CN115798575A - 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
- CN115798575A CN115798575A CN202310065212.7A CN202310065212A CN115798575A CN 115798575 A CN115798575 A CN 115798575A CN 202310065212 A CN202310065212 A CN 202310065212A CN 115798575 A CN115798575 A CN 115798575A
- Authority
- CN
- China
- Prior art keywords
- klebsiella
- gene
- predicted
- copy number
- exp
- 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.)
- Granted
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 stored thereon a computer program; the computer program, when executed by a processor, implements a method for Exp (-k) power value computation; the Exp (-k) power value calculation method comprises the following calculation steps: s1: the k value is calculated according to the following formula I: formula I:
(ii) a S2: solving an Exp (-k) power value which takes a natural constant e as a base number and takes-k as an exponent; wherein C1-C5 are KPC-1, sul1, CTX-M-55, CTX-M-15, TEM-1 genes respectively in Klebsiella to be predictedCopy number in a strain of the genus. The accuracy rate of the prediction of the sensitivity of the Klebsiella to the ceftazidime by adopting the prediction method and the prediction system 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
Antibiotic drugs were a "secret weapon" of human beings against many diseases, and in the early 20 th century, the life span of human beings was greatly improved due to the discovery of a series of antibiotics. In recent years, continuous application of antibiotics gradually leads to drug abuse, so that clinical antibiotic resistance and adverse reactions are increased continuously, and heavy burden is brought to global economy. Effective control of antibiotic abuse in medical settings is an important link to the problem of global antibiotic resistance.
Pathogenic microorganisms refer to microorganisms, or pathogens, that can invade the body and cause infections and even infectious diseases. Mainly comprises bacteria, viruses, fungi, parasites, mycoplasma, chlamydia, rickettsia, spirochete and the like. Microbial samples are of a wide variety of types. Intestinal samples include feces, mucous membranes, etc., fluid samples include urine, blood, cerebral medullary 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 Genus)Klebsiella) Is known as LatinKlebsiella TrevisanPhylogenetic Classification level of Genus (Genus)，It is a corynebacterium, diameter: 0.3-1.0 μm and 0.6-6.0 μm in length. In single, paired or short chain-like arrangement, species (strains) of the genus have been reported so far to include: klebsiella pneumoniae (K.pneumoniae: (B))Klebsiella pneumoniae) Klebsiella aerogenes (Klebsiella aerogenes) ((R))Klebsiella aerogenes) Klebsiella oxytoca (C.), (Klebsiella oxytoca) Klebsiella pneumoniae (C.) (Klebsiella quasipneumoniae）、Klebsiella variicola (Klebsiella variicola) (A)Klebsiella variicola) Klebsiella michiganensis (K.michiganensis) ((R))Klebsiella michiganensis) And so on.
Wherein Klebsiella pneumoniae (C.) (Klebsiella pneumoniae) As a Klebsiella (Genus)Klebsiella) The model strain (strain) is widely existed in the environment, is easy to be planted in the respiratory tract and the intestinal tract of a patient, causes common conditional pathogenic bacteria of infection of multiple parts of the digestive tract, the respiratory tract, blood and the like, is one of pathogenic bacteria causing human pneumonia, and is one of common drug-resistant bacteria in a hospital. According to the research of the second medical profession, the drug resistance rate of the carbapenem-resistant Klebsiella pneumoniae separated in 2014-2017 to ceftazidime is 62.5% (252/403).
Ceftazidime is the third generation cephalosporin antibiotic. Similar to other third-generation cephalosporins, they have a broad response against both gram-positive and gram-negative bacteria. Can be used for treating septicemia caused by gram-negative sensitive bacillus, lower respiratory infection, celiac and biliary infection, complicated urinary tract infection, severe skin soft tissue infection, etc.
The bacterial drug susceptibility test is the most commonly used bacterial drug resistance detection method in domestic and foreign clinics and laboratories at present, and comprises a paper sheet method, an agar dilution method, a broth dilution method, a concentration gradient method and the like, wherein except the paper sheet method, other methods can obtain relatively accurate Minimum Inhibitory Concentration (MIC) of drugs. The bacterial drug susceptibility test firstly needs to obtain pure culture, is not applicable to hard-to-culture and non-culture bacteria, is long in time consumption, and sometimes is difficult to meet the requirements of rapid diagnosis and symptomatic treatment of clinical severe and acute infection at present. The traditional detection and identification means of pathogenic microorganisms cannot meet the comprehensive requirements of wide coverage, rapidness and accuracy, the diagnosis and treatment of infectious diseases are mainly based on an empirical and directive method, and the clinical doctors and patients urgently need an innovative detection method, so that the infectious pathogens are identified more comprehensively, accurately and rapidly, the diagnosis is assisted, the medication is reasonably standardized, the treatment course is shortened, the fatality rate is reduced, and the medical cost is reduced.
With the popularization of emerging technologies such as PCR technology, whole genome sequencing technology, microfluidic technology, VITEK-2compact full-automatic bacteria identification/drug sensitive system and the like, the exploration of new bacterial drug resistance detection technologies is deepened gradually, and new bacterial drug resistance detection technical methods are matured gradually. Although the VITEK-2compact full-automatic bacteria identification/drug sensitivity system is simple, convenient and quick, the accuracy of identification/drug sensitivity evaluation of the strains is influenced by the state of a sample and the culture condition of the strains, and the use cost is higher.
Therefore, there is a need in the art to develop a system and method for predicting the sensitivity of klebsiella strains to ceftazidime quickly, accurately and at low cost.
Disclosure of Invention
In view of the above-mentioned deficiencies and needs in the art, the present invention is directed to a system and method for predicting susceptibility of klebsiella to ceftazidime.
The technical scheme of the invention is as follows:
a system for predicting the susceptibility of klebsiella to ceftazidime provided with: a calculation unit; the calculation unit includes: a computer-readable storage medium having stored thereon a computer program; the computer program, when executed by a processor, implements a method for Exp (-k) power value computation; the Exp (-k) power value calculation method comprises the following calculation steps:
s1: the k value is calculated according to the following formula I:
formula I:
s2: solving an Exp (-k) power value which takes a natural constant e as a base number and takes-k as an exponent;
in formula I:
c1 is the copy number of KPC-1 gene in 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 outputs a drug resistance result R when identifying that the Exp (-k) power value is less than 1;
the result output unit outputs a sensitive result S when the Exp (-k) power value is larger than or equal to 1;
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 sensitivity result S means that the klebsiella genus to be predicted is sensitive to ceftazidime; the drug resistance result R refers to the drug resistance of the Klebsiella to the ceftazidime to be predicted.
The system for predicting the sensitivity of the Klebsiella to the ceftazidime is also 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 experiment unit outputs an experiment result, transmits the experiment result to the data input unit through the data path and converts the experiment result into independent variable data;
the data input unit is communicated with the computing unit through a data path; the argument data is transmitted to the calculation unit via a data path.
The independent variable data includes: c1, C2, C3, C4, C5;
preferably, the experimental results include: the copy number of KPC-1 gene in Klebsiella strain to be predicted, the copy number of sul1 gene in Klebsiella strain to be predicted, the copy number of CTX-M-55 gene in Klebsiella strain to be predicted, the copy number of CTX-M-15 gene in Klebsiella strain to be predicted, and the copy number of TEM-1 gene in Klebsiella strain to be predicted.
A method of predicting susceptibility of klebsiella to ceftazidime comprising:
s1: the k value is calculated according to the following formula I:
formula I:
s2: solving an Exp (-k) power value which takes a natural constant e as a base number and takes-k as an exponent;
in formula I:
c1 is the copy number of KPC-1 gene in 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 prediction result corresponding to the Exp (-k) power value <1 is that the Klebsiella is resistant to ceftazidime, and the prediction result corresponding to the Exp (-k) power value greater than or equal to 1 is that the Klebsiella is sensitive to the 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 strain to be predicted are obtained by a second generation high-throughput sequencing method.
Copy number of gene in Klebsiella strain to be predicted =
；
Preferably, the genome contigs is the longest contigs fragment obtained by assembling the sequencing result by SPAdes v3.13.0 assembly software;
the depth of the genome contigs is calculated by SPAdes v3.13.0 assembly software;
the contigs depth where the gene is located refers to the sum of the depths of the genes on each contigs with the gene copy;
preferably, each contigs 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 cds and protein sequences of the gene with the CARD database;
preferably, the depth of the gene on each contigs with a copy of the gene is calculated by the SPAdes v3.13.0 assembly software.
In one aspect of the invention, a method for predicting the sensitivity of klebsiella to ceftazidime is provided.
In the invention, after the obtained microorganism sample is subjected to conventional treatment, necessary links such as DNA extraction and the like for sequencing can be carried out, the state of relevant characteristics of a Klebsiella prediction system in the sample can be obtained through bioinformatics flow analysis, and the characteristic state information is introduced into the system to predict the drug sensitivity condition of the sample. 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 judge the performance of a prediction system, a group of data sets which do not participate in the establishment of the prediction system are needed, and the accuracy of the prediction system is evaluated on the data sets, wherein the independent data sets are called test sets. The system prediction effect evaluation method comprises F1-score, precision (Precision), recall (Recall) and a confusion matrix.
The method of the invention also has the following advantages:
the present invention utilizes a test set to evaluate the accuracy of the system, with an average accuracy of 0.906, an F1-score of 0.873, and a recall score of 0.917. On one hand, the invention is slightly influenced by subjective factors such as operators and the like, and has good detection stability; on the other hand, the method realizes the rapid and accurate identification of infection pathogens, the prediction of drug sensitivity of the sample to be tested, the auxiliary diagnosis and the reasonable and standard medication, has high flux and reduces the medical cost.
Drawings
Fig. 1 is a schematic structural diagram (within a dashed box) of a drug resistance prediction system according to some embodiments of the present invention and a flowchart thereof.
Fig. 2 is a schematic structural diagram (within a dashed-line box) of a drug resistance prediction system according to another embodiment of the present invention and a flowchart of the structure.
Detailed Description
In order that the invention may be more fully understood, reference will now be made to the following examples.
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 in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.
The reagents used in the following examples are all commercially available unless otherwise specified.
Sources of biological material
171 samples used in the experimental examples of the present invention were pure cultures of clinical blood culture-isolated Klebsiella, which were obtained from Beijing cooperative Hospital of Chinese academy of medical sciences.
All the strains tested (species) were identified as Klebsiella (Genus) by mass spectrometry MALDI-TOF MSKlebsiella) And Latin name:Klebsiella Trevisanthe system classification level: genus).
In the Illumina Novaseq NGS sequencing platform, these strains cover 127 cases of klebsiella pneumoniae (c.pneumoniae: (c.Klebsiella pneumoniae) 20 cases of Klebsiella aerogenes (C.) (Klebsiella aerogenes) 8 examples of Klebsiella oxytoca: (Klebsiella oxytoca) 7 cases of Klebsiella pneumoniae (Klebsiella pneumoniae) ((II))Klebsiella quasipneumoniae) 6 cases of Klebsiella variicola (C.), (Klebsiella variicola) 3 cases of Klebsiella Michii: (A), (B), (C)Klebsiella michiganensis) All reported strains (species) of the genus Klebsiella.
The strain or strains can be obtained from common cases of Klebsiella pneumoniae or from laboratories of the applicant. The applicant promises to distribute strains to the public within 20 years from the filing date of the present invention for verifying the technical effects of the present invention.
Group 1 example, drug resistance prediction System of the present invention
The present group of embodiments provides a system for predicting the susceptibility 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 the sensitivity of klebsiella to ceftazidime is provided with: a calculation unit; the calculation unit includes: a computer-readable storage medium having stored thereon a computer program; the computer program, when executed by a processor, implements a method for Exp (-k) power value computation; the Exp (-k) power value calculation method comprises the following calculation steps:
s1: the k value is calculated according to the following formula I:
formula I:
s2: solving an Exp (-k) power value which takes a natural constant e as a base number and takes-k as an exponent;
in formula I:
c1 is the copy number of KPC-1 gene in 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 present invention, the natural constant e is 2.718281828459045.
In more specific examples, the above genes are all reported in the art, and are as follows:
the KPC-1 gene and TEM-1 gene are KPC-1 and TEM-1 genes described in "Novel Carbapenem-hydrolizing beta-lactase, KPC-1, from a Carbapenem-Resistant Strain of Klebsiella pneumoniae".
The sul1 gene is the sul1 gene described in "Co-occurrence of Klebsiella variicola and Klebsiella pneumoniae Both Carrying blaKPC from a Respiratory Intelligent Care Unit patent".
The CTX-M-55 gene is the CTX-M-55 gene described in "CTX-M-55-Type Extended-Spectrum. Beta. -lactamase-Producing Shigella sonnei Isolated from a Korean patent Who Had transformed to China".
The CTX-M-15 gene is a CTX-M-15 gene described in "High prediction of CTX-M-15-producing Klebsiella pneumoniae isolates in Asian cultures: reverse clones and clone isolation".
In a further embodiment, as shown in fig. 1 and 2, the system for predicting the susceptibility 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 refers to 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 transmitted to the result output unit via the data path.
In a further embodiment, as shown in fig. 1, the system for predicting susceptibility 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 result output by the experimental unit is transmitted to the data input unit through the data path and is converted into independent variable data;
the data input unit is communicated with the computing unit through a data path; the independent variable data is transmitted to the computing unit through a data path;
in a more specific embodiment, the data path is a data transmission carrier known to those skilled in the computer and electronics arts. The data path is selected from a wired or wireless form, and may be, for example, a wired path, a line, a wireless path, a wifi connection, a wireless channel, and the like.
Preferably, the independent variable data includes: c1, C2, C3, C4, C5;
preferably, the experimental results include: KPC-1, sul1, CTX-M-55, CTX-M-15, TEM-1 gene copy number in Klebsiella strain to be predicted.
The copy number of a known gene in a known strain can be conventionally known by a person skilled in the fields of molecular biology and bioinformatics by means of conventional techniques (e.g., sequencing, bioassay). 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 genes reported in the field, and the gene information and the primary structure sequence can be inquired through an NCBI website or other known bioinformatics databases. And (3) 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 KPC-1, sul1, CTX-M-55, CTX-M-15, TEM-1 genes in the Klebsiella strain to be predicted is determined by a second generation high throughput sequencing method.
In a more specific embodiment of the present invention,
preferably, the genome contigs is the longest contigs fragment obtained by assembling the sequencing result by SPAdes v3.13.0 assembly software;
the depth of the genome contigs is calculated by SPAdes v3.13.0 assembly software;
the contigs depth where the gene is located refers to the sum of the depths of the genes on each contigs with the gene copy;
preferably, each contigs 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 cds and protein sequences of the gene with the CARD database;
preferably, the depth of the gene on each contigs with a copy of the gene is calculated by the 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 the use of the second generation high throughput sequencing method to obtain gene copy number is a conventional technique well known to those skilled in the art.
In some specific embodiments, the specific method of gene copy number calculation is as follows:
the strains were sequenced using a second generation high throughput sequencing method. The average sequencing depth was around 150X, and the approximate amount of sequencing for Klebsiella was about 1G. The depth of contigs obtained by calculation in the assembly process by using SPAdes (v3.13.0) assembly software is taken as a standard, the longest contigs fragment is defined as a genome fragment, the contigs is subjected to gene prediction by using prokka software (1.14.6) to obtain all genes cds and protein sequences on the contigs, the cd and protein sequences are subjected to comparison of a CARD database by using blat (v.36) software and diamond (v 2.0.4.142) software respectively, the sequences with the similarity degree of more than 90 percent are positive sequences, and the annotation results of all drug-resistant genes are obtained. The copy number of all genes on contigs was calculated according to formula II as follows:
formula II:
。
if a gene has two or more genomic copies on different contigs or on the same contigs, the final gene copy number is equal to the sum of all calculated copy numbers for that gene. An example of a calculation method is as follows:
assuming that the KPC-1 gene has only one copy of all contigs, the copy number of the KPC-1 gene is:
assuming that the KPC-1 gene has 2 copies on one contigs and no copies on the other contigs, 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 KPC-1 gene is:
in a more specific embodiment, the result output unit, the experiment unit, and the data input unit are each provided with a computer-readable storage medium having a computer program stored thereon.
In some embodiments, a computer program on a computer readable storage medium of the result output unit, when executed by a processor, implements a larger and smaller method of Exp (-k) power value to 1 and outputs a result;
the method for comparing the Exp (-k) power value with the 1 is a large and small method, and the output result indicates that:
when the Exp (-k) power value is less than 1, the result output unit outputs a drug resistance result R;
and 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 computer program on a computer readable storage medium of the experimental unit, when executed by a processor, implements a gene copy number calculation method;
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: the maximum value of the depth of the genome contigs calculated by the SPAdes v3.13.0 assembly software is taken to obtain the genome contigs;
s2: comparing cd and protein sequences of a certain gene with a CARD database by using blat (v.36) software and diamond (v 2.0.4.142) software, and annotating to obtain contigs with the gene copy;
s3: the SPAdes v3.13.0 assembly software calculates the depth of the gene on each contigs with the gene copy;
s4: obtaining the sum of the depths of the gene on each contigs with the gene copy to obtain the depth of the contigs where the gene is located;
s5: the copy number of the gene is 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 non-dimensionalizing process of the copy number of the gene.
The non-dimensionalization 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., independent variable data. In general, the data dimension or data unit for copy number of a gene is: copies, individuals, 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 to the calculation unit through a data path, so that the gene copy number (experimental result) or independent variable data calculated by the experimental unit can be directly input to the calculation unit for calculating the Exp (-k) power value.
Group 2 example, method for predicting ceftazidime resistance of pneumococci of the present invention
The present set of embodiments provides a method for predicting susceptibility of klebsiella to ceftazidime. The present group of embodiments has the following common features: the method comprises the following steps:
s1: the k value is calculated according to the following formula I:
formula I:
s2: solving an Exp (-k) power value which takes a natural constant e as a base number and takes-k as an exponent;
in formula I:
c1 is the copy number of KPC-1 gene in 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 prediction result corresponding to the Exp (-k) power value <1 is that the Klebsiella is resistant to ceftazidime, and the prediction result corresponding to the Exp (-k) power value greater than or equal to 1 is that the Klebsiella is sensitive to ceftazidime.
In the above formula I, e is used as a mathematical constant, and is a base number of a natural logarithm function, which is also called a natural constant, a natural base number, or an euler number, and is an infinite acyclic decimal number, which has a conventional technical meaning generally understood by those skilled in the art of mathematics, and its value is about: e = 2.71828182845904523536.
In some embodiments of the present invention, the natural constant e is 2.718281828459045.
In some specific embodiments, the copy number of KPC-1, sul1, CTX-M-55, CTX-M-15, TEM-1 genes in the Klebsiella strain to be predicted is determined by a second generation high throughput sequencing method.
In a more specific embodiment of the present invention,
preferably, the genome contigs is the longest contigs fragment obtained by assembling the sequencing result by SPAdes v3.13.0 assembly software;
the depth of the genome contigs is calculated by SPAdes v3.13.0 assembly software;
the contigs depth at which the gene is located refers to the sum of the depths of the genes at each contigs having copies of the gene;
preferably, each contigs 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 cds and protein sequences of the gene with the CARD database;
preferably, the depth of the gene on each contigs with a copy of the gene is calculated by the 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 the use of the second generation high throughput sequencing method to obtain gene copy number is a conventional technique 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 a second generation high throughput sequencing method. The average sequencing depth was around 150X, and the approximate amount of sequencing for Klebsiella was about 1G. Using SPAdes (v3.13.0) assembly software to calculate the obtained contigs depth in the assembly process as a standard, defining the longest contigs fragment as a genome fragment, using prokka software (1.14.6) to perform gene prediction on contigs to obtain all gene cds and protein sequences on contigs, using blat (v.36) software and diamond (v 2.0.4.142) software to perform comparison of a CARD database on the cds and protein sequences, respectively, using sequences with the similarity degree of more than 90% as positive sequences, and obtaining the annotation results of all drug-resistant genes. The copy number of all genes on contigs was calculated according to formula II as follows:
formula II:
。
if a gene has two or more genomic copies on different contigs or on the same contigs, the final gene copy number is equal to the sum of all calculated copy numbers for that gene. An example of a calculation method is as follows:
assuming that the KPC-1 gene has only one copy of all contigs, the copy number of the KPC-1 gene is:
assuming that the KPC-1 gene has 2 copies on one contigs and no copies on the other contigs, 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 KPC-1 gene is:
experimental example, performance evaluation of prediction System and prediction method of the present invention
The prediction system of the present invention was evaluated using 171 clinical samples, and the broth microdilution classification results and the system prediction results of 171 clinical samples are shown in table 1 below. In the following table, S represents sensitivity, and R represents drug resistance.
TABLE 1
The test result data generates the confusion matrix as shown in table 2 below:
TABLE 2
Suppose TP (True Positive) represents the number of True Positive cases, FP (False Positive) represents the number of False Positive cases, FN (False Negative) represents the number of False Negative cases, and TN (True Negative) represents the number of True Negative cases. Precision (precision) refers to the proportion of positive samples in positive examples that are determined by the classifier. Recall refers to the proportion of total positive examples that are predicted to be positive examples. Accuracy (accuracy) refers to the specific gravity that the classifier determines the correct sample for the entire 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 each index are as follows:
the above-mentioned embodiments only express the embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. A system for predicting the susceptibility of klebsiella to ceftazidime provided with: a calculation unit; the calculation unit includes: a computer-readable storage medium having stored thereon a computer program; wherein the computer program when executed by the processor implements a method for Exp (-k) power value computation; the Exp (-k) power value calculation method comprises the following calculation steps:
s1: the k value is calculated according to the following formula I:
formula I:
s2: solving an Exp (-k) power value which takes a natural constant e as a base number and takes-k as an exponent;
in formula I:
c1 is the copy number of KPC-1 gene in 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.
2. The system for predicting the sensitivity of klebsiella to ceftazidime according to claim 1, further comprising: 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;
and/or, the natural constant e =2.718281828459045.
3. The system of claim 2, wherein the result output unit outputs the drug resistance result R when the Exp (-k) power value is < 1;
and the result output unit identifies that the Exp (-k) power value is more than or equal to 1 and outputs a sensitive result S.
4. The system according to claim 2 or 3, wherein the result output unit is connected to the calculating unit via a data path, and the Exp (-k) power value calculated by the calculating unit is transmitted to the result output unit via the data path;
and/or 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 the ceftazidime to be predicted.
5. The system for predicting the sensitivity of Klebsiella to ceftazidime according to any one of claims 1-4, 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 experiment unit outputs an experiment result, transmits the experiment result to the data input unit through the data path and converts the experiment result into independent variable data;
the data input unit is communicated with the computing unit through a data path; the argument data is transmitted to the calculation unit via the data path.
6. The system of claim 5, wherein the independent variable data comprises: c1, C2, C3, C4, C5;
and/or, the experimental results include: the copy number of KPC-1 gene in Klebsiella strain to be predicted, the copy number of sul1 gene in Klebsiella strain to be predicted, the copy number of CTX-M-55 gene in Klebsiella strain to be predicted, the copy number of CTX-M-15 gene in Klebsiella strain to be predicted, and the copy number of TEM-1 gene in Klebsiella strain to be predicted.
7. A method of predicting susceptibility of klebsiella to ceftazidime comprising:
s1: the k value is calculated according to the following formula I:
formula I:
s2: solving an Exp (-k) power value which takes a natural constant e as a base number and takes-k as an exponent;
in formula I:
c1 is the copy number of KPC-1 gene in 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 prediction result corresponding to the Exp (-k) power value <1 is that the Klebsiella is resistant to ceftazidime, and the prediction result corresponding to the Exp (-k) power value greater than or equal to 1 is that the Klebsiella is sensitive to ceftazidime.
8. The method of claim 7, wherein the natural constant e =2.718281828459045.
9. The method for predicting the sensitivity of Klebsiella to ceftazidime according to claim 7, wherein the copy number of KPC-1, sul1, CTX-M-55, CTX-M-15, TEM-1 genes in the Klebsiella strain to be predicted is determined by a second generation high throughput sequencing method.
10. The method of claim 9, wherein the copy number of the gene in the Klebsiella strain to be predicted = is the number of copies of the gene in the Klebsiella strain to be predicted
；
And/or the genome contigs is the longest contigs fragment obtained by assembling the sequencing result by SPAdes v3.13.0 assembling software;
the depth of the genome contigs is calculated by SPAdes v3.13.0 assembly software;
the contigs depth where the gene is located refers to the sum of the depths of the genes on each contigs with the gene copy;
and/or, each contigs with the gene copy is obtained by comparing the cds and protein sequences of the gene with the CARD database and annotating by using blat (v.36) software and diamond (v 2.0.4.142) software;
and/or the depth of the gene on each contigs with a copy of the gene is calculated by the 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 true CN115798575A (en) | 2023-03-14 |
| CN115798575B 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 (7)
| 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 |
| 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 |
| CN114333987A (en) * | 2021-12-30 | 2022-04-12 | 天津金匙医学科技有限公司 | Metagenome sequencing-based data analysis method for predicting drug resistance phenotype |
| 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 |
| CN114898808A (en) * | 2022-07-14 | 2022-08-12 | 中国医学科学院北京协和医院 | Method and system for predicting sensitivity of Klebsiella pneumoniae to cefepime |
| CN114898800A (en) * | 2022-07-14 | 2022-08-12 | 中国医学科学院北京协和医院 | Method and system for predicting sensitivity of klebsiella pneumoniae to ceftriaxone |
-
2023
- 2023-02-06 CN CN202310065212.7A patent/CN115798575B/en active Active
Patent Citations (7)
| 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 |
| 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 |
| 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 |
| CN114333987A (en) * | 2021-12-30 | 2022-04-12 | 天津金匙医学科技有限公司 | Metagenome sequencing-based data analysis method for predicting drug resistance phenotype |
| CN114898808A (en) * | 2022-07-14 | 2022-08-12 | 中国医学科学院北京协和医院 | Method and system for predicting sensitivity of Klebsiella pneumoniae to cefepime |
| CN114898800A (en) * | 2022-07-14 | 2022-08-12 | 中国医学科学院北京协和医院 | Method and system for predicting sensitivity of klebsiella pneumoniae to ceftriaxone |
Non-Patent Citations (3)
| Title |
|---|
| 刘芳: "临床常见革兰阴性菌的耐药性检测及耐药基因和耐消毒剂基因的分析" * |
| 林伟苗;李秋明;邱芳华;吴清;梁冬艳;薛志锋;王海英;黎勇强;: "肺炎克雷伯菌耐药分析及形成生物膜菌株的基因型检测" * |
| 王喜仁: "大肠埃希菌和肺炎克雷伯菌对头孢菌素的耐药性与ESBLs基因分型研究" * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115798575B (en) | 2023-06-02 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| De Goffau et al. | Human placenta has no microbiome but can contain potential pathogens | |
| CN114898800B (en) | Method and system for predicting sensitivity of klebsiella pneumoniae to ceftriaxone | |
| Dong et al. | The microbial communities in male first catch urine are highly similar to those in paired urethral swab specimens | |
| Pechal et al. | A large-scale survey of the postmortem human microbiome, and its potential to provide insight into the living health condition | |
| Sperling et al. | Comparison of bacterial 16S rRNA variable regions for microbiome surveys of ticks | |
| Wolfe et al. | Evidence of uncultivated bacteria in the adult female bladder | |
| Forney et al. | Comparison of self-collected and physician-collected vaginal swabs for microbiome analysis | |
| CN114898808B (en) | Method and system for predicting sensitivity of Klebsiella pneumoniae to cefepime | |
| WO2017013219A2 (en) | Genetic testing for predicting resistance of gram-negative proteus against antimicrobial agents | |
| Benny et al. | Placentas delivered by pre‐pregnant obese women have reduced abundance and diversity in the microbiome | |
| Komiya et al. | MinION, a portable long-read sequencer, enables rapid vaginal microbiota analysis in a clinical setting | |
| Bulfoni et al. | Validation of a saliva-based test for the molecular diagnosis of SARS-CoV-2 infection | |
| CN113186311B (en) | Application of vaginal microorganism in differential diagnosis of chronic pelvic pain syndrome | |
| JP6860771B2 (en) | Primer set for detecting chorioamnionitis-related microorganisms, assay kit, and method for detecting chorioamnionitis | |
| Gao et al. | The vaginal microbiome: disease, genetics and the environment | |
| CN111286551A (en) | Primer and kit for rapidly detecting mycobacterium tuberculosis and using method thereof | |
| CN115798575B (en) | System and method for predicting sensitivity of klebsiella to ceftazidime | |
| CN115798577B (en) | System and method for predicting sensitivity of Klebsiella to levofloxacin | |
| CN115798574B (en) | System and method for predicting sensitivity of klebsiella to meropenem | |
| CN115881213B (en) | System and method for predicting sensitivity of klebsiella to piperacillin-tazobactam | |
| CN115798576B (en) | System and method for predicting sensitivity of Klebsiella to imipenem | |
| CN115862730B (en) | System and method for predicting sensitivity of Klebsiella to cefoxitin | |
| CN115938478B (en) | System and method for predicting sensitivity of Klebsiella to amikacin | |
| EP4347895A1 (en) | Rna profiling of the microbiome and molecular inversion probes | |
| Chen et al. | The female urinary microbiota in relation to the reproductive tract microbiota |
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 |