 Oslomorella NA Oslomorella 99% Oslomorella 53% Escherichia coli 2% 11 Serratia rubrum Enterobacteriales Serratia rubrum 97% Serratia rubrum 91% 12 Klebsiella mutans Enterobacteriaceae Klebsiella pneumoniae Klebsiella mutans Klebsiella pneumoniae 53% 41% Klebsiella mutans Klebsiella pneumoniae 85% 2% 13 Acinetobacter guilinensis NA Guilin Acinetobacter rubrum Serratia rubrum 84% 9% Acinetobacter guilinensis 80% NA: Not applicable

The test results of 44 samples are shown in Table 9 below. In general, the identification results of the disclosed method for Gram-negative and Gram-positive bacteria samples are consistent with the clinical culture results, with a positive detection rate of 100%, and a positive detection rate of 100% for mixed bacteria samples, with a high degree of consistency. As for the Filmarray BCID2 platform, only 28 samples were identified as Gram-negative bacteria, with a positive detection rate of only 82%, and 80% for mixed bacteria samples. In addition, no strains were detected for negative samples by the disclosed method, and the proportion of human gene sequences was less than 1%, indicating that the accuracy of the disclosed method is in line with expectations. Table 9. Comparison of pathogen positive detection rates of the disclosed method (i.e., TCDC detection system) and the Filmarray BCID2 platform against the reference standard method (i.e., microbial culture) Pathogen species identification consistency Testing platform type Single strain Multiple strains Gram-negative bacteria: 34 samples Gram-positive bacteria: 5 samples 5 samples Filmarray BCID2 Platform 82% (28) ^* 100% (5) 80% (4) TCDC Detection System 100% (34) 100% (5) 100% (5) *The numbers in brackets are the number of samples that are consistent with the identification results of the microbial culture method

The results of the identification of drug resistance genes carried by bacteria are shown in Table 10 below, where the two reference standards used are: (1) the clinical gold standard consisting of available culture and antibiotic sensitivity test (AST); (2) the BIOFIRE BCID2 platform (Filmarray), which is a multiplex PCR system certified for bloodstream infection. Table 10. Comparison of antimicrobial drug resistance genes between the disclosed method (i.e., TCDC detection system) and the reference standard method (i.e., microbial culture and Filmarray BCID2) Sample Number Microbial culture Microbial culture AST results** Filmarray BCID2 TCDC detection system 1 Klebsiella pneumoniae S ^* : TGC, GM, AN, CMZ R: AM, SAM, TZP, CZ, CTX, FEP, CIP, LVX, SXT, MEM, ETP, IPM CTX-M type OXA-48 aadA16, aph(3')-Ia, aph(6)-Id, aph(3'')-Ib, blaOXA-48, blaSHV-1, blaCTX-M-15, blaTEM-1C, fosA, aac (6')-Ib-cr, qnrB6, ARR-3, tet (A), tet (D), OqxA, OqxB, qacE, dfrA7, dfrA27, sul1, sul2 2-1 Citrobacter freundii S: GM, AN, TZP, CTX, FEP, CIP, LVX, ETP, IPM, SXT R: AM, SAM, CZ, CMZ ND ^**** blaCMY-124, qnrB13 2-2 Serratia marcescens S: GM, AN, TZP, CTX, FEP, CIP, LVX, ETP, IPM, SXT R: AM, SAM, CZ, CMZ ND aac(6')-Ic, blaSRT-2, tet(41) 3 Enterococcus S: TGC, GM, AN, TZP, CTX, FEP, CIP, LVX, ETP, SXT R: AM, SAM, CZ, CMZ, IPM ND blaMIR-2、fosA 4 Oslomorella NA ^*** ND aac(6')-Ib3, blaIMP-26, mph(E), msr(E), qacE, sul1 5 Escherichia coli S: GM, AN, CMZ, TZP, CIP, LVX, ETP, IPM, SXT R: AM, CZ, CTX, FEP I: SAM ND tet(B),mdf(A),blaCTX-M-3 6 Escherichia coli S: AN, CIP, LVX R: GM, AM, SAM, TZP, CZ, CMZ, CTX, FEP, SXT, ETP, IPM KPC VanA/B aph(6)-Id, aph(3'')-Ib, ant(6)-Ia, aac(3)-IId, aadA1, aadA2, aph(3')-III, aac(6')-aph(2''), aac(6')-Il, floR, cmlA1, blaTEM-1B , blaSHV-11, blaKPC-2, blaCMY-2, sul2, sul3, dfrA12, fosA, VanHAX, VanC1XY, mdf(A), erm(42), msr(C), tet(A), tet(L), tet(M), tet(S) Klebsiella pneumoniae S: AN, SXT R: GM, AM, SAM, TZP, CZ, CMZ, CTX, FEP, CIP, LVX, ETP, IPM Enterococcus S: P, GMS, LZD, DAP R: VA, TEC 7 Staphylococcus aureus S: CC, VA, TEC, LZD, DAP, D, SXT, DAP, FA R: P, OX, E, CIP I: TE mecA/C and MREJ (MRSA) aac(6')-aph(2''), aadD, aph(3')-III, ant(6)-Ia, blaZ, mecA, lnu(A), mph(C), msr(A), qacA, tet(K) 8 Pseudomonas aeruginosa S: AN, CAZ, FEP, R: GM, CIP, LVX, IPM, TZP, SXT ND aadA3, aac(6')-Ib3, aph(3')-IIb, blaCARB-2, blaPAO, blaOXA-494, fosA, sul1, qacE, crpP, catB7 9 Escherichia coli S: GM, AN, SAM, CZ, CMZ, TZP, CTX, ETP, IPM, FEP R: AM, CIP, LVX, SXT ND tet(A), aph(6)-Id, aph(3'')-Ib, blaTEM-1B, aadA5, sul1, sul2, mph(A), qacE, dfrA17, mdf(A) 10 Klebsiella pneumoniae S: TGC, GM, AN, CMZ, SAM, TZP, CZ, CTX, FEP, CIP, LVX, SXT, MEM, ETP, IPM R: AM ND blaOKP-B-2, blaACT-6, OqxA, OqxB, fosA Enterococcus S: TGC, GM, AN, TZP, CTX, FEP, CIP, LVX, ETP, IPM, SXT R: AM, SAM, CZ, CMZ 11 Serratia rubrum S: GM, AN, TZP, CMZ, CTX, FEP, CIP, LVX, ETP, IPM, SXT R: AM, SAM, CZ ND ND 12 Klebsiella mutans S: GM, AN, TZP, SAM, CZ, CMZ, CTX, FEP, CIP, LVX, ETP, IPM, SXT R: AM ND blaLEN22, fosA, OqxA, OqxB 13 Acinetobacter guilinensis S: TGC R: SAM, GM, CIP, LVX, CAZ, FEP, IPM, MEM, TZP, SXT I: AN ND aph(3')-VI, aph(3')-VIb, aph(3')-Ia, aac(3)-IId, aph(6)-Id, blaNDM-1, blaOXA-274, blaOXA-58, tet(39), sul2 14 Serratia marcescens S: SXT R: AM, SAM, CZ, CMZ, GM, TZP, CTX, FEP, CIP, LVX, ETP, IPM I: AN VIM aac(6')-Ic, ant(2'')-Ia, aac(6')-Ib3, aph(3')-Ia, blaSRT-2, blaVIM-1, blaOXA-10, qnrS1, tet(41), sul1, qacE, catB3 15 Klebsiella pneumoniae S: TGC, GM, AN R: AM, SAM, TZP, CZ, CMZ, CTX, FEP, CIP, LVX, SXT, MEM, ETP, IPM CTX-M KPC aph(6)-Id, aph(3'')-Ib, blaTEM-67, blaCTX-M-14, blaCTX-M-65, blaKPC-2, blaSHV-11, tet(A), sul2, fosA 16 Escherichia coli S: GM, AN, SAM, CMZ, TZP, ETP, IPM, SXT R: AM, CZ, CTX, FEP, CIP, LVX CTX-M aph(6)-Id, aph(3'')-Ib, blaCTX-M-27, mph(A), sul1, sul2, tet(A), qacE, mdf(A) 17 Klebsiella pneumoniae S: GM, AN, TZP, SAM, CZ, CMZ, CTX, FEP, CIP, LVX, ETP, IPM, SXT R: AM ND blaSHV-1, OqxA, OqxB, fosA 18 Green bacillus S: AN, CAZ, FEP, GM, CIP, LVX, IPM, TZP R: SXT ND aph(3')-IIb, blaPAO, blaOXA-488, catB7, fosA 19 Serratia marcescens S: SXT, CMZ, GM, TZP, CTX, FEP, CIP, LVX, ETP R: AM, AN, SAM, CZ I: IPM ND aac(6')-Ic, blaSRT-2, tet(41) 20 Escherichia coli S: GM, AN, CMZ, TZP, CIP, LVX, ETP, IPM, SXT R: AM, CZ, CTX, FEP I: SAM CTX-M blaTEM-1B, blaCTX-M-27, mdf (A) twenty one Streptococcus beta S:P,VA R:CC ND aph(3')-llll,ant(6)-Ia,erm(B),mre(A),tet(M) 22-1 Escherichia coli S: GM, AN, CMZ, TZP, CTX, FEP, ETP, IPM, SXT R: AM, CIP, LVX I: SAM, CZ ND blaTEM-1B、mdf（A） 22-2 Green bacillus S: AN, CAZ, FEP, GM, CIP, LVX, TZP R: IPM, MEM, SXT VanA/B aph(3')-llb, blaIPO, blaOXA-50, catB7, crpP, fosA twenty three Escherichia coli S: GM, AN, CMZ, TZP,, CZ, CTX, FEP, CIP, LVX, ETP, IPM R: AM, SXT I: SAM ND aph(3'')-lb, aph(6)-ld, blaTEM-1B, dfrA14, mdf(A), sul2 twenty four Escherichia coli S: GM, AN, CMZ, TZP, CZ, CTX, FEP, CIP, LVX, ETP, IPM, SXT R: AM I: SAM ND blaTEM-1B, mdf (A), tet (B) 25 Escherichia coli S: AN, CMZ, TZP, CZ, CTX, FEP, CIP, LVX, ETP, IPM, SXT R: GM, AM I: SAM ND blaTEM-1B, aac (3)-lld, mdf (A) 26 Escherichia coli S: GM, AN, CMZ, TZP, CZ, CTX, FEP, CIP, LVX, ETP, IPM, SXT, AM, SAM ND mdf(A) 27 Escherichia coli S: GM, AN, TZP, FEP, CIP, LVX, ETP, IPM, SXT R: AM, SAM, CZ, CMZ, CTX ND aph(6)-ld, aph(3'')-lb, blaCMY-2, mdf(A), tet(A), sul2, floR 28 Klebsiella pneumoniae S: TGC, GM, AN, CMZ, SAM, TZP, CZ, CTX, FEP, CIP, LVX, SXT, MEM, ETP, IPM R: AM ND blaSHV-11, fosA, OqxA, OqxB 29 Klebsiella pneumoniae S: TGC R: GM, AN, CMZ, AM, SAM, TZP, CZ, CTX, FEP, CIP, LVX, SXT, MEM, ETP, IPM CTX-M KPC NDM aac(3)-lld, aph(3'')-lb, aph(6)-ld, aadA1, rmtB, aac(6')-lb-cr, catB3, blaTEM-67, blaCTX-M-14, blaSHV-11, blaTEM-1B, blaOXA-1, blaNDM-1, blaKPC-2, dfrA14, sul1, sul2, fosA, qacE, qnrB1, tet(A) 30 Faecalis Candida albicans S: LZD, GMS R: P, VA, TEC I: DAP VanA/B aac(6')-aph(2''), aac(6'')-li, aph(3')-lll, ant(6)-la, dfrG, VanHAX, msr(C), tet(M), tet(L) 31-1 Enterococcus faecium S: LZD R: P, GMS, VA, TEC I: DAP VanA/B VanHAX, aac(6')-Ii, msr(C), dfrG, erm(B), ant(6)-Ia, aac(6')-aph(2''), cat(pC194), aph(3')-III, ant(6)-Ia 31-2 Klebsiella aerogenes S: GM, AN, TZP, CTX, FEP, CIP, LVX, ETP, IPM, SXT R: AM, SAM, CZ I: CMZ F 32 Proteobacterium mirabilis Klebsiella pneumoniae Staphylococcus epidermidis S: CMZ, AM, SAM, GM, AN, TZP, CTX, FEP, CIP, LVX, ETP, IPM R: SXT I: CZ mecA/C aph(6)-Id, aph(3'')-Ib, aadA2, aac(3)-IId, aph(3')-Ia, aac(6')-aph(2''), aadD, aph(3')-IIIa, ant(6)-Ia, cat, floR, cat(pC 221), OqxA, OqxB, blaDHA-1, blaTEM-1B, blaSHV-11, blaZ, sul1, sul2, dfrA1, fosA, vga(A)LC, mph(A), erm(C), qacA, qnrB4, tet(A) S: AN, TZP, FEP, CIP, LVX, SXT, MEM, ETP, IPM R: GM, AM, SAM, CZ, CTX, CMZ 33 Escherichia coli S: GM, AN, TZP, CMZ, SXT, ETP, IPM R: AM, SAM, CZ, CIP, LVX, CTX, FEP CTX-M blaCTX-M-55, mdf (A) 34 Klebsiella pneumoniae S: TGC, AN, FEP, CIP, LVX, IPM R: CMZ, AM, SAM, TZP, CZ, CTX, SXT I: GM, ETP ND aph(3')-Ia, aadA2, aac(3)-IId, aph(6)-Id, floR, blaSHV-65, blaTEM-1B, blaDHA-1, sul1, sul2, dfrA12, fosA, mph(A), qacE, qnrB4, OqxA, OqxB 35 Klebsiella pneumoniae S: TGC, AN, SXT R: GM, CMZ, AM, SAM, TZP, CZ, CTX, MEM, ETP, IPM, FEP, CIP, LVX CTX-M KPC aph(6)-Id, aph(3'')-Ib, aac(3)-IId, blaKPC-2, blaSHV-11, blaTEM-1B, blaCTX-M-14, sul2, fosA 36 Acinetobacter boutonii R: SAM, AN, LVX, FEP, GM, CIP, CAZ, IPM, MEM, TZP, SXT I: TGC ND armA, aadA1, aac(6')-Ib3, aph(3')-Ia, aph(6)-Id, aph(3'')-Ib, aadA24, aac(3)-Ia, catB8, blaADC-25, blaOXA-23, blaTEM-1D, blaOXA-66, sul1, mph(E), msr(E), qacE, tet(B) 37 Citrobacter S: GM, SAM, AN, TZP, CTX, FEP, CIP, LVX, ETP, IPM, SXT, CMZ, CZ R: AM ND blaCKO-1 38 Acinetobacter boutonii R: SAM, AN, LVX, FEP, GM, CIP, CAZ, IPM, MEM, TZP, SXT I: TGC ND aph(3')-Ia, aadA1, aac(3)-Ia, armA, aac(6')-Ib3, aph(6)-Id, aph(3'')-Ib, catB8, blaOXA-23, blaOXA-66, blaTEM-1D, blaADC-25, sul1, mph(E), msr(E), qacE, tet(B) 39 Staphylococcus capitis NA mecA/C aac(6')-aph(2''), aadD, mecA, blaZ, fosB, erm(C), qacA, fusB 40 Stenotrophomonas maltophilia S：LVX、MI、SXT R：CAZ ND aph(3'')-IlC, aac(6')-lz 41 Enterococcus faecium S: GMS, LZD R: P, VA, TEC I: DAP VanA/B aph(3')-llll,ant(6)-Ia,aac(6')-li,Inu(B),Isa(E),dfrG,VanHAX,msr(C),tet(M),tet(L) *S: susceptible; R: resistant; I: intermediate. **AM: ampicillin; AN: amikacin; CIP: ciprofloxacin; CMZ: cefmetazole; CTX: cefotaxime; CZ: cefazolin; DAP: daptomycin; ETP: ertapenem; FEP: cefepime; GM: gentamicin; GMS: gentamicin-Syn; IPM: imipenem; LZD: linezolidinone linezolid; LVX: levofloxacin; MEM: meropenem; P: penicillin; SAM: ampicillin-sulbactam; SXT: trimethoprim/sulfamethoxazole; TEC: teicoplanin; TGC: tigecycline; TZP: piperacillin/tazobactam; VA: vancomycin. ***NA: not applicable. ****ND: not detectable.

According to the identification results of Table 10 above, Table 11 below further shows that the samples detected by the method of the present disclosure are compared with samples with stronger antibiotic resistance such as AmpC β-lactamase, extended-spectrum β-lactamase (ESBL), carbapenem-resistant, vancomycin-resistant Enterococcus (VRE), methicillin-resistant Staphylococcus aureus (MRSA) based on traditional detection. Specifically, 6 of the 44 samples were recorded to be resistant to AmpC β-lactamase, 7 were resistant to extended-acting β-lactamase, 12 were carbapenem-resistant, 4 were vancomycin-resistant Enterococci, and 1 was methicillin-resistant Staphylococcus aureus by traditional antibiotic susceptibility testing (AST). The disclosed method identified 359 drug resistance genes in the plasmids of the samples and drug resistance-related mutations on their chromosomes, with an identification rate of 100%. Among these drug resistance genes and mutations, the Filmarray BCID2 platform only detected 23 drug resistance genes, and was unable to further analyze the subtypes of these drug resistance genes, with identification rates of 0%, 43%, 50%, 100%, and 100%, respectively. Table 11. Statistics of antimicrobial resistance (AMR) between the disclosed method (i.e., TCDC detection system) and the Filmarray BCID2 platform Prediction of drug resistance Number of drug resistance genes AmpC β-lactamase ESBL Carbapenem resistance VRE MRSA Filmarray BCID2 Platform twenty three 0% (0/6) ^* 43% (3/7) 50% (6/12) 100% (4/4) 100% (1/1) TCDC detection system 359 100% (6/6) 100% (7/7) 100% (12/12) 100% (4/4) 100 (1/1) *The numbers in brackets represent the number of resistant strains consistent with the drug sensitivity test / total number of samples

These results indicate that the disclosed system and method can be used to rapidly identify pathogen species and obtain sequences with a 20-fold coverage rate sufficient for identification of drug-resistant genes within a sequencing time of 2 to 4 hours for genome assembly and accurate prediction of antibiotic sensitivity. In summary, the disclosed system and method can remove non-target nucleic acids from humans or other sources from blood culture samples by using solid phase adsorption to obtain high-purity bacterial DNA. The extracted high-purity bacterial DNA can be used for rapid sequencing on a nanopore sequencing platform to generate long sequence reads, thereby quickly obtaining sufficient nucleic acid sequence coverage to assemble the bacterial genome. The bacterial species and the antimicrobial resistance genes they carry can then be fully identified through a real-time bioinformatics analysis process.

Generally speaking, antibiotic sensitivity testing (AST) after microbial culture requires a turnaround time of more than 3 days (Figure 7). In contrast, blood culture specimens pre-treated for 2 hours by solid-phase adsorption disclosed herein can be used for nanopore sequencing, and the pathogens and the drug-resistant genes they carry can be identified within 2 to 6 hours. In other words, through the system and method disclosed herein, the required information for selecting appropriate antibiotics can be obtained within 4 to 10 hours after the positive signal of blood culture appears. In addition, compared with commercially available rapid detection systems such as GeneXpert and FilmArray, the system and method disclosed herein can be used to identify a relatively wide range of known bacterial species and drug-resistant genes, demonstrating its increased identification applicability.

Therefore, the present disclosure provides a rapid detection platform for identifying pathogen species and drug-resistant genes, which can provide medical personnel with relevant information in real time for accurate selection of effective antimicrobial drugs, thereby helping to improve the cure rate of the disease and reduce the emergence and spread of drug-resistant strains caused by ineffective drug use.

It is obvious to those skilled in the art that, with the advancement of technology, the basic concepts of the present disclosure can be implemented in various ways. Therefore, the embodiments are not limited to the above examples; on the contrary, there are many variations without departing from the scope of the present disclosure.

The embodiments described above may be used in any combination with each other. Several embodiments may be combined together to form another embodiment. The system and method disclosed herein may include at least one of the above embodiments. It should be understood that the above benefits and advantages may relate to one embodiment or several embodiments. The embodiments within the scope of the disclosure are not limited to embodiments that solve any or all of the above problems or embodiments that have any or all of the above benefits and advantages.

10: Detection system 100: Cell lysis unit 101: Container 102: Non-ionic surfactant 200: Target nucleic acid enrichment unit 201: Solid-phase nucleic acid adsorption device 202: Nucleic acid extraction device 300: Sequencing encoding unit 301: DNA library preparation kit 302: Sequencer 400: Sequence analysis and comparison unit 401: Microbial identification module 402: Drug resistance gene identification module S1, S2, S3, S4: Operation process steps of the detection system for identifying target microorganisms in samples

In order to fully understand the present disclosure, reference should be made to the following detailed description in conjunction with the accompanying drawings.

FIG. 1 is a schematic diagram of a detection system according to at least one embodiment of the present disclosure.

FIG. 2 is a schematic diagram of a microbial nucleic acid enrichment process performed by a detection system according to at least one specific embodiment of the present disclosure.

FIG. 3 is a flowchart of the operation of the detection system of at least one specific embodiment of the present disclosure.

FIG. 4A and FIG. 4B are respectively the distribution diagrams of the ratio of host nucleic acid and target bacterial nucleic acid in blood culture samples containing Klebsiella pneumoniae ( FIG. 4A ) or Staphylococcus aureus ( FIG. 4B ) after pretreatment using the method disclosed herein or a commercially available kit. Ctrl: control group, no pretreatment; Molysis: MolYsis Basic 5 Kit; NEB: NEBNext Microbial Genomic DNA Enrichment Kit; QiaBB: QIAamp BiOstic Bacteremia DNA Kit; TCDC: the method disclosed herein.

Figures 5A and 5B show the relationship between the nanopore read time and the number of drug resistance genes identified for blood culture samples containing Klebsiella pneumoniae (Figure 5A) or Staphylococcus aureus (Figure 5B) after pretreatment using the method of the present disclosure or a commercially available kit. Ctrl: control group; NEB: NEBNext Microbial Genomic DNA Enrichment Kit; QiAamp BB: QIAamp BiOstic Bacteremia DNA Kit; TCDC: method of the present disclosure.

FIG6 shows the relationship between the nanopore reading time and the number of drug resistance genes identified in clinical samples after the samples were pre-treated by the method disclosed herein.

FIG. 7 shows a comparison of the turnaround time required for traditional blood culture, Filmarray detection platform, next-generation whole genome sequencing, and the method of the present disclosure (TCDC).

10: Detection system

100: Cell lysis unit

101:Container

102: Non-ionic surfactant

200: Target nucleic acid enrichment unit

201: Solid-phase nucleic acid adsorption device

202: Nucleic acid extraction device

300: Sequential coding unit

301:DNA library preparation kit

302: Sequencer

400: Sequence analysis and alignment unit

401: Microbial identification module

402: Drug resistance gene identification module

Claims (8)

A detection system for identifying target microorganisms and/or drug-resistant genes in a sample, comprising: a cell lysis unit for lysing non-target cells in the sample, wherein the target microorganism and the non-target cells are derived from different species, and the cell lysis unit comprises a non-ionic surfactant selected from the group consisting of saponin, polyoxyethylene (10) oleyl ether, polyol, polyoxyethylene-polyoxypropylene, polyoxyethylene ether, alkylethanolamide, glucosides, fatty alcohols and any combination thereof; a target nucleic acid enrichment unit, which comprises a solid-phase nucleic acid An adsorption device is used to remove the nucleic acid of the non-target cells after cell lysis, thereby enriching the nucleic acid of the target microorganism in the sample; a sequencing encoding unit is used to decode the sequence of the nucleic acid of the target microorganism in the sample; and a sequence analysis and comparison unit is connected to the sequencing encoding unit to receive the decoded data generated by the sequencing encoding unit and compare the decoded data with the microbial gene database and/or the drug resistance gene database to obtain the identification result of the target microorganism and/or the drug resistance gene it carries. The detection system as described in claim 1, wherein the solid-phase nucleic acid adsorption device comprises a solid-phase adsorbent selected from the group consisting of silica glass beads, silica-containing magnetic beads, column extraction filters, alkyl bonded silica gels, biochar, cellulose, anion exchange resins, and any combination thereof. A method for enriching target nucleic acids in a sample comprises: providing a sample containing a target microorganism and a non-target cell, wherein the target microorganism and the non-target cell are derived from different species; adding a non-ionic surfactant to the sample by means of a cell lysis unit of a detection system, selectively lysing the non-target cells in the sample and releasing the non-target nucleic acids contained therein, wherein the non-ionic surfactant is selected from a group consisting of saponin, polyoxyethylene (10) oleyl ether, polyol, polyoxyethylene-polyoxypropylene, polyoxyethylene ether, alkylethanolamide, glucosides, fatty alcohols and any combination thereof; and removing the non-target nucleic acid by means of a target nucleic acid enrichment unit of the detection system, thereby enriching the target nucleic acid contained in the target microorganism in the sample. As described in claim 3, the target nucleic acid enrichment unit comprises a solid-phase nucleic acid adsorption device having a solid-phase adsorbent, and the removal of the non-target nucleic acid comprises: contacting the sample with the solid-phase adsorbent so that the non-target nucleic acid binds to the solid-phase adsorbent; and removing the solid-phase adsorbent to enrich the target nucleic acid. The method of claim 3, wherein the sample is a biological sample selected from the group consisting of blood, serum, plasma, urine, sputum, saliva, cerebrospinal fluid, interstitial fluid, mucus, sweat, fecal extract, feces, synovial fluid, tears, semen, peritoneal fluid, nipple aspirate fluid, breast milk, vaginal fluid, and any combination thereof, or an environmental sample selected from the group consisting of dust, soil, water, air, artificial water system, food, and any combination thereof. A method for identifying a target microorganism and/or a drug-resistant gene, comprising: providing the target nucleic acid enriched by the method described in claim 3, wherein the enriched target nucleic acid has a length of at least 2,000 nucleotides; decoding the sequence of the enriched target nucleic acid by sequencing detection to obtain decoding data; and comparing the decoding data with a microbial gene database and/or a drug-resistant gene database to obtain the identification result of the target microorganism and/or the drug-resistant gene it carries. The method as claimed in claim 6, wherein the sequencing assay is selected from the group consisting of next-generation sequencing assay, high-throughput sequencing assay, nanopore sequencing assay, PacBio sequencing assay, Sanger sequencing assay, and any combination thereof. The method as claimed in claim 6, wherein the sequence decoding of the target nucleic acid includes generating, by the sequencing detection, decoding data with a genome coverage depth of at least 20 times the genome size of the target microorganism.