Method for predicting content of anabaena toxins by using RT-qQCR

Abstract

The invention discloses a method for predicting the content of anabaena toxins by using RT-qQCR, and relates to the technical field of prediction of the content of anabaena toxins. Based on reverse transcription real-time fluorescent quantitative PCR monitoring of toxin genes, the expression change of active genes of toxins and the variety of toxin-producing blue-green algae are reflected; in addition, various stages of toxin synthesis of toxin-producing cyanobacteria on the community level are characterized: start, growth, peak, decrease and stagnation, indicating start and end of toxin synthesis. In view of importance of a starting point and an ending point of toxin synthesis of the toxin-producing cyanobacteria on toxin prediction, a reverse transcription real-time fluorescent quantitative PCR method is used for monitoring generation and ending of the toxin and abundance change of the toxin-producing cyanobacteria, so that the method is more direct and sensitive. Aiming at a microcystin specific gene, a reverse transcription real-time fluorescent quantitative PCR (Polymerase Chain Reaction) technology is utilized to monitor the toxin production capacity of potential microcystin-producing blue-green algae in a field water body, and a prediction method for the risk of microcystin is established, so that the method is more beneficial to prevention and control of harmful longspora algal bloom.

Description

Technical Field

[0001] The invention belongs to the technical field of anabaena toxin content prediction, and specifically is a method for predicting anabaena toxin content by utilizing RT-qQCR. Background Art

[0002] Longisporium is a common freshwater bloom-causing cyanobacteria, widely distributed in freshwater bodies such as lakes, rivers, and reservoirs in my country. It easily becomes a dominant species in eutrophic water bodies and forms blooms.

[0003] In recent years, with the increase in water environment governance efforts, the water quality of most lakes and reservoirs in my country has been significantly improved, and the nitrogen and phosphorus concentrations in the water bodies have dropped significantly. However, the dominance of long-spored algae with nitrogen-fixing ability has increased significantly in some lakes and reservoirs. Long-spored algae can produce rapidly lethal anabaena toxins, threatening the health of aquatic ecosystems and the safety of drinking water. The increase in the dominance of long-spored algae has significantly increased the risk of anabaena toxins in aquatic ecosystems to the safety of drinking water.

[0004] In view of the hazards of anabaena toxin-producing cyanobacteria, the top priority of current research is to monitor the toxin-producing potential of common algae in water blooms in my country and establish a reliable method for predicting anabaena toxins in order to effectively prevent and control their occurrence and hazards.

[0005] Because the mechanisms of bloom formation and toxin synthesis are not fully understood, toxic cyanobacterial blooms are difficult to predict. At present, field monitoring of harmful cyanobacteria usually relies on parameters such as algal species, quantity, biomass, and remote sensing images. These parameters are effective for field observations, but have limited predictive utility for short-term predictions. These methods also fail to reflect the species composition of cyanobacteria, the synthesis of specific toxin-producing cyanobacteria, and toxins. Methods for detecting various cyanobacterial toxins using mass spectrometry techniques such as HPLC-MS have been established, but because these methods require cumbersome pretreatment and only retrospectively measure existing toxins at a measurable level, their effectiveness in predicting toxin-producing cyanobacteria and toxins is also limited. In recent years, the use of real-time fluorescence quantitative PCR (qPCR) technology to quantify the copy number of specific genes of cyanobacteria and toxin-producing cyanobacteria in water bodies can achieve dynamic monitoring of toxic cyanobacteria in water bodies, making it possible to effectively predict the abundance of specific toxin-producing cyanobacteria and toxin levels in water bodies.

[0006] However, qPCR studies the dynamic changes of toxic cyanobacteria at the DNA level, and the corresponding algal toxin data obtained are only the existing algal toxin content, which reflects the changes in the biomass and toxin levels of the existing toxic cyanobacterial population, and cannot accurately understand the toxin-producing ability of toxic cyanobacteria in actual water bodies. In order to accurately predict the risks of toxic cyanobacteria and algal toxins in natural water bodies, it is also necessary to study the expression of the gene transcribed by living potential toxin-producing cyanobacteria at the level of toxin gene expression, so as to understand the toxin-producing ability of potential toxin-producing cyanobacteria and its relationship with toxin content.

[0007] Reverse transcription real-time fluorescence quantitative PCR (RT-qPCR) can detect potential toxin-producing cyanobacteria from the transcription level of gene mRNA, and characterize their ability to transcribe toxin-producing gene expression and synthesize algal toxins, with high sensitivity. Reverse transcription RT-qPCR monitoring based on toxin genes not only reflects the changes in the expression of active genes of toxins and the types of toxin-producing cyanobacteria, but also characterizes the various stages of toxin synthesis by toxin-producing cyanobacteria at the community level: start-up, growth, peak, reduction and stagnation, indicating the beginning and end of toxin synthesis. In view of the importance of the starting and ending points of toxin synthesis by toxin-producing cyanobacteria for toxin prediction, the use of reverse transcription real-time fluorescence quantitative RT-qPCR method to monitor the production and end of toxins is more direct and sensitive; based on this, a solution is now provided. Summary of the invention

[0008] The present invention aims to solve at least one of the technical problems existing in the prior art;

[0009] To this end, the present invention proposes a method for predicting the content of anabaena toxins using RT-qQCR, which specifically includes:

[0010] Field water sample collection;

[0011] RNA extraction and reverse transcription;

[0012] Construction of standards and standard curves;

[0013] Reverse transcription RT-qPCR quantified the expression of specific anabaena toxin genes;

[0014] Determination of anabaena toxins;

[0015] Obtain a dataset for building a prediction model;

[0016] Construction of a method for predicting the content of anabaena toxins in water based on specific toxin genes;

[0017] The accuracy of the prediction model was evaluated using the receptivity curve (ROS);

[0018] Select the best model to predict the future trend of Anabaena toxin content.

[0019] Compared with the prior art, the present invention has the following beneficial effects:

[0020] The reverse transcription real-time fluorescence quantitative RT-qPCR monitoring of specific anabaena toxin genes in this application not only reflects the changes in the expression of active genes of toxins and the types of toxin-producing cyanobacteria, but also characterizes the various stages of toxin synthesis by toxin-producing cyanobacteria at the community level: start-up, growth, peak, reduction and stagnation, indicating the beginning and end of toxin synthesis. In view of the importance of the starting and ending points of toxin synthesis by cyanobacteria for toxin prediction, the reverse transcription real-time fluorescence quantitative RT-qPCR method is used to monitor the production, end and abundance changes of toxin-producing cyanobacteria, which appears to be more direct and sensitive. However, there are currently no reports on the prediction of anabaena toxins. The reverse transcription real-time fluorescence quantitative RT-qPCR technology is used to monitor the toxin-producing ability of potential microcystin-producing cyanobacteria in wild water bodies for the specific genes of anabaena toxins, and the establishment of a prediction method for the risk of anabaena toxins will be more helpful in preventing and controlling harmful long spore algae blooms. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 The figure is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0022] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. 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.

[0023] like Figure 1 As shown, the present application provides a method for predicting the content of anabaena toxin using RT-qPCR, comprising:

[0024] At preset time intervals, water samples were collected from a freshwater lake, 200 mL of subsamples were collected and filtered using an on-site 0.40 μm microporous filter membrane. The filter membrane was immediately placed in a 1.5 mL sterile polypropylene tube containing 600 μL of RNA protection solution and brought back to the laboratory on ice for storage at -80 °C.

[0025] The preserved samples were taken out, total RNA was extracted using a kit, and the purity and concentration of RNA were detected by an ultra-micro spectrophotometer; after extraction, RNA was immediately reverse transcribed to obtain cDNA, which was stored at -80°C for future use;

[0026] Standards were constructed using potential toxin-producing Longispora algae strains. RNA of the toxin-producing Longispora algae was extracted and reverse transcribed. Using cDNA as a template, PCR was performed with specific primers for the reference gene 16SrRNA and the target gene anaC to amplify the target fragments. The plasmid standards of the target and reference genes were obtained through purification, gel recovery, PMD18-T vector ligation, TA cloning, transformation, plasmid extraction, purification, enzyme digestion, sequencing identification, and determination of the concentration and purity of the positive plasmids. A 10-fold gradient series dilution was performed with 3 replicates for each sample, and the above-mentioned fluorescent quantitative RT-PCR amplification reaction was performed to construct a standard curve.

[0027] Data collection and generation of a standard curve are achieved by existing technologies, specifically by existing ABI7500 Sequence Detection System software.

[0028] Using cDNA as a template, the expression of the target gene anabaena toxin gene anaC was quantitatively analyzed by RT-qPCR; the internal reference gene 16SrRNA and the target gene anaC were amplified by specific primers Aph-16S-F / R and anaC-genF / R for qPCR reaction, respectively, and the reaction system was 25μL: 12.5μL 2×SYBR premix ExTaqTM, 0.5μL forward and reverse primers (10μM), 1μL cDNA, 10.5μL ddH2O. The double standard curve method was used to simultaneously amplify the target gene and the internal reference gene in the standard and the sample to be tested, and a positive control and a blank control without template were set up. After the amplification reaction, a melting curve analysis was performed to generate data collection and standard curves, and the expression of the target gene was calculated according to the standard curve. Take a certain volume of water sample and filter it through GF / C filter membrane. Cut the filter membrane into pieces to break the cells. Add methanol, mix well and centrifuge to collect the supernatant. Repeat the above operation twice. Centrifuge and combine the supernatants. Concentrate the supernatant. Add 20% methanol solution to dissolve it and centrifuge it. Transfer the supernatant to a sample bottle, store it at -20℃, and determine it by HPLC-MS.

[0029] During the bloom of Longisporium algae, based on many years of multi-point high-frequency monitoring in the field, reverse transcription real-time fluorescence quantitative PCR technology was used to quantify the expression of specific anabaena toxin genes in the Longisporium cells of the bloom in different seasons in each lake area, so as to analyze the actual toxin-producing capacity of the toxin-producing Longisporium algae in different lake areas and seasons. At the same time, HPLC-MS was used to determine the anabaena toxins, so as to obtain many years of historical field monitoring data.

[0030] The historical monitoring data of anabaena toxin gene expression, anabaena toxin content and physical and chemical indicators were obtained to construct a data set; such data from year N were used as training and validation sets, and the data from year N+1 were used as the test set.

[0031] Through years of high-frequency monitoring, we obtained data on the expression of toxic genes of Longispora algae, the content of anabaena toxins, and the changes in physical and chemical factors of water bodies at different stages of Longispora algae blooms. We used R language software to analyze their Pearson correlation, and used the general additive model (GAM) to analyze the quantitative contribution of key water body physical and chemical factors to the spatiotemporal dynamic changes of the expression of toxic Longispora algae toxin genes and the content of anabaena toxins, and screened the physical and chemical indicators with significant influence.

[0032] The Stepwise regression statistical analysis method was used, with the Anabaena toxin content as the target variable, the expression of the Anabaena toxin-producing genes and physicochemical indicators of Longisporium as independent variables, and a regression model was constructed using N years of training set data to predict the changes in Anabaena toxin content in water bodies. A method was established to accurately predict the start and end of Anabaena toxin production and content by using the expression signals of Anabaena toxin genes.

[0033] The constructed model is evaluated using the validation set data. The performance and accuracy of the model prediction are evaluated by the curve position of the sensitivity curve ROC and the area under the curve AUC value. The model is then tuned based on the results of the model evaluation.

[0034] The optimized model was verified using the test set data from year N+1 to obtain the prediction results, select the best model, and apply the trained model to field monitoring to predict the risk of anabaena toxins in the next week and verify its reliability.

[0035] It can be seen from the above technical solutions that the reverse transcription real-time fluorescence quantitative RT-qPCR monitoring of specific anabaena toxin genes in this application not only reflects the changes in the expression of active genes of toxins and the types of toxin-producing cyanobacteria, but also characterizes the various stages of toxin synthesis by toxin-producing cyanobacteria at the community level: start-up, growth, peak, reduction and stagnation, indicating the beginning and end of toxin synthesis. In view of the importance of the starting point and end point of toxin synthesis by toxic cyanobacteria for toxin prediction, the reverse transcription real-time fluorescence quantitative RT-qPCR method is used to monitor the production, end and abundance changes of toxin-producing cyanobacteria, which appears to be more direct and sensitive. However, there are currently no reports on the prediction of anabaena toxins. Using reverse transcription RT-qPCR technology for anabaena toxin-specific genes to monitor the toxin-producing ability of potential anabaena toxin-producing cyanobacteria in wild water bodies and establish a prediction method for anabaena toxin risks will be more helpful in preventing and controlling harmful long spore algae blooms.

[0036] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A method for predicting the content of anabaena toxin using RT-qQCR, characterized in that: The method specifically includes: RNA extraction and reverse transcription were performed on the collected field water samples; Construct standards and standard curves; Reverse transcription RT-qPCR quantified the expression of specific anabaena toxin genes; Determination of anabaena toxins; Obtaining a data set for building anabaena toxin prediction model; Construction of a method for predicting the content of anabaena toxins in water based on specific toxin genes; Use perception curves to evaluate the accuracy of prediction models; Select the best model to predict the future trend of Anabaena toxin content.

2. The method for predicting the content of anabaena toxin using RT-qQCR according to claim 1, characterized in that: Before RNA extraction, field RNA samples need to be collected. The specific collection method is as follows: After collecting water samples from eutrophic algal lakes, they were divided into two parts. One of the water samples was filtered through a microporous filter membrane, treated with RNA protection solution, and then brought back to the laboratory on ice and stored at -80℃ for RNA extraction; the other water sample was filtered through a filter membrane for the determination of anabaena toxins.

3. The method for predicting the content of anabaena toxins using RT-qQCR according to claim 1, characterized in that: The specific method of RNA extraction and reverse transcription is as follows: The preserved samples were taken out, total RNA was extracted using a kit, and the purity and concentration of RNA were detected by ultra-micro spectrophotometer; after extraction, RNA was immediately reverse transcribed to obtain cDNA, which was stored at -80°C for future use.

4. The method for predicting the content of anabaena toxins using RT-qQCR according to claim 1, characterized in that: The specific method for constructing standard products and standard curves is as follows: The standard was processed to obtain the plasmid standard of the target and reference genes, and 10-fold gradient serial dilution was performed with 3 replicates for each sample, and the above reverse transcription RT-qPCR amplification reaction was performed to construct the standard curve; The specific method for obtaining plasmid standards of target and reference genes is: Standard samples were obtained, and RNA of toxin-producing Longisporium algae was extracted and reverse transcribed using potential toxin-producing Longisporium strains. PCR was performed using cDNA as a template and specific primers of the internal reference gene 16SrRNA and the target gene anaC to amplify the target fragment, which was then purified, recovered by gel, connected to a PMD18-T vector, cloned by TA, transformed, extracted by plasmid, purified, digested by enzymes, sequenced and identified, and the concentration and purity of the positive plasmid were determined.

5. The method for predicting the content of anabaena toxin using RT-qQCR according to claim 1, characterized in that: The specific method of RT-qPCR reaction to quantify the expression of specific anabaena toxin genes is as follows: The expression level of the target gene was calculated according to the RT-qPCR standard curve; Using the sample cDNA as a template, RT-qPCR was performed to quantitatively analyze the expression level of the target gene Anabaena toxin gene anaC; Use specific primers to amplify the internal reference gene 16S rRNA and the target gene anaC for qPCR reaction; use the double standard curve method to simultaneously amplify the target gene and the internal reference gene in the standard and the sample to be tested, and set up a positive control and a blank control without template. After the amplification reaction, perform melting curve analysis, generate data collection and standard curves, and calculate the expression level of the target gene based on the standard curve; The specific method of RT-qPCR reaction is as follows: The reference gene 16SrRNA and the target gene anaC were amplified by specific primers Aph-16S-F / R and anaC-genF / R for qPCR reaction. The reaction system was 25 μL: 12.5 μL 2×SYBR premix ExTaqTM, 0.5 μL each of forward and reverse primers (10 μM), 1 μL cDNA, and 10.5 μL ddH2O.

6. The method for predicting the content of anabaena toxin using RT-qQCR according to claim 1, characterized in that: The specific method for determining anabaena toxins is as follows: Take a certain volume of water sample and filter it through GF / C filter membrane. Cut the filter membrane into pieces to break the cells. Add methanol, mix well and centrifuge to collect the supernatant. Repeat the above operation twice. Centrifuge and combine the supernatants. Concentrate the supernatant. Add 20% methanol solution to dissolve it and centrifuge it. Transfer the supernatant to a sample bottle, store it at -20℃, and determine it by HPLC-MS.

7. The method for predicting the content of anabaena toxins using RT-qQCR according to claim 1, characterized in that: The specific method for obtaining the dataset for building a prediction model is as follows: Obtain historical monitoring data on the expression of anabaena toxin genes, anabaena toxin content, and physical and chemical indicators to construct a data set; use such data from year N as the training set and validation set, and the data from year N+1 as the test set; The method for acquiring historical detection data is as follows: during the bloom of Longisporium algae, high-frequency monitoring is carried out at several set points in the wild for several consecutive years, and the expression of specific anabaena toxin genes in the Longisporium algae cells of the blooms in different seasons in each lake area is quantified using reverse transcription real-time fluorescence quantitative PCR technology, and the anabaena toxin is determined by HPLC-MS at the same time.

8. The method for predicting the content of anabaena toxins using RT-qQCR according to claim 1, characterized in that: The specific method of constructing the prediction model of anabaena toxin content in water based on specific toxin genes is as follows: Through continuous high-frequency monitoring, we can obtain data on the expression of toxic genes of Longisporium, the content of anabaena toxins, and the changes in physical and chemical factors of water bodies at different stages of Longisporium blooms. The Pearson correlation was analyzed using R language software, and the general additive model was used to analyze the quantitative contribution of key water physicochemical factors to the spatiotemporal dynamic changes of the gene expression of toxic longisporin-producing algae and the content of anabaena toxins, and to screen the physicochemical indicators; Stepwise regression analysis method was used, with Anabaena toxin content as the target variable, and the expression of Anabaena toxin-producing genes and physicochemical indicators of Longisporium as independent variables. A regression model was constructed using N years of training set data to predict the changes in Anabaena toxin content in water bodies. A method was established to accurately predict the start and end of Anabaena toxin production and content by using the expression signals of Anabaena toxin genes.

9. The method for predicting the content of anabaena toxins using RT-qQCR according to claim 1, characterized in that: The specific method for evaluating the accuracy of the prediction model is: The constructed model is evaluated using the validation set data. The performance and accuracy of the model prediction are evaluated by the curve position of the sensitivity curve ROC and the area under the curve AUC value. The model is then tuned based on the results of the model evaluation.

10. The method for predicting the content of anabaena toxins using RT-qQCR according to claim 1, characterized in that: The specific method of selecting the best model prediction is: The optimized model was verified using the test set data from year N+1 to obtain the prediction results, select the best model, and apply the trained model to field monitoring to predict the risk of anabaena toxins in the next week and verify its reliability.

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