Application of polypeptide in distinguishing SARS-CoV-2 infections with different severity degrees

Abstract

The invention belongs to the technical field of biological medicine, and particularly relates to application of polypeptide in distinguishing infection of novel coronavirus (SARS-CoV-2) with different severity degrees, and the polypeptide is KTFPPTEPKKDKKKK (SEQ ID NO: 1) and / or ALPQRQKKQQTVTLL (SEQ ID NO: 2). By detecting the abundance of the IgA antibody combined with the polypeptide, the severity of the SARS-CoV-2 infected patient can be effectively distinguished.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of biological medicine, and particularly relates to application of polypeptides in distinguishing SARS-CoV-2 infections with different severity. BACKGROUND

[0002] Virus detection is very important in the fields of biology and medicine, and can be used for diagnosing infected persons, monitoring the epidemic trend of infectious diseases, and evaluating the treatment effect, etc. The currently commonly used virus detection techniques can be divided into electron microscope (electron microscope) observation, cell culture, and nucleic acid detection according to different principles.

[0003] The electron microscope utilizes a high-speed electron beam to interact with a sample, and obtains a sample image by detecting and processing the reflection, scattering, or transmission of the electron beam. The electron microscope observation method has ultra-high resolution and can directly observe the virus structure, and is suitable for the research on new and unknown viruses. The electron microscope observation method has high requirements for sample preparation and operation technology, and requires professional personnel with certain experience and skills to handle the sample and operate the microscope. Moreover, the electron microscope is expensive, and the use and maintenance costs are also high. If the instrument is not properly maintained or the image processing equipment performance is insufficient, the resolution may be affected.

[0004] The cell culture method is divided into plaque assay method and TCID 50 analysis method. The plaque forming units obtained by the plaque method represent the specific concentration of viruses, while the TCID 50 analysis method represents the approximate virus concentration estimated by the statistical cytopathic effect, so the plaque method has higher accuracy than the TCID 50 method. However, the TCID 50 method can compensate for the sensitivity of the plaque method to operation errors to some extent and obtain relatively accurate results. However, the traditional cell culture virus detection method is time-consuming, complex, and requires a laboratory environment, which limits its application in emergency situations.

[0005] The nucleic acid detection method includes PCR, quantitative PCR, reverse transcription PCR, isothermal amplification technology, and high-throughput sequencing, etc.

[0006] PCR is a commonly used method for virus detection, by specific amplification of viral DNA fragments, the presence of virus in the sample is determined by agarose gel electrophoresis to observe the amplification results. Quantitative PCR is developed on the basis of PCR technology, its principle is to add fluorescent substances in the PCR reaction system to realize the real-time quantitative of the target nucleic acid fragment amplification. The fluorescence intensity of quantitative PCR can be directly read and processed in the computer, which is simpler than PCR, and can accurately quantify the virus and determine the viral load in the sample. Reverse transcription PCR method can also combine fluorescent dye or probe for real-time quantitative analysis of target fragments, which is called RT-qPCR. This method also has the characteristics of high specificity and high sensitivity. Isothermal amplification technology can be used for high-efficiency and specific amplification of nucleic acids at a constant temperature. Unlike the traditional PCR amplification technology which requires cyclic heating and annealing program, isothermal amplification technology only needs to be carried out in a mild and constant environment, and the optimum reaction temperature is between 37℃ and 65℃. According to whether specific amplification occurs in the detection system, the presence of the target virus is determined, and the result is judged by optical or electrochemical method (see non-patent literature: Cao HX, Li DM. Research status and prospect of virus detection methods [J]. International Journal of Biological Products, 2024, 47(6): 390-396).

[0007] Current detection techniques can only be used to diagnose whether a virus infection is present, but cannot distinguish the severity of the infection. SUMMARY

[0008] To solve the problems of the prior art, the present application screens two polypeptides, which can indicate the severity of SARS-CoV-2 infection alone and in combination, and has excellent specificity and sensitivity in distinguishing between asymptomatic and mild cases, and between mild and severe cases.

[0009] In a first aspect, the present application provides the use of polypeptides KTFPPTEPKKDKKKK (SEQ ID NO: 1) and / or ALPQRQKKQQTVTLL (SEQ ID NO: 2) in the preparation of products for distinguishing different severity of SARS-CoV-2 infection or in the preparation of products for diagnosing SARS-CoV-2 infection.

[0010] Preferably, the product includes a kit, a test paper, a chip or a device.

[0011] Preferably, the product includes a kit, a test paper, a chip or a device.

[0012] Preferably, the product includes a kit, a test paper, a chip or a device.

[0013] Preferably, the body fluid is selected from blood, plasma, serum, saliva, urine, tears, amniotic fluid, cerebrospinal fluid, or lymph.

[0014] Preferably, the antibody is an IgA antibody.

[0015] Preferably, the abundance of the antibody in the sample is detected by enzyme-linked immunosorbent assay.

[0016] Further preferably, the enzyme-linked immunosorbent assay comprises one or more of direct ELISA, indirect ELISA, double antibody sandwich ELISA, or competitive ELISA.

[0017] The different severity of SARS-CoV-2 infection includes asymptomatic, mild, and severe.

[0018] The asymptomatic includes no fever, and no respiratory symptoms. Preferably, the respiratory symptoms include, but are not limited to, cough, dyspnea, nasal congestion, runny nose, sore throat, pneumonia, etc.

[0019] The mild includes fever and cough as the main symptoms, no pneumonia (e.g. diagnosed by imaging), no dyspnea, and no oxygenation disorder;

[0020] The severe includes any of the following symptoms:

[0021] (1) dyspnea, respiratory rate ≥ 30 times / min;

[0022] (2) oxygen saturation ≤ 93% at rest;

[0023] (3) arterial partial pressure of oxygen (PaO2) / oxygen concentration (FiO2) ≤ 300 mmHg.

[0024] In one embodiment of the application, the application comprises distinguishing asymptomatic and mild SARS-CoV-2 infection. For example, detecting the abundance of the antibody binding to the polypeptide in the patient, and then comparing with a threshold value.

[0025] In one embodiment of the application, the application comprises distinguishing mild and severe SARS-CoV-2 infection. For example, detecting the abundance of the antibody binding to the polypeptide in the patient, and then comparing with a threshold value.

[0026] In one embodiment of the application, the application comprises distinguishing asymptomatic and severe SARS-CoV-2 infection. For example, detecting the abundance of the antibody binding to the polypeptide in the patient, and then comparing with a threshold value.

[0027] The threshold value includes the threshold value for distinguishing asymptomatic and mild, the threshold value for distinguishing mild and severe, and the threshold value for distinguishing asymptomatic and severe.

[0028] The threshold value is obtained from previous experiments, that is, by experiment and data analysis of SARS-CoV-2 infection of asymptomatic patients, SARS-CoV-2 infection of mild patients, SARS-CoV-2 infection of severe patients, the difference degree of IgA antibody is determined to determine the threshold value.

[0029] In a second aspect of the present application, a method for constructing a model for distinguishing different severity of SARS-CoV-2 infection or diagnosing SARS-CoV-2 infection is provided, and the method comprises:

[0030] i) collecting the detection results of the abundance level of antibodies binding to KTFPPTEPKKDKKKK (SEQ ID NO: 1) and / or ALPQRQKKQQTVTLL (SEQ ID NO: 2) in patients with different severity of SARS-CoV-2 infection;

[0031] ii) using the results collected in i) to construct a model; or,

[0032] The method comprises:

[0033] 1) collecting samples of subjects, and detecting the abundance of antibodies binding to KTFPPTEPKKDKKKK (SEQ ID NO: 1) and / or ALPQRQKKQQTVTLL (SEQ ID NO: 2);

[0034] 2) diagnosing the subjects clinically, and dividing the subjects according to the severity of SARS-CoV-2 infection;

[0035] 3) constructing a model for distinguishing different severity of SARS-CoV-2 infection according to the detection results of step 1) and the diagnosis results of step 2).

[0036] Preferably, the algorithm used for constructing the model for distinguishing different severity of SARS-CoV-2 infection comprises one or more than two of logistic regression (LR), k-nearest neighbor algorithm (KNN), naive Bayes (NB), support vector machine (SVM Radial), random forest (RF), XGBoost, TreeNet, gradient boosting machine (GBM), LASSO or neural network (NNET).

[0037] In a third aspect of the present application, a model obtained by the method of the second aspect is provided.

[0038] In a fourth aspect of the present application, a polypeptide composition comprising KTFPPTEPKKDKKKK (SEQ ID NO: 1) and ALPQRQKKQQTVTLL (SEQ ID NO: 2) is provided.

[0039] In a fifth aspect, the present application provides use of the model of the third aspect or the polypeptide composition of the fourth aspect in the preparation of a product for differentiating SARS-CoV-2 infections of different severities or in the preparation of a product for diagnosing SARS-CoV-2 infection.

[0040] In a sixth aspect, the present application provides a method for differentiating SARS-CoV-2 infections of different severities, which comprises detecting the abundance of the polypeptide.

[0041] The polypeptide is KTFPPTEPKKDKKKK (SEQ ID NO: 1) and / or ALPQRQKKQQTVTLL (SEQ ID NO: 2).

[0042] In a seventh aspect, the present application provides a method for diagnosing SARS-CoV-2 infection, which comprises detecting the abundance of the polypeptide.

[0043] The polypeptide is KTFPPTEPKKDKKKK (SEQ ID NO: 1) and / or ALPQRQKKQQTVTLL (SEQ ID NO: 2).

[0044] The "subject" of the present application can be a human or a non-human animal, including a "patient", a "suspected patient" and a "healthy individual", and the term does not indicate a specific age or gender, covering adult or neonatal subjects and fetuses. The non-human animal can be a wild animal, a zoo animal, an economic animal, a pet, a laboratory animal, etc. Specifically, the non-human animal includes but is not limited to pigs, cows, sheep, horses, donkeys, foxes, raccoon dogs, minks, camels, dogs, cats, rabbits, mice (e.g. rats, mice, guinea pigs, hamsters, gerbils, chinchillas, squirrels) or monkeys, etc.

[0045] The "and / or" of the present application includes all combinations of the items connected by the term, which should be considered as each combination being listed in the present application individually, for example, "A and / or B" includes "A", "B" and "A and B". For another example, "A, B and / or C" includes "A", "B", "C", "A and B", "A and C", "B and C" and "A and B and C".

[0046] The "comprising" or "including" of the present application is an open-ended writing, containing the specified components or steps described, and other specified components or steps which do not materially affect.

[0047] The "threshold value" of the present application refers to a critical value for determining the positivity and negativity of a test. For example, in the present application, the threshold value is used to determine whether a disease is present or not, or the severity of the disease. The threshold value can be determined by a cut-off value, and the most commonly used method for determining the cut-off value is the receiver operating characteristic curve. In some cases, the threshold value can be the cut-off value. In some cases, the threshold value is standardized to be the cut-off value. BRIEF DESCRIPTION OF DRAWINGS

[0048] Figure 1A Shown is the antibody abundance difference result between the healthy group and the disease group in the training set for the polypeptide shown in SEQ ID NO: 1, **** represents p<0.0001.

[0049] Figure 1B Shown is the antibody abundance difference result of the asymptomatic group, mild group and severe group in the training set for the polypeptide shown in SEQ ID NO: 1, **** represents p<0.0001.

[0050] Figure 1C Shown is the comparison of antibody abundance between the healthy group and the disease group in the validation set for the polypeptide shown in SEQ ID NO: 1, *** represents p<0.001.

[0051] Figure 1D Shown is the antibody abundance difference result of the asymptomatic group, mild group and severe group in the validation set for the polypeptide shown in SEQ ID NO: 1, **** represents p<0.0001.

[0052] Figure 2A Shown is the antibody abundance difference result between the healthy group and the disease group in the training set for the polypeptide shown in SEQ ID NO: 2, *** represents p<0.001.

[0053] Figure 2B Shown is the antibody abundance difference result of the asymptomatic group, mild group and severe group in the training set for the polypeptide shown in SEQ ID NO: 2, ** represents p<0.01, **** represents p<0.0001.

[0054] Figure 2C Shown is the comparison of antibody abundance between the healthy group and the disease group in the validation set for the polypeptide shown in SEQ ID NO: 2, ** represents p<0.01.

[0055] Figure 2D Shown is the antibody abundance difference result of the asymptomatic group, mild group and severe group in the validation set for the polypeptide shown in SEQ ID NO: 2, * represents p<0.05, **** represents p<0.0001.

[0056] Figure 3Shown is a comparison of the antibody abundances for each group in the training set for the polypeptide set forth in SEQ ID NO: 3.

[0057] Figure 4A Shown is a ROC plot for SEQ ID NO: 1 in the training set to distinguish between the healthy and disease groups.

[0058] Figure 4B Shown is a ROC plot for SEQ ID NO: 1 in the training set to distinguish between the asymptomatic and mild groups.

[0059] Figure 4C Shown is a ROC plot for SEQ ID NO: 1 in the training set to distinguish between the asymptomatic and severe groups.

[0060] Figure 4D Shown is a ROC plot for SEQ ID NO: 1 in the training set to distinguish between the mild and severe groups.

[0061] Figure 5A Shown is a ROC plot for SEQ ID NO: 1 in the validation set to distinguish between the healthy and disease groups.

[0062] Figure 5B Shown is a ROC plot for SEQ ID NO: 1 in the validation set to distinguish between the asymptomatic and mild groups.

[0063] Figure 5C Shown is a ROC plot for SEQ ID NO: 1 in the validation set to distinguish between the asymptomatic and severe groups.

[0064] Figure 5D Shown is a ROC plot for SEQ ID NO: 1 in the validation set to distinguish between the mild and severe groups.

[0065] Figure 6A Shown is a ROC plot for SEQ ID NO: 2 in the training set to distinguish between the healthy and disease groups.

[0066] Figure 6B Shown is a ROC plot for SEQ ID NO: 2 in the training set to distinguish between the asymptomatic and mild groups.

[0067] Figure 6C Shown is a ROC plot for SEQ ID NO: 2 in the training set to distinguish between the asymptomatic and severe groups.

[0068] Figure 6D Shown is a ROC plot for SEQ ID NO: 2 in the training set to distinguish between the mild and severe groups.

[0069] Figure 7A Shown is a ROC plot for SEQ ID NO: 2 in the validation set to distinguish between the healthy and disease groups.

[0070] Figure 7B Shown is the ROC plot of SEQ ID NO: 2 in the validation set to distinguish the asymptomatic group from the mild group.

[0071] Figure 7C Shown is the ROC plot of SEQ ID NO: 2 in the validation set to distinguish the asymptomatic group from the severe group.

[0072] Figure 7D Shown is the ROC plot of SEQ ID NO: 2 in the validation set to distinguish the mild group from the severe group.

[0073] Figure 8A Shown is the ROC plot of SEQ ID NO: 1 and SEQ ID NO: 2 in the validation set to distinguish the healthy group from the disease group.

[0074] Figure 8B Shown is the ROC plot of SEQ ID NO: 1 and SEQ ID NO: 2 in the validation set to distinguish the asymptomatic group from the mild group.

[0075] Figure 8C Shown is the ROC plot of SEQ ID NO: 1 and SEQ ID NO: 2 in the validation set to distinguish the asymptomatic group from the severe group.

[0076] Figure 8D Shown is the ROC plot of SEQ ID NO: 1 and SEQ ID NO: 2 in the validation set to distinguish the mild group from the severe group. DETAILED DESCRIPTION

[0077] The application will be further described below in conjunction with the embodiments. The following description is only the preferred embodiments of the application and does not limit the application in other forms. Any skilled person in the art can modify the disclosed technical content to obtain equivalent embodiments. Any simple modification or equivalent change of the following embodiments without departing from the technical essence of the application falls within the protection scope of the application.

[0078] The experimental methods used in the following examples are conventional methods unless otherwise specified.

[0079] The reagents, materials, etc. used in the following examples can be obtained from commercial channels unless otherwise specified.

[0080] 1. Sample source and severity classification used in the examples

[0081] 1) Inclusion criteria for patients infected with the new coronavirus (SARS-Cov-2): (1) Age: ≥ 18 years old. (2) Diagnosis basis: positive nucleic acid detection, antigen detection (3) Compliance: able to cooperate with the research process, including follow-up and treatment.

[0082] 2) Exclusion criteria: (1) Allergy history: allergic to the study drug or related ingredients. (2) Comorbidities: suffering from severe cardiopulmonary diseases, immune deficiency, malignancies and other diseases that may affect the study results. (3) Recent medication: using drugs that may interfere with the study (such as glucocorticoids, immunosuppressants) in recent period. (4) Special population: pregnant women, lactating women, minors. (5) Others: those with cognitive impairment, unable to cooperate with the study or have difficulty in follow-up.

[0083] 3) Criteria for classification of COVID-19 severity: (1) Asymptomatic: positive RT-PCR test result but no fever or respiratory symptoms (including but not limited to cough, dyspnea, nasal congestion, runny nose, sore throat, pneumonia, etc.) during clinical care. (2) Mild: mild clinical symptoms (mainly fever and cough), no pneumonia on imaging. No dyspnea. No oxygenation disorder. (3) Severe: patients with any of the following symptoms are considered severe: 1. Dyspnea, respiratory rate ≥ 30 times / min. 2. In a resting state, pulse oxygen saturation ≤ 93%. 3. Arterial partial pressure of oxygen (PaO2) / oxygen concentration (FiO2) ≤ 300 mmHg. All samples were stored at -80°C in the biological sample bank of the Beijing Ditan Hospital, Capital Medical University after being collected by clinical personnel.

[0084] 4) 377 serum samples from COVID-19 patients and 46 serum samples from healthy controls were collected according to the inclusion and exclusion criteria in 1) and 2), and the patients and healthy subjects were from Peking Union Medical College Hospital. According to the classification criteria in 3), the results are shown in Table 1.

[0085] Table 1: Sample information

[0086]

[0087] 2, Sample cohort used in the examples

[0088] The samples were randomly divided into training set and validation set in the ratio of 6:4, as shown in Table 2.

[0089] Table 2: COVID-19 sample cohort

[0090] Group Total (n) Training set (n) Validation set (n) Healthy controls 46 28 18 Asymptomatic group 150 90 60 Mild group 118 71 47 Severe group 109 65 44

[0091] 3, Data preprocessing method of data set

[0092] In the proteomic microarray, the original fluorescence signal intensity of each sample was standardized to Z-score.

[0093]

[0094] Where P1…Pn represent individual polypeptides from the same patient; intensity P represents the fluorescence intensity of a certain polypeptide; mean intensity P1…Pn represents the mean value of the fluorescence intensity of all polypeptides detected in the patient; SD P1…Pn represents the standard deviation of the fluorescence intensity of all polypeptides detected in the patient.

[0095] 4. Proteome microarray

[0096] The proteome microarray is composed of full-length N protein, full-length E protein and truncated S protein of SARS-CoV-2 and a total of 966 peptides of the full-length sequence of SARS-CoV-2 protein, with a peptide segment of 15 amino acids, a step of 10, and an overlap of 5 amino acids between adjacent peptide segments.

[0097] 5. Data analysis

[0098] The analysis of variance uses R 4.4.3 package, the drawing uses R 4.4.3 package ggplot2 v3.5.2, and the ROC analysis uses R 4.4.3 package pROC v1.18.5.

[0099] Example 1

[0100] The proteome microarray was assembled in an incubation tray and blocked with 5% (w / v) milk in PBS containing 0.05% (v / v) Tween-20 (PBST) for 10 minutes at room temperature before antibody detection. After aspirating the blocking solution, 1:100 diluted serum was added to the array and incubated at room temperature for 30 minutes. After washing 3 times with PBST, the array was incubated with Cy TM 3. The array was incubated with AffiniPure goat anti-human serum IgA antibody (Jackson ImmunoResearch, USA, CAT#109-165-011) (4 μg / mL). Finally, the array was washed with PBST and deionized water, separated from the tray, and dried with a vacuum pump. The proteome microarray chip was scanned at 532 and 635 nm using a GenePix 4300A microarray scanner (Molecular Devices). The median of the fluorescence signal intensity after background subtraction was extracted using GenePix Pro7 software.

[0101] For the training set, the samples were grouped according to the severity (healthy group, asymptomatic group, mild group, severe group), and time-series clustering (Mfuzz) was performed using the Maiwei cloud online platform. From the clustering results, clusters with continuously rising polypeptide expression according to disease severity were selected. ANOVA variance analysis was performed on the data of these clusters, and polypeptides with significant differences were screened.

[0102] The polypeptide expression profiles of the healthy group, asymptomatic group, mild group and severe group in the training set and the validation set were tested for inter-group differences by one-way ANOVA, and it was found that the antibodies against the polypeptides of SEQ ID NO: 1 and SEQ ID NO: 2 had significant statistical differences between groups. Specifically, the results between the healthy group and the disease group for SEQ ID NO: 1 are shown in Figure 1A , 1C , and the results between the healthy group and the disease group for SEQ ID NO: 2 are shown in Figure 2A , 2C Further, Figure 1B , 1D and 2B, 2D show that the antibody abundance induced by the polypeptides of SEQ ID NO: 1 and SEQ ID NO: 2 continues to increase with the severity of the disease (asymptomatic → mild → severe). This indicates that the polypeptides of SEQ ID NO: 1 and SEQ ID NO: 2 can be used to distinguish different disease severities. However, there is no significant statistical difference between the groups in the antibody abundance detected by the polypeptide of MSDNGPQNQRNAPRI (SEQ ID NO: 3) Figure 3 .

[0103] Example 2: Diagnostic efficiency of the polypeptide of SEQ ID NO: 1

[0104] ROC analysis shows that SEQ ID NO: 1 has good efficiency in distinguishing between the healthy group and the disease group, and different severities of the disease group. The diagnostic efficiency results in the training set are shown in Table 3 and Figure 4A-Figure 4D ; and the diagnostic efficiency results in the validation set are shown in Table 4 and Figure 5A-Figure 5D .

[0105] Table 3: Diagnostic efficiency of SEQ ID NO: 1 in the training set

[0106]

[0107] Table 4: Diagnostic efficiency of SEQ ID NO: 1 in the validation set

[0108]

[0109] Example 3: Diagnostic efficiency of the polypeptide of SEQ ID NO: 2

[0110] ROC analysis shows that SEQ ID NO: 2 has good efficiency in distinguishing between the healthy group and the disease group, and different severities of the disease group. The diagnostic efficiency results in the training set are shown in Table 5 and Figure 6A-Figure 6D ; and the diagnostic efficiency results in the validation set are shown in Table 6 and Figure 7A-Figure 7D .

[0111] Table 5: Diagnostic performance of SEQ ID NO: 2 in training set

[0112]

[0113] Table 6: Diagnostic performance of SEQ ID NO: 2 in validation set

[0114]

[0115]

[0116] Example 4: Diagnostic performance of polypeptide combination

[0117] Based on the logistic regression model, the polypeptides of SEQ ID NO: 1 and SEQ ID NO: 2 were combined to distinguish different disease severity, and the ROC analysis of the validation set showed that the combination of SEQ ID NO: 1 and SEQ ID NO: 2 had good performance in distinguishing the healthy group from the disease group, and the different severity of the disease group, especially in distinguishing asymptomatic and mild, asymptomatic and severe, with AUC value close to 1, see Table 7 and Figure 8A-Figure 8D .

[0118] Table 7: Diagnostic performance of SEQ ID NO: 1 and SEQ ID NO: 2 combination diagnosis in validation set

[0119] Group Sensitivity (%) Specificity (%) AUC Healthy vs disease 57.7 100 0.779 Asymptomatic vs mild 97.6 97.7 0.996 Asymptomatic vs severe 97.7 100 0.997 Mild vs severe 54.5 97.6 0.787

[0120] The preferred embodiments of the present application are described in detail above, but the present application is not limited to the specific details of the above embodiments, and various simple modifications can be made to the technical solutions of the present application within the technical concept of the present application, and these simple modifications all belong to the protection scope of the present application.

Claims

1. Use of a polypeptide for the manufacture of a product for differentiating SARS-CoV-2 infections of different severity, characterized in that, The polypeptide is KTFPPTEPKKDKKKK (SEQ ID NO: 1) and / or ALPQRQKKQQTVTLL (SEQ ID NO: 2).

2. Use according to claim 1, characterized in that, The product includes a kit, a test paper, a chip or a device.

3. Use according to claim 1, characterized in that, Differentiating SARS-CoV-2 infections of different severity includes detecting the abundance of antibodies in the sample using polypeptides.

4. Use according to claim 3, characterized in that, The sample includes cells, tissues or body fluids.

5. Use according to claim 4, characterized in that, The body fluid is selected from blood, plasma, serum, saliva, urine, tears, amniotic fluid, cerebrospinal fluid or lymph.

6. Use according to claim 3, characterized in that, The antibody is an IgA antibody.

7. Use according to claim 4, characterized in that, The abundance of antibodies in the sample is detected by enzyme-linked immunosorbent assay.

8. Use according to any one of claims 1 to 7, characterized in that, The different severity of SARS-CoV-2 infection includes asymptomatic, mild and severe; The asymptomatic includes no fever, and no respiratory symptoms; The mild includes fever and cough as the main symptoms, no pneumonia, no dyspnea, no oxygenation disorder; The severe includes any of the following symptoms: (1) dyspnea, respiratory rate ≥ 30 times / min; (2) oxygen saturation ≤ 93% at rest; (3) arterial partial pressure of oxygen (PaO2) / oxygen concentration (FiO2) ≤ 300 mmHg.

9. A method of constructing a model for differentiating different severity of SARS-CoV-2 infection, characterized in that, The construction method includes: i) collecting the detection results of the abundance of antibodies binding to KTFPPTEPKKDKKKK (SEQ ID NO: 1) and / or ALPQRQKKQQTVTLL (SEQ ID NO: 2) in patients with SARS-CoV-2 infections of different severity; ii) using the results collected in i) to construct a model; or, The construction method includes: 1) collecting the sample of the subject, detecting the abundance of antibodies binding to KTFPPTEPKKDKKKK (SEQ ID NO: 1) and / or ALPQRQKKQQTVTLL (SEQ ID NO: 2); 2) diagnosing the subject clinically, and dividing the subject according to the severity of SARS-CoV-2 infection; 3) constructing a model for differentiating SARS-CoV-2 infections of different severity according to the detection results of step 1) and the diagnosis results of step 2).

10. A polypeptide composition, characterized in that, The polypeptide composition includes KTFPPTEPKKDKKKK (SEQ ID NO: 1) and ALPQRQKKQQTVTLL (SEQ ID NO: 2).

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