Metabolic marker for detecting novel coronavirus combined echovirus infection and application thereof

Abstract

The invention discloses a metabolic marker for detecting novel coronavirus combined with echovirus infection and application of the metabolic marker, and belongs to the technical field of biomedicine. The metabolic marker comprises at least one of lysophosphatidyl ethanolamine (0: 0 / 22: 5), lysophosphatidyl ethanolamine (22: 5 / 0: 0), choline glycerophosphatidylcholine, carnitine (C14: 3) and phenylalanine-isoleucine-valine-glycine-aspartic acid. According to the invention, serum samples of a healthy control group, a novel coronavirus infection group and a novel coronavirus combined with echovirus infection group are subjected to metabonomics analysis, and the metabolic marker capable of rapidly and accurately detecting and / or diagnosing the novel coronavirus combined with echovirus infection is obtained through screening, and has the advantages of non-invasiveness, high sensitivity, high sensitivity and the like. The method has the advantages of simple operation, convenience in sample acquisition, high detection and / or diagnosis efficiency, low cost and the like, thereby having a better application prospect.

Description

Technical Field

[0001] The present invention belongs to the field of biomedical technology, and specifically relates to metabolic markers and applications thereof for detecting novel coronavirus combined with echovirus infection. Background Art

[0002] Although vaccination has been carried out in many countries, COVID-19 continues to spread worldwide due to the continued mutation of the SARS-CoV-2 Spike protein and the emergence of new SARS-CoV-2 variants. Currently, most COVID-19 patients are asymptomatic or have mild symptoms and are usually able to recover on their own. However, for elderly patients with weakened immunity, COVID-19 may cause severe infection. In the first half of 2024 alone, China has reported a total of 1,671 severe cases and 78 deaths.

[0003] COVID-19 often co-infects with a variety of bacteria, fungi or viruses in people with low immunity, including Aspergillus, Klebsiella pneumoniae, Streptococcus pneumoniae, Legionella pneumophila, influenza virus, respiratory syncytial virus, etc. In addition, regardless of the type of co-infection, co-infection of COVID-19 with viruses often aggravates the patient's clinical symptoms, such as dyspnea, chest pain, headache, etc., and increases the patient's mortality rate. The co-infection of COVID-19 patients poses a major challenge to the prevention and control of the disease, and it is urgent to closely monitor and observe susceptible populations, especially high-risk populations such as the elderly.

[0004] Echovirus (ECHO) is a type of single-stranded positive-sense RNA virus with 34 serotypes, belonging to the human enterovirus group B. The virus is widely distributed worldwide and can be transmitted through the respiratory or digestive tract. The clinical symptoms are mainly acute upper respiratory tract infection, followed by viral encephalitis, bronchitis and pneumonia. Some infected patients may have serious sequelae or even death. During the clinical respiratory pathogen detection process, it was found that some COVID-19 patients with worsening clinical symptoms were also infected with ECHO.

[0005] Considering that ECHO virus can cause respiratory tract infection symptoms, it may cause COVID-19 patients with co-infection to be misdiagnosed or neglected in clinical diagnosis. There are no reports on the impact of ECHO infection on the severity of symptoms in COVID-19 patients in current studies. After viral infection, human serum undergoes significant metabolic changes, and some metabolites are closely related to the type and prognosis of viral infection, which suggests that early diagnosis and prognosis evaluation indicators of viral infection can be developed based on serum metabolomics.

[0006] However, there are no reports on the use of serum metabolites to predict or diagnose SARS-CoV-2 combined with ECHO infection and product application. Therefore, the development of new metabolic marker detection products for SARS-CoV-2 combined with ECHO infection is of great significance for the early diagnosis and treatment of patients with SARS-CoV-2 combined with ECHO infection and reducing mortality. Summary of the invention

[0007] The object of the present invention is to provide metabolic markers for detecting novel coronavirus combined with echovirus infection and their applications. The metabolic markers screened in the present invention can be used for rapid and accurate detection and / or diagnosis of novel coronavirus combined with echovirus infection, and are non-invasive, and therefore have good application prospects.

[0008] In a first aspect, the present invention provides a metabolic marker for detecting novel coronavirus combined with echovirus infection, the metabolic marker comprising at least one of lysophosphatidylethanolamine (0:0 / 22:5), lysophosphatidylethanolamine (22:5 / 0:0), glycerophosphatidylcholine, carnitine (C14:3), and phenylalanine-isoleucine-valine-glycine-aspartic acid.

[0009] In the present invention, metabolomics analysis is performed on serum samples of a healthy control (Control) group, a COVID-19 infection group, and a COVID-19 combined with echovirus (COVID-19+ECHO) infection group to screen out metabolic markers that can quickly and accurately detect and / or diagnose COVID-19 combined with echovirus infection, and the method has the advantages of being non-invasive, easy to obtain samples, high detection and / or diagnosis efficiency, and low cost.

[0010] In some embodiments, the metabolic markers include lysophosphatidylethanolamine (0:0 / 22:5), lysophosphatidylethanolamine (22:5 / 0:0), glycerophosphatidylcholine, carnitine (C14:3), phenylalanine-isoleucine-valine-glycine-aspartate.

[0011] In the present invention, the combination of five metabolic markers is used to detect and / or diagnose novel coronavirus combined with echovirus infection with higher sensitivity and specificity.

[0012] In a second aspect, the present invention provides the use of the above-mentioned metabolic markers in the preparation of products for detecting and / or diagnosing novel coronavirus combined with echovirus infection.

[0013] In some embodiments, the product is used to detect the relative abundance and / or amount of a metabolic marker.

[0014] In some embodiments, the method for detecting the relative abundance and / or quantity of a metabolite marker comprises at least one of liquid chromatography-mass spectrometry and gas chromatography-mass spectrometry.

[0015] It is understandable that the method for detecting the relative abundance and / or quantity of metabolic markers can select conventional quantitative detection methods in the prior art according to actual use needs. In the present invention, at least one of liquid chromatography-mass spectrometry and gas chromatography-mass spectrometry is preferably used.

[0016] In some embodiments, the product includes at least one of a reagent, a kit, a chip, a device, and an apparatus.

[0017] In some embodiments, the sample used in the detection and / or diagnosis process includes at least one of plasma, serum, and whole blood.

[0018] In a third aspect, the present invention provides a product for detecting and / or diagnosing novel coronavirus combined with echovirus infection, including a substance for detecting the relative abundance and / or quantity of the above-mentioned metabolic markers contained in a sample.

[0019] In some embodiments, the substance includes at least one of a reagent, a kit, a chip, a device, and an apparatus.

[0020] In some embodiments, the sample includes at least one of plasma, serum, and whole blood.

[0021] The beneficial effect of the present invention is that, different from the prior art, the present invention performs metabolomics analysis on serum samples of a healthy control (Control) group, a COVID-19 infection group, and a COVID-19 combined with echovirus (COVID-19+ECHO) infection group, and screens out metabolic markers that can quickly and accurately detect and / or diagnose COVID-19 combined with echovirus infection, and has the advantages of being non-invasive, easy to obtain samples, high detection and / or diagnosis efficiency, low cost, etc., and therefore, has good application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1A This is a graph showing the analysis results of serum metabolite profile components in the Control, COVID-19 and COVID-19+ECHO groups in Example 1 of the present invention;

[0023] Figure 1B This is a result diagram of 15 important characteristic metabolites after analysis of serum metabolite profile components of the Control, COVID-19 and COVID-19+ECHO groups in Example 1 of the present invention;

[0024] Figure 2This is a hierarchical clustering heat map of 50 metabolites in the serum of the Control, COVID-19, and COVID-19+ECHO groups in Example 1 of the present invention;

[0025] Figure 3 This is a fuzzy C-means clustering result diagram of the expression patterns of serum metabolites in the Control, COVID-19 and COVID-19+ECHO groups in Example 1 of the present invention;

[0026] Figure 4A This is a volcano plot of the differential analysis of serum metabolites in the Control and COVID-19 groups in Example 1 of the present invention;

[0027] Figure 4B This is a volcano plot of differential analysis of serum metabolites in the Control and COVID-19+ECHO groups in Example 1 of the present invention;

[0028] Figure 4C This is a volcano plot of differential analysis of serum metabolites in the COVID-19 and COVID-19+ECHO groups in Example 1 of the present invention;

[0029] Figure 4D This is a pathway bubble diagram of differential metabolites in the Control and COVID-19 groups in Example 1 of the present invention;

[0030] Figure 4E This is a pathway bubble diagram of differential metabolites in the Control and COVID-19+ECHO groups in Example 1 of the present invention;

[0031] Figure 4F This is a pathway bubble diagram of differential metabolites in the COVID-19 and COVID-19+ECHO groups in Example 1 of the present invention;

[0032] Figure 5 A Venn diagram for differential analysis of metabolites between any two groups among the Control, COVID-19, and COVID-19+ECHO groups in Example 1 of the present invention;

[0033] Fig. 6A This is a graph showing the results of the intersection analysis of differential metabolites in the COVID-19 and COVID-19+ECHO groups in Example 1 of the present invention;

[0034] Figure 6B It is a box plot of lysophosphatidylethanolamine (0:0 / 22:5) in the Control, COVID-19 and COVID-19+ECHO groups in Example 1 of the present invention;

[0035] Figure 6CIt is a box plot of lysophosphatidylethanolamine (22:5 / 0:0) in the Control, COVID-19 and COVID-19+ECHO groups in Example 1 of the present invention;

[0036] Fig.6D It is a box plot of glycerophosphatidylcholine in the Control, COVID-19 and COVID-19+ECHO groups in Example 1 of the present invention;

[0037] Fig. 6E It is a box plot of carnitine (C14:3) in the Control, COVID-19 and COVID-19+ECHO groups in Example 1 of the present invention;

[0038] Fig. 6F It is a box plot of phenylalanine-isoleucine-valine-glycine-aspartic acid in the Control, COVID-19 and COVID-19+ECHO groups in Example 1 of the present invention;

[0039] Fig. 7A This is a ROC curve diagram of lysophosphatidylethanolamine (0:0 / 22:5) in Example 2 of the present invention for distinguishing the COVID-19 group from the COVID-19+ECHO group;

[0040] Figure 7B This is a ROC curve diagram of lysophosphatidylethanolamine (22:5 / 0:0) in Example 2 of the present invention for distinguishing the COVID-19 group from the COVID-19+ECHO group;

[0041] Figure 7C This is a ROC curve diagram of glycerophosphatidylcholine distinguishing between the COVID-19 group and the COVID-19+ECHO group in Example 2 of the present invention;

[0042] Fig.7D This is a ROC curve diagram of carnitine (C14:3) in Example 2 of the present invention for distinguishing the COVID-19 group and the COVID-19+ECHO group;

[0043] Fig. 7E This is a ROC curve diagram for distinguishing the COVID-19 group and the COVID-19+ECHO group by phenylalanine-isoleucine-valine-glycine-aspartic acid in Example 2 of the present invention;

[0044] Figure 7FThe figure is a ROC curve diagram for distinguishing the COVID-19 group and the COVID-19+ECHO group using the combination of five metabolite markers in Example 2 of the present invention (lysophosphatidylethanolamine (0:0 / 22:5), lysophosphatidylethanolamine (22:5 / 0:0), glycerophosphatidylcholine, carnitine (C14:3), and phenylalanine-isoleucine-valine-glycine-aspartic acid). DETAILED DESCRIPTION

[0045] The following will be combined with the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0046] The experimental methods without specific conditions in the examples are usually carried out according to conventional conditions and conditions described in the manual, or according to conditions recommended by the manufacturer. The general equipment, materials, reagents, etc. used are all available from commercial channels unless otherwise specified.

[0047] Example 1 Screening of differential metabolites in the 2019-nCoV combined with echovirus infection group, healthy control group, and 2019-nCoV infection group

[0048] 1.1 Grouping of research subjects and detection of routine clinical laboratory indicators

[0049] In this trial, 85 subjects (43 males and 42 females) were selected, and all subjects obtained written informed consent before the trial. Among them, there were 29 healthy people in the control group (Control), 29 in the COVID-19 infection group, and 27 in the COVID-19 combined with ECHO infection group. The grouping criteria were based on the results of SARS-CoV-2 nucleic acid detection, antigen detection, and ECHO IgM antibody serological detection. The age, gender, clinical symptoms and signs of the subjects were collected, and the subjects in the COVID-19 group and the COVID-19+ECHO group were further subjected to routine blood tests, biochemical tests, and coagulation function tests. The results are shown in Tables 1 and 2 below, respectively.

[0050] The test data were expressed as mean ± standard deviation SD (normal distribution) or median (interquartile range, IQR) (non-normal distribution), the p-value of the normal distribution numerical parameter was calculated by two-tailed Student's t-test with unequal variance, the p-value of the non-normal distribution numerical parameter was calculated by Wilcoxon test, and the p-value of the categorical parameter was calculated by chi-square test; and the abbreviation NA means: not applicable.

[0051] Table 1 Clinical symptoms and signs of the subjects

[0052]

[0053]

[0054] The data in Table 1 show that there was no significant difference in gender among the groups, but there was a significant difference in age (p<0.001). In terms of clinical manifestations, the highest body temperature in the COVID-19+ECHO group was higher than that in the COVID-19 group, and the number of patients with shortness of breath was significantly higher than that in the COVID-19 group (p=0.010), while no significant differences were observed in other clinical manifestations between the COVID-19 group and the COVID-19+ECHO group.

[0055] Table 2 Clinical laboratory routine index test results of subjects in the COVID-19 and COVID-19+ECHO groups

[0056]

[0057]

[0058] The data in Table 2 show that the inflammatory immune indicators such as white blood cell count (WBC), neutrophil count (NEUT), C-reactive protein (CRP) and erythrocyte sedimentation rate (ESR) in the COVID-19+ECHO group were significantly higher than those in the COVID-19 group (p<0.05), indicating that the patients in the COVID-19+ECHO group had enhanced inflammatory response and immune dysfunction. In addition, the fibrinogen (FIB) level in the COVID-19+ECHO group was also higher than that in the COVID-19 group (p<0.05), indicating that the coagulation function of patients in the COVID-19+ECHO group was more severely impaired.

[0059] 1.2 Screening of serum differential metabolites

[0060] Serum sample pretreatment: 2 mL of cubital venous blood was drawn from the subjects of the above treatment groups into a clean blood collection tube. The blood samples were centrifuged at 4000 r / min for 10 min. 200 μL of serum was taken from each sample and placed in a 1.5 mL EP tube and stored in a -80°C refrigerator. The serum samples were thawed at 4°C, vortexed for 10 seconds, and 50 μL of the samples were transferred to the corresponding labeled centrifuge tubes. 300 μL of 20% acetonitrile / methanol internal standard extract was added, vortexed for 3 minutes, and centrifuged at 12000 g for 10 minutes at 4°C; the supernatant (200 μL) was transferred to another corresponding labeled centrifuge tube and allowed to stand at -20°C for 30 minutes; at 4°C, it was centrifuged again at 12000 r / min for 3 minutes, and then the supernatant (180 μL) was transferred to a suitable vial liner in the autosampler bottle for analysis.

[0061] Metabolomics analysis: First, LC-ESI-MS / MS system (UPLC, ExionLC AD, ESI-MS / MS), MS, System (SCIEX) was used to analyze serum samples of each treatment group. The specific analysis conditions are as follows: UPLC conditions: chromatographic column Waters ACQUITY UPLC HSS T3 C18 (1.8 μm, 2.1 mm × 100 mm); column temperature 40 ° C; flow rate 0.4 mL / min; injection volume 2 μL or 5 μL; mobile phase: water (0.1% formic acid): acetonitrile (0.1% formic acid); gradient program, 95:5 (V / V, aqueous phase / organic phase) at 0 min, 10:90 (V / V, aqueous phase / organic phase) at 10.0 min, 10:00 (V / V, aqueous phase / organic phase) at 11.0 min, 95.5 (V / V, aqueous phase / organic phase) at 11.1 min, and 95:5 (V / V, aqueous phase / organic phase) after 14.0 min.

[0062] In the LC / MS experiments, a Triple TOF mass spectrometer was used to acquire MS / MS spectra. The acquisition software (TripleTOF6600, AB SCIEX) continuously evaluated the full scan measurement MS data while collecting and triggered MS / MS spectral acquisition according to preselected criteria. LIT and triple quadrupole (QQQ) scans were performed on a triple quadrupole linear ion trap mass spectrometer (QTRAP) equipped with an ESI Turbo ion Spray interface. The samples were acquired on a LC-MS / MS system operated in positive and negative ion modes and controlled by Analyst 1.6.3 software (SCIEX).

[0063] The ESI source operating parameters were as follows: source temperature 500 °C; ion spray voltage (IS) 5500 V (positive), -4500 V (negative); ion source gas I (GSI), gas II (GSII), curtain gas (CUR) were set to 50, 50, and 25 psi, respectively; collision gas (CAD) high. Instrument tuning and mass calibration were performed with 10 and 100 μmol / L polypropylene glycol solutions in QQQ and LIT modes, respectively. A specific set of MRM transitions was monitored in each period to obtain multiple reaction monitoring (MRM) ion pair information according to the metabolites eluted during this period; then, a comprehensive target library composed of public databases such as Metlin, HMDB, and KEGG was used to qualitatively analyze the substances in the high-resolution mass spectrometry data through artificial intelligence prediction. The qualitatively identified substance ion pairs were then transferred to the mass spectrometer for retention time (RT) data acquisition to obtain peak areas and build a specific library for each sample. Finally, a triple quadrupole linear ion trap mass spectrometer was used to accurately detect the substances in the library using MRM to obtain the metabolites of serum samples in each treatment group.

[0064] First, partial least squares discriminant analysis (PLS-DA) was used to compare the serum metabolite profile components of the Control, COVID-19, and COVID-19+ECHO groups. Figure 1A and 1B shown.

[0065] from Figure 1A As can be seen, component 1 explains 18.4% of the variance and component 2 explains 7.6% of the variance. The results show that there is a clear separation between the groups, among which the distance between the Control group and the COVID-19 group and the COVID-19+ECHO group is far, reflecting the large difference between the groups; the distance between the COVID-19 group and the COVID-19+ECHO group is close, indicating that the difference between the two groups is smaller than the difference between them and the Control group.

[0066] Furthermore, based on the VIP scores, 15 important characteristic metabolites were identified (see Figure 1B). These characteristic metabolites are ranked according to their VIP values ​​as follows: 2-Hydroxyglutarate, Quinmerac, Methyl dioxindole-3-acetate, 6-Dimethylaminopurine, 2,6-Di-tert-butyl-4-(hydroxymethyl)phenol, Cyasterone, Arg-Glu-Ala-Phe-Arg, Gly-Leu-Arg-Asn-Gln, 1-O-Hexadecyl-2-deoxy-2-thio-R-(heptanoyl)-snglyc eryl-3-phosphocholine (1-O-hexadecyl-2-deoxy-2-thio-R-(heptanoyl)-sn-glyceryl-3-phosphatidylcholine), Anthranoylcoctonine (coumaryl scopolamine), Asp-Pro-Lys-Ile-Ala (aspartic acid-proline-lysine-isoleucine-alanine), Asp-Lys-Arg-Glu-Lys (aspartic acid-lysine-arginine-glutamic acid-lysine), Lys-His-Ile-Glu-Glu (lysine-histidine-isoleucine-glutamic acid-glutamic acid), Lys-Leu-His-Val-Asp (lysine-leucine-histidine-valine-aspartic acid), Ammeline (cyanuric acid diamide).

[0067] Next, a one-way analysis of variance (ANOVA) was performed on the metabolites in the Control, COVID-19, and COVID-19+ECHO groups to confirm the significance of the sample cohort. The screening conditions were VIP>1, p<0.05, and a total of 539 differential metabolites were obtained. Further sorting by p value, the top 50 metabolites with the highest significance were selected to draw a cluster heat map. The results are shown below: Figure 2 shown.

[0068] from Figure 2 It can be seen that the samples in each group are clustered according to the group, reflecting the differences between the groups; among them, the metabolite heat maps of the COVID-19 group and the COVID-19+ECHO group are more similar, reflecting the similarities and differences between the two groups in metabolomics.

[0069] Furthermore, the mfuzz package of R language was used to perform cluster analysis on the metabolite expression patterns of the three groups: Control, COVID-19, and COVID-19+ECHO, using the fuzzy C-means clustering algorithm. The results are as follows Figure 3 shown.

[0070] from Figure 3 It can be seen that 8 different clusters contain 298, 289, 155, 205, 214, 202, 236 and 245 metabolites, respectively. According to the clinical characteristics of the subjects obtained in step 1.1 and the test data of routine clinical laboratory indicators, it can be seen that patients in the COVID-19+ECHO group showed more severe clinical symptoms at the inflammatory and immune levels compared with patients in the COVID-19 group. In addition, the expression patterns of metabolites in Cluster 1, Cluster 2 and Cluster 7 are significantly consistent with the progression of the disease, that is, they show a gradient increase or decrease trend in the Control group, COVID-19 group and COVID-19+ECHO group. A total of 823 metabolites were obtained in these three Clusters.

[0071] Then, the metabolites of any two groups among Control, COVID-19 and COVID-19+ECHO were differentially analyzed, and the results were as follows: Figures 4A-4C shown.

[0072] Among them, the screening criteria for differential metabolites were: p < 0.05, VIP value greater than 1 and FC> 1.5 were considered up-regulated, and p < 0.05, VIP value greater than 1 and FC< 1 / 1.5 were considered down-regulated.

[0073] from Figure 4A As can be seen from the table, the differential analysis between the Control group and the COVID-19 group found 341 differential metabolites, of which 103 metabolites were upregulated and 238 metabolites were downregulated in the COVID-19 group; Figure 4B As can be seen from the table, the differential analysis between the Control group and the COVID-19+ECHO group found 279 differential metabolites, of which 111 metabolites were upregulated and 168 metabolites were downregulated in the COVID-19+ECHO group; Figure 4C As can be seen, the differential analysis between the COVID-19 group and the COVID-19+ECHO group found 121 differential metabolites, of which 58 metabolites were upregulated and 63 metabolites were downregulated in the COVID-19+ECHO group.

[0074] Continue to draw a Venn diagram for the difference analysis results of metabolites between any two groups in the above Control, COVID-19 and COVID-19+ECHO groups. The results are as follows Figure 5 shown.

[0075] from Figure 5 It can be seen that there are 190 overlaps between the differential metabolites of the Control group and the COVID-19 group and the differential metabolites of the Control group and the COVID-19+ECHO group, accounting for more than 50% in both differential groups, indicating that the differential metabolites between the COVID-19 group and the COVID-19+ECHO group are both different and similar.

[0076] The differential analysis results of metabolites in any two groups among the Control, COVID-19 and COVID-19+ECHO groups were further subjected to KEGG enrichment analysis, and the results were as follows: Figures 4D-4F shown.

[0077] from Figure 4D It can be seen that the differential metabolites between the Control group and the COVID-19 group are mainly enriched in metabolic pathways such as butanoate metabolism, fructose and mannose metabolism, and arginine biosynthesis; Figure 4E It can be seen that the differential metabolites between the Control group and the COVID-19+ECHO group are also mainly enriched in the butyrate metabolism and arginine biosynthesis pathways, which play an important role in the process of inflammation and immune response; Figure 4F It can be seen that the differential metabolites between the COVID-19 group and the COVID-19+ECHO group are mainly enriched in the glycerophospholipid metabolism pathway. This suggests that the difference in inflammatory immunity between the COVID-19+ECHO group and the COVID-19 group may be related to the glycerophospholipid metabolism pathway.

[0078] Finally, the 539 differential metabolites obtained by Anova analysis, the 121 differential metabolites obtained by differential analysis (between COVID-19 group and COVID-19+ECHO group), and the 823 metabolites that were consistent with clinical trends in the cluster analysis of metabolite expression patterns were subjected to intersection analysis. The results are as follows: Fig. 6A shown.

[0079] from Fig. 6AAs can be seen, five metabolite markers are present in all three analytical methods. These metabolite markers are LPE (0:0 / 22:5) (lysophosphatidylethanolamine (0:0 / 22:5)), LPE (22:5 / 0:0) (lysophosphatidylethanolamine (22:5 / 0:0)), Sn-Glycero-3-Phosphocholine (glycerophosphatidylcholine), Carnitine C14:3 (carnitine (C14:3)), Phe-Ile-Val-Gly-Asp (phenylalanine-isoleucine-valine-glycine-aspartic acid).

[0080] Furthermore, the changes of the above five metabolite markers in each treatment group were plotted into box plots, and the results were as follows: Figure 6B-6C shown.

[0081] from Figure 6B It can be seen that the content of lysophosphatidylethanolamine (0:0 / 22:5) in the COVID-19+ECHO group increased compared with that in the COVID-19 group, and there was a significant difference; Figure 6C It can be seen that the content of lysophosphatidylethanolamine (22:5 / 0:0) in the COVID-19+ECHO group increased compared with that in the COVID-19 group, and there was a significant difference; Fig.6D It can be seen that the content of glycerophosphatidylcholine in the COVID-19+ECHO group increased compared with that in the COVID-19 group, and there was a significant difference; Fig. 6E It can be seen that the content of carnitine (C14:3) in the COVID-19+ECHO group decreased compared with the COVID-19 group, and there was a significant difference; Fig. 6E It can be seen that the content of phenylalanine-isoleucine-valine-glycine-aspartic acid in the COVID-19+ECHO group was increased compared with that in the COVID-19 group, and the difference was significant.

[0082] Example 2 ROC curve analysis

[0083] In this example, receiver operating curve (ROC) analysis was performed on the five metabolite markers obtained above (lysophosphatidylethanolamine (0:0 / 22:5), lysophosphatidylethanolamine (22:5 / 0:0), glycerophosphatidylcholine, carnitine (C14:3), phenylalanine-isoleucine-valine-glycine-aspartic acid) to evaluate the detection and / or diagnostic effect of the COVID-19+ECHO group relative to the COVID-19 group. The results are as follows: Figures 7A-7E shown.

[0084] from Figures 7A-7EIt can be seen that the area under the curve (AUC) of lysophosphatidylethanolamine (0:0 / 22:5) is 0.697, the area under the curve (AUC) of lysophosphatidylethanolamine (22:5 / 0:0) is 0.699, the area under the curve (AUC) of glycerophosphatidylcholine is 0.745, the area under the curve (AUC) of carnitine (C14:3) is 0.708, and the area under the curve (AUC) of phenylalanine-isoleucine-valine-glycine-aspartic acid is 0.699. The above AUCs are greater than 0.5. The results show that the five metabolite markers can better distinguish the COVID-19+ECHO group and the COVID-19 group. Therefore, the five metabolite markers have good detection and / or diagnostic effects on the COVID-19+ECHO group and the COVID-19 group.

[0085] The combination of the above five metabolite markers was further selected for ROC curve analysis. Figure 7F shown.

[0086] from Figure 7F It can be seen that in the multivariate prediction based on logistic regression, the comprehensive AUC of the combination of these five metabolite markers reached 0.828. The results show that the simultaneous use of a combination of five metabolite markers can improve the detection and / or diagnosis of the COVID-19+ECHO group and the COVID-19 group.

[0087] In summary, one or a combination of lysophosphatidylethanolamine (0:0 / 22:5), lysophosphatidylethanolamine (22:5 / 0:0), glycerophosphatidylcholine, carnitine (C14:3), and phenylalanine-isoleucine-valine-glycine-aspartic acid can effectively distinguish the COVID-19+ECHO group from the COVID-19 group; therefore, the above-mentioned metabolite markers have good application prospects in the preparation of products for the detection and / or diagnosis of novel coronavirus combined with echovirus infection.

[0088] It should be noted that the above embodiments all belong to the same inventive concept, and the description of each embodiment has its own focus. For matters that are not described in detail in some embodiments, reference may be made to the description in other embodiments.

[0089] The above-mentioned embodiments only express the implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the invention patent. It should be pointed out that for ordinary technicians in this field, several modifications and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be based on the attached claims.

Claims

1. A metabolic marker for detecting novel coronavirus combined with echovirus infection, characterized in that: The metabolic markers include at least one of lysophosphatidylethanolamine (0:0 / 22:5), lysophosphatidylethanolamine (22:5 / 0:0), glycerophosphatidylcholine, carnitine (C14:3), and phenylalanine-isoleucine-valine-glycine-aspartic acid.

2. The metabolic marker according to claim 1, characterized in that The metabolic markers include lysophosphatidylethanolamine (0:0 / 22:5), lysophosphatidylethanolamine (22:5 / 0:0), glycerophosphatidylcholine, carnitine (C14:3), and phenylalanine-isoleucine-valine-glycine-aspartic acid.

3. Use of the metabolic marker as described in claim 1 or 2 in the preparation of a product for detecting and / or diagnosing infection with a novel coronavirus combined with echovirus.

4. The use according to claim 3, characterized in that: The product is used to detect the relative abundance and / or quantity of the metabolite marker.

5. The use according to claim 4, characterized in that: The method for detecting the relative abundance and / or quantity of the metabolite marker comprises at least one of liquid chromatography-mass spectrometry and gas chromatography-mass spectrometry.

6. The use according to claim 3, characterized in that: The product includes at least one of a reagent, a kit, a chip, a device, and an equipment.

7. The use according to claim 3, characterized in that: The sample used in the detection and / or diagnosis process includes at least one of plasma, serum, and whole blood.

8. A product for detecting and / or diagnosing infection with a novel coronavirus combined with echovirus, characterized in that: It comprises a substance for detecting the relative abundance and / or quantity of the metabolic marker as claimed in claim 1 or 2 contained in a sample.

9. The product according to claim 8, characterized in that The substance includes at least one of a reagent, a kit, a chip, a device, and an equipment.

10. The product according to claim 8, characterized in that The sample includes at least one of plasma, serum and whole blood.

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