A method for predicting the severity of COVID-19 using protein markers in blood exosomes.

Abstract

The present invention provides a method of predicting progressive severity in a COVID-19 patient by using RNA levels of COPB2, KRAS, PRKCB, RHOC, CD147, CAPN2, ECM1, FGG, MFAP4, ADI1, AK1, MGAT1, CLDN3, CRP, UQCRC2, FGA, FGB, FGL1, GPX1, GSK3B, LBP, PDGFC, RAB13, RAP1B, SLC6A4, UBA7, and the like in the patient's blood.

Description

[Technical Field]

[0001] This invention relates to a method for predicting the risk of severe SARS-CoV-2 infection by detecting proteins contained in exosomes in the blood. [Background technology]

[0002] Severe acute respiratory syndrome (SARS-CoV-2) coronavirus infection (COVID-19) has spread worldwide, becoming a pandemic. SARS-CoV-2 is a virus belonging to the coronavirus genus, and is an extremely small particle (0.1 nm in diameter) consisting of nucleic acid (RNA) that functions as genetic information, a protein shell (capsid) surrounding it, and a lipid bilayer shell (envelope) with spikes. This virus spreads from person to person through droplet infection, where the virus released from droplets such as coughs, sneezes, and nasal discharge of an infected person enters the body through the mouth or eyes, and through contact infection, where the person touches the mouth or nose with hands that have touched the virus-containing droplets of an infected person.

[0003] This infection has a long incubation period of 1 to 14 days, after which initial symptoms similar to a common cold, such as fever and cough, appear. The majority of infected individuals experience mild or no symptoms. The problem with COVID-19 is that some of the remaining patients develop severe respiratory distress syndrome (ARDS), and further complications such as myocarditis, vasculitis, and meningitis, which can be life-threatening.

[0004] Currently, testing for SARS-CoV-2 infection is being actively conducted in various countries, including PCR testing, antigen testing, and antibody testing. However, these tests only determine the presence of the virus or whether a person has been infected in the past; they cannot predict the severity of the illness in infected patients. While some patients with underlying medical conditions, such as cardiovascular disease, respiratory disease, and diabetes, as well as pregnant women and the elderly, tend to develop severe symptoms, severe cases have also been observed in healthy young people, highlighting the need for more accurate methods to predict the severity of the illness. [Overview of the project] [Problems that the invention aims to solve]

[0005] Therefore, the present invention aims to provide a method for predicting the severity of SARS-CoV-2 infection in patients. [Means for solving the problem]

[0006] The inventors collected blood samples from patients who tested positive for SARS-CoV-2 by PCR and exhibited moderate symptoms, and observed the subsequent progression of their condition. Subsequently, a retrospective analysis of the relationship between the progression of the patient's disease and the proteins contained in exosomes in blood samples revealed that the exosomes derived from the patient's blood contained COPB2, KRAS, PRKCB, RHOC, CD147, CAPN2, ECM1, FGG, MFAP4, ADI1, AK1, MGAT1, CLDN3, CRP, UQCRC2, FGA, FGB, FGL1, GPX1, GSK3B, LBP, PDGFC, RAB13, RAP1B, SLC6A4, UBA7, ORM1, RNPEP, ANGPT1, APOB, B4GALT1, BHMT, CPN1, GNAZ, ICAM2, SELL, MAN1A1, SERPINA5, PACSIN2, NCF1B, TMEM59, YWHAB, ABAT, A We found that the levels of DH1B, ASL, ASS1, CDH2, CAB39, CPS1, CD226, COL6A3, CUL4A, DSC1, ENTPD5, EIF4A1, FN1, PGC, RHEB, GNAI2, GNB1, GNA13, ITGA2B, ITGB1, ILK, F11R, LTA4H, LIMS1, NAV2, FAM129B, NNMT, NID1, PPIA, PLA1A, PPBP, PECAM1, GP1BB, PCSK9, MENT, SERPINA10, F2RL3, LOX, SFTPB, RAB5B, RALB, REEP6, RETN, AGXT, CCT2, THBD, ISG15, and ZYX serve as markers suggesting that COVID-19 patients may or may not develop severe symptoms. Therefore, the present invention provides a method for predicting the severity of COVID-19 in patients using the above-mentioned proteins contained in blood exosomes.

[0007] In this specification, "COVID-19 patient" refers to a person who has been confirmed to be infected with SARS-CoV-2, or a person suspected of being infected with SARS-CoV-2, regardless of whether they are showing symptoms. For example, a COVID-19 patient may be a person who has tested positive for SARS-CoV-2 in a PCR test. SARS-CoV-2 is a coronavirus called severe acute respiratory syndrome coronavirus 2, or 2019 novel coronavirus (2019-nCoV), and is a single-stranded positive-sense RNA virus with a total length of 29.9 kb. The gene sequence of the first Wuhan strain (Wuhan-Hu-1) is published in GenBank_ID MN908947, and according to Nextstrain, the mutation rate is reported to be 25.9 base mutations / genome / year, and it is thought that at least 9 base mutations occur randomly. Therefore, in this specification, "SARS-CoV-2" includes the Wuhan strain of SARS-CoV-2 and all derivative strains that have arisen from mutations therefrom.

[0008] In this specification, marker proteins include COPB2 (COPI Coat Complex Subunit Beta 2) (e.g., SEQ ID NO: 11), KRAS (KRAS proto-oncogene) (e.g., SEQ ID NO: 12), PRKCB (Protein kinase C beta type) (e.g., SEQ ID NO: 13), RHOC (Ras homologous family member C) (e.g., SEQ ID NO: 14), CD147 (Basigin, extracellular matrix metalloproteinase inducer (EMMPRIN)) (e.g., SEQ ID NO: 15), CAPN2 (Calpain-2) (e.g., SEQ ID NO: 16), ECM1 (Extracellular matrix protein 1) (e.g., SEQ ID NO: 17), FGG (Fibrinogen gamma chain) (e.g., SEQ ID NO: 18), MFAP4 (microfibril-associated protein 4) (e.g., SEQ ID NO: 19), and ADI1 (1,2-dihydroxy-3-keto-5-methylthiopentene). dioxygenase,APL1,ARD,Fe-ARD,HMFT1638,MTCBP1,Ni-ARD,SIPL,mtnD),AK1(Adenylate kinase isoenzyme 1),MGAT1(Alpha-1,3-mannosyl-glycoprotein 2-beta-N-acetylglucosaminyltransferase),CLDN3(Claudin 3),CRP(C-reactive protein), UQCRC2 (Cytochrome b-c1 complex subunit 2, mitochondrial), FGA (Fibrinogen alpha chain), FGB (Fibrinogen beta chain), FGL1 (Fibrinogen-like protein 1), GPX1 (Glutathione peroxidase 1), GSK3B (Glycogen synthase kinase 3 beta), LBP (Lipopolysaccharide binding protein),PDGFC(Platelet Derived Growth Factor C),RAB13(Ras-related protein Rab-13),RAP1B(Ras-related protein Rap-1b),SLC6A4(Sodium-dependent serotonin transporter),UBA7(Ubiquitin Like Modifier Activating Enzyme 7),ORM1(Orosomucoid 1,Alpha-1-acid glycoprotein 1), RNPEP (Aminopeptidase B), ANGPT1 (Angiopoietin 1),APOB,B4GALT1,BHMT,CPN1,GNAZ,ICAM2,SELL,MAN1A1,SERPINA5,PACSIN2,NCF1B,TMEM59,YWHAB,ABAT,AD H1B,ASL,ASS1,CDH2,CAB39,CPS1,CD226,COL6A3,CUL4A,DSC1,ENTPD5,EIF4A1,FN1,PGC,RHEB,GNAI2,GNB1,G Select from NA13, ITGA2B, ITGB1, ILK, F11R, LTA4H, LIMS1, NAV2, FAM129B, NNMT, NID1, PPIA, PLA1A, PPBP, PECAM1, GP1BB, PCSK9, MENT, SERPINA10, F2RL3, LOX, SFTPB, RAB5B, RALB, REEP6, RETN, AGXT, CCT2, THBD, ISG15, and ZYX. Preferably, selected from COPB2, KRAS, PRKCB, RHOC, CD147, CAPN2, ECM1, FGG, MFAP4, ADI1, AK1, MGAT1, CLDN3, CRP, UQCRC2, FGA, FGB, FGL1, GPX1, GSK3B, LBP, PDGFC, RAB13, RAP1B, SLC6A4, and UBA7, and more preferably selected from COPB2, KRAS, PRKCB, RHOC, CD147, CAPN2, ECM1, FGG, and MFAP4.

[0009] The marker proteins described herein may be isoforms, precursor proteins, mature proteins, or trunk forms of the marker proteins described above, and some of their amino acids, for example, 1 to 50, 1 to 30, 1 to 20, 1 to 10, 1 to 8, 1 to 5, 1 to 3, 1 to 2, or 1 amino acid, may be substituted or deleted, or amino acids not present in those proteins (for example, 1 to 50, 1 to 30, 1 to 20, 1 to 10, 1 to 8, 1 to 5, 1 to 3, 1 to 2, or 1 amino acid) may be added or inserted. Proteins having amino acid sequences that are 90% or more, 91% or more, 92% or more, 93% or more, 94% or more, 95% or more, 96% or more, 97% or more, 98% or more, or about 99% or more identical to the marker proteins described above are also included as marker proteins. Identity can be determined, for example, by BLAST. Unless otherwise inconsistent, the marker proteins described herein include these variants and variants.

[0010] In this specification, "blood sample" includes whole blood, plasma, serum, blood fractions or processed products such as hemolysis of whole blood or blood cells, and dilutions or concentrates thereof, preferably serum or its dilution.

[0011] In this specification, "severe disease" means a score of 5 or higher (Hospitalized-severe disease) in the WHO 2020 scoring for COVID-19 cases.

[0012] [Table 1]

[0013] In this specification, "exosome" refers to an extracellular vesicle with a diameter of approximately 20-200 nm or 50-150 nm released from various cells, and is also abbreviated as EV. Exosomes are known to have various functions, including intercellular communication, antigen presentation, and transport of proteins and nucleic acids such as mRNA and miRNA. Preferably, the exosomes in this specification have CD9 and CD63 on their surface.

[0014] In this specification, marker RNA is selected from miR-122-5p, SNORD33, AL732437.2, RNU2-29P, CDKN2B-AS1, AL365184.1, AL365184.1, AL365184.1, AL365184.1, let-7c-5p, miR-21-5p, miR-140-3p, and C5orf66-AS2, which may, for example, have the nucleic acid sequences described in sequence numbers 1 to 10, in order, but are not limited thereto.

[0015] If an isoform or variant exists for the marker RNA, such isoform or variant is also included in the marker RNA as defined herein. For example, the marker RNA as defined herein may have some bases substituted or deleted in the sequence known as the marker RNA described above, for example, 1 to 5, 1 to 3, 1 to 2, or 1 base, or may have bases not included in the marker RNA described above (for example, 1 to 5, 1 to 3, 1 to 2, or 1 base) added or inserted. Furthermore, RNA having a base sequence that is 90% or more, 91% or more, 92% or more, 93% or more, 94% or more, 95% or more, 96% or more, 97% or more, 98% or more, or approximately 99% or more identical to the marker RNA described above is also included in the marker RNA as defined herein. Identity can be determined, for example, by BLAST. Unless otherwise inconsistent, the marker RNA described herein includes these variants and variants. [Effects of the Invention]

[0016] Since the prognosis of COVID-19 patients can be predicted by the method of the present invention and the like, it can be used to determine the admissibility of hospitalization and the necessity of a monitoring system. In particular, for patients predicted to deteriorate, appropriate treatment and measures can be enabled by checking symptoms more frequently.

Brief Description of Drawings

[0017] [Figure 1] The patient recruitment flowchart of the cohort in the example is shown. [Figure 2] The workflow of LC-MS identification of proteome from CD9+ / CD63+ EV from serum samples of 31 mild COVID-19 patients and 10 uninfected healthy controls is shown. The 31 mild COVID-19 patients were divided into Group 1 (mild; n = 22) and Group 2 (severe; n = 9) based on the course after sample collection. [Figure 3] PCA map of 723 proteins from three test subject groups. [Figure 4] Graph showing the correlation of COPB2, KRAS, PRKCM, RHOC, CD147, CAPN2, ECM1, FGG, and MFAP4 between three subject groups. The P value is for the trend by Pearson correlation analysis. Error bars represent mean ± SEM. The vertical axis represents the protein amount, and the horizontal axis represents the patient group (non-infected, Group 1 (mild), Group 2 (severe)). [Figure 5] Graph of ROC analysis for nine EV proteins. The numerical values represent the evaluated AUC values (95% CI). [Figure 6] Kaplan-Meier curves of nine EV proteins by log-rank test are shown. The vertical axis represents the proportion of patients whose condition does not progress, and the horizontal axis time (days) represents the number of days elapsed from the registration date. The high and low groups were defined using the optimal cut-off value. [Figure 7]Shows the workflow for determining the exRNA profile from serum samples of 31 mild COVID-19 patients and 10 uninfected healthy controls by NGS. The 31 mild COVID-19 patients were divided into Group 1 (mild; n = 22) and Group 2 (severe; n = 9) based on the course after sample collection. [Figure 8] PCA map for 43 transcripts in three test subject groups. [Figure 9] The color indicates the Pearson correlation coefficient. In the upper triangular part, positive correlations are represented in purple and negative correlations are represented in brown. The color intensity and ellipticity of the circles are proportional to the correlation coefficient. The actual correlation values are displayed in the lower triangle, and the pink highlights represent P < 0.05. Cluster 1 (PRKCB, RHOC, COPB2, and KRAS) included a group of EV proteins related to the antiviral response. Cluster 2 (smoking, age, and MFPA4) and Cluster 3 (CM1, CDKN2B.AS1, AL365184.1, CAPN2, CRP, FGG, and CD147) included groups of coagulation-related markers. Cluster 4 (ALT, RNU2-29P, SNORD33, miR-122-5p, and AL732437.2) included a group of exRNAs related to liver impairment. [Figure 10] Flowchart showing the process of predicting the severity of COVID-19. [Figure 11] Diagram of home treatment for COVID-19.

Embodiments for Carrying Out the Invention

[0018] 1. Method for Determining the Likelihood of Disease Progression to Severe In one aspect, the present invention is a method for determining the likelihood of a COVID-19 patient progressing to severe disease, comprising measuring the level of one or more marker proteins present in exosomes in the blood derived from the patient, and comparing the measured level of the marker protein with a control proteinThe method includes determining the likelihood of the patient becoming severely ill by comparing it to a certain level. The marker protein is selected from the following group of proteins: COPB2, KRAS, PRKCB, RHOC, CD147, CAPN2, ECM1, FGG, MFAP4, ADI1, AK1, MGAT1, CLDN3, CRP, UQCRC2, FGA, FGB, FGL1, GPX1, GSK3B, LBP, PDGFC, RAB13, RAP1B, SLC6A4, UBA7, ORM1, RNPEP, ANGPT1, APOB, B4GALT1, BHMT, CPN1, GNAZ, ICAM2, SELL, MAN1A1, SERPINA5, PACSIN2, NCF1B, TMEM59, YWHAB, AB AT,ADH1B,ASL,ASS1,CDH2,CAB39,CPS1,CD226,COL6A3,CUL4A,DSC1,ENTPD5,EIF4A1,FN1,PGC,RHEB,GNAI2,GNB1,GNA13,ITGA2B,ITGB1,ILK,F11R,LTA4H,LIMS1,N AV2,FAM129B,NNMT,NID1,PPIA,PLA1A,PPBP,PECAM1,GP1BB,PCSK9,MENT,SERPINA10,F2RL3,LOX,SFTPB,RAB5B,RALB,REEP6,RETN,AGXT,CCT2,THBD,ISG15,and ZYX.

[0019] Therefore, the determination of the likelihood of severe illness according to the present invention may include comparing the level of a marker protein in exosomes derived from the blood of a subject who is a COVID-19 patient with the level of a control marker protein. For example, if the protein being measured is COPB2 or KRAS, when the level of the protein is higher than that of a healthy person, it can be determined that the patient is unlikely to develop severe illness or will maintain a mild condition, or when the level is not higher than that of a healthy person (lower or equivalent), it can be determined that the patient is likely to develop severe illness. Also, if the protein being measured is PRKCB or RHOC, when the level of the protein is lower than that of a healthy person or a patient who maintained a mild condition at the time of infection detection, it can be determined that the patient is likely to develop severe illness, or when the level is not lower than that of a healthy person or a patient who maintained a mild condition at the time of infection detection (higher or equivalent), it can be determined that the patient is unlikely to develop severe illness. If the protein being measured is CD147, CAPN2, ECM1, or FGG, the patient is judged to have a high probability of developing severe illness if the level of the protein is higher than that of a healthy person or a patient who maintained a mild condition at the time of infection detection, or if the level of the protein is not higher (lower or equivalent) than that of a healthy person or a patient who maintained a mild condition at the time of infection detection, or if the level of the protein is not higher (lower or equivalent) than that of a healthy person or a patient who maintained a mild condition at the time of infection detection, or if the level of the protein is not lower (higher or equivalent) than that of a healthy person

[0020] The marker proteins measured to determine the likelihood of developing severe illness are: COPB2, KRAS, PRKCB, RHOC, CD147, CAPN2, ECM1, FGG, MFAP4, ADI1, AK1, MGAT1, CLDN3, CRP, UQCRC2, FGA, FGB, FGL1, GPX1, GSK3B, LBP, PDGFC, RAB13, RAP1B, SLC6A4, UBA7, ORM1, RNPEP, ANGPT1, APOB, B4GALT1, BHMT, CPN1, GNAZ, ICAM2, SELL, MAN1A1, SERPINA5, PACSIN2, NCF1B, TMEM59, YWHAB, ABAT, ADH1B, ASL, ASS1, CDH2, CAB39, CPS1, CD226, COL6A3, CUL4A. The RNA to be measured can be one or more selected from DSC1, ENTPD5, EIF4A1, FN1, PGC, RHEB, GNAI2, GNB1, GNA13, ITGA2B, ITGB1, ILK, F11R, LTA4H, LIMS1, NAV2, FAM129B, NNMT, NID1, PPIA, PLA1A, PPBP, PECAM1, GP1BB, PCSK9, MENT, SERPINA10, F2RL3, LOX, SFTPB, RAB5B, RALB, REEP6, RETN, AGXT, CCT2, THBD, ISG15, and ZYX, and preferably one or more selected from COPB2, KRAS, PRKCB, RHOC, CD147, CAPN2, ECM1, FGG, and MFAP4. For example, the RNA to be measured can be two or more, three or more, four or more, or five or more.

[0021] When using two or more types in combination, preferably, the following combinations (a) or (b) can be used: (a) A combination of two or more types selected from PRKCB, RHOC, COPB2, and KRAS (b) Two or more combinations selected from ECM1, CAPN2, FGG, and CD147.

[0022] In this specification, "control protein level" refers to the marker protein level in the comparison group. Here, "comparison group" refers to the marker protein level in blood exosomes of healthy individuals or COVID-19 patients who did not develop severe symptoms (maintained mild symptoms or remained asymptomatic) at the time of infection detection (early stage of infection) or hospitalization. The "control protein level" can be obtained by simultaneously measuring the marker protein level in exosomes derived from the blood of the test subject, along with exosome samples derived from the blood of healthy individuals or exosome samples derived from the blood of COVID-19 patients who did not develop severe symptoms (maintained mild symptoms or remained asymptomatic) at the time of infection detection or hospitalization. Alternatively, information on the marker protein level measured for such a negative comparison group can be obtained in advance, and that level or a value set considering that level can be used as the control protein level. Such a value may be a cutoff value set by performing ROC analysis from the results of already conducted tests. Alternatively, a sample containing the marker protein at such a pre-set level can be used as a control sample. preparation Furthermore, when measuring the marker protein levels in exosomes derived from the patient's blood, control protein levels may be obtained by simultaneously measuring them.

[0023] If the method of the present invention includes a method for measuring a marker protein, the method may optionally include a step of preparing exosomes from a blood sample derived from a test patient. Exosome preparation can be carried out using any known method with blood collected from a subject. For example, exosomes can be recovered from samples such as serum by ultracentrifugation (e.g., Thery C., Curr. Protoc. Cell Biol. (2006) Chapter 3: Unit 3.22.), polymer precipitation, immunoprecipitation, FACS, ultrafiltration, gel filtration, HPLC, and by adsorption onto a carrier such as beads using antibodies or lectins. Alternatively, exosomes may be recovered using a commercially available exosome isolation kit.

[0024] When using an antibody-based method, exosomes can be isolated using a carrier to which anti-CD9 antibodies and anti-CD63 antibodies are bound, utilizing the CD9 and CD63 on the surface of the exosomes. The exosome preparation step may include, for example, mixing a blood sample from the test patient with a carrier to which anti-CD9 antibodies and anti-CD63 antibodies are bound, and recovering the carrier to which the exosomes are bound. This step may also include steps such as washing the carrier to which the exosomes are bound and dissociating the exosomes from the carrier.

[0025] Of the recovery methods described above, ultracentrifugation is the most commonly used and standard method for the isolation of exosomes. The centrifugal force in ultracentrifugation may be, for example, 50,000 × g or more, 100,000 × g or more, or 150,000 × g or more, and may also be 300,000 × g or less, 250,000 × g or less, or 200,000 × g or less. The centrifugation time is not limited, but may be, for example, 30 to 120 minutes, 60 to 90 minutes, or 70 to 80 minutes. In addition, if necessary, impurities may be removed or reduced by filter filtration and / or centrifugation at a lower centrifugal force before centrifugation.

[0026] The recovery of exosomes or the confirmation of their physical properties can be carried out according to known methods, for example, by visual confirmation using an electron microscope, or by measuring the particle size and number of exosomes using NTA (Nano Tracking Analysis) technology. Alternatively, the presence of exosomes can be confirmed by checking the expression of proteins and / or genes that can serve as exosome markers.

[0027] Protein levels may be measured using the prepared exosomes as they are, or after disrupting the membrane with a surfactant such as SDS or RIPA Buffer / RIPA Lysis Buffer. When using SDS, the proteins will be denatured, but when using RIPA Buffer / RIPA Lysis Buffer, an undenatured protein sample can be prepared. Alternatively, proteins may be further extracted from the prepared exosomes and measured. Therefore, the method of the present invention may optionally include the extraction and purification of proteins from the prepared exosomes. When extracting proteins, commercially available exosome protein extraction kits (Cosmo Bio Co., Ltd.), ExoMS Surface Protein Capture Kit (System Biosciences), etc., can be used.

[0028] While the measurement of protein levels is not particularly limited as long as it is a method capable of measuring protein quantity, it generally involves using a substance that specifically binds to a marker protein. Examples of "substances that specifically bind to a marker protein" include antibodies or their antigen-binding fragments, aptamers, ligands / receptors or their binding fragments, or fusions of these with other substances. An "antigen-binding fragment" refers to a protein or peptide containing a part (partial fragment) of an antibody, which retains the antibody's action (immunoreactivity / binding ability) towards the antigen. Examples of such immunoreactive fragments include F(ab')2, Fab', Fab, Fab3, single-stranded Fv (hereinafter referred to as "scFv"), (tandem) bispecific single-stranded Fv (sc(Fv)2), single-stranded triple body, nanobody, divalent VHH, pentavalent VHH, minibody, (double-stranded) diabody, tandem diabody, bispecific tribody, bispecific bibody, dual affinity retargeting molecule (DART), tribody (or tribody), tetrabody (or [sc(Fv)2] 2 young (scFv-SA)4) ,Examples include disulfide-bonded Fv (hereinafter referred to as "dsFv"), compact IgG, heavy-chain antibodies, or polymers thereof (see Nature Biotechnology, 29(1):5-6(2011); Maneesh Jain et al., TRENDS in Biotechnology, 25(7)(2007):307-316; and Christoph stein et al., Antibodies(1):88-123(2012)). In this specification, antibodies and immunoreactive fragments may be monospecific, bispecific, trispecific, or multispecific.

[0029] Typically, protein-level measurements are performed by determining the binding level of a marker protein bound to a substance that specifically binds to the marker protein. The measured "binding level" can be the amount, number, or percentage of binding of these substances, or a numerical value representing them (for example, the measured value itself, such as the measured fluorescence intensity). In this case, a labeled substance may be used as the substance that specifically binds to the marker protein, or the marker protein itself may be labeled. Generally, a standard sample is measured simultaneously, and the binding level is determined by calculating a value based on the standard sample by creating a standard curve or calibration curve, or by standardizing the value using the standard sample level as an indicator.

[0030] Therefore, for example, the method of the present invention may include the following: (a) Contacting a substance that binds to at least one marker protein with a protein in an exosome derived from the patient's blood; (b) Determining the binding level of the marker protein in the exosome bound to the substance that binds to the marker protein; and, (c) Determine the level of the marker protein in the exosome from the measured binding level.

[0031] When an antibody or its antigen-binding fragment is used as the substance that binds to a marker protein, the measurement of binding can be based on known detection and / or measurement methods. For example, enzyme immunoassay (EIA), simplified EIA, enzyme-linked immunosorbent assay (ELISA), radioimmunoassay (RIA) law) Binding can be measured by methods such as labeled immunoassay (FIA), immunoblotting (Western blotting), immunochromatography (GOLD colloid agglutination), chromatography (ion exchange chromatography, affinity chromatography), turbidimetric assay (TIA), nephrite assay (NIA), colorimetric assay, latex agglutination assay (LIA), particle counting assay (CIA), chemiluminescence assay (CLIA, CLEIA), sedimentation assay, surface plasmon resonance assay (SPR), resonant mirror detector assay (RMD), and comparative interference assay.

[0032] Specifically, the level of the marker protein can be determined by contacting the test sample with the antibody of the present invention or its antigen-binding fragment immobilized on a solid phase, washing, adding a labeled antibody capable of binding to a marker protein, removing the unbound antibody by washing, detecting the label of the antibody or measuring the amount of labeling (e.g., the intensity of the label). Alternatively, when performed by immunochromatography, the sample can be contacted with a labeled antibody capable of binding to a first marker protein that is not immobilized, and then the mixture can be contacted with a carrier on which an antibody capable of binding to a second marker protein or its antigen-binding fragment is immobilized at a specific site. The level of the marker protein can then be determined by detecting the labeled antibody at that site or measuring the amount of labeling (e.g., the intensity of the label).

[0033] Methods of labeling include, for example, radioisotope (RI) labeling, fluorescent labeling, and enzyme labeling. Examples of radioisotopes used for RI labeling include 32P, 131I, 35S, 45Ca, 3H, and 14C. Furthermore, examples of fluorescent dyes for fluorescent labeling include DAPI, SYTOX(registered trademark) Green, SYTO(registered trademark) 9, TO-PRO(registered trademark)-3, Propidium Iodide, Alexa Fluor(registered trademark) 350, Alexa Fluor(registered trademark) 647, Oregon Green(registered trademark), Alexa Fluor(registered trademark) 405, Alexa Fluor(registered trademark) 680, Fluorescein(FITC), Alexa Fluor(registered trademark) 488, Alexa Fluor(registered trademark) 750, Cy(registered trademark) 3, Alexa Fluor(registered trademark) 532, Pacific Blue(trademark), Pacific Orange(trademark), Alexa Fluor(registered trademark) 546, Coumarin, Tetramethylrhodamine(TRITC), Alexa Fluor(registered trademark) 555, BODIPY(registered trademark) FL, Texas Red(registered trademark), Alexa Fluor(registered trademark) 568, Pacific Green(trademark), Cy(registered trademark) 5, and Alexa Fluor(registered trademark) 594. Enzyme labels include biotin (biotin-16-dUTP, biotin-11-dUTP, etc.) and digoxigenin (DIG: a steroidal natural product). , Deoxyuridine 5'-triphosphate acid, Alkaline phosphatase and similar enzymes can be used.

[0034] Furthermore, the step of determining the marker protein level of the present invention may utilize a composition or device having a substance that binds to the marker protein described below.

[0035] In this specification, "specifically binding" of a protein means that the substance has a higher affinity for the marker protein than for other proteins with different amino acid sequences. QualityIn contrast, this means binding with substantially high affinity. Here, "substantially high affinity" means an affinity that allows the marker protein to be detected separately from proteins with other amino acid sequences. Preferably, the other amino acid sequences are different to a degree that they can be distinguished from the marker protein sequence, and may be amino acid sequences with an identity of 50% or less, 40% or less, 30% or less, 20% or less, or 10% or less. For example, substantially high affinity may mean that the amount of binding to the marker protein is 3 times or more, 4 times or more, 5 times or more, 6 times or more, 7 times or more, 8 times or more, 9 times or more, 10 times or more, 15 times or more, 20 times or more, 30 times or more, or 50 times or more.

[0036] The likelihood of developing severe illness according to the present invention may be determined using the level of marker RNA in the patient's blood, in addition to the marker protein described above. Therefore, the above method may further include measuring the level of one or more marker RNAs in a blood sample from the patient, and the likelihood of developing severe illness may be determined by combining the marker protein level and the marker RNA level. Specifically, the method includes determining that the patient is likely to develop severe illness when the measured marker RNA level is higher than the control marker RNA level. The marker RNA is selected from the group consisting of the following RNAs: miR-122-5p, SNORD33, AL732437.2, RNU2-29P, CDKN2B-AS1, AL365184.1, AL365184.1, AL365184.1, AL365184.1, AL365184.1, let-7c-5p, miR-21-5p, miR-140-3p, and C5orf66-AS2.

[0037] The method of the present invention may optionally include the extraction of RNA from the blood of a COVID-19 patient. RNA can be extracted using a commercially available RNA extraction kit (e.g., miRNeasyMini Kit, or QIAzol and miRNeasy Mini Kit (both from Qiagen, Hilden, Germany)) according to the manufacturer's protocol.

[0038] The marker RNA measured to determine the likelihood of developing severe illness can be one or more selected from miR-122-5p, SNORD33, AL732437.2, RNU2-29P, CDKN2B-AS1, AL365184.1, AL365184.1, AL365184.1, AL365184.1, let-7c-5p, miR-21-5p, miR-140-3p, and C5orf66-AS2, preferably one or more selected from miR-122-5p, SNORD33, AL732437.2, RNU2-29P, CDKN2B-AS1, and AL365184.1. For example, the RNAs being measured can be two or more, three or more, four or more, or five or more.

[0039] When using two or more types in combination, preferably, the following combinations (a) or (b) can be used: (a) Combination of CDKN2B-AS1 and AL365184.1 (b) A combination of two or more types selected from miR-122-5p, SNORD33, AL732437.2, and RNU2-29P.

[0040] According to the inventors' findings, if the levels of these RNAs in the blood of a COVID-19 patient are higher than those of a healthy, uninfected person or a COVID-19 patient who maintained a mild condition after being diagnosed with the infection, the patient is more likely to develop severe symptoms. Therefore, the determination of the likelihood of severe symptoms according to the present invention can be made by comparing the marker RNA levels in a blood sample of a subject who is a COVID-19 patient with the RNA levels of a control. If the marker RNA levels in the blood sample from the subject are higher than the marker RNA levels of the control, the patient is judged to have a high likelihood of developing severe symptoms. Conversely, if the marker RNA levels in the sample from the subject are not higher than (i.e., equivalent to or lower than) the marker RNA levels of the control, the patient is judged to have a low likelihood of developing severe symptoms or to have a high likelihood of maintaining a mild condition.

[0041] In this specification, "control marker RNA level" refers to the marker RNA level in the negative comparison sample. Here, "negative comparison sample" refers to the marker RNA level in blood samples of healthy individuals or COVID-19 patients who did not develop severe symptoms (maintained mild symptoms or remained asymptomatic) at the time of infection detection (early stage of infection) or hospitalization. The "control marker RNA level" can be obtained by simultaneously measuring the marker RNA level in the blood sample of the test subject with a blood sample from a healthy individual or a blood sample from a COVID-19 patient who did not develop severe symptoms (maintained mild symptoms or remained asymptomatic) at the time of infection detection or hospitalization, using these as controls. Alternatively, information on the marker RNA level measured for such negative comparison samples can be obtained in advance, and these levels or a value set considering these levels can be used as the control marker RNA level. For example, such a value may be a cutoff value set by performing ROC analysis from the results of already conducted tests. Alternatively, a sample containing a marker RNA level set in advance may be used as a control sample. preparationThe control marker RNA level can be obtained by simultaneously measuring the marker RNA level in the blood sample of the test patient.

[0042] RNA level measurement is not particularly limited as long as it is a method capable of measuring RNA quantity, but is generally performed using a substance that specifically binds to a marker RNA. In one example, the "substance that specifically binds to a marker RNA" may be a nucleic acid molecule, preferably a nucleic acid molecule having a sequence complementary to the marker RNA. A nucleic acid molecule having a sequence complementary to the marker RNA can specifically bind to the marker RNA by hybridization. In this specification, "nucleic acid" includes DNA, RNA, or artificially created nucleic acids (including cross-linked nucleic acids such as PNA and Locked Nucleic Acid (2',4'-BNA)), or combinations thereof. A nucleic acid molecule that specifically binds to a marker RNA may contain at least a part of an artificially designed sequence (e.g., a sequence for labeling or tagging).

[0043] A "probe" is typically a nucleic acid molecule that has a sequence complementary to a marker RNA sequence and is used to measure binding to the marker RNA sequence. Probes are usually nucleic acid molecules of 10-30mer or 10-20mer that can specifically bind to the marker RNA. Methods for measuring the binding level between marker RNA and probe include Southern hybridization, Northern hybridization, dot hybridization, fluorescence in situ hybridization (FISH), microarrays, and ASO methods. Specifically, GeneChip... TM Methods using miRNA Array Strips (Thermo Fisher Scientific, Inc.) or Agilent miRNA microarrays (Agilent Technologies) can be used.

[0044] RNA level measurement can be performed by measuring the binding level of a substance that specifically binds to a marker RNA. The "binding level" can be the amount bound, the number of bound substances, the binding ratio, or a numerical value representing these (for example, the measured value itself, such as the measured fluorescence intensity). In this case, a labeled substance that specifically binds to the marker RNA may be used, or the marker RNA may be labeled and used. Generally, a standard sample is measured simultaneously, and a standard curve or calibration curve is created based on the standard sample, and the binding level is determined by calculating a value from the measured values ​​of the sample, or by a numerical value standardized using the standard sample level as an indicator.

[0045] Methods of labeling include, for example, radioisotope (RI) labeling, fluorescent labeling, and enzyme labeling. Examples of radioisotopes used for RI labeling include 32P, 131I, 35S, 45Ca, 3H, and 14C. Furthermore, examples of fluorescent dyes for fluorescent labeling include DAPI, SYTOX(registered trademark) Green, SYTO(registered trademark) 9, TO-PRO(registered trademark)-3, Propidium Iodide, Alexa Fluor(registered trademark) 350, Alexa Fluor(registered trademark) 647, Oregon Green(registered trademark), Alexa Fluor(registered trademark) 405, Alexa Fluor(registered trademark) 680, Fluorescein(FITC), Alexa Fluor(registered trademark) 488, Alexa Fluor(registered trademark) 750, Cy(registered trademark) 3, Alexa Fluor(registered trademark) 532, Pacific Blue(trademark), Pacific Orange(trademark), Alexa Fluor(registered trademark) 546, Coumarin, Tetramethylrhodamine(TRITC), Alexa Fluor(registered trademark) 555, BODIPY(registered trademark) FL, Texas Red(registered trademark), Alexa Fluor(registered trademark) 568, Pacific Green(trademark), Cy(registered trademark) 5, and Alexa Fluor(registered trademark) 594. Suitable enzyme labels include biotin (biotin-16-dUTP, biotin-11-dUTP, etc.), digoxigenin (DIG: a steroidal natural product) (deoxyuridine 5'-triphosphate), and alkaline phosphatase.

[0046] For example, the method of the present invention may include the following (a) to (c): (a) Contacting a patient's blood sample with a nucleic acid molecule (probe) that binds to the base sequence or a portion thereof of at least one marker RNA; (b) Measuring the binding level of the marker RNA in the blood sample bound to the probe; and, (c) Determine the marker RNA level in the blood sample from the measured binding level.

[0047] In the above method, instead of a nucleic acid molecule (probe) that binds to the base sequence or a portion thereof of at least one marker RNA, a composition, kit, or device containing such nucleic acid molecule as described herein may be used.

[0048] Alternatively, RNA levels can be measured using PCR-based methods, for example, by performing qPCR, ARMS (Amplification Refractory Mutation System), RT-PCR (Reverse transcriptase-PCR), or Nested PCR using nucleic acids (primers) that specifically bind to the marker RNA. Alternatively, the Invader® method may be used. For example, a method using the GenoExplorer™ miRNA qRT-PCR Kit (GenoSensor Corporation) with appropriate primers can be used. A "primer" is typically a nucleic acid molecule of 10-30 mers (preferably 17-25 mers, 15-20 mers, etc.) used for nucleic acid amplification, and at least a portion of it has a sequence complementary to the terminal sequence of the marker RNA (preferably 7 mers or more, 8 mers or more, 9 mers or more, or 10 mers or more).

[0049] Therefore, RNA levels may be measured by the following steps: (a) Using a patient's blood sample as a template, amplifying all or part of the marker RNA in the patient's blood sample using nucleic acid molecules (primers) that can specifically bind to the marker RNA: (b) Measuring the level of amplified nucleic acid molecules; and, (c) Determining the level of marker RNA in the blood sample from the level of amplified nucleic acid molecules.

[0050] Amplification of all or part of marker RNA in a patient's blood sample can be performed by performing a PCR reaction or the like using the blood sample as a template. The level of the amplified nucleic acid can be measured by dot blot hybridization, surface plasmon resonance (SPR), PCR-RFLP, in situ RT-PCR, PCR-SSO (sequence-specific oligonucleotide), PCR-SSP, AMPFLP (Amplifiable fragment length polymorphism), MVR-PCR, and PCR-SSCP (single strand conformation polymorphism).

[0051] In this specification, "specifically binding" to RNA means that the substance binds to the nucleic acid containing the marker RNA sequence with substantially higher affinity than it binds to nucleic acids containing other base sequences. Here, "substantially high affinity" means an affinity that allows the nucleic acid containing the marker RNA sequence to be distinguished and detected from nucleic acids containing other base sequences. The other base sequences are preferably different to a degree that they can be distinguished from the marker RNA sequence, and may be base sequences having an identity of 50% or less, 40% or less, 30% or less, 20% or less, or 10% or less. For example, substantially high affinity may mean that the amount of binding to the marker RNA is 3 times or more, 4 times or more, 5 times or more, 6 times or more, 7 times or more, 8 times or more, 9 times or more, 10 times or more, 15 times or more, 20 times or more, 30 times or more, or 50 times or more.

[0052] The method of the present invention may further utilize age, smoking index, serum CRP level, and serum ALT level in addition to the marker protein and marker RNA described above. That is, the possibility of severe illness may be determined by combining age, smoking index, serum CRP level, and / or serum ALT level with the marker protein level, or by combining age, smoking index, serum CRP level, and serum ALT level with the marker protein level and the marker RNA level. Here, if the values ​​of age, smoking index, CRP, and ALT are all higher than those of healthy individuals or controls that maintain mild symptoms, it is determined that there is a high possibility of severe illness. Conversely, if the values ​​are not higher than those of healthy individuals or controls that maintain mild symptoms (they are lower or equivalent), it is determined that there is a low possibility of severe illness. The smoking index, CRP, and ALT can be determined or measured by conventional methods.

[0053] Therefore, the severity prediction of the present invention may include determining the severity of the condition by a combination selected from (a) to (d) below: (a) Two or more factors selected from the group consisting of PRKCB, RHOC, COPB2, and KRAS. (b) Two or more factors selected from the group consisting of smoking index, age, and MFAP4. (c) Two or more factors selected from the group consisting of CDKN2B-AS1, AL365184.1, ECM1, CAPN2, CRP, FGG, and CD147. (d) Two or more factors selected from the group consisting of ALT, RNU2-29P, SNORD33, miR-122-5p, and AL732437.2.

[0054] Whether the marker level in a sample derived from a subject is higher or lower than the control level used for comparison can be determined by statistical analysis. Statistical significance can be determined by statistical methods such as the T-test, F-test, or chi-squared test, for example, by comparing two or more samples and determining the confidence interval and / or p-value (Dowdy and Wearden, Statistics for Research, John Wiely & Sons, New York, 1983). The confidence intervals of the present invention may be, for example, 90%, 95%, 98%, 99%, 99.5%, 99.9%, or 99.99%. The p-value may be, for example, 0.1, 0.05, 0.025, 0.02, 0.01, 0.005, 0.001, 0.0005, 0.0002, or 0.0001.

[0055] Throughout this specification, “level” means an index relating to a quantified abundance, including, for example, concentration, quantity, or an index that can be used in place of it (preferably a numerical index). Therefore, the level may be the measured value itself, such as fluorescence intensity, or it may be a value converted to concentration. Furthermore, the level may be an absolute numerical value (abundance, abundance per unit area, etc.), or it may be a relative numerical value compared to a set comparison control as needed.

[0056] In this specification, a method for determining the likelihood of developing severe illness may be used as a method for determining or evaluating the likelihood of developing severe illness, a method for predicting whether or not a patient will develop severe illness, a method for determining, judging, or evaluating the likelihood of not developing severe illness, or a method for providing information to do so.

[0057] In this specification, "method for determining possibility" includes a method for monitoring changes in the likelihood of severe illness, unless such an interpretation is inconsistent. Therefore, in this specification, the phrase "determine possibility" may be interpreted as "monitoring changes in the likelihood of severe illness," unless such an interpretation is particularly inconsistent. Furthermore, the determination of possibility in the monitoring method may be performed continuously or intermittently.

[0058] Furthermore, the feasibility determination method of the present invention may be performed in vivo, ex vivo, or in vitro, but is preferably performed ex vivo or in vitro.

[0059] The probability assessment means predicting the course or outcome of the patient's condition, but it does not mean that the course or outcome of the condition can be determined with 100% accuracy. A high probability of severe illness means that the likelihood of severe illness occurring is increased, and it does not mean that it is more likely to occur compared to cases where severe illness does not occur. In other words, the probability assessment result means that patients with elevated marker RNA are more likely to develop severe illness compared to patients who do not exhibit such characteristics.

[0060] The method for determining the likelihood of developing severe illness according to the present invention may further include administering preventive measures to COVID-19 patients who are determined to be at high risk of developing severe illness. Examples of such preventive measures include administering vaccines, therapeutic drugs, or prophylactic drugs; providing treatment or procedures such as mechanical ventilation, ECMO, or IMPELLA; and increasing the frequency of monitoring the patient's symptoms (for example, once a day or more, twice a day or more, three times a day or more, etc.).

[0061] 2. Markers for the progression of COVID-19 severity In another aspect, the invention provides COPB2,KRAS,PRKCB,RHOC,CD147,CAPN2,ECM1,FGG,MFAP4,ADI1,AK1,MGAT1,CLDN3,CRP,UQCRC2,FGA,FGB,FGL1,GPX1,GSK3B,LBP,PDGFC,RA B13,RAP1B,SLC6A4,UBA7,ORM1,RNPEP,ANGPT1,APOB,B4GALT1,BHMT,CPN1,GNAZ,ICAM2,SELL,MAN1A1,SERPINA5,PACSIN2,NCF1B,TMEM59,YWHAB,ABAT,ADH1B ,ASL,ASS1,CDH2,CAB39,CPS1,CD226,COL6A3,CUL4A,DSC1,ENTPD5,EIF4A1,FN1,PGC,RHEB,GNAI2,GNB1,GNA13,ITGA2B,ITGB1,ILK,F11R,LTA4H,LIMS1,NAV2 ,FAM129B,NNMT,NID1,PPIA,PLA1A,PPBP,PECAM1,GP1BB,PCSK9,MENT,SERPINA10,F2RL3,LOX,SFTPB,RAB5B,RALB,REEP6,RETN,AGXT,CCT2,THBD,ISG15,and ZY X or At least one of the following will be selected too This relates to a single protein that serves as a marker for the severity of COVID-19. Preferably, it is at least one protein selected from COPB2, KRAS, PRKCB, RHOC, CD147, CAPN2, ECM1, FGG, MFAP4, ADI1, AK1, MGAT1, CLDN3, CRP, UQCRC2, FGA, FGB, FGL1, GPX1, GSK3B, LBP, PDGFC, RAB13, RAP1B, SLC6A4, and UBA7, and more preferably at least one protein selected from COPB2, KRAS, PRKCB, RHOC, CD147, CAPN2, ECM1, FGG, and MFAP4. too It is a single type of protein.

[0062] In this specification, "marker" means a biomolecule whose expression or presence level in a subject suggests a specific disease, condition, or symptom that has already occurred, or suggests a risk of developing a specific disease, condition, or symptom in the future. In other words, a marker is an indicator, or a molecule measured as an indicator, for determining or predicting a specific disease, condition, or symptom, whether current or future. Specifically, if the marker in this invention is any of COPB2, KRAS, PRKCB, RHOC, or MFAP4, a low presence level indicates a high risk of severe COVID-19, and if the marker in this invention is any of CD147, CAPN2, ECM1, or FGG, a high presence level indicates a high risk of severe COVID-19.

[0063] 3. Compositions or kits for predicting the severity of COVID-19 In another aspect, the invention provides COPB2, KRAS, PRKCB, RHOC, CD147, CAPN2, ECM1, FGG, MFAP4, ADI1, AK1, MGAT1, CLDN3, CRP, UQCRC2, FGA, FGB, FGL1, GPX1, GSK3B, LBP, PDGFC, RAB13, RAP1B, SLC6 A4,UBA7,ORM1,RNPEP,ANGPT1,APOB,B4GALT1,BHMT,CPN1,GNAZ,ICAM2,SELL,MAN1A1,SERPINA5,PACSIN2,NCF1B,TMEM59,YWHAB,ABAT,ADH1B,ASL,ASS1,CDH2,CAB39,CPS1,C This invention relates to a composition for predicting the severity of COVID-19, comprising a substance capable of binding to at least one protein selected from D226, COL6A3, CUL4A, DSC1, ENTPD5, EIF4A1, FN1, PGC, RHEB, GNAI2, GNB1, GNA13, ITGA2B, ITGB1, ILK, F11R, LTA4H, LIMS1, NAV2, FAM129B, NNMT, NID1, PPIA, PLA1A, PPBP, PECAM1, GP1BB, PCSK9, MENT, SERPINA10, F2RL3, LOX, SFTPB, RAB5B, RALB, REEP6, RETN, AGXT, CCT2, THBD, ISG15, and ZYX.Alternatively, the present invention can be applied to BA7,ORM1,RNPEP,ANGPT1,APOB,B4GALT1,BHMT,CPN1,GNAZ,ICAM2,SELL,MAN1A1,SERPINA5,PACSIN2,NCF1B,TMEM59,YWHAB,ABAT,ADH1B,ASL,ASS1,CDH2,CAB39,CPS1,CD226 The kit for predicting the severity of COVID-19 may also include a substance capable of binding to at least one protein selected from COL6A3, CUL4A, DSC1, ENTPD5, EIF4A1, FN1, PGC, RHEB, GNAI2, GNB1, GNA13, ITGA2B, ITGB1, ILK, F11R, LTA4H, LIMS1, NAV2, FAM129B, NNMT, NID1, PPIA, PLA1A, PPBP, PECAM1, GP1BB, PCSK9, MENT, SERPINA10, F2RL3, LOX, SFTPB, RAB5B, RALB, REEP6, RETN, AGXT, CCT2, THBD, ISG15, and ZYX.

[0064] The substance that can bind to the marker protein is one of the substances described as "substance that can bind to the marker protein" in "1. Method for Determining the Likelihood of Severity" above. For example, the kit may be an immunochemical measurement kit comprising a solid phase or hapten on which an antibody or its binding fragment that can bind to the marker protein is immobilized, and an antibody or its binding fragment that can bind to a labeled marker protein. Alternatively, the kit of the present invention may be an immunochemical measurement kit comprising an antibody or its binding fragment that can bind to the marker protein, and a substance that can bind to the antibody or its binding fragment (for example, an antibody or its binding fragment).

[0065] The COVID-19 severity prediction composition or kit may optionally include a buffer for stably storing a substance that can bind to a marker protein. Furthermore, the present invention's severity prediction composition or kit can be based on the known detection and / or measurement methods described above.

[0066] A composition or kit for predicting the severity of COVID-19 may be used to determine the binding level of a marker protein to a substance that specifically binds to the marker protein. In this case, the composition or kit may be used in conjunction with a system for detecting the binding to the marker protein by fluorescence or the like.

[0067] The above composition or kit may further contain substances capable of measuring one or more, two or more, three or more, four or more, or five or more of the aforementioned marker RNAs.

[0068] For example, the composition or kit may include the following combinations: (a) A combination of two or more substances selected from substances capable of measuring PRKCB, RHOC, COPB2, and KRAS. (b) A combination of two or more substances selected from those capable of measuring ECM1, CAPN2, FGG, and CD147.

[0069] The kit of the present invention may optionally include a chromogenic reagent, a reaction termination reagent, a standard antigen reagent, a sample pretreatment reagent, a blocking reagent, etc. Furthermore, if the kit of the present invention includes a labeled substance, it may further include a substrate that reacts with the label. In addition, the kit of the present invention may include packaging for storing the kit components, such as a cardboard box or plastic case, and instructions for use, etc. The kit may contain different components packaged in separate containers, or it may contain only a substance that can bind to a marker protein, with other components provided separately from the kit.

[0070] 4. Device for predicting the severity of COVID-19 The device described herein may include an array, beads, chip, immunochromatographic plate, or column to which a substance capable of binding to one or more, two or more, three or more, four or more, or five types of marker proteins is bound. The device measures the binding level of the above-mentioned marker proteins and the substance that specifically binds to the marker proteins. Ruta It can be something else.

[0071] The device described herein may further be a microarray, beads, or column to which substances (probes) capable of binding to one or more, two or more, three or more, four or more, or five types of marker RNAs are bound. The device measures the binding level between the aforementioned marker RNAs and substances (probes) that specifically bind to the marker RNAs. Ruta It can be something else.

[0072] A "microarray" refers to a device used in a method for quantifying one or more markers simultaneously. A microarray may have multiple types of probes or antibodies, or their antigen-binding fragments, bound to a single marker. A DNA microarray, for example, may have a full-length cDNA complementary to the marker RNA or a cDNA fragment that hybridizes to a portion of the marker RNA bound as a probe.

[0073] 5. Method for measuring marker proteins In another embodiment, the present invention relates to patient-derived blood exosomes containing COPB2, KRAS, PRKCB, RHOC, CD147, CAPN2, ECM1, FGG, MFAP4, ADI1, AK1, MGAT1, CLDN3, CRP, UQCRC2, FGA, FGB, FGL1, GPX1, GSK3B, LBP, PDGFC, RAB13, RAP1B, SLC6A4, UBA7, ORM1, RNPEP, ANGPT1, APOB, B4GALT1, BHMT, CPN1, GNAZ, ICAM2, SELL, MAN1A1, SERPINA5, PACSIN2, NCF1B, TMEM59, YWHAB, ABAT, ADH1B, ASL, ASS A method for determining the level of at least one protein selected from 1, CDH2, CAB39, CPS1, CD226, COL6A3, CUL4A, DSC1, ENTPD5, EIF4A1, FN1, PGC, RHEB, GNAI2, GNB1, GNA13, ITGA2B, ITGB1, ILK, F11R, LTA4H, LIMS1, NAV2, FAM129B, NNMT, NID1, PPIA, PLA1A, PPBP, PECAM1, GP1BB, PCSK9, MENT, SERPINA10, F2RL3, LOX, SFTPB, RAB5B, RALB, REEP6, RETN, AGXT, CCT2, THBD, ISG15, and ZYX, The present invention relates to a method comprising contacting a protein contained in exosomes derived from the blood of the patient with the composition or the device.

[0074] More specifically, the present invention relates to a method for measuring the level of one or more marker proteins contained in exosomes derived from patient blood, Contacting the protein contained in the exosomes derived from the patient's blood with the composition or the device, To measure the binding level of the marker protein in the exosome bound to a substance in the composition or device that can bind to the marker protein; and, The method may also include determining the level of a marker protein in the exosome from the measured binding level.

[0075] In another embodiment, the present invention provides a method for measuring the level of one or more marker proteins contained in exosomes derived from patient blood, and the level of one or more marker RNAs contained in patient blood, Contacting proteins contained in exosomes derived from the patient's blood with the composition or the device, and The present invention relates to a method comprising contacting RNA in the blood of the patient with the composition or the device.

[0076] More specifically, the present invention relates to a method for measuring the level of one or more marker proteins contained in exosomes derived from patient-derived blood, and the level of one or more marker RNAs contained in patient-derived blood, Contacting proteins contained in exosomes derived from the patient's blood with the composition or device having a substance capable of binding to a marker protein, To measure the binding level of the marker protein in the exosomes bound to a substance in the composition or device that can bind to the marker protein; and, Determining the level of marker proteins in the exosome from the measured binding level, and, Contacting RNA contained in a blood sample derived from the patient with the composition or device having a probe capable of binding to a marker RNA, To measure the binding level of marker RNA in blood bound to the probe in the composition or the device; and, The method may also include determining the level of marker RNA in the blood from the measured binding level.

[0077] Throughout this specification, the number of marker RNAs or marker proteins measured, or the number of marker RNAs or marker proteins contained in a composition or device, may be one or more, two or more, three or more, four or more, or five or more. When measuring two or more types, the likelihood of severe illness may be determined by considering all measured results together. For example, if all measured markers indicate a high likelihood of severe illness, the likelihood of severe illness may be determined to be higher than if some of the measured markers indicate a high likelihood of severe illness. Alternatively, each marker may be weighted, and the results of the marker RNA with higher importance may be given more weight to determine the likelihood of severe illness. For example, in the results of the embodiments of this application, proteins with high sensitivity and specificity (e.g., COPB2, KRAS, PRKCB, RHOC, CD147) and proteins with low P values ​​(RHOC, ECM1, FGG, MFAP4) may be used to identify patients who developed severe illness (d i Proteins that have been shown to be more beneficial in the process of scrimination can be given a higher importance rating.

[0078] Throughout this specification, there may be two or more substances that can bind to marker RNA or marker proteins used in the methods of the present invention or contained in the compositions, kits, and devices of the present invention, for a single marker RNA or a single marker protein. For example, in the methods of the present invention, two or more (e.g., three or more, four or more) different probes or two or more (e.g., three or more, four or more) that can bind to the same marker RNA may be used. Also, in the methods of the present invention, two or more (e.g., three or more, four or more) different antibodies or antigen-binding fragments thereof that can bind to the same marker protein may be used. Furthermore, the compositions, kits, and devices of the present invention may contain two or more (e.g., three or more, four or more) different probes or two or more (e.g., three or more, four or more) that can bind to the same marker RNA or two or more (e.g., three or more, four or more) primers that can bind to the same marker RNA. Furthermore, the compositions, kits, and devices of the present invention may contain two or more different antibodies or antigen-binding fragments thereof (e.g., three or more, four or more) capable of binding to the same marker protein.

[0079] Furthermore, the present invention may also be the following invention. (1) A test method used to predict whether or not symptoms of coronavirus infection (COVID-19) are likely to become severe, The procedure involves collecting bodily fluids such as blood, nasal secretions, and saliva from the subject as specimens, The steps include: recovering exosomes from collected bodily fluids, The steps include extracting proteins from the recovered exosomes, The steps include analyzing proteins extracted from exosomes to obtain protein information (information on protein type and expression level), A step of predicting whether the subject is likely to develop severe symptoms based on the acquired protein information (information on the type and expression level of the protein), A coronavirus testing method characterized by including [the following].

[0080] (2) The coronavirus testing method according to (1), characterized in that one or more of the proteins derived from the exosomes, which are predetermined to be specific types of proteins, are used as a biomarker to predict whether or not the subject is likely to develop severe symptoms.

[0081] (3) In the step of predicting the likelihood of severe illness, If the expression level of a predetermined type of protein exceeds a predetermined reference value, it is predicted that the subject may develop severe symptoms. The coronavirus testing method according to (1), characterized in that

[0082] (4) The protein includes, exosome membrane proteins, Proteins contained within exosomes The coronavirus testing method according to (1), characterized in that it includes one or both of the above.

[0083] (5) The subject of the examination said, People infected with coronavirus (SARS-CoV-2), A person suspected of being infected with coronavirus (SARS-CoV-2), or People who do not know whether or not they are infected with the coronavirus (SARS-CoV-2) The coronavirus testing method according to (1), characterized in that it is the same as the above.

[0084] (6) A system used for implementing any of the coronavirus testing methods described in (1) through (5), A means for collecting bodily fluids such as blood, nasal secretions, and saliva from a subject as a specimen, A means for preserving bodily fluids such as blood, nasal secretions, and saliva collected as specimens, An exosome recovery method for recovering exosomes from collected and preserved bodily fluids, A protein extraction method for extracting proteins from recovered exosomes, Derived from the aforementioned exosomes Ru Ta A protein analysis method for analyzing proteins and obtaining protein information (information on protein type and expression level), A means for predicting whether the subject is likely to develop severe symptoms based on the acquired protein information (information on the type and expression level of the protein), A coronavirus testing system characterized by having the following features.

[0085] (7) A user system equipped with communication functions for use by users such as those being tested for the virus, infected individuals, patients, hospitals, and government agencies, A communication-enabled analysis and judgment system used by testing centers responsible for virus testing, A virus testing method that utilizes, The user system is A means for collecting bodily fluids such as blood, nasal secretions, and saliva from a subject as a specimen, A means for preserving bodily fluids such as blood, nasal secretions, and saliva collected as specimens, A means for recovering exosomes from collected and preserved bodily fluids, Extract proteins from the recovered exosomes. means and, A protein analysis method for analyzing proteins contained in recovered exosomes to obtain protein information (information on protein type and expression level), A means of communication for communicating with the analysis and judgment system via the internet, It has, The analysis and judgment system is A means for predicting whether the subject is likely to develop severe symptoms based on protein information transmitted from the user system, A means of communication for communicating with the user system via the internet, It has, (a) The user system includes the step of recovering exosomes from bodily fluids collected from the subject, (b) The user system includes the step of extracting proteins from the recovered exosomes, (d) A user system analyzes the proteins extracted from exosomes to obtain protein information (information on the type and expression level of the protein), (f) The user system transmits the acquired protein information to the analysis and judgment system via the Internet, (g) The analysis and judgment system predicts whether the subject is likely to develop severe symptoms based on the protein information (information on the type and expression level of the protein) received from the user system, (k) The analysis and judgment system transmits information regarding the prediction result of step (g) to the user system via the Internet, A coronavirus testing method characterized by including [the following].

[0086] (8) A step of collecting bodily fluids such as blood, nasal secretions, and saliva as specimens, The steps include: recovering exosomes from collected bodily fluids, The steps include extracting proteins from the recovered exosomes, The steps include analyzing proteins extracted from exosomes to obtain protein information, The steps involve analyzing the type of coronavirus (SARS-CoV-2) based on protein information obtained from exosomes, Based on the type of coronavirus analyzed, a step is taken to determine whether or not there is a coronavirus infection, If a person is diagnosed with coronavirus infection, there is a step to determine the severity of the coronavirus infection (COVID-19) on a multi-level scale, A virus testing method characterized by including [the following].

[0087] (9)(8) A system used for carrying out the virus testing method described above, A means for collecting bodily fluids such as blood, nasal secretions, and saliva as specimens, A means for preserving bodily fluids such as blood, nasal secretions, and saliva collected as specimens, An exosome recovery method for recovering exosomes from collected and preserved bodily fluids, A protein extraction method for extracting proteins from recovered exosomes, A protein analysis method for obtaining protein information by analyzing the proteins contained in the recovered exosomes, A viral analysis method for analyzing the type of coronavirus (SARS-CoV-2) based on protein information obtained from exosomes, Based on the type of coronavirus analyzed, a first determination means for determining whether or not a person is infected with the coronavirus, If a person is diagnosed with coronavirus infection, a second diagnostic method is used to determine the severity of coronavirus infection (COVID-19) in multiple stages. A virus testing system characterized by having the following features.

[0088] (10) A user system equipped with communication functions for use by users such as those being tested for the virus, infected individuals, patients, hospitals, and government agencies, A communication-enabled analysis and judgment system used by testing centers responsible for virus testing, A virus testing method that utilizes, The user system is A means for collecting bodily fluids such as blood, nasal secretions, and saliva as specimens, A means for preserving bodily fluids such as blood, nasal secretions, and saliva collected as specimens, A means for recovering exosomes from collected and preserved bodily fluids, Extract proteins from the recovered exosomes. means and, A method for analyzing proteins contained in recovered exosomes to obtain protein information. Protein analysis means, A means of communication for communicating with the analysis and judgment system via the internet, It has, The analysis and judgment system is A virus analysis method for analyzing the type of coronavirus (SARS-CoV-2) based on protein information transmitted from the user system, Based on the type of coronavirus analyzed, a first determination means for determining whether or not a person is infected with the coronavirus, In cases where a person is diagnosed with coronavirus infection, a second diagnostic method is used to determine the severity of coronavirus infection (COVID-19) in multiple stages, A means of communication for communicating with the user system via the internet, It has, (a) The user system includes the step of recovering exosomes from bodily fluids collected from individuals tested for the virus, infected individuals, and patients, (b) The user system includes the step of extracting proteins from the recovered exosomes, (d) A user system analyzes proteins extracted from exosomes to obtain protein information, (f) The user system transmits protein information obtained from exosomes to the analysis and judgment system via the internet, (g) The analysis and determination system analyzes the type of coronavirus (SARS-CoV-2) based on the protein information received from the user system, (h) The analysis and determination system determines whether or not there is a coronavirus infection based on the type of coronavirus it has analyzed, (i) If a person is diagnosed with coronavirus infection, the step of determining the severity of coronavirus infection (COVID-19) on a multi-level scale, (j) The analysis and judgment system transmits information regarding the judgment result of step (h) to the user system via the Internet, (k) The analysis and judgment system transmits information regarding the judgment result of step (i) to the user system via the Internet, A virus testing method characterized by including [the following].

[0089] (11) A coronavirus test method that involves collecting and storing bodily fluids such as blood, nasal secretions, and saliva as specimens, extracting proteins from exosomes in these bodily fluids, and instantly analyzing the type of coronavirus (SARS-CoV-2) based on this protein information to determine whether or not a person is infected with the coronavirus, and whether, if infected, the symptoms will be mild or severe.

[0090] (12) A testing device consisting of a fluid collection device for collecting and storing bodily fluids such as blood, nasal secretions, and saliva as specimens, and a measuring device for extracting proteins from exosomes from these bodily fluids, in order to determine whether or not a person is infected with the coronavirus, and whether or not they will develop mild or severe symptoms if they are infected; and an analyzer for instantly determining the type of coronavirus based on this protein information. Consists of A device comprising an analysis device for performing the coronavirus testing method described in (11).

[0091] (13) The coronavirus testing method described in (10), characterized by a home testing method for coronavirus, in which, regardless of whether or not a patient is infected with the coronavirus, in order to determine whether the infection will be mild or severe if infected, the user transmits sample information electronically to the testing center based on a communication means such as a personal computer that has been agreed upon between the user, such as a patient, hospital, or government agency, and the testing center, and the testing center reports the results of the instantaneous analysis of the sample information electronically to the user.

[0092] Firstly, the equipment for testing and analyzing the novel coronavirus consists of testing instruments and analytical instruments. Testing equipment refers to a liquid sampling device and a measuring device. The former type of fluid collection device is used to collect and store bodily fluids such as blood as specimens. This varies depending on the type of specimen. Commercially available containers and reagents are acceptable for both types. The latter measuring device extracts proteins from exosomes in bodily fluids collected as a sample. The analysis device, based on the test results, uses deep learning to determine the cause of COVID-19 cormorant This system uses a mechanism to instantly identify the type of virus.

[0093] Secondly, the COVID-19 test involves collecting bodily fluids as a sample using a fluid collection device and then preserving those fluids. From these bodily fluids, a measuring device is used to extract proteins from exosomes. Based on the test results, an analyzer is used to instantly determine the type of COVID-19 virus according to deep learning.

[0094] Thirdly, the home testing method for COVID-19 (19) is shown in Figure 11 As stated above, the user (14) and the testing center (13) are as follows: the user (14) requests a COVID-19 test and pays the fee, while the testing center ( 13 ) agrees to undertake this inspection and to send the inspection device (8). This agreement is communicated between the two parties using means of communication such as personal computers, smartphones, and televisions (15)(16). The user (14) collects bodily fluids from the specimen (4) based on video instructions, extracts proteins from these bodily fluids using a testing device (7) (5), and then digitizes the results and transmits the test information (17). In response, the laboratory (13) analyzes the test information using an analysis device (9), digitizes the results, and reports them to the user (18).

[0095] The present invention will be described in more detail below using examples, but this will not limit the scope of the invention. References made throughout this specification are incorporated entirely by reference.

[0096] (Example 1) Patient group and clinical parameters influencing the progression of the disease Forty-two patients who tested positive for SARS-CoV-2 and visited Jikei University School of Medicine Hospital between March and May 2020 were registered (approved by the Tokyo Jikei University School of Medicine Medical Review Board (number: 32-055(10130))). Patients who tested positive for SARS-CoV-2 RNA by PCR testing of nasopharyngeal swabs were classified as COVID-19 (SARS-CoV-2 infection) patients. The severity of the patients was assessed according to the WHO 2020 Scor (Severity of Disease) scale, as shown in Table 2. ing The decision was made based on the following criteria. 31 COVID-19 patients with mild status (WHO score = 3) at the time of registration were registered, and 11 severely ill patients were excluded. All COVID-19 patients received standard treatment (non-steroidal) based on the WHO's interim guidance for COVID-19 clinical management. Among healthy donors who visited Omiya City Clinic in Saitama City for routine health checkups from March to April 2019, those with COVID-19 patient The study targeted 10 individuals of the same age (approved by the Institutional Review Board of the Institute of Medical Science, University of Tokyo (number: 28-19-0907)).

[0097] Serum samples were collected from the supernatant obtained by centrifuging blood from subjects at 4°C and 3000 rpm for 10 minutes at the time of registration, and stored at -80°C. The correlation between each marker in the obtained serum samples and the clinical course after registration was retrospectively examined. Based on the progression of the disease after registration, 31 COVID-19 patients were divided into the following two groups (Figure 1): Group 1 (mild cases) consisted of 22 patients, and Group 2 (severe cases) consisted of 9 patients. Group 1 (mild cases): Patients who maintained a mild status (WHO score ≤ 4) Group 2 (Severely Ill Patients): Patients whose status has progressed to severe (WHO score ≥ 5)

[0098] Two patients in Group 2 died due to complications from COVID-19. Table 3 shows the individual patient characteristics and the progression of symptoms after sample collection. Table 4 shows statistical values ​​for clinical parameters. No significant differences were observed between healthy individuals and COVID-19 patients in age, sex, BMI, smoking index, serum urea nitrogen (BUN), creatinine (Cr), alanine aminotransferase (ALT), history of hypertension, diabetes, dyslipidemia, and coronary heart disease (P>0.05). However, significant differences were observed in white blood cell (WBC) count and C-reactive protein (CRP) levels (P<0.05). Furthermore, between Group 1 and Group 2... C No significant differences were observed among OVID-19 patients in terms of sex, BMI, WBC count, BUN, Cr, creatinine kinase (CK), D-dimer, fibrinogen, history of hypertension, diabetes, dyslipidemia, and coronary heart disease (P>0.05). However, significant differences were found in age, smoking index, CRP levels, and ALT levels (P<0.05). Therefore, these four parameters—age, smoking index, CRP levels, and ALT levels—were associated with disease severity.

[0099] [Table 2]

[0100] [Table 3] (In the table, M in the Sex column indicates male, and F indicates female.) * "Onset of severe events (day)" indicates the number of days from the day sampling was permitted.

[0101] [Table 4]

[0102] (Example 2) Protein measurement in exosomes (EVs) (1) Isolation of exosomes (EVs) Anti-CD9 and anti-CD63 antibodies (HU Group Research Institute, Tokyo) conjugated to Dynabeads M-280 Tosylactivate (Thermo Fisher Scientific Inc., Waltham, MA, USA) were treated with chelate-based PEVIA® reagent (HU Group Research Institute) and incubated on a rotator at 4°C for 18 hours. The beads were washed three times with PBS and stored at 4°C until further analysis.

[0103] (2) Preparation of peptides The obtained exosomes (EVs) were processed using an S-Trap microspin column (AMR Inc., Tokyo, Japan) with slight modifications to the manufacturer's instructions. Specifically, the exosomes were suspended in 50 mM TEAB buffer (Honeywell Inc., Charlotte, North Carolina, USA), pH 7.5, containing 50 μL of 5% SDS (FUJIFILM Wako Pure Chemical Corporation, Osaka, Japan). After removing the beads, the amount of protein from the EVs was measured using MicroBCA. TM The protein assay was performed using a protein assay kit (Thermo Fisher Scientific Inc.). 13.8 ng Pierce for mass spectrometry. TM A digestion indicator (Thermo Fisher Scientific Inc.) was added to the dissolved samples for quality control of digestion efficiency.

[0104] (3) Proteomic analysis by LC-MS Peptides obtained from EV proteins were reconstituted in 10 μl of water containing 0.1% formic acid (FA) (Fisher Chemical, Thermo Fisher Scientific Inc.). Peptide quantification was performed using Pierce TMThe analysis was performed using a Quantitative Fluorometric Peptide Assay (Thermo Fisher Scientific Inc.). Proteomics analysis of the peptides was performed using a Q Exactive (Thermo Fisher Scientific Inc.) equipped with an UltiMate 3000 Nano LC System (Thermo Fisher Scientific Inc.). Peptide samples (1 μg) were injected using a Dreamspray interface (AMR Inc.) into an Acclaim PepMap 1000 trap column (75 μm × 2 cm, nanoViper C18 3 μm, 100 Å, Thermo Fisher Scientific Inc.) heated to 40°C in a chamber connected to a C18 reversed-phase Aurora UHPLC emitter column equipped with a nano Zero & Captive Spray Insert (75 μm × 25 cm, Ion Opticks Pty Ltd). The nanopump flow rate was set to 250 nL / min with a 302-minute gradient, and the mobile phases were A (0.1% FA in water, Fisher Chemical, Thermo Fisher Scientific Inc.) and B (0.1% FA in acetonitrile, Fisher Chemical, Thermo Fisher Scientific Inc.). The chromatography gradient was designed to increase linearly from 2%B from 0 to 8 minutes, with the gradient increasing from 2%B to 35%B from 8 to 272 minutes, from 35%B to 70%B from 272 to 282 minutes, and from 70%B to 95%B from 282 to 283 minutes, followed by an 8-minute wash and a 10-minute equilibration. Data dependency was acquired in cation mode.The mass spectrometry parameters and the parameters for the Proteome Discoverer 2.2.0.388 software (Thermo Fisher Scientific Inc.) were determined according to the method described in the previous report (Ayako Kurimoto et al., Enhanced recovery of CD9-positive extracellular vesicles from human specimens by chelating reagent, doi: https: / / doi.org / 10.1101 / 2020.06.17.155861).

[0105] (Example 3) Serum miRNA measurement The aliq of the serum sample collected in Example 1 uo Total RNA was extracted from ts (200 μL) using QIAzol and the miRNeasy Mini Kit (Qiagen, Hilden, Germany) according to the manufacturer's protocol. A library was prepared using the QIAseq miRNA Library Kit (Qiagen). preparation The libraries were quality-controlled using a Bioanalyzer 2100 or TapeStation 4200 system (Agilent Technologies, Santa Clara, CA, USA). The library pool was quantified using a Library Quantification Kit (Takara, Shiga, Japan) and sequenced using a NovaSeq6000 sequencing platform (Illumina Inc, San Diego, CA, USA). The determined sequences were preprocessed using CLC Genomics Workbench v20.0.1 and annotated to miRBase v22.1 and the Ensembl non-coding DNA database release 100.

[0106] (Example 4) Statistical Processing Clinical data between the two groups were compared using Fisher's exact test for categorical variables and Student's unpaired t-tests for continuous variables. To identify biomarker candidates from EV proteins and exRNAs, one-way analysis of variance (ANOVA) was first used to select candidates present at different levels (P < 0.05) among the three subject groups (uninfected, COVID-19 group 1, and group 2). Principal component analysis (PCA) was performed on the selected candidates using Partek Genomics Suite 7.0 (Partek, St. Louis, Missouri, USA). Next, candidates with superior discriminative power between Group 1 and Group 2 were selected based on linear discriminant analysis using leave-one outcross validation. ROC analysis was then performed using R version 3.6.3 (R Foundation for Statistics Computing, http: / / www.R-project.org), compute.es package version 0.2-2, hash package version 2.2.6.1, MASS package version 7.3-51.5, mutoss package version 0.1-12, and pROC package version 1.16.2. The optimal cutoff value for each candidate was set based on the maximum sum of sensitivity and specificity (Youden index). Predictive sensitivity, specificity, and accuracy were calculated using the corresponding cutoff value for each candidate. Kaplan-Meier analysis and Cox regression analysis using log-rank tests were performed using IBM SPSS Statistics 25 (IBM Japan, Tokyo, Japan). Correlation plots were generated using R version 3.6.3 and the corrplot package version 0.84, and unsupervised hierarchical clustering analysis was performed using Partek Genomics Suite 7.0. The limit of statistical significance for all analyses was defined as a two-tailed p-value of 0.05.

[0107] (result)

[0108] (1) LC-MS analysis of proteome profiles from extracellular viable cells (EVs) in serum samples from COVID-19 patients and non-infected controls. In clinical practice, analysis of extracellular viable cells (EVs) from liquid biopsies is attracting attention as a potential means of providing biomarkers for the diagnosis and prognosis of various diseases. However, this strategy is not yet widely used because there is no standardized method for isolating EVs from patients. After various studies on optimal EV preparation methods, it was found that immunoprecipitation (IP)-based methods targeting EV surface marker proteins enabled rapid and specific separation. Specifically, recovery of CD9+ or CD63+ positive EVs from serum samples using IP in the presence of chelating reagents improved yield and purity, and was suitable for subsequent EV proteome analysis by LC-MS (Figure 2).

[0109] LC-MS analysis was performed on 41 serum samples, and after excluding proteins absent in all samples, 1676 proteins were identified. Of these 1676 proteins, 723 were present at different levels among the three groups (P<0.05; one-way ANOVA). Unsupervised multivariate statistics based on principal component analysis (PCA) mapping were used to compare the expression patterns of these 723 EV-derived proteins among the three patient cohorts. PCA plotted the first principal component (PC1) and the second principal component (PC2) using the term frequency of all LC-MS data, showing the tendency to separate the three groups (Figure 3). The first PC1 accounted for 28.1% of the variance, and the second PC2 accounted for 14.5% of the variance, indicating a tendency for the two principal components to separate the three groups with a contribution of 54.5%.

[0110] These observations indicated that the EV proteome profiles of serum from COVID-19 patients deviated considerably from those of non-infected subjects (healthy individuals). Despite slight overlap or variance, PCA score plots revealed clear differences in EV proteomics between Group 1 and Group 2. To further identify the differences in EV-derived proteins from patients in Group 1 and Group 2, cross-validation scores (Urabe F et al., Clin Cancer Res. 2019;25(10):3016-25.) indicating the robustness of their discriminative performance were calculated based on Fisher linear discriminant analysis of each selected protein. From the candidate proteins, 91 proteins with cross-validation scores exceeding 0.75 were listed (Tables 5-7).

[0111] [Table 5]

[0112] [Table 6]

[0113] [Table 7]

[0114] Furthermore, Figure 4 shows the abundance of the top nine proteins with cross-validation scores exceeding 0.85 across the three patient groups. Of these proteins, four—COPI coat complex subunit beta-2 (COPB2), KRAS proto-oncogene (KRAS), protein kinase C beta (PRKCB), and ras homolog family member C (RHOC)—were more abundant in Group 1 than in Group 2 or the non-infected control (P trend > 0.05). Additionally, CD147, calpain 2 (CAPN2), extracellular matrix protein 1 (ECM1), and fibrinogen gamma chain (FGG) were significantly more abundant in Group 2 than in the non-infected control or Group 1 (P trend < 0.05). Only microfibril-associated protein 4 (MFAP4) was less abundant in Group 1 than in the non-infected control or Group 2 (P trend > 0.05).

[0115] (2) Predictive values ​​of nine EV proteins for COVID-19 severity ROC analysis was performed by combining Group 1 and Group 2 to conduct robust tests for sensitivity, specificity, and AUC for a set of nine predictive markers (Figure 5). The AUC values ​​for the four markers that showed increased abundance in Group 1—COPB2, KRAS, PRKCM, and RHOC—were 1.00 (95% CI: 1.00-1.00), 0.93 (95% CI: 0.85-1.00), 0.93 (95% CI: 0.83-1.00), and 0.96 (95% CI: 0.89-1.00), respectively. The AUC values ​​for the other five markers, CD147, CAPN2, ECM1, FGG, and MFAP4, were 0.73 (95% CI: 0.48-0.98), 0.84 (95% CI: 0.67-1.00), 0.82 (95% CI: 0.60-1.00), 0.87 (95% CI: 0.73-1.00), and 0.75 (95% CI: 0.52-0.97), respectively.

[0116] These results suggest that this series of markers examined at the time of patient admission can significantly separate patients who develop mild and severe COVID-19. Additional ROC curves were generated to identify the optimal cut-off values for nine marker proteins according to the Youden index.

[0117] For further analysis, the cut-off values were used to divide COVID-19 patients into low and high groups and the onset of severe COVID-19-related events was compared. Kaplan-Meier curves showing the time to the onset of severe events were created for each of the nine proteins (Figure 6). The non-severity period (progression-free period) of the group with low amounts of COPB2, KRAS, PRKCM, and RHOC, which are proteins with increased abundance in Group 1, was significantly shorter than that of the group with high amounts of the same proteins (P = 9.8×10 -10 ; P = 1.0×10 -5 ; P = 5.1×10 -7 ; P = 4.2×10 -8 ), respectively). Conversely, the non-severity period (progression-free period) of the group with high amounts of CD147, CAPN2, ECM1, FGG, and MFAP4, which are proteins with increased abundance in Group 2, was significantly shorter than that of the group with low amounts of the same proteins (P = 7.0×10 -5 ; P = 0.00060; P = 2.2×10 -5 ; P = 1.9×10 -7 ; P = 2.0×10 -6 ), respectively). These results indicate that this series of markers can be useful for predicting the likelihood that a patient will experience severe COVID-19-related events.

[0118] (3) NGS measurement of ExRNA profiles in serum samples from COVID-19 patients and non-infected controls Circulating exRNAs may function as biomarkers for a wide range of diseases. ExRNAs consist of a diverse population of RNA subpopulations that are protected from degradation by incorporation into extracellular viable cells (EVs) and binding to lipids and proteins. ExRNA profiles in blood samples are dynamic and include mRNA, miRNA, piRNA, and lncRNA (Murillo OD et al., Cell. (2019) 177(2):463-77 e15). In this example, next-generation sequencing (NGS) was used to analyze exRNAs present in patient serum samples (Figure 7).

[0119] NGS analysis identified 408 transcripts from 41 serum samples. However, transcripts with fewer than 50 reads in all samples were excluded. Of these exRNAs, 43 transcripts were differentially expressed among the three groups (P<0.05; one-way ANOVA). Unsupervised multivariate statistics were performed based on PCA mapping to identify the expression patterns of these 43 transcripts among the three groups. PCA plots from NGS data revealed a tendency to separate the three groups (Figure 8). This separation could be explained by the first principal components (PS1), which accounted for 28.1% of the variance. The second principal components (PS2) accounted for 14.5% of the variance. These observations indicated that the serum exRNA profiles of COVID-19 patients deviated considerably from those of non-infected donors. Furthermore, despite some overlap or variance, the PCA plots were able to detect clear differences in exRNA profiles between group 1 and group 2.

[0120] To distinguish between Group 1 and Group 2, the cross-validation score for each selected transcript was calculated based on Fisher linear discriminant analysis. From the candidate transcripts, 14 transcripts with a cross-validation score greater than 0.75 were selected (Table 8).

[0121] [Table 8]

[0122] (4) Correlation between markers selected to predict disease severity Next, hazard ratios (HRs) for exRNA markers and EV protein markers were calculated using univariate Cox regression analysis. In particular, the HR for low COBP2 could not be statistically calculated using an optimal cutoff value, suggesting that EVCOPB2 had the highest predictive value among the two sets of markers. High age (HR 28.1; 95%CI 3.4-231.9; P=0.0019), high CRP (HR 8.4; 95%CI 1-67.5; P=0.045), low PRKCB (HR 32.1; 95%CI 3.9-261.9; P=0.0012), low RHOC (HR 23.6; 95%CI 4.7-118; P=0.0001). 2), high CD147 (HR10.7; 95%CI2.5-45.1; P=0.0013), high CAPN2 (HR15.5; 95%CI1.9-125.9; P=0.010), high ECM1 (HR11.6; 95%CI2.8-48.4; P=0.00079), high FGG (HR21.4; 95%CI4.2-110 .4; P=0.00025), high MFAP4 (HR12.7; 95%CI3.3-48.6; P=0.00022), high miR-122-5p (HR10. 5;95%CI2.7-40.4;P=0.00063), high AL732437.2(HR9.9;95%CI1.2-79.9;P=0.031),high RN U2-29P (HR 10.4; 95%CI 2.6-40.8; P=0.00081), high CDKN2B-AS1 (HR 14.4; 95%CI 3.4-61.3; P=0.00031), and high AL365184.1 (HR 14.2; 95%CI 1.8-114.4; P=0.013) were statistically significant (Table 9).

[0123] [Table 9]

[0124] To investigate the potential relationships between the selected markers, Spearman's correlation coefficient was calculated based on the marker level. Correlation plots were created to visualize the correlation coefficients of the 19 markers (Figure 9). This revealed four hierarchical clusters of markers that shared strong positive correlations within their respective groups. Each marker appeared to fit into one of four clearly defined clusters (i.e., clusters 1, 2, 3, and 4). In particular, cluster 1 (PRKCB, RHOC, COPB2, and KRAS) was negatively correlated with the other clusters, while clusters 2, 3, and 4 were substantially strongly positively correlated with each other.

[0125] All four EV proteins in Cluster 1 showed significantly higher levels in Group 1 patients than in Group 2 COVID-19 patients. MFAP4 levels were not significantly correlated with smoking or age. Cluster 3, on the other hand, included ECM1, CDKN2B.AS1, AL365184.1, CAPN2, CRP, FGG, and CD147. Levels of one exRNA (CDKN2B.AS1 in Cluster 3) and four proteins associated with extracellular matrix formation (MFPA4 in Cluster 2 and ECM1, CAPN2, and CD147 in Cluster 3) correlated with FGG levels, which play a crucial role in coagulation (P<0.05) (Figure 4). Levels of markers in Cluster 3 correlated with age, which is associated with vascular endothelial dysfunction and coagulation (Donato AJ et al., Circ Res. 2018;123(7):825-48). The majority of the data suggested that Clusters 2 and 3 represent groups of coagulation-related markers. The components of cluster 4, ALT, RNU2-29P, SNORD33, miR-122-5p, and AL732437.2, may reflect phenomena at least partially related to liver damage. Levels of ALT, a representative transaminase primarily associated with liver dysfunction, correlated with levels of these three exRNA species (P<0.05).

[0126] The results above demonstrate that the EV protein and exRNA profiles in patients' serum clearly reflect specific host responses to SARS-CoV-2 infection and disease progression. [Explanation of Symbols]

[0127] 13. Testing Station 14. User 15.Central Computer 16.Individual computer 17. Sending Information 18. Reporting Information 19. Home testing methods for COVID-19

Claims

1. A method for indicating the likelihood of severe illness in COVID-19 patients, To measure the level of one or more marker proteins present in exosomes in the blood of the patient, and By comparing the measured marker protein levels with control protein levels, the likelihood of the patient developing severe symptoms was indicated. Here, the marker protein is one or more proteins selected from the following group: COPB2, KRAS, PRKCB, RHOC, CD147, CAPN2, ECM1, FGG, MFAP4, ADI1, AK1, MGAT1, CLDN3, CRP, UQCRC2, FGA, FGB, FGL1, GPX1, GSK3B, LBP, PDGFC, RAB13, RAP1B, SLC6A4, UBA7, ORM1, RNPEP, ANGPT1, APOB, B4GALT1, BHMT, CPN1, GNAZ, ICAM2, SELL, MAN1A1, SERPINA5, PACSIN2, NCF1B, TMEM59, YW HAB, ABAT, ADH1B, ASL, ASS1, CDH2, CAB39, CPS1, CD226, COL6A3, CUL4A, DSC1, ENTPD5 , EIF4A1, FN1, PGC, RHEB, GNAI2, GNB1, GNA13, ITGA2B, ITGB1, ILK, F11R, LTA4H, LIMS 1, NAV2, FAM129B, NNMT, NID1, PPIA, PLA1A, PPBP, PECAM1, GP1BB, PCSK9, MENT, SERPI NA10, F2RL3, LOX, SFTPB, RAB5B, RALB, REEP6, RETN, AGXT, CCT2, THBD, ISG15, and ZYX.

2. The method according to claim 1, wherein the marker protein is one or more proteins selected from the following group: COPB2, KRAS, PRKCB, RHOC, CD147, CAPN2, ECM1, FGG, and MFAP4. Here, if the measured protein is COPB2 or KRAS, it is indicated that when the level of this protein is higher than that of a healthy person, there is a high probability that the patient will not develop severe symptoms. If the measured protein is PRKCB or RHOC, a lower level of this protein compared to that of a healthy person or a patient with mild symptoms indicates a higher likelihood of the patient developing severe symptoms. If the measured protein is one of CD147, CAPN2, ECM1, or FGG, a higher level of this protein compared to that of a healthy person or a patient with mild symptoms indicates a higher likelihood of the patient developing severe symptoms, and If the measured protein is MFAP4, a lower level of this protein compared to that of a healthy individual indicates a lower likelihood of the patient developing severe symptoms.

3. The method according to claim 1 or claim 2, wherein the control protein level is the level of the marker protein in a blood sample obtained in the early stages of infection from a healthy person or a COVID-19 patient who did not develop severe symptoms.

4. The method according to any one of claims 1 to 3, characterized by using two or more types in combination.

5. The method according to claim 4, wherein the two or more combinations are the combinations (a) or (b) below. (a) A combination of two or more types selected from PRKCB, RHOC, COPB2, and KRAS (b) A combination of two or more types selected from ECM1, CAPN2, FGG, and CD147.

6. Furthermore, this includes measuring the level of one or more marker RNAs in the blood of the patient, The combination of the aforementioned marker protein level and the marker RNA level suggests the possibility of severe illness. Here, the marker RNA is one or more RNAs selected from the following group: miR-122-5p, SNORD33, AL732437.2, RNU2-29P, CDKN2B-AS1, AL365184.1, AL365184.1, AL365184.1, AL365184.1, let-7c-5p, miR-21-5p, miR-140-3p, and C5orf66-AS2, and The method according to any one of claims 1 to 5, wherein the patient is likely to develop severe symptoms when the measured RNA level is higher than the control RNA level.

7. The method according to claim 6, wherein the marker RNA is selected from the group consisting of the following RNAs: miR-122-5p, SNORD33, AL732437.2, RNU2-29P, CDKN2B-AS1, and AL365184.

1.

8. Furthermore, the method according to any one of claims 1 to 7, comprising determining or measuring one or more selected from the patient's age, smoking index, blood CRP level, and blood ALT level, characterized in that the age, smoking index, blood CRP level, and / or blood ALT level, in combination with the marker protein level, or in combination with the marker protein level and the marker RNA level, indicate the possibility of severe illness. Here, higher values ​​for age, smoking index, CRP, and ALT compared to healthy individuals or controls with mild symptoms indicate a higher likelihood of developing severe symptoms.

9. This includes showing severe symptoms in combination selected from (a) to (d) below. The method according to claim 8. (a) Two or more factors selected from the group consisting of PRKCB, RHOC, COPB2, and KRAS (b) Two or more factors selected from the group consisting of smoking index, age, and MFAP4; (c) Two or more factors selected from the group consisting of CDKN2B-AS1, AL365184.1, ECM1, CAPN2, CRP, FGG, and CD147. (d) Two or more factors selected from the group consisting of ALT, RNU2-29P, SNORD33, miR-122-5p, and AL732437.

2.

10. A method for determining the likelihood of a COVID-19 patient developing severe symptoms, To measure the level of one or more marker proteins present in exosomes in the blood of the patient, and A method for determining the likelihood of a patient developing severe symptoms by comparing the measured level of a marker protein with the level of a control protein, COPB2, KRAS, PRKCB, RHOC, CD147, CAPN2, ECM1, FGG, MFAP4, ADI1, AK1, MGAT1, CLDN 3, UQCRC2, FGA, FGB, FGL1, GPX1, GSK3B, LBP, PDGFC, RAB13, RAP1B, SLC6A4, UBA7, OR M1, RNPEP, ANGPT1, APOB, B4GALT1, BHMT, CPN1, GNAZ, ICAM2, SELL, MAN1A1, SERPINA 5, PACSIN2, NCF1B, TMEM59, YWHAB, ABAT, ADH1B, ASL, ASS1, CDH2, CAB39, CPS1, CD22 6. A COVID-19 severity marker, which is at least one protein selected from COL6A3, CUL4A, DSC1, ENTPD5, EIF4A1, FN1, PGC, RHEB, GNAI2, GNB1, GNA13, ITGA2B, ITGB1, ILK, F11R, LTA4H, LIMS1, NAV2, FAM129B, NNMT, NID1, PPIA, PLA1A, PPBP, PECAM1, GP1BB, PCSK9, MENT, SERPINA10, F2RL3, LOX, SFTPB, RAB5B, RALB, REEP6, RETN, AGXT, CCT2, THBD, ISG15, and ZYX.

11. The marker according to claim 10, which is at least one protein selected from COPB2, KRAS, PRKCB, RHOC, CD147, CAPN2, ECM1, FGG, and MFAP4.

12. A method for determining the likelihood of a COVID-19 patient developing severe symptoms, To measure the level of one or more marker proteins present in exosomes in the blood of the patient, and A method for determining the likelihood of a patient developing severe symptoms by comparing the measured level of a marker protein with the level of a control protein, COPB2, KRAS, PRKCB, RHOC, CD147, CAPN2, ECM1, FGG, MFAP4, ADI1, AK1, MGAT1, CLDN3, U QCRC2, FGA, FGB, FGL1, GPX1, GSK3B, LBP, PDGFC, RAB13, RAP1B, SLC6A4, UBA7, ORM1, RNP EP, ANGPT1, APOB, B4GALT1, BHMT, CPN1, GNAZ, ICAM2, SELL, MAN1A1, SERPINA5, PACSIN 2, NCF1B, TMEM59, YWHAB, ABAT, ADH1B, ASL, ASS1, CDH2, CAB39, CPS1, CD226, COL6A3, CU A composition for predicting the severity of COVID-19, comprising one or more substances capable of binding to at least one protein selected from L4A, DSC1, ENTPD5, EIF4A1, FN1, PGC, RHEB, GNAI2, GNB1, GNA13, ITGA2B, ITGB1, ILK, F11R, LTA4H, LIMS1, NAV2, FAM129B, NNMT, NID1, PPIA, PLA1A, PPBP, PECAM1, GP1BB, PCSK9, MENT, SERPINA10, F2RL3, LOX, SFTPB, RAB5B, RALB, REEP6, RETN, AGXT, CCT2, THBD, ISG15, and ZYX.

13. COPB2, KRAS, PRKCB, RHOC, CD147, CAPN2, ECM1, A substance capable of binding to at least one protein selected from FGG and MFAP4. The composition according to claim 12, comprising one or more of the above.

14. The composition according to claim 13, comprising two or more substances capable of binding to the protein.

15. The composition according to claim 14, comprising the following combination of (a) or (b): (a) Two or more combinations selected from substances that can bind to PRKCB, substances that can bind to RHOC, substances that can bind to COPB2, and substances that can bind to KRAS. (b) A combination of two or more substances selected from substances that can bind to ECM1, substances that can bind to CAPN2, substances that can bind to FGG, and substances that can bind to CD147.

16. The composition according to any one of claims 12 to 15, wherein the substance is an antibody or an antigen-binding fragment thereof.

17. A COVID-19 severity prediction kit or COVID-19 severity prediction device comprising the composition described in any one of claims 12 to 16.

18. COPB2, KRAS, PRKCB, RHOC, CD147, CAPN2, ECM1, FGG, MFAP4, ADI1, AK1, MGAT1, CLDN3, CRP, UQCRC2, FGA, FGB, FGL1, GPX1, GSK3B, LBP, PDGFC, RAB13, RAP1 B, SLC6A4, UBA7, ORM1, RNPEP, ANGPT1, APOB, B4GALT1, BHMT, CPN1, GNAZ, ICAM2, SELL, MAN1A1, SERPINA5, PACSIN2, NCF1B, TMEM59, YWHAB, ABAT, ADH1B, ASL, ASS1, CDH2, CAB 39. A method for measuring the level of at least one protein selected from CPS1, CD226, COL6A3, CUL4A, DSC1, ENTPD5, EIF4A1, FN1, PGC, RHEB, GNAI2, GNB1, GNA13, ITGA2B, ITGB1, ILK, F11R, LTA4H, LIMS1, NAV2, FAM129B, NNMT, NID1, PPIA, PLA1A, PPBP, PECAM1, GP1BB, PCSK9, MENT, SERPINA10, F2RL3, LOX, SFTPB, RAB5B, RALB, REEP6, RETN, AGXT, CCT2, THBD, ISG15, and ZYX, A method comprising contacting a blood sample derived from the patient with the composition described in any one of claims 12 to 17.

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