Substance, kit and application thereof for detecting differentially methylated genes of EB virus genome of CAEBV and IM

Abstract

The present invention discloses an application of a substance for detecting the methylation levels of EBNA-LP 30069bp, EBNA-3A 53968bp, A73 157064bp, BALF5 15437bp and BNRF1 2422bp sites in the genome of a sample to be tested in the preparation of a product for identifying or assisting in identifying chronic active EB virus infection and infectious mononucleosis. The present invention can detect the methylation levels of the above gene sites by using the detection substance to perform early identification of CAEBV and IM.

Description

Technical Field

[0001] The present invention relates to a primer set and a kit for identifying methylation differences, in particular to a primer set, a kit and applications thereof for identifying differentially methylated genes of Epstein-Barr virus (EBV) in chronic active Epstein-Barr virus infection (CAEBV) and infectious mononucleosis (IM), and belongs to the field of biotechnology. Background Art

[0002] Epstein-Barr virus (EBV) is a member of the subfamily of gammaherpesvirus of the family Herpesviridae. It is a human lymphotropic virus that is widely distributed throughout the world. About 90% of healthy adults worldwide carry the virus. EBV proliferates in oropharyngeal epithelial cells and then infects B lymphocytes. These cells enter the blood circulation in large numbers, causing systemic infection, and can remain latent in human lymphoid tissues for a long time. EBV infection can manifest as proliferative infection and latent infection. Different infection states express different antigens. The antigens expressed in the proliferative infection period include EBV early antigens, EBV capsid proteins, and EBV membrane antigens, and the antigens expressed in the latent infection period include EBV nuclear antigens and latent membrane proteins. EBV is associated with the occurrence and development of a variety of human lymphocytic and epithelial tumors, such as Hodgkin's lymphoma, nasopharyngeal carcinoma, and EBV-related gastric cancer. It is a recognized DNA tumor virus.

[0003] EBV is the pathogen of infectious mononucleosis (IM) and chronic active EBV infection (CAEBV). IM is an acute lymphoproliferative disease that often occurs after the first EBV infection in adolescence. Its clinical manifestations include fever, pharyngitis, lymphadenitis, splenomegaly, abnormal liver function, and a large increase in monocytes and atypical lymphocytes in peripheral blood. CAEBV is a lymphoproliferative disease caused by EBV infection of T lymphocytes, NK cells or B lymphocytes. CAEBV usually occurs after primary EBV infection and is characterized by fever, lymphadenopathy and splenomegaly. Laboratory tests can reveal high copy numbers of EBV-DNA and / or abnormal EBV-related antibodies, and pathological examinations can detect EBV-encoded RNA and a variety of virus-related proteins in damaged tissues. Studies have found that early allogeneic hematopoietic stem cell transplantation is an effective way to cure the disease.

[0004] Studies have found that epigenetic modifications, especially DNA methylation modifications, play an important role in EBV infection-related diseases. After EBV infects host cells, EBV viral genes may regulate the DNA methylation level of host genes through methyltransferases, thereby participating in the EBV pathogenic process. The methylation characteristics of the EBV genome are different in different EBV-related diseases, so the corresponding diseases can be identified and diagnosed by detecting differential DNA methylation genes.

[0005] At present, the potential role of changes in EBV genome-wide DNA methylation in the occurrence and development of chronic active EBV infection (CAEBV) and infectious mononucleosis (IM) has not been studied. Comparing differentially methylated genes in the EBV genome between CAEBV and IM, and screening DNA methylation biomarkers that can be used for clinical diagnosis and prognosis, can further reveal the mechanism of methylation in the occurrence and development of the disease, and is of great significance as a basis for early detection and diagnosis of the disease. Summary of the invention

[0006] The primary technical problem to be solved by the present invention is to provide a new use of a substance for detecting the methylation level of relevant sites in the EB virus genome of a sample to be tested in identifying or assisting in identifying chronic active EB virus infection and infectious mononucleosis.

[0007] Another technical problem to be solved by the present invention is to provide a detection primer set for detecting the above-mentioned related sites.

[0008] In order to achieve the above technical objectives, the present invention adopts the following technical solutions:

[0009] The use of substances for detecting the methylation levels of EBNA-LP, EBNA-3A, A73, BALF5 and BNRF1 sites in the EB virus genome of the sample to be tested in the preparation of products for identification or auxiliary identification of chronic active EB virus infection and infectious mononucleosis.

[0010] Preferably, the substance is a primer set.

[0011] Preferably, the primer set is selected from a primer pair consisting of SEQ ID No.1 and SEQ ID No.2, a primer pair of SEQ ID No.3 and SEQ ID No.4, a primer pair of SEQ ID No.5 and SEQ ID No.6, a primer pair of SEQ ID No.7 and SEQ ID No.8, and a primer pair of SEQ ID No.9 and SEQ ID No.10.

[0012] A primer set for detecting differentially methylated genes in the Epstein-Barr virus genome of chronic active Epstein-Barr virus infection and infectious mononucleosis, wherein the primer set is used to detect the methylation levels of five sites, namely, EBNA-LP, EBNA-3A, A73, BALF5 and BNRF1, in the Epstein-Barr virus genome.

[0013] Preferably, the primer set is selected from a primer pair consisting of SEQ ID No.1 and SEQ ID No.2, a primer pair of SEQ ID No.3 and SEQ ID No.4, a primer pair of SEQ ID No.5 and SEQ ID No.6, a primer pair of SEQ ID No.7 and SEQ ID No.8, and a primer pair of SEQ ID No.9 and SEQ ID No.10.

[0014] Use of the primer set in a detection reagent or detection kit for detecting or assisting in detecting the differential methylation level in the EB virus genome of chronic active EB virus infection and infectious mononucleosis.

[0015] A kit for detecting differentially methylated genes in the EB virus genome of chronic active EB virus infection and infectious mononucleosis, comprising the above primer set.

[0016] Compared with the prior art, the present invention has the following technical effects:

[0017] (1) The present invention optimizes and improves the screening method to screen out the differential methylation sites between CAEBV and IM with strong stability and significant specificity. The present invention proposes for the first time a method for distinguishing CAEBV from IM by detecting the methylation level of the target EBV gene.

[0018] (2) The present invention provides five methylation sites located on the EB virus genome. Changes in the methylation sites caused by EBV infection are associated with CAEBV. Early diagnosis of CAEBV can be performed by detecting changes in the methylation sites of the EB virus genome.

[0019] (3) The five differential methylation sites between CAEBV and IM provided by the present invention have good specificity and excellent stability. The changes in the methylation sites at EBNA-LP (30069bp), EBNA-3A (53968bp), A73 (157064bp), BALF5 (154371bp) and BNRF1 (2422bp) of the EB virus genome can be further detected for early identification of CAEBV and IM.

[0020] (4) The present invention provides primers for detecting methylation sites, which can be used to perform PCR amplification and pyrophosphate sequencing on the sequences to detect the methylation levels of the corresponding genes, thereby enabling early identification of CAEBV and IM. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 This is the methylation level map of the whole genome of EBV in CAEBV and IM groups;

[0022] Figure 2 The figure shows the analysis of EBV DNA methylation levels in PBMC samples of CAEBV and IM cases;

[0023] Figure 3 This is an analysis diagram of differentially methylated genes in the EBV genome between CAEBV and IM detected according to the first screening criteria;

[0024] Figure 4 This is an analysis diagram of differentially methylated genes in the EBV genome between CAEBV and IM detected according to the second screening criteria;

[0025] Figure 5A The methylation level analysis diagram of the five genes EBNA-LP, EBNA-3A, A73, BALF5 and BNRF1 that showed significant methylation differences between CAEBV and IM after the optimized second screening criteria;

[0026] Figure 5B This is the methylation level analysis chart of the five genes BcRF1, EBNA1, BPLF1, LMP-2A and BSLF1 that showed significant methylation differences between CAEBV and IM according to the first screening criteria.

[0027] Figure 6 To verify the methylation levels of EBNA-LP, A73, BALF5, BNRF1 and EBNA-3A genes in 5 children with CAEBV and 5 children with IM in the sample set. DETAILED DESCRIPTION

[0028] The technical content of the present invention is described in detail below with reference to the accompanying drawings and specific embodiments.

[0029] Example 1 Screening and analysis of differentially methylated sites between CAEBV and IM

[0030] 1. Sample source

[0031] Thirty-six peripheral blood mononuclear cell (PBMC) samples, 19 from children with chronic active EBV infection (CAEBV) and 17 from infectious mononucleosis (IM).

[0032] 2. Extraction of Total DNA

[0033] PBMC sample DNA was extracted using a magnetic bead method universal genomic DNA extraction kit (Tiangen). After the test was qualified, the sample DNA was subjected to bisulphate treatment (EZ DNA Method Gold Kit, ZymoRes earch). After treatment, unmethylated C was converted to U (converted to T after PCR amplification), while methylated C remained unchanged.

[0034] 3. Development of a Customized EBV Methylation Primer Panel Platform

[0035] Using the EBV (NC_007605.1) genome as the EBV reference sequence, 98 pairs of BS-PCR primers were designed and synthesized. Data quality control and comparison were performed after sequencing.

[0036] 4. BSP amplification of the Bisul fit treated template using a high fidelity U base tolerant DNA polymerase

[0037] The 1 / 20 eluted product was used as a template for 35 PCR amplifications using the KAPA HiFi HotStart Ultra+ReadyMix PCR Kit (KAPA Bio systems, Wilmington, MA, USA).

[0038] 5. Library Construction

[0039] The BSP amplification products from the same sample were mixed, amplified with label primers, and attached with Illumina sequencing adapters to finally obtain sequencing libraries with different labels for each sample. Each sample library was purified, quantified, mixed with multiple libraries, and quality checked before being sequenced.

[0040] 6. Data quality control and comparison (Excel)

[0041] Qualified data is the basis of information analysis. Therefore, quality control of the data after sequencing is the primary content of data analysis, which mainly includes:

[0042] (1) Perform basic statistics on the output and quality of raw reads after the machine is launched;

[0043] (2) The software Trimmomat i c-0.36 was used to remove sequencing adapters and low-quality bases and reads. A sliding window was used, with 4 bases as a window. If the average base quality value of the window was less than 15, reads were truncated from that window. The parameter selection was: SLIDINGWINDOW:4:15. Clean reads were obtained.

[0044] (3) Next, the cluster data were aligned with the amplified target sequence. The methylation sequencing data alignment software (bsmap, version v2.73) was used to align and analyze the cluster reads and the target sequence. The alignment mode was map to 2 forward strands, i.e., BSW(++) and BSC(-+).

[0045] 7. Methylation site detection and analysis

[0046] After the alignment is completed, the methylation level of the CG base on each target sequence of each sample is calculated using the BSMAP built-in Python program for calculating methylation. The methylation level is calculated according to the following formula:

[0047] The methylation level of site C = the number of reads supporting methylation / (the number of reads supporting methylation + the number of reads supporting non-methylation).

[0048] The methylation level of each site in each sample was counted, and if the depth was less than 30X, it would be represented as NA. Finally, according to the methylation levels calculated above, the R language was used for statistical and visual display, and the two-tailed t-test was used for inter-group difference analysis. When the p value of the test result was less than 0.05 and the absolute value of the difference in the average methylation level between the groups was greater than 0.1, the difference was considered significant. In this way, the effective methylation information of the single site on the whole genome was obtained.

[0049] like Figure 3 As shown, the site specificity obtained by the current screening method is poor and cannot effectively screen out specific differentially methylated gene sites for distinguishing CAEBV and IM. In order to further screen out effective methylation sites, the inventors optimized and improved the screening method as follows.

[0050] 8. Screening of significant differentially methylated sites

[0051] (1) To screen for specific heteromethylated gene loci that can effectively distinguish CAEBV from IM, we further optimized the screening scheme. Using 500 bp as a sliding window, we scanned the methylation information of each segment of the whole genome, screened the loci with the largest absolute difference in the average methylation level between groups in each segment, and counted the effective methylation information of each segment.

[0052] (2) Raise the screening threshold of differentially methylated sites between groups and perform screening again. The screening conditions are as follows:

[0053] 1) For sites where the methylation levels of the EBV genome differed between the CAEBV group and the IM group, sites where the absolute value of the difference between the mean methylation level of the CAEBV group and the mean of the IM group was greater than 0.3 were selected;

[0054] 2) Evaluate the stability within the differentially methylated site group, that is, calculate the coefficient of variation (CV), the formula is CV = S / M*100% (S is the sample standard deviation, M is the sample mean), and sites with CV less than or equal to 20% are considered stable. Sites with CV less than or equal to 20% in the CAEBV group are selected as significant differentially methylated sites.

[0055] 9. After repeated experiments, screening and verification, primers with high amplification efficiency and good specificity were obtained, which can detect the above-mentioned significantly differentially methylated gene levels in CAEBV and IM samples.

[0056] Example 2 Screening and analysis results of CAEBV and IM differentially methylated sites

[0057] 1. The methylation sites of 19 CAEBV and 17 IM samples were detected, and a total of 1019 differentially methylated sites were detected.

[0058] Based on the 1019 differentially methylated sites measured, the methylation levels of CAEBV and IM samples were visualized. Based on the absolute coordinates of the detected gene regions, a graph was drawn to display the methylation level of each site in each sample, and different colors were used according to biological groups. The average methylation level of each site in the group was displayed in the form of a dot map, which intuitively displays the location of differential methylation on the gene, the methylation level, and the significance of the difference. The visualization results are shown in the figure below. Figure 1 As shown, Figure 1 Figure 2 shows the DNA methylation levels of CpG sites in the whole EBV genome in the sequencing data of PBMC samples of CAEBV and IM cases. Each point in the figure represents the methylation level of the corresponding CpG site. Figure 1 The CAEBV methylation level was higher in the IM group than in the IM group.

[0059] 2. Differential methylation analysis between CAEBV and IM

[0060] The t-test was used to detect whether there were differences in each site of each amplicon in each comparison group. The average methylation level of each amplicon was calculated. The methylation distribution of each region between groups was displayed using a violin plot, and the inter-group difference analysis was performed. The results are shown in the figure. Figure 2 As shown, Figure 2 is the EBV DNA methylation level of PBMC samples of CAEBV and IM cases. Each point in the figure represents the average methylation level of a sample. The EBV genome methylation level in the CAEBV group was 78.4%, and the EBV genome methylation level in the IM group was 66.7%. The EBV genome methylation level in the CAEBV group was higher than that in the IM group (P<0.0001). According to the first screening criteria (two-tailed t-test was used for inter-group difference analysis, when the test result p value was less than 0.05, and the absolute value of the difference in the average methylation level between the groups was greater than 0.1), the differential methylation sites between the groups were screened, and the methylation status of each gene between the groups was displayed using a heat map. The results are shown in the figure. Figure 3 shown. Figure 3 Heat map of cluster analysis of differentially methylated genes in the EBV genome in PBMC samples from CAEBV and IM cases. Each column represents a sample. Each row represents the average methylation level of each EBV-encoded gene in the viral genome in all samples. The methylation level corresponding to each region of all samples is represented by a different color. The color gradient from dark to light represents the change from low to high gene methylation. CAEBV: chronic active EBV infection; IM: infectious mononucleosis.

[0061] 3. Screening of specific differentially methylated sites between CAEBV and IM

[0062] Since the 1019 differentially methylated sites initially detected had poor specificity, in order to further screen out differentially methylated sites that can specifically distinguish CAEBV and IM, the inventors optimized the screening conditions. The specific optimization method is as follows:

[0063] (1) We selected sites where the absolute value of the difference in methylation levels between the two groups was greater than 0.3. A total of 171 EBV differentially methylated sites were detected. A heat map was used to display the methylation status of each gene between the two groups. The results are as follows: Figure 4 As shown; Figure 4This is a heat map of cluster analysis of differentially methylated genes in the EBV genome in PBMC samples of CAEBV and IM cases according to the second screening criteria. Each column in the figure represents a sample. Each row represents the average methylation level of each EBV-encoded gene in the viral genome in all samples. The methylation level corresponding to each region of all samples is represented by a different color. The color gradient from blue to red indicates the change from low to high gene methylation. CAEBV: chronic active EBV infection; IM: infectious mononucleosis.

[0064] (2) The intra-group stability of the 171 differentially methylated sites was further evaluated by calculating the coefficient of variation (CV) using the formula CV = S / M*100% (S is the sample standard deviation, M is the sample mean). Sites with a CV less than 20% were considered stable.

[0065] After the method was optimized, five genes with significant methylation differences between CAEBV and IM were screened out: EBNA-LP (30069 bp), EBNA-3A (53968 bp), A73 (157064 bp), BALF5 (154371 bp), and BNRF1 (2422 bp). The methylation levels and CV values ​​of each gene are shown in Tables 1 and Figure 5A and Figure 5B As shown, Figure 5A and Figure 5B is the methylation level of representative differentially methylated genes in the EBV genome of CAEBV and IM groups, where Figure 5A The methylation levels of five genes EBNA-LP, EBNA-3A, A73, BALF5 and BNRF1 that showed significant methylation differences between CAEBV and IM were screened according to the optimized second screening criteria. Figure 5B The methylation levels of the five genes BcRF1, EBNA1, BPLF1, LMP-2A and BSLF1 that were screened out according to the first screening criteria and showed differential methylation between CAEBV and IM. Figure 5A The methylation differences of five sites were significantly higher than Figure 5B as shown in .

[0066] Table 1 lists some of the site data obtained from the first screening, and Table 2 lists the site data obtained from the second optimization screening. It can be clearly seen from the diff value that the methylation level of the methylation site in the second screening is higher, and it can also be clearly seen from the CV value that the specificity of the second screening site is also much higher than other sites.

[0067] Table 1

[0068]

[0069] Table 2

[0070]

[0071] Example 2 Primers and detection method for detecting significantly differentially methylated gene levels in the EB virus genome in CAEBV and IM samples

[0072] 1. Primer Set

[0073] The inventors designed a primer set with high amplification efficiency and good specificity for the above-mentioned differentially methylated sites, which can detect the gene levels of the above-mentioned significantly differentially methylated sites in CAEBV and IM samples. The sequences of each primer are shown in the following table.

[0074] Table 3

[0075]

[0076]

[0077] 2. Basic method for detecting target methylation levels in CAEBV and IM samples using the above primers:

[0078] The above primers were used to perform pyrophosphate sequencing PCR detection on the methylation level of the target gene in CAEBV and IM samples.

[0079] (1) DNA sample preparation: DNA was extracted using a magnetic bead-based universal genomic DNA extraction kit (Tiangen);

[0080] (2) Bisulfite conversion: The extracted DNA samples were modified with sodium bisulfite using the EZ DNA Methylation-Gold Kit D5005 (ZYMORESEARCH), so that unmethylated cytosine was converted to uracil, while methylated cytosine remained unchanged.

[0081] (3) PCR amplification reaction: The gene fragment to be detected is enriched and amplified through PCR reaction, and the product length is between 200-700 bp.

[0082] a. The sample was sulfite converted and centrifuged briefly at low speed for later use.

[0083] b. Prepare the PCR reaction mixture as shown in the table below (the reaction system preparation process is completed under low temperature conditions to prevent inactivation due to long-term exposure to high temperature).

[0084] Table 4 Methylation detection amplification PCR reaction components

[0085] Reagents Usage <![CDATA[ddH2O]]> 5.5μL 2×TaqPCRMasterMix 12.5μL forwardprimer 1.5μL reverseprimer 1.5μL DNA template 4μL TotalVolume 25μL

[0086] c. Amplification PCR reaction procedure

[0087] The temperature and time were programmed as follows: 95°C, 5 min → (94°C, 30 s → 60°C, 45 s → 72°C, 45 s) 40 cycles → 72°C, 10 min → 10°C, 10 min.

[0088] d. After the reaction is completed, take 10 μL of PCR product and run it on 2% agarose gel electrophoresis for 45 min. After the electrophoresis, take a UV photo to verify the amplification effect of the BSP reaction product.

[0089] (4) Pyrophosphate sequencing:

[0090] a. Use the single-strand purification device of the PyroMark Q96 ID pyrophosphate sequencer to separate single-stranded DNA from the PCR product;

[0091] b. Place the sequencing reaction plate in the sequencer for sequencing.

[0092] c. Data analysis: The methylation analysis software provided with the pyrophosphate sequencer was used to analyze the methylation results.

[0093] Example 3 Using the above primer set of the present invention to detect the methylation level of the target gene in CAEBV and IM samples for verification

[0094] (1) Based on the above research, the present invention further validated another validation sample set using the primer set in Table 3. The validation sample set was from 5 children with CAEBV and 5 children with IM. The methylation levels of EBNA-LP, A73, BALF5, BNRF1 and EBNA-3A genes in the sample set were detected to identify chronic active EB virus infection and infectious mononucleosis, thereby validating the feasibility and effectiveness of the methylation sites described in the present invention. The detection method and analysis method are as described in Examples 1 and 2.

[0095] (2) Experimental results

[0096] The results are shown in Table 5 and Figure 6 As shown, in the PBMC samples of children in the CAEBV group, the average methylation levels of EBNA-LP, A73, BALF5, BNRF1 and EBNA-3A genes were higher than those in the IM group, and the difference was statistically significant. The experimental results show that early identification of CAEBV and IM can be performed by detecting changes in the differential methylation sites of the EB virus genome described in the present invention. The verification experiment shows that the present invention has high sensitivity, good specificity and repeatability.

[0097] Table 5

[0098]

Claims

1. Application of a primer set for detecting the methylation levels of EBNA-LP 30069bp, EBNA-3A 53968bp, A73 157064bp, BALF515437bp and BNRF1 2422bp sites in the genome of the sample to be tested in the preparation of a product for distinguishing or assisting in distinguishing chronic active EB virus infection and infectious mononucleosis; The primer set is selected from a primer pair consisting of SEQ ID No.1 and SEQ ID No.2, a primer pair of SEQ ID No.3 and SEQ ID No.4, a primer pair of SEQ ID No.5 and SEQ ID No.6, a primer pair of SEQ ID No.7 and SEQ ID No.8, and a primer pair of SEQ ID No.9 and SEQ ID No.10.

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