CN115798575B - System and method for predicting sensitivity of klebsiella to ceftazidime - Google Patents
System and method for predicting sensitivity of klebsiella to ceftazidime Download PDFInfo
- Publication number
- CN115798575B CN115798575B CN202310065212.7A CN202310065212A CN115798575B CN 115798575 B CN115798575 B CN 115798575B CN 202310065212 A CN202310065212 A CN 202310065212A CN 115798575 B CN115798575 B CN 115798575B
- Authority
- CN
- China
- Prior art keywords
- klebsiella
- gene
- ceftazidime
- predicted
- copy number
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A50/00—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE in human health protection, e.g. against extreme weather
- Y02A50/30—Against vector-borne diseases, e.g. mosquito-borne, fly-borne, tick-borne or waterborne diseases whose impact is exacerbated by climate change
Landscapes
- Measuring Or Testing Involving Enzymes Or Micro-Organisms (AREA)
Abstract
The invention discloses a system and a method for predicting sensitivity of klebsiella to ceftazidime, and belongs to the technical field of biological information. The system comprises: a calculation unit; the calculation unit includes: a computer readable storage medium having a computer program stored thereon; the computer program realizes an Exp (-k) power value calculation method when being executed by a processor; the Exp (-k) power value calculation method comprises the following calculation steps: s1: the k value is calculated as follows: formula I:
the method comprises the steps of carrying out a first treatment on the surface of the S2: solving Exp (-k) power values taking a natural constant e as a base and taking-k as an index; wherein, C1-C5 are the copy numbers of KPC-1, sul1, CTX-M-55, CTX-M-15 and TEM-1 genes in the Klebsiella strain to be predicted. The accuracy of the prediction of the sensitivity of the Klebsiella to ceftazidime by adopting the prediction method and the prediction system of the invention is about 90.6 percent.
Description
Technical Field
The invention belongs to the technical field of biological information, and particularly relates to a system and a method for predicting sensitivity of klebsiella to ceftazidime.
Background
Antibiotics have been a "secret weapon" for humans against a number of diseases, and the life of humans has been greatly increased by the discovery of a range of antibiotics in the beginning of the last 20 th century. In recent years, the continuous application of antibiotics gradually causes the abuse of drugs, so that the clinical antibiotic resistance and adverse reaction are continuously increased, and the global economy is heavily burdened. Effective control of antibiotic abuse in medical applications is an important element in dealing with the problem of global antibiotic resistance.
Pathogenic microorganisms refer to microorganisms, or pathogens, that can invade the human body, causing infection or even infectious disease. Mainly comprises bacteria, viruses, fungi, parasites, mycoplasma, chlamydia, rickettsia, spirochetes and the like. Microbial samples are of a wide variety. Intestinal tract samples include feces, mucous membranes, etc., liquid samples include urine, blood, cerebral spinal fluid, saliva, sputum, alveolar lavage fluid, amniotic fluid, etc., swab samples include oral cavity, genital tract, skin, etc., and others include tissues, liver, eyes, placenta, etc.
Klebsiella (Genus)Klebsiella) Latin is known asKlebsiella TrevisanThe system classification level is Genus (Genus)，It is a straight bacillus, diameter: 0.3-1.0 μm and 0.6-6.0 μm long. The species (strains) of this genus, which have been reported to date, are single, paired or short-chain arrangement, including: klebsiella pneumoniaeKlebsiella pneumoniae) Klebsiella oxytocaKlebsiella aerogenes) Klebsiella oxytocaKlebsiella oxytoca) Klebsiella pseudopneumoniaeKlebsiella quasipneumoniae) Klebsiella variabilis (L.) kuntzeKlebsiella variicola) Klebsiella michigan @ bacteriaKlebsiella michiganensis) Etc.
Wherein, klebsiella pneumoniae is a kind of Chinese medicineKlebsiella pneumoniae) As Klebsiella (Genus)Klebsiella) The model strain (strain) of the strain is widely existed in the environment, is easy to colonize on the respiratory tract and intestinal tract of a patient, causes common conditional pathogenic bacteria of infection of multiple parts such as digestive tract, respiratory tract, blood and the like, is one of pathogenic bacteria causing human pneumonia and is also one of common drug-resistant bacteria in hospitals. According to the second army medical university study, the drug resistance rate of the isolated carbapenem-resistant klebsiella pneumoniae to ceftazidime in 2014-2017 is 62.5% (252/403).
Ceftazidime is a third generation cephalosporin antibiotic. Similar to other third generation pioneers, there is a broader response against gram positive and gram negative bacteria. Can be used for treating septicemia, lower respiratory infection, abdominal cavity biliary infection, complicated urinary tract infection, and serious skin soft tissue infection caused by sensitive gram-negative bacillus.
Bacterial drug susceptibility tests are currently the most commonly used bacterial drug resistance detection methods in domestic and foreign clinics and laboratories, and include a paper sheet method, an agar dilution method, a broth dilution method, a concentration gradient method and the like, wherein the other methods except the paper sheet method can obtain relatively accurate minimum inhibitory concentration (minimum inhibitory concentration, MIC) of the drug. Bacterial drug susceptibility tests first require pure cultures to be obtained, are not applicable to difficult and non-cultured bacteria, are time-consuming, and sometimes are difficult to meet the current requirements for rapid diagnosis and symptomatic treatment of clinical severe and emergency infections. The traditional detection and identification means of pathogenic microorganisms can not meet the comprehensive requirements of wide coverage, rapidness and accuracy, the diagnosis and treatment of infectious diseases are mainly based on empirical and directional methods, and clinical doctors and patients urgently need an innovative detection method, so that the infectious pathogens can be identified more comprehensively, accurately and rapidly, the auxiliary diagnosis and reasonable and standard medication treatment can be realized, the treatment course can be shortened, the death rate of diseases can be reduced, and the medical cost can be reduced.
With the popularization of the new technologies such as PCR technology, whole genome sequencing technology, microfluidic technology, VITEK-2compact full-automatic bacterial identification/drug sensitivity system and the like, the exploration of new technologies for detecting bacterial drug resistance is gradually deep, and the new technologies for detecting various bacterial drug resistance are increasingly mature. The full-automatic bacterial identification/drug sensitivity system of VITEK-2compact is simple and rapid, but the accuracy of the identification/drug sensitivity evaluation of the strain is influenced by the sample state and the culture condition of the strain, and the use cost is high.
Therefore, there is a need in the art to develop a system and method for rapidly and accurately predicting the sensitivity of klebsiella strains to ceftazidime at a low cost.
Disclosure of Invention
In view of the foregoing deficiencies and needs in the art, it is an object of the present invention to provide a system and method for predicting the sensitivity of klebsiella to ceftazidime.
The technical scheme of the invention is as follows:
a system for predicting sensitivity of klebsiella to ceftazidime, provided with: a calculation unit; the calculation unit includes: a computer readable storage medium having a computer program stored thereon; the computer program realizes an Exp (-k) power value calculation method when being executed by a processor; the Exp (-k) power value calculation method comprises the following calculation steps:
s1: the k value is calculated as follows:
formula I:
s2: solving Exp (-k) power values taking a natural constant e as a base and taking-k as an index;
in formula I:
c1 is the copy number of the KPC-1 gene in the Klebsiella strain to be predicted,
c2 is the copy number of the sul1 gene in the Klebsiella strain to be predicted,
c3 is the copy number of the CTX-M-55 gene in the Klebsiella strain to be predicted,
c4 is the copy number of the CTX-M-15 gene in the Klebsiella strain to be predicted,
c5 is the copy number of the TEM-1 gene in the Klebsiella strain to be predicted.
The system for predicting the sensitivity of the klebsiella to the ceftazidime is further provided with: a result output unit; the computing unit transmits the computed Exp (-k) power value to the result output unit, and the result output unit identifies the Exp (-k) power value and outputs a result;
preferably, the natural constant e= 2.718281828459045.
The result output unit recognizes that the drug resistance result R is output when the Exp (-k) power value is less than 1;
the result output unit recognizes that when the Exp (-k) power value is more than or equal to 1, a sensitive result S is output;
the result output unit is communicated with the calculation unit through a data path, and the Exp (-k) power value calculated by the calculation unit is transmitted to the result output unit through the data path;
preferably, the sensitive result S refers to that the klebsiella to be predicted is sensitive to ceftazidime; the drug resistance result R refers to the drug resistance of the klebsiella to be predicted to ceftazidime.
The system for predicting the sensitivity of the klebsiella to the ceftazidime is further provided with: an experiment unit and a data input unit;
the experimental unit is communicated with the data input unit through a data path; the experimental unit outputs experimental results, and the experimental results are transmitted to the data input unit through the data path and converted into self-variable data;
the data input unit is communicated with the computing unit through a data path; the self-variable data is transmitted to the computing unit through the data path.
The self-variable data includes: numerical values of C1, C2, C3, C4, C5;
preferably, the experimental results include: the copy number of the KPC-1 gene in the Klebsiella strain to be predicted, the copy number of the sul1 gene in the Klebsiella strain to be predicted, the copy number of the CTX-M-55 gene in the Klebsiella strain to be predicted, the copy number of the CTX-M-15 gene in the Klebsiella strain to be predicted, the copy number of the TEM-1 gene in the Klebsiella strain to be predicted.
A method of predicting sensitivity of klebsiella to ceftazidime, comprising:
s1: the k value is calculated as follows:
formula I:
s2: solving Exp (-k) power values taking a natural constant e as a base and taking-k as an index;
in formula I:
c1 is the copy number of the KPC-1 gene in the Klebsiella strain to be predicted,
c2 is the copy number of the sul1 gene in the Klebsiella strain to be predicted,
c3 is the copy number of the CTX-M-55 gene in the Klebsiella strain to be predicted,
c4 is the copy number of the CTX-M-15 gene in the Klebsiella strain to be predicted,
c5 is the copy number of TEM-1 gene in the Klebsiella strain to be predicted;
the result of the prediction corresponding to the Exp (-k) power value <1 is that the Klebsiella is resistant to ceftazidime, and the result of the prediction corresponding to the Exp (-k) power value more than or equal to 1 is that the Klebsiella is sensitive to ceftazidime.
The natural constant e= 2.718281828459045;
the copy numbers of KPC-1, sul1, CTX-M-55, CTX-M-15 and TEM-1 genes in the Klebsiella strains to be predicted are obtained by a second-generation high-throughput sequencing method.
Copy number of genes in klebsiella strain to be predicted =
；
Preferably, the genome connigs are the longest connigs fragments obtained by assembling sequencing results by SPADes v3.13.0 assembling software;
the depth of the genome contigs is the depth of the genome contigs calculated by SPADes v3.13.0 assembly software;
the depth of the gene in the contigs refers to the sum of the depths of the gene on each contig with the copy of the gene;
preferably, each connigs with a copy of the gene is annotated using blat (v.36) software and diamond (v 2.0.4.142) software after alignment of the cd and protein sequences of the gene in the CARD database;
preferably, the depth of the gene on each contig with a copy of the gene is calculated by SPADes v3.13.0 assembly software.
In one aspect of the invention, a method of predicting drug sensitivity of klebsiella to ceftazidime is provided.
In the invention, the obtained microorganism sample is subjected to conventional treatment, and then the necessary links of DNA extraction and the like can be performed, the state of relevant characteristics of the Klebsiella prediction system in the sample can be obtained through bioinformatics flow analysis, and the drug sensitivity of the sample can be predicted by introducing characteristic state information into the system. Compared with the traditional method, the method has the advantages of simple and convenient operation, short detection time, accurate species identification and the like.
In order to effectively determine the performance of a predictive system, a set of data sets is required that are not involved in the predictive system build-up and the accuracy of the predictive system is evaluated on the data sets, the set of independent data sets being referred to as the test set. The system prediction effect evaluation method comprises F1-score, precision, recall and confusion matrix.
The method of the invention also has the following advantages:
the invention evaluates the accuracy of the system by using a test set, wherein the average accuracy of the method is 0.906, the F1-score is 0.873, and the recall score is 0.917. On one hand, the invention is less influenced by subjective factors such as operators and the like, and has good detection stability; on the other hand, the method realizes rapid and accurate identification of infectious etiology and prediction of drug sensitivity of a sample to be tested, assists diagnosis and reasonably standardizes medication, has high flux and reduces medical cost.
Drawings
Fig. 1 is a schematic structural diagram (within a dashed box) and a workflow diagram of a drug resistance prediction system according to some embodiments of the present invention.
Fig. 2 is a schematic structural diagram (within a dashed box) and a workflow diagram of a drug resistance prediction system according to other embodiments of the present invention.
Detailed Description
In order that the invention may be understood, a more complete description of the invention will be provided in the examples below.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.
The reagents used in the examples below, unless otherwise specified, are all commercially available.
Sources of biological materials
The 171 samples used in the experimental example of the invention are pure culture of klebsiella isolated by clinical blood culture, and come from Beijing co-ordination hospital of China medical science center.
All the test strains (species) were identified as Klebsiella by mass spectrometry MALDI-TOF MS (academic name: klebsiella (Genus)Klebsiella) Latin name:Klebsiella Trevisansystem classification level: genus).
In the Illumina Novaseq NGS sequencing platform, these strains cover 127 Klebsiella pneumoniae @Klebsiella pneumoniae) Klebsiella oxytoca of 20 casesKlebsiella aerogenes) 8 cases of Klebsiella oxytocaKlebsiella oxytoca) Klebsiella pneumoniae of 7 casesKlebsiella quasipneumoniae) Klebsiella variabilis of 6 casesKlebsiella variicola) 3 cases of Michigan Klebsiella @Klebsiella michiganensis) All reported strains (species) of Klebsiella.
The strain or strain can be obtained from common Klebsiella pneumoniae cases or from the applicant laboratory. The applicant promises to distribute strains to the public within 20 years from the date of application of the present invention for verification of the technical effect of the present invention.
Group 1 example, drug resistance prediction System of the present invention
The present set of examples provides a system for predicting sensitivity of klebsiella to ceftazidime. All embodiments of this group share the following common features: as shown in fig. 1 and 2, the system for predicting sensitivity of klebsiella to ceftazidime is provided with: a calculation unit; the calculation unit includes: a computer readable storage medium having a computer program stored thereon; the computer program realizes an Exp (-k) power value calculation method when being executed by a processor; the Exp (-k) power value calculation method comprises the following calculation steps:
s1: the k value is calculated as follows:
formula I:
s2: solving Exp (-k) power values taking a natural constant e as a base and taking-k as an index;
in formula I:
c1 is the copy number of the KPC-1 gene in the Klebsiella strain to be predicted,
c2 is the copy number of the sul1 gene in the Klebsiella strain to be predicted,
c3 is the copy number of the CTX-M-55 gene in the Klebsiella strain to be predicted,
c4 is the copy number of the CTX-M-15 gene in the Klebsiella strain to be predicted,
c5 is the copy number of the TEM-1 gene in the Klebsiella strain to be predicted.
In some embodiments of the invention, the natural constant e has a value of 2.718281828459045.
In a more specific embodiment, each of the above genes is a gene reported in the art, specifically as follows:
the KPC-1 gene and the TEM-1 gene are the KPC-1 and TEM-1 genes described in the section "Novel Carbapenem-hydroyzing beta-Lactamase, KPC-1, from a Carbapenem-ResistantStrain of Klebsiella pneumoniae".
The sul1 gene is the sul1 gene described in the section "Co-occurrence of Klebsiella variicola and Klebsiella pneumoniae Both Carrying blaKPC from a Respiratory Intensive Care Unit Patient".
The CTX-M-55 gene is the CTX-M-55 gene described in the text "CTX-M-55-Type Extended-Spectrum. Beta. -lactase-Producing Shigella sonnei Isolated from a Korean Patient Who Had Travelled to China".
The CTX-M-15 gene is the CTX-M-15 gene described in the text of "High prevalence of CTX-M-15-producing Klebsiella pneumoniae isolates in Asian countries: diverse clones and clonal dissemination".
In a further embodiment, as shown in fig. 1 and 2, the system for predicting sensitivity of klebsiella to ceftazidime is further provided with: a result output unit; the result output unit outputs a sensitive result or a drug resistance result; the sensitive result means that the Klebsiella to be predicted is sensitive to ceftazidime; the drug resistance result refers to the drug resistance of the klebsiella to be predicted to ceftazidime;
when the Exp (-k) power value is less than 1, the result output unit outputs a drug resistance result R;
when the Exp (-k) power value is more than or equal to 1, the result output unit outputs a sensitive result S;
preferably, the result output unit is communicated with the calculation unit through a data path;
preferably, the Exp (-k) power value calculated by the calculation unit is delivered to the result output unit via the data path.
In a further embodiment, as shown in fig. 1, the system for predicting sensitivity of klebsiella to ceftazidime further comprises: an experiment unit and a data input unit;
the experimental unit is communicated with the data input unit through a data path; the experimental unit outputs experimental results, and the experimental results are transmitted to the data input unit through the data path and converted into self-variable data;
the data input unit is communicated with the computing unit through a data path; the self-variable data is transmitted to a computing unit through a data path;
in a more specific embodiment, the data path is a data transmission carrier well known to those skilled in the computer arts, electronics arts. The data path is selected from a wired form or a wireless form, and may be a wired path, a line, a wireless path, a wifi connection, a wireless channel, or the like, for example.
Preferably, the self-variable data includes: numerical values of C1, C2, C3, C4, C5;
preferably, the experimental results include: copy number of KPC-1, sul1, CTX-M-55, CTX-M-15, TEM-1 genes, respectively, in the Klebsiella strain to be predicted.
The copy number of a known gene in a known strain is routinely known to those skilled in the art of molecular biology and bioinformatics by conventional techniques (e.g., sequencing, bioinformatics analysis). The KPC-1, sul1, CTX-M-55, CTX-M-15 and TEM-1 genes related in the experimental results output by the experimental unit of the prediction system are all reported genes in the field, and the gene information and the primary structure sequence of the genes can be inquired through NCBI websites or other known bioinformatics databases. And carrying out whole genome sequencing on the Klebsiella strain to be predicted to obtain the copy number of each gene in the strain.
In other specific embodiments, the copy number of the KPC-1, sul1, CTX-M-55, CTX-M-15, TEM-1 genes in the Klebsiella strain to be predicted is obtained by a second generation high throughput sequencing method.
In a more specific embodiment of the present invention,
preferably, the genome connigs are the longest connigs fragments obtained by assembling sequencing results by SPADes v3.13.0 assembling software;
the depth of the genome contigs is the depth of the genome contigs calculated by SPADes v3.13.0 assembly software;
the depth of the gene in the contigs refers to the sum of the depths of the gene on each contig with the copy of the gene;
preferably, each connigs with a copy of the gene is annotated using blat (v.36) software and diamond (v 2.0.4.142) software after alignment of the cd and protein sequences of the gene in the CARD database;
preferably, the depth of the gene on each contig with a copy of the gene is calculated by SPADes v3.13.0 assembly software.
The second generation high throughput sequencing method has the meaning of conventional techniques well known to those skilled in the art, and obtaining the copy number of a gene using the second generation high throughput sequencing method is a conventional means well known to those skilled in the art.
In some specific embodiments, the specific method of gene copy number calculation is as follows:
strains were sequenced using the second generation high throughput sequencing method. The average sequencing depth was about 150x, and the approximate sequencing amount for Klebsiella was about 1G. And (3) utilizing the depth of the contigs calculated in the assembly process of SPades (v3.13.0) assembly software as a standard, defining the longest contigs fragment as a genome fragment, carrying out gene prediction on the contigs by using prokka software (1.14.6) to obtain all gene cds and protein sequences on the contigs, and respectively carrying out CARD database comparison on the cds and the protein sequences by using blat (v.36) software and diamond (v2.0.4.142) software, wherein the sequence with the similarity more than 90% is a positive sequence, so as to obtain annotation results of all drug-resistant genes. The copy numbers of all genes on the connigs were calculated as follows according to formula II:
formula II:
。
if a gene has two or more copies of the genome on different constitues or the same constitues, the final gene copy number is equal to the sum of all calculated copy numbers of the gene. The calculation method is exemplified as follows:
assuming that the KPC-1 gene has only one copy on all contigs, the copy number of the KPC-1 gene is:
assuming that the KPC-1 gene has 2 copies on one contig and no copies on the other contig, the copy number of the KPC-1 gene is:
assuming that the KPC-1 gene has 1 copy on one contig1, contig2 and no copy on the other contigs, the copy number of the KPC-1 gene is:
in a more specific embodiment, the result output unit, the experimental unit, the data input unit are each provided with a computer readable storage medium on which a computer program is stored.
In some embodiments, a computer program on a computer readable storage medium of the result output unit when executed by a processor implements a comparison size method of Exp (-k) power values with 1 and outputs a result;
the comparison of the Exp (-k) power value and 1 is performed by a size method and output results are as follows:
when the Exp (-k) power value is less than 1, the result output unit outputs a drug resistance result R;
when the Exp (-k) power value is more than or equal to 1, the result output unit outputs a sensitive result S.
In other embodiments, a method of gene copy number calculation is implemented when a computer program on a computer readable storage medium of the experimental unit is executed by a processor;
the gene copy number calculation method is a conventional technical means well known to those skilled in the art, and specifically comprises the following steps:
s1: obtaining genome connigs by taking the maximum value of the depth of the genome connigs calculated by SPADes v3.13.0 assembly software;
s2: comparing cds of a certain gene with a protein sequence by using blat (v.36) software and diamond (v 2.0.4.142) software in a CARD database, and annotating to obtain each contigs with the gene copy;
s3: SPADes v3.13.0 assembly software calculates the depth of the gene on each contigs with copies of the gene;
s4: obtaining the sum of the depths of the genes on the contigs with the gene copies to obtain the contigs depth of the genes;
s5: the copy number of the gene was obtained by calculation according to the following formula,
in some embodiments, the computer program on the computer readable storage medium of the data input unit, when executed by the processor, implements a dimensionless processing of the copy number of the gene.
The dimensionless treatment refers to: the copy number of the gene is removed from the data dimension or data unit to obtain a dimensionless number, i.e., self-variable data. In general, the data size or data unit of the copy number of a gene is: copy, individual or copies.
In other embodiments, as shown in fig. 2, the system for predicting the sensitivity of klebsiella to ceftazidime may not be provided with a data input unit, and the experimental unit is directly connected with the calculating unit through a data path, so that the gene copy number (experimental result) obtained by the calculation of the experimental unit or the self-variable data can be directly input into the calculating unit to calculate the Exp (-k) power value.
Group 2 example, method of predicting drug resistance of Kefei to ceftazidime of the invention
The present set of examples provides a method for predicting sensitivity of klebsiella to ceftazidime. This group of embodiments has the following common features: the method comprises the following steps:
s1: the k value is calculated as follows:
formula I:
s2: solving Exp (-k) power values taking a natural constant e as a base and taking-k as an index;
in formula I:
c1 is the copy number of the KPC-1 gene in the Klebsiella strain to be predicted,
c2 is the copy number of the sul1 gene in the Klebsiella strain to be predicted,
c3 is the copy number of the CTX-M-55 gene in the Klebsiella strain to be predicted,
c4 is the copy number of the CTX-M-15 gene in the Klebsiella strain to be predicted,
c5 is the copy number of the TEM-1 gene in the Klebsiella strain to be predicted.
The result of the prediction corresponding to the Exp (-k) power value <1 is that the Klebsiella is resistant to ceftazidime, and the result of the prediction corresponding to the Exp (-k) power value more than or equal to 1 is that the Klebsiella is sensitive to ceftazidime.
In the above formula I, e is a base of a natural logarithmic function, also called a natural constant, a natural base, or euler number, which is an infinite non-cyclic fraction having a conventional technical meaning generally understood by those skilled in the art of mathematics, and takes on a value of about: e= 2.71828182845904523536.
In some embodiments of the invention, the natural constant e has a value of 2.718281828459045.
In some specific embodiments, the copy number of the KPC-1, sul1, CTX-M-55, CTX-M-15, TEM-1 genes in the Klebsiella strain to be predicted is obtained by a second generation high throughput sequencing method.
In a more specific embodiment of the present invention,
preferably, the genome connigs are the longest connigs fragments obtained by assembling sequencing results by SPADes v3.13.0 assembling software;
the depth of the genome contigs is the depth of the genome contigs calculated by SPADes v3.13.0 assembly software;
the depth of the gene in the contigs refers to the sum of the depths of the gene on each contig with the copy of the gene;
preferably, each connigs with a copy of the gene is annotated using blat (v.36) software and diamond (v 2.0.4.142) software after alignment of the cd and protein sequences of the gene in the CARD database;
preferably, the depth of the gene on each contig with a copy of the gene is calculated by SPADes v3.13.0 assembly software.
The second generation high throughput sequencing method has the meaning of conventional techniques well known to those skilled in the art, and obtaining the copy number of a gene using the second generation high throughput sequencing method is a conventional means well known to those skilled in the art.
In some specific embodiments, the specific method of gene copy number calculation is as follows:
strains were sequenced using the second generation high throughput sequencing method. The average sequencing depth was about 150x, and the approximate sequencing amount for Klebsiella was about 1G. And (3) utilizing the depth of the contigs calculated in the assembly process of SPades (v3.13.0) assembly software as a standard, defining the longest contigs fragment as a genome fragment, carrying out gene prediction on the contigs by using prokka software (1.14.6) to obtain all gene cds and protein sequences on the contigs, and respectively carrying out CARD database comparison on the cds and the protein sequences by using blat (v.36) software and diamond (v2.0.4.142) software, wherein the sequence with the similarity more than 90% is a positive sequence, so as to obtain annotation results of all drug-resistant genes. The copy numbers of all genes on the connigs were calculated as follows according to formula II:
formula II:
。
if a gene has two or more copies of the genome on different constitues or the same constitues, the final gene copy number is equal to the sum of all calculated copy numbers of the gene. The calculation method is exemplified as follows:
assuming that the KPC-1 gene has only one copy on all contigs, the copy number of the KPC-1 gene is:
assuming that the KPC-1 gene has 2 copies on one contig and no copies on the other contig, the copy number of the KPC-1 gene is:
assuming that the KPC-1 gene has 1 copy on one contig1, contig2 and no copy on the other contigs, the copy number of the KPC-1 gene is:
experimental example, prediction System and prediction method of the present invention
The predictive system of the present invention was evaluated using 171 clinical samples, and the broth microdilution classification results and the system predictive results of 171 clinical samples were compared as shown in table 1 below. In the following table S represents sensitivity and R represents resistance.
TABLE 1
Test result data generation confusion matrix is as follows table 2:
TABLE 2
Assuming TP (True Positive) to represent the number of true cases, FP (False Positive) to represent the number of false positive cases, FN (False Negative) to represent the number of false negative cases, and TN (Ture Negative) to represent the number of true negative cases. Precision refers to the specific gravity of the positive sample in the positive example determined by the classifier. Recall refers to the proportion of the total positive examples predicted to be positive. Accuracy (accuracy) refers to the classifier's determination of the correct specific gravity for the whole sample. F1-score is the harmonic mean of precision and recall, with a maximum of 1 and a minimum of 0. The calculation results of the indexes are as follows:
the foregoing examples merely illustrate embodiments of the invention and are described in more detail and are not to be construed as limiting the scope of the invention. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the invention, which are all within the scope of the invention. Accordingly, the scope of protection of the present invention is to be determined by the appended claims.
Claims (16)
1. A system for predicting sensitivity of klebsiella to ceftazidime, provided with: a calculation unit and a result output unit; the calculation unit includes: a computer readable storage medium having a computer program stored thereon; the method is characterized in that the computer program realizes an Exp (-k) power value calculation method when being executed by a processor; the Exp (-k) power value calculation method comprises the following calculation steps:
s1: the k value is calculated as follows:
formula I:
s2: solving Exp (-k) power values taking a natural constant e as a base and taking-k as an index;
in formula I:
c1 is the copy number of the KPC-1 gene in the Klebsiella strain to be predicted,
c2 is the copy number of the sul1 gene in the Klebsiella strain to be predicted,
c3 is the copy number of the CTX-M-55 gene in the Klebsiella strain to be predicted,
c4 is the copy number of the CTX-M-15 gene in the Klebsiella strain to be predicted,
c5 is the copy number of TEM-1 gene in the Klebsiella strain to be predicted;
the result output unit recognizes that the drug resistance result R is output when the Exp (-k) power value is less than 1;
the result output unit recognizes that the sensitive result S is output when the Exp (-k) power value is more than or equal to 1.
2. The system for predicting sensitivity of klebsiella to ceftazidime according to claim 1, wherein the calculating unit transmits the calculated Exp (-k) power value to the result output unit, and the result output unit recognizes the Exp (-k) power value and outputs the result.
3. A system for predicting the sensitivity of klebsiella to ceftazidime according to claim 1, wherein the natural constant e = 2.718281828459045.
4. The system for predicting the sensitivity of klebsiella to ceftazidime according to claim 1, wherein the result output unit is communicated with the calculation unit through a data path, and the Exp (-k) power value calculated by the calculation unit is transmitted to the result output unit through the data path.
5. The system for predicting the sensitivity of klebsiella to ceftazidime according to claim 2, wherein the result output unit is communicated with the calculation unit through a data path, and the Exp (-k) power value calculated by the calculation unit is transmitted to the result output unit through the data path.
6. A system for predicting the sensitivity of klebsiella to ceftazidime according to claim 1, wherein the sensitivity result S means that the klebsiella to be predicted is sensitive to ceftazidime; the drug resistance result R refers to the drug resistance of the klebsiella to be predicted to ceftazidime.
7. A system for predicting the sensitivity of klebsiella to ceftazidime according to any one of claims 1-6, further comprising: an experiment unit and a data input unit;
the experimental unit is communicated with the data input unit through a data path; the experimental unit outputs experimental results, and the experimental results are transmitted to the data input unit through the data path and converted into self-variable data;
the data input unit is communicated with the computing unit through a data path; the self-variable data is transmitted to the computing unit through the data path.
8. A system for predicting the sensitivity of klebsiella to ceftazidime as set forth in claim 7, wherein said self-variable data comprises: values of C1, C2, C3, C4, C5.
9. The system for predicting the sensitivity of klebsiella to ceftazidime of claim 7, wherein the experimental result comprises: the copy number of the KPC-1 gene in the Klebsiella strain to be predicted, the copy number of the sul1 gene in the Klebsiella strain to be predicted, the copy number of the CTX-M-55 gene in the Klebsiella strain to be predicted, the copy number of the CTX-M-15 gene in the Klebsiella strain to be predicted, the copy number of the TEM-1 gene in the Klebsiella strain to be predicted.
10. A method of predicting sensitivity of klebsiella to ceftazidime, comprising:
s1: the k value is calculated as follows:
formula I:
s2: solving Exp (-k) power values taking a natural constant e as a base and taking-k as an index;
in formula I:
c1 is the copy number of the KPC-1 gene in the Klebsiella strain to be predicted,
c2 is the copy number of the sul1 gene in the Klebsiella strain to be predicted,
c3 is the copy number of the CTX-M-55 gene in the Klebsiella strain to be predicted,
c4 is the copy number of the CTX-M-15 gene in the Klebsiella strain to be predicted,
c5 is the copy number of TEM-1 gene in the Klebsiella strain to be predicted;
the result of the prediction corresponding to the Exp (-k) power value <1 is that the Klebsiella is resistant to ceftazidime, and the result of the prediction corresponding to the Exp (-k) power value more than or equal to 1 is that the Klebsiella is sensitive to ceftazidime.
11. A method of predicting the sensitivity of klebsiella to ceftazidime according to claim 10, wherein the natural constant e = 2.718281828459045.
12. The method for predicting the sensitivity of klebsiella to ceftazidime according to claim 10, wherein the copy number of the KPC-1, sul1, CTX-M-55, CTX-M-15, TEM-1 genes in the klebsiella strain to be predicted is obtained by a second generation high throughput sequencing method.
13. The method for predicting the sensitivity of klebsiella to ceftazidime according to claim 12, wherein the gene is in the klebsiella to be predictedCopy number in strain =
。
14. The method for predicting the sensitivity of klebsiella to ceftazidime according to claim 13, wherein the genomic connigs are longest connigs fragments obtained by assembling sequencing results by SPADes v3.13.0 assembling software;
the depth of the genome contigs is the depth of the genome contigs calculated by SPADes v3.13.0 assembly software;
the depth of the gene at the connigs refers to the sum of the depths of the gene at each connigs with a copy of the gene.
15. The method of claim 14, wherein each connig having a copy of the gene is obtained by comparing the cds and protein sequences of the gene using the blat (v.36) software and the diamond (v 2.0.4.142) software using a CARD database.
16. A method of predicting the sensitivity of klebsiella to ceftazidime according to claim 14, wherein the depth of the gene on each contigs having a copy of the gene is calculated by SPAdes v3.13.0 assembly software.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310065212.7A CN115798575B (en) | 2023-02-06 | 2023-02-06 | System and method for predicting sensitivity of klebsiella to ceftazidime |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310065212.7A CN115798575B (en) | 2023-02-06 | 2023-02-06 | System and method for predicting sensitivity of klebsiella to ceftazidime |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115798575A CN115798575A (en) | 2023-03-14 |
| CN115798575B true CN115798575B (en) | 2023-06-02 |
Family
ID=85429923
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310065212.7A Active CN115798575B (en) | 2023-02-06 | 2023-02-06 | System and method for predicting sensitivity of klebsiella to ceftazidime |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115798575B (en) |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101519688A (en) * | 2008-09-28 | 2009-09-02 | 钟建平 | Detection method for drug resistant genes of Klebsiella pneumoniae against belta-lactamase |
| CN114729398A (en) * | 2019-11-20 | 2022-07-08 | 豪夫迈·罗氏有限公司 | Compositions and methods for rapid identification of bacteria and fungi and phenotypic antimicrobial drug susceptibility testing |
Family Cites Families (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106754907A (en) * | 2017-01-11 | 2017-05-31 | 博奥生物集团有限公司 | LAMP primer composition and its application for detecting three kinds of bacterial resistance genes of Extended-spectrum β-lactamases |
| US20210301326A1 (en) * | 2017-10-11 | 2021-09-30 | California Institute Of Technology | Antibiotic susceptibility of microorganisms and related methods and systems |
| CN114333987B (en) * | 2021-12-30 | 2023-05-12 | 天津金匙医学科技有限公司 | Data analysis method for predicting drug resistance phenotype based on metagenomic sequencing |
| CN114898800B (en) * | 2022-07-14 | 2022-09-16 | 中国医学科学院北京协和医院 | Method and system for predicting sensitivity of klebsiella pneumoniae to ceftriaxone |
| CN114898808B (en) * | 2022-07-14 | 2022-09-16 | 中国医学科学院北京协和医院 | Method and system for predicting sensitivity of Klebsiella pneumoniae to cefepime |
-
2023
- 2023-02-06 CN CN202310065212.7A patent/CN115798575B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101519688A (en) * | 2008-09-28 | 2009-09-02 | 钟建平 | Detection method for drug resistant genes of Klebsiella pneumoniae against belta-lactamase |
| CN114729398A (en) * | 2019-11-20 | 2022-07-08 | 豪夫迈·罗氏有限公司 | Compositions and methods for rapid identification of bacteria and fungi and phenotypic antimicrobial drug susceptibility testing |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115798575A (en) | 2023-03-14 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| De Goffau et al. | Human placenta has no microbiome but can contain potential pathogens | |
| Simner et al. | Understanding the promises and hurdles of metagenomic next-generation sequencing as a diagnostic tool for infectious diseases | |
| US11380421B2 (en) | Pathogen detection using next generation sequencing | |
| Pechal et al. | A large-scale survey of the postmortem human microbiome, and its potential to provide insight into the living health condition | |
| Dong et al. | The microbial communities in male first catch urine are highly similar to those in paired urethral swab specimens | |
| CN114898800B (en) | Method and system for predicting sensitivity of klebsiella pneumoniae to ceftriaxone | |
| Zemanick et al. | Inflammation and airway microbiota during cystic fibrosis pulmonary exacerbations | |
| DeBiasi et al. | Polymerase chain reaction in the diagnosis and management of central nervous system infections | |
| CN114898808B (en) | Method and system for predicting sensitivity of Klebsiella pneumoniae to cefepime | |
| Wright et al. | Progress report: next-generation sequencing, multiplex polymerase chain reaction, and broad-range molecular assays as diagnostic tools for fever of unknown origin investigations in adults | |
| Li et al. | Identification of Enterococcus faecalis in a patient with urinary-tract infection based on metagenomic next-generation sequencing: a case report | |
| WO2017012659A1 (en) | Genetic testing for predicting resistance of salmonella species against antimicrobial agents | |
| Horiba et al. | Next-generation sequencing to detect pathogens in pediatric febrile neutropenia: a single-center retrospective study of 112 cases | |
| Lian et al. | High-throughput next-generation sequencing for identifying pathogens during early-stage post-lung transplantation | |
| WO2017012661A1 (en) | Genetic testing for predicting resistance of pseudomonas species against antimicrobial agents | |
| CN116189919B (en) | Computer analysis method and system for microbial drug sensitivity and application of computer analysis method and system | |
| CN115798575B (en) | System and method for predicting sensitivity of klebsiella to ceftazidime | |
| CN115798577B (en) | System and method for predicting sensitivity of Klebsiella to levofloxacin | |
| CN115862730B (en) | System and method for predicting sensitivity of Klebsiella to cefoxitin | |
| CN115798576B (en) | System and method for predicting sensitivity of Klebsiella to imipenem | |
| CN115798574B (en) | System and method for predicting sensitivity of klebsiella to meropenem | |
| CN115938478B (en) | System and method for predicting sensitivity of Klebsiella to amikacin | |
| CN115881213B (en) | System and method for predicting sensitivity of klebsiella to piperacillin-tazobactam | |
| CN116072241A (en) | System and method for predicting sensitivity of klebsiella pneumoniae to ciprofloxacin | |
| CN116110511A (en) | System and method for predicting sensitivity of klebsiella pneumoniae to ertapenem |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |