Method for screening Wilson disease serum exosome miRNA biomarker based on transcriptomics technology

Abstract

The invention discloses a method for screening a Wilson disease serum exosome miRNA biomarker based on a transcriptomics technology, and belongs to the field of bioinformatics analysis of diseases. According to the invention, the effect of miRNA in WD pathogenesis is discussed by identifying serum exosome miRNA, and a potential biomarker is provided for accurate diagnosis and treatment of the disease. According to the invention, 59 DE-miRNAs are identified by analyzing the expression of differential miRNAs of WD and control group patients, and the DE-miRNAs are related to key approaches such as metabolic regulation, cancer progression, signal transduction and the like. Besides, the reliability of the key DE-miRNA, including miR-451a, miR-204-5p and the like, is confirmed through experimental verification, and the potential of the key DE-miRNA as a non-invasive biomarker is indicated. Research results provide important theoretical basis and new insight for accurate diagnosis of WD and development of potential therapeutic targets.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of bioinformatics analysis of diseases, and particularly relates to a method for screening Wilson disease serum exosome miRNA biomarkers based on transcriptomics technology. BACKGROUND

[0002] Wilson disease (WD) is a copper metabolism disorder caused by mutations in the ATP7B gene, characterized by abnormal accumulation of copper in the liver, brain and other organs, leading to liver damage, neurological symptoms and multiple system complications. The diagnosis of Wilson disease currently mainly relies on serum ceruloplasmin detection, 24-hour urinary copper excretion and gene analysis, but these methods still have limitations in early diagnosis of the disease, disease classification and treatment effect monitoring. Exploring new biomarkers for Wilson disease is of great significance for non-invasive diagnosis, targeted treatment and efficacy evaluation of the disease.

[0003] Exosomes are nanoscale vesicles secreted by cells, with a diameter of about 50-150 nm, which can carry bioactive molecules such as proteins, nucleic acids and lipids. Exosomes have a unique mode of action, i.e. through endocytosis to combine with receptors in the form of ligand and receptor, to interact with receptor cells, so that the integrated substances can be transmitted between cells, thereby participating in cell-to-cell communication and disease development. MicroRNA (miRNA) is a small endogenous non-coding RNA that can regulate the expression of target proteins by acting on mRNA. Studies have found that exosomes released by human macrophages, NK cells and other cells contain a variety of different types of RNA, including mRNA, miRNA, etc. These RNAs are delivered to the corresponding receptor cells to exert specific functions.

[0004] More and more studies have shown that exosome-derived miRNAs, as important regulatory molecules, exhibit specific expression profiles in various diseases such as neurological diseases and liver diseases, and can serve as potential diagnostic markers and therapeutic targets. Studies have found that serum and exosome miR-7-1-5p and miR-223-3p may serve as biomarkers for Parkinson's disease. In addition, the expression of serum-derived exosomal miRNAs can be used to assess the severity of non-alcoholic fatty liver disease, which helps to select potential targets for NAFLD treatment. Some researchers have also found that primary liver macrophage-derived exosomal miRNA-342-3p can promote liver fibrosis by inhibiting HPCAL1 in stellate cells. However, the exact role of exosome-derived miRNAs in Wilson disease has not been reported. SUMMARY

[0005] The application aims to provide a method for screening Wilson disease serum exosome miRNA biomarkers based on transcriptomics technology, taking exosome miRNA in serum of Wilson disease patients as a breakthrough point, using high-throughput RNA sequencing technology to deeply analyze the obtained differential gene expression, and accurately screening specific biomarkers of Wilson disease serum miRNA, thereby opening up a new idea and laying a solid scientific foundation for the diagnosis and treatment of the disease.

[0006] To achieve the above-mentioned purpose, the technical scheme adopted by the application is as follows:

[0007] A method for screening Wilson disease serum exosome miRNA biomarkers based on transcriptomics technology, comprising the following steps:

[0008] Step 1, separation, extraction and identification of exosomes

[0009] Exosomes in the blood sample are extracted by ultracentrifugation, the extracted exosomes are observed by transmission electron microscopy, and the particle size of the extracted exosomes is analyzed, and the expression of exosome fluorescence markers is detected by nano-flow;

[0010] Step 2, total RNA extraction and quality control

[0011] The exosome RNA is separated and purified, the integrity of the RNA is detected, and then the total RNA is separated;

[0012] Step 3, library construction, miRNA sequencing and identification

[0013] The sequencing library preparation adopts TruSeq Small RNA Sample Prep Kits (Illumina, San Diego, USA) kit; after the library preparation work is completed, the constructed library is sequenced using Illumina Hiseq 2000 / 2500, and the sequencing read length is single-end 50bp; first, the raw data is filtered, the raw reads obtained by sequencing are processed to obtain clean reads; the sRNA length is screened, and the base length range is 18-26nt; various RNA databases are compared and analyzed, the remaining sequences are compared with mRNA, RFam and Repbase databases (not containing miRNA), and filtered to obtain effective data; the effective data is compared with the mature body and precursor sequence of human in miRBase 22.0 database, and the genome sequence of the species is compared, known miRNA and new predicted miRNA are identified; the data filtered by length is used to calculate the expression level of miRNA in each sample by ACGT101-miR, and the expression amount is statistically processed by data normalization method;

[0014] Step 4, miRNA differential expression and GO, KEGG analysis

[0015] The input data is the normalized value (norm value), and the p value calculation model based on normal distribution is used for p value calculation; with P<0.05 as the screening standard, and cluster heat map, volcano plot, scatter plot analysis, according to the analysis results, the differentially expressed genes of the health group and the WD group are screened out;

[0016] With the help of GO analysis, the specific utility of DE-miRNA in molecular function, cell composition and biological process dimensions is analyzed; at the same time, KEGG analysis is used to track various intracellular signal transduction pathways and metabolic pathways involved in DE-miRNA;

[0017] Step 5, prediction and construction of miRNA target gene network

[0018] Based on the differential expression analysis of miRNA and the prediction of the relationship between miRNAs and target genes, the miRNA target interaction network is constructed by using Cytoscapev3.10.1 software to further evaluate the interaction relationship between miRNAs and target genes;

[0019] Step 6, RT-qPCR verification

[0020] The expression of the identified DE-miRNA is detected by using RT-qPCR technology.

[0021] Compared with the prior art, the beneficial effects of the present application are as follows:

[0022] The present application uses RNA sequencing and bioinformatics methods to explore the role of serum exosome miRNA in the pathogenesis of WD. By analyzing the differential expression of miRNA in WD and control group patients, 59 DE-miRNAs are identified, which are related to key pathways such as metabolic regulation, cancer progression and signal transduction. In addition, the reliability of key DE-miRNAs is confirmed by experimental verification, including miR-451a and miR-204-5p, etc., indicating their potential as non-invasive biomarkers. The research results provide important theoretical basis and new insights for the precise diagnosis of WD and the development of potential therapeutic targets. BRIEF DESCRIPTION OF DRAWINGS

[0023] Figure 1 It is a general data statistical result. (A) (B) Comparison of age and disease duration of two groups of patients (not marked "ns" means no statistical significance). (C) (D) Comparison of gender composition of CG group and WDG group. (E) (F) Comparison of basic disease composition of CG group and WDG group.

[0024] Figure 2 Observation and identification of exosomes. (A) Observation of the morphological characteristics of exosomes by transmission electron microscopy; (B), (C) analysis of the particle size and concentration of exosomes; (D), (E), (F) detection of the expression of exosome marker proteins CD9 and CD81 by nanoflow cytometry.

[0025] Figure 3 Quantitative analysis of miRNA expression in two groups (Wayne diagram of miRNA expression in two groups).

[0026] Figure 4 Quantitative analysis of miRNA expression in two groups (distribution of miRNA expression chromosomes in CG group).

[0027] Figure 5 Quantitative analysis of miRNA expression in two groups (distribution of miRNA expression chromosomes in WDG group).

[0028] Figure 6 Expression and distribution characteristics of DE-miRNAs in serum-derived exosomes in CG and WDG groups (principal component analysis chart).

[0029] Figure 7 Expression and distribution characteristics of DE-miRNAs in serum-derived exosomes in CG and WDG groups (DE-miRNA expression volcano plot).

[0030] Figure 8 Expression and distribution characteristics of DE-miRNAs in serum-derived exosomes in CG and WDG groups (DE-miRNA expression distribution scatter plot).

[0031] Figure 9 Expression and distribution characteristics of DE-miRNAs in serum-derived exosomes in CG and WDG groups (DE-miRNA distribution heat map).

[0032] Figure 10 GO analysis of DE-miRNAs expression in serum-derived exosomes of WD patients (summary bar chart of enrichment classification).

[0033] Figure 11 GO analysis of DE-miRNAs expression in serum-derived exosomes of WD patients (top 20 functional classification enrichment chart).

[0034] Figure 12 GO analysis of DE-miRNAs expression in serum-derived exosomes of WD patients (20 cell component classification enrichment chart).

[0035] Figure 13GO analysis of serum-derived exosomal DE-miRNAs expression in WD patients (top 20 cell function category enrichment plot).

[0036] Figure 14 KEGG analysis of serum-derived exosomal DE-miRNAs expression in WD patients (summary bar plot of KEGG enrichment categories).

[0037] Figure 15 KEGG analysis of serum-derived exosomal DE-miRNAs expression in WD patients (top 20 items in KEGG enrichment chart).

[0038] Figure 16 KEGG analysis of serum-derived exosomal DE-miRNAs expression in WD patients (KEGG enrichment scatter plot).

[0039] Figure 17 Interaction network diagram between the top 100 differentially expressed miRNAs and predicted targets.

[0040] Figure 18 Interaction network diagram between the top 500 differentially expressed miRNAs and predicted targets.

[0041] Figure 19 Interaction network diagram between the top 1000 differentially expressed miRNAs and predicted targets.

[0042] Figure 20 Bar chart of RT-qPCR results for verification of selected differentially expressed miRNAs (*P<0.05). (A) (B) (C) (D) (E) (F) correspond to differentially expressed miRNAs (hsa-miR-183-5p, hsa-miR-451a, hsa-miR-144-3p, hsa-miR-204-5p, hsa-miR-483-5p, hsa-miR-150-5p), respectively. DETAILED DESCRIPTION

[0043] The present application will be further described in conjunction with the examples and drawings.

[0044] 1. Materials and methods

[0045] 1.1. Study subjects

[0046] This study mainly included 15 WD patients who met the criteria and were treated in the inpatient department of the Encephalopathy Center of the First Affiliated Hospital of Anhui University of Chinese Medicine from January 2024 to December 2024, and 15 healthy volunteers as controls.

[0047] 1.1.1 WD diagnostic criteria

[0048] According to the WD clinical diagnostic criteria in the Guidelines for the Diagnosis and Treatment of Wilson Disease in China 2021, patients with unexplained liver disease, neurological symptoms (especially extrapyramidal symptoms) or mental symptoms should be considered as Wilson disease. The age of onset cannot be used as a basis for diagnosis or exclusion of Wilson disease. The specific diagnostic points are recommended as follows: 1. Neurological and / or mental symptoms. 2. Unexplained liver damage. 3. Decreased serum copper blue protein and / or increased 24-hour urinary copper. 4. Positive corneal K-F ring. 5. Family segregation and pathogenic analysis of gene variation determine that both chromosomes of the patient carry ATP7B gene pathogenic variation. Wilson disease can be diagnosed when (1 or 2) + (3 and 4) or (1 or 2) + 5 are met; Wilson disease can be diagnosed when 3 + 4 or 5 are met but no obvious clinical symptoms are present; Wilson disease can be diagnosed when any two of the first three are met, further observation is required, and ATP7B gene detection is recommended to confirm the diagnosis.

[0049] 1.1.2 Inclusion criteria

[0050] The WD patients included should meet the following criteria: (1) meet the diagnostic criteria in the Guidelines for the Diagnosis and Treatment of Wilson Disease in China 2021; (2) the patient and family members agree to participate in this study and sign the informed consent form.

[0051] 1.1.3 Exclusion criteria

[0052] The exclusion criteria include: (1) age <10 years or >50 years; (2) pregnant or lactating women; (3) combined with severe mental and behavioral abnormalities; (4) combined with viral hepatitis, autoimmune hepatitis, alcoholic liver disease or brain organic disease such as brain tumor, encephalitis, epilepsy and brain trauma; (5) patients with unstable conditions such as hepatic encephalopathy, hepatorenal syndrome, upper gastrointestinal bleeding, portal vein thrombosis, etc.

[0053] 1.2 Collection of general information and blood samples

[0054] General and clinical information of the included control group (CG) and WD group (WDG) were collected, including name, age, gender, etc. In all enrolled cases, three pairs of blood samples (A1-A3, D1-D3) were randomly selected for serum exosome extraction, and another twelve pairs of blood samples were used for sequencing data verification.

[0055] 1.3 Isolation, extraction and identification of exosomes

[0056] Exosomes were extracted by ultracentrifugation. After the sample was slowly melted at 37°C, it was moved to a new centrifuge tube and centrifuged at 2000 x g for 30 min at 4°C. The supernatant was moved to a new centrifuge tube and centrifuged again at 10,000 x g for 45 min at 4°C to remove larger vesicles. The supernatant was collected and filtered through a 0.45 μm filter membrane. The filtrate was moved to a new centrifuge tube and ultracentrifuged at 100,000 x g for 70 min at 4°C. The supernatant was removed, resuspended with 10 ml of pre-cooled 1 x PBS, and ultracentrifuged again at 100,000 x g for 70 min at 4°C. The supernatant was removed again, resuspended with 150 μL of pre-cooled 1 x PBS, and divided into two groups, with 20 μL of each sample mixed into one sample. 20 μL was taken for electron microscopy, 10 μL for particle size, and 20 μL for fluorescence. The remaining exosomes were stored at -80°C.

[0057] Exosomes were observed by transmission electron microscopy. 10 μL of exosomes were taken, 10 μL of the sample was dropped onto a copper grid and allowed to settle for 1 min, and the floating liquid was absorbed with filter paper. 10 μL of uranyl acetate was dropped onto the copper grid and allowed to settle for 1 min, and the floating liquid was absorbed with filter paper. After drying for several minutes at room temperature, electron microscopy was performed at 80 kv to obtain images.

[0058] The particle size of the extracted exosomes was analyzed. First, 10 μL of exosomes was diluted to an appropriate multiple. After the instrument performance test was passed using a standard sample, the exosome sample was loaded for testing, and gradient dilution was performed to avoid sample blockage of the sample needle. After the sample was completed, the particle size and concentration information of the exosomes were obtained.

[0059] The expression of exosome fluorescent markers was detected by nano-flow. 20 μL of exosomes was diluted to 60 μL, and 30 μL of the diluted exosomes was added to 20 μL of fluorescently labeled antibody (CD9, CD81), mixed well, and incubated at 37°C for 30 min in the dark. 1 ml of pre-cooled PBS was added, and ultracentrifugation was performed at 110,000 x g for 70 min at 4°C. The supernatant was removed, 1 ml of pre-cooled PBS was added, and ultracentrifugation was performed again at 110,000 x g for 70 min at 4°C. The supernatant was removed again, and the sample was resuspended with 50 μL of pre-cooled 1 x PBS. After the instrument performance test was passed using a standard sample, the exosome sample was loaded for testing, and gradient dilution was performed to avoid sample blockage of the sample needle. After the sample was completed, the protein index results were obtained.

[0060] 1.4 Total RNA extraction and quality control

[0061] Exosomal RNA was isolated and purified using exoRNeasy Midi / Maxi (QIAGEN, Germany) kit, and the integrity of the RNA was detected by 300 / 5400 Fragment Analyzer, (Agilent, CA, USA). The specific operation is as follows: (1) Exosome purification: the pre-filtered serum (not containing more than 0.8 μM microparticles) was mixed with Buffer XBP, and added to the exoEasay affinity membrane centrifugal column to be combined on the column. The vesicles combined on the membrane were washed with Buffer XWP, and then QIAzol lysis was used. (2) Total RNA isolation: chloroform was added to the QIAzol eluent, and the aqueous phase was recovered and mixed with ethanol. The total RNA including miRNA was combined on the centrifugal column, washed three times and eluted.

[0062] 1.5 Library construction and miRNA sequencing and identification

[0063] The experimental procedure was performed according to the standard steps provided by Illumina, including library preparation and sequencing experiment. The sRNA sequencing library preparation used TruSeq Small RNA Sample Prep Kits (Illumina, San Diego, USA) kit. After the library preparation work was completed, the constructed library was sequenced using Illumina Hiseq 2000 / 2500, and the sequencing read length was single-end 50bp. First, the raw data was filtered, and the raw reads obtained by sequencing were processed to obtain clean reads. The sRNA length was screened, and the base length range of 18-26nt was retained. The various RNA databases were compared and analyzed, the remaining sequences were compared with mRNA, RFam, Repbase databases (not containing miRNA), and filtered to obtain effective data. The effective data was compared with the mature body and precursor sequence of human in miRBase 22.0 database, and the genomic sequence of the species was compared to identify known miRNA and new predicted miRNA. The expression level of miRNA in each sample was calculated using ACGT101-miR after length filtering the data, and the expression was statistically analyzed using data normalization method.

[0064] 1.6 miRNA differential expression and GO, KEGG analysis

[0065] The input data is the normalized value (norm value), and the p-value calculation model based on the normal distribution is used for p-value calculation. With P<0.05 as the screening standard, cluster heat map, volcano plot, scatter plot and other analysis are carried out, and according to the analysis results, the differentially expressed genes with significant expression in the health group and the WD group are screened out. In order to further explore the function and regulation mechanism of DE-miRNA, this study uses GO (Gene Ontology) analysis to analyze the specific functions of DE-miRNA in molecular function, cell composition and biological process dimensions; at the same time, KEGG and KEGG (Kyoto Encyclopedia of Genes and Genomes) analysis are used to track the various signal transduction pathways and metabolic pathways in which DE-miRNA is involved, in order to further clarify its key regulatory role.

[0066] 1.7 Prediction and construction of miRNA target gene network

[0067] Based on the differential expression analysis of miRNA and the prediction of the relationship between miRNAs and target genes, the miRNA target interaction network was constructed using Cytoscape v3.10.1 software (http: / / www.cytoscape.org / ) to further evaluate the interaction between miRNAs and target genes.

[0068] 1.8 RT-qPCR verification

[0069] The expression of DE-miRNA identified was detected by RT-qPCR technology. First, the total RNA was carefully extracted from the sample, and then it was put into the reverse transcription process to convert it into the corresponding cDNA. Then, specific primers were selected for different types of cDNA for PCR amplification. In this process, two microliters of cDNA template were accurately added to each PCR reaction system to ensure the consistency of the reaction conditions. Subsequently, the 2-ΔΔCT method was used for data processing, and the CT value of the target gene was compared with the CT value of β-actin as the endogenous control. The difference between the two was used to strictly estimate the relative expression degree of the target gene, so as to realize the reliable quantitative evaluation of the expression level of DE-miRNA.

[0070] 1.9 Statistical analysis

[0071] All data in this study were analyzed using SPSS 27.0 statistical software. For comparisons of two independent numerical variables, an independent samples t-test was used if the data conformed to a normal distribution. If the conditions for application such as normality, homogeneity of variance, and independence were not met, nonparametric tests were used. For completely randomized, unordered categorical variables, correlation and difference analyses were performed using the chi-square test. 2 The results were analyzed using the Benjamini-Hochberg method. A p-value < 0.05 was considered statistically significant. When p < 0.05, the entries or pathways involved were considered to have reached a statistically significant enrichment level.

[0072] 2 Results

[0073] 2.1 Comparison of General Data

[0074] This study included 30 patients with WD and a healthy control group. General information is shown in Table 1. Results showed no significant differences between the two groups in terms of age, sex, disease duration, and underlying diseases (mainly diabetes, hyperlipidemia, and hypertension) (p > 0.05) (Table 1). Data comparison and composition are as follows. Figure 1 As shown in AF.

[0075] Table 1

[0076] CG WDG X 2 / t]] P-value Age 25.80±4.92 26.67±5.37 0.461 0.648 Gender (male) 7（46.70%） 8（53.30%） 0.133 0.715 Course of disease(years) 6.87±2.29 6.27±1.91 0.779 0.443 Basic diseases 3(20.00%) 2(13.33%) 0.382 0.547

[0077] 2.2 Observation and identification of exosome morphology and characteristics

[0078] Transmission electron microscopy revealed that the extracted exosomes were roundish vesicles with a double-membrane structure and clear outlines, consistent with the morphological characteristics of exosomes. Figure 2 A). Particle size analysis results showed that the average diameter of the extracted exosome vesicles was approximately 94.2 nm, with a concentration of 5.47 × 10⁻⁶. 9 Particles / mL ( Figure 2 B, C). This indicates that the extracted exosomes were of a reasonable quantity and good activity. Nanoflow cytometry analysis of exosome surface marker protein expression showed that, compared to the blank control group, the extracted exosomes exhibited positive expression of CD9 and CD81. Figure 2 DF).

[0079] 2.3 Quantitative analysis of miRNA expression between the two groups of samples

[0080] A total of 1364 miRNAs were screened, of which 893 miRNAs were co-expressed in CG and WDG. In addition, 199 miRNAs were mainly expressed in CG, and 272 miRNAs were specifically expressed in WDG. Figure 3). Except for chr4, chr8, chr10, chr16, chrY, the detected miRNAs were distributed on the rest of the chromosomes, and there was a correlation between miRNA expression and different degrees of correlation (Table 2). Figure 3 、 4 、5)。

[0081] 2.4 Identification of DE-miRNAs in serum-derived exosomes of WD patients

[0082] Under the screening criteria of Log2|fold change (FC)|≥1 and p-value < 0.05, 59 DE-miRNAs were obtained from the serum exosomes of WD patients. Specifically, among the identified DE-miRNAs, there were 23 significantly up-regulated miRNAs and 34 significantly down-regulated miRNAs, of which 4 were significantly down-regulated miRNAs with p-value < 0.01. The Top10 known DE-miRNAs identified are listed in the table (Table 2). The principal component analysis diagram, volcano plot, scatter plot and heat map drawn show the distribution characteristics of the identified DE-miRNAs (Fig. 2). Figure 6 、 7 、8、9)。

[0083] Table 2

[0084] miR_name miR_seq log2FC P-value up / down hsa-miR-3912-3p TAACGCATAATATGGACATGT -1.89 0.00 down hsa-miR-141-3p_R-1 TAACACTGTCTGGTAAAGATG -1.64 0.01 down hsa-miR-451a AAACCGTTACCATTACTGAGTT 1.31 0.01 up hsa-miR-146a-3p_L+1R-1 ACCTCTGAAATTCAGTTCTTCA -1.56 0.02 down hsa-miR-127-5p_R+1 CTGAAGCTCAGAGGGCTCTGATT -1.31 0.03 down hsa-miR-204-5p TTCCCTTTGTCATCCTATGCCT -2.86 0.03 down hsa-miR-483-5p AAGACGGGAGGAAAGAAGGGAG -1.69 0.03 down sha-miR-125a_R+2 TCCCTGAGACCCTAACTTGTGAAA 2.40 0.04 up hsa-miR-150-5p TCTCCCAACCCTTGTACCAGTG -1.61 0.04 down hsa-miR-941 CACCCGGCTGTGTGCACATGTGC -1.18 0.04 down

[0085] 2.5 GO analysis of the expression of DE-miRNAs in serum-derived exosomes of WD patients

[0086] By GO analysis of the DE-miRNAs expression in serum-derived exosomes of WD patients, 50 significantly enriched biological processes and functions were found, consisting of 25 biological processes (BP), 15 cellular components (CC) and 10 molecular functions (MF). Among them, the BP items included signal transduction (GO:0007165), regulation of transcription by RNA polymerase II (GO:0006357), regulation of DNA-templated transcription (GO:0006355), positive regulation of transcription by RNA polymerase II (GO:0045944), cell differentiation (GO:0030154), etc. The CC items included membrane (GO:0016020), cytoplasm (GO:0005737), nucleus (GO:0005634), cytosol (GO:0005829), membrane (GO:0016021), etc. The MF items included protein binding (GO:0005515), metal ion binding (GO:0046872), DNA binding (GO:0003677), transferase activity (GO:0016740), nucleotide binding (GO:0000166), etc. The GO analysis results of the DE-miRNAs expression and the TOP20 GO enrichment items are shown in the figures (12, 13). Figure 10 、 11 、12、13)。

[0087] 2.6 KEGG analysis of the DE-miRNAs expression in serum-derived exosomes of WD patients

[0088] KEGG analysis was further applied to identify the enrichment of DE-miRNAs in biological pathways. The results showed that the identified DE-miRNAs obtained enrichment in 6 categories and 30 related biological pathways including Cellular Processes, Environmental Information Processing, Genetic Information Processing, Human Diseases, Metabolism and Organismal Systems. The top-ranking pathways included Metabolic pathways, Pathways in cancer, Rap1 signaling pathway, Axon guidance, Ras signaling pathway, Calcium signaling pathway, Regulation of actin cytoskeleton, Hippo signaling pathway, Proteoglycans in cancer, P13K-Akt signaling pathway, Focal adhesion, Ubiquitin mediated proteolysis, Hepatocellular carcinoma, Oxytocin signaling pathway, Glutamatergic synapse, ErbB signaling pathway, etc. The classification summary chart, TOP20 enrichment pathway chart and bubble chart drawn according to the KEGG analysis results are shown in FIGS. Figure 14 、 15 , 16.

[0089] 2.7 DE-miRNAs target prediction and target network interaction relationship

[0090] In order to further analyze the role of exosome-derived DE-miRNAs and related target genes in WD, we predicted the targeting relationship of the obtained genes and listed part of the results in the table (Table 3). In addition, according to the DE-miRNAs target prediction results, the TOP 100, 500 and 1000 DE-miRNA target interaction networks were further constructed (FIGS. Figure 17 、 18 , 19).

[0091] Table 3

[0092] miRNA.ID Transcript.ID Gene.ID Target Gene TargetScan.score PC-5p-2372428_4 ENST00000000233 ENSG00000004059 ARF5 61 PC-5p-6237779_2 ENST00000000233 ENSG00000004059 ARF5 89 hsa-miR-301a-5p ENST00000000412 ENSG00000003056 M6PR 94 PC-5p-586292_16 ENST00000000442 ENSG00000173153 ESRRA 75 PC-3p-354125_32 ENST00000001008 ENSG00000004478 FKBP4 94 PC-3p-642433_14 ENST00000001008 ENSG00000004478 FKBP4 70 PC-3p-485817_21 ENST00000001146 ENSG00000003137 CYP26B1 50 PC-5p-1691926_6 ENST00000001146 ENSG00000003137 CYP26B1 71 PC-5p-295635_42 ENST00000001146 ENSG00000003137 CYP26B1 66 hsa-miR-17-3p_R+1 ENST00000002125 ENSG00000003509 NDUFAF7 94

[0093] 2.8 RT-qPCR verification

[0094] Randomly selected 6 DE-miRNAs (including up-regulated hsa-miR-183-5p, hsa-miR-451a, hsa-miR-144-3p and down-regulated hsa-miR-204-5p, hsa-miR-483-5p, hsa-miR-150-5p) identified, RT-qPCR was used to verify the gene level in the remaining samples of two groups. The results showed that there was a statistically significant difference in the expression of differential genes between CG and WDG (P<0.05), suggesting that the detection results were reliable. Figure 20 ).

[0095] 3 Discussion

[0096] WD is a rare genetic metabolic disorder involving complex mechanisms such as abnormal copper metabolism. Traditional diagnosis and treatment methods have certain limitations. Exosomal miRNA, as a key regulator of gene expression, plays an important role in the progression of various diseases. In this study, we extracted serum exosomes from WD patients for miRNA expression profile analysis, and obtained 59 DE-miRNAs, including 23 significantly up-regulated miRNAs and 34 significantly down-regulated miRNAs. GO analysis of the identified DE-miRNAs expression obtained multiple significantly enriched biological processes and functional items such as signal transduction, transcriptional regulation, and metal ion binding. KEGG analysis suggested that the enriched signaling pathways were Metabolic pathways, Pathways in cancer, Rap1 signaling pathway, Axon guidance, Ras signaling pathway, Calcium signaling pathway, Regulation of actin cytoskeleton, Hippo signaling pathway, Proteoglycans in cancer, P13K-Akt signaling pathway, Focal adhesion, Ubiquitin-mediated proteolysis, Hepatocellular carcinoma, Oxytocin signaling pathway, Glutamatergic synapse, and ErbB signaling pathway. Based on the target prediction results, we further constructed the miRNA target gene interaction network. Finally, RT-qPCR verification of the obtained DE-miRNAs was performed, which was consistent with the detection results (P<0.05).

[0097] Among the DE-miRNAs identified in this study, miR-483-5p was also found to be associated with HSCs activation-induced liver fibrosis and neuroprotective effects. Studies have found that overexpression of miR-483-5p / 3p inhibits transgenic mouse TGF-stimulated HSCs and synergistically inhibits mouse liver fibrosis. miR-483-5p can inhibit hepatocellular carcinoma cell proliferation, cellular steatosis, and fibrosis by targeting PPARα and TIMP2. Some researchers have also found that miR-483-5p has a neuroprotective effect on cardiac arrest-induced mitochondrial dysfunction through the TNFSF8 / AMPK / JNK signaling pathway.

[0098] Similar to miR-483-5p, miR-204-5p was also found to be associated with liver tumors. Studies have found that miR-204-5p can target SIRT1 to regulate hepatocellular carcinoma progression. Through the JAK2 / STAT3 axis to regulate angiogenesis, miR-204-5p is closely related to hepatoblastoma.

[0099] MiR-451a has been found to play an important role in some liver diseases. Studies have found that MiR-451a can improve alcoholic hepatitis by inhibiting hdac8-mediated pro-inflammatory response. By targeting glycerol kinase, MiR-451a can reduce liver steatosis and hepatitis C virus replication. It is suggested that the mechanism of action of MiR-451a in WD may be related to inflammation and metabolism. miR-125a has also been found to be significantly associated with certain metabolic diseases. Studies have found that miR-125a-5p improves liver glucose and lipid metabolism disorders in type 2 diabetes by targeting STAT3. Studies have also shown that abnormally regulated miR-125a can promote angiogenesis by enhancing glycolysis. It is suggested that miR-125a may play a key role in WD pathogenesis by regulating liver metabolism.

[0100] In addition, studies have found that miR-146a-3p inhibits the differentiation of hAMSCs into Schwann cells by inhibiting the expression of ERBB2. For miR-127-5p, current research mainly focuses on osteoarthritis, bone differentiation, and osteogenesis. Some scholars have also proposed that miR-127-5p can be used as a diagnostic and prognostic marker for traumatic brain injury (TBI) in trauma patients. In addition to affecting certain tumor diseases and bone diseases, miR-150-5p can activate fibroblasts to affect renal fibrosis after unilateral ischemia-reperfusion injury, suggesting that it may play a role in WD by targeting the kidney. For miR-3912 and miR-941, there is currently less research, and some scholars have found that both mainly have specific expression in certain tumor diseases.

[0101] In summary, the present application explores the pathogenesis of WD from the perspective of exosome miRNA, providing new targets and research ideas for the diagnosis and treatment of the disease. Based on this, the present application precisely screens specific biomarkers of Wilson disease serum miRNA, opening up new ideas and laying a solid scientific foundation for the diagnosis and treatment of the disease.

Claims

1. A method for screening Wilson disease serum exosomal miRNA biomarkers based on transcriptomic technology, characterized by, The steps are as follows: Step 1, isolation, extraction and identification of exosomes Exosomes in blood samples were extracted by ultracentrifugation, and the extracted exosomes were observed by transmission electron microscopy. Particle size analysis was performed on the extracted exosomes, and the expression of exosome fluorescent markers was detected by nano-flow; Step 2, total RNA extraction and quality control The exosome RNA was separated and purified, and the integrity of the RNA was detected, and then the total RNA was separated; Step 3, library construction and miRNA sequencing and identification The sequencing library preparation used TruSeq Small RNA Sample Prep Kits (Illumina, San Diego, USA) kit; after the library preparation work was completed, Illumina Hiseq 2000 / 2500 was used for sequencing, and the sequencing read length was single-end 50bp; first, the raw data was filtered, and the raw reads obtained by sequencing were processed to obtain clean reads; screen sRNA length, retain base length range of 18-26nt; align various RNA databases, align the remaining sequences to mRNA, RFam, Repbase database (not including miRNA), and filter to obtain effective data; align the effective data with the mature body and precursor sequence of human in miRBase 22.0 database, and the genome sequence of the species, identify known miRNA and new predicted miRNA; use ACGT101-miR to calculate the expression level of miRNA in each sample after length filtering, and use data normalization method to statistically analyze the expression amount; Step 4, miRNA differential expression and GO, KEGG analysis The input data is the normalized value (norm value), and the p-value calculation model based on normal distribution is used for p-value calculation; take P<0.05 as the screening standard, and perform clustering heat map, volcano plot, scatter plot analysis, according to the analysis results, screen out the differentially expressed genes expressed significantly in the health group and the WD group; With the help of GO analysis, the specific utility of DE-miRNA in molecular function, cell composition and biological process dimensions is analyzed; at the same time, KEGG analysis is used to track the various signal transduction pathways and metabolic pathways involved in DE-miRNA; Step 5, prediction and construction of miRNA target gene network Based on the differential expression analysis of miRNA and the prediction of the relationship between miRNAs and target genes, the miRNA target interaction network was constructed by using Cytoscape v3.10.1 software to further evaluate the interaction relationship between miRNAs and target genes; Step 6, RT-qPCR verification The expression of DE-miRNA identified was detected by RT-qPCR technology.

2. The method of claim 1, wherein, In step 1, the exosomes are extracted by ultracentrifugation; after the sample is melted at 37° at a medium speed, the sample is moved to a new centrifugal tube, centrifuged at 2000xg at 4° for 30 min; the supernatant is moved to a new centrifugal tube, centrifuged again at 10,000xg at 4° for 45 min to remove larger vesicles; the supernatant is taken, filtered through a 0.45μm filter membrane, and the filtrate is collected; the filtrate is moved to a new centrifugal tube, an ultracentrifuge rotor is selected, and the sample is centrifuged at 100,000xg at 4° for 70 min; the supernatant is removed, the sample is resuspended with 10ml of pre-cooled 1xPBS, an ultracentrifuge rotor is selected again, and the sample is centrifuged at 100,000xg at 4° for 70 min again; the supernatant is removed again, the sample is resuspended with 150μL of pre-cooled 1xPBS, and the sample is divided into two groups, 20μL of each sample is taken to make one case, 20μL of the sample is taken for electron microscopy, 10μL of the sample is taken for particle size, 20μL of the sample is taken for fluorescence, and the remaining exosomes are stored at -80°C; The extracted exosomes are observed by transmission electron microscopy; 10μL of the exosomes is taken, 10μL of the sample is taken and dropped on a copper grid to precipitate for 1min, and the floating liquid is absorbed by filter paper; 10μL of uranyl acetate is dropped on the copper grid to precipitate for 1min, and the floating liquid is absorbed by filter paper; after drying for several minutes at room temperature, electron microscopy detection imaging is performed at 80kv; The extracted exosomes are subjected to particle size analysis; first, 10μL of the exosomes is taken and diluted to an appropriate multiple; after the instrument performance test is passed using a standard sample, the exosome sample is loaded, and gradient dilution is performed to avoid sample blockage of the sample needle; After the sample is completed, the particle size and concentration information of the exosomes detected by the instrument are obtained; The expression of the fluorescence marker of the exosomes is detected by nanoflow; 20μL of the exosomes is diluted to 60μL, 30μL of the diluted exosomes is taken and added to 20μL of the fluorescence-labeled antibody (CD9, CD81), mixed, incubated at 37°C for 30min in the dark; 1ml of pre-cooled PBS is added, an ultracentrifuge rotor is selected, and the sample is ultracentrifuged at 110,000xg at 4°C for 70min; the supernatant is removed, 1ml of pre-cooled PBS is added, an ultracentrifuge rotor is selected again, and the sample is ultracentrifuged at 110,000xg at 4°C for 70min again; The supernatant is removed again, and the sample is resuspended with 50μL of pre-cooled 1xPBS; the instrument performance test is performed using a standard sample, and the exosome sample is loaded after passing the test; gradient dilution is required to avoid sample blockage of the sample needle; After the sample is completed, the protein index results detected by the instrument are obtained.

3. The method of claim 1, wherein, In Step 2, the exosome RNA is isolated and purified using an exoRNeasy Midi / Maxi (QIAGEN, Germany) kit, and the integrity of the RNA is then detected by a 300 / 5400 Fragment Analyzer (Agilent, CA, USA); the specific operation is as follows: (1) exosome purification: the pre-filtered serum (not containing microparticles greater than 0.8 μM) is mixed with Buffer XBP and added to an exoEasay affinity membrane centrifugal column to be combined on the column; the vesicles combined on the membrane are washed using Buffer XWP, and then lysed using QIAzol; (2) total RNA isolation: chloroform is added to the QIAzol eluent, the aqueous phase is recovered, and mixed with ethanol; the total RNA including miRNA is combined on a centrifugal column, washed three times, and then eluted.

4. The method of claim 1, wherein, In Step 6, first, the total RNA is carefully extracted from the sample, and then put into the reverse transcription process to convert it into the corresponding cDNA; next, specific primers are selected for different types of cDNA to carry out PCR amplification operation; in this process, two microliters of cDNA template are accurately added to each PCR reaction system to ensure the consistency of the reaction conditions; subsequently, the 2-ΔΔCT method is used for data processing, the CT value of the target gene is carefully compared with the CT value of β-actin as the endogenous control, and the difference between the two is used to strictly estimate the relative expression degree of the target gene, so as to realize the reliable quantitative evaluation of the expression level of DE-miRNA.

5. Use of the serum exosome miRNA biomarker of Wilson disease screened by the method according to any one of claims 1-4 in the preparation for diagnosing Wilson disease.

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