Medicine for treating Long COVID as well as screening method and application thereof

Abstract

The invention discloses a medicine for treating Long COVID as well as a screening method and application thereof, and belongs to the technical field of Long COVID medicines. According to the invention, nine drug targets of Long COVID are obtained through screening, and effective therapeutic targets are provided for development of novel therapeutic drugs of Long COVID; seven natural compound components which have biological activity, are good in docking activity with Long COVID target protein and have an adjusting effect on Long COVID cells are obtained, and new candidate drugs are provided for Long COVID treatment; the provided seven natural synthesis compounds with biological activity are tripterygium compounds, so that the functions of the tripterygium compounds are enriched; the screening method of the medicine for treating the Long COVID is simple, effective, low in cost and environmentally friendly.

Description

Technical Field

[0001] The present invention relates to the technical field of Long COVID drugs, and in particular to a drug for treating Long COVID, a screening method and an application thereof. Background Art

[0002] Long COVID is a multi-system pathological state that persists after SARS-CoV-2 infection. Its occurrence is related to multiple factors such as immune inflammatory response, microecological imbalance, vagus nerve dysfunction and metabolic changes. According to studies, the incidence of Long COVID in hospitalized patients can be as high as 50% or more, and tens of millions of people around the world have varying degrees of Long COVID symptoms and signs. The symptoms of Long COVID are diverse, and common ones include fatigue, dysbiosis, joint pain, chest pain, palpitations, loss or reduction of smell or taste, cognitive dysfunction (brain fog), headache, muscle pain, persistent cough, digestive problems and sleep problems. Patients may also experience mental health problems such as anxiety and depression.

[0003] Long COVID patients' cardiovascular system, respiratory system, nervous system, endocrine system and other systems are adversely affected, increasing patients' medical needs and economic burden. Long COVID lacks in-depth mechanism research, insufficient drug target research, and lack of effective therapeutic drugs. Currently, treatment is mainly symptomatic treatment, rehabilitation training, psychological support, etc. Long COVID mechanism research and drug target development are currently hot and difficult issues.

[0004] The herbal plants of the genus Tripterygium mainly include Tripterygium wilfordiiHook.f., Tripterygium regelii Sprague et Takeda and Tripterygium hypoglaucum (Levl.) Hutch. etc. The plants of the genus Tripterygium contain a variety of natural compounds, which have anti-inflammatory, antiviral and immunomodulatory effects. Triptolide can significantly downregulate the levels of VEGF, ICAM-1, TNF-α and IL-6 in the RPE-choroid-sclera complex, exert its anti-angiogenic effect, and has therapeutic potential in choroidal neovascularization; Celastrol shows good therapeutic effects in arthritis animal models by inhibiting the NF-κB signaling pathway; Tripterygium wilfordii polyglycoside improves experimental rat colitis by regulating innate immune responses and Th17 / Treg balance in intestinal mucosa, peripheral blood, mesenteric lymph nodes (MLN) and spleen, etc. Summary of the invention

[0005] The purpose of the present invention is to provide a drug for treating Long COVID and a screening method and application thereof, so as to solve the problem of lack of effective drugs in the current treatment of Long COVID, provide candidate drugs for the treatment of Long COVID, and provide effective targets for the research and development of new drugs for treating Long COVID.

[0006] To achieve the above objectives, the present invention provides a drug for treating Long COVID, wherein the drug comprises one or more combinations of "Tripdiolide", "Hyponine E", "Wilforine", "Wilforgine", "Triptophenolide", "Triptolide" and "Celastrol" as main ingredients and / or active ingredients.

[0007] A drug target for treating Long COVID as described above, comprising HSP90AA1, BCL2A1, COL7A1, BIRC3, PTGS1, TIMP1, CXCR4, CASP3, and CASP8.

[0008] A method for screening drugs for treating Long COVID as described above, comprising the following steps:

[0009] S1. Obtain differentially expressed genes between Long COVID patients and healthy subjects;

[0010] S2. Obtain drug targets of drugs to be screened through databases;

[0011] S3, intersecting the drug target information obtained in step S2 with the differentially expressed genes obtained in step S1, and screening to obtain drugs to be screened corresponding to drug-disease common targets whose expression levels are significantly upregulated or downregulated;

[0012] S4. By analyzing the obtained drug-disease common targets, 4 drug-disease core targets and their corresponding drugs to be screened are obtained;

[0013] S5. Perform molecular docking on the drugs screened in steps S3 and S4 and the obtained targets, examine the regulatory effects of the drugs screened in steps S3 and S4 on Long COVID PBMC cells, verify the screened drugs, and ultimately determine the drugs for treating or improving Long COVID symptoms.

[0014] Preferably, the method for obtaining the differentially expressed genes between Long COVID patients and healthy people in step S1 is: importing the differential genes into Rstudio, using the "ggplot" R package, drawing a volcano map with P<0.05 and |LogFC|≥0.5 as conditions, and obtaining genes that are upregulated and downregulated relative to healthy people.

[0015] Preferably, the database in step S2 includes four databases: TCMSP, SwissTargetPrediction, TargetNet, and GeneCards; the method for obtaining the drug target to be screened through the database is: using the English name of the drug to be screened as a keyword to analyze its related targets in the TCMSP database and the GeneCards database; obtaining the SMILES of the drug to be screened in the PubChem database, and using SMILES to search for related targets in the SwissTargetPrediction database and the TargetNet database.

[0016] Preferably, the method for screening and obtaining drug-disease common targets in step S3 is: intersecting the differentially expressed genes obtained in step S1 with the drug targets in S2 to obtain common targets, importing the obtained drug-disease common targets into Rstudio, and using the "ggplot" R package to make upper and lower bar graphs.

[0017] Preferably, in step S4, the method for analyzing the drug-disease common targets to obtain four drug-disease core targets is as follows: analyzing the obtained drug-disease common targets in the STRING database, using humans as the species in the screening conditions, and analyzing the interaction relationship between proteins; performing GO and KEGG enrichment analysis on the obtained drug-disease common targets to obtain pathway information, making network files and type files in Excel tables for the pathways, targets, and drugs to be screened corresponding to the obtained drug-disease common targets, respectively, and importing the two files into Cytoscape3.10.2 to make a target-component-pathway network diagram; downloading the protein interaction network constructed by analysis in the STRING database as a "TSV" file, and importing it into the software Cytoscape3.10.2, using its cytoHubba plug-in, selecting BottleNeck, Closeness, Degress, EcCentricity, MCC and MNC topology analysis algorithms to obtain the top 4 core targets HSP90AA1, PTGS1, BCL2A1, and BIRC3 in the PPI network.

[0018] Preferably, step S5 performs molecular docking on the drugs screened in steps S3 and S4 and the obtained targets, verifies the regulatory effects of the drugs screened in steps S3 and S4 on Long COVID PBMC cells, and verifies that the primers for the core targets are HSP90AA1, BCL2A1, COL7A1, BIRC3, PTGS1, and GAPDH.

[0019] Preferably, the upstream primer sequence of HSP90AA1 is shown as SEQ ID NO.1, and the downstream primer sequence is shown as SEQ ID NO.2; the upstream primer sequence of primer BCL2A1 is shown as SEQ ID NO.3, and the downstream primer sequence is shown as SEQ ID NO.4; the upstream primer sequence of primer COL7A1 is shown as SEQ ID NO.5, and the downstream primer sequence is shown as SEQ ID NO.6; the upstream primer sequence of primer BIRC3 is shown as SEQ ID NO.7, and the downstream primer sequence is shown as SEQ ID NO.8; the upstream primer sequence of primer PTGS1 is shown as SEQ ID NO.9, and the downstream primer sequence is shown as SEQ ID NO.10; the upstream primer sequence of primer GAPDH is shown as SEQ ID NO.11, and the downstream primer sequence is shown as SEQ ID NO.12.

[0020] A method for screening drugs for treating Long COVID as described above is used in drug target screening.

[0021] The present invention obtains the simplified linear input system (SMILES) of natural chemical components of Tripterygium wilfordii in the PubChem database by screening the differential genes between Long COVID patients and normal people, and uses SMILES to screen related targets in the SwissTargetPrediction database and the TargetNet database. The drug targets of natural chemical components of Tripterygium wilfordii and the differentially expressed genes of Long COVID patients are screened to obtain drug-disease common targets. It is confirmed that the natural chemical components of Tripterygium wilfordii, Tripdiolide, HyponineE, Wilforine, Wilforgine, Triptophenolide, Triptolide, Celastrol, etc., act on the drug targets of Long COVID, and their effective effects are verified by experiments, and can be developed into Long COVID drugs and pharmaceutical compositions.

[0022] Therefore, the drug for treating Long COVID and its screening method and application provided by the present invention have the following specific technical effects:

[0023] (1) The present invention screened and obtained 7 natural compounds with biological activity: Tripdiolide, HyponineE, Wilforine, Wilforgine, Triptophenolide, Triptolide, and Celastrol. Molecular docking experiments were performed on these 7 natural compounds and proteins encoded by common targets of Long COVID patients, and the regulatory effects on Long COVID PBMC cells were investigated. It was confirmed that these 7 natural compounds had good molecular docking activity and had a regulatory effect on Long COVID PBMC cells, providing new candidate drugs for the treatment of Long COVID;

[0024] (2) The 7 biologically active natural compounds provided by the present invention are synthesized into Tripterygium wilfordii compounds, which enrich the functions of Tripterygium wilfordii compounds;

[0025] (3) The present invention provides 9 common targets of candidate drugs and Long COVID, namely HSP90AA1, BCL2A1, COL7A1, BIRC3, PTGS1, TIMP1, CXCR4, CASP3, and CASP8, which provide effective therapeutic targets for the development of new therapeutic drugs for Long COVID;

[0026] (4) The screening method for drugs for treating Long COVID provided by the present invention is simple, effective, low-cost, and environmentally friendly. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative labor.

[0028] Figure 1 It is the Long COVID differential gene volcano map drawn in Example 1 of the present invention;

[0029] Figure 2 is a bar graph of common target expression levels drawn in Example 1 of the present invention;

[0030] Figure 3 is a GO and KEGG enrichment analysis bar chart drawn in Example 2 of the present invention;

[0031] Figure 4 is the protein interaction network diagram mediated by the common target drawn in Example 3 of the present invention;

[0032] Figure 5 is the target-component-pathway network diagram drawn in Example 3 of the present invention;

[0033] Figure 6 It is the core target map predicted based on BottleNeck, Closense and Degress algorithms drawn in Example 3 of the present invention;

[0034] Figure 7 It is the core target map predicted based on the EcCentricity, MCC and MNC algorithms drawn in Example 3 of the present invention;

[0035] Figure 8 It is the core target function map predicted based on the GeneMANIA database drawn in Example 3 of the present invention;

[0036] Fig. 9 is a visualization diagram of the docking result of tripterygium wilfordii obtained in Example 4 of the present invention;

[0037] Fig.10 is a visualization diagram of the docking result of triptolide obtained in Example 4 of the present invention;

[0038] Fig.11 This is a visualization diagram of the docking results of triptolide and triptolide obtained in Example 4 of the present invention;

[0039] Fig.12 This is the result of treating Long COVID mononuclear cells with the Tripterygium compound obtained in Example 5 of the present invention. DETAILED DESCRIPTION

[0040] The technical solution of the present invention is further described below through the accompanying drawings and embodiments.

[0041] In order to make the purpose, technical scheme and advantages of the present application clearer, more thorough and more complete, the technical scheme of the present invention is clearly and completely described below through the accompanying drawings and examples. The following detailed descriptions are all descriptions of the embodiments, and are intended to provide further detailed descriptions of the present invention. Unless otherwise specified, all technical terms used in the present invention have the same meaning as those generally understood by those skilled in the art to which the present application belongs.

[0042] The instruments, equipment, reagents and materials used in the examples were all obtained through commercial channels, and the Tripterygium wilfordii compounds used were all purchased from Inotech Technology Co., Ltd. with a purity of >98% (HPLC).

[0043] Embodiment 1

[0044] Screening of drug targets for Long COVID patients, as follows:

[0045] 1. Screening of Long COVID drug targets.

[0046] (1) Screening of Long COVID differentially expressed genes.

[0047] The transcriptome data of normal people and Long COVID patients were analyzed. The differential expression of genes in Long COVID patients was analyzed compared with that in normal people. The differentially expressed genes were imported into Rstudio (https: / / cran.r-project.org / ), and the "ggplot" R package was used to draw a three-color volcano map with P < 0.05 and |LogFC| ≥ 0.5 as conditions, as shown in the figure. Figure 1 A total of 968 differentially expressed genes were identified between Long COVID patients and healthy controls. Compared with healthy controls, 128 genes were downregulated in Long COVID patients, and 402 genes were upregulated in Long COVID patients.

[0048] (2) Analyze and integrate drug component targets.

[0049] The drug targets of 20 currently known natural compounds of the genus Tripterygium were screened and integrated from the databases, including four databases: TCMSP, SwissTargetPrediction, TargetNet, and GeneCards.

[0050] The English names of these 20 natural compounds of the genus Tripterygium were used as keywords to analyze their related targets in the TCMSP database and GeneCards database.

[0051] The simplified linear input system (SMILES) of 20 components was obtained from the PubChem database, and SMILES was used to search for related targets in the SwissTargetPrediction database and the TargetNet database.

[0052] Use Excel to take the common intersection of the drug targets collected from the four databases for each component to obtain the intersection target, which is the drug target of the component, and save the data for subsequent analysis.

[0053] (3) Analyze common drug-disease targets.

[0054] The drug targets of 20 natural compounds of Tripterygium wilfordii were intersected with the differentially expressed genes of Long COVID, and 9 common drug-disease targets were obtained: HSP90AA1, BCL2A1, COL7A1, BIRC3, PTGS1, TIMP1, CXCR4, CXCR4, and CASP3, and the corresponding compounds were Tripdiolide, HyponineE, Wilforine, Wilforgine, Triptophenolide, Triptolide, and Celastrol.

[0055] The obtained 9 drug-disease common targets were imported into Rstudio, and the “ggplot” R package was used to make an up-and-down bar graph. Figure 2 shown.

[0056] Embodiment 2

[0057] The drug-disease common targets obtained in Example 1 were enriched and analyzed as follows:

[0058] (1) GO enrichment analysis.

[0059] The obtained 9 common drug-disease targets were imported into Rstudio, and GO enrichment analysis was performed using the "ClusterProfiler" R package. First, ID conversion was performed to convert the gene symbol to ENTREZID. The enrichment conditions were: species limited to humans, pvalue cutoff set to 0.1, q value vutoff set to 0.1, and the method of multiple hypothesis testing correction was Benjamini-Hochberg. After analysis, it was found that the 9 common drug-disease targets played a regulatory role in programmed cell death, response to viruses, regulation of immune responses, cytokine-mediated signaling pathways, NF-kappaB signaling transduction, RIG-I signaling pathways, and integrin-mediated signaling pathways in biological processes; they played a regulatory role in the basement membrane, collagen-containing extracellular matrix, mitochondrial membrane, and organelle membrane in cellular components; in terms of molecular function, they could regulate chemokine receptor activity, protein folding chaperones, antioxidant activity, immune receptor activity, endopeptidase inhibitor activity, and serine / threonine kinase inhibitor activity.

[0060] (2) KEGG enrichment analysis.

[0061] The 9 drug-disease common targets screened were imported into Rstudio, and KEGG enrichment analysis was performed using the "ClusterProfiler" R package. First, ID conversion was performed to convert the gene name to ENTREZID. The enrichment conditions were: the species was selected as human, the p value cutoff was set to 0.1, and the q value vutoff was set to 0.1. The analysis results showed that the 9 drug-disease common targets played a regulatory role in NOD-like receptor signal transduction, cytomegalovirus infection, NF-kappa B signaling pathway, apoptosis pathway, TNF signaling pathway, and necroptosis.

[0062] (3) Visualization of enrichment analysis results.

[0063] After obtaining the GO and KEGG enrichment analysis results, the “ggplot2” R package was used for visualization.

[0064] Figure 3 These are GO and KEGG enrichment analysis diagrams, which respectively show the pathways of the three categories of BP, CC, and MF in GO enrichment analysis and the pathways of KEGG enrichment analysis.

[0065] Embodiment 3

[0066] Drug-disease core target screening, PPI network construction and function prediction, as follows:

[0067] 1. Screening of drug-disease core targets and construction of PPI network.

[0068] (1) Construct a drug-disease target-mediated protein-protein interaction network.

[0069] The 9 drug-disease common targets obtained in Example 1 were analyzed in the STRING database, and the species in the screening condition was human. The interaction relationship between proteins was analyzed, such as Figure 4 As shown, among the nine common drug-disease targets, there are direct interactions between eight proteins, namely PTGS1, TIMP1, CXCR4, CASP3, BCL2A1, HSP90AA1, BIRC3, and CASP8, and there is an indirect interaction relationship between COL7A1 and other proteins.

[0070] (2) Construct a target-component-pathway network diagram.

[0071] The 9 drug-disease common targets, 20 natural compounds (ingredients) of Tripterygium wilfordii, and the 10 most significant pathways in GO and KEGG enrichment analysis were made into network files and type files in Excel tables, and the two files were imported into Cytoscape3.10.2 to make a target-ingredient-pathway network diagram. The target-ingredient-pathway network diagram made by Cytoscape is shown in the figure below: Figure 5 shown.

[0072] 2. Acquisition of core drug targets.

[0073] The protein interaction network constructed by analyzing the STRING database was downloaded as a "TSV" file and imported into the software Cytoscape3.10.2. Using its cytoHubba plug-in, the six topological analysis algorithms BottleNeck, Closeness, Degress, EcCentricity, MCC and MNC were selected to predict and explore the top 4 core targets in the PPI network. The results obtained by the above six algorithms were taken as the intersection, and the result obtained was the drug-disease core target, such as Figure 6 and Figure 7 As shown, the core drug-disease targets are PTGS1, HSP90AA1, BIRC3 and BCL2A1.

[0074] 3. Study the functions of core targets in the GeneMANIA database.

[0075] The core targets obtained by screening were input into the GeneMANIA database for analysis and research, and interaction maps were constructed, such as Figure 8As shown. Each circle in the figure represents a target. The left circle represents the four identified drug-disease core targets PTGS1, HSP90AA1, BIRC3 and BCL2A1, and the right circle represents 20 related prediction genes MPO, PTGIS, TBXASB, etc. The four core targets and 20 prediction genes are enriched in the network, with 77.84% physical interaction, 8.01% co-expression, 5.37% prediction, 3.63% co-localization, 2.87% genetic interaction, 1.88% pathway, and 0.60% shared protein domain.

[0076] The main functions enriched are apoptosis signaling pathway, reactive oxygen metabolism process, regulation of reactive oxygen biosynthesis process, biosynthesis process, metabolism process, protein folding, etc. The drug acts on 4 drug-disease core targets, regulating the apoptosis pathway in Long COVID patients, regulating the metabolism and biosynthesis process of intracellular molecules, etc., thereby alleviating symptoms and treating diseases.

[0077] Embodiment 4

[0078] Molecular docking analysis was performed on five of the nine drug-disease common targets, namely PTGS1, HSP90AA1, BCL2A1, COL7A1, and BIRC3, as follows:

[0079] (1) Structure and pretreatment of natural small molecule drugs (ligands).

[0080] Search the corresponding small molecule compound in the PubChem database and download its chemical structure in SDF file format. Import the SDF format file into Chem3D software, optimize the structural energy, and export the result in mol2 format. Then import it into AutoDockTool1.5.7 to add polar hydrogen, check the torsion bond, and select the torsion bond. After processing, select the small molecule as the ligand (Ligand-Input-Choose) and save the result in pdbqt format, thus completing the preparation of the molecular docking ligand file.

[0081] (2) Download and preprocess the structure of core disease targets (protein receptors).

[0082] Search for the corresponding protein in the PDB database and download its chemical structure in the PDB file format. In the PyMOL2.5.4 software, import the PDB format file of the protein receptor, enter the Remove solvent and Remove organic commands, remove the water molecules and residues of the protein, and export the results in the PDB format. Then import the results into the AutoDockTool1.5.7 software to add polar hydrogen. After processing, select the protein as a ligand (Grid-Macromole cule-Choose-Protein), and save the results in the pdbqt format, completing the relevant preparation of the molecular docking receptor file.

[0083] (3) Use AutoDockTool1.5.7 for molecular docking.

[0084] Molecular docking was performed using AutoDockTool1.5.7. Semi-flexible docking was used as the docking method, and the genetic algorithm (GA) was used for molecular docking calculations. The final docking results were evaluated based on binding energy and binding sites. The results are shown in Table 1.

[0085] Table 1 Results of molecular docking using AutoDockTool1.5.7

[0086]

[0087]

[0088] Studies have shown that when the binding energy between the ligand and the receptor is less than -4.25 kcal / mol, it indicates that there is a certain binding activity between the two; when it is less than -5.0 kcal / mol, it indicates that there is a good binding activity between the two; when it is less than -7.0 kcal / mol, it indicates that there is a strong binding activity between the ligand and the receptor.

[0089] The docking results showed that Celastrol had a high binding activity with COL7A1 protein, and had good binding activity with HSP90AA1 protein and BCL2A1 protein. Triptolide had a strong docking activity with HSP90AA1 protein, and had good docking activity with BCL2A1 protein. Tripdiolide had good docking activity with HSP90AA1 protein.

[0090] (4) Visualization of docking results.

[0091] The docking results were visualized using PyMOL software.

[0092] like Fig. 9As shown, Celastrol forms hydrogen bonds with HSP90AA1 protein at ASP-93, SER-52, and ASN-51, with bond lengths of 2.2, 2.5, and 3.4, respectively; forms a hydrogen bond with BCL2A1 protein at GLN-25 with a bond length of 1.9, and forms two hydrogen bonds at ASN-39 with bond lengths of 2.3 and 2.6, respectively; forms a hydrogen bond with COL7A1 protein at ASP-1124 with a bond length of 2.2, and forms two hydrogen bonds at ARG-1120 with bond lengths of 2.9 and 2.3, respectively.

[0093] like Fig.10 As shown, triptolide forms hydrogen bonds with BCL2A1 protein at CYS-19 and GLN-25, with bond lengths of 2 and 2.3, respectively; and forms hydrogen bonds with HSP90AA1 protein at LEU-122 and ASP-127, with bond lengths of 2 and 3.3, respectively.

[0094] like Fig.11 As shown, tripdiolide forms a hydrogen bond with HSP90AA1 protein at LEU-122 with a bond length of 2.2, and forms two hydrogen bonds at ASP-127 with bond lengths of 2.2 and 3.5 respectively. Wilforgine forms a hydrogen bond with HSP90AA1 protein at ILE-128 with a bond length of 2.1.

[0095] Embodiment 5

[0096] The regulatory effects of natural compounds of Tripterygium wilfordii on Long COVID PBMC cells were investigated as follows:

[0097] 1. Extraction of mononuclear cells from Long COVID patients.

[0098] (1) The mononuclear cell separation solution was equilibrated at room temperature for 30 minutes. Prepare 50 mL of whole blood, add the same volume of PBS to dilute 1:1, and gently pipette to mix; first add 20 mL of mononuclear cell separation solution to the bottom of a 50 mL centrifuge tube, then pipette 20 mL of PBS-diluted whole blood sample along the tube wall and slowly add it to the upper layer of the mononuclear cell separation solution.

[0099] (2) Transfer the centrifuge tube to a centrifuge, set the ascending speed to 1 and the descending speed to 0, and centrifuge at room temperature and 700×g for 30 minutes; after centrifugation, make a circle to aspirate the cells in the middle layer of the centrifuge tube, transfer them to a 15mL centrifuge tube, add buffer PBS+ (10% serum and 0.2uM EDTA are added to PBS buffer) to a final volume of 14mL, invert the cell suspension 3-5 times to mix, and centrifuge at 400×g for 7 minutes; remove the supernatant and collect the cell precipitate.

[0100] (3) PBS+ was added again and washed twice. The cell pellet was resuspended in 1 mL of RPMI-1640. The cell suspension was passed through a 70 μm mesh to remove cell clusters and obtain a blood mononuclear cell sample in a single cell suspension state.

[0101] 2. Treatment of mononuclear cells with natural compounds of Tripterygium wilfordii, RNA extraction and reverse transcription. The prepared mononuclear cells were concentrated to 2×10 6 / well was inoculated in a 6-well plate, and 25umol / L Tripdiolide, 6.25umol / L Hyponine E, 6.25umol / L Wilforine, 6.25umol / L Wilforgine, 25umol / L Triptophenolide, 25umol / L Triptolide, and 400umol / L Celastrol were added to the cells, and the cell wells without drug addition were used as controls. The cells were cultured in a cell culture incubator at 37°C and 5% CO2 for 24 hours, the supernatant was removed, and the cells were collected for later use.

[0102] (1) After adding Trizol lysis buffer to cell samples treated with different drug concentrations, the samples were placed at room temperature for 5 minutes to allow the samples to be fully lysed. The samples were centrifuged at 12000 rpm for 5 minutes and the supernatant was collected.

[0103] (2) Add 200 μL of chloroform to every 1 mL of lysate, shake and mix, and place at room temperature for 15 minutes to allow natural stratification. Do not use a vortex shaker to prevent gene breakage. After centrifugation at 4°C and 12,000 rpm for 15 minutes, the sample will separate into three layers: a yellow organic layer, a middle layer, and an upper colorless aqueous layer. RNA is mainly in the aqueous layer.

[0104] (3) Carefully pipette the upper layer into another new 1.5 mL centrifuge tube. Do not pipette the middle interface to avoid DNA and protein contamination. Add an equal volume of ice-cold isopropanol to the supernatant and place at -20°C for 1 hour to increase RNA precipitation. Centrifuge at 4°C, 12,000 rpm for 10 minutes, discard the supernatant, and the RNA will settle to the bottom of the tube.

[0105] (4) Add 1 mL of 75% ethanol (prepared with DEPC water) to the RNA precipitate and gently shake the centrifuge tube to suspend the precipitate. Centrifuge at 8000 rpm for 5 min at 4°C and carefully aspirate the supernatant with a pipette. Allow to dry for 5 to 10 min to allow the remaining traces of ethanol to evaporate. Dissolve the RNA precipitate with 50 μL of nuclease-free water.

[0106] (5) Prepare the reaction system to remove genomic DNA from the RNA solution: 1 μg RNA, 4 μL 4×gDNA Remover Mix, and add nuclease-free water to 16 μL. Vortex and mix well, centrifuge briefly, and collect the liquid at the bottom of the centrifuge tube. Run at 37°C for 5 min and 85°C for 5 min. Place on ice after completion and continue the reverse transcription reaction as soon as possible.

[0107] (6) Reverse transcription reaction system and reaction conditions: On ice, add 5 μL of 4× reverse transcription reaction mix and 1 μg of total RNA to a PCR tube without nuclease, add nuclease-free water to 20 μL, mix well and centrifuge briefly. Perform reverse transcription reaction on a PCR instrument according to the following reaction program: 55°C for 15 min, 85°C for 5 min. The reverse transcription product cDNA can be used for subsequent qPCR reaction immediately or stored at 20°C for later use.

[0108] 3. qPCR quantitatively analyzes the changes in target gene expression in drug-treated mononuclear cells.

[0109] (1) Dilution primers: HSP90AA1 (the upstream primer sequence is shown in SEQ ID NO.1, and the downstream primer sequence is shown in SEQ ID NO.2), primers: BCL2A1 (the upstream primer sequence is shown in SEQ ID NO.3, and the downstream primer sequence is shown in SEQ ID NO.4), primers: COL7A1 (the upstream primer sequence is shown in SEQ ID NO.5, and the downstream primer sequence is shown in SEQ ID NO.6), primers: BIRC3 (the upstream primer sequence is shown in SEQ ID NO.7, and the downstream primer sequence is shown in SEQ ID NO.8), primers: PTGS1 (the upstream primer sequence is shown in SEQ ID NO.9, and the downstream primer sequence is shown in SEQ ID NO.10), and primers: GAPDH (the upstream primer sequence is shown in SEQ ID NO.11, and the downstream primer sequence is shown in SEQ ID NO.12).

[0110] SEQ ID NO.1:AGGAGGTTGAGACGTTCGC

[0111] SEQ ID NO.2:AGAGTTTCGATCTTGTTTGTTCGG

[0112] SEQ ID NO.3:GGATAAGGCAAAACGGAGGC

[0113] SEQ ID NO.4:TGGAGTGTCCTTTCTGGTCAA

[0114] SEQ ID NO.5:GGCTGCAATTCTCCATGTGG

[0115] SEQ ID NO.6:CTGTGAGGCAACTCGCTTCA

[0116] SEQ ID NO.7: CGCTTGAAAAGACTGGGCTTG

[0117] SEQ ID NO.8:ATGTGCCAGTAGGAGACTGC

[0118] SEQ ID NO.9:CCCTGTTGTTACTATCCATGCCA

[0119] SEQ ID NO.10:GGGCGGGTACATTTCTCCAT

[0120] SEQ ID NO.11:CCGAGCCACATCGCTCAGACAC

[0121] SEQ ID NO.12:GTGACCAGGCGCCCAATACGAC

[0122] (2) Prepare qPCR reaction system: 2×SYBR qPCR Mix 10 mL, cDNA 10 ng template, 10 μM forward primer 0.5 μL, 10 μM reverse primer 0.5 μL, and add nuclease-free water to 20 μL.

[0123] (3) qPCR reaction program: 95°C pre-denaturation for 3 min, 95°C for 5 s, 60°C for 30 s, 40 cycles of reaction. Melting curve was set.

[0124] (4) The amount of the starting template of the gene to be tested / the amount of the starting template of the reference gene GAPDH = 2^-[ct(the gene to be tested)-ct(the reference gene)] = 2^-Act. The result of the division between the two - ΔΔct represents the changing trend of the expression of the target gene. The amount of the starting template of the gene to be tested / the amount of the starting template of the reference gene GAPDH in the group without drug treatment is compared. If the ratio is greater than 1, it indicates that the expression of the target gene is upregulated after drug treatment. If the ratio is less than 1, it indicates that the expression of the target gene is downregulated after drug treatment.

[0125] The results are as follows Fig.12 As shown, HSP90AA1 was downregulated, BCL2A1 was downregulated, and COL7A1 was upregulated after treatment with Celastrol, Triptolide, Tripdiolide, Wilforgine, HyponineE, Wilforine, and Triptophenolide.

[0126] Therefore, the present invention screened and obtained 7 natural chemical components with biological activity, which have good docking activity with protein molecules encoded by common targets of Long COVID patients, have a regulatory effect on Long COVID PBMC cells, and provide new candidate drugs for the treatment of Long COVID; the 7 natural chemical components with biological activity provided are Tripterygium compounds, which enrich the functions of Tripterygium compounds; 9 candidate drugs and common targets of Long COVID patients are provided, which provides effective therapeutic targets for the development of new therapeutic drugs for Long COVID; the screening method for drugs for treating or improving Long COVID symptoms is simple and effective, does not require a large number of experiments, saves time and labor, is low in cost, and is environmentally friendly.

[0127] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solution of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solution to deviate from the spirit and scope of the technical solution of the present invention.

Claims

1. A drug for treating Long COVID, characterized in that: The drug contains one or more of "Tripdiolide", "Hyponine E", "Wilforine", "Wilforgine", "Triptophenolide", "Triptolide" and "Celastrol" as main ingredients and / or active ingredients.

2. A drug target for treating Long COVID as claimed in claim 1, characterized in that: Including HSP90AA1, BCL2A1, COL7A1, BIRC3, PTGS1, TIMP1, CXCR4, CASP3, and CASP8.

3. A method for screening drugs for treating Long COVID as claimed in claim 1, characterized in that: Here are the steps: S1. Obtain differentially expressed genes between Long COVID patients and healthy subjects; S2. Obtain drug targets of drugs to be screened through databases; S3, intersecting the drug target information obtained in step S2 with the differentially expressed genes obtained in step S1, and screening to obtain drugs to be screened corresponding to drug-disease common targets whose expression levels are significantly upregulated or downregulated; S4. By analyzing the obtained drug-disease common targets, 4 drug-disease core targets and their corresponding drugs to be screened are obtained; S5. Perform molecular docking on the drugs screened in steps S3 and S4 and the obtained targets, examine the regulatory effects of the drugs screened in steps S3 and S4 on Long COVID PBMC cells, verify the screened drugs, and ultimately determine the drugs for treating or improving Long COVID symptoms.

4. A method for screening drugs for treating Long COVID according to claim 3, characterized in that: The method for obtaining the differentially expressed genes between Long COVID patients and healthy people in step S1 is as follows: the differentially expressed genes are imported into Rstudio, and the "ggplot" R package is used to draw a volcano plot with P < 0.05 and |LogFC| ≥ 0.5 as conditions to obtain the genes that are up-regulated and down-regulated relative to healthy people.

5. The method for screening a drug for treating Long COVID according to claim 3, characterized in that: The databases in step S2 include four databases: TCMSP, SwissTargetPrediction, TargetNet, and GeneCards. The method for obtaining drug targets to be screened through the databases is: using the English names of the drugs to be screened as keywords to analyze their related targets in the TCMSP database and the GeneCards database; The SMILES of the drugs to be screened were obtained in the PubChem database, and the relevant targets were searched in the SwissTargetPrediction database and the TargetNet database using SMILES.

6. The method for screening a drug for treating Long COVID according to claim 3, characterized in that: The method for screening and obtaining drug-disease common targets in step S3 is: the differentially expressed genes obtained in step S1 are intersected with the drug targets in S2 to obtain common targets, the obtained drug-disease common targets are imported into Rstudio, and the upper and lower bar graphs are made using the "ggplot" R package.

7. The method for screening a drug for treating Long COVID according to claim 3, characterized in that: In step S4, the drug-disease common targets are analyzed to obtain four drug-disease core targets as follows: the obtained drug-disease common targets are analyzed in the STRING database, the species in the screening condition is human, and the interaction relationship between proteins is obtained by analysis; the obtained drug-disease common targets are subjected to GO and KEGG enrichment analysis to obtain pathway information, and the pathways, targets, and drugs to be screened corresponding to the obtained drug-disease common targets are respectively made into network files and tpye files in Excel tables, and the two files are imported into Cytoscape3.10.2 to make a target-component-pathway network diagram; the protein interaction network obtained by analysis and construction in the STRING database is downloaded as a "TSV" file, and imported into the software Cytoscape3.10.2, and the cytoHubba plug-in is used to select BottleNeck, Closeness, Degress, EcCentricity, MCC and MNC topological analysis algorithms to obtain the top 4 core targets HSP90AA1, PTGS1, BCL2A1, and BIRC3 in the PPI network.

8. The method for screening a drug for treating Long COVID according to claim 3, characterized in that: Step S5 performs molecular docking on the drugs screened in steps S3 and S4 and the obtained targets, verifies the regulatory effects of the drugs screened in steps S3 and S4 on LongCOVID PBMC cells, and verifies that the primers for the core targets are HSP90AA1, BCL2A1, COL7A1, BIRC3, PTGS1, and GAPDH.

9. A method for screening drugs for treating Long COVID according to claim 8, characterized in that: The upstream primer sequence of HSP90AA1 is shown in SEQ ID NO.1, and the downstream primer sequence is shown in SEQ ID NO.2; The upstream primer sequence of primer BCL2A1 is shown in SEQ ID NO.3, and the downstream primer sequence is shown in SEQ ID NO.4; The upstream primer sequence of primer COL7A1 is shown in SEQ ID NO.5, and the downstream primer sequence is shown in SEQ ID NO.6; The upstream primer sequence of primer BIRC3 is shown in SEQ ID NO.7, and the downstream primer sequence is shown in SEQ ID NO.8; the upstream primer sequence of primer PTGS1 is shown in SEQ ID NO.9, and the downstream primer sequence is shown in SEQ ID NO.10; the upstream primer sequence of primer GAPDH is shown in SEQ ID NO.11, and the downstream primer sequence is shown in SEQ ID NO.

12.

10. Use of the screening method for a drug for treating Long COVID as described in any one of claims 3 to 9 in drug target screening.

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