A plant product origin identification method based on detection of circular RNA and application thereof

Abstract

The application provides a plant product origin identification method based on detection of circular RNA and application, and belongs to the technical field of detection of plant products for origin identification. The identification method comprises collecting plant product samples from different origins, sequencing circular RNA of the plant product samples, analyzing and obtaining differentially expressed circular RNA between the samples from different origins, designing and synthesizing fluorescent quantitative nucleic acid amplification primers for the circular RNA, performing fluorescent quantitative nucleic acid amplification to obtain circular RNA expression data, using multivariate statistical analysis software to construct an origin identification model based on partial least squares discriminant analysis / orthogonal partial least squares discriminant analysis according to the circular RNA expression data, and bringing the expression data of circular RNA of a to-be-detected plant sample into the model to predict the origin. The method of the application is suitable for plant products with large spatial scales and long distances, and is also suitable for origin identification of plant products with small spatial scales and short distances.

Description

Technical Field

[0001] This invention belongs to the technical field of identifying the origin of plant products, specifically relating to a method and application for identifying the origin of plant products based on the detection of circular RNA. Background Technology

[0002] The growing location has a significant impact on the quality of plant products. Plant products from different origins often have significant differences in the content of specific substances. This is especially true for some local specialties, such as precious Chinese medicinal herbs and geographical indication agricultural products. Due to differences in the production environment, the quality of these products varies, and the corresponding prices differ significantly. As a result, counterfeit brands and counterfeit origins are rampant in the market, even leading to a chaotic market phenomenon of "bad money driving out good." This not only affects the brand reputation and consumer confidence of products but also hinders the high-quality development of the industry.

[0003] Existing methods for identifying the origin of products mainly include spectroscopic techniques, stable isotope techniques, elemental analysis, and organic component analysis. However, these methods are more suitable for products located geographically far apart, and their effectiveness is less than ideal when the product originates from a nearby region. While combining multiple technologies can improve the accuracy of origin identification to some extent, it also introduces problems such as operational complexity and increased costs. Furthermore, metabolomics technology based on high-resolution mass spectrometry can be used for the regional identification of plant-derived products with relatively high accuracy. However, this technology requires high-resolution, large-scale mass spectrometers, and the instrument platform needs professional maintenance and operation to ensure its stability and reproducibility. It also requires extensive data analysis using bioinformatics and chemometrics methods, and currently lacks unified standards for non-target detection, making commercial application difficult in the short term.

[0004] Circular RNA (circRNA) is a class of closed circular single-stranded RNA molecules covalently linked at the 3' and 5' ends. Plant circRNAs are primarily 200-600 bp in length. Unlike linear RNA, the unique circular structure of circRNAs makes them less susceptible to degradation by exonucleases and ribonucleases, thus exhibiting higher stability than linear RNA. Furthermore, studies have shown that plant circRNAs possess conservation characteristics and can be expressed in cell- or tissue-specific ways, giving them the potential to become biomarkers. Summary of the Invention

[0005] The purpose of this invention is to provide a method for identifying the origin of plant products based on the detection of circular RNA.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] A method for identifying the origin of plant products based on the detection of circular RNA, comprising the following steps:

[0008] S1. Collect plant product samples from different origins; extract total RNA from plant product samples from each origin; use a high-throughput sequencing platform to sequence circular RNA (circRNA) to obtain the circRNA sequences of product samples from each origin.

[0009] S2. The obtained circRNA sequences are compared with the product genome to obtain the sequence information of circRNA junction sites; and significantly differentially expressed circRNAs are screened based on the number of sequencing results obtained from the circRNAs obtained from sequencing.

[0010] S3. Primers were designed for differentially expressed circRNAs, and real-time quantitative PCR analysis of RNA from plant samples from various origins was performed using these primers to obtain expression data of differentially expressed circRNAs.

[0011] S4. Using multivariate statistical analysis software, construct a regional differentiation model based on partial least squares discriminant analysis (PLS-DA) or orthogonal partial least squares discriminant analysis (OPLS-DA) based on the expression data of significantly differentially expressed circRNAs obtained in step S3, so as to distinguish the origin of the samples.

[0012] S5. After extracting total RNA from the plant samples to be tested, real-time quantitative PCR analysis was performed using the primers in S3 to obtain the expression levels of significantly differentially expressed circRNAs in the samples to be tested. The data were then substituted into the model to determine the origin of the samples.

[0013] In the above-described method for identifying the origin of plant products, preferably, in step S1, the plant products are agricultural products, vegetables, fruits, flowers, medicinal plants, etc.

[0014] In the above-described method for identifying the origin of plant products, preferably, in step S2, the sequencing data is analyzed using two software programs, find_circ and CIRI, and the corresponding sequence information is obtained by comparing it with the product genome, while the circRNA junction position is also obtained.

[0015] The criteria for screening differentially expressed circRNAs were a fold change greater than or equal to 2 and a significance level less than 0.05.

[0016] Furthermore, it is preferable to use the negative binomial distribution (DESeq2) method to perform differential expression analysis of circular RNAs to screen for significantly differentially expressed circRNAs; the differentially expressed circRNAs are screened based on two perspectives: fold change and corrected significance level.

[0017] In the above-described method for identifying the origin of plant products, preferably, in step S3, primer design is performed using BeaconDesigner 8 software, the target gene is a significantly differentially expressed circRNA containing a cross-connection sequence, the primer length is 18-24 bases, the GC content is 40-60%, and the amplification product length is between 100-200 bases.

[0018] In the above-described method for identifying the origin of plant products, preferably, in step S4, the variable explanatory power R of the constructed origin differentiation model is... 2 X and the predictive index Q 2 All should be greater than 0.5.

[0019] The method described above is used to identify the origin of peaches, wherein the nucleic acid sequences of the differentially expressed circular RNA are shown in SEQ ID NO.1-6, and are used to identify peaches from Pinggu District of Beijing, Shunping County of Hebei Province, and Leting County of Hebei Province.

[0020] The method described above is used to identify the origin of Beijing white pears. The nucleic acid sequences of the differentially expressed circular RNA are shown in SEQ ID NO.19-23, which are used to identify Beijing white pears from Mentougou District and Fangshan District of Beijing.

[0021] The application of nucleic acid sequences as shown in SEQ ID NO.1-6 in distinguishing different peach producing areas.

[0022] A primer set for distinguishing the circular RNA sequences of peaches from different origins, comprising sequences as shown in SEQ ID NO.7-18.

[0023] The application of nucleic acid sequences as shown in SEQ ID NO.19-23 in distinguishing different origins of Beijing white pears.

[0024] The primer set used to distinguish the circular RNA sequences of Beijing white pears from different origins includes sequences as shown in SEQ ID NO. 24-33.

[0025] The beneficial effects of this invention are as follows:

[0026] This invention provides a method for identifying the origin of plant products based on the detection of circular RNA. By screening for significantly differentially expressed circRNAs in plants, and designing nucleic acid amplification primers for quantitative real-time PCR amplification, the origin can be distinguished by constructing an origin differentiation model using circRNA expression data combined with multivariate statistical analysis strategies. The accuracy of origin differentiation is greater than 98%, providing a new method for identifying the origin of plant products from different sources. This method has high accuracy and wide applicability, suitable not only for identifying samples from distant origins such as intercontinental, inter-national, or north-south regions, but also particularly applicable to samples from neighboring origins, such as adjacent provinces, different cities and counties within the same province, and different districts and counties within the same city or county. Moreover, this technology, based on nucleic acid amplification, is simple to operate and low in cost, and holds promise for developing rapid detection products, providing a new method and technology for identifying the origin of plant products. Attached Figure Description

[0027] Figure 1 Results of RNA detection in peach samples using 1% agarose gel electrophoresis.

[0028] Figure 2 The results show the differentiation of peach samples from three different production areas based on circRNA markers.

[0029] Figure 3 The results are the permutation test results for the model in Example 1.

[0030] Figure 4 The results of RNA detection in a Jingbai pear sample were obtained by 1% agarose gel electrophoresis.

[0031] Figure 5 The results of distinguishing between Beijing white pear samples from two different origins based on circRNA markers.

[0032] Figure 6 The results are the permutation test results for the model in Example 2. Detailed Implementation

[0033] Plant products from different origins exhibit variations in circRNA expression due to differences in their environmental conditions. Therefore, this invention is the first to utilize this technology to identify the origin of plant products from different regions. It provides a novel method for identifying the origin of plant products from diverse sources. This method is highly accurate and widely applicable, suitable not only for identifying samples from distant origins (e.g., between continents, countries, or north and south), but also for samples from neighboring origins (e.g., adjacent provinces, different cities / counties within the same province, or different districts / counties within the same city / county). Furthermore, this technology, based on nucleic acid amplification, is simple to operate, low-cost, and can be developed into rapid detection products, providing a new method and technology for identifying the origin of plant products.

[0034] The following embodiments are used to further illustrate the present invention, but should not be construed as limiting the present invention. Any modifications or substitutions made to the present invention without departing from its spirit and essence are within the scope of the present invention.

[0035] Unless otherwise specified, the technical means used in the embodiments are conventional means well known to those skilled in the art. Unless otherwise specified, all reagents used in this invention are of analytical grade or higher.

[0036] Example 1: Identification of peach samples from different origins based on circRNA biomarker combinations

[0037] 1. Collection of plant samples

[0038] Mature peach samples from different origins were collected using a five-point sampling method. Samples from each point were combined into a single sample and brought back to the laboratory. A total of peach samples were collected from three origins: Pinggu District (PG) in Beijing, Shunping County (SP) in Hebei Province, and Leting County (LT) in Hebei Province. Sixteen representative samples were collected from each origin.

[0039] 2. RNA extraction and high-throughput sequencing

[0040] After samples were brought back for the experiment, they were cryogenically ground in liquid nitrogen. Total RNA was extracted using the Quick RNA Isolation Kit (Huayueyang, China). Following RNA extraction, the RNA was subjected to 1% agarose gel electrophoresis (150V constant voltage electrophoresis for 20 min), and RNA bands were observed using a gel imaging system to assess their integrity. Figure 1 The electrophoretic bands in the total RNA were clear, and the concentration of the upper 25S rRNA band was approximately 1.5-2.0 times that of the lower 18S band, indicating good RNA sample quality. The concentration of total RNA was determined using a Nanodrop 2000c UV spectrophotometer, and RNA purity was analyzed based on OD260 / 280 and OD260 / 230 values. An RNA concentration greater than 100 ng / μL and OD260 / 280 and OD260 / 230 values ​​between 1.8 and 2.0 were required, indicating that the extracted RNA concentration and purity met the requirements. After the RNA quality was deemed satisfactory, three representative samples were selected from each origin. Ribosomal RNA was first removed from the samples using the Epicentre RiboZero™ kit (Epicentre, USA), and then high-throughput circRNA sequencing was performed using the Illumina HiSeq™ 2500 sequencing platform.

[0041] 3. CircRNA identification and differential expression analysis

[0042] Sequencing data were analyzed using find_circ and CIRI software. Sequence information was obtained by aligning the data with the peach genome (ftp: / / ftp.ensemblgenomes.org / pub / plants / ), and circRNA junction positions were also determined. The read counts of each circRNA were used as data for circRNA differential expression analysis. A negative binomial distribution (DESeq2) method was employed for circRNA differential expression analysis. Based on the differential expression among samples from different production areas, a fold change greater than or equal to 2 and a corrected significance level (p-value) less than 0.05 were used as screening criteria to identify significantly differentially expressed circRNAs. These significantly differentially expressed circRNAs were then used as candidate biomarkers for subsequent modeling analysis. The experiment ultimately identified six significantly differentially expressed circRNAs among production areas, and the corresponding partial junction-crossing sequences (29 bases before and after the junction) are shown in Table 1.

[0043] Table 1. Crossover sequence information of peach circRNA

[0044]

[0045] 4. Analysis of circRNA expression levels in samples

[0046] First, reverse transcription was performed using the Transcript One-step gDNA Removal and cDNA Synthesis SuperMix kit (TransGen Biotech, Beijing). The reverse transcription system is shown in Table 2. After mixing, the system was placed in a PCR instrument and incubated at 25°C for 10 min, 42°C for 30 min, and heated at 85°C for 5 s. After the reaction, cDNA for quantitative real-time PCR was obtained.

[0047] Then, using Beacon Designer 8 software, primers were designed for the cross-junction sequences of the above 6 significantly differentially expressed circRNAs. The sequence template can be appropriately extended in terms of the number of bases before and after the junction so that the amplification product contains the junction sequence of the circRNA. At the same time, the GC content of the primers should be between 40 and 60, the length between 18 and 24 bp, and the length of the amplification product should be between 100 and 200 bp. Primer information is shown in Table 3.

[0048] Real-time quantitative PCR analysis was performed on all samples using these primers. Amplification was performed using the SYBR Green PremixPro Taq HS qPCR kit (AGbio, Hunna). The qPCR amplification was carried out according to the kit instructions, following the corresponding system and procedure. The amplification system is shown in Table 4. After preparation, the system was mixed, centrifuged, and placed in a LightCycler 96 (Roche, Switzerland) qPCR instrument for reaction and fluorescence signal collection. The qPCR reaction program was: 95℃ for 30s, followed by 40 cycles of 95℃ for 5s and 60℃ for 30s. Fluorescence signals were collected at the end of each cycle. After amplification, a melting curve program was run: 95℃ for 10s, 65℃ for 60s, and 97℃ for 1s. Three replicates were set for each primer pair and each template to obtain the threshold cycle number (Cq) for each sample. After amplification, the standard deviation of Cq in the three replicate wells of each primer pair and each template should not be greater than 0.4 for the amplification to be considered qualified. Otherwise, the amplification should be repeated until the requirement is met. Finally, the Cq values ​​of all samples under the primers corresponding to the six significantly differentially expressed circRNAs are obtained, which are the expression level data of the significantly differentially expressed circRNAs for each sample.

[0049] Table 2 RNA reverse transcription system

[0050]

[0051] Table 3 Primer information for peach circRNA

[0052]

[0053] Table 4 Real-time quantitative PCR amplification system

[0054]

[0055] 5. Constructing an origin identification model and predicting the origin of samples

[0056] Using multivariate statistical analysis software (SIMCA-P), and with the expression levels of differentially expressed circRNAs corresponding to the samples as input data, a place of origin identification model based on partial least squares discriminant analysis (PLS-DA) was constructed. The model is as follows: Figure 2 As shown. From Figure 2 As can be seen, the samples from the three different production areas clustered together, indicating that the model was successfully constructed. Meanwhile, the model's explanatory power R0 is [missing information]. 2 X=0.986, Predictive exponent Q 2 The values ​​of 0.868 are all greater than 0.5, indicating that the model has good reliability and predictive ability. The model validation results are as follows: Figure 3 As shown in the result, all the blue Qs on the left...2 The values ​​are all lower than the original point on the right, Q 2 The blue regression line at the point intersects the vertical axis at zero or below zero, while all the green R lines to the left... 2 The fact that all values ​​are lower than the original point on the right also indicates the effectiveness of the original model.

[0057] After the model was built, the expression data of the six significantly differentially expressed circRNAs of the six peach samples to be tested were input into the model. The model will automatically give the region where each sample to be tested falls, and then automatically calculate its origin and corresponding accuracy probability. The results are shown in Table 5. There are two samples from each origin in the six samples, and the prediction accuracy is greater than 98%.

[0058] Table 5 Model prediction results for the test samples

[0059]

[0060] By using the methods described in 1-5 above to construct an origin identification model for products from different plant sources, and by inputting the corresponding plant source samples into the model, the origin of the samples to be tested can be accurately identified.

[0061] Example 2: Identification of Beijing White Pears from Different Origin Based on circRNA Biomarker Combinations

[0062] 1. Collection of plant samples

[0063] Mature Beijing white pears from different origins were collected using a five-point sampling method. Samples from each point were combined into a single sample and brought back to the laboratory. The experiment collected Beijing white pear samples from two origins: Mentougou District (MTG) and Fangshan District (FS) in Beijing. Fifteen representative samples were collected from each origin.

[0064] 2. RNA extraction and high-throughput sequencing

[0065] After samples were brought back for the experiment, they were cryogenically ground in liquid nitrogen. Total RNA was extracted using the Quick RNA Isolation Kit (Huayueyang, China). Following RNA extraction, the RNA was subjected to 1% agarose gel electrophoresis (150V constant voltage electrophoresis for 20 min), and RNA bands were observed using a gel imaging system to assess their integrity. Figure 4The electrophoretic bands in the total RNA were clear, and the concentration of the upper 25S rRNA band was approximately 1.5-2.0 times that of the lower 18S band, indicating good RNA sample quality. The concentration of total RNA was determined using a Nanodrop 2000c UV spectrophotometer, and RNA purity was analyzed based on OD260 / 280 and OD260 / 230 values. An RNA concentration greater than 100 ng / μL and OD260 / 280 and OD260 / 230 values ​​between 1.8 and 2.0 were required, indicating that the extracted RNA concentration and purity met the requirements. After the RNA quality was deemed satisfactory, 3-4 representative samples were selected from each origin. Ribosomal RNA was first removed from the samples using the Epicentre RiboZero™ kit (Epicentre, USA), and then high-throughput circRNA sequencing was performed using the Illumina HiSeq™ 2500 sequencing platform.

[0066] 3. CircRNA identification and differential expression analysis

[0067] Sequencing data were analyzed using both find_circ and CIRI software. Sequence information was obtained by aligning the data with the genome of *Pyrus bretschneideri* (https: / / ftp.ncbi.nlm.nih.gov / genomes / all / GCF / 019 / 419 / 815 / GCF_019419815.1_Pyrus_bretschneideri_v1 / ), and the locations of circRNA junctions were also determined. The number of read counts for each circRNA was used as the data for differential expression analysis. A negative binomial distribution (DESeq2) method was employed for circRNA differential expression analysis. Based on the differential expression among samples from different origins, circRNAs with significantly differential expression were selected using a fold change greater than or equal to 2 and a corrected significance level (p-value) less than 0.05. These significantly differentially expressed circRNAs were then used as candidate biomarkers for subsequent modeling analysis. The experiment ultimately screened out 5 circRNAs with significant differential expression among different production sites, and the corresponding partial cross-junction sequences (29 bases before and after the junction) are shown in Table 6.

[0068] Table 6. Crossover sequence information of pear circRNA

[0069]

[0070] 4. Analysis of circRNA expression levels in samples

[0071] First, reverse transcription was performed using the Transcript One-step gDNA Removal and cDNA Synthesis SuperMix kit (TransGen Biotech, Beijing). The reverse transcription system is shown in Table 2. After mixing, the system was placed in a PCR instrument and incubated at 25°C for 10 min, 42°C for 30 min, and heated at 85°C for 5 s. After the reaction, cDNA for quantitative real-time PCR was obtained.

[0072] Then, using Beacon Designer 8 software, primers were designed for the cross-junction sequences of the five significantly differentially expressed circRNAs. The sequence template can be appropriately extended in terms of the number of bases before and after the junction to ensure that the amplification product contains the junction sequence of the circRNA. At the same time, the GC content of the primers should be between 40-60%, the length between 18-24 bp, and the length of the amplification product should be between 100-200 bp. Primer information is shown in Table 7.

[0073] Real-time quantitative PCR analysis was performed on all samples using these primers. Amplification was performed using the SYBR Green PremixPro Taq HS qPCR kit (AGbio, Hunan). The qPCR amplification was conducted according to the kit instructions, following the corresponding system and procedure. The amplification system is shown in Table 4. After preparation, the system was mixed, centrifuged, and then placed in a LightCycler 96 (Roche, Switzerland) qPCR instrument for reaction and fluorescence signal collection. The qPCR reaction program was: 95℃ for 30s, followed by 40 cycles of 95℃ for 5s and 60℃ for 30s. Fluorescence signals were collected at the end of each cycle. After amplification, a melting curve program was run: 95℃ for 10s, 65℃ for 60s, and 97℃ for 1s. Three replicates were set for each primer pair and each template to obtain the threshold cycle number (Cq) for each sample. After amplification, the standard deviation of Cq in the three replicate wells of each primer pair and each template should not be greater than 0.4 for the amplification to be considered qualified. Otherwise, the amplification should be repeated until the requirement is met. Finally, the Cq values ​​of all samples under the primers corresponding to the five significantly differentially expressed circRNAs are obtained, which are the expression level data of the significantly differentially expressed circRNAs for each sample.

[0074] Table 7 Primer information for pear circRNA

[0075]

[0076] 5. Constructing an origin identification model and predicting the origin of samples

[0077] Using multivariate statistical analysis software (SIMCA-P), and with the expression levels of differentially expressed circRNAs corresponding to the samples as input data, an origin identification model based on orthogonal partial least squares discriminant analysis (OPLS-DA) was constructed. The model is as follows: Figure 5 As shown. From Figure 5 As can be seen, the samples from the two different production areas clustered together, indicating that the model was successfully constructed. Meanwhile, the model's explanatory power R0 is [missing information]. 2 X=0.887, Predictive Index Q 2 The values ​​of 0.93 are all greater than 0.5, indicating that the model has good reliability and predictive ability. The model validation results are as follows: Figure 6 As shown in the result, all the blue Qs on the left... 2 The values ​​are all lower than the original point on the right, Q 2 The blue regression line at the point intersects the vertical axis at zero or below zero, while all the green R lines to the left... 2 The fact that all values ​​are lower than the original point on the right also indicates the effectiveness of the original model.

[0078] After the model was built, the expression data of five significantly differentially expressed circRNAs corresponding to the four pear samples were input into the model. The model will automatically give the region where each sample falls, and then automatically calculate its origin and corresponding accuracy probability. The results are shown in Table 8. There are two samples from each origin in the four samples, and the prediction accuracy is greater than 98%.

[0079] Table 8 Model prediction results for the samples to be tested

[0080]

[0081] As can be seen from the above, the present invention provides a method for distinguishing the origin of plant products by detecting circular RNA in various plant products, screening for significantly differentially expressed circRNAs, and constructing an origin differentiation model by combining the expression level data of differentially expressed circRNAs with nucleic acid amplification primers and multivariate statistical analysis strategies. The accuracy of origin differentiation is greater than 98%. This method is based on nucleic acid amplification technology, is simple to operate, and has low cost. It is expected to be developed into a rapid detection product, providing a new method and technology for the identification of the origin of plant products.

Claims

1. An application of a method for identifying the origin of plant products based on the detection of circular RNA in identifying the origin of Jingbai pear, characterized in that, It includes the following steps: S1. Collect samples of plant products from different origins, perform circular RNA sequencing, and obtain the circular RNA sequences of samples from each origin. S2. The obtained circular RNA sequences are compared with the product genome to obtain the sequence information of circRNA junction sites; and significantly differentially expressed circular RNAs are screened based on the number of sequences obtained from the sequencing of the circular RNAs. S3. Primers were designed for differentially expressed circular RNAs, and real-time quantitative PCR analysis was performed on RNAs from plant samples from different origins using these primers to obtain expression levels of differentially expressed circular RNAs. The primer set used to distinguish the circular RNA sequences of Beijing white pears from different origins included sequences as shown in SEQ ID NO. 24-33. S4. Using multivariate statistical analysis software, construct a regional differentiation model based on partial least squares discriminant analysis or orthogonal partial least squares discriminant analysis based on the expression data of significantly differentially expressed circular RNA obtained in step S3, so as to distinguish the origin of the samples. S5. After extracting total RNA from the plant sample to be tested, amplify it using the primers designed in S3 to obtain the expression level data of significantly differentially expressed circular RNA. Substitute the expression level data into the model obtained in S4 to obtain the origin of the plant sample to be tested. In step S2, the sequencing data is analyzed using two software programs, find_circ and CIRI. The corresponding sequence information is obtained by comparing it with the product genome, and the location of the circular RNA junction is also obtained. The criteria for screening differentially expressed circular RNAs were a fold change greater than or equal to 2 and a significance level less than 0.

05. Differentially expressed circular RNAs were screened using a negative binomial distribution method. The differentially expressed circular RNAs were screened based on two criteria: fold change and corrected significance level. The nucleic acid sequences of the differentially expressed circular RNAs are shown in SEQ ID NO. 19-23, and they were used to identify Jingbai pears from Mentougou District and Fangshan District of Beijing.

2. The application according to claim 1, characterized in that, In step S1, the plant-based product is Beijing white pear.

3. The application according to claim 1, characterized in that, In step S3, primer design was performed using BeaconDesigner 8 software. The target gene was a differentially expressed circular RNA containing a cross-junction sequence. The primer length was 18-24 bases, the GC content was 40-60%, and the amplification product length was between 100-200 bases.

4. The application according to claim 1, characterized in that, In step S4, the variable explanation degree R2X and the prediction index Q2 of the constructed production area differentiation model should both be greater than 0.

5.

5. Application of nucleic acid sequences as shown in SEQ ID NO.19-23 in distinguishing the production areas of Jingbai pears in Mentougou District and Fangshan District of Beijing.

6. A primer set for distinguishing the circular RNA sequences of Beijing white pears from different origins, comprising sequences as shown in SEQ ID NO. 24-33.

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