Development of a Triplex FMCA Assay for Genotyping Three Genes, ADH1B, ADH1C, and ALDH2, Involved in Alcohol Metabolism

Development of a Triplex FMCA Assay for Genotyping Three Genes, ADH1B, ADH1C, and ALDH2, Involved in Alcohol Metabolism | 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 Article Development of a Triplex FMCA Assay for Genotyping Three Genes, ADH1B, ADH1C, and ALDH2, Involved in Alcohol Metabolism Mikiko Soejima, Yoshiro Koda This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8635444/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 31 Mar, 2026 Read the published version in Scientific Reports → Version 1 posted 12 You are reading this latest preprint version Abstract Three functional single nucleotide variants (SNVs)‒ ALDH2 rs671 (p.E504K), ADH1C rs698 (p.I350V), and ADH1B rs1229984 (p.R48H)‒are key genetic determinants of human alcohol metabolism. These variants significantly affect drinking behavior and are associated with liver disease and increased risks of several malignancies, including esophageal and gastric cancers. We developed a triplex fluorescent probe-based melting curve analysis (FMCA) assay for the simultaneous detection of these three SNVs. The assay was validated by comparing FMCA results with Sanger sequencing using genomic DNA from 94 Japanese individuals. The automated detection algorithm reliably identified genotypes of rs671 and rs698. Although the melting peaks of rs1229984 exhibited lower resolution and necessitated manual visual inspection for definitive genotype discrimination, all genotypes were nevertheless correctly identified. The assay demonstrated 100% accuracy. In conclusion, this triplex FMCA assay provides a rapid, cost-effective, and streamlined method for the simultaneous genotyping of ADH1B , ADH1C , and ALDH2 . Given its high accuracy and ease of implementation, this method serves as a practical alternative to conventional sequencing, positioning it as a valuable tool for both large-scale epidemiological research and routine clinical assessment of alcohol-related health risks. Health sciences/Biomarkers Biological sciences/Cancer Biological sciences/Computational biology and bioinformatics Biological sciences/Genetics Biological sciences/Molecular biology Health sciences/Oncology alcohol metabolism rs671 rs698 rs1229984 fluorescence melting curve analysis multiplex genotyping Figures Figure 1 Figure 2 Introduction Ethanol is metabolized through a sequential oxidative pathway in which alcohol dehydrogenase (ADH) converts ethanol to acetaldehyde, followed by aldehyde dehydrogenase (ALDH)–mediated oxidation of acetaldehyde to acetic acid 1 , 2 . Interindividual variation in the catalytic efficiency of these enzymes affects alcohol consumption patterns, susceptibility to alcohol dependence, the development of alcohol-associated liver disease, and the severity of physiological responses after alcohol intake 3 , 4 . Because both ethanol and acetaldehyde are classified as human carcinogens, elucidating the genetic determinants of alcohol metabolism is essential for understanding the etiology of several cancers including esophageal and gastric cancers. Importantly, the risk of these malignancies is not determined by a single locus but is substantially influenced by the synergistic interactions of multiple functional variants 5 – 7 . Therefore, a comprehensive assessment of these risks‒rather than analyzing individual single nucleotide variants (SNVs) in isolation‒is crucial for identifying high-risk individuals and implementing personalized cancer prevention strategies. Among the loci involved in ethanol metabolism, ADH1B (rs1229984, p.R48H), ADH1C (rs698, p.I350V), and ALDH2 (rs671, p.E504K) are well-established nonsynonymous SNVs with clear functional consequences 5 – 7 . The A alleles of rs1229984 ( ADH1B*2 ) and rs698 ( ADH1C*1 ) encode ADH isoforms with markedly high catalytic turnover, whereas the corresponding G alleles ( ADH1B*1 , ADH1C*2 ) produce low-activity isoforms 8 , 9 . Individuals carrying low-activity ADH variants exhibit slower ethanol-to-acetaldehyde conversion, resulting in prolonged systemic ethanol exposure. Recent large-scale genomic studies and global burden assessments have reinforced this factor as a critical driver of increased carcinogenic risk, not only for the aero-digestive tract but also for various systemic malignancies and other alcohol-related disorders 10 – 13 . Reduced acetaldehyde accumulation also diminishes aversive physiological responses, thereby facilitating higher alcohol intake and increasing vulnerability to alcohol dependence 14 . The rs1229984 A allele is highly enriched in East Asian populations‒including Japanese, Chinese, and Koreans‒where its frequency approaches 70%, but it remains uncommon (< 10%) in Europeans, Africans, and South Asians. In contrast, the rs698 A allele is prevalent in East Asians and Africans (80–90%) and moderately frequent in Europeans (~ 60%) 5 . ALDH2 rs671 also exhibits pronounced population stratification. The G allele ( ALDH2*1 ) encodes an enzyme with robust acetaldehyde-detoxifying capacity, whereas the A allele ( ALDH2*2 ) produces an enzyme with severely diminished (G/A heterozygote) or absent (A/A homozygotes) activity 15 . Individuals with low or null ALDH2 function accumulate high levels of acetaldehyde after alcohol intake, leading to characteristic alcohol-induced reactions such as facial flushing, headache, and nausea, and markedly increasing the risk of upper gastrointestinal carcinogenesis 16 . In East Asian populations, the rs671 A allele frequency ranges from 20% to 30% (Source: dbSNP, https://www.ncbi.nlm.nih.gov/snp/rs671 ). Specifically, among Japanese individuals, approximately 37% carry the low-activity G/A heterozygous genotype and ~ 7% carry the null-activity A/A homozygous genotype 17 . In contrast, more than 90% of non-East Asian populations‒including Europeans, Africans, and Indians‒are homozygous for the high-activity G allele 5 . Given these striking interpopulation differences and their substantial implications for alcohol-related disease susceptibility, a rapid and reliable genotyping platform for these three SNVs would be highly useful for assessing drinking behavior, alcohol-related liver disease, and carcinogenic risk. However, current PCR-based methods such as Sanger sequencing suffer from limited throughput or high cost when analyzing multiple loci 18 . To overcome the limitations of conventional assays, TaqMan genotyping assays, which employ dual-labeled hydrolysis probes, are widely used for SNV discrimination 19 . During amplification, the 5’-3’ exonuclease activity of Taq polymerase cleaves the probe, generating a fluorescence signal. Alternatively, fluorescence melting curve analysis (FMCA) has emerged as a high-resolution method for SNV identification 20 . Because FMCA does not rely on probe degradation, it is compatible with polymerases regardless of their 5’-3’ exonuclease activity. A key advantage of FMCA over TaqMan assays is that only a single probe is required per SNV, enabling efficient multiplex genotyping within a single reaction 20 – 23 . Recently, a genotyping method using FMCA for ALDH2 rs671 was reported. However, this method is limited to the detection of a single SNV 24 . Therefore, in this study, we developed a triplex FMCA platform capable of simultaneously genotyping rs671, rs698, and rs1229984 with high analytical performance. This platform provides a practical tool for relatively large-scale screening in various populations, particularly East Asians, among whom the synergistic effects of these alcohol-metabolizing gene variants significantly impact public health. Results Genotypes of rs671, rs698, and rs1229984 determined by Sanger sequencing To compare the results of FMCA, we first determined the genotypes of ALDH2 (rs671), ADH1C (rs698), and ADH1B (rs1229984) in 94 Japanese subjects by Sanger sequencing of the PCR products. Raw sequence data were deposited in the DDBJ repository (BioProject PRJDB40219; Run accessions DRR902565-DRR902658). As described in the Materials and methods, in the method designed here, the alleles of rs698 and rs1229984 are represented as C and T instead of G and A (Table 1 Available in the Supplementary Files). The genotype and allele frequencies are summarized in Table 2 (Available in the Supplementary Files). To assess the genetic representativeness and data quality of our cohort, the genotype distributions of the three SNVs were tested for Hardy-Weinberg equilibrium (HWE) using the chi-square test. All variants‒rs671, rs698, and rs1229984‒showed no significant deviation from HWE ( p = 0.5407, 0.8691, and 0.7261, respectively). Furthermore, the minor allele frequencies observed in our study population were highly comparable to those reported for East Asian populations in the dbSNP database (Source: https://www.ncbi.nlm.nih.gov/snp/ ): rs671 (0.255 vs. 0.225), rs698 (0.096 vs. 0.070), and rs1229984 (0.245 vs. 0.296). These results confirm the genetic representativeness and reliability of our study cohort. Optimization of triplex FMCA conditions We then optimized PCR and FMCA conditions to enable simultaneous detection of these three SNVs in a single triplex FMCA assay. Based on preliminary testing of multiple DNA polymerases, Probe qPCR Mix MultiPlus was selected for the final protocol due to its superior robustness and reproducibility in generating distinct, sharp melting peaks. Under default analysis settings, the genotypes of rs671 and rs698 were clearly distinguishable. For rs671, the T m values were approximately 62°C for G/G, 55°C and 62°C for A/G, and 55°C for A/A (Fig. 1 a). For rs698, the T m values were approximately 58°C for C/C, 49°C and 58°C for C/T, and 49°C for T/T (Fig. 1 b). Challenges in automated genotyping of rs1229984 Compared to rs671 and rs698, rs1229984 exhibited more complex melting curve characteristics. FMCA revealed two peaks corresponding to the C allele ( T m approximately 65°C) and the T allele ( T m approximately 56°C). However, the peak intensities and baseline behaviors differed significantly between genotypes (Fig. 1 c). In C/C homozygotes, while the higher-temperature peak was prominent, a subtle baseline dip was frequently observed at the expected position of the T-allele peak. In C/T heterozygotes, the lower-temperature peak (T-allele) exhibited an upwardly convex shape but with markedly low intensity compared to the C-allele peak. Therefore, as shown in Fig. 1 c, C/T heterozygotes with low C-allele peak intensity were often categorized as “unknown” or incorrectly assigned as T/T homozygotes even under modified analysis settings (score threshold 0.30, resolution threshold 0.15, normal sensitivity). In addition, some C/C homozygotes with low peak intensity were also misclassified as C/T heterozygotes (Fig. 1 c), and negative controls were misidentified as T/T homozygotes due to minor baseline fluctuations. Notably, while negative controls exhibited only irregular baseline noise without any distinct thermal transitions, the T-allele in sample DNA consistently formed a discernible upward-pointing peak, even when its intensity was low. These findings indicate that genotyping accuracy depends not only on peak position but also on peak intensity. Crucially, despite these automated miscalls, each genotype possessed a consistent “morphological signature.” The T-allele peak in heterozygotes, though low in amplitude, maintained a reproducible derivative shape that was visually distinguishable from the baseline noise or dips seen in C/C samples. By performing manual visual inspection focused on these curve characteristics, we were able to resolve all automated ambiguities. This combined approach‒automated clustering followed by expert visual confirmation‒ensured 100% genotyping accuracy for rs1229984, as subsequently validated by Sanger sequencing. Quantitative performance and error rates Low-intensity peaks also caused rare automated misidentifications for rs671 and rs698 (Figs. 1 a and 1 b), though these occurred much less frequently than for rs1229984. Table 3 (Available in the Supplementary Files) summarizes the error rates across four independent triplex FMCA experiments. As detailed in the previous section, rs1229984 C/C homozygotes exhibited the highest automated misclassification rate (25.0%; 5/20 tests), primarily due to the software recognizing baseline fluctuations as heterozygous peaks. Additionally, 7.6% of C/T heterozygotes (11/144 tests) were misclassified as T/T or labeled as “unknown” due to the inherently low intensity of the T-allele peak. In contrast, rs671 and rs698 demonstrated exceptional reliability, with automated error rates of only 0.3% (1/376 tests) each. Notably, the vast majority of these rare errors for rs671 and rs698 resulted in “unknown” or “Group 4” (unclassified) labels rather than the assignment of an incorrect valid genotype. Because these samples are automatically segregated into distinct error categories by the software, they are easily flagged for subsequent manual review. This characteristic minimizes the risk of overlooking genotype miscalls and ensures the high diagnostic fidelity of the assay. Representative melting curve profiles demonstrating the consistency of these profiles across four independent experiments are shown in Figs. 2 a, 2 b, and 2 c. Discussion In this study, we developed a triplex FMCA method capable of simultaneously detecting three alcohol metabolism-related SNVs‒ ALDH2 (rs671), ADH1C (rs698), and ADH1B (rs1229984)‒within a single reaction. Under default analysis settings, rs671 and rs698 were automatically detected with high accuracy, with only one misclassification each observed among 376 reactions. These rare errors were attributable to low-intensity peaks and were categorized by the automated system as “unknown” or assigned to an additional cluster distinct from the three valid genotypes, allowing straightforward correction through visual inspection. In contrast, rs1229984 exhibited the substantially higher automated misclassification rate of approximately 4.3%. Unlike rs671 and rs698, miscalls for rs1229984 frequently involved assignment to an incorrect valid genotype rather than “unknown” or assigned to an additional cluster. This behavior appears to stem from the unique melting curve morphology of rs1229984. Specifically, C/C homozygotes displayed a baseline dip at the expected T-allele position, while C/T heterozygotes exhibited an extremely low intensity T-allele peak. These features likely interfered with the automated clustering algorithm, leading to misrecognition. Nevertheless, because the characteristic curve shapes remain visually distinguishable, accurate genotype calling is readily achievable through manual inspection. An important observation in this study was that samples exhibiting low peak intensity varied across experiments. This indicates that low peaks are not attributable to poor DNA quality in specific samples but instead arise from stochastic factors inherent to FMCA, such as minor fluctuations in PCR efficiency, subtle temperature variations, and slight differences in reaction composition. Therefore, for multiplex SNV detection using FMCA, incorporating retesting and visual confirmation when necessary is effective for ensuring reproducibility and reliability. This study also highlighted a weakness in the current automated detection algorithm. FMCA-based automated calling relies on clustering based on peak position, height, and curve shape; however, fixed threshold settings may not adequately accommodate SNV-specific curve characteristics or low-intensity peaks. SNVs such as rs1229984, which inherently exhibit weaker signals, are particularly susceptible to misclassification. The observed baseline dip and low peak intensity for the rs1229984 T-allele may be attributed to the specific sequence context or thermodynamic instability of the probe-target duplex at that locus. While this poses a challenge for current automated clustering, the distinct ‘morphological signature’ of the melting curves ensures high fidelity in manual calling. Future integration of machine learning-based signal processing could potentially eliminate this manual step, further streamlining the workflow for clinical application. Additionally, FMCA offers several advantages, including the ease of multiplexing, rapid turnaround time, and low cost. However, the results also demonstrate that detection performance depends on enzyme selection and probe design. In this study, Probe qPCR Mix MultiPlus produced the most robust and reproducible results among the tested polymerases. This is consistent with our previous experience in developing other triplex FMCA assays 22 , suggesting that this specific enzyme mix may be particularly well-suited for the thermodynamic demands of multiplex FMCA. While further validation across a broader range of SNV targets will be beneficial to confirm its versatility, the choice of a high-performance polymerase appears to be a critical factor for successful multiplexing. Furthermore, incorporating probes that achieve higher T m values even with short sequences, such as Minor Groove Binder or Locked Nucleic Acid probes 25 , 26 , may further improve the detection of challenging SNVs like rs1229984. For instance, Minor Groove Binder-conjugated probes have been shown to enhance signal-to-noise ratios and stabilize duplexes in AT-rich regions 27 , while Locked Nucleic Acid modifications can significantly broaden the T m difference (Δ T m ) between alleles, facilitating clearer automated genotype clustering in FMCA assays 28 . Furthermore, optimizing the probe orientation might enhance the signal-to-noise ratio for challenging targets like rs1229984. In this study, probes for rs698 and rs1229984 were designed based on the reverse-complement strand. Future comparative studies evaluating probes complementary to the sense (mRNA) strand may reveal whether orientation-specific sequence contexts can improve the distinctness of the T-allele melting peak, thereby facilitating more robust automated genotype calling. The ability to simultaneously analyze ADH1B , ADH1C , and ALDH2 genotypes has significant implications for clinical and epidemiological research. These SNVs are strongly associated with drinking behavior, alcohol dependence, and risks of esophageal cancer, gastric cancer, and alcohol-related liver injury. Notably, previous studies have demonstrated that the synergistic interaction between the rapid-activity ADH1B*2 allele (T allele in this study) and the inactive ALDH2*2 allele (A allele in this study) dramatically elevates the risk of esophageal cancer‒by more than several hundred-fold in heavy drinkers compared to those without these variants 29 , and were recently confirmed by meta-analysis 11 . By integrating these critical loci into a single assay, our FMCA-based method ensures the reliable identification of such ultra-high-risk individuals without the need for multiple independent tests. This makes it an exceptionally efficient tool for stratifying disease risk in clinical settings. Triplex FMCA is therefore useful for determining genotype frequencies in large populations, stratifying disease risk, and conducting population genetic studies, including regional allele frequency gradients. Despite the clinical utility of this triplex FMCA assay, several limitations should be acknowledged. First, although the assay demonstrated 100% concordance with Sanger sequencing in this study, the validation was conducted on a relatively small cohort of 94 Japanese individuals. Further validation with larger and more diverse East Asian populations is necessary to confirm the robustness of the assay across different genomic backgrounds. Second, the requirement for manual visual inspection of rs1229984 introduces a degree of subjectivity, which may limit its application in fully automated, ultra-high-throughput settings. As previously mentioned, refining the probe chemistry or integrating more sophisticated signal-processing algorithms would be essential to enhance objectivity. Finally, while these three SNVs are major contributors to alcohol metabolism, they do not account for the entire spectrum of alcohol-related disease risk. Environmental factors, such as total alcohol consumption and smoking, also play critical roles; therefore, this assay should be integrated into a broader, multifaceted risk assessment strategy. While Sanger sequencing remains the gold standard for SNV analysis, its low throughput limits its utility for large-scale studies. TaqMan assays offer high specificity but are constrained in multiplexing capacity. Next-generation sequencing provides comprehensive genomic information 30 , but is excessive and costly for rapid detection of a small number of SNVs. In contrast, FMCA can be performed using widely available real-time PCR instruments and a simple reaction setup, making it one of the most practical and scalable options for multiplex SNV detection 18 . Furthermore, the low cost and rapid turnaround time of this assay make it an ideal tool for population-based cancer screening programs and routine health check-ups. Instead of relying solely on drinking frequency questionnaires, which are often subject to recall bias, clinicians can use this genetic data to provide evidence-based counseling. Identifying ‘high-risk’ genotypes before the onset of disease could facilitate early interventions, such as intensive lifestyle modifications or prioritized endoscopic surveillance, ultimately reducing the burden of alcohol-related malignancies in high-risk populations like East Asians. Conclusion This study established a triplex FMCA method for the simultaneous genotyping of ADH1B rs1229984, ADH1C rs698, and ALDH2 rs671 in a single reaction. While rs1229984 requires visual inspection due to unique melting peak characteristics, the assay reliably identifies all genotypes with high precision. This rapid and cost-effective platform serves as a practical tool for large-scale screening and personalized cancer risk stratification, potentially contributing to the prevention of alcohol-related malignancies. Materials and methods Ethical statements and DNA samples All methods were carried out in accordance with relevant guidelines and regulations, including the Declaration of Helsinki. This study utilized existing anonymized genomic DNA samples from 94 randomly selected healthy Japanese individuals, as previously described 31 . These samples were originally collected in 1995 after obtaining verbal informed consent for genetic polymorphism analysis, with the consent procedure approved by the Ethical Committee of Kurume University at that time. The protocol for the current study, which involves the use of these existing anonymized samples as well as commercially available genomic DNA, was initially approved by the Ethical Committee of Kurume University in 2002. Since then, the protocol has been renewed every five years, with the most recent approval granted on October 31, 2022 (Approval No. 22158). Probes and primers The three SNVs‒rs671, rs698, and rs1229984‒were genotyped using specific PCR primer sets and fluorophore-labeled probes (Table 1 ). PCR primers were designed using Primer3Plus (source: https://www.bioinformatics.nl/cgi-bin/primer3plus/primer3plus.cgi ) 32 . In addition, the thermodynamic properties and potential secondary structures of the designed oligonucleotides‒including melting temperature ( T m ) self-dimerization, and hairpin formation‒were verified using OligoCalc (Source: http://oligocalc.eu/ ) 33 . All oligonucleotides, including FAM-, HEX-, and Cy5-labeled probes with appropriate quenchers (black hole quencher 1/black hole quencher 2), were custom-synthesized by Eurofins Genomics (Tokyo, Japan). For rs698 and rs1229984, the target genomic regions corresponded to the reverse complement of the mRNA sequence. Consequently, in this assay, the C and T alleles represented the low-activity and high-activity alcohol metabolism phenotypes, respectively. It is important to note that for these two SNVs, the T allele in this assay corresponds to the A allele in the standard genomic forward strand nomenclature. This orientation was maintained throughout the study to ensure consistent data interpretation. Genotyping of rs671, rs698, and rs1229984 by Sanger sequencing To determine the genotypes of rs671, rs698, and rs1229984 in this study, PCR products from all 94 individuals were subjected to direct Sanger sequencing. The target regions were amplified using specific primers (Table 1 ) in a 10 µL reaction mixture containing 1–10 ng of genomic DNA, 5 µL of Premix Ex Taq (Probe qPCR) (Takara Bio, Shiga, Japan), and 250 nM of each primer. The thermal profile comprised an initial denaturation at 95°C for 30 sec, followed by 45 cycles of denaturation at 95°C for 5 sec and annealing/extension at 60°C for 15 sec. The same primers used for PCR were employed for the sequencing reactions, which were outsourced to Eurofins Genomics. Sanger sequencing served as the reference method to validate the genotyping results obtained via FMCA. Real-time PCR monitoring and FMCA Asymmetric PCR followed by fluorescence melting curve analysis (FMCA) was conducted for rs671, rs698, and rs1229984 using the LightCycler 480 II system (Roche Diagnostics, Tokyo, Japan). Each 10 µL reaction contained Probe qPCR Mix MultiPlus (Takara Bio) and 1–10 ng of genomic DNA. Primer and probe concentrations were optimized for each SNV. For rs671 and rs698, concentrations were 50/500/200 nM, while for rs1229984, they were 25/250/100 nM (forward/reverse/probe). In both cases, a 10-fold excess of reverse primers was maintained to promote asymmetric amplification 20 , 22 . Thermal cycling conditions consisted of an initial denaturation at 95°C for 20 sec, followed by 45 cycles of 95°C for 1 sec and 60°C for 20 sec. Fluorescence signals were monitored using FAM, VIC/HEX/Yellow555, and Cy5/Cy5.5 filter sets during both the amplification phase and the subsequent melting curve analysis (40‒80°C at a ramp rate of 0.10°C/sec). The T m values and genotypes were analyzed using LightCycler 480 Gene Scanning Software (v1.5). Default analysis parameters (score threshold 0.70, resolution threshold 0.10, normal sensitivity) were applied to rs698 and rs671. For rs1229984, which exhibited lower peak intensity and a distinct curve morphology, modified settings (score threshold 0.30, resolution threshold 0.15, normal sensitivity) were used to improve allele discrimination. Automated calls were generated based on clustering algorithms, followed by manual visual inspection to ensure definitive genotype discrimination, particularly for samples with low peak intensities. Abbreviations ADH1B: alcohol dehydrogenase 1B, ADH1C: alcohol dehydrogenase 1C, ALDH2: aldehyde dehydrogenase 2, FMCA: fluorescence melting curve analysis, Tm: melting temperature Declarations Competing interests The authors declare no competing interests. Author Contribution M.S. contributed to planning and conducting experiments, data analysis, writing of the original draft, reviewing and editing the manuscript. Y.K. contributed to planning and conducting experiments, supervision, data analysis, reviewing and editing the manuscript. Acknowledgement We thank Katherine Ono for editing the English in this manuscript. Data Availability The datasets generated and analyzed during the current study are available in the DDBJ repository under BioProject accession number PRJDB40219 and Run accession numbers DRR902565-DRR902658. References Wilson, D. F. & Matschinsky, F. M. Ethanol metabolism: The good, the bad, and the ugly. Med. Hypotheses . 140 , 109638. 10.1016/j.mehy.2020.109638 (2020). 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Supplementary Files Tables.pdf Tables 1-3 Cite Share Download PDF Status: Published Journal Publication published 31 Mar, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 05 Mar, 2026 Reviews received at journal 27 Feb, 2026 Reviews received at journal 23 Feb, 2026 Reviewers agreed at journal 22 Feb, 2026 Reviewers agreed at journal 19 Feb, 2026 Reviewers agreed at journal 18 Feb, 2026 Reviewers agreed at journal 09 Feb, 2026 Reviewers invited by journal 04 Feb, 2026 Editor assigned by journal 04 Feb, 2026 Editor invited by journal 04 Feb, 2026 Submission checks completed at journal 03 Feb, 2026 First submitted to journal 03 Feb, 2026 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. 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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-8635444","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":585996533,"identity":"ac7962e0-26eb-4ea3-858a-b9aaf729f494","order_by":0,"name":"Mikiko Soejima","email":"","orcid":"","institution":"Kurume University","correspondingAuthor":false,"prefix":"","firstName":"Mikiko","middleName":"","lastName":"Soejima","suffix":""},{"id":585996536,"identity":"0fe32c74-d8f4-46cd-9bce-b1a79c00b316","order_by":1,"name":"Yoshiro Koda","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/0lEQVRIiWNgGAWjYDACHgYGCYYKIOMAG4o4G3blcC1nSNbC2IapBTcwOHP44I2f87bJ8R1gS5OubNsmxyCRwPjhBwNfHk4tZ9uSLXu33TaWPMB2TPJs221joBZmyR4GtmKcWs7zmEnwbruduOEAe5tkY9vtxP03EhikgX5JbMCjRfLvnNv1MC31DUBbfuPVcrbHTJq34XaCAchhQC0JQIex4bVF8syxZGuZY7cNZx5mS7ZsOHfbsIHnYZtljwFuv/CdST54803NbXm+422GNxvKbsszsCcfvvGj4hjOEFM4AGMxw8UYgU4yOJaAS4s8LhfX4NQyCkbBKBgFIw4AAESPWGb4KcIYAAAAAElFTkSuQmCC","orcid":"","institution":"Kurume University","correspondingAuthor":true,"prefix":"","firstName":"Yoshiro","middleName":"","lastName":"Koda","suffix":""}],"badges":[],"createdAt":"2026-01-19 05:53:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8635444/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8635444/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-026-46895-y","type":"published","date":"2026-03-31T15:59:04+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":102310255,"identity":"32c60e36-16a9-470c-a9ef-64961d9a1723","added_by":"auto","created_at":"2026-02-10 11:53:02","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":495587,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRepresentative melting peak profiles for the genotyping of rs671 (a), rs698 (b), and rs1229984 (c). \u003c/strong\u003eThe automated software categorized genotypes into three primary clusters: green, blue, and red (refer to individual panels for genotype assignments). Arrows indicate specific instances of automated misclassification that required manual resolution: (1) an rs671 A/A subject misidentified as “Group 4” (pink); (2) an rs698 T/T subject misidentified as “Unknown” (brown). For rs1229984 (C), subject (3) (genotype C/C) was misidentified as C/T (red), while subjects (4) and (5) (genotype C/T) were misidentified as T/T (blue) and “Unknown” (brown), respectively.\u003c/p\u003e","description":"","filename":"Figures1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8635444/v1/6d55708a255cd880e97c9734.jpg"},{"id":102310302,"identity":"c91575a7-1d02-48fe-bf14-dfdbd9de197d","added_by":"auto","created_at":"2026-02-10 11:53:12","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":613955,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComprehensive triplex FMCA profiles of 94 Japanese subjects across four independent experiments. (a)\u003c/strong\u003e rs671 (\u003cem\u003eALDH2\u003c/em\u003e): genotypes A/A (green), A/G (red), and G/G (blue) are clearly clustered. \u003cstrong\u003e(b)\u003c/strong\u003e rs698 (\u003cem\u003eADH1C\u003c/em\u003e): genotypes T/T (blue), C/T (red), and C/C (green) are well-differentiated. \u003cstrong\u003e(c)\u003c/strong\u003e rs1229984 (\u003cem\u003eADH1B\u003c/em\u003e): genotypes are shown as T/T (blue), C/T (red), and C/C (green). Light blue lines in all panels represent negative controls. Notably, the automated software misidentified the negative control in \u003cstrong\u003e(c)\u003c/strong\u003e as T/T due to baseline fluctuations. One subject with genotype C/C was misclassified as “Unknown” (brown), and one subject with C/T was misidentified as T/T (blue); both cases were corrected through manual visual inspection.\u003c/p\u003e","description":"","filename":"Figures2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8635444/v1/6dcc737f2d9cc77307cf6de1.jpg"},{"id":106343842,"identity":"7bb2d770-130e-4f3e-af72-8ba649d8021e","added_by":"auto","created_at":"2026-04-07 16:10:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1845508,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8635444/v1/9da53b72-ce7e-40ad-a59e-fd2145a30306.pdf"},{"id":102428389,"identity":"4ca2aa9c-6fff-4af2-b011-b236c6da0914","added_by":"auto","created_at":"2026-02-11 14:56:48","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":38966,"visible":true,"origin":"","legend":"\u003cp\u003eTables 1-3\u003c/p\u003e","description":"","filename":"Tables.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8635444/v1/cde78aa9fe160cb9f0a9104a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Development of a Triplex FMCA Assay for Genotyping Three Genes, ADH1B, ADH1C, and ALDH2, Involved in Alcohol Metabolism","fulltext":[{"header":"Introduction","content":"\u003cp\u003eEthanol is metabolized through a sequential oxidative pathway in which alcohol dehydrogenase (ADH) converts ethanol to acetaldehyde, followed by aldehyde dehydrogenase (ALDH)\u0026ndash;mediated oxidation of acetaldehyde to acetic acid\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Interindividual variation in the catalytic efficiency of these enzymes affects alcohol consumption patterns, susceptibility to alcohol dependence, the development of alcohol-associated liver disease, and the severity of physiological responses after alcohol intake\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Because both ethanol and acetaldehyde are classified as human carcinogens, elucidating the genetic determinants of alcohol metabolism is essential for understanding the etiology of several cancers including esophageal and gastric cancers. Importantly, the risk of these malignancies is not determined by a single locus but is substantially influenced by the synergistic interactions of multiple functional variants\u003csup\u003e\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Therefore, a comprehensive assessment of these risks‒rather than analyzing individual single nucleotide variants (SNVs) in isolation‒is crucial for identifying high-risk individuals and implementing personalized cancer prevention strategies.\u003c/p\u003e \u003cp\u003eAmong the loci involved in ethanol metabolism, \u003cem\u003eADH1B\u003c/em\u003e (rs1229984, p.R48H), \u003cem\u003eADH1C\u003c/em\u003e (rs698, p.I350V), and \u003cem\u003eALDH2\u003c/em\u003e (rs671, p.E504K) are well-established nonsynonymous SNVs with clear functional consequences\u003csup\u003e\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. The A alleles of rs1229984 (\u003cem\u003eADH1B*2\u003c/em\u003e) and rs698 (\u003cem\u003eADH1C*1\u003c/em\u003e) encode ADH isoforms with markedly high catalytic turnover, whereas the corresponding G alleles (\u003cem\u003eADH1B*1\u003c/em\u003e, \u003cem\u003eADH1C*2\u003c/em\u003e) produce low-activity isoforms\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Individuals carrying low-activity ADH variants exhibit slower ethanol-to-acetaldehyde conversion, resulting in prolonged systemic ethanol exposure. Recent large-scale genomic studies and global burden assessments have reinforced this factor as a critical driver of increased carcinogenic risk, not only for the aero-digestive tract but also for various systemic malignancies and other alcohol-related disorders\u003csup\u003e\u003cspan additionalcitationids=\"CR11 CR12\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Reduced acetaldehyde accumulation also diminishes aversive physiological responses, thereby facilitating higher alcohol intake and increasing vulnerability to alcohol dependence\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. The rs1229984 A allele is highly enriched in East Asian populations‒including Japanese, Chinese, and Koreans‒where its frequency approaches 70%, but it remains uncommon (\u0026lt;\u0026thinsp;10%) in Europeans, Africans, and South Asians. In contrast, the rs698 A allele is prevalent in East Asians and Africans (80\u0026ndash;90%) and moderately frequent in Europeans (~\u0026thinsp;60%)\u003csup\u003e5\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003cem\u003eALDH2\u003c/em\u003e rs671 also exhibits pronounced population stratification. The G allele (\u003cem\u003eALDH2*1\u003c/em\u003e) encodes an enzyme with robust acetaldehyde-detoxifying capacity, whereas the A allele (\u003cem\u003eALDH2*2\u003c/em\u003e) produces an enzyme with severely diminished (G/A heterozygote) or absent (A/A homozygotes) activity\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Individuals with low or null ALDH2 function accumulate high levels of acetaldehyde after alcohol intake, leading to characteristic alcohol-induced reactions such as facial flushing, headache, and nausea, and markedly increasing the risk of upper gastrointestinal carcinogenesis\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. In East Asian populations, the rs671 A allele frequency ranges from 20% to 30% (Source: dbSNP, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/snp/rs671\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/snp/rs671\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Specifically, among Japanese individuals, approximately 37% carry the low-activity G/A heterozygous genotype and ~\u0026thinsp;7% carry the null-activity A/A homozygous genotype\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. In contrast, more than 90% of non-East Asian populations‒including Europeans, Africans, and Indians‒are homozygous for the high-activity G allele\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eGiven these striking interpopulation differences and their substantial implications for alcohol-related disease susceptibility, a rapid and reliable genotyping platform for these three SNVs would be highly useful for assessing drinking behavior, alcohol-related liver disease, and carcinogenic risk. However, current PCR-based methods such as Sanger sequencing suffer from limited throughput or high cost when analyzing multiple loci\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTo overcome the limitations of conventional assays, TaqMan genotyping assays, which employ dual-labeled hydrolysis probes, are widely used for SNV discrimination\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. During amplification, the 5\u0026rsquo;-3\u0026rsquo; exonuclease activity of Taq polymerase cleaves the probe, generating a fluorescence signal. Alternatively, fluorescence melting curve analysis (FMCA) has emerged as a high-resolution method for SNV identification\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Because FMCA does not rely on probe degradation, it is compatible with polymerases regardless of their 5\u0026rsquo;-3\u0026rsquo; exonuclease activity. A key advantage of FMCA over TaqMan assays is that only a single probe is required per SNV, enabling efficient multiplex genotyping within a single reaction\u003csup\u003e\u003cspan additionalcitationids=\"CR21 CR22\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eRecently, a genotyping method using FMCA for \u003cem\u003eALDH2\u003c/em\u003e rs671 was reported. However, this method is limited to the detection of a single SNV\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Therefore, in this study, we developed a triplex FMCA platform capable of simultaneously genotyping rs671, rs698, and rs1229984 with high analytical performance. This platform provides a practical tool for relatively large-scale screening in various populations, particularly East Asians, among whom the synergistic effects of these alcohol-metabolizing gene variants significantly impact public health.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eGenotypes of rs671, rs698, and rs1229984 determined by Sanger sequencing\u003c/h2\u003e \u003cp\u003eTo compare the results of FMCA, we first determined the genotypes of \u003cem\u003eALDH2\u003c/em\u003e (rs671), \u003cem\u003eADH1C\u003c/em\u003e (rs698), and \u003cem\u003eADH1B\u003c/em\u003e (rs1229984) in 94 Japanese subjects by Sanger sequencing of the PCR products. Raw sequence data were deposited in the DDBJ repository (BioProject PRJDB40219; Run accessions DRR902565-DRR902658). As described in the Materials and methods, in the method designed here, the alleles of rs698 and rs1229984 are represented as C and T instead of G and A (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e Available in the Supplementary Files).\u003c/p\u003e \u003cp\u003eThe genotype and allele frequencies are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e (Available in the Supplementary Files). To assess the genetic representativeness and data quality of our cohort, the genotype distributions of the three SNVs were tested for Hardy-Weinberg equilibrium (HWE) using the chi-square test. All variants‒rs671, rs698, and rs1229984‒showed no significant deviation from HWE (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.5407, 0.8691, and 0.7261, respectively). Furthermore, the minor allele frequencies observed in our study population were highly comparable to those reported for East Asian populations in the dbSNP database (Source: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/snp/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/snp/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e): rs671 (0.255 vs. 0.225), rs698 (0.096 vs. 0.070), and rs1229984 (0.245 vs. 0.296). These results confirm the genetic representativeness and reliability of our study cohort.\u003c/p\u003e \n\u003ch3\u003eOptimization of triplex FMCA conditions\u003c/h3\u003e\n\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eWe then optimized PCR and FMCA conditions to enable simultaneous detection of these three SNVs in a single triplex FMCA assay. Based on preliminary testing of multiple DNA polymerases, Probe qPCR Mix MultiPlus was selected for the final protocol due to its superior robustness and reproducibility in generating distinct, sharp melting peaks.\u003c/p\u003e \u003cp\u003eUnder default analysis settings, the genotypes of rs671 and rs698 were clearly distinguishable. For rs671, the \u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003em\u003c/em\u003e\u003c/sub\u003e values were approximately 62\u0026deg;C for G/G, 55\u0026deg;C and 62\u0026deg;C for A/G, and 55\u0026deg;C for A/A (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). For rs698, the \u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003em\u003c/em\u003e\u003c/sub\u003e values were approximately 58\u0026deg;C for C/C, 49\u0026deg;C and 58\u0026deg;C for C/T, and 49\u0026deg;C for T/T (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eChallenges in automated genotyping of rs1229984\u003c/h3\u003e\n\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eCompared to rs671 and rs698, rs1229984 exhibited more complex melting curve characteristics. FMCA revealed two peaks corresponding to the C allele (\u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003em\u003c/em\u003e\u003c/sub\u003e approximately 65\u0026deg;C) and the T allele (\u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003em\u003c/em\u003e\u003c/sub\u003e approximately 56\u0026deg;C). However, the peak intensities and baseline behaviors differed significantly between genotypes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec).\u003c/p\u003e \u003cp\u003eIn C/C homozygotes, while the higher-temperature peak was prominent, a subtle baseline dip was frequently observed at the expected position of the T-allele peak. In C/T heterozygotes, the lower-temperature peak (T-allele) exhibited an upwardly convex shape but with markedly low intensity compared to the C-allele peak. Therefore, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec, C/T heterozygotes with low C-allele peak intensity were often categorized as \u0026ldquo;unknown\u0026rdquo; or incorrectly assigned as T/T homozygotes even under modified analysis settings (score threshold 0.30, resolution threshold 0.15, normal sensitivity). In addition, some C/C homozygotes with low peak intensity were also misclassified as C/T heterozygotes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec), and negative controls were misidentified as T/T homozygotes due to minor baseline fluctuations. Notably, while negative controls exhibited only irregular baseline noise without any distinct thermal transitions, the T-allele in sample DNA consistently formed a discernible upward-pointing peak, even when its intensity was low. These findings indicate that genotyping accuracy depends not only on peak position but also on peak intensity.\u003c/p\u003e \u003cp\u003eCrucially, despite these automated miscalls, each genotype possessed a consistent \u0026ldquo;morphological signature.\u0026rdquo; The T-allele peak in heterozygotes, though low in amplitude, maintained a reproducible derivative shape that was visually distinguishable from the baseline noise or dips seen in C/C samples. By performing manual visual inspection focused on these curve characteristics, we were able to resolve all automated ambiguities. This combined approach‒automated clustering followed by expert visual confirmation‒ensured 100% genotyping accuracy for rs1229984, as subsequently validated by Sanger sequencing.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eQuantitative performance and error rates\u003c/h3\u003e\n\u003cp\u003eLow-intensity peaks also caused rare automated misidentifications for rs671 and rs698 (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea and \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb), though these occurred much less frequently than for rs1229984. Table\u0026nbsp;3 (Available in the Supplementary Files) summarizes the error rates across four independent triplex FMCA experiments.\u003c/p\u003e \u003cp\u003eAs detailed in the previous section, rs1229984 C/C homozygotes exhibited the highest automated misclassification rate (25.0%; 5/20 tests), primarily due to the software recognizing baseline fluctuations as heterozygous peaks. Additionally, 7.6% of C/T heterozygotes (11/144 tests) were misclassified as T/T or labeled as \u0026ldquo;unknown\u0026rdquo; due to the inherently low intensity of the T-allele peak. In contrast, rs671 and rs698 demonstrated exceptional reliability, with automated error rates of only 0.3% (1/376 tests) each.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eNotably, the vast majority of these rare errors for rs671 and rs698 resulted in \u0026ldquo;unknown\u0026rdquo; or \u0026ldquo;Group 4\u0026rdquo; (unclassified) labels rather than the assignment of an incorrect valid genotype. Because these samples are automatically segregated into distinct error categories by the software, they are easily flagged for subsequent manual review. This characteristic minimizes the risk of overlooking genotype miscalls and ensures the high diagnostic fidelity of the assay. Representative melting curve profiles demonstrating the consistency of these profiles across four independent experiments are shown in Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea, \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb, and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we developed a triplex FMCA method capable of simultaneously detecting three alcohol metabolism-related SNVs‒\u003cem\u003eALDH2\u003c/em\u003e (rs671), \u003cem\u003eADH1C\u003c/em\u003e (rs698), and \u003cem\u003eADH1B\u003c/em\u003e (rs1229984)‒within a single reaction. Under default analysis settings, rs671 and rs698 were automatically detected with high accuracy, with only one misclassification each observed among 376 reactions. These rare errors were attributable to low-intensity peaks and were categorized by the automated system as \u0026ldquo;unknown\u0026rdquo; or assigned to an additional cluster distinct from the three valid genotypes, allowing straightforward correction through visual inspection.\u003c/p\u003e \u003cp\u003eIn contrast, rs1229984 exhibited the substantially higher automated misclassification rate of approximately 4.3%. Unlike rs671 and rs698, miscalls for rs1229984 frequently involved assignment to an incorrect valid genotype rather than \u0026ldquo;unknown\u0026rdquo; or assigned to an additional cluster. This behavior appears to stem from the unique melting curve morphology of rs1229984. Specifically, C/C homozygotes displayed a baseline dip at the expected T-allele position, while C/T heterozygotes exhibited an extremely low intensity T-allele peak. These features likely interfered with the automated clustering algorithm, leading to misrecognition. Nevertheless, because the characteristic curve shapes remain visually distinguishable, accurate genotype calling is readily achievable through manual inspection.\u003c/p\u003e \u003cp\u003eAn important observation in this study was that samples exhibiting low peak intensity varied across experiments. This indicates that low peaks are not attributable to poor DNA quality in specific samples but instead arise from stochastic factors inherent to FMCA, such as minor fluctuations in PCR efficiency, subtle temperature variations, and slight differences in reaction composition. Therefore, for multiplex SNV detection using FMCA, incorporating retesting and visual confirmation when necessary is effective for ensuring reproducibility and reliability.\u003c/p\u003e \u003cp\u003eThis study also highlighted a weakness in the current automated detection algorithm. FMCA-based automated calling relies on clustering based on peak position, height, and curve shape; however, fixed threshold settings may not adequately accommodate SNV-specific curve characteristics or low-intensity peaks. SNVs such as rs1229984, which inherently exhibit weaker signals, are particularly susceptible to misclassification. The observed baseline dip and low peak intensity for the rs1229984 T-allele may be attributed to the specific sequence context or thermodynamic instability of the probe-target duplex at that locus. While this poses a challenge for current automated clustering, the distinct \u0026lsquo;morphological signature\u0026rsquo; of the melting curves ensures high fidelity in manual calling. Future integration of machine learning-based signal processing could potentially eliminate this manual step, further streamlining the workflow for clinical application.\u003c/p\u003e \u003cp\u003eAdditionally, FMCA offers several advantages, including the ease of multiplexing, rapid turnaround time, and low cost. However, the results also demonstrate that detection performance depends on enzyme selection and probe design. In this study, Probe qPCR Mix MultiPlus produced the most robust and reproducible results among the tested polymerases. This is consistent with our previous experience in developing other triplex FMCA assays\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e, suggesting that this specific enzyme mix may be particularly well-suited for the thermodynamic demands of multiplex FMCA. While further validation across a broader range of SNV targets will be beneficial to confirm its versatility, the choice of a high-performance polymerase appears to be a critical factor for successful multiplexing. Furthermore, incorporating probes that achieve higher \u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003em\u003c/em\u003e\u003c/sub\u003e values even with short sequences, such as Minor Groove Binder or Locked Nucleic Acid probes\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e, may further improve the detection of challenging SNVs like rs1229984. For instance, Minor Groove Binder-conjugated probes have been shown to enhance signal-to-noise ratios and stabilize duplexes in AT-rich regions\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e, while Locked Nucleic Acid modifications can significantly broaden the \u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003em\u003c/em\u003e\u003c/sub\u003e difference (Δ\u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003em\u003c/em\u003e\u003c/sub\u003e) between alleles, facilitating clearer automated genotype clustering in FMCA assays\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Furthermore, optimizing the probe orientation might enhance the signal-to-noise ratio for challenging targets like rs1229984. In this study, probes for rs698 and rs1229984 were designed based on the reverse-complement strand. Future comparative studies evaluating probes complementary to the sense (mRNA) strand may reveal whether orientation-specific sequence contexts can improve the distinctness of the T-allele melting peak, thereby facilitating more robust automated genotype calling.\u003c/p\u003e \u003cp\u003eThe ability to simultaneously analyze \u003cem\u003eADH1B\u003c/em\u003e, \u003cem\u003eADH1C\u003c/em\u003e, and \u003cem\u003eALDH2\u003c/em\u003e genotypes has significant implications for clinical and epidemiological research. These SNVs are strongly associated with drinking behavior, alcohol dependence, and risks of esophageal cancer, gastric cancer, and alcohol-related liver injury. Notably, previous studies have demonstrated that the synergistic interaction between the rapid-activity \u003cem\u003eADH1B*2\u003c/em\u003e allele (T allele in this study) and the inactive \u003cem\u003eALDH2*2\u003c/em\u003e allele (A allele in this study) dramatically elevates the risk of esophageal cancer‒by more than several hundred-fold in heavy drinkers compared to those without these variants\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e, and were recently confirmed by meta-analysis\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. By integrating these critical loci into a single assay, our FMCA-based method ensures the reliable identification of such ultra-high-risk individuals without the need for multiple independent tests. This makes it an exceptionally efficient tool for stratifying disease risk in clinical settings. Triplex FMCA is therefore useful for determining genotype frequencies in large populations, stratifying disease risk, and conducting population genetic studies, including regional allele frequency gradients.\u003c/p\u003e \u003cp\u003eDespite the clinical utility of this triplex FMCA assay, several limitations should be acknowledged. First, although the assay demonstrated 100% concordance with Sanger sequencing in this study, the validation was conducted on a relatively small cohort of 94 Japanese individuals. Further validation with larger and more diverse East Asian populations is necessary to confirm the robustness of the assay across different genomic backgrounds. Second, the requirement for manual visual inspection of rs1229984 introduces a degree of subjectivity, which may limit its application in fully automated, ultra-high-throughput settings. As previously mentioned, refining the probe chemistry or integrating more sophisticated signal-processing algorithms would be essential to enhance objectivity. Finally, while these three SNVs are major contributors to alcohol metabolism, they do not account for the entire spectrum of alcohol-related disease risk. Environmental factors, such as total alcohol consumption and smoking, also play critical roles; therefore, this assay should be integrated into a broader, multifaceted risk assessment strategy.\u003c/p\u003e \u003cp\u003eWhile Sanger sequencing remains the gold standard for SNV analysis, its low throughput limits its utility for large-scale studies. TaqMan assays offer high specificity but are constrained in multiplexing capacity. Next-generation sequencing provides comprehensive genomic information\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e, but is excessive and costly for rapid detection of a small number of SNVs. In contrast, FMCA can be performed using widely available real-time PCR instruments and a simple reaction setup, making it one of the most practical and scalable options for multiplex SNV detection\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFurthermore, the low cost and rapid turnaround time of this assay make it an ideal tool for population-based cancer screening programs and routine health check-ups. Instead of relying solely on drinking frequency questionnaires, which are often subject to recall bias, clinicians can use this genetic data to provide evidence-based counseling. Identifying \u0026lsquo;high-risk\u0026rsquo; genotypes before the onset of disease could facilitate early interventions, such as intensive lifestyle modifications or prioritized endoscopic surveillance, ultimately reducing the burden of alcohol-related malignancies in high-risk populations like East Asians.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study established a triplex FMCA method for the simultaneous genotyping of \u003cem\u003eADH1B\u003c/em\u003e rs1229984, \u003cem\u003eADH1C\u003c/em\u003e rs698, and \u003cem\u003eALDH2\u003c/em\u003e rs671 in a single reaction. While rs1229984 requires visual inspection due to unique melting peak characteristics, the assay reliably identifies all genotypes with high precision. This rapid and cost-effective platform serves as a practical tool for large-scale screening and personalized cancer risk stratification, potentially contributing to the prevention of alcohol-related malignancies.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eEthical statements and DNA samples\u003c/h2\u003e \u003cp\u003eAll methods were carried out in accordance with relevant guidelines and regulations, including the Declaration of Helsinki.\u003c/p\u003e \u003cp\u003eThis study utilized existing anonymized genomic DNA samples from 94 randomly selected healthy Japanese individuals, as previously described\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. These samples were originally collected in 1995 after obtaining verbal informed consent for genetic polymorphism analysis, with the consent procedure approved by the Ethical Committee of Kurume University at that time. The protocol for the current study, which involves the use of these existing anonymized samples as well as commercially available genomic DNA, was initially approved by the Ethical Committee of Kurume University in 2002. Since then, the protocol has been renewed every five years, with the most recent approval granted on October 31, 2022 (Approval No. 22158).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eProbes and primers\u003c/h2\u003e \u003cp\u003eThe three SNVs‒rs671, rs698, and rs1229984‒were genotyped using specific PCR primer sets and fluorophore-labeled probes (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). PCR primers were designed using Primer3Plus (source: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.bioinformatics.nl/cgi-bin/primer3plus/primer3plus.cgi\u003c/span\u003e\u003cspan address=\"https://www.bioinformatics.nl/cgi-bin/primer3plus/primer3plus.cgi\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003csup\u003e32\u003c/sup\u003e. In addition, the thermodynamic properties and potential secondary structures of the designed oligonucleotides‒including melting temperature (\u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003em\u003c/em\u003e\u003c/sub\u003e) self-dimerization, and hairpin formation‒were verified using OligoCalc (Source: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://oligocalc.eu/\u003c/span\u003e\u003cspan address=\"http://oligocalc.eu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003csup\u003e33\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAll oligonucleotides, including FAM-, HEX-, and Cy5-labeled probes with appropriate quenchers (black hole quencher 1/black hole quencher 2), were custom-synthesized by Eurofins Genomics (Tokyo, Japan).\u003c/p\u003e \u003cp\u003eFor rs698 and rs1229984, the target genomic regions corresponded to the reverse complement of the mRNA sequence. Consequently, in this assay, the C and T alleles represented the low-activity and high-activity alcohol metabolism phenotypes, respectively. It is important to note that for these two SNVs, the T allele in this assay corresponds to the A allele in the standard genomic forward strand nomenclature. This orientation was maintained throughout the study to ensure consistent data interpretation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eGenotyping of rs671, rs698, and rs1229984 by Sanger sequencing\u003c/h2\u003e \u003cp\u003eTo determine the genotypes of rs671, rs698, and rs1229984 in this study, PCR products from all 94 individuals were subjected to direct Sanger sequencing. The target regions were amplified using specific primers (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) in a 10 \u0026micro;L reaction mixture containing 1\u0026ndash;10 ng of genomic DNA, 5 \u0026micro;L of Premix Ex Taq (Probe qPCR) (Takara Bio, Shiga, Japan), and 250 nM of each primer. The thermal profile comprised an initial denaturation at 95\u0026deg;C for 30 sec, followed by 45 cycles of denaturation at 95\u0026deg;C for 5 sec and annealing/extension at 60\u0026deg;C for 15 sec. The same primers used for PCR were employed for the sequencing reactions, which were outsourced to Eurofins Genomics. Sanger sequencing served as the reference method to validate the genotyping results obtained via FMCA.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eReal-time PCR monitoring and FMCA\u003c/h2\u003e \u003cp\u003eAsymmetric PCR followed by fluorescence melting curve analysis (FMCA) was conducted for rs671, rs698, and rs1229984 using the LightCycler 480 II system (Roche Diagnostics, Tokyo, Japan). Each 10 \u0026micro;L reaction contained Probe qPCR Mix MultiPlus (Takara Bio) and 1\u0026ndash;10 ng of genomic DNA. Primer and probe concentrations were optimized for each SNV. For rs671 and rs698, concentrations were 50/500/200 nM, while for rs1229984, they were 25/250/100 nM (forward/reverse/probe). In both cases, a 10-fold excess of reverse primers was maintained to promote asymmetric amplification\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThermal cycling conditions consisted of an initial denaturation at 95\u0026deg;C for 20 sec, followed by 45 cycles of 95\u0026deg;C for 1 sec and 60\u0026deg;C for 20 sec. Fluorescence signals were monitored using FAM, VIC/HEX/Yellow555, and Cy5/Cy5.5 filter sets during both the amplification phase and the subsequent melting curve analysis (40‒80\u0026deg;C at a ramp rate of 0.10\u0026deg;C/sec).\u003c/p\u003e \u003cp\u003eThe \u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003em\u003c/em\u003e\u003c/sub\u003e values and genotypes were analyzed using LightCycler 480 Gene Scanning Software (v1.5). Default analysis parameters (score threshold 0.70, resolution threshold 0.10, normal sensitivity) were applied to rs698 and rs671. For rs1229984, which exhibited lower peak intensity and a distinct curve morphology, modified settings (score threshold 0.30, resolution threshold 0.15, normal sensitivity) were used to improve allele discrimination. Automated calls were generated based on clustering algorithms, followed by manual visual inspection to ensure definitive genotype discrimination, particularly for samples with low peak intensities.\u003c/p\u003e \u003c/div\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eADH1B: alcohol dehydrogenase 1B, ADH1C: alcohol dehydrogenase 1C, ALDH2: aldehyde dehydrogenase 2, FMCA: fluorescence melting curve analysis, Tm: melting temperature\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eM.S. contributed to planning and conducting experiments, data analysis, writing of the original draft, reviewing and editing the manuscript. Y.K. contributed to planning and conducting experiments, supervision, data analysis, reviewing and editing the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe thank Katherine Ono for editing the English in this manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated and analyzed during the current study are available in the DDBJ repository under BioProject accession number PRJDB40219 and Run accession numbers DRR902565-DRR902658.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWilson, D. F. \u0026amp; Matschinsky, F. M. Ethanol metabolism: The good, the bad, and the ugly. \u003cem\u003eMed. 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OligoCalc: an online oligonucleotide properties calculator. \u003cem\u003eNucleic Acids Res.\u003c/em\u003e \u003cb\u003e35\u003c/b\u003e, W43\u0026ndash;46. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/nar/gkm234\u003c/span\u003e\u003cspan address=\"10.1093/nar/gkm234\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2007).\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":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"alcohol metabolism, rs671, rs698, rs1229984, fluorescence melting curve analysis, multiplex genotyping","lastPublishedDoi":"10.21203/rs.3.rs-8635444/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8635444/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThree functional single nucleotide variants (SNVs)‒\u003cem\u003eALDH2\u003c/em\u003e rs671 (p.E504K), \u003cem\u003eADH1C\u003c/em\u003e rs698 (p.I350V), and \u003cem\u003eADH1B\u003c/em\u003e rs1229984 (p.R48H)‒are key genetic determinants of human alcohol metabolism. These variants significantly affect drinking behavior and are associated with liver disease and increased risks of several malignancies, including esophageal and gastric cancers. We developed a triplex fluorescent probe-based melting curve analysis (FMCA) assay for the simultaneous detection of these three SNVs. The assay was validated by comparing FMCA results with Sanger sequencing using genomic DNA from 94 Japanese individuals. The automated detection algorithm reliably identified genotypes of rs671 and rs698. Although the melting peaks of rs1229984 exhibited lower resolution and necessitated manual visual inspection for definitive genotype discrimination, all genotypes were nevertheless correctly identified. The assay demonstrated 100% accuracy. In conclusion, this triplex FMCA assay provides a rapid, cost-effective, and streamlined method for the simultaneous genotyping of \u003cem\u003eADH1B\u003c/em\u003e, \u003cem\u003eADH1C\u003c/em\u003e, and \u003cem\u003eALDH2\u003c/em\u003e. Given its high accuracy and ease of implementation, this method serves as a practical alternative to conventional sequencing, positioning it as a valuable tool for both large-scale epidemiological research and routine clinical assessment of alcohol-related health risks.\u003c/p\u003e","manuscriptTitle":"Development of a Triplex FMCA Assay for Genotyping Three Genes, ADH1B, ADH1C, and ALDH2, Involved in Alcohol Metabolism","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-10 11:38:55","doi":"10.21203/rs.3.rs-8635444/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-05T13:16:13+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-27T14:43:36+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-23T07:16:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"20658959764895430030352424269938380720","date":"2026-02-23T04:33:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"92125780283467577538864738999462953145","date":"2026-02-19T11:39:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"51961013090048596413552510588330716447","date":"2026-02-19T02:33:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"206778330582320931476511918830632660464","date":"2026-02-09T14:28:21+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-04T14:25:42+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-04T14:18:31+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-04T08:39:27+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-03T06:36:58+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-02-03T06:21:01+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"bc15a157-810e-4f0e-8b28-222f9e53fcc2","owner":[],"postedDate":"February 10th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":62345243,"name":"Health sciences/Biomarkers"},{"id":62345244,"name":"Biological sciences/Cancer"},{"id":62345245,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":62345246,"name":"Biological sciences/Genetics"},{"id":62345247,"name":"Biological sciences/Molecular biology"},{"id":62345248,"name":"Health sciences/Oncology"}],"tags":[],"updatedAt":"2026-04-07T16:05:36+00:00","versionOfRecord":{"articleIdentity":"rs-8635444","link":"https://doi.org/10.1038/s41598-026-46895-y","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2026-03-31 15:59:04","publishedOnDateReadable":"March 31st, 2026"},"versionCreatedAt":"2026-02-10 11:38:55","video":"","vorDoi":"10.1038/s41598-026-46895-y","vorDoiUrl":"https://doi.org/10.1038/s41598-026-46895-y","workflowStages":[]},"version":"v1","identity":"rs-8635444","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8635444","identity":"rs-8635444","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}