Polypeptide or polypeptide composition for diagnosing SARS-CoV-2 infection

Abstract

The invention belongs to the technical field of biological medicines, and particularly relates to a polypeptide or a polypeptide composition for diagnosing SARS-CoV-2 infection.

Description

Technical Field

[0001] This invention belongs to the field of biomedical technology, specifically relating to a polypeptide or polypeptide composition for diagnosing SARS-CoV-2 infection. Background Technology

[0002] Coronaviruses are a class of enveloped, linear, single-stranded, positive-sense RNA viruses. To date, coronaviruses that can cause human diseases include HCoV-229E, HCoV-OC43, HCoV-NL63, HCoV-HKU1, SARS-CoV, MERS-CoV, and SARS-CoV-2.

[0003] The most common clinical symptoms of SARS-CoV-2 infection are fever, fatigue, and dry cough. A few patients also experience upper respiratory and digestive symptoms such as nasal congestion, runny nose, and diarrhea. Severe cases often develop respiratory distress after one week, and in severe cases, this can progress to acute respiratory distress syndrome, septic shock, uncorrectable metabolic acidosis, and coagulation disorders. SARS-CoV-2 infection is a multisystem disease, affecting not only the lungs but also the kidneys, liver, heart, brain, and nervous system to varying degrees. It has been reported that most critically ill patients suffer from organ dysfunction, primarily cardiac injury, acute kidney injury, liver dysfunction, and pneumothorax. Currently, there are no specific effective drugs for SARS-CoV-2 infection; treatment primarily involves isolation and supportive care.

[0004] Therefore, there is an urgent need to develop an efficient and rapid method for detecting the novel coronavirus, so as to achieve early diagnosis and identification of SARS-CoV-2 virus infection. Summary of the Invention

[0005] In a first aspect, the present invention provides a polypeptide as shown in SEQ ID NO: 1.

[0006] In a second aspect, the present invention provides a polypeptide composition comprising polypeptide sequences as shown in SEQ ID NO: 1-3.

[0007] A third aspect of the present invention provides the use of the polypeptides described in the first aspect and the polypeptide compositions described in the second aspect in the preparation of products for diagnosing SARS-CoV-2 infection.

[0008] Preferably, the product includes a reagent kit, test strip, chip, or device.

[0009] Preferably, the diagnosis of SARS-CoV-2 infection includes detecting the abundance of antibodies in a sample using a peptide or peptide composition.

[0010] The samples mentioned include cells, tissues, or body fluids.

[0011] Preferably, the body fluid is selected from blood, plasma, serum, saliva, urine, tears, amniotic fluid, cerebrospinal fluid, or lymph.

[0012] Preferably, the antibody includes one or more of IgG antibody, IgA antibody, IgE antibody, IgD antibody or IgM antibody.

[0013] Preferably, enzyme-linked immunosorbent assay (ELISA) is used to detect the abundance of antibodies in the sample.

[0014] More preferably, the enzyme-linked immunosorbent assay (ELISA) includes one or more of the following: direct ELISA, indirect ELISA, double-antibody sandwich ELISA, or competitive ELISA.

[0015] A fourth aspect of the present invention provides a method for assessing the severity of SARS-CoV-2 infection, the method comprising detecting the abundance of antibodies in a sample that bind to the polypeptide or polypeptide composition using the polypeptide described in the first aspect or the polypeptide composition described in the second aspect.

[0016] A fifth aspect of the present invention provides the use of the polypeptide described in the first aspect or the polypeptide composition described in the second aspect in the preparation of a product for assessing the severity of SARS-CoV-2 infection.

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

[0018] The term "asymptomatic" includes the absence of both fever and respiratory symptoms. Preferably, the respiratory symptoms include, but are not limited to, cough, difficulty breathing, nasal congestion, runny nose, sore throat, and pneumonia.

[0019] The severe illness includes any of the following symptoms: (1) Dyspnea, respiratory rate ≥30 breaths / min; (2) Under resting conditions, oxygen saturation is ≤93%; (3) Arterial partial pressure of oxygen (PaO2) / oxygen concentration (FiO2) ≤ 300 mmHg.

[0020] The severe condition includes any of the following symptoms: (1) Respiratory failure occurs and mechanical ventilation is required; (2) Shock occurs; (3) If other organ failure is present, ICU monitoring and treatment are required.

[0021] In one specific embodiment of the invention, the application includes distinguishing between healthy individuals and asymptomatic SARS-CoV-2 infection, for example, by detecting the abundance of antibodies in a subject that bind to a peptide or peptide composition, and then comparing it to a threshold.

[0022] In one specific embodiment of the invention, the application includes distinguishing between healthy individuals and those severely infected with SARS-CoV-2. For example, it involves detecting the abundance of antibodies binding to a peptide or peptide composition in a subject and then comparing it to a threshold.

[0023] In one specific embodiment of the invention, the application includes distinguishing between healthy and critically ill subjects infected with SARS-CoV-2. For example, it involves detecting the abundance of antibodies binding to peptides or peptide compositions in the subject and then comparing it to a threshold.

[0024] In one specific embodiment of the invention, the application includes distinguishing between asymptomatic and severe SARS-CoV-2 infection. For example, this involves detecting the abundance of antibodies binding to peptides or peptide compositions in patients and then comparing it to a threshold.

[0025] In one specific embodiment of the invention, the application includes distinguishing between asymptomatic and severe SARS-CoV-2 infection. For example, this involves detecting the abundance of antibodies binding to peptides or peptide compositions in patients and then comparing it to a threshold.

[0026] In one specific embodiment of the invention, the application includes distinguishing between severe and critical SARS-CoV-2 infection. For example, this involves detecting the abundance of antibodies binding to peptides or peptide compositions in patients and then comparing it to a threshold.

[0027] The thresholds include thresholds for distinguishing between healthy and asymptomatic individuals, thresholds for distinguishing between healthy and severely ill individuals, and thresholds for distinguishing between healthy and critically ill individuals.

[0028] The thresholds also include thresholds for distinguishing between asymptomatic and severe cases, as well as thresholds for distinguishing between asymptomatic and critical cases, and thresholds for distinguishing between severe and critical cases.

[0029] The threshold was obtained from previous experiments, that is, the threshold was determined by the degree of difference in antibodies among healthy subjects, asymptomatic patients infected with SARS-CoV-2, severely infected patients with SARS-CoV-2, and critically infected patients with SARS-CoV-2 through experiments and data analysis.

[0030] A sixth aspect of the present invention provides a method for constructing a model to assess the severity of SARS-CoV-2 infection, the method comprising: i): Collect the results of antibody abundance levels binding to peptides or peptide combinations in SARS-CoV-2 infected patients of different severities; ii): Build a model using the results collected in i); or, The construction method includes: 1) Collect samples from subjects and detect the abundance of antibodies binding to peptides or peptide combinations; 2) Clinical diagnosis was performed on the subjects, and they were divided into groups according to the severity of SARS-CoV-2 infection; 3) Construct a model to distinguish SARS-CoV-2 infections of different severity based on the test results of step 1) and the diagnosis results of step 2).

[0031] Preferably, the algorithm used to construct the model for assessing the severity of SARS-CoV-2 infection includes one or more of the following: logistic regression (LR), k-nearest neighbor algorithm (KNN), Naive Bayes (NB), support vector machine (SVM Radial), random forest (RF), XGBoost, TreeNet, gradient booster (GBM), LASSO, or neural network (NNET).

[0032] The logistic regression is a binary logistic regression.

[0033] A seventh aspect of the present invention provides a method for constructing a model for diagnosing SARS-CoV-2 infection, the method comprising: i): Collect the results of antibody abundance levels that bind to peptides or peptide compositions in healthy groups and SARS-CoV-2 infected patients; ii): Build a model using the results collected in i); or, The construction method includes: 1) Collect samples from subjects and detect the abundance of antibodies binding to peptides or peptide combinations; 2) Clinical diagnosis was performed on the subjects, and they were divided into a healthy group and a SARS-CoV-2 infected patient group; 3) Construct a model for diagnosing SARS-CoV-2 infection based on the test results of step 1) and the diagnosis results of step 2).

[0034] Preferably, the algorithm used to construct the model for diagnosing SARS-CoV-2 infection includes one or more of the following: logistic regression (LR), k-nearest neighbor algorithm (KNN), Naive Bayes (NB), support vector machine (SVM Radial), random forest (RF), XGBoost, TreeNet, gradient booster (GBM), LASSO, or neural network (NNET).

[0035] The logistic regression is a binary logistic regression.

[0036] An eighth aspect of the invention provides a model for diagnosing SARS-CoV-2 infection and / or assessing the severity of SARS-CoV-2 infection obtained by the construction method described above.

[0037] The ninth aspect of the present invention provides an application of the model described herein in the preparation of products that differentiate between different degrees of SARS-CoV-2 infection.

[0038] In a tenth aspect of the present invention, a method for diagnosing SARS-CoV-2 infection is provided, the method comprising detecting the abundance of antibodies in a sample using the polypeptide described in the first aspect or the polypeptide composition described in the second aspect. Attached Figure Description

[0039] Figure 1A The results show the difference in antibody abundance binding to the peptide shown in SEQ ID NO: 1 between the healthy group and the disease group (including the asymptomatic group, the severe group, and the critical group), with ** indicating p < 0.01; Figure 1B The results show the differences in antibody abundance among the asymptomatic, severe, and critically ill groups for the peptide shown in SEQ ID NO: 1. * indicates p < 0.05, ** indicates p < 0.01, and **** indicates p < 0.0001. Figure 2 The figure shows a comparison of antibody abundance among the asymptomatic, severe, and critically ill groups for the peptide shown in SEQ ID NO: 4, where ns indicates no significant difference. Figure 3A The figure shows the ROC plot of the healthy group and the asymptomatic group of the validation cohort for antibodies against the peptide shown in SEQ ID NO: 1; Figure 3B The figure shows the ROC plots of the healthy group and the severely ill group of the antibody against the peptide shown in SEQ ID NO: 1 in the validation queue. Figure 3C The figure shows the ROC plot of the healthy group and the critical group of antibodies against the peptide shown in SEQ ID NO: 1 in the validation queue; Figure 3D The figure shows the ROC plots of the asymptomatic and severe groups of antibodies against the peptide shown in SEQ ID NO: 1 in the validation queue. Figure 3E The figure shows the ROC plots of the asymptomatic and critically ill groups of the antibody against the peptide shown in SEQ ID NO: 1 in the validation queue. Figure 3FThe figure shows the ROC plots of antibodies against the peptide shown in SEQ ID NO: 1 in the severe and critically ill groups in the validation queue. Figure 4A The figure shows the ROC plot of antibody diagnosis of SARS-CoV-2 infection against the peptide shown in SEQ ID NO: 1 in the validation queue; Figure 4B The figure shows the ROC plot of the peptides shown in SEQ ID NO: 1-3 for the combined diagnosis of SARS-CoV-2 infection in the validation queue. Detailed Implementation

[0040] The present invention will be further described below with reference to embodiments. The following description is merely a preferred embodiment of the present invention and is not intended to limit the invention in any other way. Any person skilled in the art may make equivalent modifications to the disclosed technical content to create equivalent embodiments. Any simple modifications or equivalent changes made to the following embodiments based on the technical essence of the present invention without departing from the scope of the invention are all within the protection scope of the present invention.

[0041] Unless otherwise specified, the experimental methods used in the following examples are conventional methods.

[0042] Unless otherwise specified, all reagents and materials used in the following examples are commercially available.

[0043] The term "subject" as used in this invention can refer to humans or non-human animals, including "patients," "suspected patients," and "healthy individuals," etc. This term does not indicate a specific age or sex and covers adult or newborn subjects as well as fetuses. Non-human animals can be wild animals, zoo animals, commercially traded animals, pets, laboratory animals, etc. Specifically, non-human animals include, but are not limited to, pigs, cattle, 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.

[0044] The term "and / or" as used in this invention encompasses all combinations of items connected by the term, and should be considered as if each combination has been individually listed in this application. For example, "A and / or B" includes "A", "B", and "A and B". Similarly, "A, B and / or C" includes "A", "B", "C", "A and B", "A and C", "B and C", and "A and B and C".

[0045] The term "comprising" or "including" as used in this invention is an open-ended expression, encompassing the specified components or steps described, as well as other specified components or steps that do not substantially affect the meaning.

[0046] The "threshold" mentioned in this invention refers to a critical value used to determine whether a test is positive or negative. For example, in this application, the threshold is used to determine whether a disease is present, or the severity of the disease. The threshold can be determined by a cutoff value, most commonly determined using a receiver operating system (ROS) curve. In some cases, the threshold can be a cutoff value. In other cases, the threshold is a cutoff value after standardization.

[0047] Example 1: Screening for peptides I. Discovering Queue Sample Information 1. Inclusion criteria for patients infected with SARS-CoV-2 (1) Age: ≥18 years old.

[0048] (2) Criteria for diagnosis: positive nucleic acid test and antigen test.

[0049] (3) Compliance: Able to cooperate with the research process, including follow-up and treatment.

[0050] 2. Exclusion criteria for patients infected with SARS-CoV-2 (1) Allergy history: Allergy to the research drug or related components.

[0051] (2) Comorbidities: serious cardiopulmonary diseases, immunodeficiency, malignant tumors, or other diseases that may affect the research results.

[0052] (3) Recent medication: Recent use of drugs that may interfere with the study (such as glucocorticoids, immunosuppressants).

[0053] (4) Special populations: pregnant women, breastfeeding women, and minors.

[0054] (5) Others: those with cognitive impairment, unable to cooperate with research, or have difficulty with follow-up.

[0055] 3. Classification of SARS-CoV-2 infection severity (1) Asymptomatic: The RT-PCR test result is positive but no fever or respiratory symptoms (including but not limited to cough, dyspnea, nasal congestion, runny nose, sore throat, pneumonia, etc.) occur during clinical care.

[0056] (2) Severe illness: A patient is considered severe if he or she has any of the following symptoms: 1. Dyspnea, respiratory rate ≥30 breaths / min. 2. Oxygen saturation ≤93% at rest. 3. Arterial partial pressure of oxygen (PaO2) / oxygen concentration (FiO2) ≤300mmHg.

[0057] (3) Critical condition: A patient is considered critically ill if he or she has any of the following symptoms: 1. Respiratory failure requiring mechanical ventilation. 2. Shock. 3. Other organ failure requiring ICU monitoring and treatment.

[0058] 4. Serum samples from the cohort were collected at Peking Union Medical College Hospital and included 14 healthy controls, 45 asymptomatic cases, 33 severe cases, and 45 critical cases. All samples were aliquoted and stored at -80°C in the biobank of the Department of Laboratory Medicine at Beijing Ditan Hospital, Capital Medical University, after being collected by clinical staff.

[0059] II. Detection Methods A proteomic microarray was constructed using the full-length N protein, full-length E protein, and truncated S protein of SARS-CoV-2, along with 966 peptides arranged in 15-amino acid segments with a step size of 10 and an overlap of 5 amino acids between adjacent segments. This array was assembled in an incubation dish and blocked for 10 minutes at room temperature with 5% (w / v) milk in PBS containing 0.05% (v / v) Tween-20 (PBST). After removing the blocking solution, serum diluted 1:101 was added to the array and incubated for 30 minutes at room temperature. After washing three times with PBST, the array was incubated with Cy™3 Affinipure donkey anti-human IgG (H+L) antibody (Jackson Immuno Research, CAT#709-165-149, USA) (4 μg / mL). Finally, the array was washed with PBST and deionized water, separated from the tray, and dried using a vacuum pump. The proteomics microarray chip was scanned at 532 nm and 635 nm using a GenePix 4300A microarray scanner (Molecular Devices). The median fluorescence signal intensity after background subtraction was extracted using GenePix Pro 7 software.

[0060] III. Data Preprocessing Methods In the proteomic microarray, the raw fluorescence signal intensity of each sample was normalized to a Z-score.

[0061]

[0062] in, Each polypeptide represents a different patient; Indicates the fluorescence intensity of a specific polypeptide; This represents the average fluorescence intensity of all peptides detected in this patient; This represents the standard deviation of the fluorescence intensity of all peptides detected in this patient.

[0063] IV. Data Analysis Analysis of variance was performed using the R 4.4.3 package, plotting was performed using the R 4.4.3 package ggplot2 v3.5.2, and ROC analysis was performed using the R 4.4.3 package pROC v1.18.5.

[0064] V. Results Data from each group were analyzed, and ANOVA was performed to screen for peptides with significant differences, particularly those that were upregulated with increasing disease severity. One-way ANOVA was then used to examine the peptide expression profiles of the healthy, asymptomatic, severe, and critically ill groups in the cohort, revealing statistically significant differences in antibody expression against the peptide shown in SEQ ID NO: 1 (Table 1) among the groups. Figure 1A The results showed antibody abundance between the healthy group and the disease group. Figure 1B The expression level of the antibody binding to SEQ ID NO: 1 was shown to be progressively upregulated with increasing disease severity (asymptomatic → severe → critical). However, the peptide shown in SEQ ID NO: 4 did not exhibit a significant gradient in expression. Figure 2 ).

[0065] Table 1: Peptide Sequences

[0066] Example 2: Peptide Validation 1. Sample The inclusion criteria, exclusion criteria, and severity classification of SARS-CoV-2 infection patients are the same as in Example 1.

[0067] Serum samples for the validation cohort were collected at Peking Union Medical College Hospital, comprising 31 healthy controls, 104 asymptomatic cases, 76 severe cases, and 104 critical cases. All samples were aliquoted and stored at -80°C in the biobank of the Department of Laboratory Medicine at Beijing Ditan Hospital, Capital Medical University, after collection by clinical staff.

[0068] 2. The detection method is the same as in Example 1. 3. Results The antibody abundance binding to the peptide shown in SEQ ID NO: 1 differed significantly between the healthy group and each disease group. Furthermore, among the disease groups, antibody abundance increased with increasing disease severity, a trend consistent with that in Example 1. The diagnostic efficacy of SEQ ID NO: 1 was further evaluated using ROC, and the results are shown in Table 2 and... Figures 3A-3F .

[0069] Table 2: Diagnostic efficacy of SEQ ID NO: 1

[0070] Example 3: Validation of Peptide Co-application The applicant previously screened and found that the antibody abundance against the peptides shown in SEQ ID NO: 2 and 3 showed significant differences between healthy individuals and diseased individuals, as well as between different disease groups. Therefore, this embodiment verifies the diagnostic efficacy of the combination of SEQ ID NO: 1-3.

[0071] The samples and testing methods were consistent with those in Example 2. The disease group included asymptomatic, severe, and critical cases.

[0072] Based on a logistic regression model, the peptides shown in SEQ ID NO: 1-3 were combined for the diagnosis of SARS-CoV-2. ROC analysis results showed that, compared to SEQ ID NO: 1 alone, the combined diagnosis significantly improved specificity and sensitivity. The results are shown in Table 3. Figure 4A -B.

[0073] Table 3

[0074] The preferred embodiments of the present invention have been described in detail above. However, the present invention is not limited to the specific details in the above embodiments. Within the scope of the technical concept of the present invention, various simple modifications can be made to the technical solution of the present invention, and these simple modifications all fall within the protection scope of the present invention.

Claims

1. A polypeptide as shown in SEQ ID NO:

1.

2. A polypeptide composition, characterized in that, The polypeptide composition comprises polypeptide sequences as shown in SEQ ID NO: 1-3.

3. The use of the polypeptide of claim 1 and the polypeptide composition of claim 2 in the preparation of products for diagnosing SARS-CoV-2 infection.

4. The application according to claim 3, characterized in that, The products mentioned include reagent kits, test strips, chips, or devices.

5. The application according to claim 3, characterized in that, Diagnosing SARS-CoV-2 infection involves detecting the abundance of antibodies in a sample using peptides or peptide compositions.

6. The application according to claim 5, characterized in that, The antibody includes one or more of IgG antibody, IgA antibody, IgE antibody, IgD antibody or IgM antibody.

7. The application according to claim 5, characterized in that, The samples mentioned include cells, tissues, or body fluids.

8. The application according to claim 7, characterized in that, The body fluids mentioned are selected from blood, plasma, serum, saliva, urine, tears, amniotic fluid, cerebrospinal fluid, or lymph.

9. The application according to claim 5, characterized in that, The abundance of antibodies in the samples was detected by enzyme-linked immunosorbent assay (ELISA).

10. A method for assessing the severity of SARS-CoV-2 infection, characterized in that, The method includes using the polypeptide of claim 1 or the polypeptide composition of claim 2 to detect the abundance of antibodies in a sample that bind to the polypeptide or polypeptide composition.

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