Sesame wilt identification method based on molecular marker

Abstract

The invention discloses a sesame fusarium wilt identification method based on a molecular marker, and relates to the technical field of phytopathology, the identification method comprises the following steps: S100, RNA extraction and purification: extracting total DNA and protein pollution RNA from a sesame sample to be detected, purifying the extracted RNA by using an efficient RNA extraction kit, removing existing impurities and inhibitors, and determining the content of the protein pollution RNA in the sesame sample to be detected; according to the method, specific long-chain non-coding RNA is adopted as a molecular marker, the molecular marker has high specificity and can accurately distinguish the sesame wilt from other diseases and healthy plants, the probability of misjudgment can be greatly reduced through data analysis, and the detection accuracy is improved. The specific long-chain non-coding RNA has the advantages that the specific long-chain non-coding RNA can be used for detecting the sesame fusarium wilt, reliable guarantee is provided for accurate identification of the sesame fusarium wilt, meanwhile, the specific long-chain non-coding RNA has good stability under different environmental conditions, and detection of a large number of samples can be completed within a short time through PCR amplification and high-throughput sequencing.

Description

Technical Field

[0001] The invention relates to the technical field of plant pathology, and in particular to a sesame wilt disease identification method based on molecular markers. Background Art

[0002] As an important oil crop, sesame is widely planted around the world. However, the frequent occurrence of sesame wilt poses a serious threat to the yield and quality of sesame. Sesame wilt is a disease caused by a variety of pathogens. Its pathogenesis is complex and the early symptoms are not obvious. Once the disease worsens, it often leads to large-scale death of sesame plants, causing huge economic losses to growers. Therefore, accurate and rapid identification of sesame wilt is crucial for timely prevention and control measures and ensuring sesame production.

[0003] However, in the identification methods of sesame wilt disease, traditional morphological observation and pathogen isolation and culture methods often rely on experienced professionals for morphological observation, which is highly subjective and difficult to accurately judge in the early stages of the disease. Pathogen isolation and culture is time-consuming, complicated to operate, and easily affected by environmental factors. Existing identification methods based on molecular markers mostly use conventional DNA fragments or short-chain RNA as molecular markers. These molecular markers are insufficient in specificity and sensitivity, and have certain cross-reactions with sesame plants in other diseased and non-disease states, leading to misjudgment. At the same time, traditional identification methods usually use known protein-coding genes as markers. These methods often cannot accurately reflect the early stages of the disease.

[0004] In summary, the existing sesame wilt disease identification methods are difficult to meet the needs of rapid and accurate identification of sesame wilt disease in actual production. Therefore, the development of a new sesame wilt disease identification method based on specific long non-coding RNA as a molecular marker has important practical significance and provides more reliable technical support for the prevention and control of sesame wilt disease. Summary of the invention

[0005] The purpose of the present invention is to make up for the deficiencies of the prior art and provide a sesame wilt disease identification method based on molecular markers. The method can use specific long non-coding lncRNA as a molecular marker. These lncRNAs have high sequence specificity and expression specificity, and can more accurately reflect the occurrence of sesame wilt disease. Through PCR amplification and high-throughput sequencing, a large number of samples can be detected in a short time, which greatly improves the identification efficiency, realizes early prediction and rapid response to sesame wilt disease, and helps to take prevention and control measures in time to reduce disease losses.

[0006] In order to solve the above technical problems, the present invention provides the following technical solution: a method for identifying sesame wilt disease based on molecular markers, the identification method comprising the following steps:

[0007] S100, RNA extraction and purification: Extract total DNA and protein-contaminated RNA from the sesame samples to be tested, and purify the extracted RNA using an efficient RNA extraction kit to remove existing impurities and inhibitors to ensure the efficiency of subsequent reverse transcription and PCR amplification;

[0008] S200, reverse transcription and cDNA synthesis: The purified RNA was reverse transcribed into cDNA using a reverse transcription kit. During the reverse transcription process, specific primers and enzymes designed for lncRNA-X123 were used to perform quality detection and concentration determination on the synthesized cDNA, and electrophoresis was used to ensure that it met the requirements of PCR amplification.

[0009] S300, PCR amplification and product detection: synthesize primer pairs for specific lncRNA to accurately amplify the target lncRNA fragment, use cDNA as a template, use specific primer pairs for PCR amplification, optimize PCR reaction conditions, and perform quality detection of the amplified product, including product size, purity and concentration;

[0010] S400, sequencing and data analysis: Sequencing the amplified products to obtain the sequence information of specific lncRNA, comparing and analyzing with the known lncRNA sequences related to sesame wilt, and performing quality control on the sequencing results to obtain accurate identification results;

[0011] S500, judging the identification result: judging whether the sesame sample to be tested is infected with Fusarium wilt according to the comparison result, and if the sequencing result is consistent with the known lncRNA sequence related to sesame Fusarium wilt, then the sesame sample to be tested is judged to be infected with Fusarium wilt, and multiple specific lncRNA molecular markers are set for joint identification to test the sensitivity and specificity of the identification.

[0012] Furthermore, the specific steps of S100 are:

[0013] S101, sample preparation and tissue destruction: select healthy sesame plants and sesame plants known to be infected with Fusarium wilt as test samples, quickly freeze them in liquid nitrogen, take out the frozen sesame tissues, and grind them into powder in liquid nitrogen;

[0014] S102, RNA extraction: using the RNA extraction kit Trizol reagent, adding lysis solution to the ground tissue to lyse the cells and release RNA, removing cell debris and insoluble matter by centrifugation to obtain the supernatant containing RNA, and using a UV spectrophotometer to determine the RNA concentration and purity;

[0015] S103, removal of DNA contamination: adding DNase to the supernatant to remove residual DNA, and inactivating the DNase by heat inactivation and adding inhibitors;

[0016] S104, RNA purification: RNA was further purified using a column purification kit to remove impurities and inhibitors generated during the extraction process, and the concentration and purity of the purified RNA were measured again using a UV spectrophotometer;

[0017] S105, RNA precipitation and washing: add isopropanol and ethanol precipitants to precipitate the RNA, collect the RNA precipitate by centrifugation, and wash with 75% ethanol to remove residual salts and small molecule impurities.

[0018] Furthermore, the S100 RNA extraction and purification measured the A260 / A280 ratio and A260 / A230 ratio of the RNA by a UV spectrophotometer to evaluate the purity of the RNA, wherein when the A260 / A280 ratio was close to 2.0 and the A260 / A230 ratio was close to 2.0-2.2, the RNA quality was considered to be good, and the extracted and purified RNA was stored at -80°C to prevent RNA degradation and used for reverse transcription and PCR amplification.

[0019] Furthermore, the specific steps of the S200 reverse transcription and synthesis are:

[0020] S201, preparation of reverse transcription system: prepare the reaction system required for reverse transcription according to the reverse transcription kit, including reverse transcriptase, specific primers designed for lncRNA-X123, RNase inhibitor, deoxyribonucleoside triphosphate and buffer;

[0021] S202, RNA template addition: adding the purified and quality-assessed RNA sample to the reverse transcription system;

[0022] S203, reverse transcription reaction: setting reaction conditions for the reverse transcription system containing the RNA template, performing a reverse transcription reaction, and completely reversely transcribing the RNA into cDNA;

[0023] S204, cDNA quality assessment and concentration determination: Perform a preliminary quality assessment on the cDNA obtained by reverse transcription, and use a spectrophotometer to determine the concentration of the cDNA. At the same time, use agarose gel electrophoresis to observe the integrity and size distribution of the cDNA and check for the presence of degradation and non-specific products;

[0024] S205, cDNA storage: Store qualified cDNA samples at -20°C to prevent degradation and prepare for PCR amplification.

[0025] Furthermore, in the S203 reverse transcription reaction, the purified RNA sample is mixed with a specific primer, and reverse transcriptase is added according to the reverse transcription kit, and the reverse transcription reaction conditions are set to 65° C., preheated for 5 minutes to denature the RNA, and then cooled on ice. The reverse transcription reaction is performed at a reaction temperature and time of 42° C. and 1 hour, and heated at 70° C. for 15 minutes to terminate the reaction. The concentration and purity of the cDNA are determined by an ultraviolet spectrophotometer through S204, so that the A260 / A280 ratio of the cDNA is close to 2.0, that is, the cDNA purity is high, and the quality of the cDNA is checked by agarose gel electrophoresis to ensure that there is no degradation.

[0026] Furthermore, the specific steps of S300 PCR amplification and product detection are:

[0027] S301, PCR reaction system preparation: in a sterile PCR tube, add cDNA template, specific primer pair, DNA polymerase, buffer, dNTPs and sterile water in sequence to form a PCR reaction system;

[0028] S302, PCR reaction condition setting: according to the characteristics of the primers and the DNA polymerase, the PCR reaction conditions are set, including the pre-denaturation temperature and time, the denaturation temperature and time, the annealing temperature and time, the extension temperature and time, and the number of cycles;

[0029] S303, PCR amplification reaction: placing the PCR reaction system into a PCR instrument, performing an amplification reaction under the set reaction conditions, and observing the operating status of the PCR instrument;

[0030] S304, preliminary observation of product detection: after the PCR amplification reaction is completed, a portion of the product is taken out for preliminary observation, and the color change and turbidity of the product in the PCR tube are observed to preliminarily determine whether an amplification product is generated;

[0031] S305, agarose gel electrophoresis detection: prepare agarose gel, perform electrophoresis separation on the PCR products, control the voltage and time during the electrophoresis process, observe the electrophoresis results, judge the band position of the PCR product and compare it with the known target product size to determine whether it is the correct amplification product;

[0032] S306, product concentration determination: Use a spectrophotometer to determine the concentration of the PCR product, and determine the efficiency of the PCR reaction and the yield of the product based on the determination results.

[0033] Furthermore, the S303 PCR amplification reaction sets the PCR amplification conditions as 94°C pre-denaturation for 5 minutes, 94°C denaturation for 30 seconds, 55°C annealing for 30 seconds, 72°C extension for 1 minute, repeating 35 cycles, and extending at 72°C for 5 minutes to complete the final extension. The configured PCR reaction system is placed in a PCR instrument for amplification, and the PCR product is subjected to 1.5% agarose gel electrophoresis at 100 V for 30 minutes. The position and clarity of the bands are recorded to evaluate the purity of the product.

[0034] Furthermore, the S400 sequencing and data analysis selects sequencing depth, base quality distribution uniformity, and low-quality base ratio as evaluation indicators, and performs quality control on the identification results by calculating the base quality at different positions, that is, preprocessing the original sequencing data, including removing the adapter sequence, filtering out low-quality bases, calculating the correlation between adjacent bases and the background frequency of each base, and improving the quality assessment accuracy of the sequencing data. The formula is: P(e|i) is the probability of an error occurring at position i, and F(b i ) is the background frequency of base b at position i, δ is the weight factor used to adjust the influence of the error probability of bases at adjacent positions, η is the weight factor used to adjust the influence of the background frequency of bases at adjacent positions, Q(i) is the new quality score for position i, and by considering the read length and position deviation factors, the error probability of each position is estimated more accurately, that is, P(e|i) = αe -β|i-μ| +γ, α is the basic value that affects the error probability, β is the degree of influence of the control position deviation on the error probability, γ represents the background error rate, μ is the center position of the read segment, and a function L(i) of the read segment length is introduced to determine the influence of the read segment length on the error rate, that is, L max is the maximum length of the read segment. By considering the impact of the read segment length on the error rate, the error rate can be accurately estimated.

[0035] Furthermore, the S400 sequencing and data analysis accurately estimates the background frequency of each position, i.e., F(b i )=F 0 (b i )×(1+ω 1 F 1 (b i-1 )+ω 2 F 2 (b i+1 )), F 0 (b i ) is the basic background frequency with no influence, F 1 (b i-1 ) is the background frequency of the previous base, F 2 (bi+1 ) makes the background frequency of the next base, ω 1 and ω 2 Adjust the weighting factors that affect the intensity, apply the above algorithm to the sequencing data, calculate the new quality score for each base in each read, and compare the quality scores of different positions in the same read.

[0036] Furthermore, the specific steps of determining the identification result in S500 are:

[0037] S501, preliminary result judgment: for each sesame sample to be tested, the sequencing results of each molecular marker are compared with the known sequence;

[0038] S502, uncertainty processing: For cases where some molecular marker results are inconsistent or the results are in an ambiguous range, further analysis is performed, the experiment is repeated, and the sample size is increased;

[0039] S503, final result determination: All information, including the results of each molecular marker, the results of the analysis, and the results of the processing of uncertainty, is integrated to determine whether the sesame sample to be tested is infected with Fusarium wilt, and the final result is presented in the form of a clear report, including the sample number, the specific situation of each molecular marker, and the final judgment conclusion.

[0040] Compared with the prior art, this method for identifying sesame wilt based on molecular markers has the following beneficial effects:

[0041] 1. The present invention adopts a specific long-chain non-coding RNA as a molecular marker. The molecular marker has a high specificity and can accurately distinguish sesame wilt from other diseases and healthy plants. Through data analysis, the probability of misjudgment can be greatly reduced, providing a reliable guarantee for the accurate identification of sesame wilt. At the same time, the specific long-chain non-coding RNA has good stability under different environmental conditions. Through PCR amplification and high-throughput sequencing, a large number of samples can be tested in a short time, which greatly improves the identification efficiency, realizes early prediction and rapid response to sesame wilt, and helps to take prevention and control measures in time to reduce disease losses.

[0042] 2. In the process of nucleic acid extraction, the present invention adopts an efficient RNA extraction kit and purification method, which can obtain high-quality total RNA in a relatively short time. The detection of long-chain non-coding RNA adopts real-time fluorescence quantitative reverse transcription polymerase chain reaction, which can quickly and accurately detect the presence and content of target molecular markers. The result judgment basis is clear, and an accurate identification conclusion can be drawn in a short time. This fast and efficient identification process enables growers and agricultural technicians to understand the health status of sesame plants in a timely manner, take corresponding prevention and control measures, and minimize the losses caused by sesame wilt disease.

[0043] Other advantages, objectives and features of the present invention will be set forth in part in the following description and, in part, will be apparent to those skilled in the art based on an examination of the following or may be taught from the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0045] Figure 1 This is an operational flow chart of a molecular marker-based identification method for sesame Fusarium wilt.

[0046] Figure 2 This is a flow chart of the specific steps of S300 PCR amplification and product detection. DETAILED DESCRIPTION

[0047] The technical solutions in the embodiments of the present invention are described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0048] Embodiment 1

[0049] A molecular marker-based method for identifying sesame wilt disease. Through high-throughput sequencing technology and bioinformatics analysis, this method found that lncRNA-X123 has a significant differential expression pattern between healthy plants and diseased plants. As a highly specific molecular marker, this molecular marker can be used to accurately detect the presence of sesame wilt disease in the early stages of the disease.

[0050] First, enter the RNA extraction and purification stage (S100), select healthy sesame plants known to be infected with Fusarium wilt as test samples, and quickly freeze them in liquid nitrogen to help maintain the stability and biological activity of the samples. Take out the frozen sesame tissue and grind it into powder in liquid nitrogen to better release the RNA in the cells. Use the RNA extraction kit Trizol reagent, add lysis solution to the ground tissue to fully lyse the cells and release RNA. Remove cell debris and insoluble matter by centrifugation to obtain a supernatant containing RNA. In order to remove residual DNA, add DNase to the supernatant, and inactivate the DNase by heat inactivation and adding inhibitors. Use a column purification kit to perform The RNA was purified in one step to remove impurities and inhibitors generated during the extraction process to improve the purity of the RNA. During the purification process, isopropanol and ethanol precipitants were added to precipitate the RNA. The RNA precipitate was collected by centrifugation and washed with 75% ethanol to remove residual salts and small molecule impurities. The A260 / A280 ratio and A260 / A230 ratio of the RNA were measured by UV spectrophotometer to evaluate the purity of the RNA. When the A260 / A280 ratio was close to 2.0 and the A260 / A230 ratio was close to 2.0-2.2, the RNA was considered to be of good quality. The extracted and purified RNA was stored at -80°C to prevent RNA degradation and ensure that it could be used for subsequent reverse transcription and PCR amplification.

[0051] Then, the reverse transcription and cDNA synthesis stage (S200) is entered. The reaction system required for reverse transcription is prepared according to the reverse transcription kit, including reverse transcriptase, specific primers designed for lncRNA-X123, RNase inhibitor, deoxyribonucleoside triphosphates and buffer. The purified and quality-assessed RNA sample is added to the reverse transcription system, and reaction conditions are set for the reverse transcription system containing the RNA template to perform a reverse transcription reaction so that the RNA is completely reverse transcribed into cDNA. Specifically, the purified RNA sample is mixed with the specific primers, and the reverse transcription kit is added according to the reaction conditions. Add reverse transcriptase, set the reverse transcription reaction conditions to 65°C for 5 minutes to preheat to denature the RNA, then cool on ice, set the reaction temperature and time to 42°C and 1 hour for the reverse transcription reaction, and heat at 70°C for 15 minutes to terminate the reaction. Use an ultraviolet spectrophotometer to determine the concentration and purity of cDNA, so that the A260 / A280 ratio of cDNA is close to 2.0, that is, the cDNA purity is high. Check the quality of cDNA by agarose gel electrophoresis to ensure that there is no degradation. Store qualified cDNA samples at -20°C to prevent degradation and prepare for PCR amplification.

[0052] Next, enter the PCR amplification and product detection stage (S300), in a sterile PCR tube, add cDNA template, specific primer pair, DNA polymerase, buffer, dNTPs and sterile water in sequence to form a PCR reaction system, set PCR reaction conditions according to the characteristics of primers and DNA polymerase, including pre-denaturation temperature and time, denaturation temperature and time, annealing temperature and time, extension temperature and time and number of cycles, put the PCR reaction system into a PCR instrument, perform amplification reaction according to the set reaction conditions, observe the running status of the PCR instrument, after the PCR amplification reaction is completed, take out a part of the product for preliminary observation, observe the color change and turbidity of the product in the PCR tube, preliminarily judge whether amplification product is generated, prepare agarose gel, and perform electrophoresis separation on the PCR product. During the electrophoresis process, control the voltage and time, observe the electrophoresis results, judge the band position of the PCR product and compare it with the known target product size to determine whether it is the correct amplification product, use a spectrophotometer to measure the concentration of the PCR product, and judge the efficiency of the PCR reaction and the yield of the product according to the measurement results.

[0053] Secondly, enter the sequencing and data analysis stage (S400), sequence the amplified product to obtain the sequence information of the specific lncRNA, select the sequencing depth, base quality distribution uniformity, and low-quality base ratio as evaluation indicators, pre-process the original sequencing data, including removing the adapter sequence, filtering out low-quality bases, calculating the correlation between adjacent bases and the background frequency of each base, improving the quality evaluation accuracy of the sequencing data, and performing quality control on the identification results by calculating the base quality at different positions. Considering the read length and position deviation factors, the error probability of each position is more accurately estimated, and a read length function is introduced. The effect of the read length on the error rate is considered to accurately estimate the error rate by considering the effect of the read length on the error rate. By considering the effect of the base, the background frequency of each position is accurately estimated. A new quality score for each base in each read is calculated, and the quality scores of different positions in the same read are compared.

[0054] Finally, the identification result judgment stage (S500) is entered. For each sesame sample to be tested, a preliminary judgment is made based on the comparison of the sequencing results of each molecular marker with the known sequence. For the case where the results of some molecular markers are inconsistent or the results are in an ambiguous range, further analysis is performed, the experiment is repeated, the sample size is increased, and all information is integrated, including the results of each molecular marker, the results of the analysis, and the results of the processing of uncertainty, to determine whether the sesame sample to be tested is infected with wilt disease, and the final result is presented in the form of a clear report, including the sample number, the specific situation of each molecular marker, and the final judgment conclusion.

[0055] In summary, this embodiment provides the specific steps and operation flow of a method for identifying sesame wilt disease based on molecular markers. Through a series of operations of RNA extraction, purification, reverse transcription, PCR amplification, sequencing and data analysis, sesame wilt disease can be accurately identified, providing a scientific basis for disease prevention and control in sesame planting.

[0056] Embodiment 2

[0057] This embodiment provides an implementation process of a sesame wilt disease identification method based on molecular markers. By introducing evaluation indicators, the quality and accuracy of the experimental results can be more accurately evaluated, thereby improving the reliability of sesame wilt disease identification. This method provides an effective tool for sesame growers and related researchers, which helps to timely detect and prevent sesame wilt disease and reduce the impact of the disease on sesame yield and quality.

[0058] First, in the sesame planting field, several areas were randomly selected, and samples of healthy sesame plants and sesame plants suspected of being infected with Fusarium wilt were collected to ensure that the samples were representative and covered sesame plants at different growth stages and growth environments. RNA was extracted and purified in strict accordance with the specific steps of S100. Healthy sesame plants and sesame plants known to be infected with Fusarium wilt were selected as test samples, and they were quickly frozen and stored in liquid nitrogen to maintain the stability and biological activity of the samples. They were ground into powder in liquid nitrogen, and RNA was extracted using the RNA extraction kit Trizol reagent and lysis buffer. Impurities and inhibitors were removed through centrifugation, DNase treatment, and RNA purification steps to obtain high-purity RNA. The A260 / A280 ratio and A260 / A230 ratio of RNA were determined using a UV spectrophotometer to ensure good RNA quality. The extracted and purified RNA was stored at -80°C for subsequent use.

[0059] Next, in a sterile PCR tube, cDNA template, specific primer pair, DNA polymerase, buffer, dNTPs and sterile water were added in sequence to form a PCR reaction system. The primer pair should be designed for a specific lncRNA to ensure the specificity of amplification. According to the characteristics of the primers and DNA polymerase, appropriate PCR reaction conditions were set. For example, the pre-denaturation temperature was set to 94°C and the pre-denaturation time was 5 minutes to fully melt the DNA double helix; the denaturation temperature was set to 94°C and the denaturation time was set to 30 seconds to ensure that the DNA double helix was completely separated; the annealing temperature was set to 55°C and the annealing time was set to 30 seconds to allow the primer to bind to the template; the extension temperature was set to 72°C and the extension time was set to 1 minute to complete the extension of the DNA chain. The number of cycles was set to 35 cycles, and finally the final extension was completed at 72°C for 5 minutes. Put it into the PCR instrument and perform amplification reaction according to the set reaction conditions. During the reaction, closely observe the operating status of the PCR instrument to ensure that the reaction proceeds normally. After the PCR amplification reaction is completed, take out a part of the product for preliminary observation, observe the color change and turbidity of the product in the PCR tube, and preliminarily judge whether there is amplification product generated. Prepare 1.5% agarose gel and separate the PCR products by electrophoresis. The electrophoresis conditions are 100V voltage for 30 minutes. During the electrophoresis process, by observing the electrophoresis results, judge whether the band position of the PCR product is consistent with the known target product size, so as to determine whether it is the correct amplification product. At the same time, use a spectrophotometer to measure the concentration of the PCR product, judge the efficiency of the PCR reaction and the yield of the product according to the measurement results, and record the position and clarity of the band to evaluate the purity of the product.

[0060] Secondly, the amplified products were sequenced to obtain the sequence information of specific lncRNA. The sequencing depth, base quality distribution uniformity, and low-quality base ratio were selected as evaluation indicators. The original sequencing data were preprocessed, including removing the adapter sequence, filtering out low-quality bases, calculating the correlation between adjacent bases and the background frequency of each base, and improving the quality assessment accuracy of the sequencing data. The new quality score of position i was calculated using the following formula, namely: P(e|i) is the probability of an error occurring at position i, and F(b i ) is the background frequency of base b at position i, δ is the weight factor used to adjust the influence of the error probability of bases at adjacent positions, η is the weight factor used to adjust the influence of the background frequency of bases at adjacent positions, Q(i) is the new quality score for position i, and by considering the read length and position deviation factors, the error probability of each position is estimated more accurately, that is, P(e|i) = αe -β|i-μ| +γ, α is the basic value that affects the error probability, β controls the influence of position deviation on the error probability, γ represents the background error rate, μ is the center position of the read segment, and a function L(i) of the read segment length is introduced to determine the influence of the read segment length on the error rate, that is, L max is the maximum length of the read segment. By considering the effect of the read segment length on the error rate, the error rate can be accurately estimated. By considering the effect of the base, the background frequency of each position can be accurately estimated, that is, F(b i )=F 0 (b i )×(1+ω 1 F 1 (b i-1 )+ω 2 F 2 (b i+1 )), F 0 (b i ) is the basic background frequency with no influence, F 1 (b i-1 ) is the background frequency of the previous base, F 2 (b i+1 ) makes the background frequency of the next base, ω 1 and ω 2 Adjust the weighting factors that affect the intensity, apply the above algorithm to the sequencing data, calculate the new quality score for each base in each read, and compare the quality scores at different positions in the same read, so as to conduct in-depth analysis of the sequencing results.

[0061] Finally, for each sesame sample to be tested, a preliminary judgment is made based on the comparison of the sequencing results of each molecular marker with the known sequence. When the sequencing result is consistent with the known sesame Fusarium wilt-related lncRNA sequence, the sesame sample to be tested is judged to be infected with Fusarium wilt. Taking into account the possibility that some molecular marker results may be inconsistent or the results may be in an ambiguous range, further analysis is carried out, such as re-experimenting, increasing the sample size, etc., to improve the accuracy of the judgment. All information, including the results of each molecular marker, the results of the analysis, and the results of the processing of uncertainty, is comprehensively considered to finally determine whether the sesame sample to be tested is infected with Fusarium wilt, and the final result is presented in the form of a clear report, including the sample number, the specific situation of each molecular marker, and the final judgment conclusion.

[0062] In summary, this embodiment provides an operating procedure of a sesame wilt disease identification method based on molecular markers. By adopting a specific long non-coding RNA (lncRNA-X123) as a molecular marker, accurate identification of sesame wilt disease can be achieved. This method not only simplifies the identification process and improves the identification efficiency, but also because the specific long non-coding RNA has good stability under different environmental conditions, it can achieve early prediction and rapid response to sesame wilt disease, which helps to take prevention and control measures in time and reduce disease losses.

[0063] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A method for identifying sesame wilt based on molecular markers, characterized in that: The identification method comprises the following steps: S100, RNA extraction and purification: Extract total DNA and protein-contaminated RNA from the sesame samples to be tested, and purify the extracted RNA using an efficient RNA extraction kit to remove existing impurities and inhibitors to ensure the efficiency of subsequent reverse transcription and PCR amplification; S200, reverse transcription and cDNA synthesis: The purified RNA was reverse transcribed into cDNA using a reverse transcription kit. During the reverse transcription process, specific primers and enzymes designed for lncRNA-X123 were used to perform quality detection and concentration determination on the synthesized cDNA, and electrophoresis was used to ensure that it met the requirements of PCR amplification. S300, PCR amplification and product detection: synthesize primer pairs for specific lncRNA to accurately amplify the target lncRNA fragment, use cDNA as a template, use specific primer pairs for PCR amplification, optimize PCR reaction conditions, and perform quality detection of the amplified product, including product size, purity and concentration; S400, sequencing and data analysis: Sequencing the amplified products to obtain the sequence information of specific lncRNA, comparing and analyzing with the known lncRNA sequences related to sesame wilt, and performing quality control on the sequencing results to obtain accurate identification results; S500, judging the identification result: judging whether the sesame sample to be tested is infected with Fusarium wilt according to the comparison result, and if the sequencing result is consistent with the known lncRNA sequence related to sesame Fusarium wilt, then the sesame sample to be tested is judged to be infected with Fusarium wilt, and multiple specific lncRNA molecular markers are set for joint identification to test the sensitivity and specificity of the identification.

2. The method for identifying sesame wilt based on molecular markers according to claim 1, characterized in that: The specific steps of S100 are: S101, sample preparation and tissue destruction: select healthy sesame plants and sesame plants known to be infected with Fusarium wilt as test samples, quickly freeze them in liquid nitrogen, take out the frozen sesame tissues, and grind them into powder in liquid nitrogen; S102, RNA extraction: using the RNA extraction kit Trizol reagent, adding lysis solution to the ground tissue to lyse the cells and release RNA, removing cell debris and insoluble matter by centrifugation to obtain the supernatant containing RNA, and using a UV spectrophotometer to determine the RNA concentration and purity; S103, removal of DNA contamination: adding DNase to the supernatant to remove residual DNA, and inactivating the DNase by heat inactivation and adding inhibitors; S104, RNA purification: RNA was further purified using a column purification kit to remove impurities and inhibitors generated during the extraction process, and the concentration and purity of the purified RNA were measured again using a UV spectrophotometer; S105, RNA precipitation and washing: add isopropanol and ethanol precipitants to precipitate the RNA, collect the RNA precipitate by centrifugation, and wash with 75% ethanol to remove residual salts and small molecule impurities.

3. The method for identifying sesame wilt based on molecular markers according to claim 2, characterized in that: The S100 RNA extraction and purification method measures the A260 / A280 ratio and A260 / A230 ratio of RNA by ultraviolet spectrophotometer to evaluate the purity of RNA, wherein when the A260 / A280 ratio is close to 2.0 and the A260 / A230 ratio is close to 2.0-2.2, the RNA quality is considered to be good. The extracted and purified RNA is stored at -80°C to prevent RNA degradation and used for reverse transcription and PCR amplification.

4. The method for identifying sesame wilt based on molecular markers according to claim 1, characterized in that: The specific steps of the S200 reverse transcription and synthesis are: S201, preparation of reverse transcription system: prepare the reaction system required for reverse transcription according to the reverse transcription kit, including reverse transcriptase, specific primers designed for lncRNA-X123, RNase inhibitor, deoxyribonucleoside triphosphate and buffer; S202, RNA template addition: adding the purified and quality-assessed RNA sample to the reverse transcription system; S203, reverse transcription reaction: setting reaction conditions for the reverse transcription system containing the RNA template, performing a reverse transcription reaction, and completely reversely transcribing the RNA into cDNA; S204, cDNA quality assessment and concentration determination: Perform a preliminary quality assessment on the cDNA obtained by reverse transcription, and use a spectrophotometer to determine the concentration of the cDNA. At the same time, use agarose gel electrophoresis to observe the integrity and size distribution of the cDNA and check for the presence of degradation and non-specific products; S205, cDNA storage: Store qualified cDNA samples at -20°C to prevent degradation and prepare for PCR amplification.

5. The method for identifying sesame wilt based on molecular markers according to claim 4, characterized in that: The S203 reverse transcription reaction mixes the purified RNA sample with specific primers, and adds reverse transcriptase according to the reverse transcription kit, sets the reverse transcription reaction conditions to 65° C., preheats for 5 minutes to denature the RNA, and then cools it on ice. The reverse transcription reaction is performed at a reaction temperature and time of 42° C. and 1 hour, and is heated at 70° C. for 15 minutes to terminate the reaction. The concentration and purity of the cDNA are determined by an ultraviolet spectrophotometer through S204, so that the A260 / A280 ratio of the cDNA is close to 2.0, that is, the cDNA purity is high, and the quality of the cDNA is checked by agarose gel electrophoresis to ensure that there is no degradation.

6. The method for identifying sesame wilt based on molecular markers according to claim 1, characterized in that: The specific steps of S300 PCR amplification and product detection are: S301, PCR reaction system preparation: in a sterile PCR tube, add cDNA template, specific primer pair, DNA polymerase, buffer, dNTPs and sterile water in sequence to form a PCR reaction system; S302, PCR reaction condition setting: according to the characteristics of the primers and the DNA polymerase, the PCR reaction conditions are set, including the pre-denaturation temperature and time, the denaturation temperature and time, the annealing temperature and time, the extension temperature and time, and the number of cycles; S303, PCR amplification reaction: placing the PCR reaction system into a PCR instrument, performing an amplification reaction under the set reaction conditions, and observing the operating status of the PCR instrument; S304, preliminary observation of product detection: after the PCR amplification reaction is completed, a portion of the product is taken out for preliminary observation, and the color change and turbidity of the product in the PCR tube are observed to preliminarily determine whether an amplification product is generated; S305, agarose gel electrophoresis detection: prepare agarose gel, perform electrophoresis separation on the PCR products, control the voltage and time during the electrophoresis process, observe the electrophoresis results, judge the band position of the PCR product and compare it with the known target product size to determine whether it is the correct amplification product; S306, product concentration determination: Use a spectrophotometer to determine the concentration of the PCR product, and determine the efficiency of the PCR reaction and the yield of the product based on the determination results.

7. The method for identifying sesame wilt based on molecular markers according to claim 6, characterized in that: The S303 PCR amplification reaction set the PCR amplification conditions as 94°C pre-denaturation for 5 minutes, 94°C denaturation for 30 seconds, 55°C annealing for 30 seconds, 72°C extension for 1 minute, repeated 35 cycles, and 72°C extension for 5 minutes to complete the final extension. The configured PCR reaction system was placed in a PCR instrument for amplification, and the PCR product was subjected to 1.5% agarose gel electrophoresis at 100V for 30 minutes. The position and clarity of the bands were recorded to evaluate the purity of the product.

8. The method for identifying sesame wilt based on molecular markers according to claim 1, characterized in that: The S400 sequencing and data analysis selects sequencing depth, base quality distribution uniformity, and low-quality base ratio as evaluation indicators, and performs quality control on the identification results by calculating the base quality at different positions, that is, preprocessing the original sequencing data, including removing the adapter sequence, filtering out low-quality bases, calculating the correlation between adjacent bases and the background frequency of each base, and improving the quality assessment accuracy of the sequencing data. The formula is: P(e|i) is the probability of an error occurring at position i, and F(b i ) is the background frequency of base b at position i, δ is the weight factor used to adjust the influence of the error probability of bases at adjacent positions, η is the weight factor used to adjust the influence of the background frequency of bases at adjacent positions, Q(i) is the new quality score for position i, and by considering the read length and position deviation factors, the error probability of each position is estimated more accurately, that is, P(e|i) = αe -β|i-μ| +γ, α is the basic value that affects the error probability, β is the degree of influence of the control position deviation on the error probability, γ represents the background error rate, μ is the center position of the read segment, and a function L(i) of the read segment length is introduced to determine the influence of the read segment length on the error rate, that is, L max is the maximum length of the read segment. By considering the impact of the read segment length on the error rate, the error rate can be accurately estimated.

9. The method for identifying sesame wilt based on molecular markers according to claim 8, characterized in that: The S400 sequencing and data analysis accurately estimates the background frequency of each position, i.e., F(b i )=F0(b i )×(1+ω1F1(b i-1 )+ω2F2(b i+1 )), F0(b i ) is the basic background frequency with no influence, F1(b i-1 ) is the background frequency of the previous base, F2(b i+1 ) makes the background frequency of the latter base, ω1 and ω2 adjust the weight factors that affect the intensity, apply the above algorithm to the sequencing data, calculate the new quality score of each base in each read segment, and compare the quality scores of different positions in the same read segment.

10. The method for identifying sesame wilt disease based on molecular markers according to claim 1, characterized in that: The specific steps of determining the identification result in S500 are: S501, preliminary result judgment: for each sesame sample to be tested, the sequencing results of each molecular marker are compared with the known sequence; S502, uncertainty processing: For cases where some molecular marker results are inconsistent or the results are in an ambiguous range, further analysis is performed, the experiment is repeated, and the sample size is increased; S503, final result determination: All information, including the results of each molecular marker, the results of the analysis, and the results of the processing of uncertainty, is integrated to determine whether the sesame sample to be tested is infected with Fusarium wilt, and the final result is presented in the form of a clear report, including the sample number, the specific situation of each molecular marker, and the final judgment conclusion.

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