A biomarker for identifying highly virulent Klebsiella pneumoniae, application, kit and identification method

Abstract

The embodiments of the present application relate to a biomarker, application, kit and detection method for identifying highly virulent Klebsiella pneumoniae. The biomarker includes one or more of arachidic acid, 3,4-dihydroxymandelic acid, 10-hydroxystearic acid, D-arginine and caproic acid in the metabolites of highly virulent Klebsiella pneumoniae, which can accurately distinguish highly virulent Klebsiella pneumoniae from classical Klebsiella pneumoniae, and can more accurately identify highly virulent Klebsiella pneumoniae.

Description

Technical Field

[0001] The present application relates to the field of biological detection technology, and more specifically, to a biomarker, application, kit and identification method for identifying highly virulent Klebsiella pneumoniae. Background Art

[0002] Klebsiella pneumoniae is an increasingly important bacterial pathogen that can cause severe organ damage and life-threatening diseases. One of its notable features is the ability to acquire new genes. Currently, this pathogen is mainly divided into two pathological types: classic Klebsiella pneumoniae (cKP) and hypervirulent Klebsiella pneumoniae (hvKp). These two types of bacteria each show unique characteristics, posing great challenges to clinicians.

[0003] The characteristics of hvKp and its differences from cKp have not been fully investigated. Although hvKp has been described as a highly virulent pathogen, infections caused by hvKp are increasingly being reported in the community. Clinical epidemiological studies have shown that hvKp infection can occur at any age and that infected individuals often present with multiple sites of infection, with the potential for metastatic spread, making K. pneumoniae an unusual member of the Enterobacteriaceae family. hvKp can infect virtually any site in the human body. These infectious syndromes include liver abscesses, pneumonia, necrotizing fasciitis, endophthalmitis, and meningitis. The hyperviscosity phenotype, which was initially thought to be susceptible and specific to hvKp strains, has been shown to be non-exclusive, as not all hvKp strains are hyperviscosity and some cKp strains also have this feature. Therefore, the use of the hyperviscosity phenotype alone to define hvKp strains is inaccurate. Similarly, the chromosomally encoded aerobic oxadiazole (iutA) receptor or iroE gene is also present in many cKp strains and cannot be used alone to define hvKp. The presence of K1 or K2 capsular type alone cannot be used as a criterion for identifying hvKp. However, hvKp has acquired virulence genes present on large virulence plasmids (such as pK2044 and pLVPK) and integrated chromosomal elements (ICEs) that confer a hypervirulent phenotype, which are referred to as biomarkers on virulence plasmids.

[0004] Traditional methods for detecting virulence genes of Klebsiella pneumoniae mainly rely on PCR technology to detect a series of known virulence genes, such as rmpA, rmpA2, magA, etc. These genes are believed to be related to the virulence characteristics of bacteria, and in theory their presence or absence can be used as an indicator to assess the virulence of strains. However, recent studies have shown that these traditional virulence gene detection methods have several limitations in actual clinical applications: the ubiquity of virulence genes, which are often detected even in classical strains. For example, genes such as rmpA and magA can also be detected in some Klebsiella pneumoniae strains with lower virulence. This finding suggests that simply detecting the presence of these genes is not sufficient to accurately assess the virulence level of strains.

[0005] The virulence genes of Klebsiella pneumoniae include capsule genes, lipopolysaccharide, pili, outer membrane proteins, nitrogen source utilization system and iron carrier system. However, the experimental results found that whether it is through the detection of pili or virulence genes to identify hvKp, the accuracy is not high enough compared with the gold standard - the greater wax moth model. Summary of the invention

[0006] The present application is provided to solve the above defects in the prior art. A biomarker, application, kit and detection method for identifying highly virulent Klebsiella pneumoniae are needed to improve the accuracy of identifying highly virulent Klebsiella pneumoniae.

[0007] In a first aspect of the present application, a biomarker for identifying highly virulent Klebsiella pneumoniae is provided, wherein the biomarker comprises one or more of arachidic acid, 3,4-dihydroxymandelic acid, 10-hydroxystearic acid, D-arginine and caproic acid in the metabolites of highly virulent Klebsiella pneumoniae.

[0008] The second aspect of the present application provides an application of the biomarker described in any embodiment of the present application in identifying the type of highly virulent Klebsiella pneumoniae.

[0009] The third aspect of the present application provides a use of the biomarker described in any embodiment of the present application in the preparation of a product for identifying highly virulent Klebsiella pneumoniae.

[0010] In a fourth aspect of the present application, a kit is provided, wherein the kit comprises the biomarker described in any embodiment of the present application.

[0011] In a fifth aspect of the present application, a method for identifying highly virulent Klebsiella pneumoniae is provided, the identification method comprising: extracting metabolites of the Klebsiella pneumoniae to be tested; detecting the metabolites using LC-MC (liquid chromatography-mass spectrometry) to obtain the content of the biomarker described in any embodiment of the present application; comparing the content of the biomarker obtained by LC-MC (liquid chromatography-mass spectrometry) detection with the content of the corresponding metabolite of low-virulence Klebsiella pneumoniae to identify the Klebsiella pneumoniae to be tested.

[0012] The biomarkers, applications, kits and detection methods for identifying highly virulent Klebsiella pneumoniae provided in various embodiments of the present application propose one or more biomarkers of arachidic acid, 3,4-dihydroxymandelic acid, 10-hydroxystearic acid, D-arginine and caproic acid. The experimental results show that compared with the high viscosity phenotype, virulence genes and the like, the biomarkers of the present application can accurately distinguish highly virulent Klebsiella pneumoniae from classical Klebsiella pneumoniae, and can more accurately identify highly virulent Klebsiella pneumoniae. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In the drawings, which are not necessarily drawn to scale, the same reference numerals may describe similar parts in different views. The same reference numerals with letter suffixes or different letter suffixes may represent different instances of similar parts. The accompanying drawings generally illustrate various embodiments by way of example and not limitation, and together with the specification and claims, are used to illustrate the embodiments applied for. When appropriate, the same reference numerals are used throughout the drawings to refer to the same or similar parts. Such embodiments are illustrative and are not intended to be exhaustive or exclusive embodiments of the present apparatus or method.

[0014] Figure 1 The genome structure of the highly virulent strain WX071 according to Experimental Example 1 of the present application is shown; Figure 2 The genome structure of the classical strain WX045 according to Experimental Example 1 of the present application is shown; Figure 3 A volcano plot showing the cationic differential metabolites of the primary metabolites according to Example 1 of the present application; Figure 4 A volcano plot showing anionic differential metabolites of primary metabolites according to Example 1 of the present application; Figure 5 A cluster analysis diagram of cationic differential metabolites of primary metabolites according to Example 1 of the present application is shown; Figure 6 A cluster analysis diagram of anionic differential metabolites of primary metabolites according to Example 1 of the present application is shown; Figure 7A volcano plot showing the secondary metabolites according to Example 1 of the present application; Figure 8 A cluster analysis diagram of secondary metabolites according to Example 1 of the present application is shown; Fig. 9 The KEGG cluster analysis diagram of the metabolites according to Example 1 of the present application is shown. DETAILED DESCRIPTION

[0015] In order to enable those skilled in the art to better understand the technical solution of the present application, the present application is described in detail below in conjunction with the accompanying drawings and specific implementation examples. The embodiments of the present application are further described in detail below in conjunction with the accompanying drawings and specific implementation examples, but are not intended to limit the present application.

[0016] The words "first", "second" and similar words used in this application do not indicate any order, quantity or importance, but are only used to distinguish. The words "include" or "comprise" and similar words mean that the elements before the word include the elements listed after the word, and do not exclude the possibility of including other elements.

[0017] According to an embodiment of the present application, a biomarker for identifying highly virulent Klebsiella pneumoniae is provided, wherein the biomarker includes one or more of arachidic acid, 3,4-dihydroxymandelic acid, (R)-10-hydroxystearate, D-arginine and caproic acid in the metabolites of highly virulent Klebsiella pneumoniae.

[0018] Arachidic acid, 3,4-dihydroxymandelic acid, 10-hydroxystearic acid, and D-arginine are up-regulated biomarkers. The contents of these four biomarkers are significantly different in the metabolites of hypervirulent Klebsiella pneumoniae compared with those of classical Klebsiella pneumoniae, and the contents of these four biomarkers are significantly up-regulated. The content of caproic acid is significantly different in the metabolites of hypervirulent Klebsiella pneumoniae compared with those of classical Klebsiella pneumoniae, and the content of caproic acid is significantly down-regulated.

[0019] Classical Klebsiella pneumoniae is considered to be low-virulence Klebsiella pneumoniae compared to high-virulence Klebsiella pneumoniae (classical Klebsiella pneumoniae is also referred to as low-virulence Klebsiella pneumoniae in this application), and the toxicity of the two Klebsiella pneumoniae is significantly different. The biomarker of the present application can clearly determine and identify Klebsiella pneumoniae as high-virulence Klebsiella pneumoniae, and can better distinguish the two Klebsiella pneumoniae.

[0020] In some embodiments, the biomarkers further include ABC transporters.

[0021] In some embodiments, the biomarkers further include one or more of aminoacyl-tRNA, phenylalanine, arginine, proline metabolism, and tyrosine.

[0022] According to an embodiment of the present application, there is also provided an application of the biomarker described in any embodiment of the present application in identifying the type of highly virulent Klebsiella pneumoniae.

[0023] According to the embodiments of the present application, there is also provided a use of the biomarker described in any embodiment of the present application in the preparation of a product for identifying highly virulent Klebsiella pneumoniae.

[0024] According to an embodiment of the present application, a kit is also provided, wherein the kit comprises the biomarker described in any embodiment of the present application.

[0025] According to an embodiment of the present application, a method for identifying highly virulent Klebsiella pneumoniae is also provided, the identification method comprising: extracting metabolites of the Klebsiella pneumoniae to be tested; detecting the metabolites using LC-MC to obtain the content of the biomarker described in any embodiment of the present application; comparing the content of the biomarker obtained by LC-MC detection with the content of the corresponding metabolite of classical Klebsiella pneumoniae to identify the Klebsiella pneumoniae to be tested.

[0026] In some embodiments, the method for extracting metabolites of Klebsiella pneumoniae to be tested includes: mixing Klebsiella pneumoniae and methanol water and vortexing; freezing and thawing the vortexed mixture, and grinding the thawed mixture; centrifuging the ground mixture, taking the supernatant, and concentrating and drying the supernatant; redissolving the concentrated and dried solid in a 2-chloro-L-phenylalanine solution, taking the supernatant and filtering, and the filtrate includes the extracted metabolites.

[0027] In some embodiments, the method of detecting metabolites using LC-MC includes: the method of detecting metabolites using LC-MC includes: performing sample cleanup by solid phase extraction to concentrate metabolites and remove impurities, and injecting them into a liquid chromatography-mass spectrometry system.

[0028] Experimental Example 1-Sequencing of Klebsiella pneumoniae genome The Klebsiella pneumoniae genome sequencing was completed by Biomike Biotechnology Co., Ltd.

[0029] 1. Sequencing principle: PacBio sequencing technology uses SMRT chips as sequencing carriers. In the nanopores inside the SMRT chip, DNA polymerase binds to the template, and four bases (dNTPs) are labeled with four-color fluorescence. During the base pairing stage, the addition of different bases will emit different colors of light. The type of base added can be determined based on the wavelength and peak value of the light.

[0030] 2. Experimental process: The experimental process was carried out according to the standard protocol provided by PacBio (PacBio Technology Co., Ltd.), including sample quality detection, library construction, library quality detection and library sequencing. The library construction includes the following steps: 1) Use g-TUBE to shear DNA samples; 2) Repair the damaged DNA samples; 3) Perform end repair on DNA; 4) Connect the dumbbell type connector; 5) Perform exonuclease digestion; 6) Use the BluePippin automated nucleic acid recovery system to screen target fragments and obtain sequencing libraries.

[0031] 3. Information analysis mainly includes the following steps: 1) Quality control of raw data, filtering CCS reads that are too short; 2) Genome assembly: assemble the filtered CCS reads from scratch and correct the assembled genome; 3) Analysis of genome components, mainly including: analysis of repetitive sequences, coding genes, non-coding RNA, prophages, gene islands, CRISPR, etc.; 4) Functional annotation, mainly including general databases such as Nr, Uniprot, COG, KEGG and other databases, as well as proprietary database annotations such as CAZyme, PHI, and CARD; 5) Genome map analysis, including: genome circle map, genome map display tools, and genome map.

[0032] 4. Klebsiella pneumoniae genome sequencing results 1) Klebsiella pneumoniae genome size and plasmids The experimental subjects were 10 clinically isolated Klebsiella pneumoniae strains (coded as WX060, WX071, WX090, WX117, WX120, WX045, WX075, WX077, WX079, and WX132). The sequencing results showed that the genome size of all bacteria was about 5-6 Mb, and all bacteria were composed of chromosomes and several carried plasmids. As shown in Table 1, all bacteria carried at least one plasmid. The genome structure of the highly virulent strain WX071 is shown in Figure 1 The genome structure of the classical strain WX045 is shown in Figure 2 As shown, the highly virulent strain WX071 carries 4 plasmids, while the classical strain WX045 carries 5 plasmids.

[0033] Table 11 Genome assembly of 10 clinically isolated Klebsiella pneumoniae strains 2) Structural analysis of the genomes of 10 clinically isolated Klebsiella pneumoniae strains According to the genome data, gene prediction was performed using the software Prodigal v2.6.3, which uses a dynamic programming algorithm to predict genes in newly sequenced genomes with high accuracy. The statistical information of the specific gene prediction results is shown in Table 2. The results show that the number of genes in these 10 strains of Klebsiella pneumoniae is about 5,000 genes. RepeatMaskerv4.0.5 software is used to compare the bacterial genome with a known repeat sequence database (such as Repbase) to search for repeat sequences in the genome. The proportion of repeat sequences in highly toxic strains is 0.49-0.55%, while the proportion of repeat sequences in low-toxic strains is larger, ranging from 0.32-0.94%. The software Infernal v1.1.3 can accurately predict the three types of rRNA in the genome based on the covariance model. The number of non-coding RNAs in most strains is about 115, among which the number of non-coding RNAs in the highly toxic strain WX117 reaches 240, while the number of non-coding RNAs in the low-toxic strain WX075 reaches 286. CRISPR (Clustered Regularly Interspaced Palindromic Repeats) is a continuous DNA region in the genome. The CRISPR region, together with some Cas-related proteins, forms an immune system that is widely present in bacteria and archaea, which prevents external nucleic acids from entering bacterial cells. The CRT v1.2 software was used to predict CRISPR in the genome, and the statistical results are shown in Table 2.2. The number of CRISPR sequences in highly virulent strains is relatively distributed between 15 and 18, while the number of CRISPR sequences in low-virulence strains is between 14 and 62, among which the number of CRISPR sequences in strain WX079 is as high as 62. Genomic islands often have specific functions. The software IslandPath-DIMOB v0.2 is a commonly used software for predicting genomic islands in bacterial genomes. The number of genomic islands is shown in Table 2. Only strain WX075 contains 3 genomic islands, and the others have 4 genomic islands. The software antiSMASH v5.0 is the most widely used tool for identifying and analyzing biosynthetic gene clusters (BGCs) in bacterial and fungal genome sequences. Gene clusters encoding all known broad chemical classes of secondary metabolites were accurately identified. The results showed that most strains contained five gene clusters, and strains WX17, WX075, and WX079 contained four gene clusters. Promoter regions are key regulatory regions that allow genes to be transcribed or repressed and can also be determined experimentally. Using the tool PromPredict v1, genomic DNA with different GC contents was analyzed and used as a universal standard for predicting promoter regions in microbial genomes.Promoters within 500 bp upstream of the gene and with a prediction confidence higher than level 2 were selected as prediction results. The results are shown in Table 2: It shows that the number of promoters in all highly virulent strains is 141, while in the low-virulence strains, the number of promoters in most strains is in the 50s, and only the strain WX075 has a promoter number as high as 187.

[0034] Table 2 Genome structures of 10 clinically isolated Klebsiella pneumoniae strains 3) Subcellular localization of proteins encoded by genes in the genomes of 10 clinically isolated Klebsiella pneumoniae strains The signal peptide is a short peptide segment at the N-terminus of the protein initially synthesized by the ribosome. After being synthesized by the ribosome, the peptide segment is recognized by the signal peptide recognition particle and guided to the biomembrane (cell membrane or organelle membrane surface, bacteria only have cell membrane) under the action of related proteins and signal peptide recognition particles, and then the signal peptide sequence is removed to form a mature protein. The software SignalP v4.0 was used to predict whether the protein contains a signal peptide and to predict the specific signal peptide removal site, as shown in Table 3. The results showed that the number of signal peptides in all strains was between 477 and 524. There was no obvious difference between high-virulence and low-virulence strains. Transmembrane proteins are proteins that exist in large numbers on the cell membrane. The transmembrane regions of these proteins contain one or more transmembrane helices, and the other regions are located on the surface of the cell membrane. According to the characteristics of the transmembrane region and the non-transmembrane region, the software TMHMM v2.0 designed based on the hidden Markov model can accurately predict whether the protein is a transmembrane helix, and find out the specific predicted transmembrane region and non-transmembrane region, as shown in Table 3. The results show that the number of transmembrane proteins in all strains is between 1221-1333, and there is no obvious difference in number between high virulence and low virulence.

[0035] Secretory proteins are proteins that are secreted outside the cell during the life of microorganisms. Secretory proteins can often improve the adaptability of bacteria to the environment. For example, some secretory proteins are proteases or polysaccharide degrading enzymes, which degrade macromolecular nutrients (proteins or polysaccharides) that cannot be directly absorbed and utilized by bacteria in the environment into small molecules that can be absorbed and utilized. Some secretory proteins are toxic to other bacteria (antibiotics, etc.), inhibiting the growth of other types of bacteria around them. Proteins with signal peptides can be guided to the cell membrane surface or outside the cell by signal peptides, and proteins located on the cell membrane generally have transmembrane helices. In the above-predicted proteins containing signal peptides, the proteins containing transmembrane helices are removed, and the remaining proteins are secretory proteins. As shown in Table 3, the number of secretory proteins is between 395-524, and there is no obvious difference in number between high toxicity and low toxicity.

[0036] Table 3 Subcellular localization of proteins encoded by genes from 10 clinically isolated Klebsiella pneumoniae strains Through Experimental Example 1, it can be obtained that among the 10 clinically isolated Klebsiella pneumoniae strains, there is no significant difference in plasmid genome size, number of genes, ratio of repeat sequences, number of non-coding RNAs and CRISPR sequences between highly toxic Klebsiella pneumoniae and classical Klebsiella pneumoniae in highly toxic and classical strains. In addition, there are no significant differences in gene islands and gene clusters (including metabolic functions and antibiotic resistance, etc.) between highly toxic and low toxic strains. It can be obtained that there are certain limitations in evaluating the virulence of strains based solely on genome sequencing data. Genome sequencing can only reveal the existence of genes, but cannot provide the expression of genes in a specific environment. There may be significant differences in the expression levels of virulence genes in different strains, but these differences cannot be directly observed through genome sequencing. For example, some virulence genes may not be expressed in classical strains due to transcriptional regulation or other mechanisms, but are expressed at high levels in highly toxic strains. Genome sequencing data provides a comprehensive perspective on the structure of bacterial genomes, but there are still limitations in its analysis and interpretation.

[0037] Compared to the existing belief that highly toxic Klebsiella pneumoniae often contains large plasmids, these plasmids carry multiple virulence genes, which significantly enhance the pathogenicity of bacteria. In this application, through genome sequencing technology, we found that there was no significant difference in the number of plasmids between highly toxic and low toxic strains, and even some low toxic strains had more plasmids. Plasmids are small DNA molecules independent of chromosomes in bacteria that can carry antibiotic resistance genes, virulence genes, and metabolic genes, giving host bacteria a variety of survival advantages. The traditional view is that the number and size of plasmids are directly related to the virulence of bacteria, especially in highly toxic Klebsiella pneumoniae. However, the results of genome sequencing in this application show that the number of plasmids is not the only factor that determines the virulence of bacteria. On the contrary, some low toxic strains may contain a larger number of plasmids, indicating that other factors may play a role in the expression of virulence.

[0038] Therefore, it can be concluded from Experimental Example 1 that there is no significant difference between highly virulent Klebsiella pneumoniae and low-virulence Klebsiella pneumoniae in terms of the number of signal peptides, transmembrane proteins, secreted proteins, number of plasmids, genome size, number of genes, repeat ratio, number of non-coding RNAs, and CRISPR sequences, and the accuracy of identifying highly virulent Klebsiella pneumoniae through these aspects is poor.

[0039] Example 1 The experimental subjects were 10 clinically isolated Klebsiella pneumoniae strains, including 5 highly virulent Klebsiella pneumoniae strains and 5 classical Klebsiella pneumoniae strains.

[0040] 1. Metabolite extraction: Step 1. Accurately weigh an appropriate amount of sample (10 strains of clinically isolated Klebsiella pneumoniae, collected by shaking) into a 2 mL centrifuge tube and add 100 mg of glass beads; Step 2. Accurately add 1000 µL of 50% methanol water and vortex for 30 s; Step 3. Place the centrifuge tube containing the sample in the 2 mL adapter of the instrument, immerse it in liquid nitrogen for rapid freezing for 5 min, take out the centrifuge tube and thaw it at room temperature, place the centrifuge tube in the 2 mL adapter again, install it in the tissue grinder, and grind it at 55 Hz for 2 min; Repeat step 3 twice; Take out the centrifuge tube, centrifuge at 12000 rpm and 4℃ for 10 min, take the supernatant, concentrate and dry; Accurately add 300 µL of 2-chloro-L-phenylalanine (4 ppm) solution prepared in 50% methanol water to dissolve the sample, take the supernatant and filter it through 0.22 μm After membrane filtration, the filtrate was added to a detection bottle for LC-MS (liquid chromatography-mass spectrometry) detection.

[0041] 2. LC-MS (liquid chromatography-mass spectrometry) detection: The sample is purified by a C18 solid phase extraction column, and then eluted with ethanol. The collected eluate is nitrogen-purged and filtered using a 0.22μm organic filter membrane to obtain the sample to be tested. The sample to be tested is injected into the liquid chromatography-mass spectrometry system. According to the characteristics of bacterial metabolites, LC-MS is operated under optimized conditions, usually using electrospray ionization (positive ion mode or negative ion mode). By analyzing the data, metabolites are identified and quantified based on the comparison of mass-to-charge ratio and retention time with known standards.

[0042] 3. Data preprocessing: The original mass spectrometry file was converted into mzXML file format by using the MSConvert tool in the Proteowizard software package (v3.0.8789). The R XCMS (v3.12.0) software package was used for peak detection, peak filtering, and peak alignment to obtain a quantitative list of metabolites. The parameters were bw=2, ppm=15, peakwidth=c(5, 30), mzwid=0.015, mzdiff=0.01, and method=”centWave”. Then, data correction was achieved by total peak area normalization to eliminate systematic errors. The public databases HMDB, massbank, LipidMaps, mzcloud, KEGG, and the Nomi Metabolism self-built standard library were used for material identification (search library). The parameters were set to ppm<30 ppm to obtain the qualitative results of metabolites. The specific principle is to determine the molecular weight of the metabolite based on the mass-to-charge ratio (m / z) of the parent ion in the primary mass spectrum, predict the molecular formula through information such as mass number deviation (ppm) and adduct ions, and then match it with the database to achieve primary identification of the metabolite; at the same time, in the quantitative list, the metabolites detected in the secondary spectrum are matched with information such as the fragment ions of each metabolite in the database to achieve secondary identification of the metabolite.

[0043] 4. Data analysis: The R software package Ropls was used to perform principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and orthogonal partial least squares discriminant analysis (OPLS-DA) dimensionality reduction analysis on the sample data. The permutation test method was used to test the overfitting of the model. R2X and R2Y represent the explanatory rate of the constructed model for the X and Y matrices, respectively, and Q2 indicates the predictive ability of the model. The closer their values ​​are to 1, the better the fit of the model is, and the more accurately the samples in the training set can be divided into their original belonging. According to the statistical test, the P value is calculated, the variable projection importance (VIP) is calculated by the OPLS-DA dimensionality reduction method, and the fold change is calculated to calculate the difference between groups. The intensity and explanatory power of each metabolite content on the classification and discrimination of samples are measured to assist in the screening of marker metabolites. When the P value is <0.05 and the VIP value is >1, the metabolite molecules are considered to have statistically significant differences.

[0044] 5. Pathway analysis: MetaboAnalyst software package was used to perform functional pathway enrichment and topological analysis on the screened differential metabolites. The enriched pathways were visualized using KEGG Mapper to browse differential metabolites and pathway maps.

[0045] It can be found that the metabolites of Klebsiella pneumoniae can be divided into primary metabolites and secondary metabolites. We screened out the differential metabolites from the primary metabolite list and used the set statistical test method to perform differential analysis. The metabolites with significant differences were screened out by P value and VIP value, such as Figure 3 and Figure 4 shown.

[0046] Among the positive ion metabolites, a total of 27,751 metabolites were detected, of which 3,298 were significantly different, including 314 up-regulated metabolites and 2,984 down-regulated metabolites. Among the negative ion metabolites, a total of 23,193 metabolites were detected, of which 2,172 were significantly different, including 567 up-regulated metabolites and 1,605 down-regulated metabolites. The differential cluster analysis of positive and negative ion metabolites is shown in the figure below. Figure 5 and Figure 6 shown.

[0047] From the list of secondary metabolites, we performed differential analysis using a set of statistical tests to identify differentially expressed metabolites. Figure 7 and Figure 8 As shown. Among the measured secondary metabolites, a total of 267 metabolites were found, of which 5 metabolites were up-regulated and 49 metabolites were down-regulated. Among these metabolites, the five metabolites with the most significant differences were down-regulated Arachidic acid, 3,4-Dihydroxymandelic, (R)-10-Hydroxystearate and D-Arginine, and up-regulated Caproic acid.

[0048] Arachidic acid is a long-chain fatty acid that may be involved in the structural and functional regulation of bacterial membranes, affecting bacterial stability and infectious capacity. 3,4-Dihydroxymandelic plays a role in antioxidant defense, helping bacteria resist host oxidative stress. (R)-10-Hydroxystearate may be associated with lipid metabolism, affecting bacterial energy production and viability. D-Arginine plays a role in amino acid metabolism and may affect bacterial growth and virulence expression. The upregulation of caproic acid may enhance bacterial metabolic activity, improving its adaptability and infectious capacity.

[0049] The metabolites were subjected to KEGG cluster analysis. Fig. 9 As shown, Fig. 9From top to bottom, they are ABCTransporters, D-arginine and D-ornithine metabolism, mTOR signaling pathway, Aminoacyl-tRNA biosynthesis, Phenylalanin metabolism, vascular smooth muscle contraction, valine, leucine and isoleucine biosynthesis, Arging biosynthesis, neurogenic ligand-receptor interaction, lysosome, insulin signaling pathway, lineleic acid metabolism, foxo signaling pathway, pantothenate and coa biosynthesis, phenylalanine, tyrosine and tryptophan biosynthesis, pentose phosphate pathway(pentose phosphate pathway)、ariginine and proline metabolism、tyrosine metabolism、adrenergic signaling in cardiomyocytes、valine,leucine and isoleucine degradation。

[0050] It can be seen that metabolic pathways such as ABC transporters, Aminoacyl-tRNA biosynthesis, Phenylalanin metabolism, Arging and prolinemetabolismm and Tyrosine metabolism were significantly enriched.

[0051] Therefore, it can be concluded that compared with the classical Klebsiella pneumoniae, the down-regulated arachidic acid, 3,4-dihydroxymandelic acid, 10-hydroxystearic acid, and D-arginine of the highly virulent Klebsiella pneumoniae showed significant down-regulation, and caproic acid showed significant up-regulation. These five metabolites are closely related to virulence.

[0052] Metabolic pathways such as ABC transporters, aminoacyl-tRNA biosynthesis, phenylalanine metabolism, arginine and proline metabolism, and tyrosine metabolism were significantly enriched in highly virulent strains, and these pathways may play a role by affecting bacterial fitness and virulence expression. In particular, the ABC transporter pathway, which is significantly enriched in transcriptome data and metabolome data, suggests its key role in virulence expression. ABC transporters are a class of proteins that drive the transport of substances across membranes through ATP hydrolysis and are widely present in bacteria. They play a key role in a variety of physiological processes, including nutrient uptake, metabolite excretion, and drug and toxin excretion. In highly virulent Klebsiella pneumoniae, the enrichment of ABC transporters may enhance bacterial fitness and survival, especially in response to host defense mechanisms and antibiotic pressure. These transporters not only help bacteria excrete toxins and avoid self-damage, but may also increase drug resistance by excreting antibiotics. Therefore, ABC transporters play an important role in bacterial virulence mechanisms, and their high expression may be one of the key factors for highly virulent strains to compete and survive in complex environments.

[0053] In addition, although exemplary embodiments have been described herein, the scope includes any and all embodiments based on the present application with equivalent elements, modifications, omissions, combinations (e.g., various embodiments intersecting schemes), adaptations or changes. The elements in the claims are to be interpreted broadly based on the language adopted in the claims, and are not limited to the examples described in this specification or during the practice of this application, and the examples are to be interpreted as non-exclusive. Therefore, this specification and examples are intended to be considered as examples only, and the true scope and spirit are indicated by the following claims and the full scope of their equivalents.

[0054] The above description is intended to be illustrative rather than restrictive. For example, the above examples (or one or more schemes thereof) may be used in combination with each other. For example, a person of ordinary skill in the art may use other embodiments when reading the above description. In addition, in the above-mentioned specific embodiments, various features may be grouped together to simplify the present application. This should not be interpreted as an intention that a feature of an application that does not require protection is necessary for any claim. On the contrary, the subject matter of the present application may be less than all the features of an embodiment of a particular application. Thus, the following claims are incorporated herein in the specific embodiments as examples or embodiments, wherein each claim is independently a separate embodiment, and it is considered that these embodiments may be combined with each other in various combinations or arrangements. The scope of the present invention should be determined with reference to the appended claims and the full scope of equivalent forms granted by these claims.

[0055] The above embodiments are only exemplary embodiments of the present application and are not intended to limit the present invention. The protection scope of the present invention is defined by the claims. Those skilled in the art may make various modifications or equivalent substitutions to the present invention within the essence and protection scope of the present application, and such modifications or equivalent substitutions shall also be deemed to fall within the protection scope of the present invention.

Claims

1. A biomarker for identifying highly virulent Klebsiella pneumoniae, characterized in that: The biomarkers include one or more of arachidic acid, 3,4-dihydroxymandelic acid, 10-hydroxystearic acid, D-arginine and caproic acid in the metabolites of highly virulent Klebsiella pneumoniae.

2. The biomarker according to claim 1, characterized in that The biomarkers also include ABC transporters.

3. The biomarker according to claim 1, characterized in that The biomarkers also include one or more of aminoacyl-tRNA, phenylalanine, arginine, proline metabolism, and tyrosine.

4. Use of the biomarker according to any one of claims 1 to 3 in identifying the type of highly virulent Klebsiella pneumoniae.

5. Use of the biomarker according to any one of claims 1 to 3 in the preparation of a product for identifying highly virulent Klebsiella pneumoniae.

6. A kit, characterized in that: The kit comprises the biomarker according to any one of claims 1 to 3.

7. A method for identifying highly virulent Klebsiella pneumoniae, characterized in that: The identification method comprises: Extracting metabolites of the Klebsiella pneumoniae to be tested; Detecting the metabolites using LC-MC to obtain the content of the biomarker according to any one of claims 1 to 3; The content of the biomarker obtained by LC-MC detection is compared with the content of the corresponding metabolites of classical Klebsiella pneumoniae to identify the Klebsiella pneumoniae to be tested.

8. The identification method according to claim 7, characterized in that: The method for extracting metabolites of Klebsiella pneumoniae to be tested comprises: mixing Klebsiella pneumoniae and methanol water and vortexing; freezing and thawing the vortexed mixture, and grinding the thawed mixture; centrifuging the ground mixture, taking the supernatant, and concentrating and drying the supernatant; re-dissolving the concentrated and dried solid in a 2-chloro-L-phenylalanine solution, taking the supernatant and filtering, wherein the filtrate includes the extracted metabolites.

9. The identification method according to claim 7, characterized in that: The detection of metabolites using LC-MS involves sample cleanup via solid phase extraction to concentrate the metabolites and remove impurities, followed by injection into a liquid chromatography-mass spectrometry system.

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