CN115798577A - System and method for predicting sensitivity of Klebsiella to levofloxacin - Google Patents
System and method for predicting sensitivity of Klebsiella to levofloxacin Download PDFInfo
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Abstract
The invention discloses a system and a method for predicting sensitivity of Klebsiella to levofloxacin, and belongs to the technical field of biological information. The system includes a computer readable storage medium having a computer program stored thereon; 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 respectively the copy numbers of TEM-1, ompA, AAC (6') -Ib-cr6, KPC-1 and QnrS1 genes in the Klebsiella strain to be predicted. MiningThe accuracy of the prediction of the sensitivity of the Klebsiella to the levofloxacin by using the prediction method and the prediction system is about 89.5 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 levofloxacin.
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. 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 TrevisanThe phylogenetic classification level is 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 arrays, species (strains) of the genus have been reported so far, including: klebsiella pneumoniae (K.pneumoniae: (B))Klebsiella pneumoniae) Klebsiella aerogenes (A) and (B)Klebsiella aerogenes) Klebsiella oxytoca (C.), (Klebsiella oxytoca) Klebsiella pneumoniae (C.) (Klebsiella quasipneumoniae) Klebsiella variicola (C.) (Klebsiella variicola) Klebsiella michiganensis (K.michiganensis) ((R))Klebsiella michiganensis) And the like.
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 levofloxacin is 62.5% (252/403).
Levofloxacin (Levofloxacin) is one of quinolone drugs, and has a broad-spectrum antibacterial effect. It is used to treat a variety of bacterial infections, including acute bacterial sinusitis, pneumonia, helicobacter pylori (in combination with other drugs), urinary tract infections, chronic bacterial prostatitis, and certain types of gastroenteritis. Can be used in combination with other antibiotics for treating pulmonary tuberculosis, meningitis or pelvic inflammatory disease.
The bacterial drug susceptibility test is the most commonly used bacterial drug resistance detection method in domestic, foreign and clinical and laboratory 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, the 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 method and system for predicting the sensitivity of klebsiella strain to levofloxacin rapidly, 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 the sensitivity of klebsiella to levofloxacin.
The technical scheme of the invention is as follows:
a system for predicting the sensitivity of klebsiella to levofloxacin, comprising: 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 the TEM-1 gene in the Klebsiella strain to be predicted,
c2 is the copy number of the OmpA gene in the Klebsiella strain to be predicted,
c3 is the copy number of the AAC (6') -Ib-cr6 gene in the Klebsiella strain to be predicted,
c4 is the copy number of KPC-1 gene in Klebsiella strain to be predicted,
c5 is the copy number of the QnrS1 gene in the Klebsiella strain to be predicted.
The system for predicting the sensitivity of the Klebsiella to the levofloxacin is further provided with: a result output unit; the calculation unit transmits the calculated 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 = 2.7182818459045.
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 identifies that a sensitive result S is output when the Exp (-k) power value is more 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 levofloxacin; the drug resistance result R refers to the drug resistance of the klebsiella to be predicted to the levofloxacin.
The system for predicting the sensitivity of the Klebsiella to the levofloxacin 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 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.
The independent variable data includes: c1, C2, C3, C4, C5;
preferably, the experimental results include: the copy number of the TEM-1 gene in the Klebsiella strain to be predicted, the copy number of the OmpA gene in the Klebsiella strain to be predicted, the copy number of the AAC (6') -Ib-cr6 gene in the Klebsiella strain to be predicted, the copy number of the KPC-1 gene in the Klebsiella strain to be predicted, and the copy number of the QnrS1 gene in the Klebsiella strain to be predicted.
A method of predicting the sensitivity of klebsiella to levofloxacin, 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 the TEM-1 gene in the Klebsiella strain to be predicted,
c2 is the copy number of the OmpA gene in the Klebsiella strain to be predicted,
c3 is the copy number of the AAC (6') -Ib-cr6 gene in the Klebsiella strain to be predicted,
c4 is the copy number of KPC-1 gene in Klebsiella strain to be predicted,
c5 is the copy number of the QnrS1 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 levofloxacin, and the prediction result corresponding to the Exp (-k) power value being more than or equal to 1 is that the Klebsiella is sensitive to levofloxacin.
The natural constant e =2.718281828459045;
the copy numbers of the TEM-1, ompA, AAC (6') -Ib-cr6, KPC-1, qnrS1 genes in the Klebsiella strain to be predicted were 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 the depth of the genome contigs 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 levofloxacin drugs is provided.
In the invention, after the obtained microorganism sample is treated conventionally, necessary links such as DNA extraction and the like for sequencing can be carried out, the state of the relevant characteristics of the Klebsiella prediction system in the sample can be obtained through bioinformatics flow analysis, and the characteristic state information is introduced into the system, so that the drug sensitivity condition of the sample can be predicted. 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 invention utilizes a test set to evaluate the system accuracy, and the average accuracy of the method is 0.895, the F1-score is 0.859, and the recall score is 0.833. 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 the drug sensitivity of the sample to be detected, the auxiliary diagnosis and the reasonable and standard medication, and has high flux and reduced 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 operation of the drug resistance prediction system.
Detailed Description
In order to facilitate an understanding of the present invention, the present invention will now be described more fully in the 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 (klebsiella pneumoniae: (r) (r))Klebsiella pneumoniae) 20 cases of Klebsiella aerogenes (Klebsiella aerogenes) ((R))Klebsiella aerogenes) 8 examples of Klebsiella oxytoca: (Klebsiella oxytoca) 7 examples of Klebsiella pneumoniae (Klebsiella pneumoniae: (C))Klebsiella quasipneumoniae) 6 cases of Klebsiella variicola (C.), (Klebsiella variicola) 3 cases of Klebsiella michiganensis: (A. Meyeri) ((B. Meyeriana))Klebsiella michiganensis) All strains (strains) of the genus Klebsiella have been reported.
The strain or strains can be obtained from common cases of Klebsiella pneumoniae and also from the laboratory 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 set of embodiments provides a system for predicting the sensitivity of klebsiella to levofloxacin. 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 levofloxacin was 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 calculation; 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 the TEM-1 gene in the Klebsiella strain to be predicted,
c2 is the copy number of the OmpA gene in the Klebsiella strain to be predicted,
c3 is the copy number of the AAC (6') -Ib-cr6 gene in the Klebsiella strain to be predicted,
c4 is the copy number of KPC-1 gene in Klebsiella strain to be predicted,
c5 is the copy number of the QnrS1 gene in the Klebsiella strain to be predicted.
In some embodiments of the present invention, the natural constant e is 2.7182818459045.
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-hydrolizng b-Lactamase, KPC-1, from a Carbapenem-Resistant Strainof Klebsiella pneumoniae".
The OmpA gene is the OmpA gene described in "Klebsiella pneumoconiae OmpA genes Resistance to Antimicrobial Peptides".
AAC (6 ') -Ib-cr6 gene is the AAC (6') -Ib-cr6 gene described in the text "Genomic Analysis of multidug-Resistant Hyperviral (Hypermucoviscous) Klebsiella pneumoniae Strain cloning of the Hypermucoviscous (rmpA/rmpA 2)".
The QnrS1 gene is the QnrS1 gene described in "Co-occurrence of Klebsiella variicola and Klebsiella pneumoniae Both culturing blaKPC from a Respiratory Intelligent Care Unit".
In a further embodiment, as shown in fig. 1 and 2, the system for predicting the sensitivity of klebsiella to levofloxacin is further provided with: a result output unit; the result output unit outputs a sensitive result or a drug resistance result; the sensitive result indicates that the Klebsiella to be predicted is sensitive to the levofloxacin; the drug resistance result refers to the drug resistance of the klebsiella to be predicted to the levofloxacin;
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 computing unit through a data path;
preferably, the Exp (-k) power value calculated by the calculation unit is transmitted to the result output unit through the data path.
In a further embodiment, as shown in fig. 1, the system for predicting the sensitivity of klebsiella to levofloxacin 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 result output by the experimental unit is transmitted to the data input unit through the data path and 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: the copy numbers of the TEM-1, ompA, AAC (6') -Ib-cr6, KPC-1, qnrS1 genes, respectively, in the 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). The TEM-1, ompA, AAC (6') -Ib-cr6, KPC-1 and QnrS1 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 examples, the copy number of the TEM-1, ompA, AAC (6') -Ib-cr6, KPC-1, qnrS1 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 method,
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 common 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 about 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 CARD database comparison on the cds and protein sequences, respectively, using sequences with the similarity degree of more than 90% as positive sequences, and obtaining the annotation result 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:
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 blat (v.36) software and diamond (v 2.0.4.142) software, and then 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 in which the gene is positioned;
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 levofloxacin may not have a data input unit, the experimental unit is directly connected to the calculation unit via a data path, and the gene copy number or the 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 drug resistance of pneumococcus to levofloxacin
The present set of embodiments provides a method for predicting the sensitivity of klebsiella to levofloxacin. The embodiments of the group have the following common characteristics: 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 the TEM-1 gene in the Klebsiella strain to be predicted,
c2 is the copy number of the OmpA gene in the Klebsiella strain to be predicted,
c3 is the copy number of the AAC (6') -Ib-cr6 gene in the Klebsiella strain to be predicted,
c4 is the copy number of KPC-1 gene in Klebsiella strain to be predicted,
c5 is the copy number of the QnrS1 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 levofloxacin, and the prediction result corresponding to the Exp (-k) power value being more than or equal to 1 is that the Klebsiella is sensitive to levofloxacin.
In the above formula I, e is a mathematical constant, which is a base number of a natural logarithm function, 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 commonly understood by those skilled in the mathematical art, and its value is about: e = 2.718282845904523536.
In some embodiments of the present invention, the natural constant e is 2.718281828459045.
In some specific examples, the copy number of the TEM-1, ompA, AAC (6') -Ib-cr6, KPC-1, qnrS1 genes in the Klebsiella strain to be predicted is derived 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 common 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 CARD database comparison on the cds and protein sequences, respectively, using sequences with the similarity degree of more than 90% as positive sequences, and obtaining the annotation result 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 specimens, and the results of broth microdilution classification and systematic prediction of the 171 clinical specimens 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 (Turenegative) 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 judges correctly 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 embodiments only express the embodiments of the present invention, and the description thereof is specific and detailed, but not to be understood 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 should be subject to the appended claims.
Claims (10)
1. A system for predicting the sensitivity of klebsiella to levofloxacin, comprising: 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 calculation; 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 the TEM-1 gene in the Klebsiella strain to be predicted,
c2 is the copy number of the OmpA gene in the Klebsiella strain to be predicted,
c3 is the copy number of the AAC (6') -Ib-cr6 gene in the Klebsiella strain to be predicted,
c4 is the copy number of KPC-1 gene in Klebsiella strain to be predicted,
c5 is the copy number of the QnrS1 gene in the Klebsiella strain to be predicted.
2. The system for predicting the sensitivity of klebsiella to levofloxacin 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 = 2.7182818459045.
3. The system for predicting the sensitivity of Klebsiella to levofloxacin according to 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 outputs a sensitive result S when the Exp (-k) power value is more than or equal to 1.
4. The system for predicting the sensitivity of Klebsiella to levofloxacin according to claim 2 or 3, wherein the result output unit is in communication with 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 sensitive result S means that the klebsiella to be predicted is sensitive to levofloxacin; the drug resistance result R refers to the drug resistance of the klebsiella to be predicted to the levofloxacin.
5. The system for predicting the sensitivity of klebsiella to levofloxacin according to any one of claims 1 to 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 experimental unit outputs experimental results, transmits the experimental results to the data input unit through the data path and converts the experimental results 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 the TEM-1 gene in the Klebsiella strain to be predicted, the copy number of the OmpA gene in the Klebsiella strain to be predicted, the copy number of the AAC (6') -Ib-cr6 gene in the Klebsiella strain to be predicted, the copy number of the KPC-1 gene in the Klebsiella strain to be predicted, and the copy number of the QnrS1 gene in the Klebsiella strain to be predicted.
7. A method of predicting the sensitivity of klebsiella to levofloxacin, 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 the TEM-1 gene in the Klebsiella strain to be predicted,
c2 is the copy number of the OmpA gene in the Klebsiella strain to be predicted,
c3 is the copy number of the AAC (6') -Ib-cr6 gene in the Klebsiella strain to be predicted,
c4 is the copy number of KPC-1 gene in Klebsiella strain to be predicted,
c5 is the copy number of the QnrS1 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 levofloxacin, and the prediction result corresponding to the Exp (-k) power value greater than or equal to 1 is that the Klebsiella is sensitive to levofloxacin.
8. The method of claim 7, wherein the natural constant e = 2.7182818459045.
9. The method of claim 7, wherein the copy number of TEM-1, ompA, AAC (6') -Ib-cr6, KPC-1, qnrS1 genes in the Klebsiella strain to be predicted is determined by a second generation high throughput sequencing method.
10. The method for predicting the sensitivity of Klebsiella to levofloxacin according to claim 9, wherein the copy of the gene in the Klebsiella strain to be predictedShell number =
；
And/or the genome contigs are longest contigs 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 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;
and/or, annotation of each contigs with the gene copy after alignment of cd and protein sequences of the gene using blat (v.36) software and diamond (v 2.0.4.142) software for CARD database;
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.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109715831A (en) * | 2016-04-14 | 2019-05-03 | 阿瑞斯遗传股份有限公司 | Improved gene resistance test is used for using the full gene information collection from bacterial genomes and plasmid |
| US20210292807A1 (en) * | 2017-04-07 | 2021-09-23 | The University Of Western Australia | Method for testing antimicrobial susceptibility |
| 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 |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109715831A (en) * | 2016-04-14 | 2019-05-03 | 阿瑞斯遗传股份有限公司 | Improved gene resistance test is used for using the full gene information collection from bacterial genomes and plasmid |
| US20210292807A1 (en) * | 2017-04-07 | 2021-09-23 | The University Of Western Australia | Method for testing antimicrobial susceptibility |
| 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 |
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