Method for detecting off-target effect of CRISPR-Cas9 gene editing system based on S-EPTS / LM-PCR

Abstract

The invention belongs to the field of biological information, and particularly relates to a method for detecting the off-target effect of a CRISPR-Cas9 gene editing system based on S-EPTS / LM-PCR, and according to the method, on the basis of a GUIDE-seq method, the S-EPTS / LM-PCR is used for replacing PCR. Compared with the prior art, the invention has the following beneficial effects: 1) by introducing the S-EPTS-PCR technology, the PCR amplification condition is optimized, the detection capability of low-frequency off-target events is obviously improved, and the effect of enhancing the detection sensitivity is achieved; 2) by developing an advanced bioinformatics algorithm, accurate distinguishing and attribution of a plurality of target spot off-target events are realized, and the effect of deeply understanding an off-target mechanism is achieved; and 3) through the development of a comprehensive analysis tool, detailed information such as the size of an edit box, visual display and whether an off-target site is on a cancer-related gene is provided, and the effect of remarkably improving the clinical application value is achieved.

Description

Technical Field

[0001] The present invention belongs to the field of bioinformatics, and specifically relates to E-GUIDEseq: a method for detecting off-target effects of a CRISPR-Cas9 gene editing system based on S-EPTS / LM-PCR. Background Art

[0002] In the context of the rapid development of gene editing therapy and cell therapy, the CRISPR-Cas9 system has become a favorite in research and clinical applications due to its high efficiency and simplicity. The CRISPR-Cas9 system can precisely modify specific sites in the genome of an organism, opening up new prospects for the treatment of genetic diseases. However, with its increasingly widespread application in gene therapy, the off-target effect of the CRISPR-Cas9 system (i.e., accidental editing of non-target sites) has become an issue that cannot be ignored. Off-target effects may lead to unexpected gene mutations, which in turn affect cell function and even cause disease. Therefore, systematically evaluating off-target effects in gene editing systems is a core step to ensure their safety and effectiveness in clinical applications, and is also a key guarantee for improving their success rate.

[0003] GUIDE-seq (Genome-wide, Unbiased Identification of DSBs Enabled by Sequencing) is a widely used CRISPR-Cas off-target site detection technology that introduces specific markers during gene editing and uses these markers to amplify and sequence sequences on both sides of the genomic break site to identify off-target sites. The principle of this technology is based on the CRISPR-Cas9 system causing double-stranded DNA breaks (DSBs) at the target site and integrating marker sequences (dsODN) of varying lengths into the genome during the repair process (see Tsai, SQ et al. "GUIDE-seq enables genome-wide profiling of off-target cleavage by CRISPR-Cas nucleases." Nat Biotechnol 33, 187–97 (2015)). Subsequently, the dsODN sequence integrated into the genome and the surrounding genomic DNA are amplified by specific primers and analyzed by high-throughput sequencing technology to reveal potential off-target sites. In order to improve the specificity of detection, Nobles and colleagues developed the i-GUIDE-seq technology, which achieved the purpose of improving specificity by increasing the sequence length of dsODN (see Nobles, CL et al. "iGUIDE: an improved pipeline for analyzing CRISPR cleavage specificity." Genome Biology 20, 14 (2019)).

[0004] Although GUIDE-seq technology has made important progress in identifying off-target effects of the CRISPR-Cas9 system, it still faces the challenge of low sensitivity in practical applications. In some cases, off-target sites may be missed due to the rarity of off-target events or insufficient labeling efficiency. The core of GUIDE-seq is to detect the integration event of dsODN in the region after a DNA double-strand break (DSB) occurs. Therefore, the detection sensitivity of the integration site determines the detection sensitivity of GUIDE-seq.

[0005] Secondly, GUIDE-seq mainly analyzes the editing of a single target, but when multiple targets are edited simultaneously, this technology has difficulty distinguishing off-target events of different targets, which poses a challenge to understanding the off-target patterns in the complex gene editing process of multiple targets at the same time.

[0006] Finally, the clinically useful information provided by GUIDE-seq is relatively limited, such as the lack of information on the size of the editing frame in target editing, the identification of non-conserved sites on the sgRNA sequence during off-target editing, the visualization of editing sites, and whether the off-target editing sites are on genes associated with adverse events. For the safety assessment of editing mutations, this information is of great value for clinical decision-making and treatment effect evaluation, but the current GUIDE-seq technology fails to provide it adequately.

[0007] GUIDEseq currently has the following problems: (1) low sensitivity in off-target site detection; (2) it is only applicable to off-target detection of a single target. When cells are edited at multiple targets simultaneously, it is impossible to determine which target caused the off-target; (3) it is unable to provide information on the size of the editing frame, the identification of non-conserved editing sites, or whether the off-target editing sites are in special functional regions of the gene or on genes related to adverse events, and therefore lacks clinical guidance significance.

[0008] In response to the above problems in the practical application of off-target site detection, we developed the E-GUIDEseq (Enhanced Genome-wide, Unbiased Identification of DSBs Enabled by Sequencing) multi-target off-target detection toolbox. The E-GUIDEseq method further optimizes the above problems from the two levels of experimental operation and bioinformatics algorithm, increasing the practicality of off-target site detection based on second-generation sequencing in the medical field of CRISPR-Cas9 system. Summary of the invention

[0009] The present invention first provides an E-GUIDE-seq method, which is a method for detecting off-target effects of a CRISPR-Cas9 gene editing system based on S-EPTS / LM-PCR.

[0010] The present invention also provides an E-GUIDE-seq method, which is based on the GUIDE-seq method and uses S-EPTS / LM-PCR to replace PCR.

[0011] In certain embodiments, the method comprises the following steps:

[0012] (1) fragmenting the target genomic DNA and purifying it to obtain purified fragments;

[0013] (2) primer extension and capture of the purified fragments, followed by ligation with adapters to obtain target products with adapters;

[0014] (3) performing nested PCR on the target product with the linker to obtain a nested PCR product.

[0015] In certain embodiments, the method further comprises purification and quality control of the nested PCR products.

[0016] In certain embodiments, the fragmentation is performed using ultrasound.

[0017] In certain embodiments, the extension uses a specific biotin-modified primer.

[0018] In certain embodiments, the capture is streptavidin magnetic bead capture.

[0019] In certain embodiments, the nested PCR comprises a first nested PCR and a second nested PCR.

[0020] In certain embodiments, the method further comprises upper level sequencing.

[0021] In certain embodiments, the method further comprises performing bioinformatics analysis on the sequencing results.

[0022] Compared with the prior art, the present invention has at least the following beneficial effects:

[0023] 1) In order to address the problem of insufficient sensitivity of the existing GUIDE-seq technology, we introduced S-EPTS-PCR technology, optimized the PCR amplification conditions, significantly improved the detection capability of low-frequency off-target events, and achieved the effect of enhancing detection sensitivity.

[0024] 2) In response to the limitation of GUIDE-seq technology that it cannot distinguish the sources of off-target events during multi-target editing, we developed advanced bioinformatics algorithms to accurately distinguish and attribute off-target events of multiple targets, achieving a deep understanding of the off-target mechanism.

[0025] 3) In response to the problem that GUIDE-seq technology does not provide enough clinically useful information, we developed a comprehensive analysis tool that provides detailed information such as editing frame size, visual display, and whether the off-target site is on a cancer-related gene, thereby significantly improving the value of clinical applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 :E-GUIDE-seq experimental process based on S-EPTS / LM-PCR.

[0027] Figure 2 :Bioinformatics analysis pipeline of E-GUIDE-seq.

[0028] Figure 3: After replacing the original processing method with S-EPTS / LM-PCR, the sensitivity increased, and more dsODN insertion sites could be detected in the same sample.

[0029] Figure 4 :Increasing the accurate distinction of off-target events of multiple targets and the comparison before and after attribution can achieve the effect of separately judging the off-target of each target.

[0030] Figure 5 : Density distribution and size judgment of the edit box.

[0031] Figure 6 : Visual display of GUIDEseq off-target sites and judgment of non-conserved sites.

[0032] Figure 7 :Determine whether the off-target site is on a cancer-related gene. DETAILED DESCRIPTION

[0033] In order to make the technical problems, technical solutions and advantages to be solved by the present invention more clear, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0034] The specific meanings of the terms used are as follows.

[0035] 1.Guide RNA, gRNA, guide RNA;

[0036] 2.DNA Double-Stranded break,DSB,DNA double-strand break;

[0037] 3.Nonhomologous End-Joining, NHEJ, non-homologous end joining;

[0038] 4.Homology-Directed Repair, HDR, homologous recombination repair;

[0039] 5.Double-stranded Oligodeoxynucleotides, dsODN;

[0040] 6.Ligation Target Amplification PCR, S-EPTS / LM-PCR, targeted ligation amplification PCR;

[0041] 7.Linear Amplification Mediated PCR,LAM-PCR, linear amplification mediated PCR.

[0042] Embodiment 1:

[0043] It is proved that E-GUIDEseq can significantly improve the detection ability of low-frequency off-target events, achieving the effect of enhancing detection sensitivity. In order to compare the performance of GUIDEseq and E-GUIDEseq, we used the same gene editing system to carry out the targeted introduction of dsODN into 2 different batches of samples, and then extracted high-quality genomic DNA by standard DNA extraction methods, and performed GUIDEseq and E-GUIDEseq library construction and sequencing respectively. In GUIDEseq, fragmentation is performed by physical methods (such as ultrasonic fragmentation) to generate 300-500bp DNA fragments. The purpose of fragmentation is to prepare suitable fragment sizes for subsequent library construction and sequencing, while ensuring that the DNA fragments integrated with dsODN can be effectively captured and amplified.

[0044] Next, the fragmented DNA is end-repaired and A-tailed, and the barcoded adapter is connected, followed by PCR amplification using specially designed primers. These primers specifically target the region where dsODN is integrated, ensuring that the amplified product mainly contains the information of the break site induced by CRISPR-Cas9. The PCR amplification step includes two rounds of amplification, the first round uses primers targeting the dsODN insertion site, and the second round uses primers targeting the adapter for library expansion. The amplified product is purified to remove unbound adapters, primer dimers, and other contaminants. The purified product is used to construct a sequencing library and is subjected to high-throughput sequencing using the Illumina platform.

[0045] In E-GUIDEseq, 500 ng of genomic DNA was sheared to an average length of 500 ± 50 bp using a Covaris M220 gene fragmentor, and the fragment size was verified by Tapestation 4150. Then, the sheared DNA was primer extended using specific biotin-modified primers, and the extension products were purified using Ampure XP magnetic beads. Next, the target DNA fragments were captured by streptavidin magnetic beads and washed with sterile deionized water. The captured DNA was then ligated to the first barcode by ligase to distinguish different samples. The ligation product was divided into two parts, one of which was used for the first nested PCR and the other was stored as an intermediate product. The biotinylated PCR product was captured by magnetic beads and enriched and washed. Half of the eluted product was used for the second exponential PCR, and the second barcode and sequencing-specific adapter were connected at the same time. The PCR product was qualitatively controlled by gel electrophoresis, and the remaining part was measured by Qubit 4.0 for concentration and mixed at 50 ng / sample. Finally, the mixed product was quality controlled using TapeStation4150 and Qubit 4.0 and diluted to 10 pM for next-generation sequencing.

[0046] The sequencing results of the above two samples in GUIDEseq and E-GUIDEseq were analyzed by the same bioinformatics method. After obtaining the dsODN insertion sites, it can be found that after replacing the original processing method of GUIDEseq with E-GUIDEseq, the sensitivity increased, and the same sample was able to detect more dsODN insertion sites. In the two sgRNAs of sample 1, 67 and 102 off-target sites were detected, respectively, while in E-GUIDEseq, 80 and 141 off-target sites were detected, respectively, increasing the number of off-target sites by 19.4% and 38.2%, respectively; in the two sgRNAs of sample 2, 72 and 113 were detected, respectively, while in E-GUIDEseq, 88 and 137 were detected, respectively; the number of off-target sites increased by 22.2% and 21.2%, respectively. It can be seen that after replacing the original processing method of GUIDEseq with E-GUIDEseq, the detection sensitivity increased, and the same sample was able to detect 19.4%-38.2% more off-target sites.

[0047] Table 1: Number of off-target sites detected by E-GUIDEseq and GUIDEseq for two sgRNAs in two samples

[0048] Sample Target Status Value Sample 1 Target 1 Before 67 Sample 1 Target 2 Before 102 Sample 1 Target 1 After 80 Sample 1 Target 2 After 141 Sample 2 Target 1 Before 72 Sample 2 Target 2 Before 113 Sample 2 Target 1 After 88 Sample 2 Target 2 After 137

[0049] Embodiment 2:

[0050] It is proved that E-GUIDEseq can accurately distinguish and attribute off-target events of multiple targets, achieving the effect of in-depth understanding of off-target mechanisms. In Example 2, a dual-target editing sample was subjected to the E-GUIDEseq experimental treatment described in Example 1, and the detected off-target sites were subjected to E-GUIDEseq bioinformatics analysis. During the bioinformatics analysis process, the BWA tool was first used to accurately locate the integration site of dsODN in the genome, and then the specific sequences around these sites were extracted from the genome and stored in data set A. Next, the reverse complementary sequence of each sgRNA sequence was calculated, and these sequences were stored in data set B together with the original sgRNA sequence. After that, the sequences in data sets A and B were locally aligned by the Smith-Waterman algorithm, and the alignment scores were recorded in the matrix M, where the rows represent the sequences of data set A and the columns represent the higher matching scores of sgRNA and its reverse complementary sequence in data set B. E-GUIDEseq determines the best matching sgRNA attribution of the sequences in data set A by analyzing the scores of matrix M, thereby distinguishing off-target events of multiple targets. In the case of the same score, E-GUIDEseq will give priority to the sgRNA with a smaller distance to the insertion site. Finally, once the sgRNA attribution of each dsODN insertion site is determined, all dsODN insertion sites attributable to that sgRNA are collected as potential off-target sites for subsequent analysis. The results show that in the E-GUIDEseq results, the sgRNA attribution of all sgRNA sequences at off-target sites is calculated (e.g. Figure 4 ), and through sequence alignment, it can be seen that the sequence of each sgRNA off-target site has a high degree of similarity with the sgRNA sequence to which it belongs (such as Figure 4 ), which shows that E-GUIDEseq can distinguish and attribute off-target events of the two targets with high accuracy.

[0051] Embodiment 3:

[0052] It is proved that E-GUIDEseq can judge the editing frame size of sgRNA, identify non-conserved sites in sgRNA sequences, and whether the off-target sites are on cancer-related genes and other detailed information. These indicators play an important role in the prediction and evaluation of the off-target safety of CRISPR drugs. In Example 3, a dual-target editing sample was subjected to the E-GUIDEseq experimental treatment described in Example 1, and the bioinformatics analysis and attribution analysis of the potential off-target sites of sgRNA described in Example 2 were performed. After completing the attribution analysis, E-GUIDEseq further performs a detailed analysis of the off-target site set of each sgRNA. First, it calculates the distance between these sites and the theoretical cutting site of sgRNA, analyzes the editing frame, and displays the editing frame in a visual form (such as Figure 5 Then, E-GUIDEseq identifies the most matched region sequence in each off-target site set and uses the MUSCLE algorithm for multiple sequence alignment to identify non-conserved sites on sgRNA and present this information in a visual way (such as Figure 6 In addition, E-GUIDEseq will determine whether the off-target sites are located in genes and their functional regions based on their specific locations in the genome. For off-target sites located in genes, E-GUIDEseq will further analyze whether these genes are associated with cancer and display these analysis results in a visual form (such as Figure 7 ), so that researchers can understand and evaluate the off-target risks of sgRNAs and their potential biological impacts.

[0053] Figure 5 The function of E-GUIDEseq to visualize the editing frame of sgRNA in the CRISPR-Cas9 system is demonstrated. In this visualization, the x-axis represents the base coordinates centered on the theoretical cutting site of the sgRNA, while the y-axis represents the frequency of editing events observed at these positions. This visualization method allows researchers to intuitively see the distribution of off-target events that may occur during gene editing. By analyzing the editing frame, researchers can optimize the design of sgRNA to reduce off-target events and improve the specificity of editing, which is crucial to improving the success rate of clinical applications of gene editing technology.

[0054] Figure 6It was demonstrated that E-GUIDEseq can not only identify off-target sites, but also evaluate the off-target conservation of each base on the sgRNA and display it in a visual way, which is crucial for understanding and optimizing the gene editing process. In the visual display of E-GUIDEseq, the off-target conservation of each site is represented by the square above. The color or mark of the square can intuitively reflect the conservation level of the site, among which the less conservative sites are usually specially marked so that researchers can quickly identify them. For example, the three sites marked with dotted lines in the figure are less conservative sites, indicating that these base positions are less conserved, and off-target events may be more likely to occur when mutations occur on the above bases.

[0055] Figure 7 It plays a key role in E-GUIDEseq analysis, showing whether off-target sites are located on genes associated with cancer. This judgment is crucial for evaluating the safety and effectiveness of CRISPR-Cas9 gene editing technology, especially in clinical applications of cancer treatment. Figure 7 With this analysis, researchers can assess the potential impact of off-target sites on cancer treatment. If the off-target site is located on a gene that is associated with cancer development or treatment, this could affect the effectiveness of the treatment or increase the risk of side effects.

[0056] It can be seen from the above examples that the E-GUIDEseq technology optimizes the PCR amplification conditions by introducing the S-EPTS-PCR technology, significantly improving the detection sensitivity of low-frequency off-target events. At the same time, by developing advanced bioinformatics algorithms, E-GUIDEseq can accurately distinguish and attribute the off-target sources during multi-target editing, and deeply understand the off-target mechanism. In addition, through the development of comprehensive analysis tools, E-GUIDEseq also provides detailed information on the size of the editing box, visual display, and whether the off-target site is on a cancer-related gene, which significantly enhances the clinical application value of GUIDEseq in the off-target safety risk assessment of cell gene therapy drugs.

[0057] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for detecting off-target effects of the CRISPR-Cas9 gene editing system based on S-EPTS / LM-PCR, characterized in that: The method is a method for detecting off-target effects of the CRISPR-Cas9 gene editing system based on S-EPTS / LM-PCR.

2. A method for detecting off-target effects of the CRISPR-Cas9 gene editing system based on S-EPTS / LM-PCR, characterized in that: The method is based on the GUIDE-seq method, using S-EPTS / LM-PCR to replace PCR.

3. A method for detecting off-target effects of the CRISPR-Cas9 gene editing system based on S-EPTS / LM-PCR, characterized in that: The method comprises the following steps: (1) fragmenting the target genomic DNA and purifying it to obtain purified fragments; (2) extending the purified fragments with primers, capturing them, and then connecting them with adapters to obtain target products with adapters; (3) performing nested PCR on the target product with the linker to obtain a nested PCR product.

4. The method according to claim 3, characterized in that The method also includes purification and quality control of nested PCR products.

5. The method according to claim 3, characterized in that: The fragmentation is performed by using ultrasound.

6. The method according to claim 3, characterized in that The extension uses specific biotin-modified primers.

7. The method according to claim 3, characterized in that The capture is streptavidin magnetic bead capture.

8. The method according to claim 3, characterized in that The nested PCR comprises a first nested PCR and a second nested PCR.

9. The method according to claim 3, characterized in that: The method further comprises upper level sequencing.

10. The method according to claim 3, characterized in that The method also includes performing bioinformatics analysis on the sequencing results.

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