CN116631500A - Non-core drug-resistant gene - Google Patents
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Abstract
The application relates to the technical field of bioinformatics, in particular to a model construction method for detecting and identifying drug resistance genes and predicting drug resistance phenotype based on gene sequencing reads comparison. The application combines important characteristic genes related to drug-resistant phenotype, and constructs and realizes direct comparison detection and identification of target pathogenic bacteria and drug-resistant genes carried by the target pathogenic bacteria based on sequencing read sequence, and prediction of drug-resistant phenotype.
Description
The application is a divisional application of a Chinese patent application CN 202111680866.8 submitted by 2021, 12 months and 30 days.
Technical Field
The application relates to the technical field of bioinformatics, in particular to a method for detecting and identifying drug resistance genes and predicting drug resistance phenotype based on gene sequencing read comparison.
Technical Field
The accurate detection of infectious pathogenic bacteria and drug resistance thereof is a key for guiding clinical accurate medication. Currently, laboratory tests for resistance to infectious pathogens are classified into phenotypic tests and genotypic tests. In the aspect of a phenotype detection method, a gold standard method of microorganism culture and drug sensitivity test is mainly used clinically, the method can provide powerful basis for diagnosis and treatment of clinical infection, but the method also has some limitations, such as time-consuming culture (generally 2-4 days), low positive rate of pathogen culture, easy influence of various uncertain factors in the culture process, and the like, and some rare pathogenic bacteria are difficult to culture or even cannot be successfully cultured. Other detection technologies such as Carba NP test, modified carbapenem inactivation test (including mCIM and eCIM), carbapenem inhibitor enhancement test, time-of-flight mass spectrometry technology and the like are detection for clinically common carbapenemases of enterobacteriales, and are only suitable for detecting drug resistance of carbapenem drugs caused by producing related enzymes, but not for other mechanisms (such as efflux pump mechanisms). Genotype detection, including enzyme immunochromatography techniques and genetic detection techniques. The enzyme immunochromatography technology and the conventional gene detection technology (such as GeneXpert, verigene and Filmarray detection systems) are used for detecting specific target genes, and have the characteristics of rapidness and easiness in interpretation, but if the genes to be detected are different from the target genes, false negative results are easy to appear.
With the continuous development of sequencing technology and the reduction of sequencing cost, the detection of pathogenic bacteria and drug resistance genes thereof based on sequencing is also becoming popular. Microbial genome sequencing, including both Whole Genome Sequencing (WGS) and metagenomic sequencing (mNGS). For whole genome sequencing of bacterial strains, studies have shown that constructing a model (rule-based model or machine-learning model) based on genome data of all strains of a single population and corresponding drug-resistant phenotype data, and then analyzing and predicting drug-resistant phenotypes of new strains by genome data has a better effect (the accuracy rate can reach more than 95%), but there is a limitation that whole genome sequencing likewise bypasses the limitation of culture enrichment of pathogenic bacteria. And the pathogen metagenome high throughput sequencing (mNGS) is a novel pathogen detection means developed in more than ten years, and is different from microbial culture, the mNGS can read the base sequences of all pathogen nucleic acids from a small number of samples at one time without screening to obtain pure cultures of all pathogens in the environment, and provide information on the types of the pathogens and the like. The mNSS has the characteristics of rapid detection, wide coverage pathogen and no deviation, and has not only been developed on the high-level SCI journal by related application research articles, but also the detection of drug-resistant genes is analyzed and discussed by a few of the published articles, so that the mNSS has the potential value of predicting the drug sensitivity characteristics of pathogenic bacteria. However, it is known that clinical specimens (alveolar lavage, blood, cerebrospinal fluid, etc.) often suffer from serious contamination by the host (the host nucleic acid content in the total nucleic acid of the extracted sample is more than 95%), and the microbial load is low, so that the bacterial genome measured by the clinical specimens is much covered by not more than 1X under the conventional sequencing of about 20M reads. In the case of this sequencing data, it is necessary to examine the detection efficiency of mNGS further, if mNGS can identify pathogenic bacteria well and detect drug resistance genes accurately to predict the drug resistance of pathogenic bacteria.
Disclosure of Invention
In order to solve the technical problems, the application combines the important characteristic genes related to the drug resistance phenotype screened and determined by BGWAS to construct the data analysis method and system for directly carrying out comparison detection identification and prediction of the drug resistance phenotype of target pathogenic bacteria and the drug resistance genes carried by the target pathogenic bacteria based on the NGS or nanopore sequencing read sequence. Aiming at genomes of all strains in a training set which is incorporated by the early BGWAS, detecting a target drug resistance gene by simulating NGS or nonopore sequencing read sequence comparison (read-based) and genome content sequence comparison (assembly-based), and verifying and optimizing a read-based detection flow by taking a detection result of an assmbly-based method as a reference so as to realize the aim of accurately detecting genotyping of the read-based. Then, a Score is calculated by a self-defined formula, and is used as an interpretation index for predicting the drug susceptibility property of the antibiotic drug, ROC analysis is carried out by combining a read sequence simulation test to determine an optimal cutoff threshold, and meanwhile, the accuracy and the performance of a prediction model are evaluated. Finally, the effectiveness of the analysis system is assessed using clinical specimens or culture isolated pure pathogenic bacterial strain specimens for validation.
Specifically, the application provides the following technical scheme:
The application firstly provides a method for detecting and identifying drug resistance genes and constructing a model for predicting drug resistance phenotype based on gene sequencing reads comparison, which comprises the following steps:
step 1): sorting and correcting drug resistance gene family information and annotation information of consistency among gene sequences by combining classification information of drug resistance genes in a CARD drug resistance database, and preferably sorting and correcting source species information and/or a mediating mode;
step 2): calculating a weight coefficient of a drug-resistant gene family, and calculating the weight coefficient of the gene family based on the weight coefficient of the member gene in the family and the sample detection frequency of the corresponding gene in the BGWAS model training set;
step 3): detection and flow correction of drug-resistant genes.
Further, in the step 1),
the gene family is defined according to family information of the genes recorded in the drug resistance database, and combing correction is carried out by referring to the family information of the drug resistance genes recorded in the NCBI NDARO database and the MEGARes database;
the source species information is obtained by comparing all drug-resistant reference genes with NCBI NT library, reserving hit of identity > =95% and subject coverage > =95% to obtain all species annotation information of each drug-resistant gene;
The mediation mode is to inquire whether the drug resistance gene is mediated by plasmid in the reference sequence description information on the comparison;
the consistency annotation information between the gene sequences is that all the drug-resistant gene sequences are compared pairwise, so that consistency values between all the gene sequences are obtained.
Further, in the step 2), the weight coefficient definition formula is as follows:
wherein arg_Ni is the number of samples detected in the BGWAS model training set of the corresponding genes in the target gene family, arg_wi is the weight coefficient of the corresponding genes in the family, j represents j key genes in the family, and j+k represents the number of all genes in the family.
Further, the detection and flow correction of the drug-resistant gene in the step 3) comprises the following steps:
a) Sequencing Reads data simulation;
b) Sequence alignment and annotation statistics;
c) Screening and filtering drug-resistant genes.
Further, the a) sequencing Reads data simulation is based on BGWAS model training set strain samples for data simulation; preferably:
short reads sequence data were sequenced using art_illumina software to simulate bacterial strain genome NGS;
sequencing reads sequence data using ReadSim software to simulate bacterial strain genome Nanopore;
more preferably, the simulation is simulating a gradient data volume of 0.05X, 0.1X, 0.2X, 0.3X, 0.4X, 0.5X, 0.6X, 0.7X, 0.8X, 0.9X, 1X, 2X, 3X, 5X, 10X, 30X.
Further, the b) sequence alignment and annotation statistics include:
comparing the simulated reads sequence with a drug-resistant gene library, and filtering low-quality hit; performing final gene annotation, counting to obtain the number of specific reads detected by the drug-resistant genes in the sample, multiple comparison of the number of reads and the number of reads of the family to which the drug-resistant genes belong, and calculating the coverage of the detected genes;
preferably, final gene annotation of reads using the optimal alignment and LCA algorithm is: selecting the hit of the highest score of each ready sequence, namely a best hit as a final hit, if multiple identical values exist in the best hit, namely multiple alignment, carrying out final annotation of the read sequence on the multiple hits by adopting an LCA algorithm, namely annotating a higher hierarchy level as a gene family level because multiple alignment leads to the fact that the annotation of a single read sequence is not up to genotype;
more preferably, the method further comprises the steps of,
aiming at NGS sequencing data, using blastn software to compare the simulated ready sequence with a drug-resistant gene library, and filtering to retain hit with identity higher than 90%;
for Nanopore sequencing data, a mini 2 software was used to align with the drug-resistant reference gene library, filtering out hit with identity below 0.7 or with subject coverage below 0.4.
Further, the screening and filtering of the drug-resistant genes in the c) is as follows: screening and filtering the drug-resistant genes of the reads sequences in the step b);
preferably, any one or more of the following screening filtration criteria are included:
a) Evaluating the influence of sequence consistency among different genotyping genes in a drug-resistant gene reference library on the accurate detection of the drug-resistant genotyping of read-based: selecting drug-resistant genes with different maximum sequence consistencies in a database, simulating short reads sequences to perform read-based flow detection analysis, and counting the number of specific reads detected by target genes and the number of reads of all target genes compared; detecting and identifying a genotyping strategy aiming at whether a specific reads sequence exists, and selecting 95% identity as a threshold standard of whether the accurate genotyping can be easily realized by the target gene;
b) Screening and filtering based on drug resistance genotyping identification: aiming at target genes with high similarity with other genes in a database, adopting all the reads capable of comparing the target genes as true positive detection, and calculating the retention of non-first positions based on accurate comparison read to obtain genes with 100% coverage as true positive detection; secondly, aiming at target genes with low similarity with other genes in the database, judging whether the target genes are true positive results or not according to the number of detected specificity reads; direct filtering out for some gene families for which no specific reads were detected;
C) Evaluation of read-based detection accuracy in identifying drug resistant genotypes: taking the result of detecting the drug-resistant gene by the assembly-based in the BGWAS model training process as a reference, aiming at the important gene or gene family screened out by the BGWAS model, obtaining the accuracy, sensitivity and specificity index of the read-based drug-resistant gene or gene family by statistics,
further, step 4): defining and calculating a negative and positive judgment index Score value, and determining a report rule and a cutoff threshold value based on ROC analysis;
the Score value is calculated as follows:
wherein arg_wi represents the weight coefficient of the corresponding genotype, and generamily_wi represents the weight coefficient of the corresponding gene family; when the genotype is detected and the genotype weight coefficient is more than 0, calculating by the genotype weight coefficient; when the genotype is detected, but the genotype weight coefficient is 0 or no weight coefficient, the genotype weight coefficient is calculated by the gene family weight coefficient.
Further, the sequencing reads are first, second and third generation sequencing reads, preferably NGS or Nanopore sequencing reads; more preferred are NGS or Nanopore metagenomic sequencing reads.
The invention also provides a model construction method for drug-resistant gene species attribution prediction, which is characterized by comprising the following steps:
Step 1): comparing genome sequences of target pathogenic bacteria, detecting the sequence number, and calculating genome coverage and coverage depth;
step 2): based on the BGWAS model training set specimen drug resistance gene detection result, counting the copy number of drug resistance genes carried by target pathogenic bacteria;
step 3): and calculating the copy number of the drug-resistant gene and judging the species attribution based on the assumed gene-species attribution relationship.
Further, the step 1) is specifically that
Selecting a clinically common pathogenic bacterium to set as a target pathogen, searching and downloading a target pathogenic bacterium reference genome from an NCBI genome database, and taking the target pathogenic bacterium reference genome as a reference sequence library for identifying target pathogenic bacteria;
comparing each sequencing reads sequence with the reference sequence library, and calculating the detected sequence number, genome coverage and coverage depth of the target pathogenic bacteria on comparison; and counting to obtain the total reads sequence number, genome coverage and coverage depth of the detected pathogenic bacteria.
Further, the step 2) specifically includes:
based on the detection result of the drug-resistant genes of the assembly-based training set sample during BGWAS model training, the detection distribution and the copy change range of the drug-resistant genes and the drug-resistant gene families of the target pathogenic bacteria are obtained through statistics.
Further, the step 3) specifically includes:
assuming drug resistance gene-species correspondence, the main basis is: a. the species annotation of the reference gene in the database contains the target species, and if so, the assumption of the relationship of the gene-species is accepted; b. if a is not satisfied, checking whether the mediation mode annotation of the reference gene in the database contains a plasmid mediation mode, and if so, firstly accepting the assumption of the attribution relation of the gene-species; c. if a and b are not satisfied, the species source is presumed according to the species annotation of ARG-like reads, and the copy number of the drug resistance gene is calculated according to the following formula:
and if the calculated copy number of the drug-resistant gene falls within the normal copy number range of the target gene family obtained based on the BGWAS model training set statistics, the assumed gene-species attribution relationship is accepted, otherwise, rejection is carried out.
The invention also provides a method for detecting the drug resistance of metagenomic sequencing data, which comprises the following steps:
1) Performing quality control and human nucleic acid sequence removal on the sample sequencing data;
2) Detecting and identifying drug resistance genes contained in a sample: based on the detection and identification method, comparing and annotating statistics of drug resistance genes of the sample sequence are carried out, and the drug resistance genes contained in the sample are detected and identified;
3) Predicting species assignment of the detected drug resistance genes in the sample: identifying target pathogenic bacteria contained in the sample according to the species attribution prediction method, and predicting the species attribution of the detected drug-resistant genes;
4) According to the detected drug resistance gene carrying condition of target pathogenic bacteria, according to the above score calculation mode, the score value of the target species-antibiotic drug is calculated and obtained, and compared with the cutoff value: when score > =cutoff, then R is predicted; score < cutoff, S is predicted if the pathogen genome coverage is detected to be higher than the minimum genome coverage or data amount required for model stabilization, otherwise it is reported as unknown.
The invention also provides a model for identifying drug resistance genes based on gene sequencing reads comparison detection, which comprises the following modules:
module 1): the method is used for combining classification information of drug resistance genes of a CARD drug resistance database, sorting and correcting drug resistance gene family information and annotation information of consistency among gene sequences, and preferably comprises sorting and correcting source species information and/or a mediation mode;
module 2): the method is used for calculating the weight coefficient of the drug-resistant gene family, and calculating the weight coefficient of the gene family based on the weight coefficient of the member genes in the family and the sample detection frequency of the corresponding genes in the BGWAS model training set;
Module 3): the method is used for detection and flow correction of drug-resistant genes.
The invention also provides a prediction model of drug-resistant gene species attribution, and the method comprises the following steps:
module 1): the method is used for comparing genome sequences of target pathogenic bacteria and calculating the number of detected sequences, genome coverage and coverage depth;
module 2): the method is used for counting the copy number of the drug-resistant gene carried by the target pathogenic strain based on the detection result of the drug-resistant gene of the training set specimen of the BGWAS model;
module 3): the method is used for calculating the copy number of the drug-resistant gene and judging the species attribution based on the assumed gene-species attribution relationship.
Further limitations of the modules in the above model are the same as the limitations of the steps in any of the above methods.
The invention also provides an apparatus comprising: at least one memory for storing a program; at least one processor for loading the program to perform the method of any of the above.
The invention also provides a storage medium having stored therein processor executable instructions which when executed by a processor are for carrying out the method as claimed in any one of the preceding claims.
The invention also provides the following contents:
the application of genes AAC (3) -IIe, AAC (3) -IV, AAC (3) -IId, rmtC, armA, rmtF, rmtB, AAC (6') -33 and ANT (2 ") -Ia as non-core type drug-resistant genes in the auxiliary drug sensitivity prediction of klebsiella pneumoniae;
The drug susceptibility prediction comprises drug resistance prediction and susceptibility prediction, preferably susceptibility prediction;
more preferably, the drug-sensitivity is directed to gentamicin drugs.
Application of detection reagents aiming at non-core drug-resistant genes AAC (3) -IIe, AAC (3) -IV, AAC (3) -IId, rmtC, armA, rmtF, rmtB, AAC (6') -33 and ANT (2 ") -Ia in preparation of klebsiella pneumoniae auxiliary drug sensitivity prediction kit;
the drug susceptibility prediction comprises drug resistance prediction and susceptibility prediction, preferably susceptibility prediction;
more preferably, the drug-sensitivity is for gentamicin drug;
it is further preferable that the detection of the high frequency occurrence and high weight of AAC (3) -IIe, AAC (3) -IV, AAC (3) -IId, rmtC, armA, rmtF, rmtB, AAC (6') -33 and ANT (2 ") -Ia genes, which mainly mediate drug resistance, is performed simultaneously, and if the detection results are negative, it is presumed to be sensitive.
Application of genes AAC (3) -IV, AAC (3) -IId, AAC (6 ') -Ib ', AAC (6 ') -Ib-cr, AAC (6 ') -Ib-Hangzhou, AAC (6 ') -Ib4, mphE, ANT (2 ") -Ia and aadA24 as non-core drug-resistant genes in klebsiella pneumoniae auxiliary drug sensitivity prediction;
the drug susceptibility prediction comprises drug resistance prediction and susceptibility prediction, preferably susceptibility prediction;
more preferably, the drug-sensitive is against tobramycin.
Application of detection reagents aiming at non-core drug-resistant genes AAC (3) -IV, AAC (3) -IId, AAC (6 ') -Ib ', AAC (6 ') -Ib-cr, AAC (6 ') -Ib-Hangzhou, AAC (6 ') -Ib4, mphE, ANT (2 ") -Ia and aadA24 in preparation of klebsiella pneumoniae auxiliary drug-sensitive prediction kit;
the drug susceptibility prediction comprises drug resistance prediction and susceptibility prediction, preferably susceptibility prediction;
more preferably, the drug-sensitive is against tobramycin;
it is further preferable that the detection of AAAC (3) -IV, AAC (3) -IId, AAC (6 ') -Ib ', AAC (6 ') -Ib-cr, AAC (6 ') -Ib-Hangzhou, AAC (6 ') -Ib4, mphE, ANT (2 ") -Ia, aadA24 genes, which mainly mediate the occurrence of high frequency of drug resistance, are performed simultaneously, and if the detection results are negative, the detection results are presumed to be sensitive.
The application of the genes CTX-M-55, CTX-M-11, CTX-M-15, SHV-155, SHV-5, SHV-11, SHV-12, SHV-76, SHV-30, SHV-53, SHV-124, SHV-182, DHA-1, KPC-3 and KPC-2 as non-core type drug resistance genes in the auxiliary drug sensitivity prediction of klebsiella pneumoniae.
The drug susceptibility prediction comprises drug resistance prediction and susceptibility prediction, preferably susceptibility prediction;
more preferably, the drug-sensitive is directed to ceftazidime.
Application of detection reagents aiming at non-core drug-resistant genes CTX-M-55, CTX-M-11, CTX-M-15, SHV-155, SHV-5, SHV-11, SHV-12, SHV-76, SHV-30, SHV-53, SHV-124, SHV-182, DHA-1, KPC-3 and KPC-2 in preparation of klebsiella pneumoniae auxiliary drug-sensitive prediction kit;
The drug sensitivity prediction is drug resistance prediction;
preferably, the drug-sensitive is directed against ceftazidime.
More preferably, the drug resistance is estimated by detecting the CTX-M-55, CTX-M-11, CTX-M-15, SHV-155, SHV-5, SHV-11, SHV-12, SHV-76, SHV-30, SHV-53, SHV-124, SHV-182, DHA-1, KPC-3 and KPC-2 genes which are high in weight and which mainly mediate the occurrence of the drug resistance, and if the detection results are positive.
The application of genes dfrA12, dfrA15, dfrA17, dfrA19, dfrA30, dfrA8, dfrA5, dfrA15b, dfrA14, dfrA 22, dfrA27 and dfrA1 as non-core drug-resistant genes in klebsiella pneumoniae auxiliary drug-sensitive prediction;
the drug sensitivity prediction is drug resistance prediction;
preferably, the drug sensitivity is aimed at a compound neonolamine drug.
Application of detection reagents aiming at non-core drug-resistant genes dfrA12, dfrA15, dfrA17, dfrA19, dfrA30, dfrA8, dfrA5, dfrA15b, dfrA14, dfrA 22, dfrA2 and dfrA1 in preparation of klebsiella pneumoniae auxiliary drug-sensitive prediction kit;
the drug sensitivity prediction is drug resistance prediction;
preferably, the drug sensitivity is aimed at a compound neonolamine drug.
More preferably, the genes dfrA12, dfrA15, dfrA17, dfrA19, dfrA30, dfrA8, dfrA5, dfrA15b, dfrA14, dfr22, dfrA27 and dfrA1, which are high in weight and mainly mediate the occurrence of drug resistance, are detected simultaneously, and if the detection results are positive, the drug resistance is estimated.
The application has the beneficial technical effects that:
1) The application is a method for detecting drug resistance based on nucleic acid molecules, which bypasses the limitation of traditional culture, directly carries out metagenome sequencing detection and identification on target pathogenic bacteria and drug resistance gene carrying conditions of the target pathogenic bacteria aiming at clinical specimens, and further predicts drug sensitivity results of antibiotic drugs based on the presence or absence of detection characteristics of the drug resistance genes.
2) When the application detects and identifies the drug-resistant genes, the drug-resistant genes are directly compared and detected based on the ready sequence of NGS or non-napore sequencing, and compared with the comparison and detection based on genome contig, the application bypasses the genome assembly step and has higher detection sensitivity. Specifically, a set of read-based drug-resistant gene comparison detection method and a corresponding database are constructed, and simultaneously, the drug-resistant gene detection result of the asembly-based method is used as a reference for the read-based drug-resistant gene comparison detection method
And verifying and evaluating the performance of the based flow, and ensuring the accuracy of detecting the drug-resistant gene by the read-based.
3) The application constructs a specific drug-resistant gene reference database aiming at read-based drug-resistant gene detection, and particularly, the application realizes the annotation strategy that LCA can be adopted for the sequence to be queried by sorting the drug-resistant gene types and the classification thereof. Specifically, all gene sequences of the CARD drug-resistant public library are recorded as reference genes, the gene multilevel level labeling mode of the MEGARes database and the family information of the drug-resistant genes recorded in the NCBI NDARO database are referenced, and each reference gene is labeled at 6 levels, so that LCA (low cost
common references) annotation strategy, i.e., the number of detected specific reads at each level is obtained. By OXA-181
The gene is exemplified by its class 6 level labeled: oxa-181__1 (L1_geneST), oxa-
181(L2_genetype),OXA-48subfamily(L3_subgroup),OXA family(L4_Group),
Class_D_betalactamases(L5_Mechanism),betalactams(L6_Class)。
4) The invention adopts a strategy combining two rules aiming at the accurate detection of the read-based drug-resistant genotyping, and can effectively improve the genotyping capability and accuracy of the detection flow. Firstly, aiming at target genes with high similarity (such as consistency exceeding 95%) with other genes in a database, adopting ARG-like reads (i.e. the reads of all the target genes can be compared, or specific reads+multiple comparison reads) to take the first position as true positive detection, and reserving genes with 100% coverage calculated based on accurate comparison reads for the non-first position as true positive detection; and secondly, aiming at target genes with low similarity (consistency is lower than 95%) with other genes in the database, judging whether the target genes are true positive results or not mainly according to the number of detected specificity reads. For some undetected specificities
The family of reads would be considered false positives and filtered directly.
5) The invention defines a method for calculating the weight coefficient of the corresponding drug-resistant gene family based on the drug-resistant gene typing weight coefficient
Aiming at the situation that the genotyping is possibly inaccurate, instead of adopting the gene family weight to participate in calculating and predicting the drug sensitivity result, the false positive possibly caused by the inaccurate genotyping is effectively avoided.
6) The invention defines a method rule for predicting drug sensitivity, namely defines a calculation formula of yin-yang interpretation index Score, and simultaneously combines gradient simulation tests of different sequencing data amounts to evaluate the performance of a prediction model and determine cutoff
And the threshold value is finally used for effectively predicting the antibiotic susceptibility result.
7) Aiming at metagenome sequencing (mixed flora sequencing) of clinical specimens, the invention defines a method (or formula) for calculating the copy number of the drug-resistant gene based on the detection sequence conditions of target pathogenic bacteria and the drug-resistant gene, and predicts and evaluates the possible pathogenic species sources of the drug-resistant gene according to the calculated copy number of the drug-resistant gene. Specifically, when the corresponding relation between the drug resistance gene and the pathogenic species is deduced, the drug resistance gene is firstly assumed to originate from a certain target species according to the information of the actually detected drug resistance gene and pathogenic species, then the copy number of the drug resistance gene is calculated, whether the calculated copy number falls within a normal range is checked, if so, the attribution relation is considered to be accepted, otherwise, rejection is carried out. Assuming drug resistance gene-species correspondence, the main basis is: a. the species annotation of the reference gene in the database contains the target species, and if so, the assumption of the relationship of the gene-species is accepted; b. if a is not satisfied, checking the information of the mediation mode of the reference genes marked in the database, and if the information contains a plasmid mediation mode, firstly accepting the assumption of the relationship of the genes and species; c. if a, b are not satisfied, the species source is presumed from the species annotation of ARG-like reads.
8) By taking the detection of drug resistance of Klebsiella pneumoniae antibiotic drugs as an example, the invention can accurately identify Klebsiella pneumoniae strains and drug resistance genes carried by the Klebsiella pneumoniae strains, and can effectively predict carbapenems (imipenem, meropenem) and aminoglycosides
The drug sensitivity result of (gentamicin and tobramycin) and the drug resistance property of the ceftazidime and the compound neonomine are predicted, and the prediction accuracy is more than 90 percent. Clinical specimen sampling verification shows that the drug sensitivity prediction accuracy of carbapenem can reach 100%, and the number of samples for clearly giving out drug sensitivity prediction result prompts is more than 80%. The invention can assist in clinical detection and diagnosis of infection drug-resistant bacteria.
Drawings
FIG. 1 is a technical roadmap of the invention;
FIG. 2 is a graph of results of drug resistance gene testing performed on 100X target gene read data for target genes of different identity from other genes in the database. In the figure, ARG-like is the ratio of the number of sequences of the detected target gene or non-target gene to the number of sequences detected by the family to which the target gene belongs, and Specific is the number of sequences Specific to the detected target gene.
FIG. 3 is a graph of accuracy performance of genotyping or genotyping of a gene family identified by read-based on the results of an analysis of an drug-resistant gene of an assembled-based.
FIG. 4 technical flow chart for Score index calculation and reporting rules based on read-based drug resistance gene detection FIG. 5 graphs simulating 6 antibiotic resistance prediction model performance (AUC values) for different sequencing data volumes FIG. 6 graphs simulating 6 antibiotic drug prediction model performance (AUC values) for 30X genome data volumes for training and validation sets.
Detailed Description
Embodiments of the present application will be described in detail below with reference to examples, but it will be understood by those skilled in the art that the following examples are only for illustrating the present application and should not be construed as limiting the scope of the present application. The specific conditions are not noted in the examples and are carried out according to conventional conditions or conditions recommended by the manufacturer. The reagents or apparatus used were conventional products commercially available without the manufacturer's attention.
Partial term definition
Unless defined otherwise hereinafter, all technical and scientific terms used in the detailed description of the application are intended to be identical to what is commonly understood by one of ordinary skill in the art. While the following terms are believed to be well understood by those skilled in the art, the following definitions are set forth to better explain the present application.
As used herein, the terms "comprising," "including," "having," "containing," or "involving" are inclusive or open-ended and do not exclude additional unrecited elements or method steps. The term "consisting of …" is considered to be a preferred embodiment of the term "comprising". If a certain group is defined below to contain at least a certain number of embodiments, this should also be understood to disclose a group that preferably consists of only these embodiments.
The indefinite or definite article "a" or "an" when used in reference to a singular noun includes a plural of that noun.
The term "about" in the present application means a range of accuracy that one skilled in the art can understand while still guaranteeing the technical effect of the features in question. The term generally means a deviation of + -10%, preferably + -5%, from the indicated value.
Furthermore, the terms first, second, third, (a), (b), (c), and the like in the description and in the claims, are used for distinguishing between similar elements and not necessarily for describing a sequential or chronological order. It is to be understood that the terms so used are interchangeable under appropriate circumstances and that the embodiments of the application described herein are capable of operation in other sequences than described or illustrated herein.
The term "drug resistance" as used herein refers to the tolerance of microorganisms, parasites and tumor cells to the action of drugs, which is significantly reduced once produced. The preferred in vivo infection of the present application is bacterial resistance to antibiotics.
The term "drug resistance phenotype" as used herein generally refers to a characteristic of drug resistance exhibited by a organism, referred to as a drug resistance phenotype (resistant phenotype), and the possessed drug resistance gene is referred to as a drug resistance genotype (resistant genotype).
The "non-core gene" as used herein is a gene which is present in only a part of strains for a certain bacterial population, and corresponds to a core gene, i.e., a gene which is present in all strains. The drug-resistant genes detected by the method of the application are mainly aimed at the non-core genes.
The "important characteristic gene" refers to the above-mentioned non-core drug-resistant gene, namely, drug-resistant characteristic or drug-resistant gene which is obviously related to a drug-resistant phenotype of a certain antibiotic drug.
The application of the "read-based" refers to the read sequence alignment: and directly comparing the sequencing read sequence with a drug-resistant gene library, and detecting and analyzing the drug-resistant gene.
The invention relates to an 'assembled-based' refers to genome Contig sequence alignment; and (3) assembling the genome of the species by using the sequencing read sequence to obtain contig, and then comparing the drug-resistant gene library based on the contig sequence to detect and analyze the drug-resistant gene.
The 'BGWAS' or 'BGWAS model' refers to the whole genome association analysis of bacteria, namely, the association analysis of bacterial genome data and drug resistance phenotype data is carried out to screen and find important drug resistance characteristics or drug resistance genes obviously related to the drug resistance phenotype.
Accordingly, the "BGWAS model training set" refers to all bacterial strain data used in developing the whole genome association analysis of bacteria, i.e., the model training set.
For details on "BGWAS" or "BGWAS model", see also applicant's early patent CN202111400540.5, which model specifically comprises the following modules:
module 1) for obtaining genome data of a target bacterial strain while collecting corresponding drug susceptibility test result data;
module 2), alignment annotation for drug resistance databases based on contig sequences of bacterial genomes;
module 3) for carrying out genotype and drug resistance phenotype data association analysis for the target drug, screening important characteristic genes related to drug resistance generation, and calculating important characteristic gene weight coefficients; preferably, the important characteristic gene is a non-core drug resistance gene.
Module 4), ROC analysis evaluation predicts model performance of drug susceptibility results based on the screened important genes.
The ROC analysis is as follows: defining and calculating a Score value based on the matrix of the important gene weight coefficient obtained in the step 3), drawing an ROC curve and determining a cut off value by taking the Score value as a negative and positive interpretation index, and verifying and evaluating the performance of the model by using a verification set sample; the saidWherein ar/_W i The weight coefficient value indicating the detection of the corresponding gene.
Further, the genome number of the strain in the step 1) =100, the strain source covers various subtypes, and the ratio of the number of the strains of the drug-resistant strain to the number of the strains of the sensitive strain is balanced; in some preferred forms, the obtaining is performed by searching and downloading published target genomic sequences from a public database, or by sequencing and assembling bacterial strains identified by current collection clinical culture; in some more preferred ways, the search download from the public database is: bacterial strain information recorded with drug susceptibility test results is collected from the NCBI NDARO database and the PATRIC database platform, phenotypic data is collated, and genome data is downloaded in batches from the NCBI genome database according to genome assembly id numbers or from the PATRIC database according to PATRIC ids. Further, the comparison annotation in the step 2) is: comparing the contig sequences with a CARD drug-resistant gene reference sequence library, filtering hit with low identity and coverage (preferably, filtering hit with identity less than 90% or reference gene coverage less than 90%), selecting best hit from the region on each contig comparison as the final comparison result of the contig region, and adding annotation information of the drug-resistant gene. Furthermore, in the step 3), the association analysis is performed by adopting a guy cable regression model. Furthermore, the association analysis method of the inhaul cable regression model in the step 3) specifically comprises the following steps: taking a gene detection distribution matrix and an antibiotic susceptibility test result data matrix as inputs, carrying out association analysis on genotype and drug resistance phenotype data by using a glmnet program package, carrying out k (preferably k=5-15) repeated cross verification, screening to obtain important characteristic genes related to the drug resistance phenotype, and calculating the weight coefficient of the important characteristic genes; further, the important characteristic genes specifically are: according to the CV error rate and AUC change curve of the model under different number of characteristic genes, selecting the number of genes corresponding to the position with the lowest CV error rate and relatively stable AUC value of the model at the moment as important characteristic genes. Further, step 3) may further include manual recall, where the manual recall is: the gene with higher PPV (preferably, PPV > =0.8) with the drug-resistant phenotype is manually recalled, and the weight coefficient of the recalled gene is calculated based on the weight coefficient value of the important gene obtained above. Further, the bacteria of the present application include, but are not limited to, escherichia coli, klebsiella pneumoniae Lei Bashi, acinetobacter baumannii, pseudomonas aeruginosa, enterobacter cloacae complex, staphylococcus aureus, enterococcus faecium, enterococcus faecalis, streptococcus pneumoniae, streptococcus pyogenes, haemophilus influenzae, staphylococcus epidermidis; klebsiella pneumoniae is preferred. Further, the drug resistant phenotypes of the present application include, but are not limited to, phenotypes resistant to carbapenems, cephalosporins, penicillins, beta lactam antibiotic inhibitors, aminoglycosides, sulfonamides, tetracyclines, quinolones, glycopeptides, oxazolidinones, polymyxin drugs; preferably, the drug resistant phenotype is a carbapenem drug resistant phenotype.
The application is illustrated below in connection with specific embodiments.
EXAMPLE 1 method of the application set up
FIG. 1 is a technical scheme of the present application, and the steps are described as follows:
firstly), based on the important genes screened by BGWAS, constructing a drug-resistant gene detection and phenotype prediction process based on sequencing reads comparison, and carrying out test verification and determining a positive cutoff value through simulation data.
1.1, by combining the classification information of the drug resistance genes in the CARD drug resistance database (V3.1.0), the annotation information such as the family to which the drug resistance genes belong, possible source species, mediation mode, and consistency among gene sequences is rearranged and corrected. The gene family definition is defined according to the family information of the genes recorded in a drug resistance database (CARD), and the family information of the drug resistance genes recorded in the NCBI NDARO database and the MEGARes database is referenced for combing and correcting. Such as: the OXA family can be subdivided into subfamilies such as OXA-48family, OXA-51family, etc., and there may be differences in the contributions of different antibiotic drugs from subfamilies that determine drug resistance, so that it is desirable to define OXA specific typing genes to subfamilies levels, e.g., OXA-181 and OXA-232 are OXA-48family. Secondly, all drug-resistant reference genes are aligned with NCBI NT library, the hit of identity > =95% and subject coverage > =95% is reserved, so as to obtain annotation information of all species of each drug-resistant gene, and keyword 'plasma' is searched in the aligned reference sequence description information to inquire whether the drug-resistant gene is mediated by plasmid. And finally, carrying out pairwise comparison on all the drug-resistant gene sequences by using blastn software to obtain the consistency value among all the gene sequences.
1.2 calculation of drug-resistant gene family weight coefficient. The weight coefficient of the gene family is calculated based on the weight coefficient of the member gene in the family and the sample detection frequency of the corresponding gene in the BGWAS model training set (see the applicant's prior patent CN202111400540.5 for details). The calculation formula is as follows:
wherein arg_N i For the number of samples detected in the training set (i.e. the samples used for model training in the early BGWAS analysis) for the corresponding genes in the target gene family, arg_W i Is the weight coefficient of the corresponding gene in the family. j represents j key genes in the family, and j+k represents the number of all genes in the family.
1.3 simulation of NGS or ONT sequencing reads for detection and flow correction of drug resistance genes
1R data simulation
Based on the training set strain samples used in the early BGWAS model, the bacterial strain genome sequencing short reads sequence (Ilumina SE 75) data (parameter settings: -ss NS50-l 75-f 5-nf 0-rs 1) were simulated using ART_Illumina software (Version 2.5.8). The sequence reads were sequenced using ReadSim software (Version 1.6), parameter settings: -revstrd on-tech nanopore-read mu 3000-read dist normal. The genomic depth of pathogenic bacteria detected under the data volume of 20 mrads of the clinical specimen is generally not more than 1X, so that gradient data volumes of 0.05X, 0.1X, 0.2X, 0.3X, 0.4X, 0.5X, 0.6X, 0.7X, 0.8X, 0.9X, 1X, 2X, 3X, 5X, 10X, 30X and the like are simulated.
1.3.2 sequence alignment and annotation statistics
The simulated Illumina reads are compared with the drug-resistant gene database by using the blastn (version 2.9.0 +) software (parameter setting: value 1e-5-outfmt 6), only the hit with the identity higher than 90% is filtered, then the hit of the highest score of each read sequence, namely the best hit, is selected as the final hit, if multiple identical values exist for the hit of the highest sore (namely multiple comparison), the final annotation of the read sequence is carried out on the multiple hits by adopting the LCA algorithm (namely, for a single read sequence, the genotype is not annotated due to multiple comparison, and then the higher hierarchy is annotated as the gene family level), then the number of specific reads detected by the drug-resistant genes in the sample and the number of multiple comparison reads and the number of specific reads of the families to which the drug-resistant genes belong are counted, and the coverage index of each detected gene is calculated.
For simulated nanopore reads sequence data, a minimap2 software (version 2.17) was used to align with the drug-resistant reference gene library, parameter settings: -c-x map-ont-L-second = no, then filtering out hit with identity below 0.7 or subject coverage below 0.4. And finally carrying out gene annotation on the reads sequence by adopting an optimal alignment or LCA algorithm, counting the number of specific reads detected by the drug-resistant genes in the obtained sample, multiple-alignment the number of reads and the number of reads of the family to which the drug-resistant genes belong, and calculating the coverage of the detected genes.
1.3.3 screening and Filtering of drug resistant genes with aligned reads sequences
A) And (3) evaluating the influence of sequence consistency among different genotyping genes in the drug-resistant gene reference library on the accurate detection of the drug-resistant genotyping by the read-based. The number of specific reads and ARG-like reads detected by the target genes (i.e. the number of reads of all the target genes aligned) are counted by picking the drug-resistant genes with different maximum sequence consistencies in the database, simulating the short reads sequence and performing read-based flow detection analysis. Finally, a strategy for identifying genotyping is detected for whether a specific reads sequence exists, and 95% identity can be selected as a threshold criterion for whether accurate genotyping can be easily achieved for the target gene (see FIG. 2).
B) Identification of drug-resistant genotyping adopts a strategy combining two rules: firstly, aiming at target genes with high similarity (such as consistency exceeding 95%) with other genes in a database, adopting ARG-like reads (i.e. the reads of all the target genes can be compared, or specific reads+multiple comparison reads) to take the first position as true positive detection, and reserving genes with 100% coverage calculated based on accurate comparison reads for the non-first position as true positive detection; and secondly, aiming at target genes with low similarity (such as consistency lower than 95%) with other genes in the database, judging whether the target genes are true positive results or not mainly according to the number of detected specific reads. For some families of genes for which no specific reads were detected, they were considered false positives and were filtered directly.
C) And evaluating the accuracy of the read-based detection to identify the drug-resistant genotyping. The result of detecting the drug-resistant genes by using the assembly-based in the BGWAS model training process is taken as a reference, and the accuracy, sensitivity and specificity indexes of the read-based detection drug-resistant genes or gene families are obtained by statistics aiming at important genes or gene families screened out by the BGWAS model, as shown in FIG. 3, the read-based detection flow has better performance when detecting most of the drug-resistant genes or gene families.
1.4 defining and calculating a negative and positive judgment index Score value, and carrying out ROC analysis by combining simulation tests under different sequencing data volumes to determine a report rule and a cutoff threshold.
1.4.1 based on the important genes (and gene families) obtained by screening and the weight coefficients thereof, score index values are defined and calculated according to the detection results of the drug-resistant genes of the samples. The calculation formula is as follows:
wherein arg_W i Weight coefficient representing corresponding genotype, generamily_W i The weight coefficients representing the corresponding gene families. When genotype (i.e., gt) is detected and the genotype weight coefficient>0, calculating by using genotype weight coefficients; when the genotype is detected but the genotype weight coefficient is 0 or no weight coefficient, the genotype weight coefficient is calculated by the gene family weight coefficient (the genotype weight coefficient is 0).
Specifically, according to the rule of fig. 4, score calculation is performed based on the read-based drug-resistant gene detection typing result and the gene weight coefficient matrix, and simultaneously considering the positive consistency (PPV) factor of each gene and drug-sensitive result in the BGWAS model training set. (in FIG. 4, gt represents ARG type, i.e., drug resistance genotyping, and gf represents ARG family, i.e., drug resistance gene family).
1.4.2 for simulation tests under different data volumes based on the training set strains, ROC analysis was performed based on the drug resistance gene detection results and the training set strain actual drug sensitivity results, drug sensitivity prediction model performance (AUC values) under different sequencing data volumes was evaluated, and a cutoff threshold was determined.
Specifically, thresholds are set for reporting "drug resistance" and "sensitivity" respectively in view of model performance and sequencing data volume influencing factors. The threshold for reporting "drug resistance" is set by taking the Score value corresponding to the maximum value of the Youden index as the threshold when the genome sequencing data of the target strain is sufficient and the model performance is stable (such as 30X), and the Score value is recorded as R_cutoff. Reporting a "sensitive" threshold setting, two sets of threshold criteria are employed. One is to take R_cutoff determined under the condition that the sequencing data volume is enough (30X data volume, model stabilization) as a threshold for reporting "sensitivity", which is marked as S_cutoff2, and the NPV is required to be above 0.9 at the moment, otherwise, the "sensitivity" cannot be reported. Secondly, the minimum sequencing data amount (denoted as gf_lod1) corresponding to the stability of the model performance found directly from the calculation of Score values based on the gene family weight coefficients, under which the maximum Score value satisfying NPV exceeding 0.9 (which Score value must be less than or equal to s_setof2) is denoted as the threshold for reporting "sensitivity", is denoted as s_setof1. Next, for each simulated sequencing data amount between gf_lod1 and 30X, the feasibility of reporting a "sensitivity" threshold criterion with s_cutoff2 was examined, i.e., finding the minimum data amount meeting NPV >0.9, noted gf_lod2. Then, two sets of threshold criteria for reporting "sensitivity" can be finally determined, namely, when the sequencing data volume is above gf_lod2, s_cutoff2 is taken as the threshold for reporting "sensitivity", and when the sequencing data volume is between gf_lod1 and gf_lod2, s_cutoff1 is taken as the threshold for reporting "sensitivity".
Specific reporting rules for drug susceptibility prediction for a certain antibiotic are as follows: when Score value is greater than r_cutoff, it is reported as "likely drug resistance"; reporting as "potentially sensitive" when the genome coverage of the detected pathogenic bacteria is greater than or equal to gf_lod2 and the Score value is less than s_cutoff2, or reporting as "potentially sensitive" when the genome coverage of the detected pathogenic bacteria is between gf_lod1 and gf_lod2 and the Score value is less than s_cutoff 1; when the genome coverage of the detected pathogenic bacteria is less than gf_lod1 and Score is less than s_setof1, it is reported as "/", i.e., unknown.
Two) drug resistant Gene species assignment prediction
2.1 detection of target pathogenic Strain and species assignment prediction of drug resistance Gene
2.1.1 target pathogen species genome sequence alignment and detection sequence number, genome coverage and coverage depth calculation
The method comprises the steps of selecting clinically common pathogenic bacteria (klebsiella pneumoniae, escherichia coli, acinetobacter baumannii, pseudomonas aeruginosa, enterobacter cloacae and the like) as target pathogens, searching and downloading target pathogen reference genome from NCBI genome database, and taking the target pathogen reference genome as a reference sequence library for identifying target pathogen strains.
The Illumina reads sequence was aligned with the target pathogen reference genome sequence library set above using minimal ap2 software (v 2.17) (alignment parameters: -x sr-a-second = no-L), and then the number of sequences detected, genome coverage, and coverage depth of the aligned target pathogen species were calculated. For the alignment of nanopore sequencing reads, the parameters were set to-x map-ont-a-second=no-L.
Then, the total reads sequence number, genome coverage and coverage depth of the detected pathogenic bacteria are counted.
2.1.2 based on BGWAS model training set specimen drug resistance gene detection results, statistics of target pathogen strain carried drug resistance gene copy number
Based on the detection result of the drug-resistant genes of the assembly-based training set sample during BGWAS model training, the detection distribution and the copy change range of the drug-resistant genes and the drug-resistant gene families of the target pathogenic bacteria are obtained through statistics.
2.1.3 based on the presumed gene-species assignment relationship, drug-resistant gene copy number calculation and species assignment judgment are performed
Assuming drug resistance gene-species correspondence, the main basis is: a. the species annotation of the reference gene in the database contains the target species, and if so, the assumption of the relationship of the gene-species is accepted; b. if a is not satisfied, checking whether the mediation mode annotation of the reference gene in the database contains a plasmid mediation mode, and if so, firstly accepting the assumption of the attribution relation of the gene-species; c. if a, b are not satisfied, the species source is presumed from the species annotation of ARG-like reads. Then, the copy number of the drug resistance gene was calculated as follows:
And if the calculated copy number of the drug-resistant gene falls within the normal copy number range of the target gene family obtained based on the BGWAS model training set statistics, the assumed gene-species attribution relationship is accepted, otherwise, rejection is carried out.
Third), detecting and verifying drug-resistant genes by metagenome sequencing of clinical specimens
And collecting a clinical specimen subjected to culture and drug sensitivity test simultaneously, transferring to a medical test laboratory according to requirements for pretreatment, nucleic acid extraction, library establishment and on-machine sequencing (Illumina CN500 SE75 or nanopore sequencing), and then comparing and analyzing target species genome and drug resistance gene database of a ready sequence obtained by sequencing to identify pathogenic bacteria and drug resistance gene conditions carried by the pathogenic bacteria in the sample.
3.1 quality control filtering and removal of human nucleic acid sequences from raw low quality sequenced sequences
Sequence data measured for the Illumina platform is processed as follows:
a. processing sequencing lower machine data by using BCL2fastq (v2.20.0.422) to convert BCL format data into fastq format sequence data;
b. the resulting raw fastq sequence data was filtered (parameter settings: -q 15-u 40-l read_length 0.67) using fastp (v0.19.5) software, removing low quality and short sequences; at the same time, the sequence information complexity (parameter settings: -F-t 0.4) was calculated using komplity (v0.3.6) software and the low complexity sequences were filtered out.
c. The clear sequence obtained by quality control filtration is compared with the ginseng genome sequence (human_38) by using bowtie2 (v2.3.4.3) software (parameter setting: minus mm- -ver-active-k 1) to filter out the human sequence.
3.2 detection and identification of drug resistance Gene contained in sample and species assignment thereof
Drug resistance gene alignment and annotation statistics of sample sequences were performed using blastn software according to step 1.3.2, target species genome alignment was performed using minimap2 software according to step 2.1.1 and detected genome coverage was calculated, and then species assignment of predicted detected drug resistance genes was assessed according to step 2.1.3.
3.3, calculating the score value of the target species-antibiotic drug according to the score calculation mode defined by 1.4.1 aiming at the pathogen bacteria concerned and detected according to the detection condition of the drug resistance genes, and comparing with the cutoff value: score > = cutoff, then R is predicted; score < cutoff, if the detected pathogen genome coverage is higher than the minimum genome coverage or data amount (e.g., > 40%) required for model stabilization based on the 1.4.2 step determination, then it is predicted as S, otherwise it is reported as "/" (indicating unknown)
And 3.4, finally comparing the drug sensitivity prediction result with the clinical specimen actual drug sensitivity test result collected simultaneously, counting the accuracy of the prediction result and the sample number proportion of the effective report, and evaluating the performance of the drug resistance detection flow.
Example 2 detection of Klebsiella pneumoniae drug resistance Gene and analysis of phenotype prediction against clinical specimens
1. Based on klebsiella pneumoniae genome, BGWAS screening to obtain important antibiotic-related drug-resistant genes and corresponding gene families, and calculating to obtain weight coefficients of the genes or gene families
Collecting and downloading genome data of klebsiella pneumoniae strains with drug sensitivity test result information from NCBI NDARO database and PATRIC database, filtering to obtain 3072 strain samples (training set, validation set are 2410 and 662) finally, filtering to obtain important genes related to antibiotic drug resistance and weight coefficient matrixes thereof by adopting a machine learning method, and calculating weight coefficients of families of the important genes according to a formula of a technical scheme 1.2, wherein the results are shown in the following table:
2. based on the genome of 2410 cases of klebsiella pneumoniae strains in a training set, according to the test verification of a read-based drug-resistant gene detection flow by simulating 75bp short reads (NGS sequencing platform) in the step of 1.3 of the technical scheme, different gradient data amounts of 0.05X, 0.1X, 0.2X, 0.3X, 0.4X, 0.5X, 0.6X, 0.7X, 0.8X, 0.9X, 1X, 2X, 3X, 5X, 10X, 30X and the like are simulated. And then carrying out drug resistance gene detection to obtain drug resistance gene detection results of each simulated specimen, calculating score values according to the technical scheme 1.4.1, carrying out ROC curve analysis to obtain model performance AUC values of each antibiotic drug under different data volumes, and then drawing a change curve of the model performance AUC values as shown in the following figure 5. At 30X data, the model performance has stabilized, and the AUC values for each antibiotic model performance at this time are shown in figure 6 below. According to the step of the technical scheme 1.4.2, the report rule and the cutoff threshold of each antibiotic drug are finally determined as follows.
mNGS drug sensitivity prediction cutoff threshold for 6 antibiotic drugs of Klebsiella pneumoniae.
Note that: "/" indicates that "sensitivity" cannot be reported.
3. A total of 48 cases of clinical specimens containing Klebsiella pneumoniae were collected and identified for preservation in a refrigerator at-80 ℃. These 48 specimens were then subjected to nucleic acid extraction to construct a metagene second generation (insert length 200-400 bp) library, and second generation (Illumina nextseq CN SE 75) was subjected to on-machine sequencing. And (3) identifying pathogens and drug resistance genes carried by the pathogens in the next machine data, and then calculating a Score value and predicting and judging drug susceptibility results. Finally, the target pathogenic bacteria of each specimen and the drug resistance gene detection and identification result and drug sensitivity prediction result are obtained. The results of a portion of the samples are shown in the following table:
description: ND represents undetected, "/" represents unknown.
The statistics shows that the medicine sensitivity prediction accuracy and the ratio of the number of the reportable samples are as follows:
the result shows that the invention can effectively and accurately identify pathogenic bacteria and drug-resistance genes carried by the pathogenic bacteria aiming at clinical specimens and forecast drug-sensitive results of antibiotic drugs, and can be used for assisting in clinical detection and diagnosis of infection drug-resistance bacteria.
Further, it is known from the above results that, for gentamicin, AAC (3) -IIe, AAC (3) -IV, AAC (3) -IId, rmtC, armA, rmtF, rmtB, AAC (6 ') -33, ANT (2 ") -Ia are important drug resistance genes, and all possible mechanisms of gene weight, gene family weight, frequency of occurrence of genes and drug resistance generation are combined, in practice, the detection can be carried out simultaneously by using AAC (3) -IIe, AAC (3) -IV, AAC (3) -IId, rmtC, armA, rmtF, rmtB, AAC (6') -33, ANT (2") -Ia genes which mainly mediate high frequency occurrence of drug resistance generation and have high weight, and if the detection results are all negative, it can be presumed to be sensitive, namely, the aim of drug sensitivity, especially sensitivity detection, can be achieved.
Further, it is known from the above results that, for tobramycin, AAC (3) -IV, AAC (3) -IId, AAC (6 ') -Ib', AAC (6 ') -Ib-cr, AAC (6') -Ib-Hangzhou, AAC (6 ') -Ib4, mphE, ANT (2 ") -Ia, aadA24 are important drug-resistant genes, and all possible mechanisms of gene weight, gene family weight, frequency of occurrence of genes, and drug resistance generation are combined, in practice, the detection results can be presumed to be sensitive by simultaneously detecting AAAC (3) -IV, AAC (3) -IId, AAC (6') -Ib ', AAC (6') -Ib-cr, AAC (6 ') -Ib-Hangzhou, AAC (6') -Ib, mpe, ANT (2") -Ia, aadA24 genes which mainly mediate high frequency of drug resistance generation, and the like, and the detection results are all negative, namely, the detection results are especially sensitive.
Further from the above results, it is understood that, for ceftazidime, CTX-M-55, CTX-M-11, CTX-M-15, SHV-155, SHV-5, SHV-11, SHV-12, SHV-76, SHV-30, SHV-53, SHV-124, SHV-182, DHA-1, KPC-3, KPC-2 are important drug resistance genes, and that, in combination with factors such as the gene weight, the gene family weight, the frequency of occurrence of the gene, and all possible mechanisms of drug resistance generation, it is possible in practice to speculate that the positive detection of the drug resistance genes can be achieved by the CTX-M-55, CTX-M-11, CTX-M-15, SHV-155, SHV-5, SHV-11, SHV-12, SHV-76, SHV-30, SHV-53, SHV-124, SHV-182, DHA-1, KPC-3 and/or KPC-2 which are high in terms of frequency of occurrence of drug resistance. Meanwhile, according to the 30X genome simulation reads test result, a proper threshold cannot be found, so that when the calculated Score value is smaller than the threshold, the model NPV is higher (for example > 0.9), and therefore the aim of sensitive detection cannot be fulfilled.
Further, as is clear from the above results, the genes dfrA12, dfrA15, dfrA17, dfrA19, dfrA30, dfrA8, dfrA5, dfrA15b, dfrA14, dfrA 22, dfrA27, dfrA1 are important drug resistance genes, and all possible mechanisms of gene weight, gene family weight, frequency of gene occurrence, and drug resistance generation are combined, and in practice, the factors can be detected by detecting the high frequency of occurrence of the drug resistance mainly mediated and the high weight dfrA12, dfrA15, dfrA17, dfrA19, dfrA30, dfrA8, dfrA5, dfrA15b, dfrA14, dfrA 22, dfrA27, and/or dfrA1 genes at the same time, and if the detection results are positive, it can be presumed that the drug resistance is obtained. Namely, the aim of drug sensitivity, especially sensitivity detection is fulfilled. Meanwhile, according to the 30X genome simulation reads test result, a proper threshold cannot be found, so that when the calculated Score value is smaller than the threshold, the model NPV is higher (for example > 0.9), and therefore the aim of sensitive detection cannot be fulfilled.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present application, and not for limiting the same; although the application has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the application.
Claims (4)
1. Application of detection reagents aiming at non-core drug-resistant genes AAC (3) -IIe, AAC (3) -IV, AAC (3) -IId, rmtC, armA, rmtF, rmtB, AAC (6') -33 and ANT (2 ") -Ia in preparation of klebsiella pneumoniae auxiliary drug sensitivity prediction kit;
the drug susceptibility prediction comprises drug resistance prediction and susceptibility prediction, preferably susceptibility prediction;
more preferably, the drug-sensitivity is for gentamicin drug;
it is further preferable that the detection of the high frequency occurrence and high weight of AAC (3) -IIe, AAC (3) -IV, AAC (3) -IId, rmtC, armA, rmtF, rmtB, AAC (6') -33 and ANT (2 ") -Ia genes, which mainly mediate drug resistance, is performed simultaneously, and if the detection results are negative, it is presumed to be sensitive.
2. Application of detection reagents aiming at non-core drug-resistant genes AAC (3) -IV, AAC (3) -IId, AAC (6 ') -Ib ', AAC (6 ') -Ib-cr, AAC (6 ') -Ib-Hangzhou, AAC (6 ') -Ib4, mphE, ANT (2 ") -Ia and aadA24 in preparation of klebsiella pneumoniae auxiliary drug-sensitive prediction kit;
the drug susceptibility prediction comprises drug resistance prediction and susceptibility prediction, preferably susceptibility prediction;
more preferably, the drug-sensitive is against tobramycin;
it is further preferable that the detection of AAAC (3) -IV, AAC (3) -IId, AAC (6 ') -Ib ', AAC (6 ') -Ib-cr, AAC (6 ') -Ib-Hangzhou, AAC (6 ') -Ib4, mphE, ANT (2 ") -Ia, aadA24 genes, which mainly mediate the occurrence of high frequency of drug resistance, are performed simultaneously, and if the detection results are negative, the detection results are presumed to be sensitive.
3. Application of detection reagents aiming at non-core drug-resistant genes CTX-M-55, CTX-M-11, CTX-M-15, SHV-155, SHV-5, SHV-11, SHV-12, SHV-76, SHV-30, SHV-53, SHV-124, SHV-182, DHA-1, KPC-3 and KPC-2 in preparation of klebsiella pneumoniae auxiliary drug-sensitive prediction kit;
the drug sensitivity prediction is drug resistance prediction;
preferably, the drug-sensitive is directed against ceftazidime.
More preferably, the drug resistance is estimated by detecting the CTX-M-55, CTX-M-11, CTX-M-15, SHV-155, SHV-5, SHV-11, SHV-12, SHV-76, SHV-30, SHV-53, SHV-124, SHV-182, DHA-1, KPC-3 and KPC-2 genes which are high in weight and which mainly mediate the occurrence of the drug resistance, and if the detection results are positive.
4. Application of detection reagents aiming at non-core drug-resistant genes dfrA12, dfrA15, dfrA17, dfrA19, dfrA30, dfrA8, dfrA5, dfrA15b, dfrA14, dfrA 22, dfrA27 and dfrA1 in preparation of klebsiella pneumoniae auxiliary drug-sensitive prediction kit;
the drug sensitivity prediction is drug resistance prediction;
preferably, the drug sensitivity is aimed at a compound neonomine drug;
more preferably, the genes dfrA12, dfrA15, dfrA17, dfrA19, dfrA30, dfrA8, dfrA5, dfrA15b, dfrA14, dfr22, dfrA27 and/or dfrA1, which are high in weight and mainly mediate the occurrence of drug resistance, are detected simultaneously, and if the detection results are positive, it is estimated that the drug resistance is obtained.
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