The Application of RPA-PfAgo Technology Combined with Multidimensional Data Analysis in the Rapid Detection of the MTHFR A1298C Polymorphism

The Application of RPA-PfAgo Technology Combined with Multidimensional Data Analysis in the Rapid Detection of the MTHFR A1298C Polymorphism | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Application of RPA-PfAgo Technology Combined with Multidimensional Data Analysis in the Rapid Detection of the MTHFR A1298C Polymorphism Yaqun Liu, Lianghui Chen, Peikui Yang, Miaofen Fang, Xiaotong Cai, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4884474/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 2 You are reading this latest preprint version Abstract This study presents a novel approach that integrates recombinase polymerase amplification (RPA) with Pf Ago protein technology for the rapid and precise detection of the MTHFR A1298C polymorphism. Although traditional genotyping methods are effective, they are often limited by complexity, high cost, and the need for specialized equipment. The RPA- Pf Ago technique harnesses the swift isothermal amplification of RPA and the high specificity and sensitivity of Pf Ago-mediated DNA cleavage, completing the entire process from sample collection to detection within 90 minutes. The utility of this method has been substantiated through a battery of optimization experiments, parameter analysis, and assessments of sensitivity, specificity, and repeatability, along with clinical validation using oral mucosal samples. The findings indicate that this new technology not only substantially reduces detection time and cost but also offers an effective tool for personalized medicine and disease prevention with high accuracy and reliability. MTHFR A1298C polymorphism Recombinase polymerase amplification (RPA) PfAgo protein Genotyping Multidimensional data analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1 Introduction With the rapid advancement of human genomics and molecular biology, our understanding of the relationship between genetic polymorphisms and disease susceptibility has deepened. Polymorphism of the methylenetetrahydrofolate reductase ( MTHFR ) gene, particularly the A1298C locus variant, has garnered widespread attention [ 1 ] . In the 7th exon of the MTHFR gene, the 1298A to C mutation results in the substitution of adenine (A) with cytosine (C), leading to the replacement of the encoded amino acid from glutamic acid (Glu) to alanine (Ala) and the elimination of an MboII restriction enzyme site [ 2 ] . Studies indicate that the CC genotype may contribute to hyperhomocysteinaemia (HHcy) by decreasing enzyme activity and folate levels. Furthermore, the MTHFR A1298C polymorphism is associated with an increased risk of cardiovascular and cerebrovascular diseases, diabetic nephropathy and congenital defects in newborns [ 3 – 5 ] . Therefore, accurate detection of the MTHFR A1298C polymorphism is highly important for disease prevention and personalized medicine. Although traditional techniques such as polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) [ 6 ] , TagMan PCR [ 7 ] , multiplex asymmetric real-time PCR-HRM [ 8 ] , electrochemical DNA chip [ 9 ] , OpenArray and real-time PCR-FRET [ 10 ] have achieved certain success in genotyping MTHFR A1298C, these methods are often complex in operation, costly in equipment, and require professional handling. The promotion and application of these technologies are restricted, particularly in resource-limited regions. Therefore, the development of a rapid and economical genotyping method for MTHFR A1298C is of significant importance for clinical applications. The application of rapid amplification technologies, such as RPA, which includes glucose−6-phosphate dehydrogenase (G6PD) [ 11 ] , apolipoprotein E (APOE) [ 12 ] , and norovirus (NoV) GII.4 [ 13 ] , has increased rapidly in recent years, and these technologies have been widely used in the field of genetic polymorphism genotyping. RPA is an isothermal amplification technique that operates at a constant temperature of 37–42°C and is capable of amplifying low concentrations of target DNA to detectable levels within 5–30 minutes. Compared to other nucleic acid amplification technologies, RPA offers high sensitivity, high specificity, and ease of operation without the need for expensive temperature control equipment, making it particularly suitable for on-site testing [ 14 ] . Additionally, Pyrococcus furiosus Argonaute ( Pf Ago) has been proven to be a highly sensitive nucleic acid detection system [ 15 ] , and recent studies have successfully and accurately utilized Pf Ago for genotyping the FUT2 gene using human oral swab samples [ 16 ] . Pf Ago proteins, in particular, have drawn attention due to their role in defense mechanisms and efficient nucleic acid cleavage. The advantages of Pf Ago include the absence of a requirement for PAM sequences in the target DNA, the use of short DNA molecules as guides, which are more cost-effective and stable, and a smaller molecular size that facilitates modification and production [ 17 ] . The emergence of the RPA and Pf Ago technologies has led to new breakthroughs in the field of gene detection, with significant enhancements in their rapidity, sensitivity, and specificity when combined. Currently, the RPA- Pf Ago technique is mostly applied for pathogen detection, such as for detecting viruses infecting rice [ 18 ] , Mycoplasma synoviae [ 19 ] , Enterocytozoon hepatopenaei [ 20 ] , and White Spot Syndrome Virus [ 21 ] . These studies demonstrate the potential of Pf Ago combined with the RPA method in the field of pathogen detection, offering the capacity for rapid on-site diagnostic tools. This study leverages the advantages of RPA and Pf Ago technologies for genotyping the MTHFR A1298C polymorphism, demonstrating technological innovation and providing new tools for a deeper understanding of the complex connections between MTHFR gene polymorphisms and disease susceptibility. With further research and optimization, the RPA- Pf Ago technique has become a significant tool in genetic disease diagnostics and personalized medicine. 2. Materials and Methods 2.1 RPA Primers, gDNA, and Probes Design The MTHFR A1298C (rs1801131) sequence was obtained from the National Center for Biotechnology Information (NCBI) database. Using Primer Premier 5 software, three sets of RPA primers targeting the mutation site were designed. The specificity of these primers was subsequently verified using the primer-BLAST tool on NCBI. To exploit the enzymatic cleavage characteristics of the Pf Ago protein, both wild-type and mutant guide DNA (gDNA), along with corresponding probes, were designed. These probes were labelled with two different fluorophores: FAM and ROX. Additionally, three sets of wild-type and mutant gDNA were designed, in which the mutation site was positioned at various locations within the gDNA. The 5’ ends of the gDNA were modified with a phosphate group (Table 1 ). Table 1 Sequences of the RPA primers, wild-type and mutant gDNA, and modified probes Category Identifier Sequence (5′-3′) Modification RPA Primers F1 TGGGCCTCCAGACCAAAGAGTTACATCTACCG - F2 TACCCAGGAGTGGGACGAGTTCCCTAACGG - F3 GGAGCTGAAGGACTACTACCTCTTCTACCTG - R1 TTTGTGACCATTCCGGTTTGGTTCTCCCGAG - R2 TCCAGGGCAGGCAAGTCACTTTGTGACCATTCCG - R3 TCCTCCTTCAGCAGGCTGGTCTCAGCCGCCAG - Wild-Type gDNA WT-gDNA7-1 TCACTTTCTTCACTGG 5’-PHO WT-gDNA7-2 TACGAAGACTTCAAAG 5’-PHO WT-gDNA11-1 TAAGACACTTTCTTCA 5’-PHO WT-gDNA11-2 TAAGAACGAAGACTTC 5’-PHO WT-gDNA14-1 TTCAAAGACACTTTCT 5’-PHO WT-gDNA14-2 TGTAAAGAACGAAGAC 5’-PHO Mutant gDNA Mut-gDNA7-1 TCACTTGCTTCACTGG 5’-PHO Mut-gDNA7-2 TACGAAGACTTCAAAG 5’-PHO Mut-gDNA11-1 TAAGACACTTGCTTCA 5’-PHO Mut-gDNA11-2 TAAGAACGAAGACTTC 5’-PHO Mut-gDNA14-1 TTCAAAGACACTTGCT 5’-PHO Mut-gDNA14-2 TGTAAAGAACGAAGAC 5’-PHO Wild-Type Probes FAM-Modified FAM-GAACGAAGACTTCAAAGACACTTTCTTCA-BHQ1 FAM ROX-Modified ROX-GAACGAAGACTTCAAAGACACTTTCTTCA-BHQ2 ROX Mutant Probes FAM-Modified FAM-GAACGAAGACTTCAAAGACACTTGCTTCA-BHQ1 FAM ROX-Modified ROX-GAACGAAGACTTCAAAGACACTTGCTTCA-BHQ2 ROX 2.2 RPA Reaction Conditions Optimization The RPA reactions followed the protocol provided with the TwistAmp® RPA Kit (TwistDx, Cambridge, UK). The amplified products were subjected to agarose gel electrophoresis for analysis. After amplification, the products were electrophoresed on a 2% agarose gel at a constant voltage of 120 V for 30 minutes to visualize the results. The optimization involved varying the MgAc concentration in five different volumes: 2.0 µL, 2.3 µL, 2.5 µL, 2.8 µL, and 3.0 µL. The reaction temperature ranged among five set points: 35°C, 37°C, 39°C, 42°C, and 45°C. The reaction times were also optimized, with durations tested at 10 min, 15 min, 20 min, 25 min, and 30 min. A negative control using nuclease-free water (ddH 2 O) as the template was included. Throughout the optimization of these three parameters, all other conditions remained constant. 2.3 Protocol Optimization for the RPA- Pf Ago Cleavage Assay Using the optimized RPA protocol, samples were prepared for the Pf Ago cleavage assay. Specifically, 4 µL of the RPA product was added to 25 µL of the Pf Ago reaction mixture, which included 2 µL of gDNA1 and gDNA2 (each at 20 µM), 1 µL of a molecular beacon (20 µM), 4 µL of the Ago enzyme (200 U/µL), 4 µL of MnCl 2 (250 nM), 2 µL of 10× buffer, and 4 µL of the RPA product. The assay was performed at 95°C for 30 minutes in a thermocycler, with FAM or ROX fluorescence signals recorded every 30 seconds. After the reaction, the samples were analysed under blue (470 nm) and green (525 nm) light. Optimization focused on five key parameters: gDNA, MnCl 2 , the probe, the Pf Ago enzyme, and the RPA product. The concentrations and volumes were varied as follows: gDNA was tested at 10, 20, 40, 60, 80, and 100 µM; MnCl 2 at 1, 2, 3, 4, 5, and 6 µL; probe at 10, 20, 40, 60, 80, and 100 µM; Ago enzyme at 1, 2, 3, 4, 5, and 6 µL; and the RPA product at 1, 2, 3, 4, 5, and 6 µL. A negative control with ddH 2 O was included for assay reliability. This optimization was conducted with all other variables held constant, aiming to identify the optimal conditions for each parameter in the RPA- Pf Ago cleavage assay. 2.4 Performance Evaluation The detection performance of the RPA- Pf Ago system was evaluated in terms of sensitivity, repeatability, and specificity. Sensitivity was determined by amplifying serially diluted positive plasmid templates at concentrations ranging from 4×10 1 ng/µL to 4×10 − 4 ng/µL within the optimized RPA- Pf Ago detection system. This approach facilitated the assessment of the system's ability to detect low concentrations of target DNA. Repeatability was assessed by performing triplicate assays of the RPA- Pf Ago under consistent reaction conditions, including system configurations and timing, across multiple samples. This was done to assess the consistency of detection outcomes. Specificity was evaluated by comparing the system's performance in detecting wild-type and mutant samples within both the wild-type and mutant-specific RPA- Pf Ago systems. This comparison aimed to determine the system’s accuracy in distinguishing between different genetic variants. 2.5 Clinical Validation Buccal mucosal cells were successfully collected from oropharyngeal swabs from 20 volunteers. Genomic DNA of the human folate genome was extracted utilizing the high-efficiency Swab Genomic DNA Extraction Kit (DP362, Tiangen, Beijing, China). The established RPA- Pf Ago method was employed for detection, with Sanger sequencing serving as the reference standard for these samples. The concordance of the results was used to evaluate the clinical applicability of the method. 2.6 Statistical Analysis Statistical analyses were conducted as follows for data that did not follow a normal distribution, the Kruskal‒Wallis test was utilized to determine significant differences among groups using SPSS Statistics software (version 26, IBM Corp., Armonk, NY, USA). To assess the temporal variability of the RPA- Pf Ago detection system, which is essential for monitoring reaction dynamics over time, time series analysis was performed in the R statistical environment (R Foundation for Statistical Computing, Vienna, Austria). Furthermore, principal component analysis (PCA) was conducted using the FactoMineR package in R to explore patterns and relationships across various testing conditions. To evaluate the significance of parameters within the RPA- Pf Ago detection system, the Critic method was applied using custom scripts in MATLAB (MathWorks, Natick, MA, USA) to assess the importance of various parameters, considering both contrast and conflict among criteria. The independence weight coefficient was calculated in SPSS Statistics software (version 26, IBM Corp., Armonk, NY, USA) using a custom macro to quantify parameter independence and ensure experimental integrity. The entropy weighting method was applied using MATLAB (MathWorks, Natick, MA, USA) to determine the relative importance of parameters based on their variability. Correlation and variability analyses were conducted as follows: Spearman's rank correlation analysis was performed using the cor.test function in R to determine the strength and direction of associations between ranked variables. Normalized hierarchical heatmaps were generated using the heatmap.2 function in the gplots package in R, with normalization applied to enhance data comparability. The coefficient of variation (CV) was calculated using Microsoft Excel (Microsoft Corporation, Redmond, WA, USA) to measure relative variability and assay repeatability. 3 Results 3.1 Design and Construction of the RPA- PfAgo Detection Platform for MTHFR A1298C To facilitate rapid and precise detection of the A1298C polymorphism in the MTHFR gene, this study developed a detection platform utilizing RPA coupled with the Pf Ago protein. The platform capitalizes on the swift amplification capability of RPA and the high specificity of Pf Ago-mediated DNA cleavage to sensitively identify genetic mutations. The RPA- Pf Ago detection process consists of initial isothermal amplification of DNA sequences containing the target site via RPA. Subsequently, the amplified product is mixed with gDNA and Pf Ago protein, where the gDNA directs Pf Ago to specifically recognize and cleave the target DNA, releasing fluorescently labelled probe fragments, thus generating a detectable fluorescent signal. The presence of the specific mutation at the target site in the sample was determined by capturing the fluorescent signal with a fluorescence detection device or direct visualization using a fluorescence imaging system (Fig. 1 A). In this study, synthetic oligonucleotides representing the antisense strand of wild-type and mutated MTHFR A1298C were used as cleavage targets. Additionally, 5'-phosphorylated 16-nucleotide long gDNAs were synthesized, and single-base mismatches were introduced to assess the cleavage efficiency of Pf Ago in both wild-type and mutant gDNAs. The cleavage site of the gDNA, which is critical for the detection of the wild-type and mutant sequences of the folate gene, was positioned between nucleotides 10 and 11. The length of gDNA should not be less than 15 nucleotides, as excessive length may impair cleavage efficiency. For this experiment, gDNAs were designed to be 16 nucleotides long, and a series of gDNAs were constructed with the mutation site shifting in gDNA, resulting in 16 distinct gDNAs tailored for both wild-type and mutant forms (Fig. 1 B) 3.2 Optimizing RPA Conditions for MTHFR A1298C To increase the efficiency and sensitivity of RPA detection, this study initially focused on the selection of RPA primers. By designing three sets of primers and conducting pairwise combination experiments, we determined that the primer combination F3R3 exhibited optimal amplification efficiency, resulting in a single, bright band. Consequently, F3R3 was selected for subsequent experiments. In the optimization of the MgAc concentration, testing different concentrations revealed that adding 3.0 µL of MgAc at 39°C produced a comparably brighter amplification product band. With respect to the adjustment of the reaction temperature, it was found that at a temperature range of 35°C to 39°C, a reaction temperature of 37°C yielded the clearest band of specific amplification products, with the brightness decreasing as the temperature increased, thereby establishing 37°C as the optimal reaction temperature. Finally, in setting the reaction time, comparing band brightness at different time points (10, 15, 20, and 25 minutes) showed that a 25-minute duration produced the clearest band, with the brightness decreasing over longer periods (Fig. 2 ). Thus, 25 minutes was determined to be the optimal reaction time. 3.3 Parameter Optimization and Analysis for the MTHFR A1298C RPA- Pf Ago Detection Platform Combining the fluorescence capture starting time, visual observation, and entire fluorescence curve, multiparameter optimization was conducted for the MTHFR A1298C RPA- Pf Ago detection platform. For both the wild-type (WT) and mutant (MuT) genotypes, three sets of gDNA primers (gDNA7, gDNA11, and gDNA14) were designed, with gDNA7 identified as optimal for both. Specificity testing using the gDNA7 primer set demonstrated accurate differentiation between wild-type and mutant MTHFR A1298C alleles without cross-reactivity. Fluorescence imaging indicated that the optimal concentration for obtaining gDNA from both the wild-type and mutant strains was 10 µM. Further optimization of the probe concentration confirmed that 10 µM was the most effective concentration for both systems. In the MnCl 2 optimization assays, the addition of 6 µL yielded the highest enzymatic cleavage efficiency for the wild-type system, while 5 µL produced the strongest fluorescence signal for the mutant system. The concentration of the Ago enzyme was also critical, with 6 µL found to be optimal for both the wild-type and mutant reactions. Finally, optimization of the volume of the RPA amplification products added to the cleavage system demonstrated that a volume of 6 µL resulted in peak fluorescence intensity for both genotypes (Fig. 3 ). To further investigate the parameters involved in the optimization of the MTHFR A1298C RPA- Pf Ago detection platform, a comprehensive multidimensional analysis of the experimental parameters was conducted. By comparing the fluorescence signals in different detection channels (FAM and ROX), it was observed that the differences between the wild type and the mutant were minimal in the FAM channel, whereas significant differences were noted in the ROX channel. Statistical tools such as difference analysis, correlation analysis, and principal component analysis were used to further confirm that the association between the wild type in the FAM channel and the mutant in the ROX channel was the lowest. Comprehensive evaluations using the Critic weighting method, independence weight coefficient, and entropy weighting method also indicate that the mutant's detection results in the ROX channel exhibit greater independence. When analysing the impact of major experimental parameters such as gDNA concentration, probes, MnCl 2 , Pf Ago protein, and RPA products, a high trend similarity between gDNA concentration and probes was observed, contrasting with their low similarity to other parameters. Comprehensive evaluations through Critic weighting and entropy weighting methods revealed that the gDNA concentration and probes had the highest weights, suggesting that these parameters might play key roles in the RPA- Pf Ago detection system (Fig. 4 ). 3.4 Comprehensive Performance Assessment of the MTHFR A1298C RPA- Pf Ago Detection Platform To evaluate the performance of the RPA- Pf Ago detection platform for the MTHFR A1298C polymorphism, this study conducted a quantitative analysis of its repeatability, sensitivity, and specificity under optimal parameter conditions. In the repeatability analysis, three independent experiments were performed using six different plasmids each time, yielding coefficients of variation (CVs) below 5%, indicating the high operational stability of the platform. Notably, in the FAM detection system, high consistency was observed for both the wild-type and mutant strains in terms of the initiation time and final fluorescence value of the fluorescence curves across three experiments. In the ROX detection system, high repeatability was observed only in a single experimental group, which may be related to the higher background fluorescence and greater environmental variability associated with the ROX system. Principal component analysis further confirmed the lower error in the FAM system, and time series analysis revealed greater variability across all four clusters in the ROX system. For the sensitivity assessment, the use of plasmids at different concentration gradients revealed that both the fluorescence value and initiation time varied with concentration in both the FAM and ROX detection systems, with the lowest detection limit of both systems reaching 4x10 − 4 ng/µL. In the specificity tests, six wild-type and mutant plasmids were tested in the two detection systems, confirming that both systems could specifically detect the corresponding genotypes (Fig. 5 ). 3.5 Clinical Validation of the MTHFR A1298C RPA- Pf Ago Detection Platform Oral mucosal samples from 20 randomly selected volunteers were subjected to clinical validation using the MTHFR A1298C RPA- Pf Ago detection platform. The findings indicated that 17 individuals tested positive for the wild-type allele using the platform designated for wild-type detection. In contrast, the platform specific for the detection of the mutant allele yielded a positive result for 9 individuals. Among these, 6 individuals were identified as positive for both wild-type and mutant alleles, suggesting heterozygous mutations according to the RPA- Pf Ago platform's indications. Confirmatory sequencing was conducted as a reference standard, and the results were in complete concordance with the RPA- Pf Ago detection results, demonstrating a 100% agreement rate (Fig. 6 ). 4 Discussions This study introduces the RPA- Pf Ago method for detecting polymorphisms at the A1298C locus of the MTHFR gene. Compared to traditional methods such as PCR-RFLP and fluorescent PCR, this technique offers advantages, including simplicity of operation, low cost, and independence from expensive laboratory equipment, making it suitable for rapid testing in resource-limited settings. The Pf Ago protein family demonstrates high sensitivity and specificity in nucleic acid detection, particularly because of its ability to guide DNA cleavage without the need for a PAM sequence [ 15 ] . This enhances the sensitivity and specificity of genetic mutation detection, which is significant for susceptibility research and the application of personalized medicine. The RPA- Pf Ago detection platform was finely optimized to enhance the efficiency and accuracy of detecting the MTHFR A1298C polymorphism. First, a specific set of conditions was determined by systematically adjusting the RPA primers, MgAc concentration, reaction temperature, and reaction time to optimize the RPA amplification efficiency. The carefully designed primer combination F3R3 showed the best amplification efficiency at a constant temperature of 37°C and a reaction time of 25 minutes. Further optimization involved the use of the RPA- Pf Ago cleavage reaction system, which is a complex process involving multiple variables whose performance improvement resulted from the combined effect of multiple parameters. The adjustment of each parameter was based on a deep understanding of Pf Ago enzyme activity and its interaction mechanism with DNA. Optimization of the gDNA, probe design, MnCl 2 concentration, Pf Ago protein, and RPA product parameters achieved precise differentiation of the wild-type and mutant alleles. Attention to gDNA design has focused on high complementarity to the target sequence, appropriate length and structure, and optimized concentration to ensure efficient binding and cleavage precision [ 22 ] . Careful control of gDNA revealed that 7 ng gDNA and 10 µM provided the optimal signal strength for the two genotypes. Probe design and concentration optimization not only improved specificity but also enhanced the ability to recognize the target sequence, minimizing background signals [ 22 ] . The probe concentrations in the wild-type and mutant detection systems were optimized to 10 µM, ensuring reaction specificity and signal clarity. Optimization of the MnCl 2 concentration revealed the catalytic role of metal ions in Pf Ago-mediated nucleic acid cleavage reactions, which is closely related to Pf Ago protein activity [ 23 ] . Experimentally, it was found that for the wild-type system, adding 6 µL of MnCl 2 achieved the highest cleavage efficiency, while for the mutant system, adding 5 µL produced the strongest fluorescence signal. This subtle difference suggests that different genotypes may require different reaction conditions to optimize detection results. Different genotypes may have different requirements for MnCl 2 , reflecting subtle differences in Pf Ago protein interactions with different DNA sequences. These differences may stem from secondary structures around the mutation site or DNA flexibility, which are worthy of further research [ 24 ] . Optimization of the Pf Ago protein and RPA product volumes relates to the ratio of enzyme to substrate in the reaction, which is crucial for achieving maximum cleavage efficiency. In this process, for different genotypes, the same concentration of the Pf Ago protein exhibited optimal cleavage efficiency, suggesting that the Pf Ago protein may have some adaptability to different target sequences. Additionally, optimization of the RPA amplification product volume demonstrated that 6 µL was the ideal volume for achieving peak fluorescence intensity, emphasizing the crucial role of amplification products in the cleavage reaction. During the optimization process of the MTHFR A1298C RPA- Pf Ago detection platform, the influence of various parameters on the detection results was thoroughly investigated through multidimensional analysis of the experimental parameters. When comparing fluorescence signals in the FAM and ROX detection channels, it was found that the difference between the wild-type and mutant types in the FAM channel was minimal, while the difference in the ROX channel was significant. This suggests that different channels may have varying sensitivities to wild-type and mutant types. Statistical tools such as differential analysis, correlation analysis, and principal component analysis further confirmed that the correlation between detecting the wild type in the FAM channel and detecting the mutant type in the ROX channel was the lowest, indicating a lower correlation between the detection signals of the two types in different channels. This is crucial for designing highly specific detection systems, as it allows for the detection of different types in different channels, reducing signal interference. Comprehensive evaluations using Critic weighting, independence weighting coefficients, and entropy weighting showed that the detection results of the mutant type in the ROX channel had greater independence, possibly because the signal provided by the ROX channel had a lower correlation with other variables and could better reflect the presence of the mutant type. Analysis of the effects of major experimental parameters, such as gDNA, probes, MnCl 2 , Pf Ago protein, and RPA products, revealed that there was a high degree of trend similarity between gDNA and probes, while the similarity between these two parameters and other parameters was low, indicating their crucial roles in the RPA- Pf Ago detection system. The comprehensive evaluation showed that gDNA and probes had the highest weights, further emphasizing their importance [ 25 ] . This multidimensional assessment helps to understand the relative importance of various parameters on the detection results and guides the focus of optimization efforts. In the experimental setup, a comprehensive evaluation is essential to ensure that the detection platform provides reliable results under various conditions. By conducting a thorough assessment of repeatability, sensitivity, and specificity, the detection platform is ensured to perform well not only under ideal conditions but also to maintain high performance in practical applications [ 26 ] . Repeatability is a measure of the stability of experimental operations, and a coefficient of variation below 5% in this study indicates the high operational stability of the RPA- Pf Ago detection platform. The high consistency observed in the FAM system suggests that the system can reliably reproduce detection results, which is crucial for the reliability of experimental outcomes. However, high repeatability was observed only in a single experimental group in the ROX system, possibly due to higher background fluorescence and greater environmental variability in the ROX system, suggesting that further parameter adjustments or optimizations may be necessary to improve its operational stability. Sensitivity is a key metric for evaluating the performance of a detection platform and is particularly crucial in clinical and research settings where precise detection of low-copy-number genetic variants is required [ 27 ] . In this study, testing with different concentrations of plasmids showed that the fluorescence values and initiation times of both systems varied with concentration, reaching a limit of detection (LOD) of 4×10 − 4 ng/µL. Compared to molecular beacon PCR detection (LOD = 2 ng/µL) [ 28 ] and multiplex asymmetric real-time fluorescent quantitative PCR (LOD = 1 ng/µL) [ 8 ] , the particularly notably lower detection limit of the RPA- Pf Ago detection platform highlights its high sensitivity, which is suitable for detecting low-concentration genetic variants. Specificity refers to the ability of a detection system to accurately differentiate between target and nontarget sequences [ 29 ] . This study verified through cross-testing of wild-type and mutant plasmids that both the FAM and ROX systems could be used to specifically detect the corresponding genotypes. This result is vital for ensuring the accuracy of detection results, as incorrect genotype determination in clinical and research contexts could lead to misdiagnosis or biases in research outcomes. In this study, the RPA- Pf Ago detection platform was employed for clinical validation on randomly selected oral mucosal samples from volunteers, and the results were compared with those of traditional Sanger sequencing. The findings demonstrate a high concordance of the RPA- Pf Ago platform with Sanger sequencing in detecting genetic variations, confirming its reliability and accuracy for clinical applications. This discovery underscores the potential of the RPA- Pf Ago technology for rapid detection of genetic polymorphisms and supports its application in personalized medicine and disease prevention. Due to its high sensitivity and good repeatability, the RPA- Pf Ago detection platform is particularly suitable for early disease diagnosis and genetic risk assessment, providing an effective tool for personalized healthcare. Future efforts should focus on further optimizing and standardizing the experimental processes and enhancing awareness of this new technology among the public and healthcare professionals, which could lead to its widespread use in clinical practice. In the future, to enhance the applicability of the RPA- Pf Ago technology, research should concentrate on further improving the detection sensitivity and specificity to accommodate more complex sample types, simplifying operational procedures to lower technical barriers, making it more suitable for routine clinical laboratory needs, and conducting broader clinical trials to validate its effectiveness under different diseases and conditions. Through these efforts, RPA- Pf Ago technology has become an indispensable tool in personalized medicine and disease prevention. Declarations Conflict of interest statement The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Funding Source This work was supported by the Guangdong Key Laboratory of Functional Substances in Medicinal Edible Resources and Healthcare Products (grant number 2021B1212040015), the Special Research Projects of Hybribio (grant number KP202304), Chaozhou Municipal Science and Technology Bureau Project (grant number 2023ZC24), Hanshan Normal University Assistance Project (grant number XBF202302) and the Scientific Projects of Key Disciplines in Guangdong Province (grant numbers 2021ZDJS042 and 2022ZDJS070). Author Contribution Author ContributionYaqun Liu: Conceptualization, Methodology, Investigation, Writing - Original Draft Preparation. Zhenxia Zhang: Data Curation, Formal Analysis, Visualization, Writing - Review & Editing. Lianghui Chen: Software, Validation. 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Dual toeholds regulated CRISPR-Cas12a sensing platform for ApoE single nucleotide polymorphisms genotyping[J]. Biosens Bioelectron. 2024;255:116255. Qian W, Huang J, Wang X, et al. CRISPR-Cas12a combined with reverse transcription recombinase polymerase amplification for sensitive and specific detection of human norovirus genotype GII.4[J]. Virology. 2021;564:26–32. Munawar MA. Critical insight into recombinase polymerase amplification technology[J]. Expert Rev Mol Diagn. 2022;22(7):725–37. He R, Wang L, Wang F, et al. Pyrococcus furiosus Argonaute-mediated nucleic acid detection[J]. Chem Commun (Camb). 2019;55(88):13219–22. Zhang Y, Gong B, Lin Y, et al. Split G-quadruplex based Pf Ago sensing platform for nucleotide mutation discrimination and human genotyping[J]. Analyst. 2024;149(3):707–11. Swarts DC, Hegge JW, Hinojo I, et al. Argonaute of the archaeon Pyrococcus furiosus is a DNA-guided nuclease that targets cognate DNA[J]. Nucleic Acids Res. 2015;43(10):5120–9. Liu Y, Xia W, Zhao W et al. RT-RPA- Pf Ago System: A Rapid, Sensitive, and Specific Multiplex Detection Method for Rice-Infecting Viruses[J]. Biosensors (Basel), 2023,13(10). Zhao Y, Zhang Y, Wu W, et al. Rapid and sensitive detection of Mycoplasma synoviae using RPA combined with Pyrococcus furiosus Argonaute[J]. Poult Sci. 2024;103(3):103244. Yang L, Guo B, Wang Y, et al. Pyrococcus furiosus Argonaute Combined with Recombinase Polymerase Amplification for Rapid and Sensitive Detection of Enterocytozoon hepatopenaei[J]. J Agric Food Chem. 2023;71(1):944–51. Wang Y, Chen Y, Tang Y, et al. A recombinase polymerase amplification and Pyrococcus furiosus Argonaute combined method for ultrasensitive detection of white spot syndrome virus in shrimp[J]. J Fish Dis. 2023;46(12):1357–65. Wang L, Chen W, Zhang C, et al. Molecular mechanism for target recognition, dimerization, and activation of Pyrococcus furiosus Argonaute[J]. Mol Cell. 2024;84(4):675–86. Fu R, Hou J, Wang Z et al. Mn(2+)-Mediated Modulation of Pf Ago Activity for Biosensing[J]. Adv Healthc Mater, 2024:e2304484. Kitamura S, Fujishima K, Sato A, et al. Characterization of RNase HII substrate recognition using RNase HII-argonaute chimeric enzymes from Pyrococcus furiosus [J]. Biochem J. 2010;426(3):337–44. Fu L, Xie C, Jin Z, et al. The prokaryotic Argonaute proteins enhance homology sequence-directed recombination in bacteria[J]. Nucleic Acids Res. 2019;47(7):3568–79. Jonker MF, Roudijk B, Maas M. The Sensitivity and Specificity of Repeated and Dominant Choice Tasks in Discrete Choice Experiments[J]. Value Health. 2022;25(8):1381–9. Gill P. Application of low copy number DNA profiling[J]. Croat Med J. 2001;42(3):229–32. Peng Q, Liao S, He Y, et al. A molecular-beacon-based asymmetric PCR assay for detecting polymorphisms related to folate metabolism[J]. J Clin Lab Anal. 2020;34(8):e23337. Saah AJ, Hoover DR. Sensitivity and specificity reconsidered: the meaning of these terms in analytical and diagnostic settings[J]. Ann Intern Med. 1997;126(1):91–4. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Submission checks completed at journal 09 Aug, 2024 First submitted to journal 09 Aug, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4884474","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":338154073,"identity":"da805b50-c6ac-4276-b405-6a595a9c1c89","order_by":0,"name":"Yaqun Liu","email":"","orcid":"","institution":"Hanshan Normal University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yaqun","middleName":"","lastName":"Liu","suffix":""},{"id":338154074,"identity":"6a822bed-e7bf-4237-8cb0-4ab1c3303b03","order_by":1,"name":"Lianghui Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAy0lEQVRIiWNgGAWjYBACNvb2gw8/VEjw2Lc3HyBOCx/PmWRjiTMWMgY8xxKI0yInkWAmwdtWYWMgkWNApMMYEtIkJM5I8Jgz5Hy88YbBTk63gaCWg4ctCoB+sWw4u9lyDkOysdkBQloYGxJvgGxhONi7TZqH4UDiNoJamBkMgH4BajnM84xILWwMRmAtBsd42IjUwsMDCmQJHskeNmPLOQZE+EV+/nNQVNbZ88s/fnjjTYWdHEEtKADoPFKUQ7SQqmMUjIJRMApGBAAA/ic9DaLqe/4AAAAASUVORK5CYII=","orcid":"","institution":"Youjiang Medical University for Nationalities","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Lianghui","middleName":"","lastName":"Chen","suffix":""},{"id":338154075,"identity":"f680843a-69ad-4abf-a830-0d6f8bb4b232","order_by":2,"name":"Peikui Yang","email":"","orcid":"","institution":"Hanshan Normal University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Peikui","middleName":"","lastName":"Yang","suffix":""},{"id":338154076,"identity":"159fd15c-9510-43cd-8cd0-4f576da74af1","order_by":3,"name":"Miaofen Fang","email":"","orcid":"","institution":"Hanshan Normal University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Miaofen","middleName":"","lastName":"Fang","suffix":""},{"id":338154077,"identity":"73cbc292-4a37-4b30-b0e5-ee76cdee7d4a","order_by":4,"name":"Xiaotong Cai","email":"","orcid":"","institution":"Hanshan Normal University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xiaotong","middleName":"","lastName":"Cai","suffix":""},{"id":338154078,"identity":"82cb1f68-cb77-4b0a-bea6-76ea58dcfb3c","order_by":5,"name":"Jinkun Han","email":"","orcid":"","institution":"Hanshan Normal University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jinkun","middleName":"","lastName":"Han","suffix":""},{"id":338154079,"identity":"9f1a15de-c26c-44cd-94fd-a38fd22b0471","order_by":6,"name":"Yicun Chen","email":"","orcid":"","institution":"Shantou University Medical College","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yicun","middleName":"","lastName":"Chen","suffix":""},{"id":338154080,"identity":"5993e6b1-0549-438d-ad6d-b6cb3112a9dc","order_by":7,"name":"Chengsong Xie","email":"","orcid":"","institution":"Guangdong Taiantang Pharmaceutical Co., Ltd","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Chengsong","middleName":"","lastName":"Xie","suffix":""},{"id":338154081,"identity":"e49e24ae-5f9a-41bf-828f-5a7f0029d331","order_by":8,"name":"Min Lin","email":"","orcid":"","institution":"Hanshan Normal University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Min","middleName":"","lastName":"Lin","suffix":""},{"id":338154082,"identity":"72f3e9cf-0f3c-4e9e-9d27-53a1f3760fd7","order_by":9,"name":"Zhenxia Zhang","email":"","orcid":"","institution":"Hanshan Normal University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Zhenxia","middleName":"","lastName":"Zhang","suffix":""},{"id":338154083,"identity":"e741e43e-f0f0-41d4-afed-f41d79f58c11","order_by":10,"name":"Yuzhong Zheng","email":"","orcid":"","institution":"Hanshan Normal University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yuzhong","middleName":"","lastName":"Zheng","suffix":""}],"badges":[],"createdAt":"2024-08-09 05:25:01","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4884474/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4884474/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":64043743,"identity":"f1475482-3303-43c4-b580-167726667f87","added_by":"auto","created_at":"2024-09-05 13:54:23","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":292540,"visible":true,"origin":"","legend":"\u003cp\u003eStrategy for the RPA-\u003cem\u003ePf\u003c/em\u003eAgo detection platform targeting the \u003cem\u003eMTHFR\u003c/em\u003e A1298C polymorphism. (A) Schematic representation of the RPA-\u003cem\u003ePf\u003c/em\u003eAgo detection system detailing the RPA amplification process and \u003cem\u003ePf\u003c/em\u003eAgo protein activity over time and at different temperatures. (B) Specific strategy for RPA-\u003cem\u003ePf\u003c/em\u003eAgo detection of the \u003cem\u003eMTHFR\u003c/em\u003e A1298C polymorphism, including \u003cem\u003ePf\u003c/em\u003eAgo target site recognition and gDNA-mediated targeted cleavage.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4884474/v1/bcf337c28bae24f7fbd294bc.png"},{"id":64043742,"identity":"c33ad968-3c81-4c1c-970f-7186ffb09b3a","added_by":"auto","created_at":"2024-09-05 13:54:23","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":61976,"visible":true,"origin":"","legend":"\u003cp\u003eOptimization of the RPA method within the RPA-\u003cem\u003ePf\u003c/em\u003eAgo detection platform for the \u003cem\u003eMTHFR\u003c/em\u003e A1298C polymorphism. The optimization parameters included primer concentration, MgAc concentration, temperature and incubation time.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4884474/v1/00476438f40bb9d6a79544fa.jpeg"},{"id":64043744,"identity":"bd359065-3e73-41e4-bf1e-8c80310cb4cd","added_by":"auto","created_at":"2024-09-05 13:54:23","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":303903,"visible":true,"origin":"","legend":"\u003cp\u003eOptimization of parameters in the \u003cem\u003eMTHFR\u003c/em\u003e A1298C RPA-\u003cem\u003ePf\u003c/em\u003eAgo detection system. This figure presents the optimization of the gDNA screening, gDNA concentration, probes, MnCl\u003csub\u003e2\u003c/sub\u003e, \u003cem\u003ePf\u003c/em\u003eAgo protein, and RPA products. The line graphs illustrate the trends of fluorescence values under various conditions. Photographs of tubes against a black background visually display the fluorescence intensity; heatmaps indicate the starting points of fluorescence under different conditions.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4884474/v1/43c65ba70542b51d10caaf03.jpeg"},{"id":64043745,"identity":"fd46c947-7b76-4bd7-a4fc-30ff4774dd56","added_by":"auto","created_at":"2024-09-05 13:54:23","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":175483,"visible":true,"origin":"","legend":"\u003cp\u003eMultivariate analysis of parameters for the \u003cem\u003eMTHFR\u003c/em\u003e A1298C RPA-\u003cem\u003ePf\u003c/em\u003eAgo platform. Statistical differences in nonnormal data were assessed using the Kruskal‒Wallis test. Comprehensive evaluation is achieved through the Critic, independence weight coefficient, and entropy methods. Correlations were examined via Spearman's, trend, and principal component analyses, supplemented by normalized hierarchical heatmaps to delineate variable interrelations.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4884474/v1/2686cfc4a1b5ae8121c6c1a0.jpeg"},{"id":64043747,"identity":"7fd0d1b1-0bf2-44fa-bb08-41c8e4be9039","added_by":"auto","created_at":"2024-09-05 13:54:23","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":171503,"visible":true,"origin":"","legend":"\u003cp\u003eRepeatability, sensitivity, and specificity of the \u003cem\u003eMTHFR\u003c/em\u003e A1298C RPA-\u003cem\u003ePf\u003c/em\u003eAgo detection platform\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4884474/v1/bbbec035f2619d0249897af3.jpeg"},{"id":64044146,"identity":"a30015b4-1e37-4ff4-9236-edb3a5e47cb9","added_by":"auto","created_at":"2024-09-05 14:02:23","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":77004,"visible":true,"origin":"","legend":"\u003cp\u003eDetection results and sequencing loci for wild-type and mutant alleles in 20 individuals using the RPA-\u003cem\u003ePf\u003c/em\u003eAgo polymorphism detection platform\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4884474/v1/0e338aef7c7c372ea5a787e6.jpeg"},{"id":64044147,"identity":"7d04b5b1-4de8-4da6-bb23-fd50e792b1f4","added_by":"auto","created_at":"2024-09-05 14:02:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1655073,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4884474/v1/087f448c-8788-4944-9e99-46cd1c6fe311.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Application of RPA-PfAgo Technology Combined with Multidimensional Data Analysis in the Rapid Detection of the MTHFR A1298C Polymorphism","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eWith the rapid advancement of human genomics and molecular biology, our understanding of the relationship between genetic polymorphisms and disease susceptibility has deepened. Polymorphism of the methylenetetrahydrofolate reductase (\u003cem\u003eMTHFR\u003c/em\u003e) gene, particularly the A1298C locus variant, has garnered widespread attention\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. In the 7th exon of the \u003cem\u003eMTHFR\u003c/em\u003e gene, the 1298A to C mutation results in the substitution of adenine (A) with cytosine (C), leading to the replacement of the encoded amino acid from glutamic acid (Glu) to alanine (Ala) and the elimination of an MboII restriction enzyme site\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. Studies indicate that the CC genotype may contribute to hyperhomocysteinaemia (HHcy) by decreasing enzyme activity and folate levels. Furthermore, the \u003cem\u003eMTHFR\u003c/em\u003e A1298C polymorphism is associated with an increased risk of cardiovascular and cerebrovascular diseases, diabetic nephropathy and congenital defects in newborns\u003csup\u003e[\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. Therefore, accurate detection of the \u003cem\u003eMTHFR\u003c/em\u003e A1298C polymorphism is highly important for disease prevention and personalized medicine.\u003c/p\u003e \u003cp\u003eAlthough traditional techniques such as polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP)\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e, TagMan PCR\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e, multiplex asymmetric real-time PCR-HRM\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e, electrochemical DNA chip\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e, OpenArray and real-time PCR-FRET\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e have achieved certain success in genotyping \u003cem\u003eMTHFR\u003c/em\u003e A1298C, these methods are often complex in operation, costly in equipment, and require professional handling. The promotion and application of these technologies are restricted, particularly in resource-limited regions. Therefore, the development of a rapid and economical genotyping method for \u003cem\u003eMTHFR\u003c/em\u003e A1298C is of significant importance for clinical applications. The application of rapid amplification technologies, such as RPA, which includes glucose\u0026minus;6-phosphate dehydrogenase (G6PD)\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e, apolipoprotein E (APOE)\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e, and norovirus (NoV) GII.4\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e, has increased rapidly in recent years, and these technologies have been widely used in the field of genetic polymorphism genotyping. RPA is an isothermal amplification technique that operates at a constant temperature of 37\u0026ndash;42\u0026deg;C and is capable of amplifying low concentrations of target DNA to detectable levels within 5\u0026ndash;30 minutes. Compared to other nucleic acid amplification technologies, RPA offers high sensitivity, high specificity, and ease of operation without the need for expensive temperature control equipment, making it particularly suitable for on-site testing\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. Additionally, \u003cem\u003ePyrococcus furiosus\u003c/em\u003e Argonaute (\u003cem\u003ePf\u003c/em\u003eAgo) has been proven to be a highly sensitive nucleic acid detection system\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e, and recent studies have successfully and accurately utilized \u003cem\u003ePf\u003c/em\u003eAgo for genotyping the FUT2 gene using human oral swab samples\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. \u003cem\u003ePf\u003c/em\u003eAgo proteins, in particular, have drawn attention due to their role in defense mechanisms and efficient nucleic acid cleavage. The advantages of \u003cem\u003ePf\u003c/em\u003eAgo include the absence of a requirement for PAM sequences in the target DNA, the use of short DNA molecules as guides, which are more cost-effective and stable, and a smaller molecular size that facilitates modification and production\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. The emergence of the RPA and \u003cem\u003ePf\u003c/em\u003eAgo technologies has led to new breakthroughs in the field of gene detection, with significant enhancements in their rapidity, sensitivity, and specificity when combined. Currently, the RPA-\u003cem\u003ePf\u003c/em\u003eAgo technique is mostly applied for pathogen detection, such as for detecting viruses infecting rice\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e, \u003cem\u003eMycoplasma synoviae\u003c/em\u003e\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e, \u003cem\u003eEnterocytozoon hepatopenaei\u003c/em\u003e\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e, and White Spot Syndrome Virus\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. These studies demonstrate the potential of \u003cem\u003ePf\u003c/em\u003eAgo combined with the RPA method in the field of pathogen detection, offering the capacity for rapid on-site diagnostic tools.\u003c/p\u003e \u003cp\u003eThis study leverages the advantages of RPA and \u003cem\u003ePf\u003c/em\u003eAgo technologies for genotyping the \u003cem\u003eMTHFR\u003c/em\u003e A1298C polymorphism, demonstrating technological innovation and providing new tools for a deeper understanding of the complex connections between \u003cem\u003eMTHFR\u003c/em\u003e gene polymorphisms and disease susceptibility. With further research and optimization, the RPA-\u003cem\u003ePf\u003c/em\u003eAgo technique has become a significant tool in genetic disease diagnostics and personalized medicine.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 RPA Primers, gDNA, and Probes Design\u003c/h2\u003e \u003cp\u003eThe \u003cem\u003eMTHFR\u003c/em\u003e A1298C (rs1801131) sequence was obtained from the National Center for Biotechnology Information (NCBI) database. Using Primer Premier 5 software, three sets of RPA primers targeting the mutation site were designed. The specificity of these primers was subsequently verified using the primer-BLAST tool on NCBI. To exploit the enzymatic cleavage characteristics of the \u003cem\u003ePf\u003c/em\u003eAgo protein, both wild-type and mutant guide DNA (gDNA), along with corresponding probes, were designed. These probes were labelled with two different fluorophores: FAM and ROX. Additionally, three sets of wild-type and mutant gDNA were designed, in which the mutation site was positioned at various locations within the gDNA. The 5\u0026rsquo; ends of the gDNA were modified with a phosphate group (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSequences of the RPA primers, wild-type and mutant gDNA, and modified probes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIdentifier\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSequence (5\u0026prime;-3\u0026prime;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModification\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eRPA Primers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTGGGCCTCCAGACCAAAGAGTTACATCTACCG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTACCCAGGAGTGGGACGAGTTCCCTAACGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGGAGCTGAAGGACTACTACCTCTTCTACCTG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTTTGTGACCATTCCGGTTTGGTTCTCCCGAG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTCCAGGGCAGGCAAGTCACTTTGTGACCATTCCG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTCCTCCTTCAGCAGGCTGGTCTCAGCCGCCAG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eWild-Type gDNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWT-gDNA7-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTCACTTTCTTCACTGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u0026rsquo;-PHO\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWT-gDNA7-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTACGAAGACTTCAAAG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u0026rsquo;-PHO\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWT-gDNA11-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTAAGACACTTTCTTCA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u0026rsquo;-PHO\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWT-gDNA11-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTAAGAACGAAGACTTC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u0026rsquo;-PHO\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWT-gDNA14-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTTCAAAGACACTTTCT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u0026rsquo;-PHO\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWT-gDNA14-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTGTAAAGAACGAAGAC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u0026rsquo;-PHO\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eMutant gDNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMut-gDNA7-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTCACTTGCTTCACTGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u0026rsquo;-PHO\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMut-gDNA7-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTACGAAGACTTCAAAG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u0026rsquo;-PHO\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMut-gDNA11-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTAAGACACTTGCTTCA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u0026rsquo;-PHO\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMut-gDNA11-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTAAGAACGAAGACTTC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u0026rsquo;-PHO\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMut-gDNA14-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTTCAAAGACACTTGCT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u0026rsquo;-PHO\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMut-gDNA14-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTGTAAAGAACGAAGAC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u0026rsquo;-PHO\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eWild-Type Probes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFAM-Modified\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFAM-GAACGAAGACTTCAAAGACACTTTCTTCA-BHQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFAM\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eROX-Modified\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eROX-GAACGAAGACTTCAAAGACACTTTCTTCA-BHQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eROX\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMutant Probes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFAM-Modified\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFAM-GAACGAAGACTTCAAAGACACTTGCTTCA-BHQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFAM\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eROX-Modified\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eROX-GAACGAAGACTTCAAAGACACTTGCTTCA-BHQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eROX\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 RPA Reaction Conditions Optimization\u003c/h2\u003e \u003cp\u003eThe RPA reactions followed the protocol provided with the TwistAmp\u0026reg; RPA Kit (TwistDx, Cambridge, UK). The amplified products were subjected to agarose gel electrophoresis for analysis. After amplification, the products were electrophoresed on a 2% agarose gel at a constant voltage of 120 V for 30 minutes to visualize the results. The optimization involved varying the MgAc concentration in five different volumes: 2.0 \u0026micro;L, 2.3 \u0026micro;L, 2.5 \u0026micro;L, 2.8 \u0026micro;L, and 3.0 \u0026micro;L. The reaction temperature ranged among five set points: 35\u0026deg;C, 37\u0026deg;C, 39\u0026deg;C, 42\u0026deg;C, and 45\u0026deg;C. The reaction times were also optimized, with durations tested at 10 min, 15 min, 20 min, 25 min, and 30 min. A negative control using nuclease-free water (ddH\u003csub\u003e2\u003c/sub\u003eO) as the template was included. Throughout the optimization of these three parameters, all other conditions remained constant.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Protocol Optimization for the RPA-\u003cem\u003ePf\u003c/em\u003eAgo Cleavage Assay\u003c/h2\u003e \u003cp\u003eUsing the optimized RPA protocol, samples were prepared for the \u003cem\u003ePf\u003c/em\u003eAgo cleavage assay. Specifically, 4 \u0026micro;L of the RPA product was added to 25 \u0026micro;L of the \u003cem\u003ePf\u003c/em\u003eAgo reaction mixture, which included 2 \u0026micro;L of gDNA1 and gDNA2 (each at 20 \u0026micro;M), 1 \u0026micro;L of a molecular beacon (20 \u0026micro;M), 4 \u0026micro;L of the Ago enzyme (200 U/\u0026micro;L), 4 \u0026micro;L of MnCl\u003csub\u003e2\u003c/sub\u003e (250 nM), 2 \u0026micro;L of 10\u0026times; buffer, and 4 \u0026micro;L of the RPA product. The assay was performed at 95\u0026deg;C for 30 minutes in a thermocycler, with FAM or ROX fluorescence signals recorded every 30 seconds. After the reaction, the samples were analysed under blue (470 nm) and green (525 nm) light. Optimization focused on five key parameters: gDNA, MnCl\u003csub\u003e2\u003c/sub\u003e, the probe, the \u003cem\u003ePf\u003c/em\u003eAgo enzyme, and the RPA product. The concentrations and volumes were varied as follows: gDNA was tested at 10, 20, 40, 60, 80, and 100 \u0026micro;M; MnCl\u003csub\u003e2\u003c/sub\u003e at 1, 2, 3, 4, 5, and 6 \u0026micro;L; probe at 10, 20, 40, 60, 80, and 100 \u0026micro;M; Ago enzyme at 1, 2, 3, 4, 5, and 6 \u0026micro;L; and the RPA product at 1, 2, 3, 4, 5, and 6 \u0026micro;L. A negative control with ddH\u003csub\u003e2\u003c/sub\u003eO was included for assay reliability. This optimization was conducted with all other variables held constant, aiming to identify the optimal conditions for each parameter in the RPA-\u003cem\u003ePf\u003c/em\u003eAgo cleavage assay.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Performance Evaluation\u003c/h2\u003e \u003cp\u003eThe detection performance of the RPA-\u003cem\u003ePf\u003c/em\u003eAgo system was evaluated in terms of sensitivity, repeatability, and specificity. Sensitivity was determined by amplifying serially diluted positive plasmid templates at concentrations ranging from 4\u0026times;10\u003csup\u003e1\u003c/sup\u003e ng/\u0026micro;L to 4\u0026times;10\u0026thinsp;\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e ng/\u0026micro;L within the optimized RPA-\u003cem\u003ePf\u003c/em\u003eAgo detection system. This approach facilitated the assessment of the system's ability to detect low concentrations of target DNA. Repeatability was assessed by performing triplicate assays of the RPA-\u003cem\u003ePf\u003c/em\u003eAgo under consistent reaction conditions, including system configurations and timing, across multiple samples. This was done to assess the consistency of detection outcomes. Specificity was evaluated by comparing the system's performance in detecting wild-type and mutant samples within both the wild-type and mutant-specific RPA-\u003cem\u003ePf\u003c/em\u003eAgo systems. This comparison aimed to determine the system\u0026rsquo;s accuracy in distinguishing between different genetic variants.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Clinical Validation\u003c/h2\u003e \u003cp\u003eBuccal mucosal cells were successfully collected from oropharyngeal swabs from 20 volunteers. Genomic DNA of the human folate genome was extracted utilizing the high-efficiency Swab Genomic DNA Extraction Kit (DP362, Tiangen, Beijing, China). The established RPA-\u003cem\u003ePf\u003c/em\u003eAgo method was employed for detection, with Sanger sequencing serving as the reference standard for these samples. The concordance of the results was used to evaluate the clinical applicability of the method.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Statistical Analysis\u003c/h2\u003e \u003cp\u003e \u003cstrong\u003eStatistical analyses were conducted as follows\u003c/strong\u003e \u003cp\u003efor data that did not follow a normal distribution, the Kruskal‒Wallis test was utilized to determine significant differences among groups using SPSS Statistics software (version 26, IBM Corp., Armonk, NY, USA). To assess the temporal variability of the RPA-\u003cem\u003ePf\u003c/em\u003eAgo detection system, which is essential for monitoring reaction dynamics over time, time series analysis was performed in the R statistical environment (R Foundation for Statistical Computing, Vienna, Austria). Furthermore, principal component analysis (PCA) was conducted using the FactoMineR package in R to explore patterns and relationships across various testing conditions.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eTo evaluate the significance of parameters within the RPA-\u003cem\u003ePf\u003c/em\u003eAgo detection system, the Critic method was applied using custom scripts in MATLAB (MathWorks, Natick, MA, USA) to assess the importance of various parameters, considering both contrast and conflict among criteria. The independence weight coefficient was calculated in SPSS Statistics software (version 26, IBM Corp., Armonk, NY, USA) using a custom macro to quantify parameter independence and ensure experimental integrity. The entropy weighting method was applied using MATLAB (MathWorks, Natick, MA, USA) to determine the relative importance of parameters based on their variability.\u003c/p\u003e \u003cp\u003eCorrelation and variability analyses were conducted as follows: Spearman's rank correlation analysis was performed using the cor.test function in R to determine the strength and direction of associations between ranked variables. Normalized hierarchical heatmaps were generated using the heatmap.2 function in the gplots package in R, with normalization applied to enhance data comparability. The coefficient of variation (CV) was calculated using Microsoft Excel (Microsoft Corporation, Redmond, WA, USA) to measure relative variability and assay repeatability.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Design and Construction of the RPA-\u003cem\u003ePfAgo\u003c/em\u003e Detection Platform for \u003cem\u003eMTHFR\u003c/em\u003e A1298C\u003c/h2\u003e \u003cp\u003eTo facilitate rapid and precise detection of the A1298C polymorphism in the \u003cem\u003eMTHFR\u003c/em\u003e gene, this study developed a detection platform utilizing RPA coupled with the \u003cem\u003ePf\u003c/em\u003eAgo protein. The platform capitalizes on the swift amplification capability of RPA and the high specificity of \u003cem\u003ePf\u003c/em\u003eAgo-mediated DNA cleavage to sensitively identify genetic mutations. The RPA-\u003cem\u003ePf\u003c/em\u003eAgo detection process consists of initial isothermal amplification of DNA sequences containing the target site via RPA. Subsequently, the amplified product is mixed with gDNA and \u003cem\u003ePf\u003c/em\u003eAgo protein, where the gDNA directs \u003cem\u003ePf\u003c/em\u003eAgo to specifically recognize and cleave the target DNA, releasing fluorescently labelled probe fragments, thus generating a detectable fluorescent signal. The presence of the specific mutation at the target site in the sample was determined by capturing the fluorescent signal with a fluorescence detection device or direct visualization using a fluorescence imaging system (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). In this study, synthetic oligonucleotides representing the antisense strand of wild-type and mutated \u003cem\u003eMTHFR\u003c/em\u003e A1298C were used as cleavage targets. Additionally, 5'-phosphorylated 16-nucleotide long gDNAs were synthesized, and single-base mismatches were introduced to assess the cleavage efficiency of \u003cem\u003ePf\u003c/em\u003eAgo in both wild-type and mutant gDNAs. The cleavage site of the gDNA, which is critical for the detection of the wild-type and mutant sequences of the folate gene, was positioned between nucleotides 10 and 11. The length of gDNA should not be less than 15 nucleotides, as excessive length may impair cleavage efficiency. For this experiment, gDNAs were designed to be 16 nucleotides long, and a series of gDNAs were constructed with the mutation site shifting in gDNA, resulting in 16 distinct gDNAs tailored for both wild-type and mutant forms (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Optimizing RPA Conditions for \u003cem\u003eMTHFR\u003c/em\u003e A1298C\u003c/h2\u003e \u003cp\u003eTo increase the efficiency and sensitivity of RPA detection, this study initially focused on the selection of RPA primers. By designing three sets of primers and conducting pairwise combination experiments, we determined that the primer combination F3R3 exhibited optimal amplification efficiency, resulting in a single, bright band. Consequently, F3R3 was selected for subsequent experiments. In the optimization of the MgAc concentration, testing different concentrations revealed that adding 3.0 \u0026micro;L of MgAc at 39\u0026deg;C produced a comparably brighter amplification product band. With respect to the adjustment of the reaction temperature, it was found that at a temperature range of 35\u0026deg;C to 39\u0026deg;C, a reaction temperature of 37\u0026deg;C yielded the clearest band of specific amplification products, with the brightness decreasing as the temperature increased, thereby establishing 37\u0026deg;C as the optimal reaction temperature. Finally, in setting the reaction time, comparing band brightness at different time points (10, 15, 20, and 25 minutes) showed that a 25-minute duration produced the clearest band, with the brightness decreasing over longer periods (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Thus, 25 minutes was determined to be the optimal reaction time.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Parameter Optimization and Analysis for the \u003cem\u003eMTHFR\u003c/em\u003e A1298C RPA-\u003cem\u003ePf\u003c/em\u003eAgo Detection Platform\u003c/h2\u003e \u003cp\u003eCombining the fluorescence capture starting time, visual observation, and entire fluorescence curve, multiparameter optimization was conducted for the \u003cem\u003eMTHFR\u003c/em\u003e A1298C RPA-\u003cem\u003ePf\u003c/em\u003eAgo detection platform. For both the wild-type (WT) and mutant (MuT) genotypes, three sets of gDNA primers (gDNA7, gDNA11, and gDNA14) were designed, with gDNA7 identified as optimal for both. Specificity testing using the gDNA7 primer set demonstrated accurate differentiation between wild-type and mutant \u003cem\u003eMTHFR\u003c/em\u003e A1298C alleles without cross-reactivity. Fluorescence imaging indicated that the optimal concentration for obtaining gDNA from both the wild-type and mutant strains was 10 \u0026micro;M. Further optimization of the probe concentration confirmed that 10 \u0026micro;M was the most effective concentration for both systems. In the MnCl\u003csub\u003e2\u003c/sub\u003e optimization assays, the addition of 6 \u0026micro;L yielded the highest enzymatic cleavage efficiency for the wild-type system, while 5 \u0026micro;L produced the strongest fluorescence signal for the mutant system. The concentration of the Ago enzyme was also critical, with 6 \u0026micro;L found to be optimal for both the wild-type and mutant reactions. Finally, optimization of the volume of the RPA amplification products added to the cleavage system demonstrated that a volume of 6 \u0026micro;L resulted in peak fluorescence intensity for both genotypes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo further investigate the parameters involved in the optimization of the \u003cem\u003eMTHFR\u003c/em\u003e A1298C RPA-\u003cem\u003ePf\u003c/em\u003eAgo detection platform, a comprehensive multidimensional analysis of the experimental parameters was conducted. By comparing the fluorescence signals in different detection channels (FAM and ROX), it was observed that the differences between the wild type and the mutant were minimal in the FAM channel, whereas significant differences were noted in the ROX channel. Statistical tools such as difference analysis, correlation analysis, and principal component analysis were used to further confirm that the association between the wild type in the FAM channel and the mutant in the ROX channel was the lowest. Comprehensive evaluations using the Critic weighting method, independence weight coefficient, and entropy weighting method also indicate that the mutant's detection results in the ROX channel exhibit greater independence. When analysing the impact of major experimental parameters such as gDNA concentration, probes, MnCl\u003csub\u003e2\u003c/sub\u003e, \u003cem\u003ePf\u003c/em\u003eAgo protein, and RPA products, a high trend similarity between gDNA concentration and probes was observed, contrasting with their low similarity to other parameters. Comprehensive evaluations through Critic weighting and entropy weighting methods revealed that the gDNA concentration and probes had the highest weights, suggesting that these parameters might play key roles in the RPA-\u003cem\u003ePf\u003c/em\u003eAgo detection system (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Comprehensive Performance Assessment of the \u003cem\u003eMTHFR\u003c/em\u003e A1298C RPA-\u003cem\u003ePf\u003c/em\u003eAgo Detection Platform\u003c/h2\u003e \u003cp\u003eTo evaluate the performance of the RPA-\u003cem\u003ePf\u003c/em\u003eAgo detection platform for the \u003cem\u003eMTHFR\u003c/em\u003e A1298C polymorphism, this study conducted a quantitative analysis of its repeatability, sensitivity, and specificity under optimal parameter conditions. In the repeatability analysis, three independent experiments were performed using six different plasmids each time, yielding coefficients of variation (CVs) below 5%, indicating the high operational stability of the platform. Notably, in the FAM detection system, high consistency was observed for both the wild-type and mutant strains in terms of the initiation time and final fluorescence value of the fluorescence curves across three experiments. In the ROX detection system, high repeatability was observed only in a single experimental group, which may be related to the higher background fluorescence and greater environmental variability associated with the ROX system. Principal component analysis further confirmed the lower error in the FAM system, and time series analysis revealed greater variability across all four clusters in the ROX system. For the sensitivity assessment, the use of plasmids at different concentration gradients revealed that both the fluorescence value and initiation time varied with concentration in both the FAM and ROX detection systems, with the lowest detection limit of both systems reaching 4x10\u0026thinsp;\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e ng/\u0026micro;L. In the specificity tests, six wild-type and mutant plasmids were tested in the two detection systems, confirming that both systems could specifically detect the corresponding genotypes (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Clinical Validation of the \u003cem\u003eMTHFR\u003c/em\u003e A1298C RPA-\u003cem\u003ePf\u003c/em\u003eAgo Detection Platform\u003c/h2\u003e \u003cp\u003eOral mucosal samples from 20 randomly selected volunteers were subjected to clinical validation using the \u003cem\u003eMTHFR\u003c/em\u003e A1298C RPA-\u003cem\u003ePf\u003c/em\u003eAgo detection platform. The findings indicated that 17 individuals tested positive for the wild-type allele using the platform designated for wild-type detection. In contrast, the platform specific for the detection of the mutant allele yielded a positive result for 9 individuals. Among these, 6 individuals were identified as positive for both wild-type and mutant alleles, suggesting heterozygous mutations according to the RPA-\u003cem\u003ePf\u003c/em\u003eAgo platform's indications. Confirmatory sequencing was conducted as a reference standard, and the results were in complete concordance with the RPA-\u003cem\u003ePf\u003c/em\u003eAgo detection results, demonstrating a 100% agreement rate (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4 Discussions","content":"\u003cp\u003eThis study introduces the RPA-\u003cem\u003ePf\u003c/em\u003eAgo method for detecting polymorphisms at the A1298C locus of the \u003cem\u003eMTHFR\u003c/em\u003e gene. Compared to traditional methods such as PCR-RFLP and fluorescent PCR, this technique offers advantages, including simplicity of operation, low cost, and independence from expensive laboratory equipment, making it suitable for rapid testing in resource-limited settings. The \u003cem\u003ePf\u003c/em\u003eAgo protein family demonstrates high sensitivity and specificity in nucleic acid detection, particularly because of its ability to guide DNA cleavage without the need for a PAM sequence\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. This enhances the sensitivity and specificity of genetic mutation detection, which is significant for susceptibility research and the application of personalized medicine.\u003c/p\u003e \u003cp\u003eThe RPA-\u003cem\u003ePf\u003c/em\u003eAgo detection platform was finely optimized to enhance the efficiency and accuracy of detecting the \u003cem\u003eMTHFR\u003c/em\u003e A1298C polymorphism. First, a specific set of conditions was determined by systematically adjusting the RPA primers, MgAc concentration, reaction temperature, and reaction time to optimize the RPA amplification efficiency. The carefully designed primer combination F3R3 showed the best amplification efficiency at a constant temperature of 37\u0026deg;C and a reaction time of 25 minutes. Further optimization involved the use of the RPA-\u003cem\u003ePf\u003c/em\u003eAgo cleavage reaction system, which is a complex process involving multiple variables whose performance improvement resulted from the combined effect of multiple parameters. The adjustment of each parameter was based on a deep understanding of \u003cem\u003ePf\u003c/em\u003eAgo enzyme activity and its interaction mechanism with DNA. Optimization of the gDNA, probe design, MnCl\u003csub\u003e2\u003c/sub\u003e concentration, \u003cem\u003ePf\u003c/em\u003eAgo protein, and RPA product parameters achieved precise differentiation of the wild-type and mutant alleles. Attention to gDNA design has focused on high complementarity to the target sequence, appropriate length and structure, and optimized concentration to ensure efficient binding and cleavage precision\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. Careful control of gDNA revealed that 7 ng gDNA and 10 \u0026micro;M provided the optimal signal strength for the two genotypes. Probe design and concentration optimization not only improved specificity but also enhanced the ability to recognize the target sequence, minimizing background signals\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. The probe concentrations in the wild-type and mutant detection systems were optimized to 10 \u0026micro;M, ensuring reaction specificity and signal clarity. Optimization of the MnCl\u003csub\u003e2\u003c/sub\u003e concentration revealed the catalytic role of metal ions in \u003cem\u003ePf\u003c/em\u003eAgo-mediated nucleic acid cleavage reactions, which is closely related to \u003cem\u003ePf\u003c/em\u003eAgo protein activity\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. Experimentally, it was found that for the wild-type system, adding 6 \u0026micro;L of MnCl\u003csub\u003e2\u003c/sub\u003e achieved the highest cleavage efficiency, while for the mutant system, adding 5 \u0026micro;L produced the strongest fluorescence signal. This subtle difference suggests that different genotypes may require different reaction conditions to optimize detection results. Different genotypes may have different requirements for MnCl\u003csub\u003e2\u003c/sub\u003e, reflecting subtle differences in \u003cem\u003ePf\u003c/em\u003eAgo protein interactions with different DNA sequences. These differences may stem from secondary structures around the mutation site or DNA flexibility, which are worthy of further research\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. Optimization of the \u003cem\u003ePf\u003c/em\u003eAgo protein and RPA product volumes relates to the ratio of enzyme to substrate in the reaction, which is crucial for achieving maximum cleavage efficiency. In this process, for different genotypes, the same concentration of the \u003cem\u003ePf\u003c/em\u003eAgo protein exhibited optimal cleavage efficiency, suggesting that the \u003cem\u003ePf\u003c/em\u003eAgo protein may have some adaptability to different target sequences. Additionally, optimization of the RPA amplification product volume demonstrated that 6 \u0026micro;L was the ideal volume for achieving peak fluorescence intensity, emphasizing the crucial role of amplification products in the cleavage reaction.\u003c/p\u003e \u003cp\u003eDuring the optimization process of the \u003cem\u003eMTHFR\u003c/em\u003e A1298C RPA-\u003cem\u003ePf\u003c/em\u003eAgo detection platform, the influence of various parameters on the detection results was thoroughly investigated through multidimensional analysis of the experimental parameters. When comparing fluorescence signals in the FAM and ROX detection channels, it was found that the difference between the wild-type and mutant types in the FAM channel was minimal, while the difference in the ROX channel was significant. This suggests that different channels may have varying sensitivities to wild-type and mutant types. Statistical tools such as differential analysis, correlation analysis, and principal component analysis further confirmed that the correlation between detecting the wild type in the FAM channel and detecting the mutant type in the ROX channel was the lowest, indicating a lower correlation between the detection signals of the two types in different channels. This is crucial for designing highly specific detection systems, as it allows for the detection of different types in different channels, reducing signal interference. Comprehensive evaluations using Critic weighting, independence weighting coefficients, and entropy weighting showed that the detection results of the mutant type in the ROX channel had greater independence, possibly because the signal provided by the ROX channel had a lower correlation with other variables and could better reflect the presence of the mutant type. Analysis of the effects of major experimental parameters, such as gDNA, probes, MnCl\u003csub\u003e2\u003c/sub\u003e, \u003cem\u003ePf\u003c/em\u003eAgo protein, and RPA products, revealed that there was a high degree of trend similarity between gDNA and probes, while the similarity between these two parameters and other parameters was low, indicating their crucial roles in the RPA-\u003cem\u003ePf\u003c/em\u003eAgo detection system. The comprehensive evaluation showed that gDNA and probes had the highest weights, further emphasizing their importance\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. This multidimensional assessment helps to understand the relative importance of various parameters on the detection results and guides the focus of optimization efforts.\u003c/p\u003e \u003cp\u003eIn the experimental setup, a comprehensive evaluation is essential to ensure that the detection platform provides reliable results under various conditions. By conducting a thorough assessment of repeatability, sensitivity, and specificity, the detection platform is ensured to perform well not only under ideal conditions but also to maintain high performance in practical applications\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. Repeatability is a measure of the stability of experimental operations, and a coefficient of variation below 5% in this study indicates the high operational stability of the RPA-\u003cem\u003ePf\u003c/em\u003eAgo detection platform. The high consistency observed in the FAM system suggests that the system can reliably reproduce detection results, which is crucial for the reliability of experimental outcomes. However, high repeatability was observed only in a single experimental group in the ROX system, possibly due to higher background fluorescence and greater environmental variability in the ROX system, suggesting that further parameter adjustments or optimizations may be necessary to improve its operational stability. Sensitivity is a key metric for evaluating the performance of a detection platform and is particularly crucial in clinical and research settings where precise detection of low-copy-number genetic variants is required\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. In this study, testing with different concentrations of plasmids showed that the fluorescence values and initiation times of both systems varied with concentration, reaching a limit of detection (LOD) of 4\u0026times;10\u0026thinsp;\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e ng/\u0026micro;L. Compared to molecular beacon PCR detection (LOD\u0026thinsp;=\u0026thinsp;2 ng/\u0026micro;L)\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e and multiplex asymmetric real-time fluorescent quantitative PCR (LOD\u0026thinsp;=\u0026thinsp;1 ng/\u0026micro;L)\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e, the particularly notably lower detection limit of the RPA-\u003cem\u003ePf\u003c/em\u003eAgo detection platform highlights its high sensitivity, which is suitable for detecting low-concentration genetic variants. Specificity refers to the ability of a detection system to accurately differentiate between target and nontarget sequences\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e. This study verified through cross-testing of wild-type and mutant plasmids that both the FAM and ROX systems could be used to specifically detect the corresponding genotypes. This result is vital for ensuring the accuracy of detection results, as incorrect genotype determination in clinical and research contexts could lead to misdiagnosis or biases in research outcomes.\u003c/p\u003e \u003cp\u003eIn this study, the RPA-\u003cem\u003ePf\u003c/em\u003eAgo detection platform was employed for clinical validation on randomly selected oral mucosal samples from volunteers, and the results were compared with those of traditional Sanger sequencing. The findings demonstrate a high concordance of the RPA-\u003cem\u003ePf\u003c/em\u003eAgo platform with Sanger sequencing in detecting genetic variations, confirming its reliability and accuracy for clinical applications. This discovery underscores the potential of the RPA-\u003cem\u003ePf\u003c/em\u003eAgo technology for rapid detection of genetic polymorphisms and supports its application in personalized medicine and disease prevention. Due to its high sensitivity and good repeatability, the RPA-\u003cem\u003ePf\u003c/em\u003eAgo detection platform is particularly suitable for early disease diagnosis and genetic risk assessment, providing an effective tool for personalized healthcare. Future efforts should focus on further optimizing and standardizing the experimental processes and enhancing awareness of this new technology among the public and healthcare professionals, which could lead to its widespread use in clinical practice. In the future, to enhance the applicability of the RPA-\u003cem\u003ePf\u003c/em\u003eAgo technology, research should concentrate on further improving the detection sensitivity and specificity to accommodate more complex sample types, simplifying operational procedures to lower technical barriers, making it more suitable for routine clinical laboratory needs, and conducting broader clinical trials to validate its effectiveness under different diseases and conditions. Through these efforts, RPA-\u003cem\u003ePf\u003c/em\u003eAgo technology has become an indispensable tool in personalized medicine and disease prevention.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConflict of interest statement\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding Source\u003c/h2\u003e \u003cp\u003eThis work was supported by the Guangdong Key Laboratory of Functional Substances in Medicinal Edible Resources and Healthcare Products (grant number 2021B1212040015), the Special Research Projects of Hybribio (grant number KP202304), Chaozhou Municipal Science and Technology Bureau Project (grant number 2023ZC24), Hanshan Normal University Assistance Project (grant number XBF202302) and the Scientific Projects of Key Disciplines in Guangdong Province (grant numbers 2021ZDJS042 and 2022ZDJS070).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAuthor ContributionYaqun Liu: Conceptualization, Methodology, Investigation, Writing - Original Draft Preparation. Zhenxia Zhang: Data Curation, Formal Analysis, Visualization, Writing - Review \u0026amp; Editing. Lianghui Chen: Software, Validation. Peikui Yang: Data Curation, Writing - Review \u0026amp; Editing. Xiaotong Cai: Resources, Data Curation. Miaofen Fang: Investigation, Resources. Kunjin Han: Resources, Data Curation. Yicun Chen: Supervision, Project Administration. Chengsong Xie: Funding Acquisition, Project Administration. Min Lin: Conceptualization, Supervision, Writing - Review \u0026amp; Editing. Yuzhong Zheng: Conceptualization, Methodology, Supervision, Writing - Review \u0026amp; Editing, Funding Acquisition.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003ePetrone I, Bernardo PS, Dos SE et al. \u003cem\u003eMTHFR\u003c/em\u003e C677T and A1298C Polymorphisms in Breast Cancer, Gliomas and Gastric Cancer: A Review[J]. Genes (Basel), 2021,12(4).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDonnelly JG. 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Ann Intern Med. 1997;126(1):91\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-ethics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"meth","sideBox":"Learn more about [BMC Medical Ethics](http://bmcmedethics.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/meth/default.aspx","title":"BMC Medical Ethics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"MTHFR A1298C polymorphism, Recombinase polymerase amplification (RPA), PfAgo protein, Genotyping, Multidimensional data analysis","lastPublishedDoi":"10.21203/rs.3.rs-4884474/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4884474/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study presents a novel approach that integrates recombinase polymerase amplification (RPA) with \u003cem\u003ePf\u003c/em\u003eAgo protein technology for the rapid and precise detection of the \u003cem\u003eMTHFR\u003c/em\u003e A1298C polymorphism. Although traditional genotyping methods are effective, they are often limited by complexity, high cost, and the need for specialized equipment. The RPA-\u003cem\u003ePf\u003c/em\u003eAgo technique harnesses the swift isothermal amplification of RPA and the high specificity and sensitivity of \u003cem\u003ePf\u003c/em\u003eAgo-mediated DNA cleavage, completing the entire process from sample collection to detection within 90 minutes. The utility of this method has been substantiated through a battery of optimization experiments, parameter analysis, and assessments of sensitivity, specificity, and repeatability, along with clinical validation using oral mucosal samples. The findings indicate that this new technology not only substantially reduces detection time and cost but also offers an effective tool for personalized medicine and disease prevention with high accuracy and reliability.\u003c/p\u003e","manuscriptTitle":"The Application of RPA-PfAgo Technology Combined with Multidimensional Data Analysis in the Rapid Detection of the MTHFR A1298C Polymorphism","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-09-05 13:54:18","doi":"10.21203/rs.3.rs-4884474/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"checksComplete","content":"","date":"2024-08-09T12:15:39+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medical Ethics","date":"2024-08-09T05:23:10+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-ethics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"meth","sideBox":"Learn more about [BMC Medical Ethics](http://bmcmedethics.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/meth/default.aspx","title":"BMC Medical Ethics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0aeb3905-6b45-4969-a8b1-9d8dd9df2f94","owner":[],"postedDate":"September 5th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-09-05T13:54:18+00:00","versionOfRecord":[],"versionCreatedAt":"2024-09-05 13:54:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4884474","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4884474","identity":"rs-4884474","version":["v1"]},"buildId":"FbvkV6FR0MCFSLy54lSbu","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}