Method for high-throughput identification and accurate quantification of active drug-resistant bacteria

Abstract

The present application relates to the field of biotechnology, and particularly relates to a method for high-throughput identification and accurate quantification of active drug-resistant bacteria. The present application applies antibiotics and other stresses to the microbial community to destroy the cell membrane of the corresponding sensitive bacteria; then, specific DNA masking agents are used to selectively shield the DNA of sensitive bacteria; finally, 16S rRNA gene absolute quantification sequencing is performed, and the absolute abundance change before and after the microbial community treatment is used to identify the species information of drug-resistant bacteria and obtain the absolute abundance. Through integrated design, the present application can realize the rapid identification and accurate quantification of all active antibiotic-resistant bacteria in the microbial community with high throughput, high precision and high efficiency, solve the technical problems that the prior art cannot realize the high-throughput identification and accurate quantification of active antibiotic-resistant bacteria in the microbial community, and make a significant contribution to the understanding of drug-resistant bacterial species information and the evaluation of drug-resistant bacterial distribution in environmental and clinical samples.

Description

Technical Field

[0001] This invention relates to the field of biotechnology, and in particular to a method for high-throughput identification and precise quantification of active drug-resistant bacteria. Background Technology

[0002] The global spread of antibiotic resistance (AMR) poses a serious challenge to human health and severely threatens the cornerstone of modern medicine. Therefore, accurate and reliable monitoring of drug-resistant microorganisms with real potential for transmission and infection in the environment and clinical settings is crucial for understanding the ecological cycle of AMR, assessing its risks to human health, and developing effective intervention and control strategies.

[0003] Current mainstream AMR surveillance technologies face significant technical bottlenecks and limitations in assessing microbial communities (microorganisms with biological activity and phenotypic drug resistance) that pose a real public health risk. While traditional microbial culture methods are considered the "gold standard" for detecting live bacteria, their most critical limitation is that over 99% of microorganisms in the natural environment cannot be cultured under standard laboratory conditions. This leads to a systematic and substantial underestimation of the true diversity and scale of drug-resistant microorganisms, resulting in surveillance results that are merely the tip of the iceberg and fail to depict a complete risk profile.

[0004] On the other hand, conventional molecular detection techniques, such as polymerase chain reaction (PCR) and metagenomics, while overcoming the limitations of culturability, suffer from four unavoidable technical challenges in interpreting their results. First, these techniques cannot distinguish whether the detected DNA signal originates from living cells, residual DNA from dead cells, or free extracellular DNA. Since residual DNA can persist in the environment for days to weeks, this can lead to a significant overestimation of drug resistance risk. Second, and most critically, these methods only detect the presence of genes (genotype), but the presence of a gene does not equate to its functional expression and actual resistance manifestation (phenotype). The difference between genotype and phenotype is a common phenomenon in microbiology, and gene detection alone cannot provide crucial information about whether a microorganism can truly survive under specific antibiotic pressure. Although bioinformatics methods have attempted to predict phenotypes by analyzing the presence or absence of drug resistance genes, the accuracy of such predictions remains highly questionable due to the complexity of gene expression regulation and the continuous emergence of new drug resistance mechanisms, and they cannot replace direct phenotypic confirmation. Secondly, the method of tracing drug resistance genes back to specific host genomes through metagenomic association analysis has low accuracy. Finally, standard high-throughput sequencing technologies essentially produce relative abundance data. This componential data sums to a constant 100%, meaning that an increase or decrease in the abundance of one species inevitably leads to a relative change in the abundance of other species, even if the absolute numbers of the latter remain unchanged. This fundamentally determines that direct, meaningful absolute quantitative comparisons of microbial load between different samples are impossible, severely hindering spatiotemporal dynamic analysis of AMR risk and the establishment of unified risk assessment standards.

[0005] Therefore, it is particularly necessary to develop new systematic methods that can achieve high-throughput identification and precise quantification of active antibiotic-resistant bacteria in a unified analytical workflow, so as to provide strong technical support for the accurate monitoring and risk management of AMRs. Summary of the Invention

[0006] In view of this, the technical problem to be solved by the present invention is to provide a method for high-throughput identification and accurate quantification of active drug-resistant bacteria.

[0007] This invention provides a method for high-throughput identification and precise quantification of active antibiotic-resistant bacteria in samples, characterized by comprising the following steps:

[0008] Step 1: Divide the samples into a treatment group and a control group; the treatment group is incubated after contact with antibiotics; the control group is not incubated after contact with antibiotics.

[0009] Step 2: After incubation, the treatment group and the control group were treated with a photocrosslinking DNA blocking agent, respectively;

[0010] Step 3: Extract DNA from the treated group and the control group after the treatment, and add a known amount of exogenous internal standard DNA to each extracted DNA sample;

[0011] Step 4: Perform 16S rRNA gene sequencing on the treatment group and the control group in Step 3 with added internal standard DNA to obtain the sequence abundance of microbial taxa units and the exogenous internal standard DNA in the treatment group and the control group;

[0012] Step 5: Based on the known number of reads of exogenous internal standard DNA, calculate the absolute abundance of each microbial taxonomic unit in the treatment group and the control group, and identify each microbial taxonomic unit as an active antibiotic-resistant or sensitive bacterium by comparing the survival rate of each microbial taxonomic unit in the treatment group and the control group, and perform absolute quantification of the microbial taxonomic unit.

[0013] Wherein, the survival rate is: the ratio of the absolute abundance of each microbial taxa in the treatment group to its absolute abundance in the control group;

[0014] In this invention, the survival rate can also be another value obtained by multiplying or dividing the ratio by the same value. Specifically, the survival rate can be the ratio multiplied by 100%.

[0015] The absolute abundance of each of the aforementioned microbial taxa is the product of the transformation factor and the number of reads for a particular taxa.

[0016] The transformation factor is the ratio of the specific quantity of the known amount of exogenous internal standard DNA to the number of reads of the known amount of exogenous internal standard DNA.

[0017] The specific formula for calculating the conversion factor is as follows:

[0018]

[0019] The formula for absolute quantification of each of the aforementioned microbial taxa is as follows:

[0020] ;

[0021] The unit for the total volume of the DNA extraction solution is μL; the total volume of the DNA extraction solution is the volume of DNA extracted from the treated group or the control group after the treatment.

[0022] The initial sample quantity includes the initial sample mass and / or the initial sample volume, wherein the initial sample mass is in g and the initial sample volume is in mL.

[0023] In the method described in this invention, the treatment group and the control group can each be one or more independently, and this invention does not limit this. In necessary experiments, the treatment group and the control group can each be one or more, or the control group can be one and the treatment group can be multiple.

[0024] In this invention, the antibiotic can be a single antibiotic, a combination of multiple antibiotics, or a single antibiotic in a combination of multiple antibiotics acting independently; this invention does not limit this. Specifically, depending on the experimental requirements, when designing multiple treatment groups, multiple different antibiotics can be used to treat the treatment groups respectively, and corresponding control groups also need to be set up.

[0025] Furthermore, in the method described in this invention, the incubation time is 5 min to 60 min.

[0026] The photocrosslinking DNA blocking agents include: propidium azide bromide (PMA), ethidium bromide monoazide (EMA), psoralen, and / or MycoLight™ vPCR350.

[0027] The concentration of the photocrosslinking DNA blocking agent is 5 μM to 40 μM.

[0028] The exogenous internal standard DNA was Spike-in Control DNA;

[0029] Furthermore, the known quantity of exogenous internal standard DNA is 10. 3 copies / μL ~10 7 copies / μL of Spike-in Control DNA.

[0030] In the method described in this invention, the identification standard is:

[0031] If the survival rate exceeds the preset resistance threshold for the antibiotic, then the microbial taxonomy unit is an active antibiotic resistant bacterium.

[0032] If the survival rate is lower than a preset sensitivity threshold for the antibiotic, then the microbial taxonomy unit is an active antibiotic-sensitive bacterium.

[0033] Further, the antibiotic is polymyxin B; the resistance threshold for the antibiotic is a ratio greater than 0.1; the sensitivity threshold for the antibiotic is a ratio less than 0.02.

[0034] Furthermore, the concentration of polymyxin B was 200 mg / L.

[0035] In the method described in this invention, the samples include environmental samples, water treatment system samples, medical clinical samples, and / or industrial samples. Preferably, the samples include, but are not limited to, river, wastewater, sludge, soil, sediment, feces, urine, or sputum samples.

[0036] This invention provides a method that overcomes the shortcomings of existing technologies in distinguishing between dead and live cells, identifying drug resistance phenotypes, achieving absolute quantification, and performing high-throughput analysis. This method enables high-throughput identification and precise quantification of biologically active and phenotypic drug-resistant microorganisms. The invention aims to provide a systematic technical platform for accurately assessing the risk of real-world antibiotic resistance.

[0037] This invention, through the synergistic effect of "antibiotic stress" and "specific DNA masking agent treatment," accurately identifies active drug-resistant microorganisms that pose a real public health risk, reducing misjudgments based solely on resistance genes. The accuracy and reliability of its assessment results far surpass traditional methods. Secondly, this invention combines internal standard absolute quantification with amplicon sequencing technology, providing both high-throughput species information on drug-resistant microorganisms and copy-level absolute abundance, facilitating the quantification of antibiotic resistance risk. Thirdly, this invention does not require long-term pre-culturing and is applicable to microbiomes in free or aggregated states in complex matrices such as water and sludge, demonstrating excellent practical applicability. Attached Figure Description

[0038] Figure 1 This is a schematic flowchart of the high-throughput identification and precise quantification method for antibiotic-resistant bacteria provided by the present invention;

[0039] Figure 2 The diagram shows the stability verification results of the method of the present invention in a complex matrix; wherein: (a) is the linear relationship between the residual proportion of the absolute abundance of 16S rRNA gene and the proportion of resistant bacteria after treatment with 200 mg / L mB of a mixture of different proportions of polymyxin B (PmB) resistant strain (R-13) and PmB sensitive strain (S-11); (b) is the effect of turbidity on the inhibition efficiency of the method of the present invention; (cd) is the residual proportion of the absolute abundance of 16S rRNA gene of strains S-11 and R-13 incorporated into the actual environmental sample matrix;

[0040] Figure 3 This diagram illustrates the DNA residue ratio discrimination standard established by the present invention for distinguishing between active PmB-resistant and susceptible bacteria; wherein, R-1 to R-13 are PmB-resistant strains, and S-1 to S-13 are PmB-sensitive strains.

[0041] Figure 4 This diagram illustrates the absolute abundance and community composition of active PmB-resistant bacteria (PmB-ARB) obtained after testing wastewater treatment plant effluent samples using the present invention. Specifically: (a) compares the number of PmB-ARB genera detected in wastewater treatment plant effluent samples using the method of the present invention and the traditional culture method; (b) shows the distribution of the drug-resistant genera at the phylum level detected by the two methods; (c) shows the absolute abundance of the 16S rRNA gene of PmB-ARB at the phylum level in wastewater treatment plant effluent samples measured by the method of the present invention; and (d) shows the composition of PmB-ARB at the genus level in wastewater treatment plant effluent samples. Detailed Implementation

[0042] This invention provides a method for high-throughput identification and precise quantification of active drug-resistant bacteria. Those skilled in the art can refer to the content of this document and appropriately modify the process parameters to achieve the desired result. It should be particularly noted that all similar substitutions and modifications are obvious to those skilled in the art and are considered to be included in this invention. The methods and applications of this invention have been described through preferred embodiments. Those skilled in the art can obviously make modifications or appropriate alterations and combinations to the methods and applications described herein without departing from the content, spirit, and scope of this invention to implement and apply the technology of this invention.

[0043] This invention provides a high-throughput and precise quantification method for antibiotic-resistant bacteria that integrates drug selection, activity detection, and absolute quantitative sequencing. The method specifically includes the following steps:

[0044] 1. Sample pretreatment steps:

[0045] After obtaining the sample to be tested, preprocessing is performed as follows:

[0046] Water samples are filtered through a membrane (e.g., 0.45 μm pore size) to enrich microorganisms. The membrane is then placed in a sterile buffer solution (e.g., phosphate-buffered saline) and prepared into a homogeneous sample suspension by means of ultrasonic oscillation or other methods.

[0047] For other types of samples, such as sludge or soil, suspensions can be prepared using conventional methods in the field, such as buffer dilution and homogenization.

[0048] 2. Antibiotic treatment steps:

[0049] The pretreated sample suspension was divided into at least two portions, which were labeled as the treatment group and the control group, respectively.

[0050] Add the target antibiotic at a predetermined concentration to the treatment group and incubate under suitable conditions (e.g., 37°C) for a time sufficient to distinguish between susceptible and resistant bacteria (e.g., 5-60 minutes). Incubate the control group under the same conditions but without antibiotics. After incubation, wash the treatment group 2-3 times with isotonic buffer to remove residual antibiotics.

[0051] 3. DNA masking agent treatment steps:

[0052] A photosensitive DNA-binding dye (e.g., 5 μM–40 μM) that is impermeable to intact cell membranes was added to both the treatment and control samples. After incubation in the dark with shaking (e.g., 2–20 min), the samples were then subjected to light treatment. The light conditions, such as light source power, wavelength, exposure time, and distance, can be optimized according to the sample type and volume to ensure that the dye is fully cross-linked with the DNA of the sensitive bacteria.

[0053] 4. DNA extraction and absolute quantitative sequencing steps:

[0054] After light exposure, total DNA was extracted from both groups of samples using a commercially available DNA extraction kit. Before PCR amplification, a known copy number of exogenous internal standard DNA was precisely added to each DNA extract, for example, 10 μL of exogenous internal standard DNA per microliter of DNA extract. 3 copies / μL ~10 7 Copies / μL of commercially available Spike-in Control DNA were then used. Subsequently, using the DNA with added internal control as a template, PCR amplification was performed using universal primers (e.g., primers targeting the V3-V4 region of the bacterial 16S rRNA gene) or specific primers, and the products were sequenced at high throughput. Any suitable high-throughput sequencer in the art can be used as the sequencing platform.

[0055] 5. Bioinformatics analysis and identification of antibiotic-resistant bacteria:

[0056] Sequencing data underwent quality control and species annotation to obtain abundance tables for each microbial taxonomic unit (e.g., amplicon sequence variants (ASVs)) and internal standards. Transformation factors were calculated using the known copy numbers of internal standards and sequencing reads, converting all ASV reads to absolute abundance (e.g., copies / mL). Subsequently, the DNA survival rate of each ASV in the treatment and control groups was calculated. Based on pre-established discrimination criteria, each ASV was classified: ASVs with a DNA survival rate exceeding a preset drug-resistant threshold were identified as "active antibiotic-resistant bacteria"; ASVs with a DNA survival rate below a preset sensitive threshold were identified as "active antibiotic-sensitive bacteria." Finally, the absolute abundance of all active antibiotic-resistant bacteria was summarized to obtain the total burden and community composition of active antibiotic-resistant bacteria in the sample.

[0057] Absolute abundance calculation method:

[0058] First: Calculate the transformation factor, the formula is:

[0059] The known copy number of the exogenous internal standard ÷ the sequencing reads of the internal standard (sequence abundance of the internal standard) = transformation factor;

[0060] Second: Calculate the absolute abundance of a specific microbial taxonomic unit using the following formula:

[0061] The absolute abundance (copies / μL) of a certain microbial taxonomic unit = transformation factor × sequencing reads (sequence abundance) of a certain microbial taxonomic unit;

[0062] Sequencing reads refer to the number of sequences that belong to a specific DNA fragment (such as a specific ASV or internal standard) and are obtained directly through high-throughput sequencing (such as the Illumina platform); specifically, it is the number of reads corresponding to each target sequence (gene, transcript, etc.) counted by alignment (such as Bowtie, STAR) or pseudo-alignment (such as Salmon, Kallisto) software.

[0063] In this invention, the threshold criteria for determining drug resistance and sensitivity are uniform for different microbial taxa in the sample. For polymyxin B in bacterial drug resistance detection, the criteria for determining drug resistance and sensitivity are as follows: if the absolute abundance of the 16S rRNA gene of the strain is greater than 10% (the residual ratio is the ratio multiplied by 100%), it is determined to be a polymyxin B resistant bacterium; if the residual ratio is less than 2%, it is determined to be a polymyxin B sensitive bacterium.

[0064] Furthermore, the antibiotics used can be replaced according to the purpose for different microbial taxa that are being targeted or may exist in the sample. In a specific embodiment of the present invention, polymyxin B is used.

[0065] The sequence abundance of the exogenous internal standard DNA is the number of reads that were successfully aligned to the exogenous internal standard sequence after sequencing. This value is the sequence abundance of the exogenous internal standard DNA.

[0066] The reagents and materials used in this invention are all commercially available products. The invention is further illustrated below with reference to specific examples:

[0067] Example 1: Verification of the stability and anti-interference ability of the method of the present invention in complex matrices.

[0068] This embodiment aims to verify the stability and accuracy of the method of the present invention in quantitative analysis when faced with different environmental disturbances and complex matrices.

[0069] 1. Mixing ratio and quantitative linearity verification:

[0070] PmB-resistant strain (R-13) and PmB-sensitive strain (S-11) were mixed in different proportions, and then the mixture was treated with 200 mg / L PmB, combined with a specific DNA masking agent (propidium azide bromide PMA) and absolute quantitative sequencing. Figure 2 As shown in (a), the residual proportion of the absolute abundance of the 16S rRNA gene in the treated mixture showed a highly linear relationship with the initial proportion of drug-resistant bacteria. This result demonstrates that the present invention still possesses accurate absolute quantification capabilities in complex microbial mixtures.

[0071] 2. Turbidity interference verification:

[0072] Environmental and clinical samples often have different physical properties. Therefore, this embodiment evaluates the impact of common turbidity factors in environmental samples on the method's inhibition efficiency. For example... Figure 2 As shown in (b), after introducing turbidity interference of different gradients, the inhibition efficiency of the method of the present invention against sensitive strains remained at a high level without significant fluctuations. This indicates that the masking and cross-linking steps of the present invention have excellent anti-turbidity interference capabilities.

[0073] 3. Real-world matrix validation:

[0074] To further confirm the reliability of the method in real-world scenarios, PmB-sensitive strains (S-11) and drug-resistant strains (R-13) were respectively incorporated into complex environmental sample matrices (sewage treatment plant influent and sludge) for detection. Figure 2As shown in (c) and (d), under the encapsulation and interference of the actual environmental matrix, the absolute abundance of the 16S rRNA gene of susceptible bacteria remained stably suppressed to an extremely low level, while the abundance of resistant bacteria remained at a high level. This result verifies the stability and feasibility of this invention in identifying and quantifying active drug-resistant microorganisms in real, complex samples.

[0075] Example 2: Identification and Quantification of Active PmB-ARB in Wastewater Samples

[0076] This embodiment aims to illustrate by example how to apply the method of the present invention to identify and quantify active PmB-ARB in effluent samples from wastewater treatment plants.

[0077] 1. Sample collection and pretreatment:

[0078] A 2L sample of effluent from a municipal wastewater treatment plant was collected and filtered through a membrane (0.45μm pore size) to enrich microorganisms. The membrane was then placed in a sterile buffer solution (phosphate-buffered saline) and prepared into a homogeneous sample suspension by means of ultrasonic oscillation.

[0079] 2. Antibiotic treatment:

[0080] The sample suspension was divided into at least two equal portions, labeled as the treatment group and the control group. PmB was added to the treatment group to a final concentration 10 to 500 times the minimum inhibitory concentration (MIC) of the microorganism (in this experiment, the PmB concentration was specifically 200 mg / L), and incubated at 37°C for 5 to 60 minutes (the incubation time was the same for the same sample, even if the bacterial species contained were different; specifically, the incubation time was 30 minutes). The control group was incubated under the same conditions, but without the addition of antibiotics. After incubation, the treatment group was washed twice with isotonic buffer to remove residual PmB and was ready for use.

[0081] Table 1. Identification information of strains and their corresponding MIC values ​​of polymyxin B

[0082]

[0083] In Table 1, G+ indicates Gram-positive bacteria; G- indicates Gram-negative bacteria.

[0084] 3. DNA masking agent treatment:

[0085] A photosensitive DNA-binding dye (PMA) that is impermeable to intact cell membranes was added to both the treatment and control samples, to a final concentration of 5 μM–40 μM (in this experiment, the specific concentration of PMA was 10 μM). After incubation in the dark with shaking for a period of time, the samples were placed on ice and irradiated for a period of time using a xenon lamp equipped with a 420 nm UV cutoff filter.

[0086] 4. DNA extraction and absolute quantitative sequencing:

[0087] After light exposure, total DNA was extracted from both groups of samples using a commercially available DNA extraction kit. Before PCR amplification, 10 μL of the extracted DNA solution was precisely added to each sample. 3 copies / μL ~10 7 Commercial Spike-in Control DNA copies / μL (the amount of Spike-in Control DNA added to the treatment and control groups of the same sample was the same, specifically 10 copies / μL). 5 (copies / μL). Subsequently, using DNA with added internal standard as a template, PCR amplification was performed using universal primers (primers targeting the V3-V4 region of the bacterial 16S rRNA gene), and the products were sequenced using high-throughput sequencing.

[0088] 5. Bioinformatics analysis and identification of antibiotic-resistant bacteria:

[0089] Sequencing data underwent quality control and species annotation to obtain abundance tables for each microbial taxonomic unit (e.g., ASV) and internal standard. Transformation factors were calculated using the known copy number of the internal standard and sequencing reads, converting all ASV reads into absolute abundance. Subsequently, the DNA survival rate of each ASV in the treatment and control groups was calculated (i.e., the ratio of absolute abundance in the antibiotic-treated group to absolute abundance in the control group). In this embodiment, ASVs with a DNA survival rate greater than 10% were identified as active PmB-ARBs, and ASVs with a survival rate less than 2% were identified as active PmB-sensitive bacteria (PmB-ASBs). Figure 3 As shown, these two thresholds were set based on a large number of known PmB-resistant strains ( Figure 3 (R-1 to R-13) and susceptible strains ( Figure 3 The discrimination criteria were established based on experimental verification of S-1 to S-13. The experimental results clearly show that the DNA survival rate of all known drug-resistant strains was significantly higher than 10%. Conversely, the DNA survival rate of all known susceptible strains was lower than 2%. The clear distinction between these two thresholds provides a reliable criterion for accurately differentiating drug-resistant and susceptible bacteria.

[0090] Absolute abundance calculation for each ASV:

[0091] (1) In the ASV table, identify and count the number of reads of the internal standard ASV in each sample. spike-in ).

[0092] (2) Calculate the conversion factor (CF) for each sample: CF = (total number of added exogenous internal standard copies) / (number of measured exogenous internal standard reads), the unit is (copies / read).

[0093] (3) Calculate the absolute abundance (copy number / μL DNA extract) of each ASV in each sample: Abundance ASV (copies / μL) = (number of reads for this ASV) × CF.

[0094] Absolute quantitative calculation:

[0095] Based on the total volume of DNA extraction solution and the initial sample volume (filtered water volume mL or treated sludge mass g), calculate the absolute abundance of each ASV in the original environmental sample (Abundance ASV, copies / mL or copies / g):

[0096] Abundance ASV (copies / mL or copies / g) = ((number of reads of this ASV) × CF × total volume of DNA extraction solution) / starting sample volume.

[0097] Analysis of active PmB resistance group:

[0098] (1) For each ASV, calculate its absolute abundance residual ratio (%) in the treatment group (i.e., the PmB+PMA treatment group) relative to the control group (i.e., the PMA-only group): ResidualRatio ASV (%) = (absolute abundance of ASV in the PmB+PMA group / absolute abundance of ASV in the PMA-only group) × 100%.

[0099] (2) Based on the established criteria, ASVs with a residual ratio > 10% are identified as active PmB-resistant ASVs, and ASVs with a residual ratio < 2% are identified as active PmB-sensitive ASVs.

[0100] (3) Calculate the total active PmB-ARB abundance = ∑ (absolute abundance of all active PmB resistant ASVs).

[0101] (4) Calculate the proportion of active PmB-ARB in the total active bacterial community (%) = (total active PmB-ARB abundance / total active bacterial abundance (total ASV abundance from the PMA-only group)) × 100%.

[0102] 6. Results Analysis:

[0103] like Figure 4As shown, this method achieves high-throughput identification and accurate quantification of active PmB-ARB. Compared with the traditional culture method, this method detected 149 drug-resistant bacterial genera in the effluent samples from the wastewater treatment plant (compared to only 27 detected by the culture method), covering 12 phyla (compared to only 5 by the culture method). By summing the absolute abundance of all ASVs identified as active PmB-ARB, the total load of active PmB-ARB in the effluent samples from this wastewater treatment plant was approximately 2.1 × 10⁻⁶. 3 The main antibiotic-resistant bacterial groups were Toxoplasma spp. (27.8%), Acinetobacter spp. (17.1%), and Bacteroides spp. (2.8%), which are clinically important opportunistic pathogens, revealing potential public health risks in wastewater treatment plant effluent.

[0104] 7. Summary of parameter optimization and method extensibility:

[0105] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention without departing from the principles and spirit of the present invention.

[0106] Furthermore, the core technology of this invention possesses platform versatility. Regarding antibiotic pressure, the method of this invention is applicable to antibacterial and bactericidal antibiotics with different mechanisms of action, and can be used to study a wide range of antibiotic resistance groups. In terms of sequencing strategies, this method is not limited to 16S rRNA gene amplicon sequencing, but can also be seamlessly integrated with metagenomic sequencing, thereby identifying the species identity of active PmB-ARBs while also obtaining richer genetic information such as their resistance gene profiles and virulence factors.

[0107] The above are merely preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for high-throughput identification and accurate quantification of active antibiotic-resistant bacteria in a sample, characterized in that, The steps include the following: Step 1: Divide the samples into a treatment group and a control group; the treatment group is incubated after contact with antibiotics; the control group is not incubated after contact with antibiotics. Step 2: After incubation, the treatment group and the control group were treated with a photocrosslinking DNA blocking agent, respectively; Step 3: Extract DNA from the treated group and the control group after the treatment, and add a known amount of exogenous internal standard DNA to each extracted DNA sample; Step 4: Perform 16S rRNA gene sequencing on the treatment group and the control group in Step 3 with added internal standard DNA to obtain the sequence abundance of microbial taxa units and the exogenous internal standard DNA in the treatment group and the control group; Step 5: Based on the known number of reads of exogenous internal standard DNA, calculate the absolute abundance of each microbial taxonomic unit in the treatment group and the control group, and identify each microbial taxonomic unit as an active antibiotic-resistant or sensitive bacterium by comparing the survival rate of each microbial taxonomic unit in the treatment group and the control group, and perform absolute quantification of each microbial taxonomic unit. Wherein, the survival rate is: the ratio of the absolute abundance of each microbial taxa in the treatment group to its absolute abundance in the control group; The absolute abundance of each of the aforementioned microbial taxa is the product of the transformation factor and the number of reads for a particular microbial taxa. The transformation factor is the ratio of the specific quantity of the known amount of exogenous internal standard DNA to the number of reads of the known amount of exogenous internal standard DNA. The formula for absolute quantification of each of the aforementioned microbial taxa is as follows: ； The unit for the total volume of the DNA extraction solution is μL; the total volume of the DNA extraction solution is the volume of DNA extracted from the treated group or the control group after the treatment. The initial sample quantity includes the initial sample mass and / or the initial sample volume, wherein the initial sample mass is in g and the initial sample volume is in mL.

2. The method according to claim 1, characterized in that, The incubation time is 5 to 60 minutes.

3. The method according to claim 1, characterized in that, The photocrosslinking DNA blocking agents include: propidium azide bromide, ethidium bromide monoazide, psoralen, and / or MycoLight™ vPCR350.

4. The method according to claim 3, characterized in that, The concentration of the photocrosslinking DNA blocking agent is 5 μM to 40 μM.

5. The method according to claim 1, characterized in that, The exogenous internal standard DNA was Spike-in Control DNA.

6. The method according to claim 5, characterized in that, The known quantity of exogenous internal standard DNA is 10 3 copies / μL ~ 10 7 copies / μL of Spike-in Control DNA.

7. The method according to claims 1 to 6, characterized in that, The criteria for identification are as follows: If the survival rate exceeds the preset resistance threshold for the antibiotic, then the microbial taxonomy unit is an active antibiotic resistant bacterium. If the survival rate is lower than a preset sensitivity threshold for the antibiotic, then the microbial taxonomy unit is an active antibiotic-sensitive bacterium.

8. The method according to claim 7, characterized in that, The antibiotic is polymyxin B; the resistance threshold for the antibiotic is a ratio greater than 0.1; the sensitivity threshold for the antibiotic is a ratio less than 0.

02.

9. The method according to claim 8, characterized in that, The concentration of polymyxin B is 200 mg / L.

10. The method according to claim 9, characterized in that, The samples include environmental samples, water treatment system samples, medical clinical samples, and / or industrial samples.

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