Method for predicting covid-19 severity and monitoring progress

Abstract

The present invention relates to a biomarker for predicting or diagnosing the severity of COVID-19 and a use thereof, a method for screening a candidate material for preventing, treating, or alleviating COVID-19, a biomarker for predicting or monitoring the progress of COVID-19, and a use thereof. The biomarker provided in the present invention can accurately determine the severity of COVID -19, and can clearly determine the condition of a lesion and the improvement of the patient after treatment.

Description

Methods for Predicting COVID-19 Severity and Monitoring Progress

[0001] The present invention relates to a method for providing information for predicting or diagnosing the severity of COVID-19, a biomarker composition for predicting or diagnosing the severity of COVID-19, a composition for predicting or diagnosing the severity of COVID-19, a method for screening candidate substances for preventing, treating, or improving COVID-19, a method for predicting or monitoring the course of COVID-19, a biomarker composition for predicting or monitoring the course of COVID-19, and a composition for predicting or monitoring the course of COVID-19.

[0002] The current coronavirus disease (COVID-19) is caused by infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Clinical manifestations of SARS-CoV-2 infection range from asymptomatic infection to severe or critical COVID-19, with acute respiratory distress syndrome and multi-organ failure.

[0003] Children and healthy young adults tend to be asymptomatic or have mild symptoms when infected with SARS-CoV-2. However, SARS-CoV-2 infection can be severe or fatal in the elderly and those with underlying chronic diseases. Since the emergence of COVID-19, research has been conducted to understand the pathophysiological mechanisms of severe or critical COVID-19, and hyper-inflammatory responses have been implicated in the progression of severe or critical COVID-19.

[0004] Although the mechanisms underlying the progression of COVID-19 are being studied, the signals and molecules involved in the healing or alleviation of COVID-19 have not been clearly identified. Therefore, if anti-inflammatory mechanisms in COVID-19 are identified, it is expected that treatments for patients with mild, severe, or critical COVID-19 can be discovered and established.

[0005] An object of the present invention is to provide a method for providing information for predicting or diagnosing the severity of COVID-19.

[0006] Another object of the present invention is to provide a biomarker composition for predicting or diagnosing the severity of COVID-19.

[0007] Another object of the present invention is to provide a composition for predicting or diagnosing the severity of COVID-19.

[0008] Another object of the present invention is to provide a method for screening candidate substances for preventing, treating, or improving COVID-19.

[0009] Another object of the present invention is to provide a method for predicting or monitoring the progress of COVID-19.

[0010] Another object of the present invention is to provide a biomarker composition for predicting or monitoring the course of COVID-19.

[0011] Another object of the present invention is to provide a composition for predicting or monitoring the progress of COVID-19.

[0012] The present invention provides a method for providing information for predicting or diagnosing COVID-19 severity, comprising the step of measuring the expression or activity level of one or more genes selected from the group consisting of NEAT1, MALAT1, and combinations thereof, or a gene encoding the same.

[0013] In addition, the present invention provides a biomarker composition for predicting or diagnosing COVID-19 severity, comprising one or more genes selected from the group consisting of NEAT1, MALAT1, and combinations thereof, or a gene encoding the same.

[0014] In addition, the present invention provides a composition for predicting or diagnosing COVID-19 severity, comprising a preparation for measuring the expression or activity level of one or more genes selected from the group consisting of NEAT1, MALAT1, and combinations thereof, or a gene encoding the same.

[0015] In addition, the present invention provides a method for screening a candidate substance for preventing, treating, or improving COVID-19, comprising the step of measuring the expression or activity level of one or more selected from the group consisting of HBEGF, VEGFA, CXCL1, CXCL2, CXCL3, and combinations thereof, or a gene encoding the same.

[0016] In addition, the present invention provides a method for predicting or monitoring the course of COVID-19, comprising the step of measuring the expression or activity level of one or more genes selected from the group consisting of HBEGF, VEGFA, CXCL1, CXCL2, CXCL3, PDGFC, CCL4, CCL7, IL1A, and combinations thereof, or a gene encoding the same, in a sample.

[0017] In addition, the present invention provides a biomarker composition for predicting or monitoring the course of COVID-19, comprising one or more genes selected from the group consisting of HBEGF, VEGFA, CXCL1, CXCL2, CXCL3, PDGFC, CCL4, CCL7, IL1A, and combinations thereof, or a gene encoding the same.

[0018] In addition, the present invention provides a composition for predicting or monitoring the course of COVID-19, comprising an agent for measuring the expression or activity level of one or more genes selected from the group consisting of HBEGF, VEGFA, CXCL1, CXCL2, CXCL3, PDGFC, CCL4, CCL7, IL1A, and combinations thereof, or a gene encoding the same.

[0019] The biomarker provided by the present invention can accurately predict and determine the severity of COVID-19, regardless of the time of infection and diagnosis or the patient's personal condition, and can provide information for preferred measures and treatments accordingly. Furthermore, it can rapidly and accurately monitor whether a COVID-19 patient's condition worsens or improves during the disease progression, providing information for more appropriate, personalized treatment.

[0020] Figure 1 shows the process of producing a precise single-cell transcriptome database for immune cells in the blood of COVID-19 patients. It shows the specific experimental design and process for blood cell isolation and temporary storage, and precise single-cell transcriptome analysis for 244,722 immune cells in the blood of 3 cohorts of COVID-19 patients, 58 individuals (including 12 healthy individuals), 171 samples, and 3 cohorts of COVID-19 patients, as well as the equipment and techniques used, and the flowchart thereof.

[0021] Figure 2 shows the expression of MALAT1 and NEAT1 according to the severity of COVID-19 patients and the change in the frequency of cells expressing them. A. Frequency distribution of myeloid cell subtypes (green) that specifically express MALAT1 and NEAT1 among immune cells in the blood (P, statistical significance; x-axis, disease severity; y-axis, frequency of each cell), B. mRNA expression levels of MALAT1 and NEAT1 according to severity (x-axis, expression levels of the corresponding genes; y-axis, disease severity), C. Volcano plot of genes differentially expressed in cells of A compared to myeloid cells of other subtypes (x-axis, Log2 fold change; y-axis - Log 10 statistical significance; red points, log2 fold ≥ 0.25 and statistical significance ≤ 0.05; blue points, log2 fold < 0.25 and statistical significance ≤ 0.05; gray points, statistical significance 0.05; indicated by the position letters of the MALAT1 and NEAT1 genes.

[0022] Figures 3a to 3g illustrate the molecular crosstalk between different immune cell types during the progression of COVID-19, showing the potential and global interactome between different immune cell types during periods of disease exacerbation (b, d, f) or improvement (c, e, g) (line and arrow widths represent relative log2 fold changes of ligands and receptors, respectively; arrowheads + dashed lines represent differential levels of receptors; dotted + solid lines represent differential levels of ligands; arrowheads + solid lines represent differential levels of ligands and receptors (simultaneously); red represents upregulated gene expression; cyan represents downregulated gene expression).

[0023] The present invention provides a method for providing information for predicting or diagnosing the severity of COVID-19, comprising the steps of: (a) measuring the expression or activity level of one or more genes selected from the group consisting of NEAT1, MALAT1, and combinations thereof, or encoding the same in a sample; and (b) comparing the expression or activity level of one or more genes selected from the group consisting of NEAT1, MALAT1, and combinations thereof, or encoding the same in step (a) with the expression or activity level of one or more genes selected from the group consisting of NEAT1, MALAT1, and combinations thereof, or encoding the same in a control group.

[0024] The method may include a step of determining that the sample is derived from a COVID-19 individual with mild or more symptoms, if the expression or activity level of one or more selected from the group consisting of NEAT1, MALAT1, and combinations thereof, or a gene encoding the same, in step (a) is higher than the expression or activity level of one or more selected from the group consisting of NEAT1, MALAT1, and combinations thereof, or a gene encoding the same, in the control group.

[0025] The method may include a step of determining that the sample is derived from a mild or moderate COVID-19 individual, if the expression or activity level of one or more selected from the group consisting of NEAT1, MALAT1, and combinations thereof or a gene encoding the same in step (a) is increased by 1.3 times or more and 1.4 times or less than the expression or activity level of one or more selected from the group consisting of NEAT1, MALAT1, and combinations thereof or a gene encoding the same in the control group.

[0026] The method may include a step of determining that the sample is derived from a severe or critical COVID-19 individual, if the expression or activity level of one or more selected from the group consisting of NEAT1, MALAT1, and combinations thereof or a gene encoding the same in step (a) is increased by more than 1.4 times and less than 1.6 times compared to the expression or activity level of one or more selected from the group consisting of NEAT1, MALAT1, and combinations thereof or a gene encoding the same in the control group.

[0027] The above severity distinguishes between asymptomatic, mild, moderate, severe, and critical, and may include the degree of increase or decrease in the risk of developing COVID-19, the degree of progression of the lesion, and the degree of increase or decrease in the risk of recurrence. The degree of increase or decrease in the risk of developing COVID-19 includes the degree of risk of developing COVID-19 or the possibility of developing COVID-19. The degree of progression of the lesion refers to the degree of COVID-19 developing and the progression of the lesion, from mild to severe. In addition, the degree of increase or decrease in the risk of recurrence refers to the degree of risk of recurrence or the possibility of recurrence after being judged to have recovered from COVID-19. The severity of COVID-19 may be expressed qualitatively and / or quantitatively, and the severity may be classified according to the degree.

[0028] The method for providing information for diagnosing COVID-19 severity according to the present invention may be used in place of, or in conjunction with, a COVID-19 severity diagnosis method known in the art. For example, the COVID-19 severity classification system and definitions are known to the Korea Centers for Disease Control and Prevention, the National Institutes of Health (NIH), and others. The method for providing information for diagnosing COVID-19 severity according to the present invention may replace or be used in conjunction with the following severity classification system.

[0029] Severity classification: National Institutes of Health, USA. Individuals who test positive for SARS-CoV-2 using a virologic test (i.e., a nucleic acid amplification test or an antigen test), but who have no symptoms that are consistent with COVID-19. Mild: Individuals who have any of the various signs and symptoms of COVID-19 (e.g., shortness of breath or other abnormalities on chest X-ray)., fever, cough, sore throat, malaise, headache, muscle pain, nausea, vomiting, diarrhea, loss of taste and smell) but who do not have shortness of breath, dyspnea, or abnormal chest imaging) Moderate Individuals who show evidence of lower respiratory disease during clinical assessment or imaging and who have saturation of oxygen (SpO2) ≥ 94% on room air at sea level Severe Individuals who have SpO2 < 94% on room air at sea level, a ratio of arterial partial pressure of oxygen to fraction of inspired oxygen (PaO2 / FiO2) < 300 mmHg, respiratory frequency > 30 breaths per minute, or lung parenchymal penetration > 50% breaths per minute, or lung infiltrates > 50%). Critical: Individuals who have respiratory failure, septic shock, and / or multiple organ dysfunction.

[0030] The above diagnosis refers to confirming the presence or characteristics of a pathological condition. This diagnosis encompasses not only the presence or absence of disease, but also the prognosis, the course of COVID-19, and the stage of the disease. For the purposes of the present invention, diagnosis refers to accurately distinguishing the severity of COVID-19.

[0031] The above NEAT1 and / or MALAT1 or the genes encoding them are used as biomarkers for predicting or diagnosing COVID-19 severity. Cohort 1 was designed for comparative analysis of normal, mild / moderate, and critical COVID-19, and it was found that as the severity increased, the long non-coding RNAs NEAT1 and MALAT1, and the proportion of cells expressing NEAT1 and MALAT1, significantly increased in patients. NEAT1 and MALAT1 can regulate the expression and activity of other immune factors in a short period of time in the mRNA state without producing proteins.

[0032] The above NEAT1 is Nuclear Paraspeckle Assembly Transcript 1, and may be of animal origin, for example, may be of human, cow, goat, sheep, pig, mouse, rabbit, etc., and may be a gene or something encoded therefrom, such as HGNC (30815), NCBI Entrez Gene (283131), Ensembl (ENSG00000245532), OMIM (612769) Open Targets Platform (ENSG00000245532).

[0033] The above MALAT1 is Metastasis Associated Lung Adenocarcinoma Transcript 1, and may be of animal origin, for example, may be of human, cow, goat, sheep, pig, mouse, rabbit, etc., and may be a gene such as HGNC (29665), NCBI Entrez Gene (378938), Ensembl (ENSG00000251562), OMIM (607924), etc., or may be encoded therefrom.

[0034] The amino acid sequence and base sequence of NEAT1 and / or MALAT1 are available from known genetic databases, NCBI, GeneCards You can check the sequence information on the back.

[0035] The above sample includes, but is not limited to, tissue, cells, whole blood, plasma, serum, blood, saliva, sputum, lymph, cerebrospinal fluid, interstitial fluid, mucus, or urine containing NEAT1 and / or MALAT1 of a subject (patient, etc.) for diagnosing the severity of COVID-19.

[0036] The sample may be a myeloid cell or a monocyte. The sample may be a classical monocyte (CM), a non-classical monocyte (NM), an intermediate monocyte (IM), etc.

[0037] The control group may be a sample from a non-COVID-19 patient, a healthy individual, a sample collected at a different time, or data from the same. The different time period may be during, after, or after recovery from COVID-19.

[0038] The expression or activity level of one or more selected from the group consisting of NEAT1, MALAT1 and combinations thereof or a gene encoding the same is increased compared to the expression or activity level of one or more selected from the group consisting of NEAT1, MALAT1 and combinations thereof or a gene encoding the same in the control group, which may mean that the proportion of myeloid cells expressing NEAT1, MALAT1 and combinations thereof is significantly increased compared to the control group.

[0039] The present invention provides a biomarker composition for predicting or diagnosing COVID-19 severity, comprising one or more genes selected from the group consisting of NEAT1, MALAT1, and combinations thereof, or a gene encoding the same.

[0040] The above biomarker refers to an indicator that can detect changes in the body using proteins, DNA, RNA, metabolites, etc., and can indicate the severity of COVID-19. The biomarker may be one or more selected from the group consisting of NEAT1, MALAT1, and combinations thereof, or a gene or transcript encoding the same.

[0041] The present invention provides a composition for predicting or diagnosing the severity of COVID-19, comprising a preparation for measuring the expression or activity level of one or more genes selected from the group consisting of NEAT1, MALAT1, and combinations thereof, or a gene encoding the same.

[0042] The above formulation refers to a substance that can be used for biomarker detection by specifically binding to one or more genes selected from the group consisting of NEAT1, MALAT1 and combinations thereof or encoding the same, and determining the expression or activity level of one or more genes selected from the group consisting of NEAT1, MALAT1 and combinations thereof or encoding the same.

[0043] For example, it may be, but is not limited to, an oligopeptide, monoclonal antibody, polyclonal antibody, chimeric antibody, ligand, peptide nucleic acid (PNA), aptamer, antisense oligonucleotide, primer pair or probe that specifically binds to NEAT1 and / or MALAT1 or the gene encoding them.

[0044] The above antibody is a term known in the art and refers to a specific protein molecule directed against an antigenic site. For the purposes of the present invention, the antibody refers to an antibody that specifically binds to the biomarker of the present invention, and such an antibody can be produced by cloning each gene into an expression vector according to a conventional method to obtain a protein encoded by the biomarker gene, and then producing the obtained protein by a conventional method. The form of the antibody of the present invention is not particularly limited, and a polyclonal antibody, a monoclonal antibody, or a part thereof that has antigen binding property is also included in the antibody of the present invention, and all immunoglobulin antibodies are included. Furthermore, the antibody of the present invention also includes special antibodies such as humanized antibodies. The antibody used for detecting the biomarker of the present invention includes not only a complete form having two full-length light chains and two full-length heavy chains, but also a functional fragment of the antibody molecule. A functional fragment of an antibody molecule means a fragment that has at least an antigen binding function, such as Fab, F(ab'), F(ab')2, and Fv.

[0045] The above primer refers to a short nucleic acid sequence having a short free 3' hydroxyl group, which can form base pairs with a complementary template and serves as a starting point for copying the template strand. The primer can initiate DNA synthesis in the presence of a reagent for polymerization (i.e., DNA polymerase or reverse transcriptase) and four different nucleoside triphosphates in an appropriate buffer and temperature. In the present invention, PCR amplification can be performed using sense and antisense primers of the polynucleotide of NEAT1 and / or MALAT1, and the production of a desired product can be diagnosed. PCR conditions and the lengths of the sense and antisense primers can be modified based on those known in the art.

[0046] The above probe refers to a nucleic acid fragment such as RNA or DNA, which is short, a few bases or long, several hundred bases, and can specifically bind to a gene, and is labeled so that the presence or absence of a specific nucleic acid can be confirmed. The probe can be produced in the form of an oligonucleotide probe, a single-stranded DNA probe, a double-stranded DNA probe, an RNA probe, etc. In the present invention, hybridization is performed using a probe complementary to NEAT1 and / or MALAT1, and diagnosis can be made through the presence or absence of hybridization. Selection of an appropriate probe and hybridization conditions can be modified based on those known in the art.

[0047] The primers or probes of the present invention can be chemically synthesized using the phosphoramidite solid support method or other well-known methods. These nucleic acid sequences can also be modified using many means known in the art. Non-limiting examples of such modifications include methylation, capping, substitution with one or more homologs of a natural nucleotide, and modification between nucleotides, such as modification with uncharged linkers (e.g., methyl phosphonate, phosphotriester, phosphoramidate, carbamate, etc.) or charged linkers (e.g., phosphorothioate, phosphorodithioate, etc.).

[0048] Measuring the expression or activity level of the NEAT1 and / or MALAT1 or the gene encoding it may be a process of confirming the presence, expression level, or activity level of NEAT1 and / or MALAT1 or the gene encoding it in a sample in order to predict or diagnose the severity of COVID-19. For example, a method of confirming the amount or activity level of the protein or the gene encoding it using a molecule that specifically binds to the NEAT1 and / or MALAT1 or the gene encoding it may be applied.

[0049] Methods for confirming the expression or activity level of the above NEAT1 and / or MALAT1 include, but are not limited to, Western blot, enzyme linked immunosorbent assay (ELISA), radioimmunoassay (RIA), radioimmunodiffusion, Ouchterlony immunodiffusion, rocket immunoelectrophoresis, tissue immunostaining, immunoprecipitation assay, complement fixation assay, FACS, and protein chip. In addition, methods for confirming the expression or activity level of a gene (DNA, mRNA, etc.) encoding NEAT1 and / or MALAT1 protein include, but are not limited to, reverse transcriptase polymerase reaction (RT-PCR), competitive reverse transcriptase polymerase reaction (competitive RT-PCR), real time RT-PCR, real time quantitative RT-PCR, RNase protection method, Northern blotting, and gene chips.

[0050] The present invention provides a kit for predicting or diagnosing COVID-19 severity, comprising the composition for predicting or diagnosing COVID-19 severity.

[0051] The above kit may include not only a preparation for measuring the expression or activity level of NEAT1 and / or MALAT1 or a gene encoding them, but also tools, reagents, etc. commonly used in the art for analysis. The tools or reagents may include, but are not limited to, suitable carriers, labeling substances capable of generating a detectable signal, chromophores, solubilizers, detergents, buffers, stabilizers, etc., for example. The carrier may include a soluble carrier, an insoluble carrier, etc., and the soluble carrier may be a physiologically acceptable buffer known in the art, for example, PBS, and the insoluble carrier may include, but is not limited to, polymers such as polystyrene, polyethylene, polypropylene, polyester, polyacrylonitrile, fluororesin, cross-linked dextran, polysaccharides, magnetic microparticles plated with metal on latex, paper, glass, metal, agarose, etc.

[0052] The present invention provides a method for providing information for determining whether to apply corticosteroid therapy to a COVID-19 subject, comprising the steps of: (a) measuring the expression or activity level of one or more genes selected from the group consisting of NEAT1, MALAT1, and combinations thereof, or encoding the same in a sample; and (b) comparing the expression or activity level of one or more genes selected from the group consisting of NEAT1, MALAT1, and combinations thereof, or encoding the same in step (a) with the expression or activity level of one or more genes selected from the group consisting of NEAT1, MALAT1, and combinations thereof, or encoding the same in a control group.

[0053] The method may determine that corticosteroid therapy is applied to the COVID-19 subject when, in step (a), the expression or activity level of one or more selected from the group consisting of NEAT1, MALAT1, and combinations thereof, or a gene encoding the same, is higher than the expression or activity level of one or more selected from the group consisting of NEAT1, MALAT1, and combinations thereof, or a gene encoding the same, in the control group.

[0054] The method may determine that corticosteroid therapy is applied to the COVID-19 subject when, in step (a), the expression or activity level of one or more selected from the group consisting of NEAT1, MALAT1, and combinations thereof, or a gene encoding the same, is increased by more than 1.4 times and less than 1.6 times compared to the expression or activity level of one or more selected from the group consisting of NEAT1, MALAT1, and combinations thereof, or a gene encoding the same in the control group.

[0055] The method can be determined as a patient with severe or critical COVID-19 if, in step (a), the expression or activity level of one or more selected from the group consisting of NEAT1, MALAT1 and combinations thereof or a gene encoding the same is increased by more than 1.4 times and less than 1.6 times compared to the expression or activity level of one or more selected from the group consisting of NEAT1, MALAT1 and combinations thereof or a gene encoding the same in the control group, and if corticosteroid therapy is applied to a patient with severe or critical COVID-19, COVID-19 is improved, so that an appropriate treatment according to the severity of the COVID-19 disease can be provided through the information providing method of the present invention.

[0056] The sample may be a myeloid cell or a monocyte. The sample may be a classical monocyte (CM), a non-classical monocyte (NM), an intermediate monocyte (IM), etc.

[0057] The control group may be a sample from a non-COVID-19 patient, a healthy individual, a sample collected at a different time, or data from the same. The different time period may be during, after, or after recovery from COVID-19.

[0058] The expression or activity level of one or more selected from the group consisting of NEAT1, MALAT1 and combinations thereof or a gene encoding the same is increased compared to the expression or activity level of one or more selected from the group consisting of NEAT1, MALAT1 and combinations thereof or a gene encoding the same in the control group, which may mean that the proportion of myeloid cells expressing NEAT1, MALAT1 and combinations thereof is significantly increased compared to the control group.

[0059] The above corticosteroids may include, but are not limited to, dexamethasone, hydrocortisone, methylprednisolone, prednisone, etc.

[0060] The present invention provides a method for screening a candidate substance for preventing, treating or improving COVID-19, comprising the steps of: (a) measuring the expression or activity level of one or more selected from the group consisting of HBEGF, VEGFA, CXCL1, CXCL2, CXCL3 and combinations thereof or a gene encoding the same after treating a sample with an unknown substance; and (b) comparing the expression or activity level of one or more selected from the group consisting of HBEGF, VEGFA, CXCL1, CXCL2, CXCL3 and combinations thereof or a gene encoding the same in the step (a) with the expression or activity level of one or more selected from the group consisting of HBEGF, VEGFA, CXCL1, CXCL2, CXCL3 and combinations thereof or a gene encoding the same in a control group.

[0061] The method may include a step of selecting a substance when, in step (a), the expression or activity level of one or more selected from the group consisting of HBEGF, VEGFA, CXCL1, CXCL2, CXCL3 and combinations thereof or a gene encoding the same is higher than the expression or activity level of one or more selected from the group consisting of HBEGF, VEGFA, CXCL1, CXCL2, CXCL3 and combinations thereof or a gene encoding the same in a sample that has not been treated with the unknown substance.

[0062] The above HBEGF (Proheparin-binding EGF-like growth factor), VEGFA (vascular endothelial growth factor A), CXCL1 (Growth-regulated alpha protein), CXCL2 (CXC motif chemokine 2), and CXCL3 (CXC motif chemokine 3) are different secreted proteins, which may be growth factors and / or cytokines, and may be involved in angiogenesis and / or wound healing.

[0063] The above HBEGF is a proheparin-binding EGF-like growth factor, and may be of animal origin, for example, may be of human, cow, goat, sheep, pig, mouse, rabbit, etc., and may be a gene such as HGNC (3059), NCBI Entrez Gene (1839), Ensembl (ENSG00000113070), OMIM (126150), UniProtKB (Q99075), etc., or may be encoded therefrom.

[0064] The above VEGFA is vascular endothelial growth factor A, and may be derived from animals, for example, humans, cows, goats, sheep, pigs, mice, rabbits, etc., and may be genes such as HGNC (12680), NCBI Entrez Gene (7422), Ensembl (ENSG00000112715), OMIM (192240), UniProtKB (P15692), etc., or something encoded therefrom.

[0065] The above CXCL1 is CXC Motif Chemokine Ligand 1 or Growth-regulated alpha protein, and may be derived from an animal, for example, from a human, cow, goat, sheep, pig, mouse, rabbit, etc., and may be a gene such as HGNC (4602), NCBI Entrez Gene (2919), Ensembl (ENSG00000163739), OMIM (155730), UniProtKB (P09341), etc., or a gene encoded therefrom.

[0066] The above CXCL2 is CXC Motif Chemokine Ligand 2 or CXC motif chemokine 2, and may be derived from an animal, for example, from a human, cow, goat, sheep, pig, mouse, rabbit, etc., and may be a gene such as HGNC (4603), NCBI Entrez Gene (2920), Ensembl (ENSG00000081041), OMIM (139110), UniProtKB (P19875), etc., or a gene encoded therefrom.

[0067] The above CXCL3 is CXC Motif Chemokine Ligand 3 or CXC motif chemokine 3, and may be derived from an animal, for example, from a human, cow, goat, sheep, pig, mouse, rabbit, etc., and may be a gene such as HGNC (4604), NCBI Entrez Gene (2921), Ensembl (ENSG00000163734), OMIM (139111) UniProtKB (P19876), or something encoded therefrom.

[0068] Their amino acid sequences and base sequences are available in known genetic databases, NCBI, GeneCards You can check the sequence information on the back.

[0069] The present invention may include a step of selecting a substance when, in step (a), the expression or activity level of at least one selected from the group consisting of HBEGF, VEGFA, CXCL1, CXCL2, CXCL3 and combinations thereof or a gene encoding the same is increased compared to the expression or activity level of at least one selected from the group consisting of HBEGF, VEGFA, CXCL1, CXCL2, CXCL3 and combinations thereof or a gene encoding the same before treatment with an unknown substance. Alternatively, in step (a), the present invention may include a step of selecting a substance when, after treatment with an unknown substance to a cell having expression or activity of at least one selected from the group consisting of HBEGF, VEGFA, CXCL1, CXCL2, CXCL3 and combinations thereof or a gene encoding the same, the expression or activity of at least one selected from the group consisting of HBEGF, VEGFA, CXCL1, CXCL2, CXCL3 and combinations thereof or a gene encoding the same is increased.

[0070] The above unknown substance refers to an unknown test substance used in screening to examine whether it affects the expression or activity of a protein or gene, and may be, but is not limited to, nucleotides, DNA, RNA, amino acids, aptamers, proteins, compounds, natural products, natural extracts, etc.

[0071] The above candidate substance refers to a substance that can be used for the prevention, treatment, or improvement of COVID-19.

[0072] The above prevention refers to any action that suppresses or delays the onset of COVID-19. The above improvement or treatment refers to any action that partially or completely improves or beneficially alters the symptoms of COVID-19.

[0073] The sample may be a myeloid cell or a monocyte. The sample may be a classical monocyte (CM) and / or an intermediate monocyte (IM).

[0074] The present invention provides a method for predicting or monitoring the progress of COVID-19, comprising the steps of: (a) measuring the expression or activity level of one or more genes selected from the group consisting of HBEGF, VEGFA, CXCL1, CXCL2, CXCL3, PDGFC, CCL4, CCL7, IL1A, and combinations thereof, or a gene encoding the same in a sample; and (b) comparing the expression or activity level of one or more genes selected from the group consisting of HBEGF, VEGFA, CXCL1, CXCL2, CXCL3, PDGFC, CCL4, CCL7, IL1A, and combinations thereof, or a gene encoding the same, in step (a) with the expression or activity level of one or more genes selected from the group consisting of HBEGF, VEGFA, CXCL1, CXCL2, CXCL3, PDGFC, CCL4, CCL7, IL1A, and combinations thereof, or a gene encoding the same in a control group.

[0075] The method may determine that the COVID-19 subject has improved if, in step (a), the expression or activity level of one or more selected from the group consisting of HBEGF, VEGFA, CXCL1, CXCL2, CXCL3, and combinations thereof, or a gene encoding the same, is higher than the expression or activity level of one or more selected from the group consisting of HBEGF, VEGFA, CXCL1, CXCL2, CXCL3, and combinations thereof, or a gene encoding the same, in the control group, or determine that the COVID-19 subject has worsened if the expression or activity level of one or more selected from the group consisting of PDGFC, CCL4, CCL7, IL1A, and combinations thereof, or a gene encoding the same, is higher than the expression or activity level of one or more selected from the group consisting of PDGFC, CCL4, CCL7, IL1A, and combinations thereof, or a gene encoding the same, in the control group.

[0076] The method determines that the COVID-19 subject has improved if, in step (a), the expression or activity level of HBEGF or the gene encoding it is increased by 1.9 times or more compared to the expression or activity level of HBEGF or the gene encoding it in the control group, if the expression or activity level of VEGFA or the gene encoding it is increased by 1.6 times or more compared to the expression or activity level of VEGFA or the gene encoding it in the control group, if the expression or activity level of CXCL1 or the gene encoding it is increased by 4 times or more compared to the expression or activity level of CXCL1 or the gene encoding it in the control group, if the expression or activity level of CXCL2 or the gene encoding it is increased by 3.7 times or more compared to the expression or activity level of CXCL2 or the gene encoding it in the control group, or if the expression or activity level of CXCL3 or the gene encoding it is increased by 7.2 times or more compared to the expression or activity level of CXCL3 or the gene encoding it in the control group,

[0077] If the expression or activity level of PDGFC or the gene encoding it is elevated by 2.9 times or more compared to the expression or activity level of PDGFC or the gene encoding it in the control group, if the expression or activity level of CCL4 or the gene encoding it is elevated by 3.2 times or more compared to the expression or activity level of CCL4 or the gene encoding it in the control group, if the expression or activity level of CCL7 or the gene encoding it is elevated by 5.7 times or more compared to the expression or activity level of CCL7 or the gene encoding it in the control group, or if the expression or activity level of IL1A or the gene encoding it is elevated by 6.3 times or more compared to the expression or activity level of IL1A or the gene encoding it in the control group, it may be judged that the COVID-19 individual has worsened.

[0078] The above PDGFC (Platelet Derived Growth Factor C), CCL4 (CC motif chemokine 4), CCL7 (CC motif chemokine 7), and IL1A (Interleukin-1 alpha) are different secreted proteins and may be growth factors and / or cytokines.

[0079] The above PDGFC is Platelet Derived Growth Factor C, which may be of animal origin, for example, may be of animal origin, such as human, cow, goat, sheep, pig, mouse, rabbit, etc., and may be a gene such as HGNC (8801), NCBI Entrez Gene (56034), Ensembl (ENSG00000145431), OMIM (608452), UniProtKB (Q9NRA1), etc., or may be encoded therefrom.

[0080] The above CCL4 is CC Motif Chemokine Ligand 4, and may be derived from an animal, for example, from a human, cow, goat, sheep, pig, mouse, rabbit, etc., and may be a gene such as HGNC (10630), NCBI Entrez Gene (6351), Ensembl (ENSG00000275302), OMIM (182284), UniProtKB (P13236), etc., or a gene encoded therefrom.

[0081] The above CCL7 is CC Motif Chemokine Ligand 7, and may be derived from an animal, for example, from a human, cow, goat, sheep, pig, mouse, rabbit, etc., and may be a gene such as HGNC (10634), NCBI Entrez Gene (6354), Ensembl (ENSG00000108688), OMIM (158106) UniProtKB (P80098), or something encoded therefrom.

[0082] The above IL1A is Interleukin-1 alpha, and may be derived from an animal, for example, from a human, cow, goat, sheep, pig, mouse, rabbit, etc., and may be a gene such as HGNC (5991), NCBI Entrez Gene (3552), Ensembl (ENSG00000115008), OMIM (147760), UniProtKB (P01583), etc., or may be encoded therefrom.

[0083] Their amino acid sequences and base sequences are available in known genetic databases, NCBI, GeneCards You can check the sequence information on the back.

[0084] The method may be such that when the expression or activity of one or more genes encoding the same selected from the group consisting of HBEGF, VEGFA, CXCL1, CXCL2, CXCL3, and combinations thereof is elevated over time or relatively, the subject may have COVID-19 improved, alleviated, or milder in severity. When the expression or activity of one or more genes encoding the same selected from the group consisting of PDGFC, CCL4, CCL7, IL1A, and combinations thereof is elevated over time or relatively, the subject may have COVID-19 worsened or increased in severity.

[0085] The sample may be a myeloid cell or a monocyte. The sample may be a classical monocyte (CM) and / or an intermediate monocyte (IM).

[0086] The control group may be a sample collected from a COVID-19 patient, a sample collected at a different time, or data from the same. The different time period may be before the COVID-19 patient's treatment. The different time period may be 3 days, 4 days, 5 days, 6 days, or 1 week prior to sample collection.

[0087] The present invention provides a biomarker composition for predicting or monitoring the course of COVID-19, comprising one or more genes selected from the group consisting of HBEGF, VEGFA, CXCL1, CXCL2, CXCL3, PDGFC, CCL4, CCL7, IL1A, and combinations thereof, or a gene encoding the same.

[0088] The above biomarker refers to a substance or indicator that can indicate improvement, improvement, or worsening of COVID-19 treatment in a sample. The biomarker may be one or more selected from the group consisting of HBEGF, VEGFA, CXCL1, CXCL2, CXCL3, PDGFC, CCL4, CCL7, IL1A, and combinations thereof, or a gene or transcript encoding the same.

[0089] The present invention provides a composition for predicting or monitoring the course of COVID-19, comprising an agent for measuring the expression or activity level of one or more genes selected from the group consisting of HBEGF, VEGFA, CXCL1, CXCL2, CXCL3, PDGFC, CCL4, CCL7, IL1A, and combinations thereof, or a gene encoding the same.

[0090] The present invention provides a kit for predicting or monitoring the progress of COVID-19 comprising the composition.

[0091] Hereinafter, to aid understanding of the present invention, examples will be given to describe it in detail. However, the following examples are provided to more fully explain the present invention to those of average skill in the art and are merely illustrative of the content of the present invention. Therefore, the scope of the present invention is not limited to the examples below.

[0092] <Experimental Example>

[0093] 1. COVID-19 patient recruitment and sample collection

[0094] This study involved patients diagnosed with COVID-19 at Chungnam National University Hospital, Seoul Asan Medical Center, and Samsung Medical Center, as well as healthy volunteers. SARS-CoV-2 RNA was obtained from nasopharyngeal swabs, saliva, and sputum collected from patients, and multiplex real-time reverse transcriptase PCR was performed using the Allplex™ 2019-nCoV Assay Kit (Seegene, Seoul, South Korea). Clinical features, test results, and chest radiographs were collected from the electronic medical records of each hospital. Patient severity was classified according to the National Institutes of Health criteria, and the results are as follows. The study protocol was reviewed and approved by the institutional review boards of all participating institutions, and written informed consent was obtained from all participants.

[0095] Peripheral blood mononuclear cells (PBMCs) were isolated from whole blood by standard Ficoll-Paque (GE Healthcare, Uppsala, Sweden) density gradient centrifugation, frozen in freezing medium, and stored in a liquid nitrogen tank.

[0096] 2. Production of single-cell RNA-seq libraries

[0097] Frozen PBMCs were thawed in a 37°C water bath for 1 min, transferred to a 50-mL tube, and RPMI 1640 medium containing 10% FCS and 0.1 mg / mL DNase I was added, followed by centrifugation at 300 g for 5 min. The cell pellet was then collected, and cell counts and viability were determined using trypan blue in a Luna-II automated cell counter (Logos Biosystems). Each PBMC sample was labeled with a multiplexing antibody (Single-Cell multiplex kit - Human; BD Biosciences). Six samples were pooled with equal numbers of 10,000 cells, resulting in a total of approximately 60,000 cells, and loaded onto a microwell cartridge (BD Rhapsody Express system, BD Biosciences). Single-cell whole transcriptome analysis libraries were constructed using the BD Rhapsody WTA reagent kit (BD, 633802) according to the manufacturer's instructions and sequenced on an Illumina HiSeq X using 2 x 75 bp paired end reads with 8 bp single index.

[0098] 3. Processing single-cell RNA-seq data

[0099] Sequencing raw data in FASTQ format were aligned to the human reference genome GRCh38 and processed with the BD Rhapsody WTA analysis pipeline (version 1.0) on the SevenBridges Genomics online platform (SevenBridges) using individual sample demultiplexing tag IDs for sample demultiplexing. A gene x cell expression matrix was extracted from a total of 171 samples using the RSEC_Adjusted_Molecules value. Unless otherwise specified, data normalization and further analysis were performed using Seurat (version 3.1.5). For initial quality control of the extracted gene-cell matrix, cells with parameters nFeature_RNA > 500 and nFeature_RNA < 3000 for the number of genes per cell and percentage of mitochondrial genes.mito < 25, and genes with parameter min.cell=3, were filtered. The filtered matrix was normalized using the LogNormalize method with a scale factor of 10,000. Variable genes were derived with parameters selection.method = vst, mean.function = ExpMean, dispersion.function = LogVMR, x.low.cutoff = 0.0125, x.high.cutoff = 3, and y.cutoff = 0.5. Reciprocal PCA was used to integrate all sample data sets.

[0100] Recurrent and variable features across datasets were selected using the Select Integration Features function, and then used for PCA of each dataset. The Find Integration Anchors and Integrate Data functions were then used for data integration. Statistically significant principal components were determined using the JackStraw method, and the first 11 principal components were selected for downstream analysis using UMAP non-linear dimensionality reduction and unsupervised hierarchical clustering analysis (at a resolution of 0.2).

[0101] Cell identity in each cluster was further confirmed using the R package SingleR, which uses the Find All Markers function to identify cluster-specific marker genes where more than 25% of cells express a particular gene (min.pct=0.25), compares the cluster-specific marker genes with conventional cell type-specific gene markers, and compares the transcriptome of each single cell to a reference dataset to determine cell identity.

[0102] 4. Cell-cell interactome analysis

[0103] The R package iTALK (doi: https: / / doi.org / 10.1101 / 507871) was used to analyze ligand-receptor interactions. Differentially expressed genes during disease progression or improvement were identified in each cell type using the non-parametric Wilcoxon rank sum test, and ligand-receptor pairs were detected using the FindLR function. The results were plotted using the LRPlot function with data type = DEG.

[0104] <Example>

[0105] 1. scRNA-seq analysis of PBMCs from the COVID-19 cohort

[0106] ScRNA-seq analysis was performed using PBMCs from an independent COVID-19 cohort (Fig. 1).

[0107] Cohort 1 comprised 47 PBMC samples from 28 patients and was used for comparative analysis with COVID-19 patients. Cohort 2 comprised 57 PBMC samples from 15 patients and was used for longitudinal analysis throughout the course of COVID-19, including disease exacerbations and improvements. Twelve PBMC samples from 12 healthy individuals served as a control group.

[0108] PBMCs were frozen and thawed immediately prior to single-cell library generation using the BD Rhapsody workflow. Six samples were pooled for each experiment using the BD Human Sample Multiplexing Kit. After quality control of the libraries, the transcriptome and sample tags were sequenced on the Illumina platform at a depth of 200 reads per cell and 20,000 reads per cell. The raw sequencing data were processed using the BD Rhapsody WTA pipeline and then subjected to in-depth bioinformatic analysis.

[0109] Eleven major cell types were identified: NK cells, CD8 T cells, CD4 T cells, B cells, plasma cells, classical monocytes (CMs), non-classical monocytes (NMs), dendritic cells (DCs), plasmacytoid DCs (pDCs), mitotic cells, and platelets. Cell types were identified and their relative proportions were determined in each cohort.

[0110] Next, we performed sub-clustering analysis into two groups: T / NK and myeloid. The T / NK cell group included NK cells, effector CD8 T cells, and GZMK. + CD8 T cells, effector CD4 T cells, naive CD4 T cells, and regulatory T cells were clustered. Myeloid cell populations were identified as CM, intermediate monocytes (IM), NM, and DCs. In addition, a subset of myeloid cells specifically expressing NEAT1 and MALAT1 among blood immune cells was identified and named lncM.

[0111] 2. Cohort 1: Identifying specific biomarkers based on the severity of COVID-19 patients.

[0112] Cohort 1 included 12 patients with severe COVID-19 and 31 PBMC samples, and 16 patients with mild / moderate COVID-19 and 16 PBMC samples.

[0113] We compared healthy controls and patient groups with asymptomatic, mild, moderate, severe, and critical COVID-19, and analyzed differentially expressed genes (DEGs) for each cell type.

[0114] lncMs, which express high levels of the noncoding RNAs NEAT1 and MALAT1, were clustered in the myeloid group. The proportion of lncMs was significantly increased in patients with severe or more severe disease compared to healthy controls or patients with mild disease (p=0.003). That is, as the severity of the disease increased, not only did the expression levels of both MALAT1 and NEAT1 increase, but also the frequency of cells expressing them increased. It was found that the two long non-coding RNAs (IncRNAs) MALAT1 and NEAT1 were highly correlated with the severity of the patients.

[0115] 3. Cohort 2: Longitudinal analysis of COVID-19 patients during their course.

[0116] Longitudinal data from Cohort 2 were analyzed to identify molecular changes in PBMCs during the course of COVID-19. In this cohort, four pairs were analyzed during periods of worsening disease, and 14 pairs were analyzed during periods of improvement.

[0117] To understand the mechanisms by which COVID-19 disease worsens and improves, we analyzed comprehensive cell-to-cell interactomes across multiple cell types. During disease progression, CMs and IMs exhibited the most significant and variable changes in signaling interactions (Fig. 3b). CMs and IMs interacted with multiple cell types. Conversely, during disease progression, cell-to-cell interactions were downregulated regardless of cell type (Fig. 3c). Notably, multiple interactions were observed in NMs.

[0118] Cytokines and growth factors were analyzed in depth. During disease progression, chemokines, including CCL4 and CCL7, and their receptors were upregulated in multiple cell types, with IL1A being upregulated (Figures 3d and 3f). These data suggest that inflammatory cell migration, along with activation of the IL-1 pathway, is involved in the exacerbation of COVID-19. Furthermore, among growth factors, interactions between PDGFC, associated with vascular inflammation, and its receptors were predominant (Figure 3d).

[0119] During disease progression, chemokines known to be involved in angiogenesis and their receptors, including CXCL1 / CXCL2 / CXCL3-CXCR2, were upregulated in multiple cell types during disease progression (Figures 3e and 3g). Furthermore, we observed upregulation of VEGFA and HBEGF, known to play important roles in angiogenesis and wound healing.

[0120] That is, during disease exacerbation, an inflammatory response characterized by inflammatory chemokines and IL-1 was enhanced, whereas during disease improvement, a tissue regenerative response characterized by angiogenic / wound healing growth factors and chemokines was observed.

[0121] While specific aspects of the present invention have been described in detail above, it should be apparent to those skilled in the art that such specific descriptions are merely preferred embodiments and do not limit the scope of the present invention. In other words, the actual scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for providing information for predicting or diagnosing COVID-19 severity, comprising the following steps: (a) measuring the expression or activity level of one or more genes selected from the group consisting of NEAT1, MALAT1 and combinations thereof or a gene encoding the same in a sample; and (b) a step of comparing the expression or activity level of one or more genes selected from the group consisting of NEAT1, MALAT1 and combinations thereof or encoding the same in the step (a) with the expression or activity level of one or more genes selected from the group consisting of NEAT1, MALAT1 and combinations thereof or encoding the same in the control group.

2. A biomarker composition for predicting or diagnosing the severity of COVID-19, comprising one or more genes selected from the group consisting of NEAT1, MALAT1, and combinations thereof, or a gene encoding the same.

3. A composition for predicting or diagnosing the severity of COVID-19, comprising an agent for measuring the expression or activity level of one or more genes selected from the group consisting of NEAT1, MALAT1, and combinations thereof, or a gene encoding the same.

4. A method for screening a candidate substance for preventing, treating, or improving COVID-19, comprising the following steps: (a) a step of measuring the expression or activity level of one or more selected from the group consisting of HBEGF, VEGFA, CXCL1, CXCL2, CXCL3 and combinations thereof or a gene encoding the same after treatment of a sample with an unknown substance; and (b) a step of comparing the expression or activity level of one or more genes selected from the group consisting of HBEGF, VEGFA, CXCL1, CXCL2, CXCL3 and combinations thereof or a gene encoding the same in the step (a) with the expression or activity level of one or more genes selected from the group consisting of HBEGF, VEGFA, CXCL1, CXCL2, CXCL3 and combinations thereof in the control group or a gene encoding the same.

5. Method for predicting or monitoring the progress of COVID-19, including the following steps: (a) measuring the expression or activity level of one or more genes selected from the group consisting of HBEGF, VEGFA, CXCL1, CXCL2, CXCL3, PDGFC, CCL4, CCL7, IL1A and combinations thereof or a gene encoding the same in a sample; and (b) a step of comparing the expression or activity level of one or more genes selected from the group consisting of HBEGF, VEGFA, CXCL1, CXCL2, CXCL3, PDGFC, CCL4, CCL7, IL1A and combinations thereof or a gene encoding the same in the step (a) with the expression or activity level of one or more genes selected from the group consisting of HBEGF, VEGFA, CXCL1, CXCL2, CXCL3, PDGFC, CCL4, CCL7, IL1A and combinations thereof in the control group or a gene encoding the same.

6. A biomarker composition for predicting or monitoring the course of COVID-19, comprising one or more genes selected from the group consisting of HBEGF, VEGFA, CXCL1, CXCL2, CXCL3, PDGFC, CCL4, CCL7, IL1A, and combinations thereof, or a gene encoding the same.

7. A composition for predicting or monitoring the course of COVID-19, comprising an agent for measuring the expression or activity level of one or more genes selected from the group consisting of HBEGF, VEGFA, CXCL1, CXCL2, CXCL3, PDGFC, CCL4, CCL7, IL1A, and combinations thereof, or a gene encoding the same.

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