Plant product producing area identification method based on circular RNA detection and application

Abstract

The invention provides a plant product producing area identification method based on circular RNA detection and application, and belongs to the technical field of producing area identification by detecting plant products. The identification method comprises the following steps: collecting plant product samples from different producing areas, and sequencing the circular RNA of the plant product samples; analyzing and obtaining differentially expressed circular RNA among samples from different producing areas and sources; a fluorescent quantitative nucleic acid amplification primer is designed and synthesized aiming at the circular RNA; performing fluorescent quantitative nucleic acid amplification to obtain circular RNA expression quantity data; constructing a producing area identification model based on partial least squares discriminant analysis / orthogonal partial least squares discriminant analysis by utilizing multivariate statistical analysis software according to the annular RNA expression quantity data; and substituting expression quantity data of circular RNA of a plant sample to be detected into the model, and predicting the origin of the plant sample. The method provided by the invention is not only suitable for identifying producing areas of plant products with large spatial scale and relatively long distance, but also suitable for identifying producing areas of plant products with small spatial scale and relatively short distance.

Description

Technical Field

[0001] The present invention belongs to the technical field of detecting plant products for origin identification, and specifically relates to a method for identifying the origin of plant products based on detecting circular RNA and its application. Background Art

[0002] The growing location has an important influence on the quality of plant products. Plant products from different origins often have obvious differences in the content of specific substances. Especially for some local specialty products, such as precious Chinese medicinal materials, geographical indication agricultural products, etc., due to differences in the production environment, their product quality is different, and the corresponding commodity prices are significantly different. Therefore, the phenomenon of counterfeit brands and counterfeit origins is prevalent in the market, and even caused the market chaos of "bad money driving out good money", which not only affects the brand reputation and consumer confidence of the product, but also restricts the high-quality development of the industry.

[0003] Existing methods for origin identification mainly include spectral technology, stable isotope technology, elemental analysis technology, and organic component analysis. However, these methods are more suitable for products that are far away in geographical space. When the products come from neighboring origins, the differentiation effect is not ideal. Although the combination of multiple technologies can improve the accuracy of origin identification to a certain extent, it will also bring about problems such as complicated operation and high cost. In addition, metabolomics technology based on high-resolution mass spectrometry can be used for the origin differentiation of products such as plant sources, and the accuracy is relatively high. However, on the one hand, this technology requires a large-scale mass spectrometer with high resolution, and the instrument platform needs to be maintained and operated by professionals to ensure its stability and reproducibility; on the other hand, it requires the use of bioinformatics and chemometric methods for a large amount of data analysis, and there is currently a lack of unified standards for non-target detection, making it difficult to commercialize in a short period of time.

[0004] Circular RNA (circRNA) is a type of closed circular single-stranded RNA molecule covalently bound by the 3' end and the 5' end. The length of plant circRNA is mainly between 200-600 bp. Unlike linear RNA, the unique circular structure of circRNA makes it less susceptible to degradation by exonucleases and ribonucleases, and thus has higher stability than linear RNA. In addition, studies have shown that plant circRNA also has the characteristics of conservatism and cell or tissue-specific expression, making it a potential biomarker. Summary of the invention

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

[0006] To achieve the above object, the present invention adopts the following technical solutions: A method for identifying the origin of plant products based on detecting circular RNA, comprising the following steps: S1. Collect samples of plant products from different origins; extract total RNA from plant product samples from each origin; use a high-throughput sequencing platform to perform circular RNA (circRNA) sequencing to obtain circRNA sequences of product samples from each origin; S2. Compare the obtained circRNA sequence with the product genome and obtain the sequence information of the circRNA junction site; and screen the circRNA with significant differential expression based on the sequencing number of the circular RNA obtained by sequencing; S3. Design primers for significantly differentially expressed circRNAs, and use the primers to perform real-time fluorescence quantitative PCR analysis on RNA of plant samples from each origin, so as to obtain expression data of significantly differentially expressed circRNAs; S4. Using multivariate statistical analysis software, construct a production area differentiation model based on partial least squares discriminant analysis (PLS-DA) or orthogonal partial least squares discriminant analysis (OPLS-DA) according to the expression data of significantly differentially expressed circRNA obtained in step S3, so as to distinguish the origin of samples; S5. After total RNA is extracted from the plant samples to be tested, real-time fluorescence quantitative PCR analysis is performed using the primers in S3 to obtain the expression level data of the significantly differentially expressed circRNA of the samples to be tested, which is then substituted into the model to determine the origin of the samples.

[0007] In the above-mentioned method for identifying the origin of plant products, preferably, in step S1, the plant products are agricultural products, vegetables, fruits, flowers, medicinal plants, etc.

[0008] In the above-mentioned method for identifying the origin of plant products, preferably, in step S2, the sequencing data are analyzed using find_circ and CIRI software, and the corresponding sequence information is obtained by comparing with the product genome, and the circRNA junction position is obtained at the same time; The criteria for screening significantly differentially expressed circRNAs were that the difference fold was greater than or equal to 2 and the significance level was less than 0.05.

[0009] Furthermore, it is preferred to screen significantly differentially expressed circRNAs by using a negative binomial distribution (DESeq2) method for circular RNA differential expression analysis; differential circRNAs are screened based on two perspectives: the difference fold and the corrected significance level.

[0010] In the method for identifying the origin of plant products as described above, preferably, in step S3, primer design is performed using BeaconDesigner 8 software, the target gene is a significantly differentially expressed circRNA containing a cross-junction sequence, the primer length is 18-24 bases, the GC content is 40-60%, and the length of the amplified product is between 100-200 bases.

[0011] In the above-mentioned method for identifying the origin of plant products, preferably, in step S4, the variable explanation degree R of the origin classification model constructed in step S4 is 2 X and the predicted index Q 2 Should be greater than 0.5.

[0012] The method as described above is used to identify the origin of peaches, wherein the nucleic acid sequences of the differentially expressed circular RNAs are shown in SEQ ID NOs. 1-6, and are used to identify peaches from Pinggu District, Beijing, Shunping County, Hebei, and Leting County, Hebei.

[0013] The method as described above is used to identify the origin of Beijing white pears, wherein the nucleic acid sequences of the differentially expressed circular RNAs are shown as SEQ ID NOs. 19-23, and is used to identify Beijing white pears from Mentougou District and Fangshan District of Beijing.

[0014] The application of nucleic acid sequences such as those shown in SEQ ID NO.1-6 in distinguishing different peach producing areas.

[0015] A primer set for distinguishing circular RNA sequences of peaches from different origins, comprising sequences as shown in SEQ ID NOs. 7-18.

[0016] The nucleic acid sequences as shown in SEQ ID NO.19-23 are used to distinguish different origins of Jingbai pear.

[0017] A primer set for distinguishing circular RNA sequences of Jingbai pears from different origins, comprising sequences as shown in SEQ ID NOs. 24-33.

[0018] The beneficial effects of the present invention are: The present invention provides a method for identifying the origin of plant products based on detecting circular RNA, through the circRNA of the plant, screen the circRNA with significant differential expression, through the design of nucleic acid amplification primers, after fluorescence quantitative PCR amplification, through the expression data of circRNA, combined with multivariate statistical analysis strategy to build a production area classification model to achieve the distinction of the origin, the production area classification accuracy is greater than 98%, and a new method is provided for the identification of the origin of plant products from different origins. The method has high accuracy and a wide range of applications. It is not only suitable for the identification of samples between production areas with large distances, such as intercontinental, inter-national, north-south, etc., but also particularly suitable for adjacent production areas, such as adjacent provinces, different cities and counties in the same province, and different districts and counties in the same city and county. And the technology is based on the technology of nucleic acid amplification, which is easy to operate and low in cost, and is expected to be developed into a rapid detection product, which provides a new method and technology for the identification of the origin of plant products. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 The results of 1% agarose gel electrophoresis detection of RNA in peach samples.

[0020] Figure 2 The results of differentiating large peach samples from three production areas based on circRNA markers.

[0021] Figure 3 This is the permutation test result of the model in Example 1.

[0022] Figure 4 The results of 1% agarose gel electrophoresis detection of RNA in Jingbai pear samples.

[0023] Figure 5 The results of differentiating the Jingbai pear samples from two origins based on circRNA markers.

[0024] Figure 6 This is the permutation test result of the model in Example 2. DETAILED DESCRIPTION

[0025] Plant products from different origins show differences in the expression of circRNA due to differences in the origin environment. Therefore, the present invention attempts to use this technology for the first time to identify the origin of plant products from different regions. A new method is provided for identifying the origin of plant products from different origins. The method has high accuracy and a wide range of applications. It is not only suitable for the identification of samples from origins with large distances, such as continents, countries, and the north and south, but also for the identification of samples from neighboring origins, such as adjacent provinces, different cities and counties in the same province, and different districts and counties in the same city and county. Moreover, the technology is based on nucleic acid amplification technology, which is easy to operate, low cost, and can be developed into a rapid detection product, providing a new method and technology for identifying the origin of plant products.

[0026] The following examples are used to further illustrate the present invention, but should not be construed as limiting the present invention. Without departing from the spirit and substance of the present invention, modifications or substitutions made to the present invention all belong to the scope of the present invention.

[0027] Unless otherwise specified, the technical means used in the examples are conventional means well known to those skilled in the art. Unless otherwise specified, the reagents used in the present invention are of analytical grade or above.

[0028] Example 1 Identification of peach samples from different origins based on circRNA marker combinations 1. Collection of plant samples Mature peach samples from different origins were collected using a 5-point sampling method. The samples collected at each point were mixed as one sample and brought back to the laboratory. This experiment collected peach samples from three origins, namely Beijing Pinggu District (PG), Hebei Shunping County (SP), and Hebei Leting County (LT). 16 representative samples were collected from each origin.

[0029] 2. RNA extraction and high-throughput sequencing After the samples were brought back to the experiment, they were frozen and ground in liquid nitrogen, and total RNA was extracted using the Quick RNA Isolation Kit (Huayueyang, China). After RNA extraction, 1% agarose gel electrophoresis (150V constant voltage electrophoresis for 20 min) was performed, and the RNA bands were observed in a gel imager to detect its integrity. Figure 1 The electrophoresis bands in the total RNA shown are clear, and the concentration of the 25SrRNA band above is about 1.5-2.0 times that of the 18S band below, indicating that the quality of the RNA sample is good. The concentration of total RNA was determined by Nanodrop2000c UV spectrophotometer, and the purity of RNA was analyzed based on OD260 / 280 and OD260 / 230 values. The concentration of RNA is required to be greater than 100 ng / μL, and the OD260 / 280 and OD260 / 230 values ​​are between 1.8-2.0, indicating that the concentration and purity of the extracted RNA meet the requirements. After the RNA quality is qualified, 3 representative samples were selected from each origin. The sample ribosomal RNA was first removed using the Epicentre RiboZeroTM kit (Epicentre, USA), and circRNA high-throughput sequencing was performed using the IlluminaHiSeqTM 2500 sequencing platform.

[0030] 3. CircRNA identification and differential expression analysis The sequencing data were analyzed using find_circ and CIRI software. The corresponding sequence information was obtained by comparing with the peach genome (ftp: / / ftp.ensemblgenomes.org / pub / plants / ), and the circRNA junction position was obtained at the same time. The number of sequencing reads (readcounts) obtained for each circRNA in the sequencing was used as the circRNA differential expression analysis data. The negative binomial distribution (DESeq2) method was used for circRNA differential expression analysis; according to the differential expression between samples from different origins, the fold change of circRNA expression between origins was greater than or equal to 2 and the corrected significance level (p value) was less than 0.05 as the screening criteria to screen the significantly differentially expressed circRNAs, and the significantly differentially expressed circRNAs were used as candidate markers for subsequent modeling analysis. The experiment finally screened out 6 circRNAs with significant differential expression between the origins, and the corresponding partial cross-junction sequences (29 bases before and after the junction) are shown in Table 1.

[0031] Table 1 Sequence information of the junctions of peach circRNA

[0032] 4. Analysis of sample circRNA expression 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 the system was mixed, it was placed in a PCR instrument and incubated at 25°C for 10 min, 42°C for 30 min, and 85°C for 5 s. After the reaction was completed, cDNA for fluorescent quantitative PCR reaction was obtained.

[0033] Then, Beacon Designer 8 software was used to design primers for the junction sequences of the above six significantly differentially expressed circRNAs. The sequence template could be appropriately extended by the number of bases before and after the junction as needed so that the amplified product contained the junction sequence of the circRNA. At the same time, the primer GC content was required to be between 40 and 60, the length was between 18 and 24 bp, and the length of the amplified product was between 100 and 200 bp. The primer information is shown in Table 3.

[0034] All samples were analyzed by real-time fluorescence quantitative PCR using this primer. The SYBR Green PremixPro Taq HS qPCR kit (AGbio, Hunna) was used for amplification. Fluorescence quantitative PCR amplification (qPCR) was performed according to the corresponding system and program in the kit manual. The amplification system is shown in Table 4. After the system is configured, it is mixed and centrifuged, and then placed in the LightCycler96 (Roche, Switzerland) fluorescence quantitative PCR instrument for reaction and fluorescence signal collection. The qPCR reaction program: 95℃ 30s, 95℃ 5s, 60℃ 30s, 40 cycles, and fluorescence signals are collected at the end of each cycle. After the amplification, the melting curve program is run, 95℃ 10s, 65℃ 60s, and 97℃ 1s. Three replicates are set for each template for each primer pair to obtain the threshold cycle number (Cq) for each sample. After amplification, the standard deviation of the Cq of three replicate wells of each template for each pair of primers is required to be no more than 0.4, and the amplification is qualified. Otherwise, the amplification is repeated until it meets the requirements. Finally, the Cq values ​​of all samples under the corresponding primers of the six significantly differentially expressed circRNAs are obtained, which are the expression data of the significantly differentially expressed circRNAs corresponding to each sample.

[0035] Table 2 RNA reverse transcription system

[0036] Table 3 Primer information of peach circRNA

[0037] Table 4 Real-time fluorescence quantitative PCR amplification system

[0038] 5. Constructing origin identification model and sample origin prediction Using multivariate statistical analysis software (SIMCA-P), the expression data of significantly differentially expressed circRNAs corresponding to the samples were used as input data to construct an origin identification model based on partial least squares discriminant analysis (PLS-DA). Figure 2 As shown. Figure 2 It can be seen that the samples from three different origins are focused together, indicating that the model is successfully constructed. At the same time, the variable explanation of the model R 2 X=0.986, prediction index Q 2 =0.868 are all greater than 0.5, indicating that the model has good reliability and predictive ability. The model verification results are as follows Figure 3 As shown, from this result, it can be seen that all the blue Q 2 The values ​​are lower than the original point on the right, Q 2The blue regression line of the point intersects the vertical axis at or below zero, and all the green R 2 The values ​​are all lower than the original point on the right, which also shows the effectiveness of the original model.

[0039] After the model was constructed, the expression data of the six significantly differentially expressed circRNAs corresponding to the six peach samples to be tested were brought into the model. The model automatically gave the area where each sample to be tested fell, and then automatically calculated its origin and the corresponding accuracy probability. The results are shown in Table 5. There were two samples from each origin among the six samples, and the prediction accuracy was greater than 98%.

[0040] Table 5 Model prediction results of the tested samples

[0041] By using the above methods 1-5 to construct an origin identification model for different plant-based products, and bringing the plant-based samples to be tested from the corresponding sources into the model, the origin of the samples to be tested can be accurately identified.

[0042] Example 2 Identification of Beijing white pears from different origins based on circRNA marker combinations 1. Collection of plant samples Mature Beijing white pear samples from different origins were collected. The samples were collected using a 5-point sampling method. The samples collected at each point were mixed as one sample and brought back to the laboratory. This experiment collected Beijing white pear samples from two origins, namely Beijing Mentougou District (MTG) and Beijing Fangshan District (FS). Fifteen representative samples were collected from each origin.

[0043] 2. RNA extraction and high-throughput sequencing After the samples were brought back to the experiment, they were frozen and ground in liquid nitrogen, and total RNA was extracted using the Quick RNA Isolation Kit (Huayueyang, China). After RNA extraction, 1% agarose gel electrophoresis (150V constant voltage electrophoresis for 20 min) was performed, and the RNA bands were observed in a gel imager to detect its integrity. Figure 4The electrophoresis bands in the total RNA shown are clear, and the concentration of the 25SrRNA band above is about 1.5-2.0 times that of the 18S band below, indicating that the quality of the RNA sample is good. The concentration of total RNA was determined by Nanodrop2000c UV spectrophotometer, and the purity of RNA was analyzed based on OD260 / 280 and OD260 / 230 values. The concentration of RNA is required to be greater than 100 ng / μL, and the OD260 / 280 and OD260 / 230 values ​​are between 1.8-2.0, indicating that the concentration and purity of the extracted RNA meet the requirements. After the RNA quality is qualified, 3-4 representative samples were selected from each origin. The sample ribosomal RNA was first removed using the Epicentre RiboZeroTM kit (Epicentre, USA), and circRNA high-throughput sequencing was performed using the IlluminaHiSeqTM 2500 sequencing platform.

[0044] 3. CircRNA identification and differential expression analysis The sequencing data were analyzed using find_circ and CIRI software. The corresponding sequence information was obtained by comparing with the Jingbai Pear genome (https: / / ftp.ncbi.nlm.nih.gov / genomes / all / GCF / 019 / 419 / 815 / GCF_019419815.1_Pyrus_bretschneideri_v1 / ), and the circRNA junction position was obtained at the same time. The number of sequencing reads (readcounts) obtained by each circRNA in the sequencing was used as the circRNA differential expression analysis data. The negative binomial distribution (DESeq2) method was used for circRNA differential expression analysis; according to the differential expression between samples from different origins, the fold change of circRNA expression between origins was greater than or equal to 2 and the corrected significance level (p value) was less than 0.05 as the screening criteria, and the significantly differentially expressed circRNAs were screened, and the significantly differentially expressed circRNAs were used as candidate markers for subsequent modeling analysis. The experiment finally screened out 5 circRNAs with significantly differential expression among the origins. The corresponding partial cross-junction sequences (29 bases before and after the junction) are shown in Table 6.

[0045] Table 6 Sequence information of the junctions of pear circRNA

[0046] 4. Analysis of sample circRNA expression 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 the system was mixed, it was placed in a PCR instrument and incubated at 25°C for 10 min, 42°C for 30 min, and 85°C for 5 s. After the reaction was completed, cDNA was obtained for fluorescent quantitative PCR reaction.

[0047] Then, Beacon Designer 8 software was used to design primers for the junction sequences of the above five significantly differentially expressed circRNAs. The sequence template could be appropriately extended by the number of bases before and after the junction as needed so that the amplified product contained the junction sequence of the circRNA. At the same time, the primer GC content was required to be between 40-60%, the length was between 18-24 bp, and the length of the amplified product was between 100-200 bp. The primer information is shown in Table 7.

[0048] All samples were analyzed by real-time fluorescence quantitative PCR using this primer. The SYBR Green PremixPro Taq HS qPCR kit (AGbio, Hunan) was used for amplification. Fluorescence quantitative PCR amplification (qPCR) was performed according to the corresponding system and program in the kit manual. The amplification system is shown in Table 4. After the system is configured, it is mixed and centrifuged, and then placed in the LightCycler96 (Roche, Switzerland) fluorescence quantitative PCR instrument for reaction and fluorescence signal collection. The qPCR reaction program: 95℃ 30s, 95℃ 5s, 60℃ 30s, 40 cycles, and fluorescence signals are collected at the end of each cycle. After the amplification, the melting curve program is run, 95℃ 10s, 65℃ 60s, and 97℃ 1s. Three replicates are set for each template for each primer pair to obtain the threshold cycle number (Cq) for each sample. After amplification, the standard deviation of the Cq of three replicate wells of each template for each pair of primers is required to be no more than 0.4, and the amplification is qualified. Otherwise, the amplification is repeated until it meets the requirements. Finally, the Cq values ​​of all samples under the corresponding primers of the five significantly differentially expressed circRNAs are obtained, which are the expression data of the significantly differentially expressed circRNAs corresponding to each sample.

[0049] Table 7 Primer information of pear circRNA

[0050] 5. Constructing origin identification model and sample origin prediction Using multivariate statistical analysis software (SIMCA-P), the expression data of significantly differentially expressed circRNAs corresponding to the samples were used as input data to construct an origin identification model based on orthogonal partial least squares discriminant analysis (OPLS-DA). Figure 5 As shown. Figure 5 It can be seen that the samples from two different origins are focused together, indicating that the model is successfully constructed. At the same time, the variable explanation of the model R 2 X=0.887, prediction index Q 2 =0.93 are all greater than 0.5, indicating that the model has good reliability and predictive ability. The model verification results are as follows Figure 6 As shown, from this result, it can be seen that all the blue Q 2 The values ​​are lower than the original point on the right, Q 2 The blue regression line of the point intersects the vertical axis at or below zero, and all the green R 2 The values ​​are all lower than the original point on the right, which also shows the effectiveness of the original model.

[0051] After the model was constructed, the expression data of the five significantly differentially expressed circRNAs corresponding to the four tested pear samples were brought into the model. The model automatically gave the area where each tested sample fell, and then automatically calculated its origin and the corresponding accuracy probability. The results are shown in Table 8. There were two samples from each origin among the four samples, and the prediction accuracy was greater than 98%.

[0052] Table 8 Model prediction results of the tested samples

[0053] As can be seen from the above, the invention provides a method for detecting circular RNA in various plant products, screening significantly differentially expressed circRNAs, and constructing a production area classification model by designing nucleic acid amplification primers and differentially expressed circRNA expression data combined with multivariate statistical analysis strategies to achieve the distinction of production areas, and the accuracy of production area classification is greater than 98%. This method is based on nucleic acid amplification technology, is easy to operate, and has low cost. It is expected to be developed into a rapid detection product, providing a new method and technology for identifying the origin of plant products.

Claims

1. A method for identifying the origin of plant products based on detecting circular RNA, characterized in that: It includes the following steps: S1. Collect samples of plant products from different origins, perform circular RNA sequencing, and obtain circular RNA sequences of samples from different origins; S2. Compare the obtained circular RNA sequence with the product genome and obtain the sequence information of the circRNA junction site; and screen the circular RNA with significant differential expression based on the sequencing number of the circular RNA obtained by sequencing; S3. Designing primers for the significantly differentially expressed circular RNAs, and using the primers to perform real-time fluorescence quantitative PCR analysis on RNA of plant samples from each origin, thereby obtaining expression data of the significantly differentially expressed circular RNAs; S4. Using multivariate statistical analysis software, a production area classification model based on partial least squares discriminant analysis or orthogonal partial least squares discriminant analysis is constructed according to the expression data of significantly differentially expressed circular RNA obtained in step S3 to distinguish the origin of the samples; S5. After extracting total RNA from the plant samples to be tested, amplification is performed using the primers designed in S3 to obtain expression data of significantly differentially expressed circular RNAs. The expression data is substituted into the model obtained in S4 to obtain the origin of the plant samples to be tested.

2. The origin identification method according to claim 1, characterized in that: In step S1, the plant products are agricultural products, vegetables, fruits, flowers, and medicinal plants.

3. The origin identification method according to claim 1, characterized in that: In step S2, the sequencing data were analyzed using find_circ and CIRI software, and the corresponding sequence information was obtained by comparing with the product genome, and the location of the circular RNA junction was obtained at the same time; The criteria for screening significantly differentially expressed circular RNAs were that the fold difference was greater than or equal to 2 and the significance level was less than 0.

05.

4. The origin identification method according to claim 1, characterized in that: In step S2, the circular RNA with significant differential expression is screened by performing circular RNA differential expression analysis based on the negative binomial distribution method; the differential circular RNA is screened based on two perspectives: the difference multiple and the corrected significance level.

5. The origin identification method according to claim 1, characterized in that: In step S3, primer design was performed using Beacon Designer 8 software. The target gene was a significantly differentially expressed circular RNA containing a cross-junction sequence. The primer length was 18-24 bases, the GC content was 40-60%, and the length of the amplified product was between 100-200 bases.

6. The origin identification method according to claim 1, characterized in that: In step S4, the variable explanatory power R of the constructed production area classification model 2 X and the predicted index Q 2 Should be greater than 0.

5.

7. Application of nucleic acid sequences such as those shown in SEQ ID NO. 1-6 in distinguishing different peach producing areas.

8. A primer set for distinguishing circular RNA sequences of peaches from different origins, comprising sequences as shown in SEQ ID NOs. 7-18.

9. Application of nucleic acid sequences such as those shown in SEQ ID NOs. 19-23 in distinguishing different origins of Jingbai pear.

10. A primer set for distinguishing circular RNA sequences of Jingbai pears from different origins, comprising sequences as shown in SEQ ID NOs. 24-33.

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